A new approach to interference cancellation in D2D 5G uplink via Non orthogonal convex optimization.
Min Zhu, Ping Guo, Xianghua Liu, Hao Zhang, Salwa Othmen, Chahira Lhioui, Aymen Flah, Ivo Perg
Author Information
Min Zhu: College of Information Science and Technology, Zhejiang Shuren University, Hangzhou, 310015, China. zhumin@zjsru.edu.cn.
Ping Guo: China Mobile Group Zhejiang Co., Ltd, Hangzhou, 310020, People's Republic of China.
Xianghua Liu: School of Artificial Intelligence, Wenzhou Polytechnic College, Wenzhou, 325000, People's Republic of China.
Hao Zhang: School of Artificial Intelligence, Wenzhou Polytechnic College, Wenzhou, 325000, People's Republic of China.
Salwa Othmen: Department of Computers and Information Technologies, College of Sciences and Arts Turaif, Northern Border University, Arar, Saudi Arabia. salwa.othmen@nbu.edu.sa.
Chahira Lhioui: Department of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of Bisha, Bisha, 67714, Saudi Arabia.
Aymen Flah: National Engineering School of Gabes, University of Gabes, Zrig Eddakhlania, Tunisia.
Ivo Perg: ENET Centre, VSB-Technical University of Ostrava, Ostrava, Czech Republic.
Heterogeneous communication modes in 5G demand integrated device connections, resource availability, and high capacity for meeting user demands. The radio resource allocation and usage for massive users results in interference between the device-to-device (D2D) uplink channels. This issue is addressed using a Non-orthogonal Convex Optimization Problem (NCOP) that identifies the chances of self-interference cancellations. This technique classifies interference and non-interference allocations in the rate of uplink communications. The channel reassignment is addressed as an NCOP based on the available interference levels. The interference levels before and after allocation and reallocation are analyzed under convex optimization. The interference cancellation convergence is computed for both channels wherein the transfer switching is performed. The convergence rate is estimated using the interference level and the number of channels reassigned for the uplink devices. Hence, the self-interference cancellation relies on non-convex channel allocations across various switching in this case. This feature is revisited if the D2D channels exceed their capacity for communication. Therefore, the 5G communication features coexist with the D2D uplinks for interference cancellations to improve channel allocation. For the SNR = 45dBm, the proposed NCOP reduces 12.4% of channel reassignment by augmenting 9.24% of interference cancellation.
Bao, J., Wang, P., Liu, C., Wu, J. & Jiang, B. Decentralized Multisubsystem weighted interference cancellation with coded caching. IEEE Internet Things J. 11(2), 3175–3189. https://doi.org/10.1109/JIOT.2023.3295128 (2023).
Lohan, P., Kantarci, B., Ferrag, M. A., Tihanyi, N. & Shi, Y. From 5G to 6G networks, a survey on AI-Based jamming and interference detection and mitigation. IEEE Open. J. Commun. Soc. 5, 3920–3974. https://doi.org/10.1109/OJCOMS.2024.3416808 (2024).
Kim, S. M., Lim, Y. G., Dai, L. & Chae, C. B. Performance analysis of self-interference cancellation in full-duplex massive MIMO systems: Subtraction versus Spatial suppression. IEEE Trans. Wireless Commun. 22(1), 642–657 (2022).
[DOI: 10.1109/TWC.2022.3197275]
Al-Makhlasawy, R. M., Khairy, M. & El-Shafai, W. Correction: recurrent neural networks for enhanced joint channel Estimation and interference cancellation in FBMC and OFDM systems: unveiling the potential for 5G networks. EURASIP J. Adv. Signal. Process. 2023(1), 130 (2023).
[DOI: 10.1186/s13634-023-01090-3]
Zhong, C., Zhai, D., Lu, Y. & Li, K. Intelligent interference cancellation and ambient backscatter signal extraction for wireless-powered UAV IoT network. EURASIP J. Adv. Signal Process. 2024(1), 30 (2024).
[DOI: 10.1186/s13634-024-01121-7]
Lu, Z. Research on anti-interference performance of 5G multi band communication antenna supported by NFC technology. Wireless Netw. 31, 915–927 (2024).
Siddiqui, M. U. A. et al. Urllc in beyond 5 g and 6 g networks: an interference management perspective. IEEe Access. 11, 54639–54663 (2023).
[DOI: 10.1109/ACCESS.2023.3282363]
Yoon, P. et al. ULTIMA: ultimate balance of centralized and distributed benefits for interference management in 5G cellular networks. IEEE Access. 11, 85694–85710. https://doi.org/10.1109/ACCESS.2023.3303482 (2023).
Kazmi, S. H. A., Qamar, F., Hassan, R. & Nisar, K. Routing-based interference mitigation in SDN enabled beyond 5G communication networks: A comprehensive survey. IEEE Access. 11, 4023–4041 (2023).
[DOI: 10.1109/ACCESS.2023.3235366]
Alraih, S., Nordin, R., Abu-Samah, A., Shayea, I. & Abdullah, N. F. A survey on handover optimization in beyond 5G mobile networks: challenges and solutions. IEEE Access. 11, 59317–59345 (2023).
[DOI: 10.1109/ACCESS.2023.3284905]
Alruwaili, M., Kim, J. & Oluoch, J. Optimizing 5G power allocation with Device-to-Device communication: A Gale-Shapley algorithm approach. IEEE Access. 12, 30781–30795. https://doi.org/10.1109/ACCESS.2024.3369597 (2024).
Wang, X., Yao, H., Mai, T., Guo, S. & Liu, Y. Reinforcement learning-based particle swarm optimization for end-to-end traffic scheduling in TSN-5G networks. IEEE/ACM Trans. Networking. 31(6), 3254–3268 (2023).
[DOI: 10.1109/TNET.2023.3276363]
Alhussan, A. A. & Towfek, S. K. 5G resource allocation using feature selection and Greylag Goose optimization algorithm. Computers Mater. Continua, 80(1). https://doi.org/10.32604/cmc.2024.049874 (2024).
Duan, S. et al. Cooperative secure beamforming optimization for Full-Duplex rate splitting multiple Access-enabled beyond-5G communication networks. Comput. Electr. Eng. 119, 109640 (2024).
[DOI: 10.1016/j.compeleceng.2024.109640]
Chen, L., Li, K. & Liu, H. L. Modeling 5G shared base station planning problem using an evolutionary bi-level optimization algorithm. Appl. Soft Comput. 165, 112079 (2024).
[DOI: 10.1016/j.asoc.2024.112079]
Algriree, W. et al. An analysis of low complexity of 5G-MIMO communication system based CR using hybrid filter detection. Alexandria Eng. J. 65, 627–648 (2023).
[DOI: 10.1016/j.aej.2022.10.050]
Alam, M. J., Chugh, R., Azad, S. & Hossain, M. R. Ant colony optimization-based solution to optimize load balancing and throughput for 5G and beyond heterogeneous networks. EURASIP J. Wirel. Commun. Netw. 2024(1), 44 (2024).
[DOI: 10.1186/s13638-024-02376-2]
Mbulwa, A. I., Yew, H. T., Chekima, A. & Dargham, J. A. Self-Optimization of handover control parameters for 5G wireless networks and beyond. IEEE Access. 12, 6117–6135. https://doi.org/10.1109/ACCESS.2023.3346039 (2023).
Mori, S., Mizutani, K. & Harada, H. A digital self-interference cancellation scheme for in-band full-duplex-applied 5G system and its software-defined radio implementation. IEEE Open. J. Veh. Technol. 4, 444–456 (2023).
[DOI: 10.1109/OJVT.2023.3281723]
Gbadamosi, S. A., Hancke, G. P. & Abu-Mahfouz, A. M. Adaptive interference avoidance and mode selection scheme for D2D-enabled small cells in 5G-IIoT networks. IEEE Trans. Industr. Inf. 20(2), 2408–2419 (2023).
[DOI: 10.1109/TII.2023.3288220]
Ni, Y. et al. December). Beamforming and interference cancellation for RIS-Assisted HAP-D2D communication systems. In 2023 IEEE Globecom Workshops (GC Wkshps) (153–159). IEEE. (2023). https://doi.org/10.1109/GCWkshps58843.2023.10464492
Mori, S., Mizutani, K. & Harada, H. Inter-User interference cancellation scheme for 5G-Based dynamic Full-Duplex cellular system. IEEE Open. J. Veh. Technol. 5, 704–720. https://doi.org/10.1109/OJVT.2024.3398566 (2024).
Sun, H., Wang, Z., Li, L. & Fan, W. Multipath Parameter Extraction in 5G-NR Multi-cell Network Using Interference Cancellation Method. IEEE Antennas and Wireless Propagation Letters. (2024).
Nguyen, L. V. & Swindlehurst, A. L. RIS-aided interference cancellation for joint device-to-device and cellular communications. In 2024 IEEE International Conference on Communica (2024), June.
Ayaz, M., Hussain, A., Hussain, T. & Ali, I. Optimization of quality of service in 5G cellular network by focusing on interference management. Wireless Pers. Commun. 135(4), 2229–2254 (2024).
[DOI: 10.1007/s11277-024-11139-7]
Al-Makhlasawy, R. M., Khairy, M. & El-Shafai, W. Recurrent neural networks for enhanced joint channel Estimation and interference cancellation in FBMC and OFDM systems: unveiling the potential for 5G networks. EURASIP J. Adv. Signal Process. 2023(1), 120 (2023).
[DOI: 10.1186/s13634-023-01077-0]
Jiang, Y., Liu, S., Li, M., Zhao, N. & Wu, M. A new adaptive co-site broadband interference cancellation method with auxiliary channel. Digit. Commun. Networks (2022). https://doi.org/10.1016/j.dcan.2022.10.025
Shaikh, F. S., Saleem, Y. & Wismüller, R. An interference-conscious reduced routing overhead protocol for Device-to-Device (D2D) Networks. Computer Communications. (2024).
Han, M. et al. Full-duplex multi-band 5G/6G coexistence mobile fronthaul network based on RoF with RF self-interference cancellation. Opt. Fiber. Technol. 81, 103551 (2023).
[DOI: 10.1016/j.yofte.2023.103551]
Li, H., Zhu, Z., Gao, C., Wang, G., Zhou, T., Li, X., … Zhao, S. (2023). High cost-effective photonic assisted in-band interference cancellation scheme for centralized radio access network towards future 5G communication. Optical Fiber Technology, 81, 103558.
De Silva, U., Silva, H. & Madanayake, A. Augmented envelope neural networks on RF-SoC for digital Self-Interference cancellation. IEEE Access. 12, 44091–44103 (2024).
[DOI: 10.1109/ACCESS.2024.3380819]
Tan, J. S. et al. Lightweight machine learning for digital cross-link interference cancellation with RF chain characteristics in flexible duplex MIMO systems. IEEE Wirel. Commun. Lett. 12(7), 1269–1273 (2023).
[DOI: 10.1109/LWC.2023.3270423]
Yang, H., Bondada, K. S., Cheng, X., Jakubisin, D. J., Tripathi, N., Yang, Y., …Reed, J. H. (2023, October). Underlay-based 5G Sidelink with Co-channel Interference Cancellation. In MILCOM 2023–2023 IEEE Military Communications Conference (MILCOM) (pp. 304–310). IEEE.
Javed, M. Y., Tervo, N., Leinonen, M. E. & Pärssinen, A. Wideband inter-beam interference cancellation for mmW/sub-THz phased arrays with squint. IEEE Trans. Veh. Technol. 72(6), 7560–7572 (2023).
[DOI: 10.1109/TVT.2023.3242133]
Nallasivan, G. et al. Clustering and Energy Efficient Power Allocation Scheme for D2D Multicast Network. In 2024 IEEE International Conference on Contemporary Computing and Communications (InC4) (Vol. 1, pp. 1–5). IEEE. (2024), March.
Mahdi, W. H. & Taşpınar, N. Resource allocation based on SFLA algorithm for D2D multicast communications. Comput. Syst. Sci. Eng., 45(2), 1517–1530. https://doi.org/10.32604/csse.2023.030069 (2023).
