Abstract
Small cells (SCs) based ultra-dense heterogeneous networks (HetNets) are one of the promising solutions for increased coverage and capacity in 5G cellular networks. However, in multi-tiered architecture, co-tier and cross-tier interferences are both performance-limiting factors. Efficient resource allocation techniques can handle interferences effectively, however, their complexity linearly increases with the density of the HetNets resulting from dynamic and unplanned deployment of SCs. Therefore, HetNets can be implemented only through an algorithm that is self-organizing and adaptive to the dynamic conditions. In this research article, a Q-Learning (QL) based adaptive resource allocation scheme is proposed and evaluated for SC-based ultra-dense HetNets. This QL scheme allocates optimal power to the small cell base station (SBS) to meet the minimum required capacity of macrocell user equipment (MUEs) and the small cell user equipment (SUEs) to provide quality of service (QoS). The proposed QL scheme not only maintains the minimum required capacities of the MUEs and SUEs but has also shown a significant improvement in the capacities of MUEs and SUEs in high interference scenarios as compared to the prior works. In the high co-tier and cross-tier interference scenario, where the state of the art schemes fail to maintain the minimum required capacity of the MUE, the proposed scheme provides a minimum MUE capacity of 2 b/s/Hz, which is twice the minimum required QoS threshold. In a similar way, the proposed solution guarantees QoS up to 16 SCs which are 37.5% more SC than the previously reported works in high interference scenario while maintaining a minimum SUE capacity of 1.5 b/s/Hz, which is 33% higher than the minimum required QoS threshold. By simultaneously mitigating co-tier and cross-tier interferences in ultra-dense HetNets, the proposed solution not only improved the minimum capacities of MUEs and SUEs but also sets a new benchmark for minimum QoS threshold.
Highlights
D URING last three decades, wireless communication technologies have evolved very rapidly in terms of increased data rate, throughput, capacity, and coverage while minimizing the latency
We focus on ultra-dense 5G small cells (SC) heterogeneous networks (HetNets) to provide solutions to the above-mentioned challenges of providing minimum Quality of Service (QoS) requirements to both MUEs and SUEs by mitigating cross-tier and co-tier interferences simultaneously through optimal power allocation to small cell base station (SBS) using QL
To mitigate the co-tier and cross-tier interferences, in multitiered architecture for 5G SC HetNets, we propose a selfadaptive framework by considering each SBS as an agent in the Markov Decision Process (MDP)
Summary
D URING last three decades, wireless communication technologies have evolved very rapidly in terms of increased data rate, throughput, capacity, and coverage while minimizing the latency. Referred to as the fundamental requirements of the future cellular networks, the concept of the small cells (SC) or ultra-densification and Self Organizing Networks (SON) are the ones which can handle the issues of coverage, capacity, high throughput, low latency, and EE [3], [5], [8], [9]. From the review of a detailed survey of challenges and future research directions in various enabling technologies for interference mitigation in 5G cellular networks, it is inferred that the intelligent or cognition enabled interference mitigation schemes perform well but some issues related to QoS of MUEs and SUEs still need to be addressed [6], [7], [9]–[14]. To improve the QoS of MUEs and SUEs in ultra-dense SC HetNets by mitigating co-tier and cross-tier interferences simultaneously, a machine learning (ML) technique that optimally allocates power to SBS is presented in this article
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