Optimal Quality-of-Service Scheduling for Energy-Harvesting Powered Wireless Communications

Abstract

XiaojingChen, Wei Ni, Xin Wang, YichuangSun, ???Optimal Quality-of-Service Scheduling for Energy-Harvesting Powered Wireless Communications???, IEEE Transactions on Wireless Communications, Vol. 15 (5): 3269-3280, January 2016. ?? 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, a new dynamic string tautening algorithm is proposed to generate the most energy-efficient off-line schedule for delay-limited traffic of transmitters with non-negligible circuit power. The algorithm is based on two key findings that we derive through judicious convex formulation and resultant optimality conditions, specifies a set of simple but optimal rules, and generates the optimal schedule with a low complexity of O(N2) in the worst case. The proposed algorithm is also extended to on-line scenarios, where the transmit schedule is generated on-the-fly. Simulation shows that the proposed algorithm requires substantially lower average complexity by almost two orders of magnitude to retain optimality than general convex solvers. The effective transmit region, specified by the tradeoff of the data arrival rate and the energy harvesting rate, is substantially larger using our algorithm than using other existing alternatives. Significantly more data or less energy can be supported in the proposed algorithm

Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey

“…The arrival processes of data traffic modeled by existing works can be categorized to two types: heavy data arrival scenario [64] and moderate data arrival scenario [13]. The former type assumes that the data to be transmitted arrive before the start of the transmissions [64]; in other words, there are always data available in the transmit buffer.…”

“…string between the harvested energy curveẼ t and the battery overflow constraint curveẼ t −E max . Such a way of producing the optimal transmission policy is therefore called the "String Tautening" method [13], [14]. The optimal transmission policy in [65] is developed over time-invariant channels.…”

“…Rules 1 and 2 still hold after replacing "transmit power" with "water-level" [65]. Here the water-levels are defined as [13], [14], [67], [69] [65], [70] Time-varying channel [64], [68], [71], [72] [73]- [77] [64]…”

“…The optimal transmission scheme obtained for a heavy data arrival scenario is no longer feasible as there might not be enough data in the buffer. Moreover, packets can have different deadlines [13], [14], [69], [73]. Let curveD min t be the total amount of data that must be transmitted by time t. ThenD t should always lie abovẽ D min t to guarantee the deadline requirement (or QoS).…”

“…We proceed to consider that both energy and data arrivals are bursty over time. A so-called "dynamic string tautening" technique is put forth to create most energy-savior offline transmit policies even in the presence of a non-ideal transmitter circuit with a considerable power consumption and a limitation of a finite battery capacity [69], or without the limitation [13]. The transmit rate r t and the "on" duration l t per epoch t are the variables to be optimized.…”