Research on Communication Signal Coverage Methods Based on Recursive Genetic Algorithms
DOI:
https://doi.org/10.4108/eetsis.11682Keywords:
Recursive Genetic Algorithm, Signal Coverage Optimization, Multi-BSS WLAN, Spectrum Management, Network Performance EvaluationAbstract
The availability of communication signals for dense wireless local area networks (WLANs) is being increasingly hindered by spectrum contention, interference, and traffic dynamics. Side effects of coverage can also be captured by higher layers, such as the transport or application layer; however, their sensing capabilities are limited, and their information may be strongly biased by the network conditions or the user traffic. In this paper, we explore a coverage-quality-aware optimization framework based on Recursive Genetic Algorithm (RGA) for multi–basic service set (BSS) WLAN environments. Signal coverage is considered as coverage quality and is then evaluated in an indirect manner using network layer performance metrics, namely throughput, packet delay, and control-frame reliability, which is measured using RTS frame loss. A private four-BSS spectrum management dataset produced by the Komondor WLAN simulator is utilized to test potential spectrum configurations. The suggested RGA refines the elite solutions to the maximum spectrum allocation iteratively under interference-aware scenarios. Experimental results demonstrate that the RGA outperforms static allocation, random selection, and the standard GA, with a best average throughput of 0.529, a lowest packet delay of 0.004, and a minimum RTS frame loss of 0.0065. Convergence and Pareto-based analyses further establish the stability as well as the multi-objective nature of the proposed approach. The results confirm that the dataset-driven design of the multi-BSS WLAN optimization by using network-level performance metrics plays a key role in providing a generalizable and reproducible tool for enhancing coverage quality of WLAN networks.
References
[1] Mottaki NA, Motameni H, Mohamadi H. A genetic algorithm-based approach for solving the target Q-coverage problem in over- and under-provisioned directional sensor networks. Physical Communication. 2022; 54:101719. doi: 10.1016/j.phycom.2022.101719
[2] Eledlebi K, et al. A hybrid Voronoi tessellation/genetic algorithm approach for the deployment of drone-based nodes of a self-organizing wireless sensor network in unknown and GPS-denied environments. Drones. 2020;4(3):33. doi:10.3390/drones4030033
[3] Zhou M, et al. Adaptive genetic algorithm-aided neural network with channel state information tensor decomposition for indoor localization. IEEE Transactions on Evolutionary Computation. 2021;25(5):913–927. doi:10.1109/TEVC.2021.3085906
[4] Sun M, et al. Indoor localization using mind evolutionary algorithm-based geomagnetic positioning and smartphone IMU sensors. IEEE Sensors Journal. 2022;22(7):7130–7141. doi:10.1109/JSEN.2022.3155817
[5] Halim Z, et al. An effective genetic algorithm-based feature selection method for intrusion detection systems. Computers & Security. 2021; 110:102448. doi: 10.1016/j.cose.2021.102448
[6] Qian L, et al. A new method of inland water ship trajectory prediction based on long short-term memory network optimized by genetic algorithm. Applied Sciences. 2022;12(8):4073. doi:10.3390/app12084073
[7] Yalçın S. An improved genetic algorithm approach to spectrum sensing for long-range based cognitive radio networks. Transactions on Emerging Telecommunications Technologies. 2022;33(9): e4526. doi:10.1002/ett.4526
[8] Alibeiki A, Motameni H, Mohamadi H. A new genetic-based approach for solving k-coverage problem in directional sensor networks. Journal of Parallel and Distributed Computing. 2021; 154:16–26. doi: 10.1016/j.jpdc.2021.03.006
[9] Datta A, Dasgupta M. Accurate localization of sensor nodes in underwater sensor networks using Doppler shift and modified genetic algorithm. Evolutionary Intelligence. 2021;14(1):119–131. doi:10.1007/s12065-019-00343-1
[10] Wagner KM, Black JT. Genetic-algorithm-based design for rideshare and heterogeneous constellations. Journal of Spacecraft and Rockets. 2020;57(5):1021–1032. doi:10.2514/1. A34527
[11] Khalili M, Khalily-Darmany M. A genetic algorithm for selecting cooperative or direct communications between nodes in wireless sensor networks. Iran Journal of Computer Science. 2020;3(1):25–33. doi:10.1007/s42044-019-00048-9
[12] Nkongolo M, et al. Cloud-based optimization for zero-day threat detection using genetic algorithm and ensemble learning. Electronics. 2022;11(11):1749. doi:10.3390/electronics11111749
[13] Xu L, et al. Hierarchical recursive signal modeling for multifrequency signals based on discrete measured data. Adaptive Control and Signal Processing. 2021;35(5):676–693. doi:10.1002/acs.3221
[14] Li B, Liu H, Wang R. Efficient sensor placement for signal reconstruction based on recursive methods. IEEE Transactions on Signal Processing. 2021; 69:1885–1898. doi:10.1109/TSP.2021.3063495
[15] Polak L, et al. Measurement and analysis of 4G/5G mobile signal coverage in heavy industry environments. Sensors. 2024;24(8):2538. doi:10.3390/s24082538
[16] Selvaraj M, et al. Reconfigurable metasurface enabling tunable reflection in 6G wireless communications. Sensors. 2023;23(22):9166. doi:10.3390/s23229166
[17] Xiang T, Wang H. Distributed 5G signal coverage detection based on PSO-BP-Kriging. Sensors. 2018;18(12):4390. doi:10.3390/s18124390
[18] Chrysikos T, Gourna S, Skouroliakou A. RF coverage design for broadband monitoring in 5G-enabled smart cities. Information. 2023;14(3):156. doi:10.3390/info14030156
[19] Guo J, et al. Optimization coverage strategy for wireless sensor network nodes based on path loss and false alarm probability. Sensors. 2025;25(2):396. doi:10.3390/s25020396
[20] Nemer IA, et al. Energy-efficient UAV movement control for fair communication coverage using deep reinforcement learning. Sensors. 2022;22(5):1919. doi:10.3390/s22051919
[21] Shao S, Wang Z, Xu S, Guo S, Qiu X. Multi-AP Cooperative Radio Resource Allocation Method for Co-Channel Interference Avoidance in 802.11be WLAN. Computers, Materials & Continua. 2025;84(3):4949–4972. doi:10.32604/cmc.2025.065053.
[22] Zhang L, Yin H, Roy S, Cao L, Gao X, Sathya V. IEEE 802.11be Network Throughput Optimization with Multi-Link Operation and AP Coordination. IEEE Internet of Things Journal. 2024;11(13):23850–23861. doi:10.1109/JIOT.2024.3386653.
[23] Park KW, Kim HM, Shin OS. Survey on intelligent reflecting surface-assisted UAV communications. Energies. 2022;15(14):5143. doi:10.3390/en15145143
[24] Hassan N, et al. User association trade-offs in integrated RF/mmWave/THz communications. Future Internet. 2023;15(12):376. doi:10.3390/fi15120376
[25] Yang Y, et al. Millimeter-wave antennas for 5G wireless communications: Technologies and future trends. Sensors. 2025;25(17):5424. doi:10.3390/s25175424
[26] Vizzarri A, Mazzenga F, Giuliano R. Future technologies for train communication: LEO HTS satellites in adaptable communication systems. Sensors. 2022;23(1):68. doi:10.3390/s23010068
[27] Chen HW, Liang CK. Genetic algorithm versus discrete particle swarm optimization for energy-efficient moving object coverage. Applied Sciences. 2022;12(7):3340. doi:10.3390/app12073340
[28] Wang Y, et al. Denoising of mining cable PD signals using genetic algorithm-optimized VMD and wavelet thresholding. Sensors. 2022;22(23):9386. doi:10.3390/s22239386
[29] Li Y, et al. Scheduling for heterogeneous signal processing platforms based on quantum genetic algorithm. Applied Sciences. 2023;13(7):4428. doi:10.3390/app13074428
[30] Li J, et al. Communication Base Station Site Selection Method Based on an Improved Genetic Algorithm. Electronics. 2025; 14(20): 3977. doi:10.3390/electronics14203977
[31] Ussipov N, et al. Mega: Maximum-entropy genetic algorithm for router nodes placement in wireless mesh networks. Sensors. 2024; 24(20): 6735. doi:10.3390/s24206735
[32] Kim H, et al. Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks. Applied Sciences. 2025; 15(1): 428. doi:10.3390/app15010428
[33] Tejedor-Romero M, Gimenez-Guzman JM, Cruz-Piris L, Herranz-Oliveros D, Marsa-Maestre I. Optimal channel assignment on dense Wi-Fi networks using Thermodynamic Threshold Accepting. Engineering Science and Technology, an International Journal. 2024;57:101797. doi: 10.1016/j.jestch.2024.101797
[34] Soufi A, Chaker J, Toukmati A, Kaabal A. Particle swarm optimization for dynamic channel allocation in IEEE 802.11 WLAN networks: Minimizing co-channel interference. International Journal of Engineering and Advanced Technology. 2025;14(6):28–33. doi:10.35940/ijeat.E4644.14060825.
[35] Barrachina-Muñoz S. Self-contained four-BSS dataset for spectrum management in WLANs. Zenodo. 2020. doi:10.5281/zenodo.4265898
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