On Dual-Path Energy-Harvesting Receivers for IoT With Batteries Having Internal Resistance

Ni, Zhengwei; Bhat, Rajshekhar Vishweshwar; Motani, Mehul

doi:10.1109/jiot.2018.2813976

Cited by 17 publications

(8 citation statements)

References 48 publications

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“…When the harvest-then-use mode is adopted, the non-ideal efficiency of the battery is often discussed. Some papers just use an energy conversion efficiency factor [28], [33], while some papers further model the internal resistance of the battery to discuss the above problems [34] .…”

Section: Introductionmentioning

confidence: 99%

“…However, they do not consider the energy harvested from ambient RF sources other than the dedicated TX. Some papers in our research group have discussed the energy decoding cost at the EH receiver in a point-to-point communication system, and have drawn many conclusions on the system design [34], [36], [37]. Ni et al [36] optimize the reception rate and time fraction of energy harvesting with different schemes.…”

Section: Introductionmentioning

confidence: 99%

“…Ni et al [37] extend the work in [36] to joint optimization of transmission power at the transmitter, reception rate and the time fraction for EH at the receiver in a point-to-point communication system. Ni et al [34] further discuss the dual-path structure of EH receivers considering both the internal resistance of the battery and the decoding cost, which constitutes a complex and difficult problem to deal with.…”

Section: Introductionmentioning

confidence: 99%

“…Ni et al [34], [36], [37] have studied the optimal TS optimization problems, accounting for decoding energy cost at an EH receiver in a point-to-point communication system; When it comes to a relay, which forwards the received information from the transmitter to the destination without a direct link, it is a very different problem. This is because the harvested energy must be carefully divided into two parts respective for decoding and forwarding the information and the process of EH also takes up part of the total processing time, in an EH system where EH and information transceiving share one antenna and one RF front-end.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Energy Harvesting Decode-and-Forward Relays With Decoding Energy Costs and Energy Storage

Yao

et al. 2021

IEEE Access

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Energy harvesting (EH) relay communication systems with decoding energy costs in multiple block cases have not been widely studied. This paper investigates the relay network with a decode-andforward relay powered by EH. Unlike other works, we consider the relay with energy decoding costs which harvests random energy from both a dedicated transmitter and other ambient radio-frequency (RF) sources. The EH relay adopts a harvest-receive-forward time-switching architecture. We optimize the time fractions of the three phases and the reception rate at the relay to maximize the offline throughput for single and multiple block cases under two EH scenarios. The multi-block optimization problem constitutes a complex non-convex problem, which we decouple into a single block problem with two auxiliary variables determined by an outer optimization problem. The original problem is finally solved at the cost of linear optimization after series of tricks. Several conclusions are derived: (i) energy storage is necessary (unnecessary) when the relay harvests energy from the transmitter (ambient RF sources), (ii) the optimal reception rate remains unchanged, while the optimal time fractions vary with the energy harvested from ambient RF sources leading to different average throughput. We give numerical simulations to verify our theoretical analysis.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimizing Energy Harvesting Decode-and-Forward Relays With Decoding Energy Costs and Energy Storage

Yao

et al. 2021

IEEE Access

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“…The Internet of things (IoT) has a crucial need for long-term or self-sustainable operations to support various applications [1] [2]. In recent years, energy harvesting (EH) has been recognized as an emerging technique that may significantly increase the network lifetime and help reduce the greenhouse gas emissions in general wireless applications [3] [4] [5].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based Multiaccess Control and Battery Prediction With Energy Harvesting in IoT Systems

Chu

Liao

et al. 2019

IEEE Internet Things J.

117

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Energy harvesting (EH) is a promising technique to fulfill the long-term and self-sustainable operations for Internet of things (IoT) systems. In this paper, we study the joint access control and battery prediction problems in a small-cell IoT system including multiple EH user equipments (UEs) and one base station (BS) with limited uplink access channels. Each UE has a rechargeable battery with finite capacity. The system control is modeled as a Markov decision process without complete prior knowledge assumed at the BS, which also deals with large sizes in both state and action spaces. First, to handle the access control problem assuming causal battery and channel state information, we propose a scheduling algorithm that maximizes the uplink transmission sum rate based on reinforcement learning (RL) with deep Q-network (DQN) enhancement. Second, for the battery prediction problem, with a fixed round-robin access control policy adopted, we develop a RL based algorithm to minimize the prediction loss (error) without any model knowledge about the energy source and energy arrival process. Finally, the joint access control and battery prediction problem is investigated, where we propose a two-layer RL network to simultaneously deal with maximizing the sum rate and minimizing the prediction loss: the first layer is for battery prediction, the second layer generates the access policy based on the output from the first layer. Experiment results show that the three proposed RL algorithms can achieve better performances compared with existing benchmarks.

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Time-Switching Energy Harvesting Relay Optimizing Considering Decoding Cost

Yao

et al. 2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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On Dual-Path Energy-Harvesting Receivers for IoT With Batteries Having Internal Resistance

Cited by 17 publications

References 48 publications

Optimizing Energy Harvesting Decode-and-Forward Relays With Decoding Energy Costs and Energy Storage

Optimizing Energy Harvesting Decode-and-Forward Relays With Decoding Energy Costs and Energy Storage

Reinforcement Learning-Based Multiaccess Control and Battery Prediction With Energy Harvesting in IoT Systems

Time-Switching Energy Harvesting Relay Optimizing Considering Decoding Cost

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