Performance Limits of Online Energy Harvesting Communications With Noisy Channel State Information at the Transmitter

Zenaidi, Mohamed Ridha; Rezki, Zouheir; Alouini, Mohamed‐Slim

doi:10.1109/access.2017.2654454

Cited by 18 publications

(16 citation statements)

References 23 publications

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“…complex Gaussian random variables with zero mean and unit variance. As in [11], we assume that there is always data available, in the buffer, for transmission. We assume that the CSI is perfectly known at the receiver.…”

Section: System Model and Eh Protocolmentioning

confidence: 99%

“…The proposed solutions do not take into account the uncertainty of the amounts of the harvested energy. To overcome this limitation, online solutions for stochastic ESI model have been proposed [8]- [11]. For the online schemes, the problem of throughput maximization is considered and solved using the first-order Markov process [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

“…For the online schemes, the problem of throughput maximization is considered and solved using the first-order Markov process [8]- [10]. In [11], authors have focused on EH systems with imperfect CSI. The design of wireless transmission systems with a TX relying exclusively on EH is proposed in [13].…”

Section: Introductionmentioning

confidence: 99%

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Save and Transmit Scheme for Energy Harvesting MIMO Systems with TAS/MRC

Issa

Ammari

2019

J. commun. softw. syst. (Online)

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In this paper, we propose and analyze a wirelesstransmitter, for multiple-input multiple-output (MIMO) systems,that relies exclusively on energy harvesting. We consider wirelesstransceivers where the transmitter harvests the total requiredenergy from its environment through various sources. We assumethat both transmitter and receiver are equipped with multipleantennas. At the transmitter, a single transmit antenna thatmaximizes the signal-to-noise ratio (SNR) at the receiver isselected for transmission. The remaining antennas are used forenergy harvesting. At the receiver side, maximal-ratio com-bining (MRC) is used. Furthermore, we assume that all theharvested power is used to power the transmitter immediately.The performance of the proposed scheme is analyzed in terms ofoutage probability (OP), symbol error rate (SER) and channelcapacity. The harvested energy comes from random sourcesand is considered as a random variable. Assuming that theharvested power follows a gamma distribution and the MIMOchannel is a Rayleigh flat fading process, we derive a closed-form expressions for the exact cumulative distribution function(CDF) and probability density function (PDF) of the SNR. Basedon this, we analyze the performance of the proposed energyharvesting scheme. The obtained analytical results are validatedby comparing them with the results of Monte-Carlo simulations.

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Section: System Model and Eh Protocolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Save and Transmit Scheme for Energy Harvesting MIMO Systems with TAS/MRC

Issa

Ammari

2019

J. commun. softw. syst. (Online)

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“…In addition, the papers in [2]- [4] assume an EH profile with discrete values from a finite set. However, empirical measurements in [6]- [8] demonstrate that the harvested energy assume continuous values in practice.…”

Section: State Of the Art And Contributionsmentioning

confidence: 99%

“…, S, where H max is the maximum energy that can be harvested, and each interval represents the S possible states of the Markov chain. This representation of states as continuous intervals simulates the actual behavior of the harvested energy, that presents a continuous nature in practice, thus, being an improvement over the studies in [2]- [4] that consider discrete values of energy. Figure 1 presents the scenario described in section VI: users served by a BS powered by hybrid energy supplies.…”

Section: System Modelingmentioning

confidence: 99%

Radio Resource Allocation with QoS Constraints in Energy Harvesting and Hybrid Power Systems

Carvalho

Lima

Maciel

et al. 2018

JCIS

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In this paper we formulate the radio resource allocation problem of maximizing throughput in an OFDMA (Orthogonal Frequency Division Multiple Access) downlink where the BS (Base Station) adapts the power allocation according to non-causal (offline) knowledge of the harvested energy and channel state. The offline case is important from a theoretical point of view since it provides a bound on the performance of the online problem (causal). Differently from previous works that consider a continuous mapping between SNR (Signal-to-Noise Ratio) and transmit data rate, we employ a discrete mapping that depends on the required MCSs (Modulation and Coding Schemes). Also, we propose a heuristic algorithm that provides near-optimal results and achieves a good complexity/performance trade-off. In addition, we analyze the online version of the problem, and we propose two novel solutions to solve the problem only with causal information. Furthermore, we reformulate our problem to satisfy QoS (Quality of Service) constraints for each user in a hybrid power system where the BS is powered by a fixed power source from the electric grid and by a stochastic power source from renewable energy sources. Lastly, we present an offline solution to this new problem that also achieves a considerable complexity/performance gain.Index Terms-Resource allocation, rate maximization, energy harvesting, OFDMA, MCS, hybrid power system, QoS. the energy consumption causality constraints that limit the used energy to the quantity available at the moment, despite the knowledge of future energy arrivals in the offline case. Both restrictions are known as energy harvesting constraints, and are present in several works [2]-[5].

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2018

Energy Harvesting Communications

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Performance Limits of Online Energy Harvesting Communications With Noisy Channel State Information at the Transmitter

Cited by 18 publications

References 23 publications

Save and Transmit Scheme for Energy Harvesting MIMO Systems with TAS/MRC

Save and Transmit Scheme for Energy Harvesting MIMO Systems with TAS/MRC

Radio Resource Allocation with QoS Constraints in Energy Harvesting and Hybrid Power Systems

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