Power Allocation in Multi-User Cellular Networks: Deep Reinforcement Learning Approaches

Meng, Fan; Chen, Peng; Wu, Lenan; Cheng, Julian

doi:10.1109/twc.2020.3001736

Cited by 194 publications

(84 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Ref. [29], the DRL framework was used to control power in multiuser wireless communication cellular networks. Transmission rate optimization was examined in Refs.…”

Section: Related Workmentioning

confidence: 99%

Deep reinforcement learning based computation offloading and resource allocation for low-latency fog radio access networks

Rahman

Dang

Ahmed

2020

Intell. and Converged Netw.

View full text Add to dashboard Cite

“…In Ref. [29], the DRL framework was used to control power in multiuser wireless communication cellular networks. Transmission rate optimization was examined in Refs.…”

Section: Related Workmentioning

confidence: 99%

Deep reinforcement learning based computation offloading and resource allocation for low-latency fog radio access networks

Rahman

Dang

Ahmed

2020

Intell. and Converged Netw.

View full text Add to dashboard Cite

“…for power allocation [1] and spectrum access [2]. In the context of densely deployed intelligent wireless networks, most of the research has focused on power allocation [3], and different model-based optimization algorithms, such as weighted minimum mean squared error (WMMSE) [1] and fractional programming (FP) [4], have been proposed. However, these methods generally rely on perfect channel state information (CSI) of the whole network, which is a strong and impractical assumption; futhermore, delayed or partial CSI is shown to deteriorate their performance.…”

Section: Introductionmentioning

confidence: 99%

“…Their applications include resource allocation and management, e.g. power allocation [3], [5], spectrum management [6], and caching and beamforming [7].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Different reinforcement learning (RL) algorithms have recently been compared to WMMSE and FP for power allocation [3,5,8] and were shown to outperform them, especially with imperfect or delayed CSI [5]. RL algorithms are also less computationally expensive in comparison to WMMSE and FP [3]. In spite of the desirable performance of the RL methods, the studied scenarios are generally not realistic; for example, their objective is to maximize sum capacity of the network, which cannot be achieved in practice due to limited code block lengths and the inefficiency of realistic modulation schemes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Link-Level Throughput Maximization Using Deep Reinforcement Learning

et al. 2020

View full text Add to dashboard Cite

A multi-agent deep reinforcement learning framework is proposed to address link level throughput maximization by power allocation and modulation and coding scheme (MCS) selection. Given the complex problem space, reward shaping is utilized instead of classical training procedures. The time-frame utilities are decomposed into subframe rewards, and a stepwise training procedure is proposed, starting from a simplified power allocation setup without MCS selection, incorporating MCS selection gradually, as the agents learn optimal power allocation. The proposed method outperforms both weighted minimum mean squared error (WMMSE) and Fractional Programming (FP) with idealized MCS selections.

show abstract

Deep reinforcement learning for resource allocation of mobile communication systems with device‐to‐device underlay

Cardoso

Carvalho

Gondim

2023

Int J Communication

View full text Add to dashboard Cite

SummaryOver the past few decades, the number of users and services of the mobile communications system has considerably increased, and since its essential resources such as spectrum and energy are limited, their optimization has drawn particular interest. Concomitantly, artificial intelligence (AI) techniques have advanced and their applications have been expanded, including problems of classification, regression, and optimization of tasks of mobile communications systems. Regarding fifth and sixth generations of such systems, the insertion of AI is foreseen toward the allocation of available resources. The present study applied two recently proposed techniques based on deep reinforcement learning algorithms (viz., deep deterministic policy gradient [DDPG] and twin‐delayed DDPG [TD3]), for the power control and spectrum allocation of a mobile communications system with device‐to‐device (D2D) underlay communications. The results show that both algorithms have superior performance to the three algorithms used for comparison: A random algorithm, a greedy algorithm, and REINFORCE, a classical reinforcement learning algorithm. Furthermore, the results show the proposed algorithms have good generalization capability and performed the allocation intelligently, taking into account the relationship between distances separating devices and interference between communications. The results also proved robust in terms of small variations in input data and noise.

show abstract

Power Allocation in Multi-User Cellular Networks: Deep Reinforcement Learning Approaches

Cited by 194 publications

References 32 publications

Deep reinforcement learning based computation offloading and resource allocation for low-latency fog radio access networks

Deep reinforcement learning based computation offloading and resource allocation for low-latency fog radio access networks

Link-Level Throughput Maximization Using Deep Reinforcement Learning

Deep reinforcement learning for resource allocation of mobile communication systems with device‐to‐device underlay

Contact Info

Product

Resources

About