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.

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.

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.