Sensing and Deep CNN-Assisted Semi-Blind Detection for Multi-User Massive MIMO Communications

Abstract

Attaining precise target detection and channel measurements are critical for guiding beamforming optimization and data demodulation in massive multiple-input multiple-output (MIMO) communication systems with hybrid structures, which requires large pilot overhead as well as substantial computational complexity. With benefits from the powerful detection characteristics of MIMO radar, we aim for designing a novel sensing-assisted semi-blind detection scheme in this paper, where both the inherent low-rankness of signal matrix and the essential knowledge about geometric environments are fully exploited under a designated cooperative manner. Specifically, to efficiently recover the channel factorizations via the formulated low-rank matrix completion problem, a low-complexity iterative algorithm stemming from the alternating steepest descent (ASD) method is adopted to obtain the solutions in case of unknown noise statistics. Moreover, we take one step forward by employing the denoising convolutional neural network (DnCNN) to preprocess the received signals due to its favorable performance of handling Gaussian denoising. The overall paradigm of our proposed scheme consists of three stages, namely (1) target parameter sensing, (2) communication signal denoising and (3) semi-blind detection refinement. Simulation results show that significant estimation gains can be achieved by the proposed scheme with reduced training overhead in a variety of system settings.

Multi-Head Transformer Architecture with Higher Dimensional Feature Representation for Massive MIMO CSI Feedback

“…Massive multiple input multiple output (MIMO) makes a substantial contribution to augmenting the throughput of wireless communication systems and providing high-speed data services, which is essential in future mobile communication systems [1,2]. Massive MIMO systems can be categorized into two principal domains: time division duplexing (TDD) systems and frequency division duplexing (FDD) systems where orthogonal time and frequency are recognized to distinguish the uplink and downlink [3,4].…”