Joint spectrum allocation and energy harvesting optimization in green powered heterogeneous cognitive radio networks

Shahini, Ali; Ansari, Nirwan

doi:10.1016/j.comcom.2018.05.011

Cited by 15 publications

(10 citation statements)

References 39 publications

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“…This assumption is consistent with the literature (see [26], [27], [30], [60] for example). We note that research [61]- [63] on time slot optimization do not fit well into the framework presented in this paper.…”

Section: Energy Harvesting Success Probabilitymentioning

confidence: 82%

Outage Minimization of Energy Harvesting-Based Relay-Assisted Random Underlay Cognitive Radio Networks With Interference Cancellation

et al. 2021

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This paper incorporates interference cancellation in the outage analysis of a wireless energy harvesting cognitive relay network. Based on an interference threshold, primary receivers are assumed to be able to cancel a fraction of the strongest interferers in the primary network which often dominates the total interference. To achieve this, the coefficient of cancellation is adapted into the analysis to reduce the level of interference. The rationale is to improve the successful transmission probability of the primary network by cancelling interference at its receivers. Interestingly, this reduces the overhead incurred by the secondary network to meet up with the primary outage constraint. In this work, the optimal relay selection range is derived and deployed to minimize the secondary outage probability. Analytical results show that this approach can be used to significantly reduce the secondary outage probability which in turn improves the network performance. However, this is at the cost of reducing the energy harvesting success probability of the relays within the secondary network.INDEX TERMS cognitive relay networks, decode and forward, energy harvesting, interference cancellation, optimal relay selection range, outage probability, stochastic geometry.

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Section: Energy Harvesting Success Probabilitymentioning

confidence: 82%

Outage Minimization of Energy Harvesting-Based Relay-Assisted Random Underlay Cognitive Radio Networks With Interference Cancellation

et al. 2021

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“…In [17], EE downlink resource allocation in heterogeneous OFDMA networks is investigated, where the EE maximisation problem is formulated as a mixed‐integer non‐linear fractional programming, and it is optimally solved using the Dinkelbach and branch‐and‐bound methods. In [18], the joint sub‐channel allocation and structure optimisation are formulated as a sum‐rate maximisation of SUs in OFDM‐based heterogeneous cognitive radio networks, with consideration of interference limitations, imperfect spectrum sensing, and various rate requirements of SUs. In [19], by taking into account the interference between two access points in LTE‐A network, Ganni et al proposed a distributed multi‐colouring algorithm for resource allocation problem to assign the resources effectively to maximise its utilisation and mitigate the interference.…”

Section: Related Workmentioning

confidence: 99%

Two‐tier trading strategy design for spectrum allocation in heterogeneous cognitive radio networks

et al. 2020

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“…Song et al [20] studied a tradeoff between Quality of Service (QoS) provisioning and the energy efficiency for IoT networks. Joint spectrum allocation and energy harvesting optimization has been proposed in [21] for green powered heterogeneous cognitive radio networks. Moreover, Liu et al [22] proposed a wireless energy harvesting protocol for an underlay cognitive relay in which the secondary users are assumed to harvest energy from the primary network.…”

Section: A Related Workmentioning

confidence: 99%

Energy Efficient Resource Allocation in EH-Enabled CR Networks for IoT

Shahini

Kiani

Ansari

2019

IEEE Internet Things J.

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With the rapid growth of Internet of Things (IoT) devices, the next generation mobile networks demand for more operating frequency bands. By leveraging the underutilized radio spectrum, the cognitive radio (CR) technology is considered as a promising solution for spectrum scarcity problem of IoT applications. In parallel with the development of CR techniques, Wireless Energy Harvesting (WEH) is considered as one of the emerging technologies to eliminate the need of recharging or replacing the batteries for IoT and CR networks. To this end, we propose to utilize WEH for CR networks in which the CR devices are not only capable of sensing the available radio frequencies in a collaborative manner but also harvesting the wireless energy transferred by an Access Point (AP). More importantly, we design an optimization framework that captures a fundamental tradeoff between energy efficiency (EE) and spectral efficiency (SE) of the network. In particular, we formulate a Mixed Integer Nonlinear Programming (MINLP) problem that maximizes EE while taking into consideration of users' buffer occupancy, data rate fairness, energy causality constraints and interference constraints. We further prove that the proposed optimization framework is an NP-Hard problem.Thus, we propose a low complex heuristic algorithm, called INSTANT, to solve the resource allocation and energy harvesting optimization problem. The proposed algorithm is shown to be capable of achieving near optimal solution with high accuracy while having polynomial complexity. The efficiency of our proposal is validated through well designed simulations.

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Joint spectrum allocation and energy harvesting optimization in green powered heterogeneous cognitive radio networks

Cited by 15 publications

References 39 publications

Outage Minimization of Energy Harvesting-Based Relay-Assisted Random Underlay Cognitive Radio Networks With Interference Cancellation

Outage Minimization of Energy Harvesting-Based Relay-Assisted Random Underlay Cognitive Radio Networks With Interference Cancellation

Two‐tier trading strategy design for spectrum allocation in heterogeneous cognitive radio networks

Energy Efficient Resource Allocation in EH-Enabled CR Networks for IoT

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