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

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

doi:10.48550/arxiv.1901.07159

Cited by 6 publications

(8 citation statements)

References 32 publications

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“…As per (20) we know that the state distributions satify m(Π T H Π, Π T x) = m(H, x). Substituting these two facts into (27) leads to…”

Section: A Permutation Equivariance Of Optimal Resource Allocationmentioning

confidence: 99%

“…Remark 4 (Permutation Matrix is Unknown) The permutation equivariance results in Propositions 1 and 2, which imply the permutation invariance result of Theorem 1, do not require knowledge of the permutation Π that equalizes the distributions m(H, x) and m( Ĥ, x) in (20). This is worth remarking because if the permutation is known, designing operators that are permutation invariant is elementary -just undo the permutation and apply the corresponding operator.…”

Section: B Equivariance Of Random Edge Graph Neural Networkmentioning

confidence: 99%

“…Proposition 1 Consider wireless networks defined by probability distributions m(H, x) and m( Ĥ, x) along with resource allocations p and p. Assume there exists a permutation matrix Π for which (20) holds and that for the same permutation matrix p( Ĥ, x) = Π T p(H, x).…”

Section: A Permutation Equivariance Of Optimal Resource Allocationmentioning

confidence: 99%

“…Perhaps the more compelling justification for statistical learning solutions to wireless resource allocation problems lies in their reliance on data over models. That is, by treating the resource allocation problem itself as a form of statistical regression, we obtain a means of training policies that solve the optimal allocation problem directly rather than via a training set [15]- [20]. This unsupervised approach exceeds the capabilities of supervised learning in that it can be applied to any arbitrary resource allocation problem and has the potential to exceed performance of existing heuristics.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Wireless Resource Allocation With Random Edge Graph Neural Networks

Eisen

Ribeiro

2020

IEEE Trans. Signal Process.

258

205

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We consider the problem of optimally allocating resources across a set of transmitters and receivers in a wireless network. The resulting optimization problem takes the form of constrained statistical learning, in which solutions can be found in a model-free manner by parameterizing the resource allocation policy. Convolutional neural networks architectures are an attractive option for parameterization, as their dimensionality is small and does not scale with network size. We introduce the random edge graph neural network (REGNN), which performs convolutions over random graphs formed by the fading interference patterns in the wireless network. The REGNN-based allocation policies are shown to retain an important permutation equivariance property that makes them amenable to transference to different networks. We further present an unsupervised model-free primal-dual learning algorithm to train the weights of the REGNN. Through numerical simulations, we demonstrate the strong performance REGNNs obtain relative to heuristic benchmarks and their transference capabilities.

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“…As per (20) we know that the state distributions satify m(Π T H Π, Π T x) = m(H, x). Substituting these two facts into (27) leads to…”

Section: A Permutation Equivariance Of Optimal Resource Allocationmentioning

confidence: 99%

Section: B Equivariance Of Random Edge Graph Neural Networkmentioning

confidence: 99%

Section: A Permutation Equivariance Of Optimal Resource Allocationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Wireless Resource Allocation With Random Edge Graph Neural Networks

Eisen

Ribeiro

2020

IEEE Trans. Signal Process.

258

205

View full text Add to dashboard Cite

show abstract

“…The principal objective is to maximize the weighted sum-rate of the system. In [17], the authors propose different DRL architectures such as REINFORCE, DQL and deep deterministic policy gradient (DDPG) for power allocation in multi-user cellular networks. The ultimate target is to maximize the overall sum-rate of the network.In [18], the authors use the DRL approach for dynamic spectrum access in wireless networks.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Radio Resource Allocation in Multi-Cell Networks

2019

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Optimal resource allocation is a fundamental challenge for dense and heterogeneous wireless networks with massive wireless connections. Because of the non-convex nature of the optimization problem, it is computationally demanding to obtain the optimal resource allocation. Recently, deep reinforcement learning (DRL) has emerged as a promising technique in solving non-convex optimization problems. Unlike deep learning (DL), DRL does not require any optimal/ near-optimal training dataset which is either unavailable or computationally expensive in generating synthetic data. In this paper, we propose a novel centralized DRL based downlink power allocation scheme for a multi-cell system intending to maximize the total network throughput. Specifically, we apply a deep Q-learning (DQL) approach to achieve near-optimal power allocation policy. For benchmarking the proposed approach, we use a Genetic Algorithm (GA) to obtain near-optimal power allocation solution. Simulation results show that the proposed DRL-based power allocation scheme performs better compared to the conventional power allocation schemes in a multi-cell scenario.

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An efficient Deep reinforcement learning with extended Kalman filter for device‐to‐device communication underlaying cellular network

Khuntia

Hazra

2019

Trans Emerging Tel Tech

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In this paper, a novel Deep Q‐learning in addition with an extended Kalman filter (EKF) is proposed to solve the channel and power allocation issue for a device‐to‐device enabled cellular network, when the prior traffic information is not known to the base station. Furthermore, this paper work explores an optimal policy for resource and power allocation between users with the aim of maximizing the sum‐rate of the overall system. The proposed work comprises of four phases, ie, cell splitting, clustering, queuing model, channel allocation, and power allocation, simultaneously. The implementation of cell splitting with novel K‐means++ clustering technique increases the network coverage, reduces co‐channel cell interference, and minimizes the transmission power of nodes, whereas the M/M/C:N queuing model solves the issue of waiting time for users in a priority based data transmission. The difficulty with the Q‐learning and Deep Q‐learning environment is to achieve an optimal policy. This is because of the uncertainty of various parameters associated with the system, especially when the state space is extremely big. In order to improve the robustness of the learner, EKF together with the Deep Q‐network is presented in this paper, which incorporates weight uncertainty of the Q‐function as well as the state uncertainty during the transition. Furthermore, the use of EKF provides an improved version of the loss function that helps the learner in achieving an optimal policy. Through numerical simulation, the advantage of our resource sharing scheme over the other existing schemes is also verified.

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

Cited by 6 publications

References 32 publications

Optimal Wireless Resource Allocation With Random Edge Graph Neural Networks

Optimal Wireless Resource Allocation With Random Edge Graph Neural Networks

Deep Learning for Radio Resource Allocation in Multi-Cell Networks

An efficient Deep reinforcement learning with extended Kalman filter for device‐to‐device communication underlaying cellular network

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