基于多维离散粒子群优化的协同 OFDMA 系统跨层资源分配

摘 要：针对多服务情况下协同OFDMA（orthogonal frequency division multiple access）系统的资源分配问题，在基站和中继单独功率约束条件下，以最大化用户的效用(utility)总和为目标，提出了一种基于多维离散粒子群（MDPSO）的渐进最优资源分配算法。该算法采用多值离散变量来编码粒子位置，并针对多维离散空间构建了新的基于概率信息的粒子速度和位置更新算法，且引入变异操作来克服粒子群算法的早熟问题。此外，还采用了迭代注水法进行最优功率分配。仿真结果表明，所提算法在总效用、吞吐量和公平性上均明显优于已有资源分配算法。

Multi-values discrete particle swarm optimization for cross layer resource allocation in cooperative OFDMA systems

Abstract: The resource allocation problem in cooperative OFDMA systems with mobile stations (MSs) on multi-services was investigated. In order to maximize the sum utility of all MSs under per-relay power constraint(PPC), an asymptotic optimal resource allocation algorithm based on multi-values discrete particle swarm optimization (MDPSO) was proposed. Unlike the traditional discrete particle swarm optimization (DPSO) algorithm, the proposed one denotes the particle position by discrete multi-value variable. Furthermore, new probability based operations for computing particle velocity and updating particle positions were, and the mutation of particle positions was also introduced to overcome the premature convergence problem. The proposed MDPSO also can be applied widely to solve the combinatorial optimization problems (COP). Furthermore, iterative waterfilling was used to complete power allocation. Simulation results show that the proposed method achieves higher sum utility of all MSs and higher degree of user fairness than the existing methods.