A Survey on Applications of Deep Reinforcement Learning in Resource Management for 5G Heterogeneous Networks

Lee, Ying Loong; Qin, Donghong

doi:10.1109/apsipaasc47483.2019.9023331

Cited by 32 publications

(20 citation statements)

References 21 publications

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“…• Even the surveys that address DRL for RRAM in wireless networks focus on specific wireless network types or applications [16], [17], [19], [20], [25], missing some of the recent research, not providing an adequate overview of the most widely used DRL algorithms for RRAM [20], or not covering the RRAM in-depth, but, rather, just covering a limited number of radio resources. Hence, the role of this paper to fill these research gaps and overcome these shortcomings.…”

Section: B Related Workmentioning

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

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<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>

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Section: B Related Workmentioning

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

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“…However, we are still in the early stages of harnessing the power of DRL in optimizing systems such as 5G networks. For more detailed challenges about DRL and networking, we refer the reader to [301].…”

Section: A Deep Reinforcement Learningmentioning

confidence: 99%

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Majid¹,

Saaybi²,

Rietbergen³

et al. 2021

Preprint

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<div>Deep Reinforcement Learning (DRL) has the potential to surpass human-level control in sequential decision-making problems. Evolution Strategies (ESs) have different characteristics than DRL, yet they are promoted as a scalable alternative. </div><div>To get insights into their strengths and weaknesses, in this paper, we put the two approaches side by side. After presenting the fundamental concepts and algorithms for each of the two approaches, they are compared from the perspectives of scalability, exploration, adaptation to dynamic environments, and multi-agent learning. Then, the paper discusses hybrid algorithms, combining aspects of both DRL and ESs, and how they attempt to capitalize on the benefits of both techniques. Lastly, both approaches are compared based on the set of applications they support, showing their potential for tackling real-world problems.</div><div>This paper aims to present an overview of how DRL and ESs can be used, either independently or in unison, to solve specific learning tasks. It is intended to guide researchers to select which method suits them best and provides a bird's eye view of the overall literature in the field. Further, we also provide application scenarios and open challenges. </div>

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“…UAVs can enable and facilitate smart agriculture, for example, by collecting necessary data collected by IoT sensors and transferring them to the local populace through the Internet (Yaacoub and Alouini, 2020). Moreover, UAVs can also be utilized for backhauling purposes in rural areas instead of using other wired/wireless options that require costly (Fadlullah et al, 2017;Fu et al, 2018), Autonomous UAV deployment (Bor-Yaliniz and Yanikomeroglu, 2016;Zeng et al, 2016;Ghanavi et al, 2018) Intelligent resource allocation (Lee and Qin, 2019;Bega et al, 2020) 2. Fault/outage detection and correction with minimum number of human personnel due to social distancing rules Automated fault tracing, outage detection and network recovery ML-based fault detection and recovery (Zoha et al, 2015;Hussain et al, 2019;Murudkar and Gitlin, 2019).…”

Section: Improved Rural Connectivitymentioning

confidence: 99%

“…Redundancy must also be built into future networks to duplicate capacity thereby making multiple connections available such that as soon as one connection goes down or does not have enough capacity to handle a particular service demand, another connection can be activated or allocated to handle that service demands (Michalopoulos et al, 2017). AI will play a very vital role in ensuring real time network response to sudden changes in networks, fault tracing and network recovery, as well as the dynamic allocation of network resources on demand basis (Lee and Qin, 2019;Bega et al, 2020).…”

Section: Lessons From Pandemic That Should Be Considered In the Desigmentioning

confidence: 99%

“…Even though AI has only been partially included in 5G, beyond 5G (B5G) networks will experience the full implementation of AI (Letaief et al, 2019;Shafin et al, 2019). AI in 5G networks will enable intelligent and autonomous operations which is of immense importance in the current and future pandemic situations due to the drastic changes in network service demands and operational procedures resulting from movement restrictions and remote working policies put in place by government and various organizations to curtail the spread of AI will enable proactive and dynamic resource allocation that will enable the network assign resources to locations that need them on a real time demand basis unlike the static resource allocation approach that is currently implemented in existing networks (Lee and Qin, 2019). This will help solve the problem of sudden shift in traffic demands from city centers to residential areas occasioned by the pandemic that is putting pressure on the RAN and backhaul of the network.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Role of Artificial Intelligence Driven 5G Networks in COVID-19 Outbreak: Opportunities, Challenges, and Future Outlook

et al. 2020

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A Survey on Applications of Deep Reinforcement Learning in Resource Management for 5G Heterogeneous Networks

Cited by 32 publications

References 21 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

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

The Role of Artificial Intelligence Driven 5G Networks in COVID-19 Outbreak: Opportunities, Challenges, and Future Outlook

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