http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/45 Telecommunication Engineering 2026-05-04T16:15:17Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/30179 Title: A Multi-Objective Particle Swarm Optimisation Approach in Interference Mitigation Underlay 5G-Enabled Machine-toMachine (M2M) Network in Comparsion with Genetic Algorithm (GA) and Simulated Annealing (SA) Algorithms Authors: Suleiman, Ahmed Danasabe; Mohammmed, Abubakar Saddiq; Salihu, Bala Alhaji; David, Micheal; Michael, Ephraim Abstract: The increase in the number of connected devices has caused a paradigm shift in cellular standards, which has characterized the Long-Term Evolution (LTE) standard. The fifth-generation (5G) standard supports several promising mobile technologies, including Machine-to-Machine (M2M) communication and Device-to-Device (D2D) communication, connecting numerous ubiquitous intelligent devices. M2M communication has found applications across various areas of human activity, such as industrial automation, geological and environmental monitoring, e-health, smart grids, intelligent monitoring, and intelligent transport systems. The deployment of M2M devices underlaid 5G cellular network has introduced new challenges in resource allocation and interference management. Interference due to decreased inter-cell distance and the seamless integration of heterogeneous devices into the 5G cellular network results in degraded Quality of Service (QoS) and network performance. This paper proposes an interference-aware architecture for M2M communication on 5G network to mitigate interference arising from the localization of M2M devices. Furthermore, the proposed interference mitigation scheme will be mathematically modeled as a multi-objective particle swarm optimization (MOPSO) problem, considering the complexity of the fitness function and the trade-off among particles. However, the proposed interference mitigation scheme was bench-marked with Genetic Algorithm (GA) and Simulated Annealing (SA) optimisation algorithms. Simulation results proved that the optimal configuration identification process of the MOPSO has achieved significant improvement in interference reduction from M2M devices relative to GA and SA strategies. Furthermore, the SINR (25.0 dB) and throughput (95.0 Mbps) of the overall network was enhanced with MOPSO in comparison with lower results obtained when GA (SINR 21.5 dB, Throughput 85.0 Mbps) and SA (SINR 18.0 dB, Throughput 78.0) were applied. The improvements achieved by MOPSO shows that the SINR when compared with SA and GA decreased by 28% and 14% respectively. While the throughput achieved by MOPSO when compared with SA and GA showed a decrease by 18% and 10% respectively. Therefore, a balance between optimal M2M separation distance and improved network performance was achieved. 2025-01-01T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/30178 Title: Development of Multi‑Objective Particle Swarm Optimisation (MOPSO) strategy in enhancing interference mitigation in Machine‑to‑Machine (M2M) communication based on fault clearance and communication delay in Smart Grid Networks Authors: Suleiman, Ahmed Danasabe; Mohammmed, Abubakar Saddiq; Salihu, Bala Alhaji; David, Michael; Mohammed, Abdullahi Ibrahim; Michael, Ephraim Abstract: The proliferation of connected devices has led to a paradigm shift in cellular standards, typified by the long-term evolution (LTE) standard. The fifth-generation (5G) standard supports numerous promising mobile technologies, including Machine-to-Machine (M2M) and Device-to-Device (D2D) communication, which enable the communication of a large number of intelligent devices and ubiquitous devices. The deployment of M2M devices in the application of smart grid (SG), specifically in power systems, has introduced new compromising challenges in the areas of resource allocation and interference management. The 5G cellular network’s quality of service (QoS) and performance deteriorate due to interference brought on by the reduced inter-cell distance and the smooth integration of heterogeneous devices.In this paper, an interference-aware multiobjective particle swarm optimisation(MOPSO)scheme is proposed for M2M communication in SG to mitigate the interference generated as a result of the localisation of M2M devices on the grid. In order to evaluate performance, the MOPSO approach was developed and implemented for smart grid situations based on pre-fault, during-fault, and post-optimisation conditions. The initial step was to use the multi-objective particle swarm optimisation (MOPSO) algorithm to optimise the smart grid network in order to decrease grid interference. According to simulation results, under different pre-fault and post-optimization settings, the system throughput and signal-to-interference-to-noise ratio (SINR) were greatly increased by 32.69 and 21.94%, respectively. Furthermore, by using MOPSO, the fault clearance time was reduced by 106.06%, reducing the amount of time needed to clear an impending fault with interference. Additionally, the smart grid network’s power loss was improved and maintained at levels comparable to those of the pre-fault conditions. In the subsequent step, the performance of the developed MOPSO technique was compared to that of the non-dominated sorting genetic algorithm (NSGA-II) in terms of convergence in fault clearance time, SINR, and throughput. Simulation results indicated that, in comparison to NSGAII’s performance, MOPSO throughput and SINR were enhanced by 5.93%, 4.65%, and 0.96%, respectively. In comparison to NSGA-II, the proposed MOPSO converges to the optimal solution more quickly for the various objective functions. The findings provided by the developed MOPSO demonstrate that it can efficiently compete with similar algorithms when tackling problems involving interference optimisation algorithms. 2025-11-24T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/27985 Title: Performance of path loss models over mid-band and high-band channels for 5G communication networks: A review Authors: Shaibu, F. E.; Onwuka, E. N.; Salawu, N.; Oyewobi, S. S.; Djouani, K.; Abu-Mahfouz, A. M. Abstract: The rapid development of 5G communication networks has ushered in a new era of highspeed, low-latency wireless connectivity, as well as the enabling of transformative technologies. However, a crucial aspect of ensuring reliable communication is the accurate modeling of path loss, as it directly impacts signal coverage, interference, and overall network efficiency. This review paper critically assesses the performance of path loss models in mid-band and high-band frequencies and examines their effectiveness in addressing the challenges of 5G deployment. In this paper, we first present the summary of the background, highlighting the increasing demand for high-quality wireless connectivity and the unique characteristics of mid-band (1–6 GHz) and high-band (>6 GHz) frequencies in the 5G spectrum. The methodology comprehensively reviews some of the existing path loss models, considering both empirical and machine learning approaches. We analyze the strengths and weaknesses of these models, considering factors such as urban and suburban environments and indoor scenarios. The results highlight the significant advancements in path loss modeling for mid-band and high-band 5G channels. In terms of prediction accuracy and computing effectiveness, machine learning models performed better than empirical models in both mid-band and high-band frequency spectra. As a result, they might be suggested as an alternative yet promising approach to predicting path loss in these bands. We consider the results of this review to be promising, as they provide network operators and researchers with valuable insights into the state-of-the-art path loss models for mid-band and high-band 5G channels. Future work suggests tuning an ensemble machine learning model to enhance a stable empirical model with multiple parameters to develop a hybrid path loss model for the mid-band frequency spectrum. 2023-01-01T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/27950 Title: Development of Model Metrics for Individuals and Pair Programmers among Software Developers in an Agile Environment Authors: Ajiboye, M. A.; Ajiboye, J. A.; Audu, W. M.; Ajiboye, D. J.; Ohize, H. O.; Majin, R. N.; Abolarin, M. S. Description: Accepted for publication in Nigeria Journal of Engineering & Applied Sciences (NJEAS) 2024-01-01T00:00:00Z