Optimal direct load control of renewable powered small cells: A shortest path approach

Abstract

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 ...

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Optimal direct load control of renewable powered small cells: Performance evaluation and bounds

“…The complexity of this optimization problem increases quasi-exponentially with the number of SBSs in the cluster and with the time horizon. In order to solve it in an efficient way, this problem has been graphically represented and subsequently transposed to a graph theory shortest-path problem in [9]. The proposed algorithm based on the Label Correcting Method has been used to obtain the results shown in the following section.…”

“…All the simulations have been performed considering a generic week of January and July, in order to highlight the differences between a generic winter (low energy arrivals) and a summer month (high energy arrivals). Moreover, the coordinator takes actions with a time step of 1 hour, considering a time horizon of 21 hours, as described in [9]. In the analysis here presented, we have assigned the same importance to the energy consumption and the system drop rate, when computing the optimal policy.…”

“…In our previous work [9], a two-tier architecture with hybrid power suppliers is introduced: macro base stations (MBSs) 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.…”