CPSO在配电网OFDM系统比特功率分配中的应用

自适应OFDM系统的比特功率分配是提高频谱利用率的关键技术，基于注水原理的注水迭代算法能够达到比特功率分配的理论上线，但实际系统中由于调制方式及传输比特整数规划的要求，不能达到比特功率分配的优化结果。鉴于此提出了云粒子群优化算法（Cloud Particle Swarm Optimization，CPSO），利用云模型的不确定特性增加群体多样性，解决粒子群优化算法易于陷入局部极值的缺点。通过给出的云粒子群进化模式，实现搜索空间的全局搜索和局部搜索;采用进化过程中动态缩小搜索空间策略提高算法收敛速度，从而解决在低压配电网上系统发射总功率和误码率限定条件下的系统传输速率最大化比特功率分配问题。通过仿真实验表明所提算法的分配结果与位添加法相当，减少了运行时间，与注水迭代算法相比，在系统传输速率相同的情况下节省功率达4.7~14.8 dBm。

Application of CPSO in bit and power allocation for OFDM system over distribution network

Bit and power allocation in adaptive Orthogonal Frequency Division Multiplexing（OFDM） systems is a crucial technique to improve spectral efficiency.Bit and power allocation based on water-filling algorithm can obtain optimal solution in theory.Due to the modulation manner and actual integer programming requirement,optimal results of bit and power allocation can not be obtained.Cloud Particle Swarm Optimization（CPSO） algorithm is proposed.A novel evolutionary mode is given using uncertain property of cloud model to improve diversity of population and overcome the shortcoming of running into local minimum in Particle Swarm Optimization（PSO）algorithm,which can realize the balance between exploration and exploitation in search space.Dynamically reducing search space in evolutionary process can improve convergence speed of algorithm proposed.Accordingly the problem of bit allocation maximizing data rate under the power and bit error rate constraints over the distribution network is solved in CPSO.Simulation results demonstrate the performance of proposed algorithm is the same as that of bit adding algorithm,reducing calculation time,and the savable power in CPSO algorithm is 4.7～14.8 dBm compared with the water-filling algorithm at the same transmission rate.