Evaluation of Particle Swarm Optimization Algorithm for Precoding in Coordinated Multi-Point System
Independent thesis Advanced level (degree of Master (Two Years))Student thesisAlternative title
Evaluation of Particle Swarm Optimization Algorithm for Precoding in Coordinated Multi-Point System (Swedish)
In future communication system, the constraint of recourse allocation are a challenging topic. One way for observing such constraint is to apply frequency reuse factor of one in commu- nication system. Taking this factor to one causes the system to have serious problems such as small sum rate and interference speci cally at cell-edge. In 3GPP, LTE Advanced, one proposed technique for next generation of communication system is Coordinated Multi-Point which is called CoMP. One of the main part of this tech- nique is a unit of precoder which prepares beamforming weight. Suitable precoding in CoMP and its subset which is called Joint Processing is a way to compensate weak performance in mutual border due to taking frequency reuse factor one. Previously some linear methods have been proposed for precoding in CoMP. Recently the literature is shifted for studying the capability of stochastic algorithms for precoding in CoMP system. In this master thesis we evaluate the Particle Swarm Optimization to form precoding matrix and compare its variations (Basic PSO, Random PSO and Multi-Start PSO) with each other and a linear technique which is known as Zero Forcing as well. The result shows for precoding, the Multi-Start PSO has better performance in com- parison to other types of algorithms and techniques in di erent system sizes and di erent objective functions.
This master thesis is the use of a stochastic algorithm Particle Swarm Optimization for precoding in Coordinated Multi-Point system. The performance of three types of such algorithm, Basic PSO, Random PSO and Multi-Start PSO are evaluated for precoding. Furthermore since that was a stochastic method, the result is compared to a famous linear technique, Zero forcing. The system throughput is considered with regard two objective functions Weight interference minimization and Sum rate maximization.
Place, publisher, year, edition, pages
2013. , 57 p.
Index Terms--- Coordinated Multi-Point, Precoding, Particle Swarm Optimization, Stochastic Optimization
Signal Processing Telecommunications
IdentifiersURN: urn:nbn:se:bth-3069Local ID: oai:bth.se:arkivex3055633B041DAC2CC1257C470038FB1COAI: oai:DiVA.org:bth-3069DiVA: diva2:830367