We consider an energy harvesting transmitter equipped with two batteries having finite storage capacities, communicating over an additive white Gaussian channel. The work is motivated by an observation that many practical batteries, when repeatedly charged after being partially discharged, suffer from degradation in the usable capacity. The capacity can be recovered by completely discharging the battery before charging it fully again. Hence, in this work, we impose the constraint that a battery must be charged (discharged) only after it is fully discharged (charged). Our goal is to maximize the longterm average throughput with non-causal and causal knowledge of the energy arrivals, which we assume to be Bernoulli. We propose two sub-optimal policies and obtain an upper bound on the performance gap (G) from the optimal long-term average throughput that is achieved with infinite capacity batteries. We find that G remains constant as the amount of energy harvested per arrival increases. Numerically, we also find that G decreases with the battery capacity faster than the inverse of the square root of the battery capacity for a specific energy arrival parameters.
翻译:这项工作的动力是,许多实用电池在被部分放电后反复充电时,其可用容量会退化。在完全充电之前,可以通过完全卸下电池来恢复能力。因此,在这项工作中,我们强制规定,电池必须在完全放电(充电)后才能充电(放电)。我们的目标是,在无因果关系和因果知识的情况下,最大限度地实现长期平均输电量,我们假定是Bernoulli。我们提出了两个次级最佳政策,并从用无限容量电池实现的最佳长期平均吞吐量中获取性能差距(G)的上限。我们发现,G保持不变,因为每次运抵的能量量会增加。从数字上看,我们还发现,在特定能量到达参数方面,G随着电池容量的下降速度比电池容量的平底部速度要快。