Optimal Spectrum Sensing in Energy Harvesting Cognitive Radio Systems

In this paper,we consider a cognitive radio system with energy harvesting,in which the secondary user operates in a saving-sensing-transmitting(SST) fashion.We investigate the tradeoff between energy harvesting,channel sensing and data transmission and focus on the optimal SST structure to maximize the SU's expected achievable throughput.We consider imperfect knowledge of energy harvesting rate,which cannot be exactly known and only its statistical information is available.By formulating the problem of expected achievable throughput optimization as a mixed-integer non-linear programming one,we derive the optimal saveratio and number of sensed channels with indepth analysis.Simulation results show that the optimal SST structure outperforms random one and performance gain can be enhanced by increasing the SU's energy harvesting rate.

Sensing Time Optimization in Energy-Harvesting Cognitive Radio with Interference Rate Control

In this paper,an energy-harvesting cognitive radio(CR) is considered,which allows the transmitter of the secondary user(SU) to harvest the primary signal energy from the transmitter of the primary user(PU) when the presence of the PU is detected.Then the harvested energy is converted into the electrical power to supply the transmission of the SU at the detected absence of the PU.By adopting the periodic spectrum sensing,the average total transmission rate of the SU is maximized through optimizing the sensing time,subject to the constraints of the probabilities of false alarm and detection,the harvested energy and the interference rate control.The simulation results show that there deed exists an optimal sensing time that maximizes the transmission rate,and the maximum transmission rate of the energy-harvesting CR can better approach to that of the traditional CR with the increasing of the detection probability.

Optimization of sensing time and cooperative user allocation for OR-rule cooperative spectrum sensing in cognitive radio network

In order to improve the throughput of cognitive radio(CR), optimization of sensing time and cooperative user allocation for OR-rule cooperative spectrum sensing was investigated in a CR network that includes multiple users and one fusion center. The frame structure of cooperative spectrum sensing was divided into multiple transmission time slots and one sensing time slot consisting of local energy detection and cooperative overhead. An optimization problem was formulated to maximize the throughput of CR network, subject to the constraints of both false alarm probability and detection probability. A joint optimization algorithm of sensing time and number of users was proposed to solve this optimization problem with low time complexity. An allocation algorithm of cooperative users was proposed to preferentially allocate the users to the channels with high utilization probability. The simulation results show that the significant improvement on the throughput can be achieved through the proposed joint optimization and allocation algorithms.

A Novel Spectrum Handoff-Based Sensing-Throughput Tradeoff Scheme in Cognitive Radio

In cognitive radio(CR), there is a tradeoff between spectrum sensing time and throughput of secondary user(SU). In the conventional sensing-throughput tradeoff scheme, when the presence of the primary user(PU) is detected, the SU will stop forwarding data and wait to redetect the PU in the following frame, yielding great throughput loss. In order to improve the SU's throughput, a novel sensing-throughput tradeoff scheme is proposed, which allows the SU to search for a new idle channel through spectrum searching and transfer to this channel to continue communication, when the presence of the PU is detected. An optimization problem is proposed to maximize the SU's throughput in the proposed scheme through jointly optimizing the sensing time, the searching time and the number of available channels, providing that the detection probability to the PU is guaranteed. By fixing the number of channels, based on alternating direction optimization, the joint optimization algorithm of sensing time and searching time is proposed, and then the optimal number of channels is obtained through enumerative searching. The simulation results show that there exist the optimal solutions to the proposed scheme, and the proposed scheme outperforms the conventional scheme notably, with different detection probabilities and sampling frequencies.

Design of Clustering Techniques in Cognitive Radio Sensor Networks

In recent decades,several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during trans-mission to a shorter distance while restricting the Primary Users(PUs)interfer-ence.The Cognitive Radio(CR)system is based on the Adaptive Swarm Distributed Intelligent based Clustering algorithm(ASDIC)that shows better spectrum sensing among group of multiusers in terms of sensing error,power sav-ing,and convergence time.In this research paper,the proposed ASDIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their connectivity.In this research,multiple random Sec-ondary Users(SUs),and PUs are considered for implementation.Hence,the pro-posed ASDIC algorithm improved the convergence speed by combining the multi-users clustered communication compared to the existing optimization algo-rithms.Experimental results showed that the proposed ASDIC algorithm reduced the node power of 9.646%compared to the existing algorithms.Similarly,ASDIC algorithm reduced 24.23%of SUs average node power compared to the existing algorithms.Probability of detection is higher by reducing the Signal-to-Noise Ratio(SNR)to 2 dB values.The proposed ASDIC delivers low false alarm rate compared to other existing optimization algorithms in the primary detection.Simulation results showed that the proposed ASDIC algorithm effectively solves the multimodal optimization problems and maximizes the performance of net-work capacity.

On the Performance of the Energy Detector Subject to Impulsive Noise in κ-μ, α-μ, and η-μ Fading Channels

This paper presents a unified theoretical analysis of the energy detection of Gaussian and M-PSK signals in κ-μ,α-μ,and η-μ fading channels at the output of an energy detector subject to impulsive noise（Bernoulli-Gaussian model）. As a result, novel, simple, and accurately approximated expressions for the probability of detection are derived. More precisely, the generalized Gauss-Laguerre quadrature is applied to approximate the probability of detection as a simple finite sum. Monte Carlo simulations corroborate the accuracy and precision of the derived approximations. The results are further extended to cooperative energy detection with hard decision combining information.

Cooperative Spectrum Sensing Algorithms Based on Correlation Matrix in Cognitive Radio Networks

Cooperative energy spectrum sensing has been widely applied in cognitive radio （CR） networks. In this paper, two cooperative sensing algorithms based on the received signals＇ correlation matrix were proposed. The first proposed algorithm made use of both diagonal elements and non-diagonal elements in the cooperative scheme. In the second algorithm, when the sensing station can obtain the information of the channel gains between the primary user and the sensing nodes, the weighted linear model can be adopted to improve the sensing performance. This paper analyzed the effectiveness of these two proposed coopera- tive algorithms and demonstrated that they can considerably improve the sensing performance compared with the traditional linear cooperative sensing algorithms. Simulation results showed that the sensing performance can be significantly enhanced by using the proposed algorithms, especially when the number of cooperative nodes is large.

Generalized Energy Storage Allocation Strategies for Load Aggregator in Hierarchical Electricity Markets

The uncertainty of user-side resource response will affect the response quality and economic benefit of load aggregator(LA).Therefore,this paper regards the flexible user-side resources as a virtual energy storage(VES),and uses the traditional narrow sense energy storage(NSES)to alleviate the uncertainty of VES.In order to further enhance the competitive advantage of LA in electricity market transactions,the operation mechanism of LA in day-ahead and real-time market is analyzed,respectively.Besides,truncated normal distribution is used to simulate the response accuracy of VES,and the response model of NSES is constructed at the same time.Then,the hierarchical market access index(HMAI)is introduced to quantify the risk of LA being eliminated in the market competition.Finally,combined with the priority response strategy of VES and HMAI,the capacity allocation model of NSES is established.As the capacity model is nonlinear,Monte Carlo simulation and adaptive particle swarm optimization algorithm are used to solve it.In order to verify the effectiveness of the model,the data from PJM market in the United States is used for testing.Simulation results show that the model established can provide the effective NSES capacity allocation strategy for LA to compensate the uncertainty of VES response,and the economic benefit of LA can be increased by 52.2%at its maximum.Through the reasonable NSES capacity allocation,LA is encouraged to improve its own resource level,thus forming a virtuous circle of market competition.

Magnetically levitated/piezoelectric/triboelectric hybrid generator as a power supply for the temperature sensor

The combination of new intelligent materials and structure technology is becoming an effective way in energy havesting and self-powered sensing. In this work, we demonstrate a magnetically levitated/piezoelectric/triboelectric hybrid generator, which does not use complex structure and has high steady output performance. It includes three parts: magnetically levitated generator(MLG), piezoelectric generator(PNG), triboelectric nanogenerator(TENG). The peak power of each is 135 μW, 22 mW and3.6 mW, which are obtained at 1 MΩ, 10 kΩ and 1 kΩ, respectively. The hybrid generator can completely light up light-emitting diodes(LEDs) under the vibration frequency of 20 Hz and the vibration amplitude of 10 mm. It also can charge a 470 μF capacitor.On this basis, we have integrated the hybrid generaor as a power supply into a self-powered tempreature sensing system. The combination of three generators can not only broaden the operating range, but also increase the operating length and sensitivity.This work will extend the application of self-powered sensor in automatic production line and promote the development of industrial control technology.