DFRC systems co-existing in licensed spectrum: cognitive beamforming designs

Article

Le, T., Ku, I., Yang, X., Masouros, C. and Le-Ngoc, T. 2025. DFRC systems co-existing in licensed spectrum: cognitive beamforming designs. IEEE Transactions on Cognitive Communications and Networking.
TypeArticle
TitleDFRC systems co-existing in licensed spectrum: cognitive beamforming designs
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 with the condition that its interference to the primary users (PUs) is 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 such a way that the mean square error (MSE) of the beam pattern between the designed and desired waveforms is minimized. The optimization problem has the following three constraints: i) the signal-to-interference-plus-noise ratio (SINR) at each data communication user is above a predetermined level; ii) the per-antenna transmit power is maintained at a given level; iii) the interference imposed on each PU is below a certain threshold. Both the semidefinite relaxation and nature-inspired firefly algorithms are proposed in order to search for the optimal solutions to the optimization problem. The simulation results indicate that our proposed algorithms can enable the DFRC system to protect the PUs while simultaneously performing its communication and radar functions.

KeywordsCognitive radio; shared spectrum; firefly algorithm; nature-inspired optimization; semidefinite relaxation; integrated sensing and communication
Sustainable Development Goals9 Industry, innovation and infrastructure
Middlesex University ThemeSustainability
Research GroupDepartment of Design and Mathematics
PublisherIEEE
JournalIEEE Transactions on Cognitive Communications and Networking
ISSN
Electronic2332-7731
Publication process dates
Accepted29 May 2025
Deposited02 Jun 2025
Output statusAccepted
Accepted author manuscript
File Access Level
Open
LanguageEnglish
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A biologically inspired network design model
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Fundamental performance trade-offs in coexisting wireless networks
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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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Robust cognitive beamforming for cell-edge coverage in multicell networks with probabilistic constraints
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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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Multiobjective cuckoo search for design optimization
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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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Cuckoo search for business optimization 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