Optimal collaborative energy harvesting spectrum sensing with limited time resource

Mohammadian, Fariba; Pourgharehkhan, Zahra; Taherpour, Abbas; Khattab, Tamer

doi:10.1109/wcnc.2016.7565033

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…In [33], throughput is optimized with respect to the sensing threshold and the amount of energy being harvested. In [34], the problem of optimal spectrum sensing strategy is studied in energy harvesting CR networks. In [35], this optimization is investigated with respect to sensing time and energy harvesting time, considering a slotted energy arrival policy.…”

Section: A Backgroundmentioning

confidence: 99%

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Sum Throughput Maximization in a Cognitive Multiple Access Channel With Cooperative Spectrum Sensing and Energy Harvesting

Biswas

Dey

Shirazinia

2019

IEEE Trans. Cogn. Commun. Netw.

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This paper focuses on the problem of sensing throughput optimization in a fading multiple access cognitive radio (CR) network, where the secondary user (SU) transmitters participate in cooperative spectrum sensing and are capable of harvesting energy and sharing energy with each other. We formulate the optimization problem as a maximization of the expected achievable sum-rate over a finite horizon, subject to an average interference constraint at the primary receiver, peak power constraints, and energy causality constraints at the SU transmitters. The optimization problem is a non-convex, mixed integer non-linear program (MINLP) involving the binary action to sense the spectrum or not, and the continuous variables, such as the transmission power, shared energy, and sensing time. The problem is analyzed under two different assumptions on the available information pattern: 1) non-causal channel state information (CSI), energy state information (ESI), and infinite battery capacity and 2) the more realistic scenario of the causal CSI/ESI and finite battery. In the non-casual case, this problem can be solved by an exhaustive search over the decision variable or an MINLP solver for smaller problem dimensions, and a novel heuristic policy for larger problems, combined with an iterative alternative optimization method for the continuous variables. The causal case with finite battery is optimally solved using a dynamic programming (DP) methodology, whereas a number of sub-optimal algorithms are proposed to reduce the computational complexity of DP. Extensive numerical simulations are carried out to illustrate the performance of the proposed algorithms. One of the main findings indicates that the energy sharing is more beneficial when there is a significant asymmetry between average harvested energy levels/channel gains of different SUs.

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Section: A Backgroundmentioning

confidence: 99%

“…, where λ and δ * i are the optimal Lagrange parameters corresponding to (34) and ( 35), respectively.…”

Section: B Ad-hoc Policymentioning

confidence: 99%

Sum Throughput Maximization in a Cognitive Multiple Access Channel With Cooperative Spectrum Sensing and Energy Harvesting

Biswas

Dey

Shirazinia

2019

IEEE Trans. Cogn. Commun. Netw.

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“…In realistic scenario, the detection result from a single user is unreliable due to the deep fading or shadow effect in wireless communications. Thus to guarantee the detection performance, the cooperative spectrum sensing [5], [6] should be adopted, however, almost all existing related works assume that there are sufficient secondary users (SU) to cooperate when a PU's state is needed to detect. Yet, it's not rational enough in realistic scenario where maybe only a limited number of SUs exist during the current timeslot and will not sense the spectrum if they have Personal use is also permitted, but republication/redistribution requires IEEE permission.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Aware Crowdsourced Spectrum Sensing and Multi-User Sharing Mechanism in Dynamic Spectrum Access Networks

Zhu

Wang

2019

IEEE Access

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Dynamic spectrum access (DSA) emerging as an effective way of improving the utilization of the scarce spectrum has attracted great attention in the communication field. A key challenge in DSA is to perform an efficient spectrum sensing and sharing mechanism. In this paper, aiming at achieving a maximal spectrum utilization, we propose a privacy-aware crowdsourced spectrum sensing and multiuser sharing mechanism for DSA. Particularly, in the sensing stage, the advanced mobile crowd sensing is adapted to economically provide sufficient candidate sensing helpers for a sensing requestor. Considering the individual rationality and energy consumption, an incentive mechanism based on both monetary and social motivation is designed to motivate the finial participations of the sensing helpers. Moreover, with the increasing attention to individual privacy, a social network and location-based k-anonymity grouping algorithm are proposed to prevent each helper's privacy being attacked by the malicious requestor or mobile users. Then, for a sensing requestor, aiming at achieving a target detection performance with minimal payment, a truthful reverse auction-based winning group selection algorithm is designed. Furthermore, in the transmitting stage, a realistic scenario is considered where multiple transmitters may discover the same idle spectrum based on sensing helpers' detections and will transmit data simultaneously. Thus, we model this problem as a potential-game-based multiuser transmission mechanism where all the transmitters act as game players and will jointly adjust their transmission powers to maximize the global throughput. Accordingly, we also take advantage of an improved differential evolution algorithm for obtaining a better equilibrium solution in a decentralized way. Both the theoretical analysis and the simulation results prove the rationality and superiority of our proposed algorithms. INDEX TERMS Privacy aware, mobile crowd sensing, incentive mechanism, dynamic spectrum access, game theory.

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“…To our best knowledge, merely works have been focused on this issue. Although some papers perform the optimization of co-operative sensing time [ 10 , 11 ], they all assume the sensing time is the same as each other and there are always sufficient co-operative SUs who sense the same channel and transmit data in it. However, this assumption may be irrational and impractical in the realistic situation where only few SUs sense the same channel and transmit in the current time slot, which will result in the degradation of co-operative sensing performance.…”

Section: Introductionmentioning

confidence: 99%

Social Incentive Mechanism Based Multi-User Sensing Time Optimization in Co-Operative Spectrum Sensing with Mobile Crowd Sensing

Zhu

2018

Sensors

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Co-operative spectrum sensing emerging as a significant method to improve the utilization of the spectrum needs sufficient sensing users to participate. Existing related papers consider only the limited secondary users in current sensing system and assume that they will always perform the co-operative spectrum sensing out of obligation. However, this assumption is impractical in the realistic situation where the secondary users are rational and they will not join in the co-operative sensing process without a certain reward to compensate their sensing energy consumption, especially the ones who have no data transmitting in current time slot. To solve this problem, we take advantage of the mobile crowd sensing to supply adequate co-operative sensing candidates, in which the sensing users are not only the secondary users but also a crowd of widely distributed mobile users equipped with personal spectrum sensors (such as smartphones, vehicle sensors). Furthermore, a social incentive mechanism is also adapted to motivate the participations of mobile sensing users. In this paper, we model the interactions among the motivated sensing users as a co-operative game where they adjust their own sensing time strategies to maximize the co-operative sensing utility, which eventually guarantees the detection performance and prevents the global sensing cost being too high. We prove that the game based optimization problem is NP-hard and exists a unique optimal equilibrium. An improved differential evolution algorithm is proposed to solve the optimization problem. Simulation results prove the better performance in our proposed multi-user sensing time optimization model and the proposed improved differential evolution algorithm, respectively compared with the non-optimization model and the other two typical equilibrium solution algorithms.

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Optimal collaborative energy harvesting spectrum sensing with limited time resource

Cited by 7 publications

References 15 publications

Sum Throughput Maximization in a Cognitive Multiple Access Channel With Cooperative Spectrum Sensing and Energy Harvesting

Sum Throughput Maximization in a Cognitive Multiple Access Channel With Cooperative Spectrum Sensing and Energy Harvesting

Privacy-Aware Crowdsourced Spectrum Sensing and Multi-User Sharing Mechanism in Dynamic Spectrum Access Networks

Social Incentive Mechanism Based Multi-User Sensing Time Optimization in Co-Operative Spectrum Sensing with Mobile Crowd Sensing

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