Capacity Analysis of Discrete Energy Harvesting Channels

• Published in: IEEE transactions on information theory Vol. 63; no. 9; pp. 5850 - 5885
• Main Authors: Wei Mao, Hassibi, Babak
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Batteries; causal CSIT; Channel capacity; Channels; Energy; Energy harvesting; Energy transmission; ergodicity; finite state channel; Lower bounds; Markov processes; Power supplies; Random variables; Receivers; Transmitters; Upper bound; Upper bounds
• ISSN: 0018-9448; 1557-9654
• DOI: 10.1109/TIT.2017.2726070

We study the channel capacity of a general discrete energy harvesting channel with a finite battery. Contrary to traditional communication systems, the transmitter of such a channel is powered by a device that harvests energy from a random exogenous energy source and has a finite-sized battery. As a consequence, at each transmission opportunity, the system can only transmit a symbol whose energy is no more than the energy currently available. This new type of power supply introduces an unprecedented input constraint for the channel, which is simultaneously random, instantaneous, and influenced by the full history of the inputs and the energy harvesting process. Furthermore, naturally, in such a channel, the energy information is observed causally at the transmitter. Both of these characteristics pose great challenges for the analysis of the channel capacity. In this paper, we use techniques developed for channels with side information and finite-state channels, to obtain lower and upper bounds on the capacity of energy harvesting channels. In particular, in a general case with Markov energy harvesting processes, we use stationarity and ergodicity theory to compute and optimize the achievable rates for the channels, and derive a series of computable capacity upper and lower bounds.