Multi-Objective Resource Allocation in a NOMA Cognitive Radio Network With a Practical Non-Linear Energy Harvesting Model

Wang, Yuhao; Wu, Yuhang; Zhou, Fuhui; Chu, Zheng; Wu, Yongpeng; Yuan, Fei

doi:10.1109/access.2017.2783880

Cited by 63 publications

(30 citation statements)

References 44 publications

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“…The noise variances at the SU-Rx and ER are set to σ 2 su = 1 and σ 2 er = 1, respectively. The convergence tolerances ε, ς and ρ defined in (16), (30) and (41), respectively, are assumed to be 10 −3 .…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Secondly, practically it is infeasible to exploit them to the mobile devices, owing to the size limitations of harvesting devices and the unstable power output caused by different geographical conditions. As such, wireless EH facilitates practical design and implementation especially in mobile devices [1], [16]- [19]. There exist three different schemes of technologies to implement wireless EH, name, magnetic induction, inductive coupling, and radio frequency-based wireless power transfer [13], [20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Energy Efficiency Optimization for Secure Transmission in MISO Cognitive Radio Network With Energy Harvesting

Zhang¹,

Cumanan²,

Thiyagalingam³

et al. 2019

IEEE Access

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In this paper, we investigate different secrecy energy efficiency (SEE) optimization problems in a multiple-input single-output underlay cognitive radio (CR) network in the presence of an energy harvesting receiver. In particular, these energy efficient designs are developed with different assumptions of channels state information (CSI) at the transmitter, namely perfect CSI, statistical CSI and imperfect CSI with bounded channel uncertainties. In particular, the overarching objective here is to design a beamforming technique maximizing the SEE while satisfying all relevant constraints linked to interference and harvested energy between transmitters and receivers. We show that the original problems are non-convex and their solutions are intractable. By using a number of techniques, such as non-linear fractional programming and difference of concave (DC) functions, we reformulate the original problems so as to render them tractable. We then combine these techniques with the Dinkelbach's algorithm to derive iterative algorithms to determine relevant beamforming vectors which lead to the SEE maximization. In doing this, we investigate the robust design with ellipsoidal bounded channel uncertainties, by mapping the original problem into a sequence of semidefinite programs This paper has been presented in part at the IEEE Wireless Communications and Signal Processing (WCSP), October 11-13, 2017 [1]. M. Zhang is with the ). Z. Ding is with the School of Electrical and Electronic Engineering, The University of Manchester, Manchester, UK (email: zhiguo.ding@manchester.ac.uk). O. A. Dobre is with the employing the semidefinite relaxation, non-linear fractional programming and S-procedure. Furthermore, we show that the maximum SEE can be achieved through a search algorithm in the single dimensional space. Numerical results, when compared with those obtained with existing techniques in the literature, show the effectiveness of the proposed designs for SEE maximization.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy Efficiency Optimization for Secure Transmission in MISO Cognitive Radio Network With Energy Harvesting

Zhang¹,

Cumanan²,

Thiyagalingam³

et al. 2019

IEEE Access

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show abstract

“…In [14], the authors considered a NOMA based heterogeneous network and studied the trade-off among the energy efficiency, fairness, harvested energy, and system sum rate. In [15], the authors designed optimal resource allocation strategies for cognitive radio networks with NOMA. The authors considered non-linear EH model, where secondary users either harvest energy or decode information.…”

Section: B Literature Surveymentioning

confidence: 99%

“…Energy harvesting is more practical with multiple antenna beamforming, however, the above works [11]- [17] do not consider the practical and complex problem of multi-antenna beamforming. In addition, wireless nodes have to either harvest energy or decode information [11]- [13], [15]- [17], and do not need to implement both EH and ID.…”

Section: B Literature Surveymentioning

confidence: 99%

NOMA Throughput and Energy Efficiency in Energy Harvesting Enabled Networks

Nasir

Tuan

Debbah

2019

IEEE Trans. Commun.

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Wireless power transfer via radio-frequency (RF) radiation is regarded as a potential solution to energize energy-constrained users, who are deployed close to the base stations (near-by users). However, energy transfer requires much more transmit power than normal information transfer, which makes it very challenging to provide the quality of service in terms of throughput for all near-by users and cell-edge users. Thus, it is of practical interest to employ non-orthogonal multiple access (NOMA) to improve the throughput of all network users, while fulfilling the energy harvesting requirements of the near-by users. To realize both energy harvesting and information decoding, we consider a transmit time-switching (transmit-TS) protocol. We formulate two important beamfoming problems of users' max-min throughput optimization and energy efficiency maximization under power constraint and energy harvesting thresholds at the nearly-located users. For these problems, the optimization objective and energy harvesting are non-convex in beamforming vectors. Thus, we develop efficient path-following algorithms to solve them. In addition, we also consider conventional power splitting (PS)-based energy harvesting receiver. Our numerical results confirm that the proposed transmit-TS based algorithms clearly outperform PS-based algorithms in terms of both, throughput and energy efficiency.Index Terms-Wireless power transfer, energy harvesting, non-orthogonal multiple access (NOMA), nonconvex optimization, throughput, energy efficiency, quality-of-service (QoS).

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“…Furthermore, we have shown here that DBCA is resource efficient. In general, it can be expected that resource efficiency implies reduced power consumption, but further study is needed on how to extend the problem formulation to account for the power consumption of UEs [48]. In this work, we have targeted 5G networks in sub-6 GHz spectrum, thus neglecting usage of mmWave and beam forming procedures.…”

Section: B Future Workmentioning

confidence: 99%

Dynamic Binary Countdown for Massive IoT Random Access in Dense 5G Networks

Vilgelm

Linares

Kellerer

2019

IEEE Internet Things J.

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Massive connectivity for Internet of Things applications is expected to challenge the way access reservation protocols are designed in 5G networks. Since the number of devices and their density are envisioned to be orders of magnitude larger, state-of-the-art access reservation, Random Access (RA) procedure, might be a bottleneck for end-to-end delay. This would be especially challenging for burst arrival scenarios: Semisynchronous triggering of a large number of devices due to a common event (blackout, emergency alarm, etc.). In this article, to improve RA procedure scalability, we propose to combine Binary Countdown Contention Resolution (BCCR) with the state-of-theart Access Class Barring (ACB). We present a joint analysis of ACB and BCCR and apply a framework for treating RA as a biobjective optimization, minimizing the resource consumption and maximizing the throughput of the procedure in every contention round. We use this framework to devise dynamic load-adaptive algorithm and simulatively illustrate that the proposed algorithm reduces the burst resolution delay while consuming less resources compared to the state-of-the-art techniques.

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Multi-Objective Resource Allocation in a NOMA Cognitive Radio Network With a Practical Non-Linear Energy Harvesting Model

Cited by 63 publications

References 44 publications

Energy Efficiency Optimization for Secure Transmission in MISO Cognitive Radio Network With Energy Harvesting

Energy Efficiency Optimization for Secure Transmission in MISO Cognitive Radio Network With Energy Harvesting

NOMA Throughput and Energy Efficiency in Energy Harvesting Enabled Networks

Dynamic Binary Countdown for Massive IoT Random Access in Dense 5G Networks

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