Joint Channel Estimation and Active User Detection for Cell-Free Massive Access System Exploiting Coarse User Location Information

Massive access is recognized as one of the main use cases of future wireless networks. The characteristic of sporadic transmission in massive access makes the processes of channel estimation (CE) and active user detection (AUD) essential prerequisites for successful data decoding. In this article, we study joint CE and AUD in massive access system with cell-free structure. Specifically, we first establish the expectation maximization approximate message passing (EM-AMP) framework tailored for cell-free structure as a benchmark. Then, we investigate three new methods that exploit coarse user location information in different ways, namely, the variance bounding method, the variance fusion method, and the proposed EM on location method, where the last two methods can also generate finer location estimation as byproduct to CE and AUD at the cost of higher complexity. For single user scenario, we theoretically prove the optimality of the proposed method in channel variance estimation, validating the foundation of the proposed method. For multiuser scenario, we conduct various simulations to compare the performance of different methods. Our findings illustrate that harnessing coarse user location information yields substantial enhancements in CE and AUD performance. Moreover, the proposed method exhibits superior localization accuracy compared to the variance fusion method, all while maintaining comparable complexity, making it a good candidate for applications with both communication and sensing requirements.