2021•Problems of Information TransmissionOpen access

Finite Blocklength Analysis of Energy Harvesting Channels

Konchady Gautam Shenoy, Vinod Pravin Sharma

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Abstract

We consider additive white Gaussian noise channels and discrete memoryless channels where the transmitter harvests energy from the environment. These can model wireless sensor networks as well as Internet of Things. By providing a unifying framework that works for any energy harvesting channel, we study these channels assuming an infinite energy buffer and provide the corresponding achievability and converse bounds on the channel capacity in the finite blocklength regime. We additionally provide moderate deviation asymptotic bounds.

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We consider additive white Gaussian noise channels and discrete memoryless channels where the transmitter harvests energy from the environment. These can model wireless sensor networks as well as Internet of Things. By providing a unifying framework that works for any energy harvesting channel, we study these channels assuming an infinite energy buffer and provide the corresponding achievability and converse bounds on the channel capacity in the finite blocklength regime. We additionally provide moderate deviation asymptotic bounds.

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Available abstract

We consider additive white Gaussian noise channels and discrete memoryless channels where the transmitter harvests energy from the environment. These can model wireless sensor networks as well as Internet of Things. By providing a unifying framework that works for any energy harvesting channel, we study these channels assuming an infinite energy buffer and provide the corresponding achievability and converse bounds on the channel capacity in the finite blocklength regime. We additionally provide moderate deviation asymptotic bounds.

Key concepts: Converse, Additive white Gaussian noise, Transmitter, Channel (broadcasting), Energy harvesting, Energy (signal processing), Gaussian, Computer science

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