Resource Allocation in Downlink OFDMA Systems An Evolutionary Approach

Abstract

A downlink wireless system features a centralized basestation communicating to a number of users physically scattered around the basestation. The purpose of resource allocation at the basestation is to intelligently allocate the limited resources, e.g. total transmit power and available frequency bandwidth, among users to meet users' service requirements. Channel-aware adaptive resource allocation has been shown to achieve higher system performance than static resource allocation, and is becoming more critical in current and future wireless communication systems as the user data rate requirements increase. Adaptive resource allocation in a multichannel downlink system is more challenging because of the additional degree of freedom for resources, but offers the potential to provide higher user data rates. Multiple channels can be created in the frequency domain using multiple carrier frequencies, a.k.a. multicarrier modulation (MCM), or in the spatial domain with multiple transmit and receive antennas, also known as multiple-input multiple-output (MIMO) systems. This thesis aims to study the system performance, e.g. total throughput and/or fairness, in multiuser multicarrier and multiuser MIMO systems with adaptive resource allocation, as well as low complexity algorithms that are suitable for cost-effective real-time implementations in practical systems. First contribution of this thesis is the use of Particle Swarm Optimization (PSO), a stochastic optimization technique, for sub-channel allocation in downlink of OFDMA systems followed by power allocation using water filling algorithm. In PSO aided subchannel allocation the search and subchannel allocation is performed simultaneously as compared to traditional methods where the subchannels are first sorted in accordance of their gains and then allocation is performed. This significantly reduces the complexity of PSO aided allocation. This fact makes PSO aided subchannel allocation a suitable choice for practical wireless systems like WiMAX (802.16e) where the convergence rate plays a very important role as the wireless channel changes rapidly. The second contribution to this thesis is a novel genetic algorithm adaptive resource allocation in MIMO OFDM systems. We impose a set of proportional fairness constraints to assure that each user can achieve a required data rate, as in a system with quality of service guarantees. With the proposed algorithm, the sum capacity can iv be distributed fairly and flexibly among users. Since the optimal solution to the constrained fairness problem is extremely computationally complex to obtain, we propose a suboptimal algorithm that separates subchannel allocation and power allocation. In the proposed algorithm, subchannel allocation is first performed using novel Genetic Algorithm, assuming an equal power distribution. An optimal power allocation algorithm then maximizes the sum capacity while maintaining proportional fairness. Finally, we present a joint solution to subchannel, bit and power allocation problem for downlink of MIMO OFDM systems. Using SVD, the MIMO fading channel of each subchannel is transformed into an equivalent bank of parallel Single Input Single Output (SISO) sub-channels. To achieve the capacity bound, one must solve a multiuser subchannel allocation and the optimal bit allocation jointly. We propose the use of Non-dominated Sorting Genetic Algorithm (NSGA) – II, which is a MultiObjective Genetic Algorithm (MOGA), for joint allocation of bits and subchannels, in the downlink of MIMO OFDMA system. NSGA – II is intended for optimization problems involving multiple conflicting objectives. Here the two conflicting objectives are Rate Maximization and Transmit Power Minimization.