A practical fusion rule with quantization combining is proposed for cooperative spectrum sensing over imperfect feedback channels that are inherent in the wireless environment. Local sensor nodes generate decision symbols regarding the existence of a primary user signal by multilevel detection and send them over feedback channels to the fusion center, in which demodulated symbols are combined to draw the cooperative decision. The proposed quantization combining scheme over imperfect feedback channels is analyzed, based on which detection probability and false alarm probability are derived. Operating parameters of the fusion rule with quantization combining are optimally determined under Neyman–Pearson criterion by using a differential evolution algorithm. The proposed fusion rule is compared with other schemes in terms of the detection performance, the amount of system information required for operation, the sensitivity to the channel estimation error, and the computational complexity required for parameter optimization. It is shown that the proposed fusion rule offers the reliable sensing capability even over imperfect feedback channels with requiring small amount of system information and complexity.

Cooperative Spectrum Sensing With Quantization Combining Over Imperfect Feedback Channels

Soft decision cooperative spectrum sensing with noise uncertainty reduction

Spectrum sensing in cognitive radio networks and metacognition for dynamic spectrum sharing between radar and communication system: A review

In cognitive radio networks, the performance of energy detection will be degraded significantly due to the cluster overlapping caused by noise uncertainty. To alleviate the noise uncertainty effect, a novel machine learning algorithm is proposed in this paper for cooperative spectrum sensing. The proposed algorithm incorporates fuzzy support vector machine and nonparallel hyperplane support vector machine. For membership assignment, kernel shadow c-means (KSCM) algorithm is utilized. Furthermore, the test statistics collected by the second users are arranged into a feature vector instead of being combined through weighted sum. Simulations results have shown that the proposed scheme, called NP-FSVM, is more robust to noise uncertainty than the existing methods.

A Fuzzy Support Vector Machine Algorithm for Cooperative Spectrum Sensing with Noise Uncertainty

Spectrum Sensing (SS) comprises the most important component in Cognitive Radio (CR) systems. Cooperative Spectrum Sensing (CSS) is proposed as an effective approach to improve detection performance in fading environments. This paper introduces an efficient energy detection based hard decision CSS algorithm to alleviate the noise uncertainty effect. In the proposed algorithm, the decision threshold is dynamically switched between two levels based on a prior prediction of the Primary User (PU) activity. The two threshold levels are evaluated using an estimated value of the noise uncertainty factor to maximize the probability of detection and minimize the probability of false alarm. The proposed algorithm is studied theoretically to deduce the enhanced detection and false alarm probabilities. Moreover, simulation analysis is used to confirm the theoretical claims and prove the high potency of the proposed scheme compared to the conventional CSS using different fusion rules.

Hard decision Cooperative Spectrum Sensing based on estimating the noise uncertainty factor

Key escrow-free attribute based encryption with user revocation

Noise uncertainty, which is unavoidable in practice, can severely limit the detection performance of cooperative spectrum sensing. Derived from the conventional hard combination scheme, the improved one-out-of-N rule is presently the most effective algorithm to reduce the influence of noise uncertainty. However, in this algorithm the abandonment of local test statistic when it falls in the uncertainty region may lose some useful information. In this paper, first we analyze the optimality of the improved one-out-of-N rule and indicate that there should be an optimal number of cooperative users that minimizes the total error rate. Then, a new weighted hard combination scheme which makes full use of the uncertainty region is further proposed. The optimizations of weight coefficient and user number in this new scheme are also investigated. Numerical results show that the minimal total error rate and its corresponding user number of this scheme are both lower than those of the improved one-out-of-N rule.

Optimization of cooperative spectrum sensing under noise uncertainty

Weighted Hard Combination for Cooperative Spectrum Sensing under Noise Uncertainty

Compared with common near space satellite Telemetry, Telecommand, and Communication (TT&C), deep space TT&C presents a more challenging environment such as long distance, very low Signal to Noise Ratio (SNR). How to acquire main carrier exactly becomes a hot focus for deep space communications. Already there emerged some main carrier acquisition algorithms, but they all require high SNR and small modulation index. In this paper, we develop a new acquire algorithm. First we use the spectral energy center algorithm to shorten the original sequence, filter out some noise and make the spectral more symmetric. Then we adopt the spectral symmetry algorithm to make full use of the whole spectrum information, and utilize FFT to reduce computation complexity. Simulation results show that our algorithm can acquire main carrier successfully under large modulation index and get good performance with low Carrier to Noise Ratio (CNR).

A new algorithm for main carrier acquisition in deep space communications

Antenna arraying is an effective method to improve the signal-to-noise ratio (SNR) of received signal in deep space communications. Although a variety of signal combining algorithms for antenna arraying have been studied, few of them can be applied to low SNR environment with small response time or short received sequence. In this paper, a new signal combining algorithm based on iterative optimization and data-aided SNR estimation is proposed. This algorithm transforms the problem of phase estimation among different antennas into a multi-function optimization, with the phase offsets as the variables and the SNR gain of combined signal as the objective function. By iteration, all variables could be well estimated. Simulation results show that the new algorithm could obtain excellent combining performance with a relatively short received sequence.

Ruyuan Zhang

Papers

Key escrow-free attribute based encryption with user revocation

Optimization of cooperative spectrum sensing under noise uncertainty

Weighted Hard Combination for Cooperative Spectrum Sensing under Noise Uncertainty

A new algorithm for main carrier acquisition in deep space communications

A new signal combining algorithm based on iteration and data-aided SNR estimation for antenna arraying