LAUSR

With the development and popularity of the renewable energy harvesting devices, the energy harvesting wireless sensor communications that can make use of the energy harvested from the nearby environments have gained more and more attentions. One key problem in the energy harvesting wireless sensor communications is the transmission strategy management, i.e., how to manage the transmission strategy at each time slot to optimize the transmission performance. In this paper, we propose to use Lyapunov optimization theory to maximize the expected good bits per packet transmission for the source node in an energy harvesting wireless communication system. Considering the channel and battery states, we adapt the transmission power and modulation type to achieve such a goal. The problem is formulated as an optimization where the objective function is the long-term average good bits per packet transmission and the constraints are the bounded long-term average battery level and bit error rate. To solve the optimization, we introduce virtual queues and employ the Lyapunov optimization theory to transform the optimization with long-term average format into optimizing the drift-plus-penalty problem. The drift-plus-penalty is further upper bounded with variables only related to current time slot, which greatly simplifies the optimization problem. Theoretic analysis is also conducted to show that the optimal solution is limited by an upper bound that is independent of the operation time index. Finally, simulation results with real solar irradiance data show that the proposed algorithm can achieve much better performance than existing approaches based on Markov decision process and water-filling.