Advances in Massive Signal Processing

Dear Colleagues,

The sixth-generation (6G) wireless networks are envisioned to provide a reliable service for a wide range of communication scenarios, including ultra-low latency, massive random access, integrated satellite-terrestrial, high-mobility, and high-frequency scenarios. In order to satisfy the growing demands for higher capacity, broader coverage, and better reliability, novel technologies, such as grant-free access, cell-free massive multiple-input multiple-output (MIMO), orthogonal time-frequency space (OTFS) modulation, and intelligent reflect surface (IRS), are conceived to be incorporated into the current communication systems. However, both the increase in the number of the antennas and that of the users have augmented the dimension of signals, resulting in extremely complex signal processing, for which conventional signal processing methods may not be efficient.

Against this background, it is necessary to investigate low-complexity signal processing approaches, including precoding, estimation, and detection algorithms for emerging techniques, such as grant-free access, IRS, OTFS, cell-free MIMO, etc. Specifically, new perspectives around signal processing, such as learning-based schemes and distributed signal processing, are encouraged to be further explored. This Special Issue aims to focus on the development of low-complexity efficient signal processing algorithms for future massive signal models. The topics of interest in this Special Issue include but are not limited to the following:

• Compressive-sensing-based signal processing;
• Matrix information geometry-based signal processing;
• Model-driven learning-based signal processing;
• Data-driven learning-based signal processing;
• Graph neural-network-aided signal processing;
• Distributed signal processing;
• Low-complexity signal processing for massive MIMO;
• Low-complexity signal processing for massive connectivity (i.e., various NOMA systems);
• Low-complexity signal processing for emerging techeniques (i.e., IRS, OTFS).

Prof. Dr. Lixia Xiao
Dr. Ping Yang
Prof. Dr. Pei Xiao
Guest Editors

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