A deep reinforcement learning for user association and power control in heterogeneous networks

Ding, Hui; Zhao, Feng; Tian, Jie; Li, Dongyang; Zhang, Haixia

doi:10.1016/j.adhoc.2019.102069

Cited by 60 publications

(27 citation statements)

References 30 publications

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“…For this, machine learning is an emerging technology to solve such issues. In [64], the problem of energy efficiency is effectively solved in uplink HetNets along with user association optimization using deep reinforcement learning. For such non-linear problems, traditional methods of problem-solving are not enough.…”

Section: ) Hetnetsmentioning

confidence: 99%

“…However, in the case of lots of user association to that particular base station, the performance degrades significantly. Several researchers also worked on user association and power allocation together [49] [63] [64] using deep reinforcement learning (DRL) and deep neural networks. According to [65], DRL is an efficient way to resolve complex issues.…”

Section: ) Hetnetsmentioning

confidence: 99%

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Towards Energy Efficient 5G Networks Using Machine Learning: Taxonomy, Research Challenges, and Future Research Directions

et al. 2020

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As the world pushes toward the use of greener technology and minimizes energy waste, energy efficiency in the wireless network has become more critical than ever. The next-generation networks, such as 5G, are being designed to improve energy efficiency and thus constitute a critical aspect of research and network design. The 5G network is expected to deliver a wide range of services that includes enhanced mobile broadband, massive machine-type communication and ultra-reliability, and low latency. To realize such a diverse set of requirement, 5G network has evolved as a multi-layer network that uses various technological advances to offer an extensive range of wireless services. Several technologies, such as software-defined networking, network function virtualization, edge computing, cloud computing, and small cells, are being integrated into the 5G networks to fulfill the need for diverse requirements. Such a complex network design is going to result in increased power consumption; therefore, energy efficiency becomes of utmost importance. To assist in the task of achieving energy efficiency in the network machine learning technique could play a significant role and hence gained significant interest from the research community. In this paper, we review the state-of-art application of machine learning techniques in the 5G network to enable energy efficiency at the access, edge, and core network. Based on the review, we present a taxonomy of machine learning applications in 5G networks for improving energy efficiency. We discuss several issues that can be solved using machine learning regarding energy efficiency in 5G networks. Finally, we discuss various challenges that need to be addressed to realize the full potential of machine learning to improve energy efficiency in the 5G networks. The survey presents a broad range of ideas related to machine learning in 5G that addresses the issue of energy efficiency in virtualization, resource optimization, power allocation, and incorporating enabling technologies of 5G can enhance energy efficiency.

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Section: ) Hetnetsmentioning

confidence: 99%

Section: ) Hetnetsmentioning

confidence: 99%

Towards Energy Efficient 5G Networks Using Machine Learning: Taxonomy, Research Challenges, and Future Research Directions

et al. 2020

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“…The authors in [167] propose a multi-agent DQN-based model to address the problem of joint user association and power control in OFDMA-based wireless HetNet. The agents are the UEs, whose action space is discrete, corresponding to jointly associate with the BS and determine the transmit power.…”

Section: ) In Cellular and Homnetsmentioning

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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“…Moreover, a goal must be introduced relating to the state of the environment. Learning can be performed using a centralised (single agent) [9,10] or a distributed approach (multi-agent) [10,11,12]. A decentralized approach of learning is effective for solving complex problems.…”

Section: Introductionmentioning

confidence: 99%

A Reinforcement Learning Approach for Interference Management in Heterogeneous Wireless Networks

Afolabi

Ahmed

Akinola

2021

Int. J. Interact. Mob. Technol.

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<span lang="EN-US">Due to the increased demand for scarce wireless bandwidth, it has become insufficient to serve the network user equipment using macrocell base stations only. Network densification through the addition of low power nodes (picocell) to conventional high power nodes addresses the bandwidth dearth issue, but unfortunately introduces unwanted interference into the network which causes a reduction in throughput. This paper developed a reinforcement learning model that assisted in coordinating interference in a heterogeneous network comprising macro-cell and pico-cell base stations. The learning mechanism was derived based on Q-learning, which consisted of agent, state, action, and reward. The base station was modeled as the agent, while the state represented the condition of the user equipment in terms of Signal to Interference Plus Noise Ratio. The action was represented by the transmission power level and the reward was given in terms of throughput. Simulation results showed that the proposed Q-learning scheme improved the performances of average user equipment throughput in the network. In particular, </span><span lang="EN-US">multi-agent systems with a normal learning rate increased the throughput of associated user equipment by a whooping 212.5% compared to a macrocell-only scheme.</span>

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A deep reinforcement learning for user association and power control in heterogeneous networks

Cited by 60 publications

References 30 publications

Towards Energy Efficient 5G Networks Using Machine Learning: Taxonomy, Research Challenges, and Future Research Directions

Towards Energy Efficient 5G Networks Using Machine Learning: Taxonomy, Research Challenges, and Future Research Directions

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

A Reinforcement Learning Approach for Interference Management in Heterogeneous Wireless Networks

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