Variational bayesian inference for channel estimation and user activity detection in C-RAN
作者：王佳星 时间：2020年02月24日 22:17 点击数：
语言：English
发表刊物：IEEE Wireless Communications Letters ( Volume: 9, Issue: 7, Pp. 953-956)
发表日期：2020年2月24日
摘要：
Cloud radio access network (C-RAN) is recognized as a key technology for the fifth generation (5G) wireless communication systems, where machine-type communication (MTC) is considered to support devices' connectivity. In this letter, we study the user activity detection (UAD) and channel estimation (CE) problems in C-RAN for MTC. Based on the user activity sparsity and signal spatial sparsity in C-RAN, we build a two-layer prior distribution graphical model to exploit the sparsity property and analyze the problem with variational Bayesian inference (VBI). We find that the width of prior distribution has a considerable impact on the algorithm performance and propose a modified VBI algorithm. Simulation results are presented to show that the proposed algorithm can achieve better performance with lower complexity than the existing approaches.
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