Generalized 3-D Constellation Design for Spatial Modulation

Generalized 3-D Constellation Design for Spatial Modulation

Generalized 3-D Constellation Design forSpatial Modulation

 

ABSTRACT:

Spatial modulation (SM) conveys information bits by utilizing both antenna index and complexsymbols to form a 3-dimension (3-D) constellation. Similar to two dimensional modulation, the structureof 3-D constellation could greatly affect the transmission reliability. In this paper, a generalized 3-Dconstellation design is proposed to optimize the constellation diagram used for each antenna, i.e.,to optimize the complex symbols and their total number for each antenna and finally to enhancethe transmission reliability. The optimal design method with exhaustive search algorithm may causeprohibitive computational complexity especially when the cardinality of 3-D constellation is large. Toovercome this issue, a recursive design algorithm is proposed with a computational complexity increasingpolynomially with the cardinality of 3-D constellation. Extensions of the proposed methods to SMconstellation design for massive multiple-input multiple-output (MIMO) transmission, generalized spatialmodulation (GSM) constellation design, and SM constellation design with transmit antenna correlationare also discussed. Simulations are done to validate those theoretical analysis, and results show that theproposed 3-D constellation design is a generalized design scheme and can be adopted in any SM/GSMsystems without constraints on the number of transceiver antennas. It is also shown that the proposedapproach offers better symbol-error-rate (SER) performance than other solutions.

 OBJECTIVE:

To SMconstellation design for massive multiple-input multiple-output (MIMO) transmission, generalized spatialmodulation (GSM) constellation design, and SM constellation design with transmit antenna correlationare also discussed. Simulations are done to validate those theoretical analysis, and results show that theproposed 3-D constellation design is a generalized design scheme and can be adopted in any SM/GSMsystems without constraints on the number of transceiver antennas. It is also shown that the proposedapproach offers better symbol-error-rate (SER) performance than other solutions.

 INTRODUCTION:

The advent of spatial modulation (SM) has enabled a brand new 3-dimensional (3-D)modulation by exploiting the potential of both the space and signal domains. Through employingthe on/off keying of antennas and the conventional amplitude and phase modulation (APM), 3-Dmodulation offers significant multiplexing gain with a single radio frequency (RF) chain. It hasdrawn a lot of attention in the research field of multiple-input multiple-output (MIMO)/massiveMIMO by virtue of its enticing advantages such as the low cost and high energy-efficiency. Moreover it has also been implemented in a testbed.

 EXISTING SYSTEM:

  1. Yang, M. D. Renzo, Y. Xiao, S. Li, and L. Hanzo, “Design guidelines for spatial modulation,” IEEE Commun. SurveysTuts., vol. 17, no. 1, pp. 6–26, 1st Quat. 2015

Investigated the star-quadrature amplitude modulation (star-QAM) based constellation design forSM, and it was shown that the performance of star-QAM based SM is better than that of theconventional APM.

  1. Zhang, C. Wang, S. Guo, and H. Zhang, “Novel APM constellation design for spatial modulation systems,” in Proc.IEEE WCSP, Nangjing, China, Oct. 2015, pp. 1–5

Investigated APMwith multiple rings, where each ring may contain different number of constellation points.

 DRAWBACKS:

  • The number of transmit antennas is not a power of two, only a power of two are chosen to form the 3-D constellation structure, which may reduce the performance.
  • Their performance is limited by transmit antenna correlation.

PROPOSED SYSTEM:

A new 3-D constellation design has been proposed for SM . Benefiting from the key idea of jointly mapping a group of informationbits to 3-D constellation (also referred as 3-D mapping) directly, transmitters can have arbitrarynumber of antennas and a flexible number of symbols for each antenna. In a 3-D constellation was designed to minimize SER by assuming that a given finite symbol set (e.g. M-QAM orM-PSK) is employed as signal constellation.

 BLOCK DIAGRAM:

Generalized 3-D Constellation Design

 DESCRIPTION:

This work has focused on generalized joint 3-D constellation design in the complexfield for SM. A constellation design optimization problem subject to the cardinality requirementand the normalized power constraint was formulated. By skillfully solving the problem, both thecomplex symbols and their total number sent by each antenna were optimized. Since the optimaldesign method involves the exhaustive search, and may introduce prohibitive computationalcomplexity especially when the 3-D constellation cardinality is large, a recursive constellationdesign algorithm whose computational complexity increases polynomially with the cardinalityof 3-D constellation has been proposed

 ADVANTAGES:

  • Both the complex symbols and their total number sent by each antenna were optimized.
  • Easily extended to SM constellation design for massive MIMO uplink/downlink transmission.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram :

SOFTWARE REQUIREMENTS:

  • Operating system : Windows 7.
  • Coding Language : MATLAB
  • Tool : MATLAB R2013A

REFERENCE:

ShuaishuaiGuo Student Member, IEEE, Haixia Zhang Senior Member, IEEE,Peng Zhang, Dalei Wu, Member, IEEE, and Dongfeng YuanSenior Member, IEEE, “Generalized 3-D Constellation Design forSpatial Modulation”, IEEE Transactions on Communications, 2017.

FBMC and LDACS Performance for Future Air to Ground Communication Systems

FBMC and LDACS Performance for Future Air to Ground Communication Systems

FBMC and LDACS Performance for Future Air to Ground Communication Systems

 

ABSTRACT:

Recently two different L-band digital aeronauticalcommunication systems (LDACS), L-DACS1 and L-DACS2 havebeen proposed as two Future Communication Infrastructure(FCI) candidates for Air-to-Ground (AG) communicationsystems, with L-DACS1 selected as the best candidate. In thispaper we describe a filter bank multicarrier (FBMC) basedcommunication system, and show its advantages over the LDACSsystems. We provide simulation results for all threecommunication systems to fairly compare their power spectraldensity (PSD), peak-to-average power ratio (PAPR), and BERperformance. We show that in a measurement-based AGcommunication channel model, FBMC has better performance(and spectral containment) than the L-DACS schemes, and this isparticularly true in the presence of actual interfering signalsfrom distance measuring equipment (DME). Simulation resultsshow that FBMC can substantially reduce the out-of-band (OOB)power, and can suppress DME interference by at least 19.5 dB,due to its well-localized subcarrier prototype filters. FBMC canalso increase throughput and spectral efficiency by reducing thenumber of guard-band subcarriers and removal of the cyclicprefix, postfix and windowing techniques used in L-DACS1.These results show that an FBMC based communication systemcan be an appealing candidate for future AG communicationsystems.

OBJECTIVE:

In thispaper we describe a filter bank multicarrier (FBMC) basedcommunication system, and show its advantages over the LDACSsystems. We provide simulation results for all threecommunication systems to fairly compare their power spectral

density (PSD), peak-to-average power ratio (PAPR), and BERperformance. We show that in a measurement-based AGcommunication channel model, FBMC has better performance(and spectral containment) than the L-DACS schemes, and this isparticularly true in the presence of actual interfering signalsfrom distance measuring equipment (DME). Simulation resultsshow that FBMC can substantially reduce the out-of-band (OOB)power, and can suppress DME interference by at least 19.5 dB,due to its well-localized subcarrier prototype filters. FBMC canalso increase throughput and spectral efficiency by reducing the

number of guard-band subcarriers and removal of the cyclicprefix, postfix and windowing techniques used in L-DACS1.These results show that an FBMC based communication systemcan be an appealing candidate for future AG communicationsystems.

 INTRODUCTION:

THE rapid growth of air traffic has led civil aviationauthorities to explore development of FCI. In the nearfuture air traffic volume will increase dramatically, whichmeans that current aeronautical communication systemsoperating at VHF will suffer from severe congestion in someregions of the world. In 2002, the need for improving thecommunication infrastructure for air traffic management(ATM) and air traffic control was recognized by theInternational Civil Aviation Organization (ICAO). Joint Euro-American research studies began in 2004 in support of ICAOto develop a FCI. The FCI consists of several links such as airto ground (AG), satellite, and may later address air-to-aircommunication systems. Initially the development of the FCIwas part of two programs: the Single European Sky ATMResearch (SESAR) supported by EUROCONTROL, theEuropean Union (EU), and the Next Generation AirTransportation System (NextGen), led by the US FederalAviation Administration (FAA) and supported by the NationalAeronautics and Space Administration (NASA) .

Significant changes in aviation technology have historicallytaken place much more slowly than in commercial andconsumer applications, hence technologies for FCI are stillbeing researched and developed. Developing AGcommunication systems depends on the accessibility ofavailable spectrum. It is expected that future AGcommunication systems will be deployed in the L-band (960-1164 MHz), allocated by the International TelecommunicationUnion (ITU).

EXISTING SYSTEM:

  1. Brandes, et al. “Physical layer specification of the L-band Digital Aeronautical Communications System (L-DACS1).” Integrated Communications, Navigationand Surveillance Conference (ICNS), IEEE, pp. 1-12, Arlington VA, 13-15 May2009.
  • The L-DACS1 physical layer is presented, covering both the deployment as an inlay and as a non-inlay system. In addition to the transmitter design, the design of the L-DACS1 receiver was addressed, including methods for mitigating interference from DME systems.
  1. Neji, et al. “Effect of the aeronautical L-DACS2 radio-frequency signals on theDME system performance.” Vehicular Technology Conference Fall (VTC 2010-Fall), IEEE, pp. 1-5, Ottawa, ON, 6-9 Sept 2010.
  • The impact of L-DACS2 on the DME system. They quantified the impact of an L-DACS2 interferer on the performance of a DME victim receiver, via computer simulations and laboratory measurements.

DRAWBACKS:

  • The FCI candidate systems must be able to operate in the presence of interference from all these systems, and also cause minimum interference to these existing systems.
  • DME is the main communication system in this band and operates very close to FCI frequencies.

PROPOSED SYSTEM:

In this paper we compare the performance of L-DACS1, LDACS2and the new system based on FBMC modulation. Wecompare this FBMC based communication system with the LDACSsystems in realistic conditions that include the AGchannel itself and the primary L-band interfering signals.we describe a filter bank multicarrier (FBMC) basedcommunication system, and show its advantages over the LDACSsystems. We provide simulation results for all threecommunication systems to fairly compare their power spectraldensity (PSD), peak-to-average power ratio (PAPR), and BERperformance.

BLOCK DIAGRAM:

FBMC and LDACS Performance for Future Airto

DESCRIPTION:

L-DACS1

Similar to the IEEE 802.16 wireless system, L-DACS1 is afrequency division duplexing (FDD) system that utilizes CPOFDMmodulation, supporting simultaneous transmission inthe forward link (FL, the ground to air channels), and reverselink (RL, the air to ground channels). Since L-DACS1 is notas well-known as similar communication systems such asIEEE 802.16 and 802.11, here we explain salient aspects ofthis technology. In L-DACS1 adaptive coding and modulation(ACM) are supported for the data channel. According to the LDACS requirements, the residual BER measured after forwarderror correction (FEC) shall be less than 10-6 at the powerlevel that corresponds to the receiver sensitivity for standardmessage and test conditions

 LDACS2

L-DACS2 uses techniques similar to those in GSM. It is anarrowband single-carrier system with 200 kHz transmissionbandwidth that uses time-division duplex (TDD). Itsmodulation is GMSK with modulation index h of 0.5 and B3Tproduct of 0.3, where B3 is the 3 dB bandwidth of the filterand T is the symbol duration. The symbol (and bit) rate is 1/T= 270.833 ksymbols/s. There is no higher order modulationavailable in L-DACS2 as we have in L-DACS1 and FBMC,and this is a main disadvantage of L-DACS2 in comparisonwith the other two systems. The available spectrum for LDACS2is divided into a number of 200 kHz wide channels.Each of these bands will be occupied by a GMSK modulatedRF carrier supporting a number of TDMA time slots .

ADVANTAGES:

  • The FBMC subcarrier based system has the ability to work without PB and have the best performance among all systems.
  • FBMC is an attractive candidate for FCI and aeronautical communication systems

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1GB

SOFTWARE REQUIREMENTS:

  • Operating system : Windows 7.
  • Coding Language : MATLAB
  • Tool : MATLAB R2013A

REFERENCE:

Hosseinali Jamal, Member, IEEE, David W. Matolak, Senior Member, IEEE, “FBMC and LDACS Performance for Future Airto Ground Communication Systems”, IEEE Transactions on Vehicular Technology, 2017.

 

Expectation-Maximization-based Channel Estimation for Multiuser MIMO Systems

Expectation-Maximization-based Channel Estimation for Multiuser MIMO Systems

Expectation-Maximization-based Channel Estimation for Multiuser MIMO Systems

 

ABSTRACT:

Multiuser multiple-input multiple-output (MUMIMO)transmission techniques have been popularly used toimprove the spectral efficiency and user experience. However,due to the coarse knowledge of channel state informationat the transmitter (CSIT), the quality of transmit precodingto control multiuser interference is degraded and hence coscheduleduser equipment (UE) may suffer from large residualmultiuser interference. In this paper, we propose a newchannel estimation technique employing reliable soft symbolsto improve the channel estimation and subsequent detectionquality of MU-MIMO systems. To this end, we pick reliabledata tones from both desired and interfering users and thenuse them as pilots to re-estimate the channel. In order tojointly estimate the channel and data symbols, we employ theexpectation maximization (EM) algorithm where the channelestimation and data decoding are performed iteratively. Fromnumerical experiments in realisticMU-MIMO scenarios, we showthat the proposed method achieves substantial performance gainin channel estimation and detection quality over conventionalchannel estimation approaches.

OBJECTIVE:

  • An improved channel estimation technique for the multiuser MIMO systems.
  • To generate the joint estimate of the channel and data symbols.
  • To reduce the computational complexity associated with the virtual pilot
    selection, we choose a small group of reliable data tones making a dominant contribution to the channel estimation quality.

INTRODUCTION

In many wireless systems, multiuser multiple-input multiple-output (MIMO) techniques have been used to improve the spectral efficiency and user experience. In contrast to the traditional single user MIMO (SU-MIMO) systems where the time-frequency resource element is dedicated to a single user, multiuser MIMO system allows multiple users to use the same time-frequency resources via a proper control of the interference among co-scheduled users at the base station.
Control of this multiuser interference is achieved by applying a precoding to the symbol vectors of all users scheduled in the same time-frequency resources. Since the precoding matrix is generated using the downlink channel state information (CSI) which relies on the feedback information from the mobile users, inaccurate precoding operation from imperfect CSI causes a severe degradation in multiuser interference cancellation at the transmitter side and the channel estimation
and detection at the receiver side, undermining the benefits of multiuser MIMO.

EXISTING SYSTEM:

  1. Ji, Y. Kim, J. Lee, E. Onggosanusi, Y. Nam, Z. Jianzhong, B. Lee, and B. Shim, “Overview of full-dimension MIMO in LTE-Advanced Pro,” IEEE Comm. Mag., pp 176-184, Oct. 2016.

LTE-AdvancedPro, the recent standard of 3rd Generation Partnership Project (3GPP) LTE, considers using up to 64 antennas at the base station.

  1. Koike, D. Ogawa, T. Seyama, T. Dateki, “MLD-Based MU-MIMO detection scheme for LTE downlink,” in Proc. IEEE Vehicular Technology Conference (VTC), 2012.

A joint detection algorithm for multiuser environment has been suggested.

DRAWBACKS:

  • Reduction of pilot density will
    cause severe degradation in the channel estimation quality
    and eventual loss of the system performance..
  • Effect of the residual interference was considered to improve
    the performance of joint demodulation and decoding.

 PROPOSED SYSTEM:

In this paper, we proposed an EM-based joint pilot and data channel estimation algorithm for the multiuser MIMO systems. Our work is motivated by the observation that the inaccurate CSI caused by the insufficient pilot signals brings severe link performance degradation in multiuser MIMO systems.
By using deliberately chosen data tones for the pilot purpose, the proposed method achieves better channel estimation and eventual link performance gain. Although our study focused on the state of the art cellular systems (LTE-Advanced and
LTE-Advanced-Pro), we expect that the effectiveness of the proposed approach can be readily extended to future wireless systems such as machine-type communication with ultra-small packets for internet of things (IoT) network.

 BLOCK DIAGRAM:

Expectation-Maximization-based ChannelEstimation

 DESCRIPTION:

        The proposed EMbased joint pilot and data channel estimation method. First, UE estimates the channels for all users and then delivers them to the multiuser MIMO detector. Multiuser MIMO detector jointly estimates the channels and produces the soft symbols for all users. Using the generated soft symbols, the channel decoder computes the soft information of information bits in a form of log likelihood ratio (LLR) and then feeds it back to the channel estimator. Using this LLR information, a small number of reliable data tones is picked for the channel reestimation. The soft symbols of selected tones are used to generate the refined channel estimates. Such an iterative process is repeated until the termination condition (e.g., max iteration number or performance convergence) is satisfied.

ADVANTAGES:

  • Achieve the ultra low latency and energy efficiency.
  • The size of packet is much smaller than that of human-centric communications.
  • The number of pilot tones in a packet is very small.
  • Effective in improving the quality of service (QoS).

 SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram :

SOFTWARE REQUIREMENTS:

  • Operating system : Windows 7.
  • Coding Language : MATLAB
  • Tool : MATLAB R2013A

REFERENCE:

Sunho Park, Member, IEEE, Jun Won Choi, Member, IEEE, Ji-Yun Seol, Member, IEEE,and Byonghyo Shim, Senior Member, IEEE, “Expectation-Maximization-based ChannelEstimation for Multiuser MIMO Systems”,IEEE TRANSACTION ON COMMUNICATIONS, 2017.

Evolutionary Channel Sharing Algorithm for Heterogeneous Unlicensed Networks

Evolutionary Channel Sharing Algorithm for Heterogeneous Unlicensed Networks

Evolutionary Channel Sharing Algorithm for Heterogeneous Unlicensed Networks

ABSTRACT:

Channel sharing in TV whitespace (TVWS) is challengingbecause of signal propagation characteristics and diversityin network technologies employed by secondary networkscoexisting in TVWS. In this paper, the TVWS sharing problemis modeled as a multiobjective optimization problem where eachobjective function tackles an important coexisting requirement,such as interference and disparity in network technologies. Wepropose an evolutionary algorithm that shares the TVWS amongcoexisting networks taking care of their channel occupancyrequirements. In this paper, the channel occupancy is definedas the time duration; a network desires to radiate on a channelto achieve its desired duty cycle. Simulation results show thatthe proposed algorithm outperforms existing TVWS sharingalgorithms regarding allocation fairness and a fraction of channeloccupancy requirements of the coexisting networks.

 OBJECTIVE:

  • A CDM procedure is implemented as a process of sharinga set of TV channels of predetermined bandwidth among a set of hetero-WSOs. Unlike existing CDM formulations in the TVWS sharing domain the proposed formulation accommodates as many as hetero-WSOs on the available TVWS by relaxing theirchannel demand satisfaction.
  • The proposed CDM system transforms the nonconvex, nonlinear multiobjective function in the TVWS sharing MOP (Section IV-B) into a max-min optimizationformulation, using a binary epsilon indicator function (Section IV-D). Such formulation enables the CDM system to achieve a true multiobjective optimization as it does not require a priori articulation of preferences of the decision maker nor does it need to scalarize the multiobjective function in the TVWS sharing MOP.
  • An evolutionary algorithm, called EvCo is proposed to obtain a feasible Pareto-optimal solution for the TVWS sharing MOP. Our evaluation studies show thesuperiority of the EvCo over existing TVWS sharing algorithms regarding scalability, fairness and WSOs’ satisfaction from the allocation.


INTRODUCTION:

TV whitespace (TVWS) refers to the TV spectrum notin use by licensed operators in a spatiotemporal region.Worldwide efforts have been initiated to permit unlicenseddevices to operate in TVWS. Therefore, several standardssuch as IEEE 802.22-2011 , 802.11af , 802.15.4m , and ECMA-392 have been developed to regulate accessto TVWS. The MAC/PHY layer technologies in these standardsare incompatible. A collocated deployment of secondarydevices operating on these standards may create coexistenceissues, such as unresolved interference due to a disparityin MAC/PHY layer technologies, spectrum congestion dueto indiscriminate spectrum usage, and spectrum scarcity in congested areas . Such issues, if left unresolved,may result in inefficient use of TVWS. Therefore, IEEE hasdeveloped a standard namely 802.19.1 to provide coexistenceamong secondary devices, namely whitespace objects (WSO),operating on heterogeneous network technologies . Thecollocated WSOs operating on heterogeneous network technologiesare referred to as hetero-WSO throughout this paper.A set of tasks to achieve peaceful coexistence amonghetero-WSOs sharing the common spectrum is referred toas coexistence decision making (CDM) procedure. A systemimplementing CDM procedure is referred to as a CDM system. Some literature work exists that implements CDM procedure in the TVWS domain.

 EXISTING SYSTEM:

  1. Bahrak, and J.-M. J. Park, “Coexistence decision making for spectrum

sharing among heterogeneous wireless systems,” IEEE TWC, vol. 31,

issue 3, pp. 1298-1307, Mar. 2014.

  • On algorithmic perspective, Bahrak and Park modeled the spectrum-sharing problem as a MOP, which was then scalarized using a weighted-sum approach and formulated using a modified Boltzmann machine.
  1. Marler, and J. Arora, “Survey of multi-objective optimization methodsfor engineering,” Struct. Mult.Opt., vol. 26, issue 6, pp. 369-395, 2004.
  • The main issue with the weighted-sum approach is its inability to find Pareto-optimal solution points in the non-convex region of the solution space boundary.

PROPOSED SYSTEM:

Wepropose an evolutionary algorithm that shares the TVWS amongcoexisting networks taking care of their channel occupancyrequirements. In this paper, the channel occupancy is definedas the time duration; a network desires to radiate on a channelto achieve its desired duty cycle. Simulation results show thatthe proposed algorithm outperforms existing TVWS sharingalgorithms regarding allocation fairness and a fraction of channeloccupancy requirements of the coexisting networks.

 BLOCK DIAGRAM:

Evolutionary Channel Sharing Algorithm forHeterogeneous Unlicensed Networks

 DESCRIPTION:

Modeling the CDM System

The proposed CDMsystem, thus, supports the channel allocation among WSOs

requesting for one TV channel, or multiple, non-contiguousTV channels. Channel allocations which are continuous infrequency slots are also promoted in the proposed system;however, such an allocation is not guaranteed.

the TVchannels must be shared among a set of coexisting WSOssuch that the following objectives are satisfied:

  • Allocation among WSOs is fair,
  • System throughput is maximized,
  • WSOs are satisfied regarding their channel demands.

The CDM system achieves the objectives of the TVWS sharingproblem by formulating them in the following functions.

1)Fairness in Allocation: Fairness, from a spectrum allocationperspective, is regarded as equity in access to radioresources. It is defined in terms of a fraction of demand servemetric as,

2) System Throughput Maximization: The gain in systemthroughput depends on multiple factors. Some common factorsare formulated as follows.

3) WSO Satisfaction from the Allocation : A WSO w issatisfied from the allocation if it achieves its desired data volumedw. A quantifiable satisfaction can be defined regardingan energy minimization function as follows.

ADVANTAGES:

  • Implements a multiobjective optimization problem (MOP) for channel sharing inTVWS.
  • Evaluate the performance of the EvCo on 802.19.1-compliant CDM system and compare its performance with existing TVWS sharing algorithms.
  • Our evaluation results show that the EvCo is superior to the comparative algorithms regarding fairness and WSO satisfaction from the allocation.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1GB

SOFTWARE REQUIREMENTS:

  • Operating system : Windows 7.
  • Coding Language : MATLAB
  • Tool : MATLAB R2013A

REFERENCE:

  1. A. Raza, Sangjun Park, and Heung-No Lee, Senior Member, IEEE, “Evolutionary Channel Sharing Algorithm forHeterogeneous Unlicensed Networks”, IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2017.

Equal Interference Power Allocation for Efficient Shared Spectrum Resource Scheduling

Equal Interference Power Allocation for Efficient Shared Spectrum Resource Scheduling

 

ABSTRACT:

Effective radio frequency spectrum sharing methodsare crucial for sustaining growth and development in mobilewireless services. In this paper, we consider a real-world scenarioinvolving spectrum sharing between mobile wireless andmeteorological satellite services as motivation for examining thegeneral problem of efficient resource scheduling in a sharedspectrum environment. We formulate an optimization frameworkfor maximizing network utility subject to stochastic interferenceprotection constraints. We design and propose a novel solutioninspired by analysis of the optimization problem, where theprimary contribution is an efficient power allocation algorithmto manage interference between systems. Using theory andsimulations, we show that our algorithm significantly outperformsalternative approaches by well approximating the optimalsolution with low enough complexity for practical, real-timeapplication to large networks.

 OBJECTIVE:

We formulate an optimization frameworkfor maximizing network utility subject to stochastic interferenceprotection constraints. We design and propose a novel solutioninspired by analysis of the optimization problem, where theprimary contribution is an efficient power allocation algorithmto manage interference between systems. Using theory andsimulations, we show that our algorithm significantly outperformsalternative approaches by well approximating the optimalsolution with low enough complexity for practical, real-timeapplication to large networks.

INTRODUCTION:

Rapid increase in the quantity and capability of consumermobile wireless devices has accelerated the growing demandfor radio frequency spectrum. In recent years, national andinternational regulators have taken steps to identify new spectrumfor use by mobile wireless services such as cellular and Wi-Fi . Though growth in demand for mobile wirelesshas been particularly acute, myriad other systems and servicesalready make use of spectrum in the frequency ranges thatcould be useful for mobile wireless.With limited opportunities to open new unencumbered

bands to mobile wireless services, interest in effective methodsfor sharing spectrum between services is high. In January2015, the US Federal Communications Commission auctionedthe band 1695-1710 MHz, making it available for cellular systemssuch as 3GPP Long-Term Evolution (LTE) uplinks .New cellular systems will share the spectrum with incumbentmeteorological satellite (METSAT) downlink services alreadyin the band, where deployment of new LTE networks mustnot cause harmful interference to fixed METSAT earth station locations . LTE operators will need to demonstrate that theycan meet the protection criteria of the METSAT receivers asdefined in a joint working group , and should be motivatedto reconsider aspects of their system design in the context of how to best make use of spectrum in a shared environmentsubject to interference constraints.

EXISTING SYSTEM:

  1. Yang et al., “Frequency-Domain Packet Scheduling for 3GPP LTE Uplink,” in IEEE Int. Conf. on Comput. Commun., 2010, pp. 1-9.
  • Several heuristic algorithms for frequency resource scheduling which trade between performance and complexity

Simonsson and A. Furuskar, “Uplink Power Control in LTE – Overview and Performance,” in Veh. Technol. Conf., 2008, pp. 1-5.

  • Examine power control mechanisms within LTE, considering performance trades between throughput, self-interference, and energy efficiency.

DRAWBACKS:

  • Resource allocation is broken into subproblems, favoring low complexity to satisfy millisecond LTE time scales.
  • LTE uplink is a combinatorial optimization problem that can be impractical to solve optimally.
  • Incorporating margins or ellipsoid uncertainty regions into the deterministic formulation.

PROPOSED SYSTEM:

In this paper, we consider this specific scenario and formulatean optimization framework for power control andtime-frequency resource scheduling on the LTE uplink withthe interference protection constraint. We design and proposea novel algorithm inspired by analysis of the optimizationproblem. Using theory and simulations, we show that ouralgorithm significantly outperforms alternative approaches,well approximates the optimal solution, and is of sufficientlylow complexity for practical implementation in large networks.

BLOCK DIAGRAM:

DESCRIPTION:

The LTE network will make use of the band for uplinks, i.e.,transmission from the user equipment (UE) to the base station(BS). We assume the number and locations of the BSs andthe UEs in the network operating around the METSAT siteare known. Consistent with LTE, we consider that each BSschedules associated UEs in time-frequency resource blocks(RBs) which are units of 180 kHz bandwidth by 0.5 ms. EachBS may assign each RB to only one UE, which is necessaryin practice to limit interference within the LTE network. RBsassigned to any single UE in a 0.5 ms time slot must be contiguousin frequency due to the use of single-carrier frequencydivision multiple access for LTE uplinks . In addition totime-frequency scheduling, we will include LTE power controlin the optimization, which allows UE transmit power levelsto be varied by the BS over a large range, typically -40 to+23 dBm in 1 dBm increments. We assume that completechannel state information for the link between any given UEand its associated BS is available to the LTE network sincethis is routinely measured at the BS. However, only partialchannel state for the UE interference to the METSAT receiveris available in the form of an estimated mean and variance for aknown distribution, where this estimation can be accomplishedwith typical models or measurements

Thus we can focusthe problem on the aggregate interference at the METSATreceiver and require that interference power be below somethreshold,

ADVANTAGES:

  • Allow networks to make efficient use of spectrum in shared environments.
  • EIPA closely approaches the performance of optimal scheduling, but with low enough complexity.
  • EIPA scheduling can be implemented within a practical LTE network.
  • Its efficiency in the context of a real-world LTE-METSAT spectrum sharing scenario.

 SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram :  1GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : MATLAB
  • Tool : MATLAB R2013A

REFERENCE:

Matthew Clarky, Konstantinos Psounis, “Equal Interference Power Allocation for EfficientShared Spectrum Resource Scheduling”, IEEE Transactions on Wireless Communications, 2017.

 

Energy and Spectral Efficiency of Cellular Networks with Discontinuous Transmission

Energy and Spectral Efficiency of Cellular Networks with Discontinuous Transmission

Energy and Spectral Efficiency of Cellular Networks with Discontinuous Transmission

 

ABSTRACT:

Cell discontinuous transmission (DTX) has beenproposed as a solution to reduce energy consumption of cellularnetworks. This paper investigates the impact of network trafficload on spectral and energy efficiency of cellular networks withDTX. The SINR distribution as a function of traffic load isderived firstly. Then sufficient condition for ignoring thermalnoise and simplifying the SINR distribution is investigated.Based on the simplified SINR distribution, the network spectraland energy efficiency as functions of network traffic load arederived. It is shown that the network spectral efficiency increasesmonotonically in traffic load, while the optimal network energyefficiency depends on the ratio of the sleep-mode power consumptionto the active-mode power consumption of base stations. Ifthe ratio is larger than a certain threshold, the network energyefficiency increases monotonically with network traffic load andis maximized when the network is fully loaded. Otherwise, thenetwork energy efficiency firstly increases and then decreases innetwork traffic load. The optimal load can be identified witha binary search algorithm. The power ratio threshold dependssolely on the path loss exponent _, e.g. 56% for _ = 4. Allthese analytic results are further validated by the numericalsimulations.

 OBJECTIVE:

  • The SINR distribution as a function of traffic load is derived firstly. Then sufficient condition for ignoring thermal noise and simplifying the SINR distribution is investigated. Based on the simplified SINR distribution, the network spectral and energy efficiency as functions of network traffic load are derived.
  • It is shown that the network spectral efficiency increases monotonically in traffic load, while the optimal network energy efficiency depends on the ratio of the sleep-mode power consumption to the active-mode power consumption of base stations. If the ratio is larger than a certain threshold, the network energy efficiency increases monotonically with network traffic load and is maximized when the network is fully loaded.
  • Otherwise, the network energy efficiency firstly increases and then decreases in network traffic load. The optimal load can be identified with a binary search algorithm.

INTRODUCTION:

Driven by the increasing usage of smart devices and mobileapplications, the traffic of cellular networks has grown dramaticallyand this trend would continue in the future. It is forecastedthat the global mobile traffic would increase by nearlytenfold from 2014 to 2019. Therefore network densificationhas been proposed to increase the network capacity by increasing the reuse of radio resources . However, deploying morebase stations (BSs) would lead to soaring energy consumption,which not only incurs severe environmental problems but alsoincreases operation cost. It is therefore critical to increase theenergy efficiency of cellular networks.

As indicated in  the energy consumption of BSs accountsfor almost 60% of all the energy consumed by cellularnetworks. Different approaches have been proposed to reducethe energy consumption of BSs. One is to develop low-energyconsuminghardware and the other is to operate BSs to trafficdemand. The latter is motivated by the fact that the existingBSs are deployed and operated to cater for the maximumtraffic demand while the network traffic may vary in time.

EXISTING SYSTEM:

  1. Oh, B. Krishnamachari, X. Liu, and Z. Niu, “Toward dynamic energyefficient

operation of cellular network infrastructure,” Communications Magazine, IEEE, vol. 49, no. 6, pp. 56–61, 2011.

  • The performance of a real network and proved the energy saving potential of dynamic BS on/off operation.
  1. S. Soh, T. Q. Quek, M. Kountouris, and H. Shin, “Energy efficient heterogeneous cellular networks,” Selected Areas in Communications, IEEE Journal on, vol. 31, no. 5, pp. 840–850, 2013.
  • The design of energy efficient cellular networks through the employment of BS sleep mode strategies as well as small cells, and investigated the tradeoff issues associated with these techniques.

 DRAWBACKS:

  • The long-term traffic variation, for which the time scale is at level of hours.
  • The average traffic intensity varies from hour to hour.
  • Incoming traffic request in certain slots and then switched into micro sleep mode during idle slots.

 PROPOSED SYSTEM:

In this paper, we investigate the impact of traffic load onnetwork performance and endeavor to discover the explicitrelationship between traffic load and spectral and energyefficiency of cellular networks using cell DTX.

1) Derive the network SINR distribution while consideringnetwork traffic load. Then we further derive network spectraland energy efficiency.

2) Present a sufficient condition for a cellular network to beinterference-limited.

3) Analyze the impact of network traffic load on networkspectral and energy efficiency.

4) Run numerical simulations to further confirm the analyticresults.

 BLOCK DIAGRAM:

Energy and Spectral Efficiency of CellularNetworks

DESCRIPTION:

In this section, we first describe the system model andthe necessary assumptions for the performance analysis. Thenthe network traffic load and power consumption model areexplained. In the end, the performance metrics are described.

Network Model

We consider the downlink transmission in a network whereboth BSs and users are randomly distributed. The network isassumed to be homogeneous in terms of both traffic demandand BS distribution. The distribution of BSs is modelled withan ergodic PPP _B with density λB. Note that we consider homogeneousnetworks and the case of heterogeneous networksis beyond the scope of this paper. Each user is associatedto its closest BS. Thus the coverage area of each BS canbe modelled using the Poisson Voronoi Tessellation (PVT)method. Fig. 1 illustrates an example of such a network. All theBSs are assumed to support DTX. The BS stays in active modeand transmits when there is any traffic request. Otherwise, it switches into sleep mode and does not transmit. The universalfrequency reuse is applied and the system bandwidth is W.The users within each cell equally share the resources inan orthogonal manner. Only path loss and fast fading areconsidered. The link between a BS and a user is modeled as follows:

wherePr, Pt, C, K, r, and α denote the receive power, thetransmit power, the antenna gain, the path loss constant at unitdistance, the distance between the BS and the user and thepath loss exponent respectively. 

ADVANTAGES:

  • The network is analyzed with theories of stochastic geometry.
  • Simplified SINR distribution, analytical expressions are obtained to describe the impact of the network load on the performances, including link spectral efficiency, network spectral and energy efficiency.
  • The average link spectral efficiency decreases while the network spectral efficiency increases

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1GB

SOFTWARE REQUIREMENTS:

  • Operating system : Windows 7.
  • Coding Language : MATLAB
  • Tool : MATLAB R2013A

REFERENCE:

Peiliang Chang and Guowang Miao, “Energy and Spectral Efficiency of CellularNetworks with Discontinuous Transmission”, IEEE Transactions on Wireless Communications, 2017.

Dynamic User Grouping and Joint Resource Allocation with Multi-Cell Cooperation for Uplink Virtual MIMO Systems

Dynamic User Grouping and Joint Resource Allocation with Multi-Cell Cooperation for Uplink Virtual MIMO Systems

Dynamic User Grouping and Joint Resource Allocation with Multi-Cell Cooperation for Uplink Virtual MIMO Systems

 

ABSTRACT:

This paper proposes a novel joint resource allocationalgorithm combining dynamic user grouping, multi-cell cooperationand resource block (RB) allocation for single carrierfrequencydivision multiple access (SC-FDMA) uplink in multicellvirtual MIMO systems. We first develop the dynamic multicelluser grouping criteria using minimum mean square error(MMSE) equalization and adaptive modulation (AM) with biterror rate (BER) constraint. Then, we formulate and solve anew throughput maximization problem whose resource allocationincludes cell selection, dynamic user grouping and RB pattern assignment.Furthermore, to reduce the computational complexitysignificantly, especially in the case of large numbers of users andRBs, we present an efficient iterative Hungarian algorithm basedon user and resource partitions (IHA URP) to solve the problemby decomposing the large scale problem into a series of smallscale sub-problems, which can obtain close-to-optimal solutionwith much lower complexity. The simulation results show that ourproposed joint resource allocation algorithm with dynamic multicelluser grouping scheme achieves better system throughput withBER guarantee than fixed user grouping algorithm and otherproposed schemes in the literature.

 OBJECTIVE:

  • To develop dynamic user grouping criteria with BER constraint in multi-cell uplink systems. The criteria are the basis of joint resource allocation algorithm.
  • To formulate a novel joint resource allocation optimization problem which combines multi-cell cooperation, dynamic user grouping and RB allocation for multi-cell SC-FDMA uplink systems.
  • To propose an efficient IHA URP algorithm for solving the joint resource allocation optimization problem by decomposing it into multiple smaller scale sub-problems. As the numbers of cells, users and RBs increase, the search scale increases dramatically so that the traditional BNB algorithm is unable to solve the problem efficiently.

.INTRODUCTION:

MULTIPLE-INPUT multiple-output (MIMO) techniqueshave been widely applied in increasing the spectralefficiency (SE) in various wireless communication systems. However, because of the cost and size of the userequipment (UE), the application of MIMO uplink is limitedby the difficulty in practical implementation at the user side,especially in a small handset. In order to deal with this problem,

virtual MIMO for uplink approach that assignstwo or more users, each with single transmitting antenna, tothe same frequency band and time slot emerged. Comparedwith a conventional MIMO system, virtual MIMO can obtainadditional multiuser diversity gain by grouping users usingwell-designed strategies.

Some research works have been performed on the criteriaof user pairing/grouping for virtual MIMO systems. Most ofthese proposed criteria are derived from the channel capacity. In n-user virtual MIMO channel capacity iscalculated using the theory and a suboptimal pairingalgorithm which selects pairing users one by one is proposed.Similarly, in the decision metric which employs instantaneousreceiving signal-to-noise ratio (SNR) after maximumlikelihood (ML) detection is equivalent to MIMO channelcapacity. In analyze the receiving signalto interference plus noise ratio (SINR) after minimum meansquare error (MMSE) equalization and use Shannon capacityas user scheduling criterion.

EXISTING SYSTEM:

  1. Nauryzbayev, E. Alsusa, “Enhanced Multiplexing Gain Using Interference Alignment Cancellation in Multi-Cell MIMO Networks,” IEEE Trans. Inform. Theory, vol. 62, no. 1, Jan. 2016.
  • To mitigate ICI, one approach is to schedule multiple users to the same resource blocks (RBs) under the constraint of interference of neighboring cells, where the ICI is treated as noise.
  1. Zhang, R. Chen, J. G. Andrews, A. Ghosh and R. W. Heath, “Networked MIMO with Clustered Linear Precoding,” IEEE Trans. Wireless Commun., vol. 8, no. 4, Apr. 2009.
  • An alternative approach is to effectively exploit rather than control ICI, where joint reception or detection is performed to improve the system performance.

DRAWBACKS:

  • It needs to share a huge amount of information among coordinated BSs.
  • In addition, no research specifically focuses on the joint resource allocation algorithm with dynamic singleantenna user grouping in multi-cell SC-FDMA uplink systems.
  • The joint optimization gains from single-antenna user grouping multiplexing, cell selection and RB allocation are not fully exploited.

PROPOSED SYSTEM:

We formulate and solve anew throughput maximization problem whose resource allocationincludes cell selection, dynamic user grouping and RB pattern assignment.Furthermore, to reduce the computational complexitysignificantly, especially in the case of large numbers of users andRBs, we present an efficient iterative Hungarian algorithm basedon user and resource partitions (IHA URP) to solve the problemby decomposing the large scale problem into a series of small

scale sub-problems, which can obtain close-to-optimal solutionwith much lower complexity.

BLOCK DIAGRAM:

Dynamic User Grouping and Joint ResourceAllocation

DESCRIPTION:

Multi-cell Virtual MIMO Model

Consider a multi-cell uplink virtual MIMO system with Lcoordinated BSs (i.e. L cells), where each cell contains oneBS equipped with Nr receiving antennas, U single antennausers. In order to increase the spectral efficiency and occupancy

level compared with classical FDMA, users in the clusterform virtual MIMO groups and operate over the same timefrequencyresource, while different groups occupy orthogonalresources to eliminate the other group interference. In thispaper, we select L coordinated cells to form a cluster in whichthe L cells share the same NRB RBs and the users can pairwith each other dynamically across cells. We name such ascheme as the multi-cell cooperation.

There should be a central controller for the implementationof the proposed algorithm in the system considered in thispaper. Compared with the single-cell approaches, the proposedmulti-cell algorithm requires the exchange of CSI and schedulinginformation between each cooperative BS and the centralprocessing unit of the cluster, which introduces the backhauloverhead. In this paper, we assume that cooperative BSs areconnected to a centralized unit with high-speed backhaul links(e.g. fiber links or microwave) whose bandwidth is sufficientto facilitate the proposed scheduling algorithm.

Dynamic User Grouping for Multi-cell SC-FDMA Uplink

The user groups are scheduled on different RBs and receivingBSs in our scheme. Considering the time-frequencycorrelation, we assume all subcarriers in one RB have thesame CSI which can be obtained by taking the average of theCSIs of the subcarriers within the RB. To keep proceduressimple, we drop the subscripts of subcarrier/RB and receivingBS in the description of user grouping.

ADVANTAGES:

  • To achieve maximum system overall throughput with MMSE equalizationand AM techniques.
  • To reduce the computation complexity
  • An efficient IHA URP algorithm for solution of the joint resource allocation problem.
  • the proposed algorithm attains better system throughput than both the traditionalalgorithms with fixed user grouping and the algorithms without multi-cell cooperation.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1GB

SOFTWARE REQUIREMENTS:

  • Operating system : Windows 7.
  • Coding Language : MATLAB
  • Tool : MATLAB R2013A

REFERENCE:

Xiaofeng Lu, Qiang Ni, Wenna Li, Hailin Zhang, “Dynamic User Grouping and Joint ResourceAllocation with Multi-Cell Cooperation for UplinkVirtual MIMO Systems”, IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2017.

Design of Simultaneous Wireless Information and Power Transfer Scheme for Short Reference DCSK Communication Systems

Design of Simultaneous Wireless Information and Power Transfer Scheme for Short Reference DCSK Communication Systems

Design of Simultaneous Wireless Information and Power Transfer Scheme for Short Reference DCSK Communication Systems

 

ABSTRACT:

Recently, a short reference differential chaos shiftkeying system (SR-DCSK) has been proposed to overcomethe dominant drawbacks related to low data rate and energyefficiency fondness of conventional DCSK systems. The fact thatterminals on a network have a limited battery capacity and arein desperate need to high energy efficiency transmission schemescompels us to tackle these crucial challenges. In this paper, wepropose an SR-DCSK system that performs simultaneous wirelessinformation and power transfer (SWIPT). This promising designexploits the saved time gained from the fact that reference signalduration of SR-DCSK scheme occupies less than half of the bitduration to transmit a signal. The aim of this system is to allowreceivers to perform without being equipped with any externalpower supply. Furthermore, at the receiver side, an RF-to-dcconversion is first performed, followed by data recovery withoutthe need to any channel estimator. Closed-form expressions ofmultiple-input single-output SR-DCSK SWIPT system, such asergodic rate, harvesting time, energy shortage, and data outageas well as exact and approximate bit error rate probabilitiesare derived under Rayleigh fading channel and are validated viasimulation. Our results show that the proposed solution savesenergy without sacrificing the non-coherent fashion of the systemor reducing the rate compared to conventional DCSK, whilekeeping the design simple.

OBJECTIVE:

To allow receivers to perform without being equipped with any external power supply. Furthermore, at the receiver side, an RF-to-DC conversion is first performed, followed by data recovery without the need to any channel estimator. Closed form expressions of multiple-input single-output (MISO) SR-DCSK
SWIPT system such as ergodic rate, harvesting time, energy shortage, data outage as well as exact and approximate bit error rate probabilities are derived under Rayleigh fading channel and are validated via simulation.

INTRODUCTION

ENERGY harvesting (EH) technique is recently consid- ered as one of the promising solutions to enhance the lifetime of wireless networks. In fact, conventional energy harvesting systems do forage energy from external green sources, such as wind or solar energy. However, such green energy methods require large circuitry and cabling and cannot be applied to mobile units. This limitation motivates the development of simultaneous wireless information and power transfer (SWIPT). In this context, wireless power transfer refers to studies related to such scenarios.

 EXISTING SYSTEM:

  1. Shi, L. Liu, W. Xu, and R. Zhang, “Joint transmit beamforming and receive power splitting for MISO SWIPT systems,” IEEE Trans.Wireless Commun., vol. 13, no. 6, pp. 3269–3280, Jun. 2014.

Harvesting is achieved by the PS method.

  1. Xiang and M. Tao, “Robust beamforming for wireless information and power transmission,” IEEE Wireless Commun. Lett., vol. 1, no. 4, pp. 372–375, Aug. 2012.

The impact of imperfect channel state information (CSI) at the transmitter in a multi-antenna wireless broadcasting system with SWIPT.

 DRAWBACKS:

  • The efficiency of wireless power transfer (WPT) drastically decreases when the distance between the transmitter and the receiver increases due to the path loss factor.
  • Sacrificing the non-coherent fashionof the system.
  • Reducing the rate compared to conventionalDCSK, while keeping the design simple

 PROPOSED SYSTEM:

In this work, we propose an SR-DCSK system that performs simultaneous wireless information and power transfer (SWIPT). This promising design exploits the saved time gained from the fact that referencesignal duration of SR-DCSK scheme occupies less than half of the bit duration to transmit a signal.

 BLOCK DIAGRAM:

Design of Simultaneous Wireless Information andPower

DESCRIPTION:

We start this section by briefly explaining the conventional non-coherent DCSK system to better evaluate the motivation behind our choice of the SR-DCSK scheme and the advantages therein. The ith transmitted bit bi = 1g in the conventional DCSK system modulator is composed of two equal-length
arrays of length β each, placed in two successive time niches (portions), such that the first time niche is allocated to the reference signal, and the second niche is dedicated to the data carrier. The data carrier simply contains the product of the
reference signal by the transmitted bit, i.e. the bit is spread by the reference sequence. In simpler terms, the content of the second niche will either be the reference signal or an inverted version of the reference signal depending on the transmitted bit, e.g. being +1 or 1. In an identical fashion to the processing gain in CDMA communication systems, the spreading factor in DCSK systems is defined as the length of the chaotic sequence that is used to spread each transmitted bit and is represented by 2β, where β is an integer.

Our model consists of a BS equippedwith L transmit antennas implementing SR-DCSK modulationto transmit the ensemble of data and power to an intended
user terminal (UT) equipped with a single receiver antenna.Without the knowledge of the CSI at the transmitter side, thisconfiguration aims to increase the transmission diversity.

ADVANTAGES:

  • Making the reference signal shorter than the data carrier signal in order to reduce the frame duration.
  • The frame becomes shorter and the integration of the EH unit, without sacrificing data rate, becomes possible.
  • Including energy shortage probability, data outage probability and bit error rate probability under Rayleigh fading channel in MISO scenario are analyzed and derived in closed form.
  • Taking BER performance, data rate, energy efficiency, user autonomy.

 SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1GB

SOFTWARE REQUIREMENTS:

  • Operating system : Windows 7.
  • Coding Language : MATLAB
  • Tool : MATLAB R2013A

REFERENCE:

Georges Kaddoum, Member, IEEE, Ha-Vu Tran, Long Kong, and Micheal Atallah, “Design of Simultaneous Wireless Information andPower Transfer Scheme for Short ReferenceDCSK Communication Systems”, IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 65, NO. 1, JANUARY 2017.

Comparison of Collision-Free and Contention-Based Radio Access Protocols for the Internet of Things

Comparison of Collision-Free and Contention-Based Radio Access Protocols for the Internet of Things

Comparison of Collision-Free and Contention-Based Radio Access Protocols for the Internet of Things

 

ABSTRACT:

The fifth-generation (5G) cellular networks will facethe challenge of integrating the traditional broadband serviceswith the Internet of Things (IoT), which is characterized bysporadic uplink transmissions of small data packets. Indeed, theaccess procedure of the previous generation cellular network (4G)is not able to support IoT traffic efficiently because it requiresa large amount of signaling for the connection setup beforethe actual data transmission. In this context, we introduce twoinnovative radio access protocols for sporadic transmissions ofsmall data packets, which are suitable for 5G networks becausethey provide a resource-efficient packet delivery exploiting aconnection-less approach. The core of this paper resides in thederivation of an analytical framework to evaluate the performanceof all the aforementioned protocols. The final goal is thecomparison between 4G and 5G radio access solutions employingboth our analytical framework and computer simulations. Theperformance evaluation results show the benefits of the protocolsenvisioned for 5G in terms of signaling overhead and accesslatency.

OBJECTIVE:

  • Derivation of an analytical framework to evaluate the performance of all the aforementioned protocols.
  • Comparison between 4G and 5G radio access solutions employingboth our analytical framework and computer simulations.

INTRODUCTION

THE Internet of Things (fundamental role in improving the quality of our lives IoT) is expected to play ain near future, allowing the activation of new services thatspan from goods tracking to e-Health. According to the\ Ciscor Visual Networking Index (VNI) Forecast, a hugegrowth of the Machine-to-Machine (M2M) market, which isthe most important enabler of the IoT paradigm, is expected inthe next five years. This implies that the number of Machine Type Devices (MTDs), i.e., smart meters, wireless sensors, andactuators, will increase with an exponential trend. Nowadays,the majority of wireless technologies for M2M traffic are adhoc, short range wireless solutions, e.g., based on the IEEE802.15.4 standard.

EXISTING SYSTEM:

C.-Y. Tu, C.-Y.Ho, and C.-Y. Huang, “Energy-Efficient Algorithms andEvaluations for Massive Access Management in Cellular Based Machineto Machine Communications,” in Proc. IEEE Vehicular Technology Conf.(VTC Fall), San Francisco, CA, USA, Sep. 2011, pp. 1–5

QoS of Machine-Type Communication by clustering the MTDs

  1. Ijaz, L. Zhang, M. Grau, A. Mohamed, S. Vural, A. U. Quddus,M. A. Imran, C. H. Foh, and R. Tafazolli, “Enabling Massive IoT in 5Gand Beyond Systems: PHY Radio Frame Design Considerations,” IEEEAccess, vol. 4, pp. 3322–3339, Jun. 2016

An entire frame design for a low-complexity Time DivisionDuplex (TDD) system with suitable radio numerology formassive connection density and bursty packet transmissions.

DRAWBACKS:

  • If more than one UEselect the same preamble, we have a collision event that
    the eNB is usually not able to detect at this stage.
  • UEs thatdo not receive a RAR within a specific time intervalmust set a random backoff timer to start a new preambletransmission.
  • Not applicable to modulation classification for MIMO-OFDM systems.

 PROPOSED SYSTEM:

             We have presented two efficient radio access protocols forsporadic small UL data traffic aiming at minimal signalingoverhead and scalability with respect to the number of IoTdevices per radio cell. Their main characteristic is to transmitthe data already in the first or in the second stage of thecommunication. In contrast to LTE, the two protocols arecontention-based and eventually connectionless, i.e.,there isno collision resolution mechanism and connection setup andrelease are not required before and after data transmission.

 BLOCK DIAGRAM:

Comparison of Collision-Free and Contention

DESCRIPTION:

              The resource-efficient radio access schemesfor IoT terminals are presented. Firstly, the PHY specificationsare described and, then, the proposed solution is introduced intwo variants, i.e., the One-Stage protocol and the Two-Stageprotocol. Possible feedback formats are discussed and, finally,a comparison with LTE is provided. Without loss of generality,in the following we assume perfect synchronization of all ULtransmissions at the eNB.

Physical Layer Design

Multiple Access (OFDMA), consisting of elementary resourceunits called Resource Elements (REs), equivalent to onesubcarrier and one time symbol (OFDM symbol). A groupof REs over S subcarriers and T symbols forms a ResourceBlock (RB). In the following we assume that a RB spans aperiod of one subframe, also called Transmission Time Interval(TTI), of duration TTTI.

ADVANTAGES:

  • Minimal delay in case of low traffic load.
  • Superior with respectto throughput and is the appropriate choice at high traffic load.
  • Effect can be in principle achieved with windowing, at the costof an additional delay.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1GB

SOFTWARE REQUIREMENTS:

  • Operating system : Windows 7.
  • Coding Language : MATLAB
  • Tool : MATLAB R2013A

REFERENCE:

Marco Centenaro, Student Member, IEEE, Lorenzo Vangelista, Senior Member, IEEE,Stephan Saur, Member, IEEE, Andreas Weber, and Volker Braun, “Comparison of Collision-Free and Contention-BasedRadio Access Protocols for the Internet of Things”, IEEE TRANSACTIONS ON COMMUNICATIONS, 2017.

Bayesian Reinforcement Learning-Based Coalition Formation for Distributed Resource Sharing by Device-to-Device Users in Heterogeneous Cellular Networks

Bayesian Reinforcement Learning-Based Coalition Formation for Distributed Resource Sharing by Device-to-Device Users in Heterogeneous Cellular Networks

 

ABSTRACT:

This paper investigates the problem of distributed resource sharing in a device-to-device (D2D) enabled heterogeneous network where the various device pairs choose their transmission channels, modes, base stations (BSs), and power levels without any control by the BSs based only on the locally-observable information. This problem is represented as a Bayesian coalition formation game where the players (device pairs) create coalitions to maximize their long-term rewards with no prior knowledge of the values of potential coalitions and the types of their members. To minimize these uncertainties, a novel Bayesian reinforcement learning (RL) model is derived. In this model, the players update (through repeated coalition formation) their beliefs about the types and coalitional values to reach a stable coalitional agreement. The proposed Bayesian RL-based coalition formation algorithms are implemented in a Long-Term Evolution Advanced (LTE-A) network and evaluated using simulations. The algorithms show a superior performance when compared to other relevant resource allocation schemes and achieve near-optimal results after a relatively small number of RL iterations.

OBJECTIVE:

This paper studies distributed resource sharing in a D2D-enabled heterogeneous network where the various device pairs select their transmission channels, modes (cellular or D2D), BSs, and power levels autonomously based only on the locally-observable information.

  • First, a new game model where players (device pairs) create coalitions (formed by the players operating on one channel) to maximize their long-term rewards, is proposed.
  • Next, note that any player can learn something about other players via interactions with them in coalition(s). In other words, if RL is deployed during repeated coalition formation, then all uncertainties about coalitional values and types will reduce and, eventually, vanish (in which case the strong BC will match the strongly-stable deterministic core where the players’ beliefs coincide, since each player knows or almost certain about the types).

INTRODUCTION:

IN D2D communication, device pairs directly exchange their data over cellular spectrum without traversing a BS which allows for the significantly increased spectrum efficiency, reduced power consumption and latency of mobile terminals .The two-fold challenge of D2D communication is interference mitigation (for different users transmitting over the same frequencies) and efficient spectrum sharing. Most works on spectrum and interference management for D2D communication consider the network as a single entity where the resources are allocated by some BS that has a global knowledge of the precise or statistical channel state information (CSI). In general, however, the D2D links operate autonomously and cannot be fully controlled by the BS. In addition, the conventional pilot signals (used in cellular communication) cannot be applied for estimation of the D2D   channels and, hence, the accurate CSI assumption in D2D systems is rather unrealistic . A distributed nature of D2D links makes the game theory a suitable tool for modeling D2D communication.Allowing cooperation among various radio terminals enables a further increase in spectrum utilization resulting in improved quality of service .

EXISTING SYSTEM:

  1. Xiao et. al., “A Bayesian Overlapping Coalition Formation Game for Device-to-Device Spectrum Sharing in Cellular Networks,” IEEE Trans. Wireless Commun., vol. 14, no. 7, pp. 4034 – 4051, Jul. 2015.
  • The goal of each player (D2D pair) is to jointly select the channel and power to maximize its reward. To solve this problem, a multi-agent Q-learning algorithm (not relying on the information exchange and/or cooperation among different users) has been developed and implemented in an LTE-A network. A new game model (the Bayesian non-transferable utility overlapping coalition formation) for spectrum sharing between cellular and D2D communication in a multi-operator network,
  1. Asheralieva, Y. Miyanaga, “An Autonomous Learning-Based Algorithm for Joint Channel and Power Level Selection by D2D Pairs in Heterogeneous Cellular Networks,” IEEE Trans. Commun., vol. 64, no. 9, pp. 3996 – 4012, Sep. 2016.
  • The authors study a basic network comprising one BS and, hence, the possibility of the inter-cell interference among the users operating in different cells on the same frequency bands is not considered.

PROPOSED SYSTEM:

This problem is represented as a Bayesian coalition formation game where the players (device pairs) create coalitions to maximize their long-term rewards with no prior knowledge of the values of potential coalitions and the types of their members. To minimize these uncertainties, a novel Bayesian reinforcement learning (RL) model is derived. In this model, the players update (through repeated coalition formation) their beliefs about the types and coalitional values to reach a stable coalitional agreement. The proposed Bayesian RL-based coalition formation algorithms are implemented in a Long-Term Evolution Advanced (LTE-A) network and evaluated using simulations.

DESCRIPTION:

Network Model

Consider a heterogeneous network formed by the set N = {1, …,N} of macro-, micro-, pico-, or femto-BSs, numbered BS1, …, BSN. The total network bandwidth comprises a set K = {1, …,K} of orthogonal channels, numbered C1, …, CK. All BSs operate on their licensed spectrum bands that may overlap with each other. A set of channels within the bandwidth of BSn, nN, is denoted by Kn K. A network serves M device pairs that communicate either in the D2D or in cellular mode. For notation consistency, the device pairs are numbered, interchangeably, as PUN+1, …, PUN+M or (UN+1, U(N+1)ʹ), …, It is assumed that each user stays in the system for the indefinitely-long time. Hence, although the users eventually leave, they are uncertain about the exact duration of their stay in the network.

Let Ck= {mM| sm(t) = k}, kK, be a set of device pairssharing one channel Ckat slot t. At any t, the number of D2Dpairs occupying one channel is unlimited. On the other hand, atmost one cellular user is allowed on each channel of every BS.

Thus,

BAYESIAN COALITION FORMATION GAME 

 Game Formulation

Definition 1 (Bayesian coalition formation game): BCFG isa coalition formation game, defined by i) a set of players M; ii)a set of types Θm, for mM; iii) the players’ beliefs Bm, for mM; iv) a set of coalitional actions ACk, for each coalition Ck;v) a set S of stochastic states (outcomes); vi) the transition dynamics Pr{s | Ck, aCk, θCk} defined as the probability of anoutcome sSfrom a particular action aCkACk realized by coalition Ck with members of the type θCk; vii) the reward functions um, for mM.

Stability Notion  

In cooperative game theory, any coalition Ck with members of a type θCk is characterized by its value V(Ck| θCk) (defined as a maximal reward that all members of Ck can jointly receive through effective cooperation.

Coalition Formation  

By analogy with a deterministic coalition formation process (presented in [24]), let us define the Bayesian coalition formation process for a considered BCFG.6 Suppose that negotiations for coalition formation take place over a (possibly) infinite number of game stages 

ADVANTAGES:

  • A novel Bayesian RL-based coalition formation approach for D2D-enabled heterogeneous networks
  • Distributed resource allocation problem for D2D users is modeled as a BCFG.
  • The players are uncertain about the values of potential coalitions and types of their members..
  • Utilize the knowledge obtained through repeated coalition formation, a Bayesian RL process (allowing the players to form their beliefs about the expected coalitional values based only on the locally-observable information) is derived and solved using the exact and approximate pomdps

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1GB

SOFTWARE REQUIREMENTS:

  • Operating system : Windows 7.
  • Coding Language : MATLAB
  • Tool : MATLAB R2013A

REFERENCE:

Alia Asheralieva, “Bayesian Reinforcement Learning-Based Coalition Formation for Distributed Resource Sharing by Device-to-Device Users in Heterogeneous Cellular Networks”, IEEE Transactions on Wireless Communications, 2017.