Joint optimization of resources allocation for quality of service aware next-generation heterogeneous cellular networks / Hayder Faeq Rasool Alhashimi

Hayder Faeq Rasool , Alhashimi (2025) Joint optimization of resources allocation for quality of service aware next-generation heterogeneous cellular networks / Hayder Faeq Rasool Alhashimi. PhD thesis, Universiti Malaya.

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Abstract

The next generation mobile communication system is expected to meet the different service needs of modern communication scenarios. Heterogeneous networks (HetNets) have received a lot of attention in recent years due to their potential as a novel paradigm for evolutionary networks. When compared to homogeneous networks, HetNets provide more potential for spatial spectrum reuse and higher Quality of Service (QoS). However, effective resource management solutions are essential to reduce interference and accomplish spectrum sharing. The research seeks to identify key challenges with the goal of developing effective approaches in 6G HetNets. In this context, resource management aspects such as user association, spectrum allocation, and power allocation are studied. The Millimeter wave band (100 GHz) for HetNet in the downlink scenario is considered. The first scheme in this thesis, joint user association (UA) and channel allocation (CA) problem in two-tier HetNets to improve QoS is investigated. An innovative scheme for user association and channel allocation is presented, wherein the user can be connected to either Macro Base Station (MBS) or a possible Small Base Station (SBS) in a direct or relay-assisted link. A matching game-based user association is proposed to find the optimal association for users. Moreover, a modified auction game is applied to allocate the optimal channel by considering the quota of each next-Generation Node Base Station (gNB). The second scheme in this thesis, a joint user association, channel assignment, and power allocation optimization scheme in 6G HetNets is proposed. The proposed optimization problem is divided into three subproblems. First, a greedy-based user association algorithm is proposed to allocate multiple users to MBS or SBSs to maximize the total sum rate. Then, an auction algorithm is proposed to solve the channel assignment optimization problem to maximize the spectrum efficiency (SE). The utility function of the auction algorithm is formulated based on a Multiple Attributes Decision-Making (MADM) strategy in which the considered attributes are SE and Energy Efficiency (EE). Finally, a State-Action-Reward-State-Action (SARSA) algorithm, which is a reinforcement learning approach, is proposed to solve the power allocation optimization problem. The SARSA algorithm determines optimal power allocation that enhances EE by investigating different power levels. The simulation results of the first scheme showed that the proposed joint UA and CA approach performs well over the state-of-the-art techniques in terms of connection probability, throughput, EE, and SE. Moreover, the significance of evaluating the MBS power level is demonstrated, in terms of probability of connection, and average data rate. The auctions allow for load balancing between the macro channels allocated to SBSs. The effectiveness of the proposed scheme in a relay-assisted scenario is demonstrated in terms of data rate, SE, EE, outage probability, and total saving power. On the other hand, the simulation results of the second scheme illustrated that the proposed SARSA algorithm outperforms the benchmark reward functions, random scheme, and maximum power-based framework in terms of network performance. Furthermore, the proposed approach demonstrates tradeoffs between SE and EE. The outcomes of this thesis demonstrate effective resource utilization that minimizes losses while achieving outstanding results with the same resources.

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