D2D Data Offloading in Vehicular Environments with Optimal Delivery Time Selection

Within the framework of a Device-to-Device (D2D) data offloading system for cellular networks, we propose a Content Delivery Management System (CDMS) in which the instant for transmitting a content to a requesting node, through a D2D communication, is selected to minimize the energy consumption required for transmission. The proposed system is particularly fit to highly dynamic scenarios, such as vehicular networks, where the network topology changes at a rate which is comparable with the order of magnitude of the delay tolerance. We present an analytical framework able to predict the system performance, in terms of energy consumption, using tools from the theory of point processes, validating it through simulations, and provide a thorough performance evaluation of the proposed CDMS, in terms of energy consumption and spectrum use. Our performance analysis compares the energy consumption and spectrum use obtained with the proposed scheme with the performance of two benchmark systems. The first one is a plain classic cellular scheme, the second is a D2D data offloading scheme (that we proposed in previous works) in which the D2D transmissions are performed as soon as there is a device with the required content within the maximum D2D transmission range..

Stochastic Game based Cooperative Alternating Q-Learning Caching in Dynamic D2D Networks

Edge caching has become an effective solution to cope with the challenges brought by the massive content delivery in cellular networks. In device-to-device (D2D) enabled caching cellular networks with time-varying content popularity distribution and user terminal (UT) location, we model these dynamic networks as a stochastic game to design a cooperative cache placement policy. The cache placement reward of each UT is defined as the caching incentive minus the transmission power cost for content caching and sharing. We consider the long-term cache placement reward of all UTs in this stochastic game. In an effort to solve the stochastic game problem, we propose a multi-agent cooperative alternating Q-learning (CAQL) based cache placement algorithm. The caching control unit is defined to execute the proposed CAQL, in which, the cache placement policy of each UT is alternatively updated according to the stable policy of other UTs during the learning process, until the stable cache placement policy of all the UTs in the cell is obtained. We discuss the convergence and complexity of CAQL, which obtains the stable cache placement policy with low space complexity. Simulation results show that the proposed algorithm can effectively reduce the backhaul load and the average content access delay in dynamic networks

Mobile Edge Computing

This is an open access book. It offers comprehensive, self-contained knowledge on Mobile Edge Computing (MEC), which is a very promising technology for achieving intelligence in the next-generation wireless communications and computing networks. The book starts with the basic concepts, key techniques and network architectures of MEC. Then, we present the wide applications of MEC, including edge caching, 6G networks, Internet of Vehicles, and UAVs. In the last part, we present new opportunities when MEC meets blockchain, Artificial Intelligence, and distributed machine learning (e.g., federated learning). We also identify the emerging applications of MEC in pandemic, industrial Internet of Things and disaster management. The book allows an easy cross-reference owing to the broad coverage on both the principle and applications of MEC. The book is written for people interested in communications and computer networks at all levels. The primary audience includes senior undergraduates, postgraduates, educators, scientists, researchers, developers, engineers, innovators and research strategists