Resource Management and Intelligent Design for Future Wireless Communication Networks
Creators: name: Nie, Dawei
Publisher: name: Lancaster University
Year: 2026

description: In recent years, data traffic has surged unprecedentedly, raising concerns
 over resource management as predictions forecast a continual exponential
 growth in devices and wireless connection demand. The increasing demand
 for high-security, sub-millisecond latency, ultra-dense networks, and
 ultra-low energy consumption has sparked concerns over resource scarcity.
 Effective strategies to address this challenge often entail optimizing
 resource consumption or managing the resource allocation of significant
 access traffic. 
 
 
 Cognitive radios (CR) emerges as a promising solution to alleviate radio
 resource scarcity, enabling secondary users (SUs) to dynamically share
 licensed spectrum with primary users (PUs). Firstly, a novel cluster-based
 cooperative sensing-after-prediction scheme is proposed and optimized
 under a system accuracy requirement and a energy consumption constraint.
 The challenging integer programming problems are solved first by relaxing
 the integer variable. Then, two low-complexity search algorithms are
 proposed to achieve the global optimum. This work demonstrate that the
 total energy consumption and the number of users contributing to learning
 and sensing can be greatly reduced by applying our optimized clustered
 sensing-after-prediction scheme. 
 
 
 Although the proposed algorithm significantly reduces energy consumption
 in CRNs, the inherent resource sharing nature renders the networks
 vulnerable to malicious attacks and reduce effective resource utilization.
 To ensure the security of our system and enhance the outcomes of our
 energy optimization efforts to increase effective resource utilization,
 minimizing the effects of malicious users (MUs) remains crucial. Two new
 types of MU effects model are proposed: normal negative effect and hidden
 negative effect, based on the behavior of malicious users in categorized
 groups. The effect models are utilized to formulate the two optimizations
 on decision fusion parameters then minimize the effect of MUs. The two
 optimizations base on MUs model yields minimal system error rates and
 effectively decreases the detection cycle for malicious user detection
 schemes, without significantly compromising decision accuracy. The
 outcomes of resource management for CR effectively meet the stringent
 requirements of both low energy consumption and high security performance.
 
 
 Finally, to fulfill the escalating demand for massive access in future
 wireless networks, it is crucial to enhance the current Random Access
 Channel (RACH) mechanism in 5G. A NOMA-enhanced 2-step RACH scheme that
 jointly leverages the benefits of the ACB, 2-step RACH, and NOMA-RA is
 proposed to reduce the latency and manage the resource allocation of
 massive access traffic. The latency performance and other theoretical
 trade-offs are analyzed by applying Markov chain model, while the optimal
 access probabilities and throughput of NOMA are derived for further
 optimization. To cope with the practical scenarios with constantly
 changing UEs traffic, the thesis proposes a Deep Contextual Multi-Armed
 Bandit (DCMAB) model that optimizes the NOMA throughput and dynamically
 adjust the barring rate to remain optimal latency based on the observable
 channel feedback, confirming the effectiveness of our proposed scheme. The
 outcomes of resource management for massive access effectively meet the
 stringent requirements of both low access latency and ultra-dense networks
 capability.