AI Empowered Resource Management for Future Wireless Networks

Shen, Yulong; Zhang, Jun; Song, S. H.; Letaief, Khaled B.

doi:10.48550/arxiv.2106.06178

Cited by 5 publications

(5 citation statements)

References 38 publications

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“…For instance, edge DL approach has been developed in [58] to deliver low-latency semantic tasks (e.g., text messages) by learning the communication strategies in an end-to-end fashion based on JSCC. Furthermore, edge AI provides a new paradigm for optimization algorithms design to enable service-driven resource allocation in 6G networks [60]. For instances, distributed RL [55], decentralized graph neural networks [23], and distributed DNN [61], are able to automatically learn the distributed resource allocation optimization algorithms.…”

Section: Edge Ai Empowered 6g Networkmentioning

confidence: 99%

Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications

Letaief

Shi

et al. 2022

IEEE J. Select. Areas Commun.

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424

143

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The thriving of artificial intelligence (AI) applications is driving the further evolution of wireless networks. It has been envisioned that 6G will be transformative and will revolutionize the evolution of wireless from "connected things" to "connected intelligence". However, state-of-the-art deep learning and big data analytics based AI systems require tremendous computation and communication resources, causing significant latency, energy consumption, network congestion, and privacy leakage in both of the training and inference processes. By embedding model training and inference capabilities into the network edge, edge AI stands out as a disruptive technology for 6G to seamlessly integrate sensing, communication, computation, and intelligence, thereby improving the efficiency, effectiveness, privacy, and security of 6G networks. In this paper, we shall provide our vision for scalable and trustworthy edge AI systems with integrated design of wireless communication strategies and decentralized machine learning models. New design principles of wireless networks, service-driven resource allocation optimization methods, as well as a holistic end-to-end system architecture to support edge AI will be described. Standardization, software and hardware platforms, and application scenarios are also discussed to facilitate the industrialization and commercialization of edge AI systems.

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Section: Edge Ai Empowered 6g Networkmentioning

confidence: 99%

Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications

Letaief

Shi

et al. 2022

IEEE J. Select. Areas Commun.

Self Cite

424

143

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“…That is why most prior work in the literature devise approximate solutions in various regimes of system parameters. With the success of machine learning, and particularly deep learning, over the past few years, learning-based algorithms have emerged to solve challenging problems in wireless communications, including for resource management [13]. As a prominent example, for the class of power allocation problems, several approaches have been proposed that leverage techniques based on supervised, unsupervised, self-supervised, and reinforcement learning, as well as meta-learning and graph representation learning [14]- [29].…”

Section: Introductionmentioning

confidence: 99%

Learning Resilient Radio Resource Management Policies with Graph Neural Networks

Naderializadeh¹,

Eisen²,

Ribeiro³

2022

Preprint

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We consider the problems of downlink user selection and power control in wireless networks, comprising multiple transmitters and receivers communicating with each other over a shared wireless medium. To achieve a high aggregate rate, while ensuring fairness across all the receivers, we formulate a resilient radio resource management (RRM) policy optimization problem with per-user minimum-capacity constraints that adapt to the underlying network conditions via learnable slack variables. We reformulate the problem in the Lagrangian dual domain, and show that we can parameterize the user selection and power control policies using a finite set of parameters, which can be trained alongside the slack and dual variables via an unsupervised primal-dual approach thanks to a provably small duality gap. We use a scalable and permutation-equivariant graph neural network (GNN) architecture to parameterize the RRM policies based on a graph topology derived from the instantaneous channel conditions. Through experimental results, we verify that the minimum-capacity constraints adapt to the underlying network configurations and channel conditions. We further demonstrate that, thanks to such adaptation, our proposed method achieves a superior tradeoff between the average rate and the 5 th percentile rate-a metric that quantifies the level of fairness in the resource allocation decisions-as compared to baseline algorithms.

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“…Therefore, the neural calibration-based method requires fewer training samples compared with the fully data-driven methods. It is shown in [35] that a high sample efficiency results in a small optimality gap, implying that the proposed method enjoys a higher scalability. Besides, since the input and output dimensions of each MLP are irrelevant to the number of users, the proposed neural calibrationbased method can be applied without re-training when the user number varies and thus, achieves higher generalization.…”

Section: Proposition 2 the Proposed Neural Calibration-based Beamform...mentioning

confidence: 99%

Learn to Communicate with Neural Calibration: Scalability and Generalization

Ma¹,

Shen²,

Yu³

et al. 2021

Preprint

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The conventional design of wireless communication systems typically relies on established mathematical models that capture the characteristics of different communication modules. Unfortunately, such design cannot be easily and directly applied to future wireless networks, which will be characterized by large-scale ultra-dense networks whose design complexity scales exponentially with the network size.Furthermore, such networks will vary dynamically in a significant way, which makes it intractable to develop comprehensive analytical models. Recently, deep learning-based approaches have emerged as potential alternatives for designing complex and dynamic wireless systems. However, existing learningbased methods have limited capabilities to scale with the problem size and to generalize with varying network settings. In this paper, we propose a scalable and generalizable neural calibration framework for future wireless system design, where a neural network is adopted to calibrate the input of conventional model-based algorithms. Specifically, the backbone of a traditional time-efficient algorithm is integrated with deep neural networks to achieve a high computational efficiency, while enjoying enhanced performance. The permutation equivariance property, carried out by the topological structure of wireless systems, is furthermore utilized to develop a generalizable neural network architecture. The proposed neural calibration framework is applied to solve challenging resource management problems in massive multiple-input multiple-output (MIMO) systems. Simulation results will show that the proposed neural calibration approach enjoys significantly improved scalability and generalization compared with the existing learning-based methods.

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AI Empowered Resource Management for Future Wireless Networks

Cited by 5 publications

References 38 publications

Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications

Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications

Learning Resilient Radio Resource Management Policies with Graph Neural Networks

Learn to Communicate with Neural Calibration: Scalability and Generalization

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