This work proposes a fully distributed improved weighted average consensus (IWAC) technique applied to a cooperative spectrum sensing (CSS) problem in cognitive radio systems. This method allows the secondary users to cooperate based on only local information exchange without a fusion centre. We have compared 4 rules of average consensus (AC) algorithms. The first rule is the simple AC without weights. The AC rule presents performance comparable to the traditional CSS techniques such as the equal gain combining rule, which is a soft combining centralised method. Another technique is the weighted AC (WAC) rule, using the weights based on the SUs' channel condition. This technique results in a performance similar to that of the maximum ratio combining with soft combining (centralised CSS). Two new AC rules are analysed, namely, WAC accuracy exchange (WAC‐AE) and IWAC; the former relates the weights to the channel conditions of the SUs' neighbours, whereas the latter combines the conditions of WAC and WAC‐AE in the same rule. All methods are compared with each other and with the hard combining centralised CSS. The WAC‐AE results in a similar performance of the WAC technique but with fast convergence, whereas the IWAC can deliver suitable performance with small complexity increment. Moreover, the IWAC method results in a similar convergence rate than the WAC‐AE method but slightly higher than the AC and WAC methods. Hence, the computational complexity of IWAC, WAC‐AE, and WAC is proven to be very similar. The analyses are based on numerical Monte Carlo simulations, whereas the algorithm's convergence is evaluated for both fixed and dynamic mobile communication scenarios and under additive white Gaussian noise and Rayleigh channels.

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SummaryIn this article, we develop a parameter and optimization analyses aiming at characterizing the performance of cooperative spectrum sensing (CSS) based on the distributed average consensus (DAC), taking into account the network topology effect, represented by adjacency matrices in cognitive radio ad hoc networks. The CSS performance is analyzed by the probability of detection, probability of false alarm, and probability of miss detection, besides the decision threshold and the number of collected samples. In this work, all secondary users are equipped with a simple energy detector. Despite the DAC technique has been successfully deployed in the distributed CSS context, however, a full analytical description taking into account the joint effects of the network topology and all the optimized system parameters remains unknown. Moreover, the CSS network decision threshold was optimized using the error probability minimization criterion. Numerical results deploying extensive Monte Carlo simulations have corroborated the proposed analytical expressions.

In cognitive radio (CR), the sensed aggregate bandwidth could be as large as several GHz. This is especially challenging if the bandwidths and central frequencies of the sensed signals are unknown and need to be estimated. This work discusses a new improved method for MB spectrum sensing (iMB-SS) based on edge detection and using Wavelet Spectrum Filtering. The proposed iMB-SS method uses a Welch power spectrum density (PSD) estimate and a multi-scale Wavelet approach to reveal the spectrum transition (edges), which is deployed to characterize the spectrum occupancy in CR scenarios where the operation frequencies of the primary users (PUs) are unknown. The focus of this work lies in improving the performance of the MB spectrum sensor, particularly by refining the spectral edge location and reducing misleading detection. A comprehensive analytical description and numerical analysis have been carried out by focusing on orthogonal-frequency-division-multiplexing (OFDM) signal applications in CR networks. Numerical results corroborate the effectiveness of the proposed iMB-SS approach. The simulated results for the multiple-PU’s OFDM-based transmission CR system demonstrate that the proposed iMB-SS method can achieve high performance even under low signal-to-noise ratio (SNR) regime, turning it out as an attractive choice for SS in the MB CR systems.

In cognitive radio (CR), the sensed aggregate bandwidth could be as large as several GHz. This is especially challenging if the bandwidths and central frequencies of the sensed signals are unknown and need to be detected. This work discusses a new method for multi-band spectrum-sensing based in edge detection. The proposed method uses a Welch power-spectrum-density (PSd) estimate and a multi-scale Wavelet approach to reveal the spectrum transition (edges), which is deployed to characterize the spectrum occupancy in CR scenarios where the operating frequency limits of the primary users are unknown. The focus in this work is to improve the performance of the multiband spectrum sensor by refining the edge location and error correcting misleading detections. In order to do so, a comprehensive analytical description and numerical analysis have been carried out by focusing on orthogonal-frequencydivision-multiplexing (OFDM) signals. Also, numerical results corroborate and give support to the effectiveness of the proposed multiband spectrum sensing method.