Abstract
This paper considers the design of online transmission strategies for slotted energy harvesting point-to-point communication systems in wireless fading channels. Online transmission strategies decide the amount of energy allocated to each transmission slot based on the energy harvested amounts and channel gains observed in the current and previous time slots. Offline strategies, in contrast, assume non-causal knowledge of future energy arrivals and channel gains. We adopt a worst case design objective. For a given online policy, we are interested in computing its maximum rate gap that is defined as the difference between the offline and online rates, maximized over all possible energy arrivals and channel states. The competitive rate gap is then defined as the minimum maximum rate gap over all possible online strategies. Here, we obtain, within a constant, the maximum rate gap for the Myopic policy, which equally distributes the available energy over the remaining slots, and provide an upper and a lower bound on the competitive rate gap. Moreover, we propose a new online policy targeting the competitive rate gap. Numerical results show that the policy proposed performs close to the competitive rate gap lower bound in constant and arbitrarily varying channels, and obtains good performance with real energy harvesting traces.
Highlights
Energy harvesting (EH) technology is considered as a major component of future wireless networks
Previous work addressing the design of transmission polices for EH devices are typically classified based on the assumptions made on the transmitter’s knowledge about the EH process [1]
The optimal offline transmission policy maximizing the throughput for an EH point-to-point additive white Gaussian noise (AWGN) channel was first studied in [2] and, extended to fading channels in [3]
Summary
Energy harvesting (EH) technology is considered as a major component of future wireless networks. Most of the works available in the literature about online optimization show performance results that are very close to those achieved by optimal offline policies [5]. Our main objective is to characterize the gap between the optimal offline and online policies Identifying this gap independent of the EH and fading statistics will determine the value of the knowledge about these random processes.
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