Energy-Efficient Detection Using Message Passing Algorithm With Dynamic Factor Graph for Uplink MIMO Communications in Cell-Free Networks

Cell-free (CF) multiple input multiple output (MIMO) is gaining traction for B5G/6G wireless communication systems due to its heightened connectivity, spectral efficiency, and quality of service (QoS). Developing an efficient data detector is imperative to harness these attributes effectively in uplink transmissions. In this paper, we propose the DMPACT2 detector, a message-passing algorithm (MPA) variant that dynamically adjusts the factor graph (FG) based on achievable rate, allocating computing resources judiciously. Additionally, two low-complexity techniques are employed to save 99% of DMPACT2’s complexity, namely local minimum mean-squared error (MMSE)-based initial constellation truncation and log-likelihood ratio (LLR)-based constellation truncation. Simulation results demonstrate that the proposed adaptive FG boosts DMPACT2’s bit error rate (BER) to approach the BER lower bound of the optimal maximum likelihood (ML) detector under high Signal-to-Noise Ratio (SNR), indicating that DMPACT2 is very competitive with the optimal detection. Moreover, DMPACT2 attains a $10^{-2}$ BER with a 3dB lower SNR than the best competitor using the same computational complexity. Furthermore, we propose a novel metric, power-normalized bit rate, which simultaneously accounts for throughput, BER, complexity, transmission power, and backhaul traffic in the cell-free context. In terms of the power-normalized bit rate, DMPACT2 exhibits superior performance compared to all other detectors.