Deep Learning in Mobile and Wireless Networking: A Survey

Zhang, Chaoyun; Patras, Paul; Haddadi, Hamed

doi:10.48550/arxiv.1803.04311

Cited by 28 publications

(33 citation statements)

References 254 publications

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“…Deep learning (DL), implemented through deep neural networks (DNNs), represents a machine-learning paradigm that has been extremely successful in the last decade, especially in computer vision and natural language processing applications [1]. This revolution has also sparked interest in applying DL in many other disciplines, including algorithm design for wireless communication systems [2]- [6]. For example, [3] uses a convolutional neural network (CNN) for channel decoding, [4] studies DL-based wireless resource allocation, and [5], [6] use DL for the classical task of radio signal (modulation) classification.…”

Section: Introductionmentioning

confidence: 99%

Adversarial Attacks on Deep-Learning Based Radio Signal Classification

Sadeghi

Larsson

2019

IEEE Wireless Commun. Lett.

267

235

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Deep learning (DL), despite its enormous success in many computer vision and language processing applications, is exceedingly vulnerable to adversarial attacks. We consider the use of DL for radio signal (modulation) classification tasks, and present practical methods for the crafting of white-box and universal black-box adversarial attacks in that application. We show that these attacks can considerably reduce the classification performance, with extremely small perturbations of the input. In particular, these attacks are significantly more powerful than classical jamming attacks, which raises significant security and robustness concerns in the use of DL-based algorithms for the wireless physical layer.

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Section: Introductionmentioning

confidence: 99%

Adversarial Attacks on Deep-Learning Based Radio Signal Classification

Sadeghi

Larsson

2019

IEEE Wireless Commun. Lett.

267

235

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“…Other related work considered radio control and signal detection problems, in which a radio signal search environment based on Gym Reinforcement Learning was developed [17] to approximate the cost of search, as opposed to asymptotically optimal search strategies [18]- [20]. Other related works on the general topic of deep learning in mobile and wireless networking can be found in a very recent comprehensive survey [21].…”

Section: A Existing Drl-based Methods For Dsamentioning

confidence: 99%

“…Thus, the strategy profile is SPE. Also, we cannot increase the reward of any user by switching to another strategy profile under the total game (played at time slots t = 1, 2, ..., T ) since it solves (21). Hence, the strategy profile is also Pareto optimal.…”

Section: B Cooperative Reward Maximizationmentioning

confidence: 99%

Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access

Naparstek

Cohen

2019

IEEE Trans. Wireless Commun.

391

207

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We consider the problem of dynamic spectrum access for network utility maximization in multichannel wireless networks. The shared bandwidth is divided into K orthogonal channels. In the beginning of each time slot, each user selects a channel and transmits a packet with a certain transmission probability. After each time slot, each user that has transmitted a packet receives a local observation indicating whether its packet was successfully delivered or not (i.e., ACK signal). The objective is a multi-user strategy for accessing the spectrum that maximizes a certain network utility in a distributed manner without online coordination or message exchanges between users.Obtaining an optimal solution for the spectrum access problem is computationally expensive in general due to the large state space and partial observability of the states. To tackle this problem, we develop a novel distributed dynamic spectrum access algorithm based on deep multi-user reinforcement leaning. Specifically, at each time slot, each user maps its current state to spectrum access actions based on a trained deep-Q network used to maximize the objective function. Game theoretic analysis of the system dynamics is developed for establishing design principles for the implementation of the algorithm. Experimental results demonstrate strong performance of the algorithm.Index Terms-Wireless networks, dynamic spectrum access, medium access control (MAC) protocols, multi-agent learning, deep reinforcement learning.Oshri Naparstek is with the Rafael Advanced

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“…Inspired by the achievements of reinforcement learning in dynamic control problems, such as the game of Atari [16], and AlphaGo [17], there has been increased interest in seeking reinforcement learning based solutions for problems in wireless communications. As summarized in [18] and [19], deep reinforcement learning algorithms have been applied in various wireless settings. For example, the authors in [20] and [21] investigate the use of Q-learning and SARSA (state-action-reward-stateaction) reinforcement learning, respectively, in power control.…”

Section: Related Workmentioning

confidence: 99%

“…We note that the reward Rt in(23) is given by(19) in the single-user case, and is equal to Rj,t in (20) when user/agent j is considered in the multi-user scenario 4. In(25), policy gradient is denoted by ∇ θ J(θ) where J(θ) stands for the policy objective function, which is generally formulated as the statistical average of the reward.…”

mentioning

confidence: 99%

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access

Zhong

Gursoy

et al. 2019

IEEE Trans. Cogn. Commun. Netw.

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To make efficient use of limited spectral resources, we in this work propose a deep actor-critic reinforcement learning based framework for dynamic multichannel access. We consider both a single-user case and a scenario in which multiple users attempt to access channels simultaneously. We employ the proposed framework as a single agent in the single-user case, and extend it to a decentralized multi-agent framework in the multi-user scenario. In both cases, we develop algorithms for the actor-critic deep reinforcement learning and evaluate the proposed learning policies via experiments and numerical results. In the single-user model, in order to evaluate the performance of the proposed channel access policy and the framework's tolerance against uncertainty, we explore different channel switching patterns and different switching probabilities. In the case of multiple users, we analyze the probabilities of each user accessing channels with favorable channel conditions and the probability of collision. We also address a time-varying environment to identify the adaptive ability of the proposed framework.Additionally, we provide comparisons (in terms of both the average reward and time efficiency) between the proposed actor-critic deep reinforcement learning framework, Deep-Q network (DQN) based approach, random access, and the optimal policy when the channel dynamics are known.

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Deep Learning in Mobile and Wireless Networking: A Survey

Cited by 28 publications

References 254 publications

Adversarial Attacks on Deep-Learning Based Radio Signal Classification

Adversarial Attacks on Deep-Learning Based Radio Signal Classification

Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access

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