Dynamic Clustering and ON/OFF Strategies for Wireless Small Cell Networks

Dynamic Clustering and ON/OFF Strategies for Wireless Small Cell Networks

Dynamic Clustering and ON/OFF Strategies for Wireless Small Cell Networks

ABSTRACT:

In this paper, a novel cluster-based approach for maximizing the energy efficiency of wireless small cell networks is proposed. A dynamic mechanism is proposed to group locally coupled small cell base stations (SBSs) into clusters based on location and traffic load. Within each formed cluster, SBSs coordinate their transmission parameters to minimize a cost function which captures the tradeoffs between energy efficiency and flow level performance, while satisfying their users’ quality-of-service requirements. Due to the lack of inter-cluster communications, clusters compete with one another in order to improve the overall network’s energy efficiency. This inter-cluster competition is formulated as a noncooperative game between clusters that seek to minimize their respective cost functions. To solve this game, a distributed learning algorithm is proposed using which clusters autonomously choose their optimal transmission strategies based on local information. It is shown that the proposed algorithm converges to a stationary mixed-strategy distribution which constitutes an epsilon-coarse correlated equilibrium for the studied game. Simulation results show that the proposed approach yields significant performance gains reaching up to 36% of reduced energy expenditures and up to 41% of reduced fractional transfer time compared to conventional approaches.

OBJECTIVE

  • To Maximizing the energy efficiency of wireless small cell networks, dynamic clustering and switching will be developed
  • Proposed to group locally coupled small cell base stations (SBSs) into clusters based on location and traffic load

INTRODUCTION

In the past decade, wireless services have evolved from traditional voice and text messaging to advanced applications such as video streaming, multimedia file sharing, and social networking. Such bandwidth-intensive applications increase the load of existing wireless cellular systems and potentially lead to increased energy consumption. The deployment of low-cost and high-capacity small cells over existing cellular networks has been introduced as a promising solution to offload the macro cellular traffic to small cell networks

EXISTING SYSTEM

  • System 1: Optimal deployment strategy.

  • Optimal deployment strategy’s Goals:
    • Low power operation
    • Effective collision avoidance
    • Simple implementation
    • Efficient at both low and high data rates
    • Tolerant to changes on the network
    • Spectral efficiency
  • System 2: Stochastic programming approach

  • Stochastic programming approach
    • Reduce latency and reduce energy consumption
    • Minimize overhearing problem.
    • More coordination of network nodes
    • High efficiency

DRAWBACKS

  • Energy conservation is high for moderate traffic.
  • Lack of security.
  • Collision and interference occurrences

PROPOSED SYSTEM

  • A dynamic mechanism is proposed to group locally coupled small cell base stations (SBSs) into clusters based on location and traffic load.
  • Within each formed cluster, SBSs coordinate their transmission parameters to minimize a cost function which captures the tradeoffs between energy efficiency and flow level performance
  • Distributed learning algorithm is proposed using which clusters autonomously choose their optimal transmission strategies based on local information.
  • 173           

BLOCK DIAGRAM

172

  • In small cell networks, performing dynamic approaches for switching BSs ON and OFF may require the knowledge of the entire network to operate effectively which incurs significant overhead. Therefore, coordination mechanisms with minimum overhead are needed to group BSs into clusters within which BSs can smartly and locally coordinate their transmissions. Unlike previous studies, we investigate not only location-based clustering methods, but we also consider the effects of BS capabilities to dynamically handle traffic, and further compare the performance of centralized and decentralized clustering solutions.

171

Software Tool description

  • Programming Language           :         C / MATLAB code 
  • Platform                         :         Windows / Linux
  • Tool                               :         MATLAB 8.1

ADVANTAGES

  • Reduce the energy consumption compare to other function
  • Lifetime efficiency
  • Reduces the overall network traffic

APPLICATIONS

  • Video streaming
  • Multimedia file sharing
  • Social networking
  • Macro and micro cellular network

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:

Sumudu Samarakoon Student Member, IEEE, Mehdi Bennis Senior Member, IEEE, Walid Saad Senior Member, IEEE and Matti Latva-aho Senior Member, “Dynamic Clustering and ON/OFF Strategies for Wireless Small Cell Networks”, IEEE Transactions on Wireless Communications, 2016.

Delay and Power Consumption in LTE/LTE-A DRX Mechanism with Mixed Short and Long Cycles

Delay and Power Consumption in LTE/LTE-A DRX Mechanism with Mixed Short and Long Cycles

Delay and Power Consumption in LTE/LTE-A DRX Mechanism with Mixed Short and Long Cycles

ABSTRACT:

Energy consumption is a major concern in today’s wireless communications due to the consensus for a greener world. LTE-Advanced (LTE-A) has been standardized for the 4th generation mobile communications to meet the growing demands for high-speed wireless communications. However, high-speed signal processing on LTE/LTE-A user equipment (UE) causes excessive power consumption. Discontinuous reception (DRX) mechanism is a critical technique for tackling this issue. Delay constraint and power saving are two contradictory performance metrics associated with the DRX mechanism. Using recursive deduction and Markov model, this paper provides an in-depth analysis on the average delay and average power consumption of the DRX mechanism. Two performance metrics, namely power saving factor and relative power saving, are devised to assess power saving performance of the DRX mechanism. The accuracy of theoretical analysis is validated by computer simulations using the parameters in compliance with LTE specifications. The performance of the DRX mechanism is governed by a set of parameters that interact with one another in an intricate manner. Therefore, the values of key parameters are tested to assess their impacts on the performance of the DRX mechanism. The results shown in this paper give an insight on the operation and further improvement of the DRX mechanism.

OBJECTIVE:

  • Provides an in-depth analysis on the average delay and average power consumption of the DRX mechanism.
  • This paper provides a simple and yet intuitive approach to analyze the average DRX delay for K and X packets to arrive at an eNB before and after the end of on uration of UE (D(K,X)),
  • Finally, the average delays of DRX mechanism with DRX short and long cycles (E[D]) are obtained.

INTRODUCTION:

  • The pursuit for handy terminals and diverse functions is the driving force of today’s smart phone designs. Smart phones provide high spectral efficiency, fast speed, and content-rich multimedia services, but high power consumption is still a major concern. When long-term evolution (LTE)/LTE-Advanced (LTE-A) technologies are deployed, the user experience will depend also on its power saving performance. Discontinuous reception (DRX) mechanism in LTE/LTE-A allows user equipment (UE) that is not receiving data from evolved Node B (eNB) to enter a power saving mode to extend its battery life.
  • In most non-real time applications, such as web browsing and instant messaging, there is a period of time in which UE does not need to continuously monitor downlink channel. For such non-delay-sensitive services, the DRX cycle can be set longer for a better power saving. However, real-time services such as VoIP applications are sensitive to delay. Therefore, for delay-sensitive services, the delay should be a priority concern over power saving.

 

 

EXISTING SYSTEM:

S.-R. Yang and Y.-B. Lin, “Modeling UMTS discontinuous receptionmechanism,” IEEE Trans. Wireless Comm., vol. 4, no. 1, pp. 312_319,Jan. 2005.

  • The performance of DRX mechanism in Universal Mobile Telecommunications System (UMTS).

ETSI Technical Report UMTS 30.03 version 3.2.0: “Universal MobileTelecommunications System (UMTS); Selection Procedures for theChoice of Radio Transmission Technologies of the UMTS,” Apr. 1998.

  • A Poisson arrival process and ETSI bursty traffic model.

DRAWBACKS:

  • Power consumption is likely to be reduced in that TTI at the cost of an increased delay.
  • The probability of false alarm for blind decoding can be reduced to almost zero with a moderate to large value of observed interval.
  • Low probability of PDCCH detection error.
  • The fact that it is unable to enter a DRX long cycle frequently.
  • The parametervalues used in the simulations were too large. DRX parameter values should be set in a specificrange.

PROPOSED SYSTEM:

  • The purpose of this paper is to present an in-depth analysis on the average delay and power consumption of the DRX mechanism for LTE/LTE-A systems when UE is in RRC CONNECTED mode, based on the specifications of LTE/LTE-A and a commonly used power consumption model. It is assumed that both DRX short and long cycles are used to improve power saving.
  • The performance of the DRX mechanism is governed by a set of parameters that interact with one another in an intricate manner. Therefore, the values of key parameters are tested to assess their impacts on the performance of the DRX mechanism. The results shown in this paper give an insight on the operation and further improvement of the DRX mechanism.

161

DESCRIPTION:

The analysis on DRX average delay, inwhich two DRX cycles, short and long cycles, are considered. When a UE is in its on-duration, it goes to its active state and monitors PDCCH message through PDCCH blind decoding. This is to ensure that a UE can correctly determine which PDCCH messages are destined for it. If a UE does not detect any PDCCH message for it, it enters a ligt sleep state in a DRX short cycle or a deep sleep state in a DRX long cycle at the end of the on-duration. It then wakes up again in on duration of the next DRX cycle to monitor PDCCH messages. Conversely, if a UE detects a PDCCH message intended for it, it initiates I-timer and continuously detects if there is a new PDCCH message. If a new PDCCH message is detected before the I-timer expires, the I-timer is re-started. These actions are repeated until the expiry of the I-timer, at which UE instantly enters to a light or deep sleep state. The major parameters of LTE/LTE-A DRX mechanism are defined as follows, and they will be used throughout this paper.

  • On-duration timer: It starts at the beginning of a DRX cycle, during which a UE monitors PDCCH messages from eNB, and its value can be 1, 2, 3, 4, 5, 6, 8, 10, 20, 30, 40, 50, 60, 80, 100, or 200 TTIs . The value is denoted by tO.
  • Inactivity timer: Whenever a UE detects a PDCCH message for it, it restarts this timer to allow an extended period for receiving packets.
  • DRX short cycle: The length is measured in TTIs and can be 2n (where n = 1, . . . , 9), or 5 ⇥ 2n (where n = 1, . . . , 6) Its value is denoted by ts.
  • DRX long cycle: The length, denoted by tL, is measured in TTIs and is typically a multiple ofts To simplify our notations, let t be the value of a DRX cycle, which can bets or tL, depending on whether the UE is in a DRX short cycle or a DRX long cycle.
  • DRX short cycle timer: This parameter, denoted by Y, specifies the number of DRX short cycles that a UE must endure without receiving a valid PDCCH message before entering a DRX long cycle.

  • ADVANTAGES:
  • A mixed DRX short and long cycles that affect the average delay and power consumption.
  • Several performance metrics were used to facilitate the analysis.
  • The results of theoretical analysis were consistent with the simulation results for both average delay and power consumption.
  • The values of the parameters in DRX were varied to investigate their effects on performance.
  • Intricate interaction among the parameters in mixed DRX short and long cycles was observed and analyzed.
  • The proper choice of parameter values can help reduce power consumption and maintain average delay below a given level.

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:

Chih-Cheng Tseng, Hwang-Cheng Wang, Fang-Chang Kuo, Kuo-Chang Ting, Members, IEEE, Hsiao-Hwa Chen, Fellow, IEEE, and Guan-Yun Chen, “Delay and Power Consumption in LTE/LTE-A DRX Mechanism with Mixed Short and Long Cycles”, IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2016.

Coupled Detection and Estimation based Censored Spectrum Sharing in Cognitive Radio Networks

Coupled Detection and Estimation based Censored Spectrum Sharing in Cognitive Radio Networks

Coupled Detection and Estimation based Censored Spectrum Sharing in Cognitive Radio Networks

ABSTRACT:

A novel spectrum sharing strategy based on coupled detection and estimation is proposed for cognitive radio networks. The proposed approach is able to trade-off throughput for reduced interference at the Primary User (PU) via censored transmissions. We derive the optimum censoring strategy that maximizes the throughput of the cognitive radio system under an average interference power constraint at the PU. We then extend the proposed framework to jointly optimize the censoring and the power allocation strategies of the Secondary User (SU) that maximize the throughput of the secondary network under average transmit power and average interference power constraints. Finally, we provide extensive simulation results to demonstrate the enhanced performance of the proposed censoring based spectrum sharing approach.

OBJECTIVE:

The concept of transmission censoring in CR networks based on coupled detection and estimation, and derived the optimum censoring strategy in terms of a censoring parameter that maximizes the total throughput of the cognitive radio network under an average interference power constraint at the PU receiver.

INTRODUCTION:

The exponential increase in the number of wireless communication users and the increasing diversity of traffic types (voice, text messaging, Web and multimedia) as well as the demand for high quality-of-service (QoS) have intensified research on efficient utilization of the electromagnetic spectrum.

In cognitive radio networks, unlicensed users, often referred to as secondary users (SUs),access the licensed spectrum allocated to the primary users(PUs) in an opportunistic manner. Primarily two different approaches have been suggested in the literature regarding the manner in which an SU can access the licensed spectrum: Opportunistic spectrum access (OSA) (or interweave accesss cheme) and spectrum sharing.

Beamforming allows coexistenceof SUs and PUs via interference control at the PU receiver, while simultaneously increasing the throughput of the SUs. However, spectrum sharing via beamforming in CR networks is a challenging problem, as any error in PU localization will increase the interference at the PU. To the best of our knowledge, beamforming based spectrum sharing strategies, in the presence of imperfect PU localization have not been addressed so far in literature.

 

EXISTING SYSTEM:

Joseph Mitola III and Gerald Q Maguire Jr, “Cognitive radio: makingsoftware radios more personal,” Personal Communications, IEEE, vol.6, no. 4, pp. 13–18, 1999.

  • Cognitive radio (CR) technology has emerged as one of the most promising solutions to alleviate the problem of spectrum scarcity, without requiring an actual increase of the licensed radio spectrum

GanZheng, Shaodan Ma, Kai-Kit Wong, and Tung-Sang Ng, “Robustbeamforming in cognitive radio,” Wireless Communications, IEEE Transactions on, vol. 9, no. 2, pp. 570–576, 2010

One way of implementing a hybrid spectrum sharing access scheme in CR networks is via null steering beam forming

DRAWBACKS:

  • In cognitive radio networks, unlicensed users, often referred to as secondary users (SUs),access the licensed spectrum allocated to the primary users
  • The spectrum allocated to an existing PU only when no PU activity is detected.
  • In spectrum sharing, the SUs can coexist with the PU under the constraint.
  • The number of wireless communication users and the increasing diversity of traffic types(voice, text messaging, Web and multimedia) as well as the demand for high quality-of-service

PROPOSED SYSTEM:

  • The proposed framework to jointly optimize the censoring andthe power allocation strategies of the Secondary User (SU) that maximize the throughput of the secondary network under average transmit power and average interference power constraints.
  • Finally, we provide extensive simulation results to demonstrate the enhanced performance of the proposed censoring based spectrum sharing approach.

BLOCK DIAGRAM:

 

 152

DESCRIPTION:

The schematic diagram of a cognitive radio network (CRN) is shown in Fig. 1, where a specific SU, SUm, and its neighbouring SUs are randomly distributed with uniform distribution over a disk D(O;RSU), centred at O with radius RSU.

The following assumptions are made throughout this paper:

  • A1: The PU and all the SUs are static during the observation period, i.e., their locations do not change.
  • A2: SURx and the PU are in the far field of the CR network.
  • A3: There is no multipath or shadowing, i.e., the effect of signal scattering is negligible.
  • A4: All the SUs are equipped with antenna arrays and can perform beamforming in an intended direction.
  • A5: All the SUs are aware of the locations of the other SUs operating in the network.
  • A6: The PU, when active, is engaged in bidirectional communication with another PU whose location and activity are not relevant for the analysis in this paper.
  • A7: The SUs lying in the disk D(O;RSU), cooperate todetect and localize the PU.

  FLOW CHART:

151

ADVANTAGES:

  • We first study a hybrid spectrum access scheme where the detection and estimation (localization) processes are carried out independently, followed by a scheme, where the detection and estimation (localization) processes are carried out in a coupled manner.
  • We provide the theoretical foundations of the approach proposed in more rigorously.
  • We derive the optimum censoring parameter that maximizes the throughput of the proposed cognitive radio system.
  • We consider the problem of joint power control and transmission censoring by an SU to satisfy the interference constraint at the PU.
  • The proposed coupled spectrum sharing strategy controls the interference temperature at the PU, via censored transmissions based on the quality of the estimate
  • We have also neglected the effect of shadowing and channel fading.

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:

Jyoti Mansukhani, Priyadip Ray, Member, IEEE, and Pramod K. Varshney, Fellow, IEEE, “Coupled Detection and Estimation based Censored Spectrum Sharing in Cognitive Radio Networks”, IEEE Transactions on Wireless Communications, 2016.

Cooperative Multicasting in Renewable Energy Enhanced Relay Networks – Expending More Power to Save Energy

Cooperative Multicasting in Renewable Energy Enhanced Relay Networks – Expending More Power to Save Energy

Cooperative Multicasting in Renewable Energy Enhanced Relay Networks – Expending More Power to Save Energy

ABSTRACT:

Power and on-off control problems are examined for renewable energy enabled base-stations (BSs) and relay nodes (RNs) in cooperative multicast networks. Renewable energy is utilized at BSs and RNs to reduce the overall grid energy cost. By considering a practical energy consumption model and the statistics of the renewable energy arrival, the optimal transmit powers are first determined by minimizing the expected grid energy consumption subject to an average outage probability constraint at MUs. The optimal solution is found via line search in the general case and is obtained in closed-form at high SNR. In addition, an on-off control policy is also proposed to further reduce the basic operational energy costs. The joint on-off and power control problems are solved approximately using two sequential deflation techniques, namely, the subset-search and the convex-relaxation based approaches. The power control problem is also extended to the multicarrier scenario with unequal transmit powers and is solved using successive convex approximation. Simulations using the photovoltaic energy arrival model are provided to demonstrate the effectiveness of the proposed schemes. The results show that expending more power at RNs allows for more efficient use of renewable energy and, thus, increases energy-savings.

OBJECTIVE

  • Propose a successive convex approximation approach is proposed to solve the problem efficiently, due to the challenges imposed by the sum power and the average outage probability constraints across subcarriers
  • Two sequential deflation algorithm – One is based on a combinatorial subset search algorithm and the other is based on a convex-relaxation approach
  • Also an On – Off control policy is proposed to reduce the basic operational energy cost

INTRODUCTION

  • Power and on-off control problems are examined for renewable energy enabled base-stations (BSs) and relay nodes (RNs) in cooperative multicast networks. Renewable energy is utilized at BSs and RNs to reduce the overall grid energy cost. By considering a practical energy consumption model and the statistics of the renewable energy arrival, the optimal transmit powers are first determined by minimizing the expected grid energy consumption subject to an average outage probability constraint at MUs. The optimal solution is found via line search in the general case and is obtained in closed-form at high SNR.
  • Multicasting refers to the delivery of a common message to multiple receivers in the system. In wireless systems, this can be done efficiently by exploiting the so called wireless broadcast advantage, i.e., the advantage that the transmission of a message can be received simultaneously by all users within the transmission range.
  • Due to the increasing demand for mass content distribution, such as multimedia streaming, file sharing, and software or firmware updates, wireless multicasting has received much attention in recent years and has been incorporated into many modern wireless standards in the form of, e.g., the Multimedia Broadcast Multicast Service (MBMS) in the third generation partnership project (3GPP) and long term evolution (LTE) systems and the Internet Protocol Television (IPTV) in WiMax

EXISTING SYSTEM

  • Systems: Multimedia Broadcast Multicast Service (MBMS), third generation partnership project (3GPP), long term evolution (LTE) systems and Internet Protocol Television (IPTV) in WiMax
  • Problem 1: Even by exploiting the broadcast nature of the wireless medium, multicasting in wireless networks can still be challenging due to the high packet loss rate and diverse channel quality among users.
  • Solution: For instance, to have all users receive a common message with the same reliability, the transmission rate of the message must be less than the channel capacity of the worst user in the system.
  • Problem 2: This results in low transmission rate and, consequently, low throughput, especially when the number of receivers is larger or when the channel quality varies drastically among users.

PROPOSED SYSTEM

Let us consider multicasts relay network with K coordinated BSs and N RNs that are dedicated to the relaying of a common message from all BSs to M multicast users (MUs), as illustrated in Fig. 1. The BSs, the RNs, and the MUs are denoted by the sets B = {b1, b2, . . . ,bK}, R = {r1, r2, . . . , rN}, and U = {u1, u2, . . . , uM}, respectively. The distance between BS b and RN r is db, r ,p(xb− xr)2 + (yb− yr)2,where (xb, yb) and (xr, yr) are the coordinates of the locations of BS b and RN r, respectively. Similarly, the distance between BS b and MU u is db,u ,p(xb− u)2 + (yb− yu)2,where (xu, yu) represents the coordinates of the locationof MU u, and the distance between RN r and MU u is dr, u ,p(xr− xu)2 + (yr− yu)2. Here, BSs’ and RNs’ locations are fixed whereas MUs’ locations are assumed to be random and are independent and identically distributed (i.i.d.) according to the joint density f X,Y(x, y). In this work, we adopt a two-hop cooperative multicasting scheme, where the multicast message is sent from all BSs to MUs by utilizing a decode-and-forward scheme at RNs.

142

BLOCK DIAGRAM

  • We adopt a two-hop cooperative multicasting scheme, where the multicast message is sent from all BSs to MUs by utilizing a decode-and-forward scheme at RNs.
  • The effectiveness of the proposed techniques are demonstrated through computer simulations using the photovoltaic (PV) power generation model as an example. However, the proposed techniques are applicable to different energy arrival distributions as well.

141

ADVANTAGES

  • Two sequential deflation algorithms, i.e., one based on the subset search approach and one based on a convex-relaxation approach, were proposed to solve this problem in an efficient manner.
  • The power control problem was also extended to the multicarrier scenario with unequal transmit powers and was solved using a successive convex approximation technique.
  • Proposed schemes can effectively reduce the grid energy consumption in different scenarios and the statistics of the renewable energy arrival indeed play a major role in determining the power and on-off control decisions.

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:

Shi-Yong Lee, Chia-Yu Liu, Min-Kuan Chang, Member, IEEE, De-Nian Yang, Senior Member, IEEE, and Y.-W. Peter Hong, Senior Member, IEEE , “Cooperative Multicasting in Renewable Energy Enhanced Relay Networks – Expending More Power to Save Energy”, IEEE Transactions on Wireless Communications, 2016.

Artificial Noise Aided Secrecy Information and Power Transfer in OFDMA Systems

Artificial Noise Aided Secrecy Information and Power Transfer in OFDMA Systems

Artificial Noise Aided Secrecy Information and Power Transfer in OFDMA Systems

ABSTRACT:

In this paper, we study simultaneous wireless information and power transfer (SWIPT) in orthogonal frequency division multiple access (OFDMA) systems with the coexistence of information receivers (IRs) and energy receivers (ERs). The IRs are served with best-effort secrecy data and the ERs harvest energy with minimum required harvested power. To enhance the physical layer security for IRs and yet satisfy energy harvesting requirements for ERs, we propose a new frequency domain artificial noise (AN) aided transmission strategy. With the new strategy, we study the optimal resource allocation for the weighted sum secrecy rate maximization for IRs by power and subcarrier allocation at the transmitter. The studied problem is shown to be a mixed integer programming problem and thus non-convex, while we propose an efficient algorithm for solving it based on the Lagrange duality method. To further reduce the computational complexity, we also propose a suboptimal algorithm of lower complexity. The simulation results illustrate the effectiveness of proposed algorithms as compared against other heuristic schemes.

OBJECTIVE

  • Study simultaneous wireless information and power transfer (SWIPT) in orthogonal frequency division multiple access (OFDMA) systems with the coexistence of information receivers (IRs) and energy receivers (ERs)
  • Our goal is to maximize the weighted sum secrecy rate of the IRs subject to minimum harvested power requirements of individual ERs
  • Propose a frequency-domain AN generation and removal method for OFDMA-based SWIPT
  • Also propose a suboptimal algorithm of lower complexity

INTRODUCTION

  • Orthogonal frequency division multiple access (OFDMA) has many advantages such as flexibility in resource allocation and robustness against multipath channel fading, and therefore has become a well established multiple access technique for multiuser wireless communications systems.
  • In our work, we study the optimal resource allocation in the AN aided secure OFDMA systems with SWIPT, where two types of receivers are assumed, i.e., IRs and ERs. Our goal is to maximize the weighted sum secrecy rate of the IRs subject to minimum harvested power requirements of individual ERs. We propose a new frequency-domain AN method in OFDMA-based SWIPT to facilitate both secrecy information transmission and energy transfer to IRs and ERs, respectively.

EXISTING SYSTEM:

  • System 1: SWIPT systems
  • Problem: The energy receivers (ERs) need to be deployed much closer to the access points than the information receivers (IRs) due to their much higher received power requirement
  • System 2: Artificial noise (AN) aided OFDMA based SWIPT systems
  • Problem: In a secure OFDMA system without AN, only the user with the largest channel gain over each subcarrier (SC) can receive secure information

PROPOSED SYSTEM:

We consider a downlink OFDMA-based SWIPT system with secrecy constraints are of one base station (BS) with a single antenna, K single-antenna receivers and N SCs. The set of receivers is denoted by K = {1; :::;K}, among which K1 receivers are IRs given by the set K1 and the rest K2 receivers are ERs given by the set K2, i.e., K1∪K2 = K. Note that the receivers (both IRs and ERs) are considered to be separated and can only decode information or harvest energy at a time, unlike the co-located receivers considered.

132

BLOCK DIAGRAM

SWIPT systems enable the users to harvest energy and decode information from the same received signal, thus making most efficient use of the wireless spectrum for both information and energy transfer. SWIPT has drawn a great amount of research interests. For instance, two practical schemes for SWIPT, namely power splitting (PS) and time switching (TS), were proposed.

131

ADVANTAGES

  • Reduce the computational complexity
  • Suboptimal algorithm of lower complexity
  • Served with best-effort secrecy data and the ERs harvest energy with minimum required harvested power

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:

Meng Zhang, Student Member, IEEE, Yuan Liu, Member, IEEE, and Rui Zhang, Senior Member, IEEE, “Artificial Noise Aided Secrecy Information and Power Transfer in OFDMA Systems”, IEEE Transactions on Wireless Communications, 2016.

Analysis of Downlink Connectivity Models in a Heterogeneous Cellular Network via Stochastic Geometry

Analysis of Downlink Connectivity Models in a Heterogeneous Cellular Network via Stochastic Geometry

Analysis of Downlink Connectivity Models in a Heterogeneous Cellular Network via Stochastic Geometry

 

ABSTRACT:

In this paper, a comprehensive study of the downlink performance in a heterogeneous cellular network (or HetNet) is conducted via stochastic geometry.

A general HetNet model is considered consisting of an arbitrary number of open-access and closed-access tiers of base stations (BSs) arranged according to independent homogeneous Poisson point processes.

 

OBJECTIVE:

The BSs within each tier have a constant transmission power, random fading factors with an arbitrary distribution and arbitrary path loss exponent of the power-law path-loss model. For such a system, analytical characterizations for the coverage probability are derived for the max-SINR connectivity and nearest-BS connectivity models.

The results also demonstrate the effectiveness and analytical tractability of the stochastic geometric approach to study the HetNet performance

 

INTRODUCTION:

THE modern cellular communication network is an overlay of multiple contributing subnetworks such as the macrocell, microcell, picocell and femtocell networks. These are denoted collectively as heterogeneous networks (or, in short,

HetNets). HetNets have been shown to sustain greater end-user data-rates as well as provide indoor and cell-edge coverage. As such, they are an important feature of fourth-generation(4G) cellular standards .

Until recently, such networks have been analyzed solely through system simulations. HetNets consist of regularly spaced macrocell base-stations (BSs) along with irregularly spaced microcell and picocell BSs and randomly placed end user deployed femto cell BSs.

The result is a complex environment with many parameters that cannot be efficiently studied through simulation. Under these circumstances, ananalytical model that captures all the design scenarios of interest is needed.

 

EXISTING SYSTEM:

  1. Haenggi and R. K. Ganti, Interference in Large Wireless Networks,vol. 3. NoW Publishers Inc., 2008.

 

  • Cellular networks modeled with randomly deployed nodes have yielded a rich set of results.
  1. Andrews, F. Baccelli, and R. Ganti, “A tractable approach to coverage and rate in cellular networks,” IEEE Transactions on Communications, vol. 59, pp. 3122 –3134, Nov 2011.

  • It can yield analytically tractable results unlike purely simulation-based studies based on a regular hexagonal grid.

 

DRAWBACKS:

  • A more complex model mightsegregate BSs into many finely distinguished tiers to represent different types of operator equipment and interference sources.
  • Closed-access tiers represent private femtocells, unlicensed devices, and other interference sources.
  • The open and closed-access tiers enable many flexible HetNet models
  • consist of actual bss or Not, closed-access tiers model interfering radios that do not Provide service to the MS

PROPOSED SYSTEM:

Using stochastic ordering, interesting properties and simplifications for the HetNet downlink performance are derived by relating these two connectivity models to the maximum instantaneous received power (MIRP) connectivity model and the maximum biased received power (MBRP) connectivity models, providing good insights about HetNets and their downlink performance in these complex networks.

Furthermore, the results also demonstrate the effectiveness and analytical tractability of the stochastic geometric approach to study the HetNet performance.

BLOCK DIAGRAM:

                         122   

DESCRIPTION:

This section describes the various elements used to modelthe wireless network: the BS layout, the radio environment,and the role of the BS connectivity model.

 

BS Layout

The HetNet is composed of K open-access and L closedaccesstiers The BS layout in each tier is according toan independent homogeneous Poisson point process in R2with density _ok, _cl for the kth open-access tier and lthclosed-access tier, respectively, where k = 1; : : : ; K andl = 1; : : : ; L.

 

Radio Environment and downlink SINR

121

BS connectivity models

Given thresholds f_kgKk=1, a MS is able to communicatewith a BS i of the kth open-access tier if SINRki > _k.In this case, the MS is said to be in coverage. The BSconnectivity model determines to which BS the MS connects

and consequently its coverage performance.

 

ADVANTAGE:

  • The result is a relatively complex semi-analytic expression.
  • Analyze the max-SINR, nearest-BS, MIRP, and MBRP connectivity models.
  • The MS is allowed to communicate with any BS of the open-access tiers
  • The BS densities are homogeneous, without loss of generality the MS is placed at the origin.

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

 

An Efficient Bayesian PAPR Reduction Method for OFDM-Based Massive MIMO Systems

An Efficient Bayesian PAPR Reduction Method for OFDM-Based Massive MIMO Systems

An Efficient Bayesian PAPR Reduction Method for OFDM-Based Massive MIMO Systems

ABSTRACT:

We consider the problem of peak-to-average power ratio (PAPR) reduction in orthogonal frequency-division multiplexing (OFDM) based massive multiple-input multiple-output (MIMO) downlink systems. Specifically, given a set of symbol vectors to be transmitted to K users, the problem is to find an OFDM-modulated signal that has a low PAPR and meanwhile enables multiuser interference (MUI) cancelation. Unlike previous works that tackled the problem using convex optimization, we take a Bayesian approach and develop an efficient PAPR reduction method by exploiting the redundant degrees-of- freedom of the transmit array. The sought-after signal is treated as a random vector with a hierarchical truncated Gaussian mixture prior, which has the potential to encourage a low PAPR signal with most of its samples concentrated on the boundaries. A variational expectation-maximization (EM) strategy is developed to obtain estimates of the hyperparameters associated with the prior model, along with the signal. In addition, the generalized approximate message passing (GAMP) is embedded into the variational EM framework, which results in a significant reduction in computational complexity of the proposed algorithm. Simulation results show our proposed algorithm achieves a substantial performance improvement over existing methods in terms of both the PAPR reduction and computational complexity.

OBJECTIVE:

The problem is to find an OFDM-modulated signal that has a low PAPR and meanwhile enables multiuser interference (MUI) cancelation. Unlike previous works that tackled the problem using convex optimization, we take a Bayesian approach and develop an efficient PAPR reduction method by exploiting the redundant degrees-of- freedom of the transmit array.

 

INTRODUCTION:

  • Massive multiple-input multiple-output (MIMO), also known as large-scale or very-large MIMO, is a promising technology to meet the ever growing demands for higher throughput and better quality-of-service of next-generation wireless communication systems . Massive MIMO systems are those that are equipped with a large number of antennas at the base station (BS) simultaneously serving a much smaller number of single-antenna users sharing the same time-frequency bandwidth. In addition to higher throughput, massive MIMO systems also have the potential to improve the energy efficiency and enable the use of inexpensive, low power components. Hence, it is expected that massive MIMO will bring radical changes to future wireless communication systems.
  • In practice, broadband wireless communications may suffer from frequency-selective fading. Orthogonal frequency division multiplexing (OFDM), a scheme of encoding digital data on multiple carrier frequencies, has been widely used to deal with frequency-selective fading. However, a major problem associated with the OFDM is that it is subject to a high peak-to-average power ratio (PAPR) owing to the independent phases of the sub-carriers.

EXISTING SYSTEM:

  1. Studer and E. G. Larsson, “PAR-aware large-scale multi-user MIMOOFDM downlink,” IEEE J. Sel. Areas Commun., vol. 31, no. 2, pp.303–313, Feb. 2013.
    • A fast iterative truncation algorithm (FITRA) was developed.
    • H. Prabhu, O. Edfors, J. Rodrigues, L. Liu, and F. Rusek, “A low complex peak-to-average power reduction scheme for OFDM based massive MIMO systems,” in Communications, Control and Signal Processing (ISCCSP), 2014 6th International Symposium on, Athens, mGreece, 2014.
  • A peak signal clipping scheme was employed to reduce the PAPR and some of the antennas at the BS are reserved to compensate for peak-clipping signals.

DRAWBACKS:

  • The FITRA algorithm shows to have a fairly low convergence rate.
  • A regularization parameter to achieve balance between the PAPR reduction and the MUI cancelation.
  • High PAPR requires a high-resolution digital-to-analog converter (DAC) and a linear power amplifier (PA) at the transmitter, which is not only expensive but also power-inefficient.
  • It is of crucial importance to reduce the PAPR of massive MIMO-OFDM systems.
  • Extension to the multiuser(MU) MIMO downlink is not straightforward, mainly because joint receiver-side signal processing is almost impossible in practice as the users are distributed

PROPOSED SYSTEM:

The generalized approximate message passing (GAMP) is embedded into the variational EM framework, which results in a significant reduction in computational complexity of the proposed algorithm. Simulation results how our proposed algorithm achieves a substantial performance improvement over existing methods in terms of both the PAPR reduction and computational complexity.

 

BLOCK DIAGRAM:

111

DESCRIPTION:

We first introduce the system model of OFDM based massive MIMO systems. Then we discuss some recent research on PAPR reduction for multi-user massive MIMO-OFDM systems.

The system model of the OFDM-based massive MIMO downlink scenario is depicted in Fig. 1, where the BS is assumed to have M transmit antennas and serve K independent single-antenna users (K M), and the total number ofOFDM tones is N.

Since cooperative detection among users is often impossible, precoding must be performed at the BS to remove multiuser interference (MUI). Usually, the signal vector on the nth tone is linearly p recoded as

Wn= Pn*sn

At the receivers, after removing the CPs of the received signals, the DFT is performed to obtain the frequency-domain signals. The receive vector consisting of K users’ signals can be described as

Rn= Hn*wn+ en,

where rn C1 denotes the receive vector associated with the nth tone, and en C1 is the receiver noise and has i.i.d. circularly symmetric complex Gaussian entries with zero mean and variance No. If the ZF precoding scheme is used, the received signal vector equals to rn= sn+ en, n, which means the MUI is perfectly removed.

ADVANTAGES:

  • Considered the problem of joint PAPR reduction and multiuser interference (MUI) cancelation in OFDM based massive MIMO downlink systems.
  • A hierarchical truncated Gaussian mixture prior model was proposed to encourage a low PAPR solution/signal.
  • The GAMP technique was embedded into the variational EM framework to facilitate the algorithm development.
  • Proposed algorithm achieves notable improvement in PAPR reduction as compared with the FITRA algorithm, and meanwhile renders better MUI cancelation and lower out-of band radiation.
  • The proposed algorithm also demonstrates a fast convergence rate, which makes it attractive for practical real-time 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:

Hengyao Bao, Jun Fang, Zhi Chen, Hongbin Li, Senior Member, IEEE, and Shaoqian Li, Fellow, IEEE, “An Efficient Bayesian PAPR Reduction Method for OFDM-Based Massive MIMO Systems”, IEEE Transactions on Wireless Communications, 2016.

Achieving High Energy Efficiency and Physical-Layer Security in AF Relaying

Achieving High Energy Efficiency and Physical-Layer Security in AF Relaying

Achieving High Energy Efficiency and Physical-Layer Security in AF Relaying

ABSTRACT:

For transmitting data in a secret and energy-efficient manner in collaborative amplify-and-forward relay networks, the secure energy efficiency (EE) defined as the secret bits transferred with unit energy, is maximized to satisfy each node power constraint and target secrecy rate requirement, based on physical security framework. The secure EE is maximized by joint source and relay power allocation which is a non-convex optimization problem. To cope with this difficulty, a solution scheme and corresponding algorithms are developed by jointly applying fractional programming, exact penalty, alternate search, and difference of convex functions programming. The key idea of the scheme is to convert the primal problem into simple subproblems step by step, such that related methods are adopted. It is verified that, compared with secrecy rate maximization, the proposed scheme improves the secure EE significantly yet with a certain loss of the secrecy rate due to the tradeoff between secure EE and secrecy rate. Furthermore, the proposed scheme achieves higher secure EE and secrecy rate than total transmission power minimization does, while with a certain increase of power consumption. These results indicate that a reasonable balance among secure EE, secrecy rate, and power consumption can be reached by the proposed scheme.

OBJECTIVE:

  • We focus on the secure EE maximization for collaborative relay networks with amplify-and-forward (AF) protocol, where the legitimate user coexists with an Eavesdropper.
  • Providing solution scheme and corresponding algorithms for secure and energy efficient Data transmission
  • This algorithm is going to be developed by jointly applying fractional programming, exact penalty, alternate search, and difference of convex functions programming.
  • Finally it is compared with secrecy rate maximization

 

INTRODUCTION

  • With the rapid and radical evolution of information transmission requirements, the security of wireless communication has attracted increasing concerns due to the potential malicious attack. Therefore, various information security technologies are applied at different layers of the protocol stack, such as cryptographic security and physical layer security mechanisms.
  • In such a network, the information should be transferred in a secret and energy-efficient manner. Therefore, the secure EE is maximized via joint source and relay power allocation, while meeting the maximum allowed node power constraints and the minimum target secrecy rate requirement. The primal optimization problem is difficult to tackle due to the non-convexity of the secure EE and secrecy rate functions. Therefore, an iterative solution scheme is developed upon several mathematical methods termed as fractional programming, exact penalty, alternate search, and difference of convex functions (DC) programming.

 

EXISTING SYSTEM WITH IT DISADVANTAGES

    • Method 1: Physical-layer security-energy tradeoffs were investigated with the metric of partial secrecy which was exploited when the equivocation rate of eavesdroppers was smaller than the transmission rate.
    • Method 2: Resource allocation for energy-efficient secure communication in an OFDMA downlink network was studied and considering the secrecy outage probability constraint.
  • Problem: However, the efficient utilization of energy in physical layer security has been studied in part for special scenarios and purposes. Additionally, these works have only considered the secure EE of general direct transmission systems without taking the collaborative relay networks into account.

PROPOSED SYSTEM

The proposed solution scheme is an iterative solution scheme is developed upon several mathematical methods termed as fractional programming, exact penalty, alternate search, and difference of convex functions (DC) programming.

WORKFLOW:

Firstly, based on fractional programming theory, the primal problem is transformed into a parametric programming which can be iteratively solved by solving a series of sequential parameterized secondary problems. After that, to convert the feasible domain into a convex set, the exact penalty function is constructed to merge the nonconvex constraint into the objective function. However, the resulting problem is still difficult to cope with due to the nonconvexity of the objective function. Thus, the current optimization problem is further transformed into a simple problem which is worked out by solving two level subproblems using alternate search. The core idea of the alternate search is that only one of the source or relay power is optimized in each step while the other is fixed. When either the source power or the relay power is given, the resulting subproblem can be reformulated as the form of DC problem and solved by DC programming. Moreover, a sequential convex program is finally solved by convex optimization methods at each iteration of the DC programming.

The following shows an AF multi-relay network with an eavesdropper. The solid links are the legitimate channels while the dotted links are the wiretap channels.

101

Flow Diagram / Block Diagram

102

Problem description, reason to occur and solution are explained in the each block of problems.

16 – Arises from the nonconvexity of both the objective function and feasible domain.

20 – Arises by transforming its objective function into a parameterized polynomial subtractive form using fractional programming

22- Parameterized secondary problem

25- penalty problem

34 – equivalent penalty problem

42 &43 – DC Subproblems

51&52 – Sequential convex subproblem

ADVANTAGES AND APPLICATIONS

  • Improves the secure EE significantly yet with a certain loss of the secrecy rate due to the tradeoff between secure EE and secrecy rate
  • Achieves higher secure EE and secrecy rate than total transmission power minimization does, while with a certain increase of power consumption
  • Reasonable balance among secure EE, secrecy rate, and power consumption can be reached

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:

Dong Wang, Bo Bai, Member, IEEE, Wei Chen, Senior Member, IEEE, and Zhu Han, Fellow, IEEE, “Achieving High Energy Efficiency and Physical-Layer Security in AF Relaying”, IEEE Transactions on Wireless Communications 2016.

Achievable Rates of Secure Transmission in Gaussian MISO Channel with Imperfect Main Channel Estimation

Achievable Rates of Secure Transmission in Gaussian MISO Channel with Imperfect Main Channel Estimation

Achievable Rates of Secure Transmission in Gaussian MISO Channel with Imperfect Main Channel Estimation

 

ABSTRACT:
A Gaussian multiple-input single-output (MISO) fading channel is considered. We assume that the transmitter, in addition to the statistics of all channel gains, is aware instantaneously of a noisy version of the channel to the legitimate receiver. On the other hand, the legitimate receiver is aware instantaneously of its channel to the transmitter, whereas the eavesdropper instantaneously knows all channel gains. We evaluate an achievable rate using a Gaussian input without indexing an auxiliary random variable. A sufficient condition for beamforming to be optimal is provided. When the number of transmit antennas is large, beamforming also turns out to be optimal. In this case, the maximum achievable rate can be expressed in a simple closed form and scales with the logarithm of the number of transmit antennas. Furthermore, in the case when a noisy estimate of the eavesdropper’s channel is also available at the transmitter, we introduce the SNR difference and the SNR ratio criterions and derive the related optimal transmission strategies and the corresponding achievable rates.
OBJECTIVE
 Achievable Rate Evaluation
 The maximumachievable rate takes a simple closed form and scales with thelogarithm of the number of transmit antennas.
 Sufficient Condition For Beam-forming To Be Optimal
 Large Scale Antennas Case
 Achievable Rate Based on SNR Criterions
 The SNR Difference Criterion
 The SNR Ratio Criterion
 Special Case When β = 1
Evaluation of numerical and Graphical results
INTRODUCTION
 According to the mathematical structure of secrecy systems set in this paper, the intended receiver and the eavesdropper (also called the “enemy”) have direct access to the transmitted signal, and the system is said “perfectly secure” if the enemy’s observation is independent of the secret message. Unfortunately, in this setting, “perfect secrecy” requires at least as many keys as secret messages.
 A Gaussian multiple-input single-output (MISO) fading channel is considered. We assume that the transmitter, in addition to the statistics of all channel gains, is aware instantaneously of a noisy version of the channel to the legitimate receiver. On the other hand, the legitimate receiver is aware instantaneously of its channel to the transmitter, whereas the eavesdropper instantaneously knows all channel gains. We evaluate an achievable rate using a Gaussian input without indexing an auxiliary random variable. A sufficient condition for beam-forming to be optimal is provided. When the number of transmit antennas is large, beam-forming also turns out to be optimal.

EXISTING SYSTEM
Method 1: Space-time transmit pre-coding with imperfect feedback and Transmitter optimization and optimality of beam-forming for multiple antenna systems

PROBLEM
These results cannot be applied to our problem since perfect CSIT is not available. Our primary concern in this paper is on how to design secure transmission strategies leveraging CSIT.
Method 2: Interference assisted Secret Communication – Used to a helping interferer is used to assist the secrecy transmission

PROBLEM
The interferer, which does not know the confidential message, can guarantee secrecy by sending independent interference signals. Multi-antenna system has gained great popularity since it can provide both spatial multiplexing and diversity gains.

PROPOSED SYSTEM
In our proposal, we consider a fast fading MISO wiretap channel. The maximum achievable rate using a Gaussian input without an auxiliary random variable is considered. While the maximization problem is not convex and thus not straightforward to solve in general, we develop a sufficient condition for beam forming to be optimal in the case where the transmitter has only the eavesdropper’s channel statistics. In the case of a large number of antennas at the transmitter, we also find that beam forming is optimal and the maximum achievable rate takes a simple closed form. Meanwhile, we also find that the maximum achievable rate scales with the logarithm of the number of transmit antennas.

BLOCK DIAGRAM FOR TRANSMITTER
91
BLOCK DIAGRAM FOR RECEIVER
92

DESCRIPTION
We consider a fast fading multiple-input single-output (MISO) channel where a transmitter is communicating to a receiver in the presence of an eavesdropper. The transmitter is equipped with Nt antennas while each of the legitimate receiver and the eavesdropper is equipped with only one antenna. The channel gains to the legitimate receiver and the eavesdropper can be represented as two Nt× 1 vectors h and g. The received signals at the legitimate receiver and the eavesdropper can be written as,

ADVANTAGES AND APPLICATIONS
 Better performance results
 A sufficient condition is provided for beam-forming to be optimal. Moreover, when the number of transmit antennas is very large
 Beam-forming is also optimal
 The achievable rate takes a simple closed form and scales with the logarithm of the number of transmit antennas
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

A Simple Recursively Computable Lower Bound on the Noncoherent Capacity of Highly Underspread Fading Channels

A Simple Recursively Computable Lower Bound on the Noncoherent Capacity of Highly Underspread Fading Channels

A Simple Recursively Computable Lower Bound on the Noncoherent Capacity of Highly Underspread Fading Channels

ABSTRACT:

Real-world wireless communication channels are typically highly underspread: their coherence time is much greater than their delay spread. In such situations it is common to assume that, with sufficiently high bandwidth, the capacity without Channel State Information (CSI) at the receiver (termed the noncoherent channel capacity) is approximately equal to the capacity with perfect CSI at the receiver (termed the coherent channel capacity). In this paper, we propose a lower bound on the non-coherent capacity of highly under spread fading channels, which assumes only that the delay spread and coherence time are known. Furthermore our lower bound can be calculated recursively, with each increment corresponding to a step increase in bandwidth. These properties, we contend, make our lower bound an excellent candidate as a simple method to verify that the noncoherent capacity is indeed approximately equal to the coherent capacity for typical wireless communication applications.

We precede the derivation of the aforementioned lower bound on the information capacity with a rigorous justification of the mathematical representation of the channel. Furthermore, we also provide a numerical example for an actual wireless communication channel and demonstrate that our lower bound does indeed approximately equal the coherent channel capacity.

OBJECTIVE:

A lower bound on the non coherent capacity of highly under spread fading channels, which assumes only that the delay spread and coherence time are known.

INTRODUCTION:

  • Actual communication channels are typically under-spread:their delay spread is much smaller than their coherence time.
  • A more exact definition can be made by describing the action of a wireless channel as a linear operator H : L2         L2. The action of H can be expressed in terms of the scattering function CH(v,t ),where v is the Doppler shift, and t_ is the time delay.
  • We work from the intuitive starting point that CSI can be learnt increasingly accurately at the receiver by increasing the bandwidth. This leads to a lower bound on the channel capacity in a recursive form, with each iteration corresponding to a step increase in bandwidth.
  • This is shown to be a monotonically non-decreasing function, which not only illustrates the role of bandwidth in the deployment of effective wireless communication systems, but also potentially reduces the computational load, for when the lower bound value becomes sufficiently large (i.e., for the application in question)then the recursive calculation can be stopped.

 

EXISTING SYSTEM:

G. Durisi, U. Schuster, H. Bolcskei, and S. Shamai, “Non coherent capacity of under spread fading channels,” Information Theory, IEEE Transactions on, vol. 56, no. 1, pp. 367–395, 2010.

  • The WSSUS model is appropriate for many scenarios and our previous work shows that, for in-vehicle channels, the channel can be assumed to be wide-sense stationary and to have uncorrelated scattering.

C. E. Shannon, “A mathematical theory of communication” The Bell System Technical Journal, vol. 27, pp. 379–423,623–656, July, October 1948.

  • Focusing on characterizing the non-coherent capacity in the infinite bandwidth limit.

 

DRAWBACKS:

  • This may lead to our analysis having a wider application.
  • Which not only illustrates the role of bandwidth in the deployment of effective wireless communication systems.
  • The capacity of under spread channels considered here.
  • The Gauss-Markov scenario is the worst-case.

PROPOSED SYSTEM:

  • In this paper, we derive a lower bound on the non-coherent capacity of highly under-spread fading channels in terms of parameters that would typically be available in actual wireless communication systems, namely the coherence time and delay spread (if available, a statistical model for the impulse response can be used to tighten the bound – but isn’t strictly necessary).
  • We precede the derivation of the aforementioned lower bound on the information capacity with a rigorous justification of the mathematical representation of the channel.
  • We also provide a numerical example for an actual wireless communication channel and demonstrate that our lower bound does indeed approximately equal the coherent channel capacity.
  • 81

DESCRIPTION:

The action of the channel can be defined:

y(t) = zH(t) _ x(t) + n(t)

where all terms are functions in time, t, with y(t) as the channel output, zH(t) the channel time varying impulse response, x(t) the input and n(t) AWGN. Shannon showed that the capacity of this channel is the mutual information between y(t) and x(t) maximised over all permissible inputs x(t) ,we denote this C:

C = sup x(t) I(x(t); y(t));

In the OFDM scheme that we will now describe, z2(t) is essentially the inter-symbol interference (ISI) plus the inter carrier interference (ICI). Treating this interference as AWGN is consistent with the initial assumption of treating noise as

AWGN (which itself is likely to comprise of interference from signals transmitted on other channels in the vicinity). As long as the power in n1(t) is small relative to the power in the actual signal and the noise, then we can assert that C = C1; and we can reason that this is in-fact a worst case scenario where the interference is not correlated with the signal and so cannot be used to improve the information transfer capability, and that when combined with the other interference and noise sources that it manifests itself as AWGN.

 

ADVANTAGES:

  • Perfect CSI available at the receiver in the presence of Additive White Gaussian Noise.
  • The noncoherent capacity is indeed approximately equal to the coherent capacity for typical wireless communication applications.
  • Provide a numerical example for an actual wireless communication channel.
  • Our lower bound does indeed approximately equal the coherent channel capacity
  • our observation that most real-world channels are highly under spread
  • provide a computationally simple technique to verify that for any given channel the non-coherent capacity is approximately equal to the coherent capacity

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