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

Investigating network services abstraction in 5G enabled device-to-device (D2D) communications

The increased demand of data rate by mobile users has led to the evolution of mobile network technologies from the fourth generation to fifth generation (5G). 5G mobile network will support various technologies that will be able to provide low latency, offload traffic and connect vertical industries. Device-to-device (D2D) communications will be used as the underlay technology for 5G network in the offloading of traffic from the cellular network and pushing content closer to the user. With D2D communication, various network services can be implemented to improve spectral efficiency and reduce energy consumption of mobile devices. This paper gives a brief overview of D2D communication and discusses different D2D applications. It proposes a network services abstraction and suggests the mapping of existing studies with the network service abstraction which can be used in the harnessing the development and implementation of D2D communication applications in 5G network. The paper also highlights possible future research for D2D communication in 5G network

Investigating network services abstraction in 5G enabled device-to-device (D2D) communications

The increased demand of data rate by mobile users has led to the evolution of mobile network technologies from the fourth generation to fifth generation (5G). 5G mobile network will support various technologies that will be able to provide low latency, offload traffic and connect vertical industries. Device-to-device (D2D) communications will be used as the underlay technology for 5G network in the offloading of traffic from the cellular network and pushing content closer to the user. With D2D communication, various network services can be implemented to improve spectral efficiency and reduce energy consumption of mobile devices. This paper gives a brief overview of D2D communication and discusses different D2D applications. It proposes a network services abstraction and suggests the mapping of existing studies with the network service abstraction which can be used in the harnessing the development and implementation of D2D communication applications in 5G network. The paper also highlights possible future research for D2D communication in 5G network

A Survey of Deep Learning for Data Caching in Edge Network

The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network as well as reducing latency to access popular content. In that respect end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e, at close proximity to the users. In addition to model based caching schemes learning-based edge caching optimizations has recently attracted significant attention and the aim hereafter is to capture these recent advances for both model based and data driven techniques in the area of proactive caching. This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, a number of key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning as well as reinforcement learning. Furthermore, a comparison of state-of-the-art literature is provided from the aspects of caching topics and deep learning methods. Finally, we discuss research challenges and future directions of applying deep learning for cachin