User Association and Resource Allocation Optimization in LTE Cellular Networks

User Association and Resource AllocationOptimization in LTE Cellular Networks



As the demand for higher data rates is growingexponentially, homogeneous cellular networks have been facinglimitations when handling data traffic. These limitations arerelated to the available spectrum and the capacity of the network.Heterogeneous Networks (HetNets), composed of Macro Cells(MCs) and Small Cells (SCs), are seen as the key solution to improvespectral efficiency per unit area and to eliminate coverageholes. Due to the large imbalance in transmit power between MCsand SCs in HetNets, intelligent User Association (UA) is requiredto perform load balancing and to favor some SCs attractionagainst MCs. As Long Term Evolution (LTE) cellular networksuse the same frequency sub-bands, User Equipments (UEs) mayexperience strong Inter-Cell Interference (ICI), especially at celledge. Therefore, there is a need to coordinate the ResourceAllocation (RA) among the cells and to minimize the ICI. In thispaper, we propose a generic algorithm to optimize user associationand resource allocation in LTE networks. Our solution, basedon game theory, permits to compute Cell Individual Offset (CIO)and a pattern of power transmission over frequency and timedomain for each cell. Simulation results show significant benefitsin the average throughput and also cell edge user throughput of40% and 55% gains respectively. Furthermore, we also obtain ameaningful improvement in energy efficiency.


  • We formulate the user association and inter-cell interference problem using a potential game.
  • We provide a dynamic solution of user association and inter-cell interference coordination optimizing the Cell.
  • Individual Offset (CIO) and transmission power over time and frequency domains to maximize the network utility.
  • We provide an analytical investigation of the algorithm and comprehensive performance study. Simulation results have shown significant improvement in the user throughputs and also energy efficiency.


Till the past few years, homogeneous LTE cellular networks,composed of identical Base Stations (BS) called macro BSs,managed to optimize the coverage and to handle the data trafficgenerated by the users. Generally, the deployment of thesemacro BSs is planned in a way that minimizes the overlapbetween the cells and at the same time guarantees a continuouscoverage for all users in the network. However, because ofthe exponential increase in the number of connected devices,

the rapid growth of data traffic and the demand for higherdata rates, LTE networks have been facing great difficultyto handle the data amount, especially in the most crowdedenvironments and at cell edges. These limitations are relatedto the available spectrum and network capacity bound. Network operatorsprefer to use the available licensed spectrum more efficiently.Another approach consists of enhancing the macro networklayer efficiency through some technology upgrades. For instance,the performance of these networks can be improvedthanks to advancement in the air interface, using multi-antennatechniques and implementing more efficient modulation andcoding schemes.


ETSI TS 36.300, “LTE Evolved Universal Terrestrial Radio Access (EUTRA)and Evolved Universal Terrestrial Radio Access Network (EUTRAN);Overall Description; Stage 2,” Tech Spec. v10.11.0, Sep. 2013.

HetNets has beenproposed by the 3rd Generation Partnership Project (3GPP)

  1. Sesia, I. Toufik, and M. Baker, LTE – The UMTS Long Term Evolution:

From Theory to Practice, 2nd Edition, Wiley, 2011

In Orthogonal Frequency Division Multiple Access(OFDMA)  cellular network, the physical radio resourcesare partitioned into a time-frequency grid


Although these techniques can be easily implemented, they cannot cope with changes in data traffic.


Our proposed framework explores the idea of a central coordinatorthat gathers some information concerning the eNBsand the users in the system to determine optimal parameters.This idea is well aligned with the emerging technology fordesigning and managing mobile networks through SoftwareDefined Wireless Networking (SDWN). This new paradigm,simplifies network management by decoupling the controlplane and data plane and enabling operators to have a completecontrol over the network from a centralized point. Forexample, we design a SDWN controller based onOpen Daylight and we validate our framework with a suboptimization algorithm, handling CIO and ABS, deployed as a north bound (NB) application.


User Association and Resource AllocationOptimization


In the following, we will describe the algorithm and itsoperation in performing user association and frequency/timeresource allocation via power patterns optimization for LTEcellular networks. This solution based on game theory is anextension to our work on coordinated scheduling via frequencyand power allocation optimization presented shows the design where a coordinator optimizes CIO values,virtually attaches the users to corresponding cells, performs adynamic resource distribution, virtually schedules the users inthe network, and computes a utility function. Then, it sends theoptimal parameters (power allocation patterns and CIO values)to each cell. The CIO values are added to RSRP measurementsand this impacts the user association and handover decision.

Then, each local eNB scheduler allocates its provided RBsaccording to its scheduling policy and uses the power settingsdetermined from the optimizer.


  • Useful for other resource allocation optimization problems and different system criteria.
  • Achieves more than 50% gain in cell edge throughput and also substantialenhancement in the average throughput and energy efficiency.



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


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


Nessrine Trabelsi, Chung Shue Chen, Rachid El Azouzi, Laurent Roullet, andEitan Altman, “User Association and Resource AllocationOptimization in LTE Cellular Networks”, IEEE Transactions on Network and Service Management, 2017.

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