改进的分布式粒子滤波盲均衡算法

A Modified Distributed Particle Filtering Algorithm for Cooperative Blind Equalization

针对多节点合作接收系统中的盲均衡问题,提出了一种基于一致优化的分布式粒子滤波盲均衡改进算法。该算法采用多个接收机组成无融合中心的分布式网络,使用分布式粒子滤波合作估计共同的发送符号序列,克服了单接收节点受信道影响大、误码率高的问题。为了保证粒子滤波中每个节点产生共同的粒子集和粒子权重,采用基于交替方向乘子法的一致优化算法获得联合似然函数,并与最大一致协议相结合,从而使每个节点获得相同的最佳重要性函数和粒子权重。理论分析与仿真结果表明,该算法只需经过有限次的一致迭代就可以达到集中式合作盲均衡的性能。全分布式的多节点合作获得了空间分集增益,降低了系统误码率。

A modified distributed particle filtering algorithm based on consensus optimization for cooperative blind equalization is proposed in cooperative receiver networks.In the proposed method,multiple receivers composed of distributed network with no fusion center estimate the transmitted sequences cooperative by using the distributed particle filtering,which improves the bit err ratio performance compared to single receiver affected by fading channel more seriously.In order to guarantee all nodes have the same set of particles and weights,the consensus optimization based on alternating-direction method of multipliers is introduced to evaluate the global likelihood function across the receiver network.Then the maximum consensus iterations are used so that the same importance function and corresponding importance weights for all particles can be same at all receivers.Theoretical analysis and simulation results show that only a few consensus iterations suffice for the proposed algorithm to approach the performance of their centralized counterparts.The fully distributed cooperative scheme achieves spatial diversity gain and decreases the bit error ratio.