Deep Reinforcement Learning for Joint Spectrum and Power Allocation in Cellular Networks

Nasir, Yasar Sinan; Guo, Dongning

doi:10.48550/arxiv.2012.10682

Cited by 6 publications

(21 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their goal was to optimize the medical data delivery from Patient Edge Nodes (PENs) via multi-radio access networks (RANs) to the core network. Nasir et al [17] presented a multi-agent DDPG-based algorithm to study the problem of joint power and spectrum allocation in wireless networks. Based on simulation results, the authors demonstrated how their proposed technique outperforms the conventional fractional programming algorithm.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Multi-Agent DRL-Based Framework for Joint Multi-RAT Assignment and Dynamic Resource Allocation in Next-Generation HetNets

Alwarafy,

Ciftler,

Abdallah

et al. 2022

Preprint

View full text Add to dashboard Cite

This paper considers the problem of cost-aware downlink sum-rate maximization via joint optimal radio access technologies (RATs) assignment and power allocation in nextgeneration heterogeneous wireless networks (HetNets). We consider a future HetNet comprised of multi-RATs and serving multiconnectivity edge devices (EDs), and we formulate the problem as a mixed-integer non-linear programming (MINP) problem. Due to the high complexity and combinatorial nature of this problem and the difficulty to solve it using conventional methods, we propose a hierarchical multi-agent deep reinforcement learning (DRL)-based framework, called DeepRAT, to solve it efficiently and learn system dynamics. In particular, the DeepRAT framework decomposes the problem into two main stages; the RATs-EDs assignment stage, which implements a single-agent Deep Q Network (DQN) algorithm, and the power allocation stage, which utilizes a multi-agent Deep Deterministic Policy Gradient (DDPG) algorithm. Using simulations, we demonstrate how the various DRL agents efficiently interact to learn system dynamics and derive the global optimal policy. Furthermore, our simulation results show that the proposed DeepRAT algorithm outperforms existing state-of-the-art heuristic approaches in terms of network utility. Finally, we quantitatively show the ability of the DeepRAT model to quickly and dynamically adapt to abrupt changes in network dynamics, such as EDs' mobility.

show abstract

Section: Related Workmentioning

confidence: 99%

Hierarchical Multi-Agent DRL-Based Framework for Joint Multi-RAT Assignment and Dynamic Resource Allocation in Next-Generation HetNets

Alwarafy,

Ciftler,

Abdallah

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Typically, most of the RRAM optimization problems in modern wireless networks are non-convex (e.g., continuous power allocation) [64], combinatorial (e.g., user association and channel access) [12], or mixed-integer nonlinear programming (MINP) (e.g., combined of continuous-and discrete-type problems) [39]. Many algorithms have been developed to systematically solve such problems and find either the global optimum [12] solution or sub-optimal solution.…”

Section: B) Optimization-based Techniquesmentioning

confidence: 99%

“…Many algorithms have been developed to systematically solve such problems and find either the global optimum [12] solution or sub-optimal solution. Such algorithms include, fractional programming (FP) [64], [65], genetic [66], Weighted Minimum Mean Square Error (WMMSE) [64], [65], among others.…”

Section: B) Optimization-based Techniquesmentioning

confidence: 99%

“…For the same system settings in [102], the authors in [64] extended their work and propose a multi-agent DDPG-based DRL framework to address the problem of the joint spectrum and power allocation in wireless networks. Two DRL-based algorithms are proposed, which are executed and trained simultaneously in two layers in order to jointly optimize the discrete subband selection and continuous power allocation.…”

Section: ) In Cellular and Homnetsmentioning

confidence: 99%

See 1 more Smart Citation

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

Alwarafy,

Abdallah,

Ciftler

et al. 2021

Preprint

View full text Add to dashboard Cite

“…However, these methods require a large number of iterations to reach a satisfying solution and are sensitive to change in system parameters. Recent works have adopted distributed RL approach for RA in multiple domains, e.g., in cellular communications [11], and C-V2X [12].…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Joint User Association and Resource Allocation in Factory Automation

Farzanullah

Le‐Ngoc

2022

2022 IEEE Wireless Communications and Networking Conference (WCNC)

View full text Add to dashboard Cite

We propose joint user association, channel assignment and power allocation for mobile robot Ultra-Reliable and Low Latency Communications (URLLC) based on multiconnectivity and reinforcement learning. The mobile robots require control messages from the central guidance system at regular intervals. We use a two-phase communication scheme where robots can form multiple clusters. The robots in a cluster are close to each other and can have reliable Deviceto-Device (D2D) communications. In Phase I, the APs transmit the combined payload of a cluster to the cluster leader within a latency constraint. The cluster leader broadcasts this message to its members in Phase II. We develop a distributed Multi-Agent Reinforcement Learning (MARL) algorithm for joint user association and resource allocation (RA) for Phase I. The cluster leaders use their local Channel State Information (CSI) to decide the APs for connection along with the sub-band and power level. The cluster leaders utilize multi-connectivity to connect to multiple APs to increase their reliability. The objective is to maximize the successful payload delivery probability for all robots. Illustrative simulation results indicate that the proposed scheme can approach the performance of the centralized algorithm and offer a substantial gain in reliability as compared to single-connectivity (when cluster leaders are able to connect to 1 AP).

show abstract

Deep Reinforcement Learning for Joint Spectrum and Power Allocation in Cellular Networks

Cited by 6 publications

References 11 publications

Hierarchical Multi-Agent DRL-Based Framework for Joint Multi-RAT Assignment and Dynamic Resource Allocation in Next-Generation HetNets

Hierarchical Multi-Agent DRL-Based Framework for Joint Multi-RAT Assignment and Dynamic Resource Allocation in Next-Generation HetNets

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

Deep Reinforcement Learning for Joint User Association and Resource Allocation in Factory Automation

Contact Info

Product

Resources

About