As a key technique for enabling artificial intelligence, machine learning (ML) is capable of solving complex problems without explicit programming. Motivated by its successful applications to many practical tasks like image recognition, both industry and the research community have advocated the applications of ML in wireless communication. This paper comprehensively surveys the recent advances of the applications of ML in wireless communication, which are classified as: resource management in the MAC layer, networking and mobility management in the network layer, and localization in the application layer. The applications in resource management further include power control, spectrum management, backhaul management, cache management, and beamformer design and computation resource management, while ML-based networking focuses on the applications in clustering, base station switching control, user association, and routing. Moreover, literatures in each aspect is organized according to the adopted ML techniques. In addition, several conditions for applying ML to wireless communication are identified to help readers decide whether to use ML and which kind of ML techniques to use. Traditional approaches are also summarized together with their performance comparison with ML-based approaches, based on which the motivations of surveyed literatures to adopt ML are clarified. Given the extensiveness of the research area, challenges and unresolved issues are presented to facilitate future studies. Specifically, ML-based network slicing, infrastructure update to support ML-based paradigms, open data sets and platforms for researchers, theoretical guidance for ML implementation, and so on are discussed.

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

This paper studies the joint communication, caching and computing design problem for achieving the operational excellence and the cost efficiency of the vehicular networks Moreover, the resource allocation policy is designed by considering the vehicle's mobility and the hard service deadline constraint These critical challenges have often been either neglected or addressed inadequately in the existing work on the vehicular networks because of their high complexity We develop a deep reinforcement learning with the multi-timescale framework to tackle these grand challenges in this paper Furthermore, we propose the mobility-aware reward estimation for the large timescale model to mitigate the complexity due to the large action space Numerical results are presented to illustrate the theoretical findings developed in the paper and to quantify the performance gains attained

Mobility-Aware Edge Caching and Computing in Vehicle Networks: A Deep Reinforcement Learning

As a key technique for enabling artificial intelligence, machine learning (ML) is capable of solving complex problems without explicit programming. Motivated by its successful applications to many practical tasks like image recognition, both industry and the research community have advocated the applications of ML in wireless communication. This paper comprehensively surveys the recent advances of the applications of ML in wireless communication, which are classified as: resource management in the MAC layer, networking and mobility management in the network layer, and localization in the application layer. The applications in resource management further include power control, spectrum management, backhaul management, cache management, beamformer design and computation resource management, while ML based networking focuses on the applications in clustering, base station switching control, user association and routing. Moreover, literatures in each aspect is organized according to the adopted ML techniques. In addition, several conditions for applying ML to wireless communication are identified to help readers decide whether to use ML and which kind of ML techniques to use, and traditional approaches are also summarized together with their performance comparison with ML based approaches, based on which the motivations of surveyed literatures to adopt ML are clarified. Given the extensiveness of the research area, challenges and unresolved issues are presented to facilitate future studies, where ML based network slicing, infrastructure update to support ML based paradigms, open data sets and platforms for researchers, theoretical guidance for ML implementation and so on are discussed.

pdf/application-of-machine-learning-in-wireless-networks-key-360ywkmhrt.pdf

Caching at mobile devices can facilitate device-to-device (D2D) communications, which may significantly improve spectrum efficiency and alleviate the heavy burden on backhaul links. However, most previous works ignored user mobility, thus having limited practical applications. In this paper, we take advantage of the user mobility pattern by the inter-contact times between different users, and propose a mobility-aware caching placement strategy to maximize the data offloading ratio , which is defined as the percentage of the requested data that can be delivered via D2D links rather than through base stations. Given the NP-hard caching placement problem, we first propose an optimal dynamic programming algorithm to obtain a performance benchmark with much lower complexity than exhaustive search. We then prove that the problem falls in the category of monotone submodular maximization over a matroid constraint, and propose a time-efficient greedy algorithm, which achieves an approximation ratio as $\frac {1}{2}$ . Simulation results with real-life data sets will validate the effectiveness of our proposed mobility-aware caching placement strategy. We observe that users moving at either a very low or very high speed should cache the most popular files, while users moving at a medium speed should cache less popular files to avoid duplication.

Mobility-Aware Caching in D2D Networks

Mobile edge caching is a promising technique to reduce network traffic and improve the quality of experience of mobile users. However, mobile edge caching is a challenging decision making problem with unknown future content popularity and complex network characteristics. In this article, we advocate the use of DRL to solve mobile edge caching problems by presenting an overview of recent works on mobile edge caching and DRL. We first examine the key issues in mobile edge caching and review the existing learning- based solutions proposed in the literature. We also discuss the unique features in the application of DRL in mobile edge caching, and illustrate an example of DRL-based mobile edge caching with trace-data-driven simulation results. This article concludes with a discussion of several open issues that call for substantial future research efforts.

Deep Reinforcement Learning for Mobile Edge Caching: Review, New Features, and Open Issues

In this paper, we study the data offloading via device-to-device (D2D) communications with proactive caching in mobile cellular networks. The problem is formulated as the optimal data caching problem, where mobile nodes have different mobility and limited cache capacities and data files have different popularity and content sizes, and is proved to be NP-hard. To deal with the problem, the contacts between mobile nodes are used to adjust the cached data files with a well-designed $(1+\alpha)$-approximation algorithm. Based on the analysis, we first propose a distributed infrastructure-assisted data offloading algorithm (IADOA), where the base station (BS) needs to provide information to mobile nodes. To further increase the flexibility, a fully-distributed data offloading algorithm (FDDOA) is designed, where mobile nodes exchange control information via D2D communications and make parameter estimations. Finally, simulation results show that our proposed algorithms have significant performance gains compared with two conventional baseline algorithms.

Device-to-Device Offloading with Proactive Caching in Mobile Cellular Networks

Content caching at network edge nodes, such as base stations (BSs) and user equipments (UEs), can significantly reduce the traffic load in future cellular networks. Considering the limited caching space, the contents cached at BSs should be selected carefully for improving caching efficiency. In this paper, we study the edge caching at BSs to minimize the transmission cost by considering traffic offloading via the device-to-device (D2D) communications. The traffic offloading reduces the traffic via cellular transmission and thus changes the utility achieved by content caching at BSs. We model the edge caching problem as a Markov decision process and propose a distributed cache replacement strategy based on Q-learning. The proposed strategy further needs the calculations of the following two key parameters: 1) To describe the effect of D2D offloading to the cellular traffic, we define the cellular serving ratio, which is calculated by the iterative maximum weighted independent sets problem for static networks and the stochastic geometry for high-dynamic networks; 2) The cache replacement rewards are calculated by analyzing the relationship between the requested and cached amounts of content data, which are obtained from the messages of the previous data request and transmissions, ignoring any extra information exchange between the BSs. Furthermore, the convergence of the proposed distributed cache replacement strategy is proved by the sequential stage game model. Simulation results verify the convergence of the proposed cache replacement strategy and show its performance gain compared with conventional strategies.

Edge Caching at Base Stations With Device-to-Device Offloading

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Device-to-Device Offloading with Proactive Caching in Mobile Cellular Networks

Edge Caching at Base Stations With Device-to-Device Offloading