Energy-Efficient Time Allocation for Wireless Energy Harvesting Communication Networks

In this paper, we study the performance of energy harvesting communication networks focusing on the system energy efficiency. We consider multiple wireless-powered users that harvest energy from a wireless power source (WPS) and then transmit information uplink through time-division multiple access...

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Vydané v:2016 IEEE Globecom Workshops (GC Wkshps) s. 1 - 6
Hlavní autori: Zewde, Tewodros A., Gursoy, M. Cenk
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.12.2016
Predmet:
Downlink
Energy harvesting
Fading channels
Quality of service
Resource management
Uplink
Wireless communication
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Shrnutí:In this paper, we study the performance of energy harvesting communication networks focusing on the system energy efficiency. We consider multiple wireless-powered users that harvest energy from a wireless power source (WPS) and then transmit information uplink through time-division multiple access channels to the access point (AP). Besides, users can also scavenge energy from an information-bearing signal transmitted by a user scheduled for uplink data transfer. Each user is subject to limitations on the buffer overflow probability, specified by the quality of service (QoS) exponent . The optimal time allocation strategies, i.e., energy harvesting and data transmission intervals, are affected by such QoS constraints in addition to the channel characteristics. Thus, we formulate optimization problems to maximize the system energy efficiency (measured by the sum effective capacity per total consumed energy) while taking statistical queuing constraints into account. In addition, we provide details for the optimal time allocation strategies in the absence of these constraints. Since the problems, in both cases, are pseudo-concave, Karush-KuhnTucker (KKT) conditions guarantee global optimality. However, it is difficult to obtain closed-form expressions for the optimal solution. Hence, we employ the Dinkelbach's method to solve the problems using standard numerical tools. Simulation results demonstrate that QoS constraints are critical, dictating time allocation, and correspondingly rate distribution, among the wireless-powered users in the presence of delay-sensitive sources.
DOI:10.1109/GLOCOMW.2016.7848893