Abstract-We propose an efficient solution to peer-to-peer localization in a wireless sensor network which works in two stages. At the first stage the optimization problem is relaxed into a convex problem, given in the form recently proposed by Soares, Xavier, and Gomes. The convex problem is efficiently solved in a distributed way by an ADMM approach, which provides a significant improvement in speed with respect to the original solution. In the second stage, a soft transition to the original, non-convex, non relaxed formulation is applied in such a way to force the solution towards a local minimum. The algorithm is built in such a way to be fully distributed, and it is tested in meaningful situations, showing its effectiveness in localization accuracy and speed of convergence, as well as its inner robustness.

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Abstract-In this survey, we discuss the role of energy in the design of future mobile networks and, in particular, we advocate and elaborate on the use of energy harvesting (EH) hardware as a means to decrease the environmental footprint of 5G technology. To take full advantage of the harvested (renewable) energy, while still meeting the quality of service required by dense 5G deployments, suitable management techniques are here reviewed, highlighting the open issues that are still to be solved to provide eco-friendly and cost-effective mobile architectures. Several solutions have recently been proposed to tackle capacity, coverage and efficiency problems, including: C-RAN, Software Defined Networking (SDN) and fog computing, among others. However, these are not explicitly tailored to increase the energy efficiency of networks featuring renewable energy sources, and have the following limitations: (i) their energy savings are in many cases still insufficient and (ii) they do not consider network elements possessing energy harvesting capabilities. In this paper, we systematically review existing energy sustainable paradigms and methods to address points (i) and (ii), discussing how these can be exploited to obtain highly efficient, energy self-sufficient and high capacity networks. Several open issues have emerged from our review, ranging from the need for accurate energy, transmission and consumption models, to the lack of accurate data traffic profiles, to the use of power transfer, energy cooperation and energy trading techniques. These challenges are here discussed along with some research directions to follow for achieving sustainable 5G systems.

In this letter, we propose an optimal direct load control of renewable powered smaller base stations (SBSs) in a two-tier mobile network based on dynamic programming (DP). We represent the DP optimization using Graph Theory and state the problem as a Shortest Path search. We use the Label Correcting Method to explore the graph and find the optimal ON/OFF policy for the SBSs. Simulation results demonstrate that the proposed algorithm is able to adapt to the varying conditions of the environment, namely renewable energy arrivals and traffic demands. The key benefit of our study is that it allows elaborating on the behavior and performance bounds of the system and gives guidance for approximated policy search methods. KEYWORDSdemand response, dynamic programming, energy sustainability, Graph Theory, mobile networks, optimal control, smart grid INTRODUCTIONThe fifth-generation (5G) mobile network is expected to support 1000 times more data volume per unit area, 100 more user data rate, 1000 more connected devices, 1/10 lower energy consumption, 1/5 lower end-to-end latency, 1/5 lower cost of network management, 10 longer device battery life, and 1/1000 lower service deployment times than fourth-generation (4G). A new architecture and new network deployments are thus necessary to satisfy such requirements. One of the most promising approaches is to densify the radio access network by deploying smaller base stations (SBSs), which may, in turn, enhance capacity and coverage of the macrocells. This approach implies the use of a high number of devices, which may drain a significant amount of energy from the power grid. This is in contrast with the energy consumption requirement of 5G networks. However, the reduced consumptions of these devices encourage the use of renewable energy sources (RESs) as distributed power suppliers. 1 This approach will allow to reduce (1) the energy drained from the power grid, (2) the carbon footprint, and (3) the cost due to the energy bills. 2 The introduction of RES entails an intermittent and erratic energy budget for the communication operations of the SBSs. Therefore, Demand Response is needed to properly manage energy inflow and spending, based on the traffic demand. In particular, SBSs may install self-organizing agents, which enable intelligent energy management policies, such as Direct Load Control. 3In our previous work, 4 a two-tier architecture with hybrid power suppliers is introduced: macro base stations (BSs) reside in the first tier to provide baseline coverage and capacity and are powered by the electrical grid, whereas SBSs operate in the second tier to provide capacity enhancement and are supplied by solar panels plus batteries. The data traffic offloaded by the SBSs has higher spectral efficiency and allows a reduction of the energy drained from the grid. In Reference 4 , we have also introduced a distributed Q-learning algorithm to direct control the load of the renewable powered SBSs. However, no proof of optimality is given in the paper. A similar resource allocation ...

Flexible functional split in Cloud Radio Access Network (CRAN) greatly overcomes fronthaul capacity and latency challenges. In such architecture, part of the baseband processing is done locally and the remaining is done remotely in the central cloud. On the other hand, Energy Harvesting (EH) technologies are increasingly adopted due to sustainability and economic advantages. Power consumption due to baseband processing has a huge share in the total power consumption breakdown of smaller base stations. Given that such base stations are powered by EH, in addition to QoS constraints, energy availability also conditions the decision on where to place each baseband function in the system. This work focuses on determining the performance bounds of an optimal placement of baseband functional split option in virtualized small cells that are solely powered by EH. The work applies Dynamic Programming (DP), in particular, Shortest Path search is used to determine the optimal functional split option considering traffic QoS requirements and available energy budget.

The deployment of dense networks of small base stations represents one of the most promising solutions for future mobile networks to meet the foreseen increasing traffic demands. However, such an infrastructure consumes a considerable amount of energy, which, in turn, may represent an issue for the environment and the operational expenses of the mobile operators. The use of renewable energy to supply the small base stations has been recently considered as a mean to reduce the energy footprint of the mobile networks. In this paper, we consider a hierarchical structure in which part of the base stations are powered exclusively by solar panels and batteries. Base stations are grouped in clusters and connected in a micro-grid. A central controller enables base station sleep mode and energy sharing among the base stations based on the available energy budget and the traffic demands. We propose three different implementations of the controller through Machine Learning models, namely Imitation Learning, Q-Learning and Deep Q-Learning, capable of learning optimal sleep mode and energy sharing policies. We provide an exhaustive discussion on the achieved performance, complexity and feasibility of the proposed models together with the energy and cost savings attained.

The massive deployment of Small Base Stations (SBSs) represents one of the most promising solutions adopted by 5G cellular networks to meet the foreseen huge traffic demand. The usage of renewable energies for powering the SBSs attracted particular attention for reducing the energy footprint and, thus, mitigating the environmental impact of mobile networks and enabling cost saving for the operators. The complexity of the system and the variability of the harvesting process suggest the adoption of learning methods. Here, we investigate techniques based on the Layered Learning paradigm to control dense networks of SBSs powered solely by solar energy. In the first layer, SBSs locally select switch ON/OFF policies according to their energy income and traffic demand based on a Heuristically Accelerated Reinforcement Learning method. The second layer relies on an Artificial Neural Network that estimates the network load conditions to implement a centralized controller enforcing local agent decisions. Simulation results prove that the control of the proposed framework mimics the behavior of the upper bound obtained offline with Dynamic Programming. Moreover, the proposed layered framework outperforms both a greedy and a distributed Reinforcement Learning solution in terms of throughput and energy efficiency under different traffic conditions.

In this paper, we focus on the design of energy self-sustainable mobile networks by enabling intelligent energy management that allows the base stations to mostly operate off-grid by using renewable energies. Many papers are available in the literature on this problem, and however, we are approaching this issue from a different angle. In fact, we advocate for future mobile networks with a hierarchical cell structure and powered by energy harvesting hardware. Base stations within the same geographical area are grouped in a micro-grid and operate almost autonomously from the power grid. To achieve this goal, we target the design of the optimal traffic and computational load control method with energy sharing within the microgrid. We solve the optimization problem using a graph-based method, and we demonstrate, via software simulations, that a combination of load control plus energy sharing represents a viable and economically convenient solution for enabling energy self-sustainability of mobile networks grouped in micro-grids.