无线中继网络中基于无似然推理的贝叶斯符号检测
Bayesian Symbol Detection in Wireless Relay Networks via Likelihood-Free Inference
摘要：
针对一类协作无线中继网络，提出了一种通用的随机模型，该模型中假设目的节点的信道状态信息是不完全的。该框架结合了在一般已知的非线性处理功能下操作的多个中继节点。当考虑非线性中继函数时，似然函数通常是难以处理的，导致极大似然和最大后验检测器不允许封闭形式的解。我们说明了我们的方法，以克服这种棘手的一个流行的最佳非线性中继函数选择的例子，并演示了我们的算法是如何能够解决以前棘手的检测问题。克服这一难题需要开发专门的贝叶斯模型。我们开发了三种新的算法来检测这个贝叶斯模型，这些算法包括一个马尔可夫链蒙特卡罗近似贝叶斯计算（MCMCMC-ABC）的方法，辅助变量MCMC（MCMCMC-AV）的方法，和一个次优穷举搜索迫零（SES-ZF）的方法。最后，数值例子比较的符号错误率（SER）性能与信噪比（SNR）的三种检测算法进行了研究，在模拟的例子。
英文摘要：
This paper presents a general stochastic model developed for a class of cooperative wireless relay networks, in which imperfect knowledge of the channel state information at the destination node is assumed. The framework incorporates multiple relay nodes operating under general known non-linear processing functions. When a non-linear relay function is considered, the likelihood function is generally intractable resulting in the maximum likelihood and the maximum a posteriori detectors not admitting closed form solutions. We illustrate our methodology to overcome this intractability under the example of a popular optimal non-linear relay function choice and demonstrate how our algorithms are capable of solving the previously intractable detection problem. Overcoming this intractability involves development of specialised Bayesian models. We develop three novel algorithms to perform detection for this Bayesian model, these include a Markov chain Monte Carlo Approximate Bayesian Computation (MCMC-ABC) approach; an Auxiliary Variable MCMC (MCMC-AV) approach; and a Suboptimal Exhaustive Search Zero Forcing (SES-ZF) approach. Finally, numerical examples comparing the symbol error rate (SER) performance versus signal to noise ratio (SNR) of the three detection algorithms are studied in simulated examples.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
