基于马尔科夫状态空间的水声OFDM资源分配

在水声自适应OFDM系统中，CSI(channel state information)是资源分配的基础。接收机必须及时地把信道信息反馈给发射机，然后接收机中的调制器根据反馈CSI信息选择合适的比特加载和功率分配方案。但在时变的水声信道中，反馈CSI与实时CSI可能不同。针对信道多变、时延大特点，本文提出了一种基于马尔可夫链的水声OFDM用户资源分配方案来解决这些问题。马尔可夫链状态空间是由初始信道状态和统计信道状态构成的，它可以充分利用感知到的时延CSI和信道统计量来预测数据传输时的CSI。利用统计CSI可以确定每个用户的最佳比特负载和功率分配，使水声系统能够容忍水声信道中较长的CSI延迟。结论表明基于马尔科夫空间的资源分配方案相比于线性预测的自适应，系统吞吐量得到有效提升

Underwater Acoustic OFDM Resource Allocation based on Markov Chain State Space

In underwater acoustic (UWA) adaptive OFDM system, channel state information (CSI) is the basis for resources allocation. The receiver must feed the CSI back to transmitter in time and the modulator selects the appropriate modulation mode according to the feedback. However the time-varying underwater acoustic channel leads that the feedback CSI may be different from the real-time CSI. This paper proposed an OFDM UWA user resource allocation scheme based on Markov chain(MC) state space to solve these problem. MC state space is composed of initial channel state and statistical channel state that can perceived time-delay CSI to predict CSI of transmission moment. The optimal bit load and power allocation for each subcarrier can be determined by using the statistical CSI that make the UWA systems can tolerate the longer delay CSI. The results show that the system throughput is improved effectively by adaptive modulation(AM) with MC compared to linear prediction