Throughput maximization for energy harvesting cognitive radio networks with finite horizon

Zhang, Fan; Jing, Tao; Huo, Yan; Jiang, Kaiwei

doi:10.1109/wcsp.2017.8171190

Cited by 3 publications

(5 citation statements)

References 18 publications

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“…In addition to spectral efficiency, energy efficiency is another important metric to consider when designing communication systems [9]- [15]. Energy harvesting (EH) has been recognized as an effective approach for improving the energy efficiency.…”

Section: A Literature Reviewmentioning

confidence: 99%

“…Aiming at a similar goal (i.e., maximizing the SU's channel capacity), the authors in [25] investigated an optimal mode selection policy (whether to access the spectrum or to harvest energy) for CR sensor networks. In addition to the transmit power, the energy allocation for sensing is considered in an energy harvesting sensor node with a finite data buffer in [15] and [6]. The authors in [7] formulated the problem differently, as they separated energy harvesting phase and spectrum sensing phase.…”

Section: A Literature Reviewmentioning

confidence: 99%

“…The discrete random process E (t) n is typically modeled as a sequence of independent and identically distributed (i.i.d.) random variables [15], regardless of the spectrum occupancy state of PU tx . We assume that the discrete random variables E (t) n 's are i.i.d.…”

Section: A Battery and Energy Harvesting Modelsmentioning

confidence: 99%

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Steady-State Rate-Optimal Power Adaptation in Energy Harvesting Opportunistic Cognitive Radios With Spectrum Sensing and Channel Estimation Errors

Yazdani¹,

Vosoughi²

2021

IEEE Trans. on Green Commun. Netw.

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We consider an uplink cognitive radio (CR) network that can access a wideband spectrum licensed to a primary network, which is divided into non-overlapping narrowband channels. The secondary users (SUs) are equipped with rechargeable battery of finite capacity that solely powered by energy harvesting from the ambient environment. The SUs which are aware of the available energy in their batteries, sense the activity of the primary transmitter. If the result of spectrum sensing is idle, SUs send pilot symbols to the secondary access point (AP) and AP estimates the secondary channel power. Then, AP feeds back the channel gain to SUs. Knowing the battery state and the feedback information, SUs optimally adapt their transmit power to AP such that the steady-state sum rate of SU-AP link is maximized, subject to average interference constraint (AIC) imposed on the primary receiver and the causality constraint of the batteries. We illustrate the effect of the AIC, energy arrival rate and battery capacity on the steady state distribution of battery, CR network rate, battery energy outage probability and transmission outage probability via simulation.

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Section: A Literature Reviewmentioning

confidence: 99%

Section: A Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Steady-State Rate-Optimal Power Adaptation in Energy Harvesting Opportunistic Cognitive Radios With Spectrum Sensing and Channel Estimation Errors

Yazdani¹,

Vosoughi²

2021

IEEE Trans. on Green Commun. Netw.

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“…2) and E t n denotes the arrival energy during slot t at sensor n. The energy arrives randomly in each slot with a fixed energy arrival rate γ e . The energy arrival process E t n is typically modeled as a sequence of independent and identically distributed (i.i.d) random variables with an exponential distribution [8], [9]. Hence, the cumulative distribution function (CDF) of…”

Section: B Battery State Modelmentioning

confidence: 99%

Power Adaptation for Distributed Detection in Energy Harvesting WSNs with Finite-Capacity Battery

Ardeshiri

Yazdani

Vosoughi

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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We consider a wireless sensor network, consisting of N heterogeneous sensors and a fusion center (FC), that is tasked with solving a binary distributed detection problem. Each sensor is capable of harvesting randomly arrived energy and storing it in a finite capacity battery. Sensors are informed of their fading channel states, via a bandwidth-limited feedback channel from the FC. Each sensor has the knowledge of its current battery state and its channel state (quantized channel gain). Our goal is to study how sensors should choose their transmit powers such that J-divergence of the received signal densities under two hypotheses at the FC is maximized, subject to certain (battery and power) constraints. We derive the optimal power map, which depends on the energy arrival rate, the battery capacity, and the battery states probabilities at the steady state. Using the optimal power map, each sensor optimally adapts its transmit power, based on its battery state and its channel state. Our simulation results demonstrate the performance of our proposed power adaptation scheme for different system parameters.

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“…the parameters that affect the performance of the energy harvesting cognitive radio network is considered [9]. To efficiently utilize the energy harvesting from the primary transmitter (PT), both energy harvesting and information transfer can be accomplished using the separated spectrum sensing and energy harvesting scheme (SSSEH).…”

Section: Introductionmentioning

confidence: 99%

A Novel Metaheuristic Optimization for Throughput Maximization in Energy Harvesting Cognitive Radio Network

Bakshi

Sharma

Khanna

2022

ELEKTRON ELEKTROTECH

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In this article, a novel technique is proposed, namely rank-based multi-objective antlion optimization (RMOALO), and applied to optimize the performance of the energy harvesting cognitive radio network (EHCRN). The original selection method in multi-objective antlion optimizer (MOALO) is suitably changed to improve the algorithm, thus reaching the optimal solution for the problem. The proposed technique shows considerable performance improvement over the method used in the multi-objective antlion optimizer (MOALO). The performance of the proposed RMOALO is demonstrated on five benchmark mathematical functions and compared to multi-objective particle swarm optimization (MOPSO), multi-objective moth flame optimization (MOMFO), MOALO-Tournament, and MOALO-Roulette. The simulation results show an improved convergence of RMOALO and find the optimal solution to the throughput maximization problem. We show that RMOALO provides 16.33 % improved average throughput with the optimal value of sensing duration for the varying amount of harvested energy compared to MOPSO, MOMFO, MOALO-Roulette, and MOALO-Tournament.

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Throughput maximization for energy harvesting cognitive radio networks with finite horizon

Cited by 3 publications

References 18 publications

Steady-State Rate-Optimal Power Adaptation in Energy Harvesting Opportunistic Cognitive Radios With Spectrum Sensing and Channel Estimation Errors

Steady-State Rate-Optimal Power Adaptation in Energy Harvesting Opportunistic Cognitive Radios With Spectrum Sensing and Channel Estimation Errors

Power Adaptation for Distributed Detection in Energy Harvesting WSNs with Finite-Capacity Battery

A Novel Metaheuristic Optimization for Throughput Maximization in Energy Harvesting Cognitive Radio Network

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