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This chapter explores the application of particle swarm optimization (PSO) search algorithm in two different optimization aspects of code division multiple access (DS/CDMA) systems, namely multiuser detection and resource allocation problem. In the first problem, a discrete PSO version is considered while in the latter, a continuous PSO algorithm is adopted. In both cases, the input parameters, specially the weight factors (acceleration coefficients), are optimized for each considered scenario. The motivation to use heuristic search algorithms is due to the nature of the NP complexity posed by the wireless network optimization problems. Hence, from the practical engineering point-of-view, the challenge is to obtain suitable performances in solving those hard complexity problem in a polynomial time, allowing the algorithm implementation using modern digital signal processing (DSP) platforms. Previous results indicated that the application of heuristic search algorithm in several wireless optimization problems have been achieved excellent performance-complexity tradeoffs, particularly the use of genetic algorithm (GA), evolutionary program (EP), particle swarm optimization (PSO), and local search (LS). For the heuristic multiuser detection (Heur-MUD) problem, the performance is extensively evaluated and characterized under single input single/multiple output (SISO/SIMO) flat Rayleigh channels, as well as frequency selective (multipath) Rayleigh channels. For this class of problem, single-objective optimization criterion (SOO) and a discrete PSO algorithm are adopted. Extensive simulations are carried out in order to demonstrate that, after convergence, the performance reached by all the analyzed Heur-MUD is much better than the conventional detector (CD), and somewhat close to the single-user bound (SuB), with the advantage of the computational complexities substantially lower than optimum multiuser detection (OMUD) and not excessively higher than CD complexity. For the heuristic resource allocation (Heur-RA) problem, there are several challenging optimization problems associated. The total network power minimization subject to multiclass information rate constraints, as well as the problem of throughput maximization subject to the transmitted power limitation are considered in this chapter. Multirate users associated with different types of traffic are aggregated to distinct classes of users, with the assurance of minimum target rate allocation per user and quality of service (QoS). For this class of problem, the SOO methodology is also carried out using a continuous PSO search algorithm, aiming to achieve promising performance-complexity tradeoffs. The numerical results are promising in terms of sum rate maximization while simultaneously minimizing the total power allocated to the multirate mobile terminals.

Multiple Access Network Optimization Aspects via Swarm Search Algorithms