Energy Harvesting Wireless Communications: A Review of Recent Advances

This article summarizes recent contributions in the broad area of energy harvesting wireless communications. In particular, we provide the current state of the art for wireless networks composed of energy harvesting nodes, starting from the information-theoretic performance limits to transmission scheduling policies and resource allocation, medium access and networking issues. The emerging related area of energy transfer for self-sustaining energy harvesting wireless networks is considered in detail covering both energy cooperation aspects and simultaneous energy and information transfer. Various potential models with energy harvesting nodes at different network scales are reviewed as well as models for energy consumption at the nodes.Comment: To appear in the IEEE Journal of Selected Areas in Communications (Special Issue: Wireless Communications Powered by Energy Harvesting and Wireless Energy Transfer

A low-complexity turbo decoder architecture for energy-efficient wireless sensor networks

Turbo codes have recently been considered for energy-constrained wireless communication applications, since they facilitate a low transmission energy consumption. However, in order to reduce the overall energy consumption, Look-Up- Table-Log-BCJR (LUT-Log-BCJR) architectures having a low processing energy consumption are required. In this paper, we decompose the LUT-Log-BCJR architecture into its most fundamental Add Compare Select (ACS) operations and perform them using a novel low-complexity ACS unit. We demonstrate that our architecture employs an order of magnitude fewer gates than the most recent LUT-Log-BCJR architectures, facilitating a 71% energy consumption reduction. Compared to state-of- the-art Maximum Logarithmic Bahl-Cocke-Jelinek-Raviv (Max- Log-BCJR) implementations, our approach facilitates a 10% reduction in the overall energy consumption at ranges above 58 m

Recent Advances in Joint Wireless Energy and Information Transfer

In this paper, we provide an overview of the recent advances in microwave-enabled wireless energy transfer (WET) technologies and their applications in wireless communications. Specifically, we divide our discussions into three parts. First, we introduce the state-of-the-art WET technologies and the signal processing techniques to maximize the energy transfer efficiency. Then, we discuss an interesting paradigm named simultaneous wireless information and power transfer (SWIPT), where energy and information are jointly transmitted using the same radio waveform. At last, we review the recent progress in wireless powered communication networks (WPCN), where wireless devices communicate using the power harvested by means of WET. Extensions and future directions are also discussed in each of these areas.Comment: Conference submission accepted by ITW 201

EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design

The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application