• Energy Research

Abstract We propose an energy-efficient resource block (RB) assignment and power control strategy for underlay device-to-device (D2D) communications in multi-cell networks, where more than one D2D pair is allowed to share the same RBs with cellular user equipments (CUEs). We first formulate the problem as a nonconvex mixed-integer fractional programming problem, which is NP-hard, and then separate the problem into two subproblems, namely RB assignment and power control, to design a two-stage energy-efficient strategy with reasonable complexity. During the first stage, the RBs are assigned to CUEs by a sequential geometric programming (SGP) algorithm and a rate-based round algorithm, while during the second stage, the power control strategy for CUEs and D2D pairs is designed using the SGP algorithm. Numerical simulations are conducted to examine the convergence behavior of our proposed strategy, as well as to evaluate the impact of system parameters on the performance of our proposed strategy.

The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which are foreseen to revolutionize our world {via} creating new services and applications in a variety of sectors such as environmental monitoring, mobile-health systems, intelligent transportation systems and so on. The {information and communication technology }(ICT) sector is experiencing a significant growth in { data} traffic, driven by the widespread usage of smartphones, tablets and video streaming, along with the significant growth of sensors deployments that are anticipated in the near future. {It} is expected to outstandingly increase the growth rate of raw sensed data. In this paper, we present the CPS taxonomy {via} providing a broad overview of data collection, storage, access, processing and analysis. Compared with other survey papers, this is the first panoramic survey on big data for CPS, where our objective is to provide a panoramic summary of different CPS aspects. Furthermore, CPS {require} cybersecurity to protect {them} against malicious attacks and unauthorized intrusion, which {become} a challenge with the enormous amount of data that is continuously being generated in the network. {Thus, we also} provide an overview of the different security solutions proposed for CPS big data storage, access and analytics. We also discuss big data meeting green challenges in the contexts of CPS.

Nowadays, there are two significant tendencies, how to process the enormous amount of data, big data, and how to deal with the green issues related to sustainability and environmental concerns. An interesting question is whether there are inherent correlations between the two tendencies in general. To answer this question, this paper firstly makes a comprehensive literature survey on how to green big data systems in terms of the whole life cycle of big data processing, and then this paper studies the relevance between big data and green metrics and proposes two new metrics, effective energy efficiency and effective resource efficiency in order to bring new views and potentials of green metrics for the future times of big data.

This paper first provides a brief survey on existing traffic offloading techniques in wireless networks. Particularly as a case study, we put forward an online reinforcement learning framework for the problem of traffic offloading in a stochastic heterogeneous cellular network (HCN), where the time-varying traffic in the network can be offloaded to nearby small cells. Our aim is to minimize the total discounted energy consumption of the HCN while maintaining the quality-of-service (QoS) experienced by mobile users. For each cell (i.e., a macro cell or a small cell), the energy consumption is determined by its system load, which is coupled with system loads in other cells due to the sharing over a common frequency band. We model the energy-aware traffic offloading problem in such HCNs as a discrete-time Markov decision process (DTMDP). Based on the traffic observations and the traffic offloading operations, the network controller gradually optimizes the traffic offloading strategy with no prior knowledge of the DTMDP statistics. Such a model-free learning framework is important, particularly when the state space is huge. In order to solve the curse of dimensionality, we design a centralized Q -learning with compact state representation algorithm, which is named QC -learning. Moreover, a decentralized version of the QC -learning is developed based on the fact the macro base stations (BSs) can independently manage the operations of local small-cell BSs through making use of the global network state information obtained from the network controller. Simulations are conducted to show the effectiveness of the derived centralized and decentralized QC -learning algorithms in balancing the tradeo- f between energy saving and QoS satisfaction.

The concept of green information and communications technologies (ICT) is relevant to both environmental sustainability and ICT. Green ICT is an interdisciplinary field relevant to a number of areas and topics, such as information systems, computer science and technologies, communications and networking, power and energy systems, electronics, environmental and civil engineering, industrial engineering and project management, social sciences, and so on. The green ICT could be considered a coined-term from two highly overlapped terms, green communications [1] and green computing [2]. Basically, two mainstream ideas may be addressed in green ICT, i.e., greening ICT and ICT for green objectives. Even if the field of green ICT has been receiving more and more attentions in research communities as well as industrial, governmental, and international organizations [1�5], more and more technical investigations and applications are still expected due to the relevant long term concerns and challenges. In early 2015, the IEEE Technical Committee on Green Communications and Computing (TCGCC), IEEE Research Project on Vision for Green ICT Standardizations, and IEEE Technical SubCommittee on Big Data (TSCBD) jointly initialized the efforts of the first IEEE International Workshop on Green Standardization and Industry Issues for ICT and Relevant Technologies (GSICT), which will be held in conjunction with the 2015 IEEE Global Communications Conference (Globe-com 2015), and the motivation of the efforts is to promote more relevant activities, especially standardization, towards green objectives in industry. Surely, the IEEE Series on Green Communications and Computing Networks can expect more submissions on the relevant industry issues and standardization activities in the future.

We investigate the energy-efficient channel estimation in wireless networks, where one pilot is inserted for every several data slots to estimate the channel coefficients. Both the channel state information estimation error and the time-varying model are considered. We first formulate the energy-efficient channel estimation problem into a mixed integer nonlinear programming (MINLP) problem, where the variables include the transmitted power and the number of data slots. Due to the NP-hard nature, we degenerate the MINLP problem into a series of non-concave optimization problems without integer variables. Then we solve these problems using successive convex approximation, geometric programming, and the Dinkelbach algorithm to obtain a point satisfying the Karush-Kuhn-Tucker (KKT) conditions. Furthermore, we develop a low-complexity sub-optimal scheme through binary variable relaxation to obtain a solution, and the convergence point satisfies the KKT conditions of the relaxed non-concave problem. Simulation results demonstrate the convergence and effectiveness of our proposed schemes.

Big data are widely recognized as being one of the most powerful drivers to promote productivity, improve efficiency, and support innovation. It is highly expected to explore the power of big data and turn big data into big values. To answer the interesting question whether there are inherent correlations between the two tendencies of big data and green challenges, a recent study has investigated the issues on greening the whole life cycle of big data systems. This paper would like to discover the relations between the trend of big data era and that of the new generation green revolution through a comprehensive and panoramic literature survey in big data technologies toward various green objectives and a discussion on relevant challenges and future directions.

Considering that the future cellular networks are more content-centric, and content sharing among user equipments (UEs) is more universal, device-to-device (D2D) communication techniques become attractive for their applications to content delivery due to the characteristic of proximal transmissions without needs of high-rate backhaul. However, D2D content delivery may not be energy efficient due to shortage of useful contents for neighbor UEs when content caching and delivery are not well designed. In this paper, we propose an architecture using D2D multicast for content delivery in cellular networks and a practical methodology, which can indicate the time for delivery, the content to be disseminated, and the UEs to receive the delivery, to achieve more energy efficient content delivery for future green cellular networks.