IEEE Access (Jan 2024)
Centralized and Decentralized IDD Schemes for Cell-Free Massive MIMO Systems: AP Selection and LLR Refinement
Abstract
In this paper, we propose iterative interference cancellation schemes with access points selection (APs-Sel) for cell-free massive multiple-input multiple-output (CF-mMIMO) systems. Closed-form expressions for centralized and decentralized linear minimum mean square error (LMMSE) receive filters with APs-Sel are derived assuming imperfect channel state information (CSI). Based on the derived expressions, insights are drawn and general expressions are devised for several cases, namely: firstly, MMSE-SIC filter for the non-scalable CF-mMIMO that uses all APs. Secondly, an MMSE-SIC filter assuming perfect channel state information. Thirdly, in this case we assume no interference cancellation and the linear MMSE filter is obtained. Additionally, we formulate a new Gaussian approximation of the likelihood function by deriving new closed-form expressions for the second order statistics (mean and variance) of the detected signal parameters in presence of channel estimation errors, APs-Sel matrix and multi-user interference (MUI). Since the MMSE-SIC filter experiences error propagation due to erroneous decisions from the previous stages, we develop a list-based detector based on LMMSE receive filters for CF-mMIMO systems that exploits interference cancellation and the constellation points to mitigate the error propagation that occurs in conventional MMSE-SIC receivers. An iterative detection and decoding (IDD) scheme that employs low-density parity-check codes is then developed. Moreover, log-likelihood ratio (LLR) refinement strategies based on censoring and a linear combination of local LLRs are proposed to improve the network performance. We assess the proposed centralized and decentralized IDD schemes against existing approaches in terms of bit error rate performance, complexity, and signaling under perfect CSI and imperfect CSI and verify the superiority of the distributed IDD architecture with LLR refinements.
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