Decentralized Multi-Agent Power Control in Wireless Networks With Frequency Reuse

Wang, Zixin; Zong, Jun; Zhou, Yong; Shi, Yuanming; Wong, Vincent W. S.

doi:10.1109/tcomm.2021.3135540

Cited by 10 publications

(6 citation statements)

References 31 publications

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“…Within each framework, we highlight the motivations, the NN design principles, type of optimization problems, toy examples of its applications in 6G wireless networks, the theoretical analysis, the related research challenges as well as the summary of advantages and disadvantages. Specifically, the algorithm unrolling [14], learning to branch-and-bound (LBB) [15], GNN for structured optimization [22], deep reinforcement learning (DRL) for stochastic optimization [26], end-to-end learning for semantic optimization [27] as well as federated learning (FL) for distributed optimization [28] will be covered in the following sections to shed light on the excellent performance of ML compared with the conventional optimization algorithm in a variety of practical domains and provide guidance on the usage of ML techniques in 6G networks, followed by the summary of network design philosophies, theoretical tools, implementation issues and discussion of future directions to drive forward the research in this area. Before the detailed elaborations of specific MOA designs, we firstly summarize the existing design paradigms of MOAs from different perspectives in the following subsections.…”

Section: B Machine Learning In 6gmentioning

confidence: 99%

“…However, the above methods require additional CSI exchange between the neighboring BSs, which can downgrade the spectrum efficiency. To address this, DEC-MAPC proposed in [26] achieved fully decentralized power control only using local CSI while maximizing the sumrate of the network. Specifically, to achieve fully distributed implementation, DEC-MAPC was proposed to decompose the global state-action value into a monotonic increasing nonlinear function of all local state-action values.…”

Section: E Application 3: Distributed Constraint Optimization Problemsmentioning

confidence: 99%

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Machine Learning for Large-Scale Optimization in 6G Wireless Networks

Shi¹,

Lian²,

Shi³

et al. 2023

Preprint

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The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from "connected things" to "connected intelligence", featured by ultra high density, largescale, dynamic heterogeneity, diversified functional requirements and machine learning capabilities, which leads to a growing need for highly efficient intelligent algorithms. The classic optimization-based algorithms usually require highly precise mathematical model of data links and suffer from poor performance with high computational cost in realistic 6G applications. Based on domain knowledge (e.g., optimization models and theoretical tools), machine learning (ML) stands out as a promising and viable methodology for many complex large-scale optimization problems in 6G, due to its superior performance, generalizability, computational efficiency and robustness. In this paper, we systematically review the most representative "learning to optimize" techniques in diverse domains of 6G wireless networks by identifying the inherent feature of the underlying optimization problem and investigating the specifically designed ML frameworks from the perspective of optimization. In particular, we will cover algorithm unrolling, learning to branch-andbound, graph neural network for structured optimization, deep reinforcement learning for stochastic optimization, as well as endto-end learning for semantic optimization, for solving challenging large-scale optimization problems arising from various important wireless applications. To enable ML implementation in distributed wireless networks across massive number of end devices, federated learning for distributed optimization will further be presented. Through the in-depth discussion, we shed light on the excellent performance of ML-based optimization algorithms with respect to the classical methods, and provide insightful guidance to develop advanced ML techniques in 6G networks. Neural network design, theoretical tools of different ML methods,

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Section: B Machine Learning In 6gmentioning

confidence: 99%

Section: E Application 3: Distributed Constraint Optimization Problemsmentioning

confidence: 99%

Machine Learning for Large-Scale Optimization in 6G Wireless Networks

Shi¹,

Lian²,

Shi³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…, where d D k ,Bm is the distance between edge device D k and edge server B m and α is the path loss exponent. Based on the Jakes' fading model [54], small-scale block fading g D k ,Bm [s] is modeled as first-order complex Gauss-Markov process, i.e.,…”

Section: Communication Modelmentioning

confidence: 99%

“…The proposed DRL-based resource allocation algorithm can be directly applied when other correlated wireless channel models are considered. However, the uncorrelated channel (e.g., Rayleigh channel) will decrease the correlation of system state in two consecutive time, which may affect the estimation accuracy of the expected future reward and decrease the performance of resource allocation decisions [54].…”

Section: A Mdp Formulationmentioning

confidence: 99%

Trustworthy Federated Learning via Blockchain

Yang

Shi

Zhou

et al. 2023

IEEE Internet Things J.

Self Cite

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The safety-critical scenarios of artificial intelligence (AI), such as autonomous driving, Internet of Things, smart healthcare, etc., have raised critical requirements of trustworthy AI to guarantee the privacy and security with reliable decisions. As a nascent branch for trustworthy AI, federated learning (FL) has been regarded as a promising privacy preserving framework for training a global AI model over collaborative devices. However, security challenges still exist in the FL framework, e.g., Byzantine attacks from malicious devices, and model tampering attacks from malicious server, which will degrade or destroy the accuracy of trained global AI model. In this paper, we shall propose a decentralized blockchain based FL (B-FL) architecture by using a secure global aggregation algorithm to resist malicious devices, and deploying practical Byzantine fault tolerance consensus protocol with high effectiveness and low energy consumption among multiple edge servers to prevent model tampering from the malicious server. However, to implement B-FL system at the network edge, multiple rounds of cross-validation in blockchain consensus protocol will induce long training latency. We thus formulate a network optimization problem that jointly considers bandwidth and power allocation for the minimization of longterm average training latency consisting of progressive learning rounds. We further propose to transform the network optimization problem as a Markov decision process and leverage the deep reinforcement learning based algorithm to provide high system performance with low computational complexity. Simulation results demonstrate that B-FL can resist malicious attacks from edge devices and servers, and the training latency of B-FL can be significantly reduced by deep reinforcement learning based algorithm compared with baseline algorithms.

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“…Especially in radio resource management, the allocation of resources is a difficult task. For instance, in [19], a deep reinforcement learning-based decentralized multiagent power control algorithm was proposed to improve the sum rate of a cellular network. In [20], multiagent deep reinforcement learning-based autonomous channel selection and transmission power selection were used to reduce the co-channel interference in a cellular network.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Algorithm for Interference Mitigation Between Cells in Networks Operating in the 250 MHz Band

et al. 2022

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The growing demand for Internet of Things (IoT) applications in agribusiness increases the necessity of reliable and secure connectivity in rural areas. Thus, in the particular case of Brazil, some initiatives aim to take advantage of frequency bands dedicated to limited private services. For instance, cellular networks based on orthogonal frequency-division multiple access (OFDMA) in 250 MHz bands require specialized adaptations because the interference between cells increases when these systems operate in the Very High Frequency (VHF) band. This work presents an analysis based on a reliable simulation of interference mitigation in OFDMA systems at 250 MHz using a network simulator. The simulator is calibrated with data obtained in the field by an extensive and rigorous drive test. Therefore, the analysis is based on a comparison of traditional frequency reuse schemes with a machine learning approach based on deep reinforcement learning (DRL) to reduce inter-cell interference. The numerical results indicate that the DRL approach outperforms the traditional frequency reuse (FR) schemes in four different typical agribusiness scenarios.INDEX TERMS Frequency reuse, Internet of Things, deep reinforcement learning, customized cellular networks, 250 MHz.

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Decentralized Multi-Agent Power Control in Wireless Networks With Frequency Reuse

Cited by 10 publications

References 31 publications

Machine Learning for Large-Scale Optimization in 6G Wireless Networks

Machine Learning for Large-Scale Optimization in 6G Wireless Networks

Trustworthy Federated Learning via Blockchain

Dynamic Algorithm for Interference Mitigation Between Cells in Networks Operating in the 250 MHz Band

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