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On Achievable Rates of AWGN Energy-Harvesting Channels With Block Energy Arrival and Non-Vanishing Error Probabilities | IEEE Journals & Magazine | IEEE Xplore

On Achievable Rates of AWGN Energy-Harvesting Channels With Block Energy Arrival and Non-Vanishing Error Probabilities


Abstract:

This paper investigates the achievable rates of an additive white Gaussian noise energy-harvesting (EH) channel with an infinite battery. The EH process is characterized ...Show More

Abstract:

This paper investigates the achievable rates of an additive white Gaussian noise energy-harvesting (EH) channel with an infinite battery. The EH process is characterized by a sequence of blocks of harvested energy, which is known causally at the source. The harvested energy remains constant within a block while the harvested energy across different blocks is characterized by a sequence of independent and identically distributed random variables. The blocks have length L , which can be interpreted as the coherence time of the energy-arrival process. If L is a constant or grows sublinearly in the blocklength n , we fully characterize the first-order term in the asymptotic expansion of the maximum transmission rate subject to a fixed tolerable error probability ε. The first-order term is known as the ε-capacity. In addition, we obtain lower and upper bounds on the second-order term in the asymptotic expansion, which reveal that the second order term is proportional to -(L/n)1/2 for any ε less than 1/2. The lower bound is obtained through analyzing the save-and-transmit strategy. If L grows linearly in n, we obtain lower and upper bounds on the ε-capacity, which coincide whenever the cumulative distribution function of the EH random variable is continuous and strictly increasing. In order to achieve the lower bound, we have proposed a novel adaptive save-and-transmit strategy, which chooses different save-and-transmit codes across different blocks according to the energy variation across the blocks.
Published in: IEEE Transactions on Information Theory ( Volume: 64, Issue: 3, March 2018)
Page(s): 2038 - 2064
Date of Publication: 23 October 2017

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