On enhancing connectivity and efficiency in 5G and emerging 6G mobile networks

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.

Grantor dc:publisher

University College Cork

Year dc:date.issued

2025

Author dc:creator

• Khan, Muhammad Farhan

Advisors dc:contributor.advisor

• Pesch, Dirk H J
• Sreenan, Cormac

• 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)

Statement dc:rights

• © 2025, Muhammad Farhan Khan.

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10468/18783

OAI identifier oai:identifier

oai:cora.ucc.ie:10468/18783