[Paper Review] Joint Multi-User Communication and Sensing Exploiting Both Signal and Environment Sparsity

Xin Tong, Zhaoyang Zhang+3 more|arXiv (Cornell University)|Sep 6, 2021

Advanced Wireless Communication Technologies38 references126 citations

TL;DR

This paper proposes an iterative, sliding-window, graph-based algorithm for joint multi-user communication and environment sensing in IRS-assisted SCMA systems, exploiting sparsity in both user signals and environmental scatterers. The method enables continuous sensing and mutual enhancement between communication decoding and environment imaging, achieving convergence and improved performance through feedback and momentum-based optimization.

ABSTRACT

As a potential technology feature for 6G wireless networks, the idea of sensing-communication integration requires the system not only to complete reliable multi-user communication but also to achieve accurate environment sensing. In this paper, we consider such a joint communication and sensing (JCAS) scenario, in which multiple users use the sparse code multiple access (SCMA) scheme to communicate with the wireless access point (AP). Part of the user signals are scattered by the environment object and reflected by an intelligent reflective surface (IRS) before they arrive at the AP. We exploit the sparsity of both the structured user signals and the unstructured environment and propose an iterative and incremental joint multi-user communication and environment sensing scheme, in which the two processes, i.e., multi-user information detection and environment object detection, interweave with each other thanks to their intrinsic mutual dependence. The proposed algorithm is sliding-window based and also graph based, which can keep on sensing the environment as long as there are illuminating user signals. The trade-off relationship between the key system parameters is analyzed, and the simulation result validates the convergence and effectiveness of the proposed algorithm.

Motivation & Objective

• Address the challenge of integrating reliable multi-user communication with accurate environment sensing in 6G wireless networks.
• Exploit sparsity in both structured multi-user signals (via SCMA) and unstructured environmental scatterers to reduce system complexity.
• Develop a joint communication and sensing framework that enables mutual enhancement between user detection and environment imaging.
• Design an incremental, sliding-window algorithm that maintains continuous environment sensing as long as user signals are received.
• Analyze the trade-off between system parameters such as number of users and sensing/communication performance

Proposed method

• Employ Sparse Code Multiple Access (SCMA) for multi-user uplink transmission, leveraging structured signal sparsity.
• Utilize Intelligent Reflective Surfaces (IRS) to reflect scattered signals from environment objects back to the access point.
• Apply compressed sensing (CS) theory to model environment sensing as a sparse signal recovery problem.
• Design a sliding-window-based iterative algorithm that alternates between multi-user detection and environment imaging.
• Introduce a 'momentum-mode' component that incorporates past data to improve convergence and sensing accuracy.
• Use feedback from decoded data to refine environment sensing, which in turn improves subsequent decoding accuracy

Experimental results

Research questions

• RQ1 How can sparsity in both user signals and environmental scatterers be jointly exploited to enable low-complexity JCAS?
• RQ2 What is the mutual dependence between multi-user detection and environment sensing, and how can it be leveraged for performance gain?
• RQ3 How does the number of users affect the trade-off between communication decoding accuracy (SER) and environment sensing accuracy (MSE)?
• RQ4 What role does feedback play in improving the convergence and accuracy of the joint detection and sensing process?
• RQ5 How does the momentum-mode component influence system performance across varying user counts?

Key findings

• The proposed iterative algorithm converges over time, with MSE decreasing and SER improving as the number of data packets increases.
• Feedback significantly improves decoding accuracy by enabling later packets to benefit from more accurate environment estimates derived from earlier sensing.
• An optimal number of users (˜12) exists where both SER and MSE reach their best performance, indicating a clear trade-off between user load and system accuracy.
• When the number of users exceeds the optimal point, further increases degrade both decoding and sensing performance due to increased interference and estimation error.
• The momentum-mode improves system performance at high user counts—especially with a high momentum coefficient (µ = 0.9), which reduces MSE and SER.
• Computational complexity increases with the number of users, as shown by runtime rising from 4.95s (5 users) to 5.64s (20 users) for a single data package