Cognitive beamforming design for dual-function radar-communications

Conference paper


Le, T., Ku, I., Yang, X., Masouros, C. and Le-Ngoc, T. 2024. Cognitive beamforming design for dual-function radar-communications. 2024 IEEE 99th Vehicular Technology Conference. Singapore 24 - 27 Jun 2024 IEEE.
TypeConference paper
TitleCognitive beamforming design for dual-function radar-communications
AuthorsLe, T., Ku, I., Yang, X., Masouros, C. and Le-Ngoc, T.
Abstract

This paper introduces a dual-function radar-communication (DFRC) system with cognitive radio capability to tackle the spectral scarcity problem in wireless communications. Particularly, a cognitive DFRC system operates on a spectrum owned by a primary system to simultaneously perform data communication and target tracking while maintaining its interference to the primary users (PUs) below a certain threshold. To achieve this, an optimization problem is formulated to jointly design the beamforming vectors for both the radar and communication functions in minimizing the mean square error (MSE) of the beam patterns between the designed and desired waveforms under three constraints: i) the signal-to-interference-plus-noise ratio (SINR) at each data communication user; ii) the per-antenna transmit power; and iii) the interference imposed on each PU. The semidefinite relaxation technique is utilized to search for the optimal solution to the optimization problem. The simulation results indicate that our proposed cognitive DFRC approach can effectively protect the PUs while simultaneously perform its communication and radar functions.

KeywordsDual-function radar-communications; cognitive radio
Sustainable Development Goals9 Industry, innovation and infrastructure
Middlesex University ThemeCreativity, Culture & Enterprise
Research GroupDesign Engineering and Mathematics
Conference2024 IEEE 99th Vehicular Technology Conference
Proceedings Title2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring)
PublisherIEEE
Publication process dates
Accepted12 Mar 2024
Deposited02 Apr 2024
Output statusAccepted
Accepted author manuscript
File Access Level
Open
Copyright Statement

© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

LanguageEnglish
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Feature selection in life science classification: metaheuristic swarm search
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A heuristic optimization method inspired by wolf preying behavior
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Sizing optimization of truss structures using flower pollination algorithm
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Color image segmentation by cuckoo search
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On the interference tolerance of the primary system in cognitive radio networks
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Oil supply between OPEC and non-OPEC based on game theory
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Bat algorithm is better than intermittent search strategy
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Downlink beamforming design for cognitive cellular systems
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Downlink beamforming in underlay cognitive cellular networks
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Computational optimization, modelling and simulation: past, present and future
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Cuckoo search: recent advances and applications
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Chaotic bat algorithm
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Bio-inspired computation: success and challenges of IJBIC
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A firefly-inspired method for protein structure prediction in lattice models
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True global optimality of the pressure vessel design problem: a benchmark for bio-inspired optimisation algorithms
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Flower pollination algorithm: a novel approach for multiobjective optimization
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Random walks, Lévy flights, Markov chains and metaheuristic optimization
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Are motorways rational from slime mould's point of view?
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Advances in simulation-driven optimization and modelling
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Downlink optimization with interference pricing and statistical CSI
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Benchmark problems in structural optimization
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Engineering optimization: an introduction with metaheuristic applications
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Bat algorithm for topology optimization in microelectronic applications
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Bat algorithm for constrained optimization tasks
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Efficiency analysis of swarm intelligence and randomization techniques
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Modelling of a pulsating heat pipe and start up asymptotics
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Free lunch or no free lunch: that is not just a question?
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Accelerated particle swarm optimization and support vector machine for business optimization and applications
Yang, X., Deb, S. and Fong, S. 2011. Accelerated particle swarm optimization and support vector machine for business optimization and applications. Fong, S. (ed.) NDT 2011: International Conference on Networked Digital Technologies. Macau, China 11 - 13 Jul 2011 Springer. pp. 53-66 https://doi.org/10.1007/978-3-642-22185-9_6
Bat algorithm for multi-objective optimisation
Yang, X. 2011. Bat algorithm for multi-objective optimisation. International Journal of Bio-Inspired Computation. 3 (5), pp. 267-274. https://doi.org/10.1504/IJBIC.2011.042259
Mixed variable structural optimization using Firefly Algorithm
Gandomi, A., Yang, X. and Alavi, A. 2011. Mixed variable structural optimization using Firefly Algorithm. Computers and Structures. 89 (23-24), pp. 2325-2336. https://doi.org/10.1016/j.compstruc.2011.08.002
Possible power-saving gains by dividing a cell into tiers of smaller cells
Le, T. and Nakhai, M. 2010. Possible power-saving gains by dividing a cell into tiers of smaller cells. Electronics Letters. 46 (16), pp. 1163-1165. https://doi.org/10.1049/el.2010.1509
A new metaheuristic bat-inspired algorithm
Yang, X. 2010. A new metaheuristic bat-inspired algorithm. in: González, J., Pelta, D., Cruz, C., Terrazas, G. and Krasnogor, N. (ed.) Nature Inspired Cooperative Strategies for Optimization (NISCO 2010) Berlin Springer.
Eagle strategy using Lévy walk and firefly algorithm for stochastic optimization
Yang, X. and Deb, S. 2010. Eagle strategy using Lévy walk and firefly algorithm for stochastic optimization. in: González, J., Pelta, D, Cruz, C., Terrazas, G. and Krasnogor, N. (ed.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) Springer. pp. 101-111
Optimization and data mining for fracture prediction in geosciences
Shi, G. and Yang, X. 2010. Optimization and data mining for fracture prediction in geosciences. Procedia Computer Science. 1 (1), pp. 1359-1366. https://doi.org/10.1016/j.procs.2010.04.151
Firefly algorithm, stochastic test functions and design optimisation
Yang, X. 2010. Firefly algorithm, stochastic test functions and design optimisation. International Journal of Bio-Inspired Computation. 2 (2), pp. 78-84. https://doi.org/10.1504/IJBIC.2010.032124
Oil and gas assessment of the Kuqa depression of Tarim Basin in western China by simple fluid flow models of primary and secondary migrations of hydrocarbons
Shi, G., Zhang, Q., Yang, X. and Mi, S. 2010. Oil and gas assessment of the Kuqa depression of Tarim Basin in western China by simple fluid flow models of primary and secondary migrations of hydrocarbons. Journal of Petroleum Science and Engineering. 75 (1-2), pp. 77-90. https://doi.org/10.1016/j.petrol.2010.10.009
Firefly algorithms for multimodal optimization
Yang, X. 2009. Firefly algorithms for multimodal optimization. in: Stochastic Algorithms: Foundations and Applications; 5th International Symposium, SAGA 2009, Sapporo, Japan, October 26-28, 2009. Proceedings Springer.
Cuckoo search via Lévy flights
Yang, X. and Deb, S. 2009. Cuckoo search via Lévy flights. in: World Congress on Nature & Biologically Inspired Computing (NaBIC 2009) IEEE Publications. pp. 210-214