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. pp. 1-5 https://doi.org/10.1109/VTC2024-Spring62846.2024.10683337
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
Page range1-5
Proceedings Title2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring)
ISSN1090-3038
Electronic2577-2465
ISBN
Paperback9798350387421
Electronic9798350387414
PublisherIEEE
Publication dates
Print24 Jun 2024
Online25 Sep 2024
Publication process dates
Accepted12 Mar 2024
Deposited02 Apr 2024
Output statusPublished
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.

Digital Object Identifier (DOI)https://doi.org/10.1109/VTC2024-Spring62846.2024.10683337
Web address (URL) of conference proceedingshttps://doi.org/10.1109/VTC2024-Spring62846.2024
LanguageEnglish
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Chaotic bat algorithm
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Mathematical modelling and parameter optimization of pulsating heat pipes
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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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Metaheuristic algorithms for self-organizing systems: a tutorial
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Downlink optimization with interference pricing and statistical CSI
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Optimal test sequence generation using firefly algorithm
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Flower pollination algorithm for global optimization
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Benchmark problems in structural optimization
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Bat algorithm for topology optimization in microelectronic applications
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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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Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization
Gandomi, A., Yang, X., Talatahari, S. and Deb, S. 2012. Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization. Computers and Mathematics with Applications. 63 (1), pp. 191-200. https://doi.org/10.1016/j.camwa.2011.11.010
Bat algorithm: a novel approach for global engineering optimization
Yang, X. and Gandomi, A. 2012. Bat algorithm: a novel approach for global engineering optimization. Engineering Computations. 29 (5), pp. 464-483. https://doi.org/10.1108/02644401211235834
Evolutionary boundary constraint handling scheme
Gandomi, A. and Yang, X. 2012. Evolutionary boundary constraint handling scheme. Neural Computing and Applications. 21 (6), pp. 1449-1462. https://doi.org/10.1007/s00521-012-1069-0
Two-stage eagle strategy with differential evolution
Yang, X. and Deb, S. 2012. Two-stage eagle strategy with differential evolution. International Journal of Bio-Inspired Computation. 4 (1), pp. 1-5. https://doi.org/10.1504/IJBIC.2012.044932
Firefly Algorithm for solving non-convex economic dispatch problems with valve loading effect
Yang, X., Hosseini, S. and Gandomi, A. 2012. Firefly Algorithm for solving non-convex economic dispatch problems with valve loading effect. Applied Soft Computing. 12 (3), pp. 1180-1186. https://doi.org/10.1016/j.asoc.2011.09.017
User position aware multicell beamforming for a distributed antenna system
Le, T. and Nakhai, M. 2011. User position aware multicell beamforming for a distributed antenna system. IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications. Toronto, ON, Canada 11 - 14 Sep 2011 IEEE. pp. 1423-1427 https://doi.org/10.1109/PIMRC.2011.6139736
An iterative algorithm for downlink multi-cell beam-forming
Le, T. and Nakhai, M. 2011. An iterative algorithm for downlink multi-cell beam-forming. 2011 IEEE Global Telecommunications Conference. Houston, TX, USA 05 - 09 Dec 2011 IEEE. pp. 1-6 https://doi.org/10.1109/GLOCOM.2011.6133913
Throughput analysis of network coding enabled wireless backhauls
Le, T. and Nakhai, M. 2011. Throughput analysis of network coding enabled wireless backhauls. IET Communications. 5 (10), pp. 1318-1327. https://doi.org/10.1049/iet-com.2010.0223
Green radio: radio techniques to enable energy-efficient wireless networks
Han, C., Harrold, T., Armour, S., Krikidis, I., Videv, S., Grant, P., Haas, H., Thompson, J., Ku, I., Wang, C., Le, T., Nakhai, M., Zhang, J. and Hanzo, L. 2011. Green radio: radio techniques to enable energy-efficient wireless networks. IEEE Communications Magazine. 49 (6), pp. 46-54. https://doi.org/10.1109/MCOM.2011.5783984
Power-efficient downlink transmission in multicell networks with limited wireless backhaul
Le, T., Nasseri, S., Zarrebini-Esfahani, A., Nakhai, M. and Mills, A. 2011. Power-efficient downlink transmission in multicell networks with limited wireless backhaul. IEEE Wireless Communications. 18 (5), pp. 82-88. https://doi.org/10.1109/MWC.2011.6056696
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