2026-09-19T22:49:25Zhttps://cora.ucc.ie/server/oai/request

oai:cora.ucc.ie:10468/187832026-05-20T02:01:30Zcom_10468_388com_10468_5com_10468_227com_10468_1col_10468_9859col_10468_11339col_10468_8983

Pesch, Dirk H J Sreenan, Cormac Khan, Muhammad Farhan Science Foundation Ireland European Regional Development Fund 2026-05-19T13:22:37Z 2026-05-19T13:22:37Z 2025-12-31 2025-12-31 The evolution of 5G and emerging 6G mobile networks introduces stringent requirements on connectivity, efficiency, reliability, and adaptability, particularly for smart factory and dense urban environments. This thesis investigates key challenges in next-generation wireless systems, including base station selection, interference management, spectral efficiency enhancement, latency reduction, and feedback overhead, in support of Industry 4.0, massive machine-type communication (mMTC), and ultra-reliable low-latency communication (URLLC). The thesis first examines base station selection strategies in 5G heterogeneous networks, comparing Maximum Received Power (MRP) and Maximum SINR-based association. Results show clear trade-offs between interference awareness, load distribution, and user throughput, highlighting the importance of interference-aware association for fairness and capacity in dense deployments. The performance of massive MIMO precoding is then analyzed, demonstrating that Zero Forcing (ZF) significantly outperforms Maximum Ratio Transmission (MRT) in interference-limited scenarios, yielding notable gains in spectral efficiency and downlink and uplink sum throughput with increasing antenna counts. To further improve reliability and efficiency, a multi-user STBC-OFDM transmission scheme is investigated, achieving reduced error rates, a twofold throughput gain, and lower peak-to-average power ratio compared to conventional OFDM. The thesis also proposes a Composite Base Station architecture with Dynamic Spectrum Scheduling, enabling coordinated use of sub-6~GHz and mmWave bands, and demonstrating substantial latency and throughput improvements in dense urban scenarios. In addition, a device-to-device relay selection framework is developed for smart industrial IoT environments, significantly improving system efficiency and mitigating coverage blind spots. The role of Intelligent Reflecting Surfaces (IRS) is then explored for mmWave and sub-6 GHz communications, showing that IRS-assisted links enhance spectral efficiency while substantially reducing required transmit power. Finally, the thesis investigates machine learning-driven optimization for compressed communication in aerial RIS-assisted CoMP-NOMA networks. Autoencoder-based feedback compression is employed to reduce RIS phase feedback overhead under fading and noise, while preserving reconstruction accuracy. Simulation results confirm that aerial RIS deployment improves network sum rate, spectral efficiency, energy efficiency, and outage performance, particularly for cell-edge users. Overall, this thesis presents a unified and experimentally grounded framework for enhancing performance, scalability, and energy efficiency in 5G and future 6G wireless networks, providing practical insights for smart industrial and urban communication systems. Not peer reviewed Accepted Version application/pdf Khan, M. F. 2025. On enhancing connectivity and efficiency in 5G and emerging 6G mobile networks. MRes Thesis, University College Cork. 237 https://hdl.handle.net/10468/18783 en University College Cork info:eu-repo/grantAgreement/SFI/Research Centres Programme/16/RC/3918/IE/Confirm Centre for Smart Manufacturing/ © 2025, Muhammad Farhan Khan. https://creativecommons.org/licenses/by-nc-nd/4.0/ 5G networks 6G wireless systems Massive MIMO Intelligent Reflecting Surfaces Spectral efficiency Industry 4.0 Dynamic Spectrum Scheduling Machine learning for wireless networks Composite Base Stations STBC-OFDM Industrial IoT (IIoT) CoMP-NOMA mmWave communications Ultra-reliable low-latency communications (URLLC) On enhancing connectivity and efficiency in 5G and emerging 6G mobile networks Masters thesis (Research) Masters MRes - Master of Researchinfo:eu-repo/semantics/openAccess