Survey of Radio Resource Management in 5G Heterogeneous Networks

Manap, Sulastri Abdul; Dimyati, Kaharudin; Hindia, Mohammad Nour; Talip, Mohamad Sofıan Abu; Tafazolli, Rahim

doi:10.1109/access.2020.3002252

Cited by 66 publications

(40 citation statements)

References 99 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, RRAM techniques can be classified into two broad categories based on their adaptivity to the wireless environment; namely, static or dynamic approaches. Each of which can be further classified based on various criteria, such as centralized or distributed, instantaneous or ergodic, optimal or sub-optimal, single-cell or multi-cell, cooperative or noncooperative, in addition to different combinations of these [45] Cognitive radio networks (CRNs) [46]- [49] Wireless HetNets [50] M2M communication networks [51]- [54] OFDM systems [55] MIMO-OFDM systems [56], [57] D2D communication networks [58] UAV communications [59], [60] Vehicular communications (V2X) [61] Railway communications Value added in this paper Focus on the applications of DRL techniques for RRAM in next generation wireless networks, such as cellular HomNets, IoT networks, satellite networks, multi-RATs networks, HetNet, etc. variants.…”

Section: B Conventional Rram Techniquesmentioning

confidence: 99%

Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

Alwarafy¹,

Abdallah²,

Çiftler³

et al. 2021

Preprint

View full text Add to dashboard Cite

<div>Next generation wireless networks are expected to be extremely complex due to their massive heterogeneity in terms of the types of network architectures they incorporate, the types and numbers of smart IoT devices they serve, and the types of emerging applications they support. In such large-scale and heterogeneous networks (HetNets), radio resource allocation and management (RRAM) becomes one of the major challenges encountered during system design and deployment. In this context, emerging Deep Reinforcement Learning (DRL) techniques are expected to be one of the main enabling technologies to address the RRAM in future wireless HetNets. In this paper, we conduct a systematic in-depth, and comprehensive survey of the applications of DRL techniques in RRAM for next generation wireless networks. Towards this, we first overview the existing traditional RRAM methods and identify their limitations that motivate the use of DRL techniques in RRAM. Then, we provide a comprehensive review of the most widely used DRL algorithms to address RRAM problems, including the value- and policy-based algorithms. The advantages, limitations, and use-cases for each algorithm are provided. We then conduct a comprehensive and in-depth literature review and classify existing related works based on both the radio resources they are addressing and the type of wireless networks they are investigating. To this end, we carefully identify the types of DRL algorithms utilized in each related work, the elements of these algorithms, and the main findings of each related work. Finally, we highlight important open challenges and provide insights into several future research directions in the context of DRL-based RRAM. This survey is intentionally designed to guide and stimulate more research endeavors towards building efficient and fine-grained DRL-based RRAM schemes for future wireless networks.</div>

show abstract

Section: B Conventional Rram Techniquesmentioning

confidence: 99%

Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

Alwarafy¹,

Abdallah²,

Çiftler³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The current 5G networks have been characterized by a combination of small cell networks, also known as HetNet, due to mmWave integration ( Figure 4) [126,127]. A straightforward but exceedingly realistic way to maximize the network capacity is by reducing the cell sizes using the concept of network densification [128].…”

Section: Small Cellmentioning

confidence: 99%

Issues, Challenges, and Research Trends in Spectrum Management: A Comprehensive Overview and New Vision for Designing 6G Networks

et al. 2020

Self Cite

View full text Add to dashboard Cite

With an extensive growth in user demand for high throughput, large capacity, and low latency, the ongoing deployment of Fifth-Generation (5G) systems is continuously exposing the inherent limitations of the system, as compared with its original premises. Such limitations are encouraging researchers worldwide to focus on next-generation 6G wireless systems, which are expected to address the constraints. To meet the above demands, future radio network architecture should be effectively designed to utilize its maximum radio spectrum capacity. It must simultaneously utilize various new techniques and technologies, such as Carrier Aggregation (CA), Cognitive Radio (CR), and small cell-based Heterogeneous Networks (HetNet), high-spectrum access (mmWave), and Massive Multiple-Input-Multiple-Output (M-MIMO), to achieve the desired results. However, the concurrent operations of these techniques in current 5G cellular networks create several spectrum management issues; thus, a comprehensive overview of these emerging technologies is presented in detail in this study. Then, the problems involved in the concurrent operations of various technologies for the spectrum management of the current 5G network are highlighted. The study aims to provide a detailed review of cooperative communication among all the techniques and potential problems associated with the spectrum management that has been addressed with the possible solutions proposed by the latest researches. Future research challenges are also discussed to highlight the necessary steps that can help achieve the desired objectives for designing 6G wireless networks.

show abstract

“…In comparison to the 4G cellular network, major features of the 5G cellular network are high throughput, reliability, EE, scalability, zero latency, better Quality of Service (QoS), and Quality of Experience (QoE). The requirements of the 5G are summarized in [1]- [5]. The vision of 5G cellular networks is not easy to achieve due to very stringent requirements.…”

Section: Introductionmentioning

confidence: 99%

“…Initially proposed solutions, multiple inputs and multiple outputs (MIMO), millimeter wave (mmW) communication, network virtualization, new layer waveforms, separation of control and data planes could not raise the EE, SE, throughput, and other performance metrics to the required limits [3], [5]- [7]. Although all of the above mentioned solutions are very important and often are HetNets based on ultra-dense deployment of SCs in a multitier architecture are the solution to meet the capacity and data rate requirements of 5G cellular networks [5], [8], [9]. The deployment of SCs in HetNets provides an efficient way of traffic offloading between network tiers to efficiently support the exponentially growing mobile traffic with increased QoS, data rates, and EE [2].…”

Section: Introductionmentioning

confidence: 99%

“…Most of the effective interference mitigation schemes are based on power control [15]- [19], user context information and load balancing among the network tiers by efficient user association [20]- [25]. Most of these schemes either handled only co-tier or cross-tier interference or could not provide the minimum required QoS and QoE to all users associated with different network tiers [5]. To handle the issue of QoS and QoE in SC HetNets, recently many schemes are proposed in the literature by imparting intelligence/ cognition in the network by ML and SON features [26]- [37].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Interference Mitigation in HetNets to Improve the QoS Using Q-Learning

2021

View full text Add to dashboard Cite

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 paper, 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.

show abstract

Survey of Radio Resource Management in 5G Heterogeneous Networks

Cited by 66 publications

References 99 publications

Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

Issues, Challenges, and Research Trends in Spectrum Management: A Comprehensive Overview and New Vision for Designing 6G Networks

Interference Mitigation in HetNets to Improve the QoS Using Q-Learning

Contact Info

Product

Resources

About