Green Cellular Networks: A Survey, Some Research Issues and Challenges

Energy efficiency in cellular networks is a growing concern for cellular operators to not only maintain profitability, but also to reduce the overall environment effects. This emerging trend of achieving energy efficiency in cellular networks is motivating the standardization authorities and network operators to continuously explore future technologies in order to bring improvements in the entire network infrastructure. In this article, we present a brief survey of methods to improve the power efficiency of cellular networks, explore some research issues and challenges and suggest some techniques to enable an energy efficient or "green" cellular network. Since base stations consume a maximum portion of the total energy used in a cellular system, we will first provide a comprehensive survey on techniques to obtain energy savings in base stations. Next, we discuss how heterogeneous network deployment based on micro, pico and femto-cells can be used to achieve this goal. Since cognitive radio and cooperative relaying are undisputed future technologies in this regard, we propose a research vision to make these technologies more energy efficient. Lastly, we explore some broader perspectives in realizing a "green" cellular network technologyComment: 16 pages, 5 figures, 2 table

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Delay-Optimal Relay Selection in Device-to-Device Communications for Smart Grid

The smart grid communication network adopts a hierarchical structure which consists of three kinds of networks which are Home Area Networks (HANs), Neighborhood Area Networks (NANs), and Wide Area Networks (WANs). The smart grid NANs comprise of the communication infrastructure used to manage the electricity distribution to the end users. Cellular technology with LTE-based standards is a widely-used and forward-looking technology hence becomes a promising technology that can meet the requirements of different applications in NANs. However, the LTE has a limitation to cope with the data traffic characteristics of smart grid applications, thus require for enhancements. Device-to-Device (D2D) communications enable direct data transmissions between devices by exploiting the cellular resources, which could guarantee the improvement of LTE performances. Delay is one of the important communication requirements for the real-time smart grid applications. In this paper, the application of D2D communications for the smart grid NANs is investigated to improve the average end-to-end delay of the system. A relay selection algorithm that considers both the queue state and the channel state of nodes is proposed. The optimization problem is formulated as a constrained Markov decision process (CMDP) and a linear programming method is used to find the optimal policy for the CMDP problem. Simulation results are presented to prove the effectiveness of the proposed scheme