Access Management in Joint Sensing and Communication Systems: Efficiency Versus Fairness

In this paper, we consider a distributed joint sensing and communication (DJSC) system in which each radar sensor as a JSC node is equipped with a sensing function and a communication function. Deploying multiple JSC nodes may require a large amount of bandwidth. Therefore, we investigate the bandwi...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology Vol. 71; no. 5; pp. 5128 - 5142
Main Authors: Nguyen, Trung Thanh, Elbassioni, Khaled, Luong, Nguyen Cong, Niyato, Dusit, Kim, Dong In
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Access control
Algorithms
Approximation
Approximation algorithms
Bandwidth
bandwidth allocation
Bandwidths
Channel allocation
Communication
Communications systems
efficiency
Estimation
fairness
Heuristic methods
Joint sensing and communication
Nodes
Optimization
Polynomials
Radar
radar estimation rate
Resource management
Sensors
ISSN:0018-9545, 1939-9359
Online Access:Get full text
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Summary:In this paper, we consider a distributed joint sensing and communication (DJSC) system in which each radar sensor as a JSC node is equipped with a sensing function and a communication function. Deploying multiple JSC nodes may require a large amount of bandwidth. Therefore, we investigate the bandwidth allocation problem for the DJSC system. In particular, we aim to optimize the bandwidth allocation to the sensing function and the communication function of the JSC nodes. To improve the allocation efficiency while benefiting the spatial diversity advantage of the DJSC systems, the objective is to maximize the sum of sensing performances, i.e., estimation rates, communication performances, i.e., communication data rates, and fairness of all the users. The optimization problem is non-convex and difficult to be solved. For this, we propose a fully polynomial time approximation algorithm, and we prove that the approximation algorithm can guarantee a near-optimal solution with an accuracy bound of <inline-formula><tex-math notation="LaTeX">\epsilon</tex-math></inline-formula>. Furthermore, we propose to use a heuristic algorithm with lower complexity. The simulation results show that both the proposed algorithms are able to achieve the solutions close to the optimum in a computationally efficient fashion.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2022.3153612