基于多用户串行干扰抵消的贝叶斯盲检测算法

Bayesian Blind Detection Algorithm Based on Multi-User Serial Interference Cancellation

在大规模机器类型通信中,免授权传输允许用户设备随机访问网络并偶发传输小数据包,接收机则需要在无调度、无导频情况下进行多用户盲检测。基于消息传递的贝叶斯盲检测算法可解决上述问题,但并行迭代计算需要消耗大量的计算资源,复杂度较高,且收敛性能不稳定。为了改善多用户盲检测性能,提出一种将串行干扰抵消与贝叶斯消息传递相结合的算法,通过不断重构与抵消正确检测用户,提高接收端信干噪比,从而改善误码性能,并降低算法复杂度。同时,通过增加阻尼和重启机制,提高算法收敛性能。仿真结果表明,所提算法在多用户盲检测中比贝叶斯盲检测算法具有明显的优势。

In the massive machine type communication,user devices are allowed to randomly access the network and transmit small packets occasionally by grant-free transmission.Correspondingly,receivers are required to perform the blind multi-user detection without scheduling and pilots.The Bayesian blind detection algorithm based on message passing can solve the above problem,but the parallel iterative calculation consumes massive computing resources with high computational complexity and unstable convergence.An algorithm combining serial interference cancellation with Bayesian message passing is proposed to improve the performance of the blind multi-user detection.By iteratively reconstructing and canceling the interference of correctly recovered users,the signal to interference plus noise ratio at the receiver is improved,which enhances the error performance and reduces the computational complexity.Meanwhile,the convergence stability is promoted by damping and re-initialization mechanisms.Simulation results show that the proposed algorithm has obvious advantages over the parallel Bayesian blind detection algorithm in the blind multiuser detection.