Sparse Signal Processing Concepts for Efficient 5G System Design

As it becomes increasingly apparent that 4G will not be able to meet the emerging demands of future mobile communication systems, the question what could make up a 5G system, what are the crucial challenges and what are the key drivers is part of intensive, ongoing discussions. Partly due to the advent of compressive sensing, methods that can optimally exploit sparsity in signals have received tremendous attention in recent years. In this paper we will describe a variety of scenarios in which signal sparsity arises naturally in 5G wireless systems. Signal sparsity and the associated rich collection of tools and algorithms will thus be a viable source for innovation in 5G wireless system design. We will discribe applications of this sparse signal processing paradigm in MIMO random access, cloud radio access networks, compressive channel-source network coding, and embedded security. We will also emphasize important open problem that may arise in 5G system design, for which sparsity will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces

Framework for a Perceptive Mobile Network using Joint Communication and Radar Sensing

In this paper, we develop a framework for a novel perceptive mobile/cellular network that integrates radar sensing function into the mobile communication network. We propose a unified system platform that enables downlink and uplink sensing, sharing the same transmitted signals with communications. We aim to tackle the fundamental sensing parameter estimation problem in perceptive mobile networks, by addressing two key challenges associated with sophisticated mobile signals and rich multipath in mobile networks. To extract sensing parameters from orthogonal frequency division multiple access (OFDMA) and spatial division multiple access (SDMA) communication signals, we propose two approaches to formulate it to problems that can be solved by compressive sensing techniques. Most sensing algorithms have limits on the number of multipath signals for their inputs. To reduce the multipath signals, as well as removing unwanted clutter signals, we propose a background subtraction method based on simple recursive computation, and provide a closed-form expression for performance characterization. The effectiveness of these methods is validated in simulations.Comment: 14 pages, 12 figures, Journal pape

Compressive Sensing-Based Grant-Free Massive Access for 6G Massive Communication

The advent of the sixth-generation (6G) of wireless communications has given rise to the necessity to connect vast quantities of heterogeneous wireless devices, which requires advanced system capabilities far beyond existing network architectures. In particular, such massive communication has been recognized as a prime driver that can empower the 6G vision of future ubiquitous connectivity, supporting Internet of Human-Machine-Things for which massive access is critical. This paper surveys the most recent advances toward massive access in both academic and industry communities, focusing primarily on the promising compressive sensing-based grant-free massive access paradigm. We first specify the limitations of existing random access schemes and reveal that the practical implementation of massive communication relies on a dramatically different random access paradigm from the current ones mainly designed for human-centric communications. Then, a compressive sensing-based grant-free massive access roadmap is presented, where the evolutions from single-antenna to large-scale antenna array-based base stations, from single-station to cooperative massive multiple-input multiple-output systems, and from unsourced to sourced random access scenarios are detailed. Finally, we discuss the key challenges and open issues to shed light on the potential future research directions of grant-free massive access.Comment: Accepted by IEEE IoT Journa