针对反射、散射影响下的非对称无线协作通信网络,提出了一种时变功率分配(Time Variant Power Allocation,TVPA)算法。根据无线协作网络中,各节点之间信道条件实时变化且不对称的特点,在信号传输过程中对信源节点和中继节点的发送信号功率进行优化分配。借助信道编码定理,将系统错误概率最小的非凸优化问题转化为最大化系统容量的凸优化问题来解。与固定功率分配(Fixed Power Allocation,FXPA)算法和平均功率分配(Average Power Allocation,AVPA)算法相比,该算法能充分利用无线信道的时变特性,重新分配功率以降低系统错误概率。在多种网络模型中的仿真结果表明,准静态瑞利衰落信道下,相比于FXPA算法,TVPA算法可获得多达5.5dB的比特错误概率性能增益。随着网络质量的进一步改善,该性能优势也逐步增大。
For quantum sparse graph codes with stabilizer formalism,the unavoidable girth-four cycles in their Tanner graphs greatly degrade the iterative decoding performance with a standard belief-propagation(BP) algorithm.In this paper, we present a jointly-check iterative algorithm suitable for decoding quantum sparse graph codes efficiently.Numerical simulations show that this modified method outperforms the standard BP algorithm with an obvious performance improvement.
