Device-to-Device (D2D) communication has emerged as a promising technology for optimizing spectral efficiency in future cellular networks. D2D takes advantage of the proximity of communicating devices for efficient utilization of available resources, improving data rates, reducing latency, and increasing system capacity. The research community is actively investigating the D2D paradigm to realize its full potential and enable its smooth integration into the future cellular system architecture. Existing surveys on this paradigm largely focus on interference and resource management. We review recently proposed solutions in over explored and under explored areas in D2D. These solutions include protocols, algorithms, and architectures in D2D. Furthermore, we provide new insights on open issues in these areas. Finally, we discuss potential future research directions.

A Survey of Device-to-Device Communications: Research Issues and Challenges

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

With the widespread adoption of various mobile applications, the amount of traffic in wireless networks is growing at an exponential rate, which exerts a great burden on mobile core networks and backhaul links. Mobile edge caching, which enables mobile edges with cache storages, is a promising solution to alleviate this problem. In this paper, we aim to review the state-of-the-art of mobile edge caching. We first present an overview of mobile edge caching and its advantages. We then discuss the locations where mobile edge caching can be realized in the network. We also analyze different caching criteria and their respective effects on the caching performances. Moreover, we compare several caching schemes and discuss their pros and cons. We further present a detailed and in-depth discussion on the caching process, which can be delineated into four phases including content request, exploration, delivery, and update. For each phase, we identify different issues and review related works in addressing these issues. Finally, we present a number of challenges faced by current mobile edge caching architectures and techniques for further studies.

On Mobile Edge Caching

In this paper, we propose a joint communication, caching and computing strategy for achieving cost efficiency in vehicular networks. In particular, the resource allocation policy is specifically designed by considering the vehicle's mobility and the hard service deadline constraint. An artificial intelligence-based multi-timescale framework is proposed for tackling these challenges. To mitigate the complexity associated with the large action and search space in the sophisticated multi-timescale framework considered, we propose to maximize a carefully constructed mobility-aware reward function using the classic particle swarm optimization scheme at the associated large timescale level, while we employ deep reinforcement learning at the small timescale level of our sophisticated twin-timescale solution. Numerical results are presented to illustrate the theoretical findings and to quantify the performance gains attained.

Twin-Timescale Artificial Intelligence Aided Mobility-Aware Edge Caching and Computing in Vehicular Networks

The Internet of Vehicles (IoV) is an emerging paradigm, driven by recent advancements in vehicular communications and networking. Advances in research can now provide reliable communication links between vehicles, via vehicle-to-vehicle communications, and between vehicles and roadside infrastructures, via vehicle-to-infrastructure communications. Meanwhile, the capability and intelligence of vehicles are being rapidly enhanced, and this will have the potential of supporting a plethora of new exciting applications, which will integrate fully autonomous vehicles, the Internet of Things (IoT), and the environment. These trends will bring about an era of intelligent IoV, which will heavily depend upon communications, computing, and data analytics technologies. To store and process the massive amount of data generated by intelligent IoV, onboard processing and Cloud computing will not be sufficient, due to resource/power constraints and communication overhead/latency, respectively. By deploying storage and computing resources at the wireless network edge, e.g., radio access points, the edge information system (EIS), including edge caching, edge computing, and edge AI, will play a key role in the future intelligent IoV. Such system will provide not only low-latency content delivery and computation services, but also localized data acquisition, aggregation and processing. This article surveys the latest development in EIS for intelligent IoV. Key design issues, methodologies and hardware platforms are introduced. In particular, typical use cases for intelligent vehicles are illustrated, including edge-assisted perception, mapping, and localization. In addition, various open research problems are identified.

Mobile Edge Intelligence and Computing for the Internet of Vehicles

In this paper, we propose a novel policy for device caching that facilitates popular content exchange through highrate device-to-device (D2D) millimeter-wave (mmWave) communication.The D2D aware caching (DAC) policy splits the cacheable content into two content groups and distributes it randomly to the user equipment devices (UEs), with the goal to enable D2D connections. By exploiting the high-bandwidth availability and the directionality of mmWaves, we ensure high rates for the D2D transmissions, while mitigating the co-channel interference that limits the D2D-communication potentials in the sub-6 GHz bands. Furthermore, based on a stochasticgeometry approach for the modeling of the network topology, we analytically derive the offloading gain that is achieved by the proposed policy and the distribution of the content retrieval delay considering both half- and full-duplex mode for the D2D communication. The accuracy of the proposed analytical framework is validated through Monte-Carlo simulations. In addition, for a wide range of a content popularity indicator the results show that the proposed policy achieves higher offloading and lower content-retrieval delays than existing state-of-the-art approaches.

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Nilesh Bodhane

Papers

Mobility-Aware Caching in D2D Networks