Deep-Unfolded Joint Activity and Data Detection for Grant-Free Transmission in Cell-Free Systems

Massive grant-free transmission and cell-free wireless communication systems have emerged as pivotal enablers for massive machine-type communication. This paper proposes a deep-unfolding-based joint activity and data detection (DU-JAD) algorithm for massive grant-free transmission in cell-free systems. We first formulate a joint activity and data detection optimization problem, which we solve approximately using forward-backward splitting (FBS). We then apply deep unfolding to FBS to optimize algorithm parameters using machine learning. In order to improve data detection (DD) performance, reduce algorithm complexity, and enhance active user detection (AUD), we employ a momentum strategy, an approximate posterior mean estimator, and a novel soft-output AUD module, respectively. Simulation results confirm the efficacy of DU-JAD for AUD and DD.Comment: Submitted to ISWCS 202

A Multi-Beam XL-MIMO Testbed Based on Hybrid CPU-FPGA Architecture

To support more users and higher data rates in future communication networks, the extremely large-scale massive multiple-input multiple-output (XL-MIMO) is considered a promising technique. The booming research on XL-MIMO necessitates a reconfigurable XL-MIMO testbed that can be used to validate new research ideas in real wireless environments and collect data for XL-MIMO channel characteristics analysis. To provide such a reliable and convenient testbed, we designed a multi-beam XL-MIMO testbed based on the hybrid CPU-FPGA architecture and channel calibration schemes. The ability to customize modules makes our testbed a convenient verification platform for future communication systems. Moreover, numerous trial measurement results in the indoor near-field scenario with moderate user equipment (UE) mobility are presented, and the excellent performance indicates that our testbed is an ideal platform for the evaluation of XL-MIMO-related algorithms