Research on theory and techniques of energy management for energy-harvesting powered wireless communications

Energy harvesting (EH) is the process of capturing renewable energy from the environment and converting it into usable electrical energy. In wireless communication systems, collecting renewable energy from the environment is a key factor in building self-sustainable networks. In addition, EH-powered wireless communications help reduce carbon footprint and enable "green" communications to solve important issues such as haze, global warming, and climate change. Due to these ecological and economic reasons, various types of EH-powered wireless communications have become current research hotspots. However, challenges arise in EH-powered wireless communication systems. First, the reliability of data transmissions is challenged due to the inherent randomness and instability of environmental energy sources. Second, due to the limited energy provided by environmental energy sources, how to make full useof the limited energy and ensure the systems obtain optimal performances is also a stringent subject. Therefore, for EH-powered wireless communication systems, we need to conduct reasonable energy management and resource allocation to ensure reliable and efficient communications, thus optimizing system performances. On the one hand, for EH-powered WSN links, we optimize energy management for the transmitters, so that the collected energy is properly distributed to data transmissions. On the other hand, we introduce smart-grid technology to jointly provide renewable energy and grid's persistent energy to base stations (BSs) in cellular networks, compensating for unstable and insufficient EH power supply. Through the optimal energy management of BSs, we make full use of renewable energy, maximize the system throughput or minimize the electricity transaction cost with the grid, while satisfying users' quality of service (QoS). Optimal energy management is first investigated for EH-powered WSN links.A new "dynamic string tautening" algorithm is proposed to generate the most energy-efficient offine schedule for delay-limited traffc of transmitters. The algorithmis based on two key findings derived through convex formulation and resultant optimality conditions, specifies a set of simple but optimal rules, and generates the optimal schedule with a low complexity. The proposed algorithm is also extended to online scenarios, where the transmit schedule is generated on-the-fly. An infinite time-horizon resource allocation problem is then formulated to maximize the time-average downlink throughput for smart-grid powered multiple input multiple-output (MIMO), subject to a time-average energy cost budget. By using the advanced time decoupling technique, a novel stochastic subgradient based online control (SGOC) approach is developed for the resultant smart-grid powered communication system. It is established analytically that the proposed online control algorithm is able to yield a feasible and asymptotically optimal solution without a-priori knowledge of the stochastic system information. Last, a two-scale stochastic control framework is put forth for smart-grid powered coordinated multi-point (CoMP) systems. The energy management taskis formulated as an infinite-horizon optimization problem minimizing the time average energy transaction cost. Leveraging the Lyapunov optimization approach as well as the stochastic subgradient method, a two-scale online control (TSOC) approach is developed for the resultant smart-grid powered CoMP systems. Using only historical data, the proposed TS-OC makes online control decisions at two timescales, and features a provably feasible and asymptotically near-optimal solution.