{"id":{"repo_id":"cork","oai_identifier":"oai:cora.ucc.ie:10468/18783"},"canonical_url":"https://search.dev.ndltd.org/etd/cork/oai:cora.ucc.ie:10468/18783","repository":{"repo_id":"cork","name":"University College Cork","base_url":"https://cora.ucc.ie/server/oai/request"},"display":{"title":"On enhancing connectivity and efficiency in 5G and emerging 6G mobile networks","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Khan, Muhammad Farhan"],"institution":"University College Cork","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Pesch, Dirk H J","Sreenan, Cormac"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12-31","date_published":"2025-12-31","updated_at":"2026-07-24T01:48:07Z","subjects":["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)"],"languages":["en"],"rights":["© 2025, Muhammad Farhan Khan."],"rights_urls":["https://creativecommons.org/licenses/by-nc-nd/4.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10468/18783","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Pesch, Dirk H J","Sreenan, Cormac"]},{"key":"dc:creator","label":"Author","values":["Khan, Muhammad Farhan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-05-19T13:22:37Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-05-19T13:22:37Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-12-31"]},{"key":"dc:publisher","label":"Institution","values":["University College Cork"]},{"key":"dc:type","label":"Dc Type","values":["Masters thesis (Research)"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["MRes - Master of Research"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["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)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["© 2025, Muhammad Farhan Khan."]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://creativecommons.org/licenses/by-nc-nd/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10468/18783"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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. 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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."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["On enhancing connectivity and efficiency in 5G and emerging 6G mobile networks"]}]}],"canonical_facts":{"dc:contributor.advisor":["Pesch, Dirk H J","Sreenan, Cormac"],"dc:creator":["Khan, Muhammad Farhan"],"dc:date.accessioned":["2026-05-19T13:22:37Z"],"dc:date.available":["2026-05-19T13:22:37Z"],"dc:date.issued":["2025-12-31"],"dc:description.abstract":["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."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/10468/18783"],"dc:language.iso":["en"],"dc:publisher":["University College Cork"],"dc:rights":["© 2025, Muhammad Farhan Khan."],"dc:rights.uri":["https://creativecommons.org/licenses/by-nc-nd/4.0/"],"dc:subject":["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)"],"dc:title":["On enhancing connectivity and efficiency in 5G and emerging 6G mobile networks"],"dc:type":["Masters thesis (Research)"],"dc:type.qualificationlevel":["Masters"],"dc:type.qualificationname":["MRes - Master of Research"]},"updated_at":"2026-07-24T01:48:07Z"}