Conventional Cooperative Spectrum Sensing Research Articles

Design and ASIC-Implementation of Hardware-Efficient Cooperative Spectrum-Sensor for Data Fusion-Based Cognitive Radio Network

This paper presents hardware-friendly algorithm for Gini-index (GI) cooperative-spectrum-sensing (CSS) algorithm for the data-fusion based cooperative cognitive-radio network. It simplifies the complex computations of sample-covariance-matrix (SCM) elements and test-statistics value of the conventional GI-based CSS algorithm. It delivers excellent detection performance under the realistic scenario of non-uniform dynamical noise and signal-power. Based on GI-based CSS algorithm, three different VLSI architectures are proposed for the cooperative spectrum sensor (CSR): CSR-VLAR1, CSR-VLAR2, and CSR-VLAR3. Here, CSR-VLAR1 is the first-time reported CSR-architecture for the conventional GI-based CSS algorithm. Subsequently, CSR-VLAR2 represents hardware-architecture of the proposed hardware-friendly GI-based CSS algorithm. Eventually, additional architectural optimization has been applied to CSR-VLAR2 that is transformed into the most hardware-efficient VLSI-architecture of CSR, referred as CSR-VLAR3, which is ASIC chip-fabricated in UMC 130 nm-CMOS technology node. Furthermore, both CSR-VLAR1 and CSR-VLAR2 are synthesized and post-layout simulated in the same technology node. Our ASIC-chip of CSR-VLAR3 occupies 0.35 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of core-area and consumes 8.31 mW of total power at 88.8 MHz of maximum clock frequency, when the supply voltage is 1.2 V. Our CSR ASIC-chip has been functionally verified with the aid of real-world signals, using USRPs and FPGAs based test-setup of cooperative cognitive-radio network. Measured results of our design are compared with reported implementations where the proposed CSR is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.52\times $ </tex-math></inline-formula> hardware-efficient and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.8\times $ </tex-math></inline-formula> power-efficient than the state-of-the-art CSR-implementations. Thus, our work addresses the key challenge of designing hardware-efficient CSR that delivers excellent detection performance in the real-world scenario.

A CR-5G network based on multi-user for various waveforms detection

The 5G network is promised to provide all the requirements surfacing from the speedily rising number of mobile phone users rather than huge amount of data transmission. Many 5G waveforms can be used by 5G network such as Filtered-Orthogonal Frequency Division Multiplexing (F-OFDM), Universal Filtered Multi-Carrier (UFMC), or Filter Bank Multi-Carrier (FBMC) waveform. To effectively capitalize on the spectrum resources, Internet of Thing (IoT), and multimedia transmission, the Cooperative Spectrum Sensing (CSS) system is being extensively utilized by Cognitive Radio (CR). It is able to meet the requisite communication services, however, the conventional CSS systems have been designed to detect only single kind of 5G waveform and poor detection performance for low signal-to-noise ratio (SNR). In this paper, a hybrid filter based CSS system is proposed for detection of various 5G waveforms by multi-user. The proposed filter includes three cascaded stages: cosine filtering, Welch segmentation, and Hamming windowing. The proposed detection system is applied on different data such as various of waveforms and SNRs. The simulation results exhibit a significant detection performance for the parameters of less than zero dB of SNR, greater than 95% global detection probability, less than 1% global system error probability, and less than 1% global false alarm probability. In addition, the detection performance reveals that the proposed system is outperformed related works as shown in the comparison in terms of the previous parameters.

A New Hardware-Efficient Spectrum-Sensor VLSI Architecture for Data-Fusion-Based Cooperative Cognitive-Radio Network

This article presents a hardware-friendly algorithm and architecture for cooperative spectrum sensing (CSS) in the data-fusion-based cognitive-radio (CR) network. The proposed VLSI-algorithm is based on the iterative power method and deflation technique that alleviate the computational complexity of conventional CSS algorithm with minimal performance degradation. In this work, a new hardware-efficient VLSI architecture of cooperative spectrum sensor (CSR) for the data-fusion center is presented, which supports up to six secondary users in the cooperative CR network. Its performance analysis under fading channel environment has been carried out where it delivers 0.8 detection probability ( $P_{d}$ ) at −8 dB of channel SNR with a false alarm rate of 0.1. It shows the minimum performance degradation of 0.057 dB at $P_{d} = 0.88$ compared to the conventional algorithm. The suggested CSR architecture has been application-specific integrated circuit (ASIC)-synthesized and postlayout simulated in UMC 90 nm-CMOS process. Thus, it occupies 2.4 mm2 of the core area, consumes 36 mW of total power, and delivers a low sensing time of $60.41~\mu \text{s}$ while operating at a maximum clock frequency of 87.7 MHz. Comparison with the reported works indicates that the proposed design requires 40.3% lesser area, and it is 41% hardware efficient than the conventional implementation. Eventually, this design has been field-programmable gate array (FPGA) prototyped, and its functionality is verified in the real-world test environment.

Improved cooperative spectrum sensing model based on machine learning for cognitive radio networks

This study presents a new machine learning (support vector machine (SVM))-based cooperative spectrum sensing (CSS) model, which utilises the methods of user grouping, to reduce cooperation overhead and effectively improve detection performance. Cognitive radio users were properly grouped before the cooperative sensing process using energy data samples and an SVM model. The resulting user group which participates in cooperative sensing procedures is safe, less redundant, or the optimised user group. Three grouping algorithms are presented in this study. The first grouping algorithm divides normal and abnormal users (malicious and severely fading users) into two groups. The second grouping algorithm distinguishes redundant and non-redundant users. The third grouping algorithm establishes an optimisation model with the objective of minimising average correlation within subsets. All users are then divided into a specific number of optimised groups, only one of which is required for cooperative sensing in each time. The performances of the three algorithms were quantified in terms of the average training time, classification speed and classification accuracy. Experimental results showed the proposed algorithms achieved their intended function and outperformed a conventional machine learning-based CSS model (proposed by Karaputugala et al. ) in terms of security, energy consumption, and sensing efficiency.

A Distance Based Reliable Cooperative Spectrum Sensing Algorithm in Cognitive Radio

Spectrum sensing is the most critical task in cognitive radio (CR) which needs to be performed very precisely in order to efficiently utilize the underutilized spectrum and to provide sufficient protection to the primary users (PUs). To improve the sensing performance under fading, shadowing and hidden terminal problems more than one CR users collaboratively perform the spectrum sensing called as cooperative spectrum sensing (CSS). In conventional CSS the decision of each CR is fused at fusion center with equal weights. But due to variable distance of each CR from the PU all decisions are not equally reliable and therefore should be assigned different weights according to their reliability. In this paper we propose a new weighting scheme for CSS under Rayleigh faded channel. In proposed weighting scheme, based on the distance of each CR from the PU reliability of CR nodes is determined and correspondingly appropriate weights are assigned to different users. The CSS algorithm using new weighting scheme gives better performance than conventional CSS algorithm.

Time-efficient cooperative spectrum sensing via analog computation over multiple-access channel

Conventional cooperative spectrum sensing (CSS) schemes in cognitive radio networks (CRNs) require that the secondary users (SUs) report their sensing data separately in the time domain to the fusion center, which yields long reporting delay especially in the case of large number of cooperative SUs. By exploiting the computation over multiple-access channel (CoMAC) method, this paper proposes a novel CoMAC-based CSS scheme that allows the cooperative SUs to encode their local statistics in transmit power and to transmit simultaneously the modulated symbols sequence carrying transmit power information to the fusion center. The fusion center then makes the final decision on the presence of primary users by recovering the overall test statistic of energy detection from the energy of the received signal. Performance metrics of the CoMAC-based CSS scheme, i.e., detection probability and false alarm probability, are further derived based on the central limit theorem. Finally, based on the derived detection and false alarm probabilities, both energy detection threshold and spectrum sensing time are optimized to improve the average throughput of the CRN. Simulations demonstrate the efficiency of this work.

Cooperative spectrum sensing with two‐stage reporting for cognitive radio networks

A new cooperative spectrum sensing (CSS) scheme with two-stage reporting is proposed so as to improve the throughput of secondary users (SUs) through reducing the reporting overhead. In the proposed scheme, the reporting process of the SUs is divided into two stages (a dedicated reporting stage and a contention-based reporting stage) and the fusion centre determines the presence of primary users through combining the sensing results successfully received in the two reporting stages by using the k-out-of-N rule. Simulation results demonstrate that the proposed scheme significantly outperforms conventional CSS schemes based on the OR rule and the k-out-of-N rule.

Optimal Sensing Time for Maximizing the Throughput of Cognitive Radio Using Superposition Cooperative Spectrum Sensing

Spectrum sensing plays an essential role in a cognitive radio network, which enables opportunistic access to an underutilized licensed spectrum. In conventional cooperative spectrum sensing (CSS), all cognitive users (CUs) in the network spend the same amount of time on spectrum sensing and waste time in remaining silent when other CUs report their sensing results to the fusion center. This problem is solved by the superposition cooperative spectrum sensing (SPCSS) scheme, where the sensing time of a CU is extended to the reporting time of the other CUs. Subsequently, SPCSS assigns the CUs different sensing times and thus affects both the sensing performance and the throughput of the system. In this paper, we propose an algorithm to determine the optimal sensing time of each CU for SPCSS that maximizes the achieved system throughput. The simulation results prove that the proposed scheme can significantly improve the throughput of the cognitive radio network compared with the conventional CSS.

An Improved Clustering Cooperative Spectrum Sensing Algorithm Based on Modified Double-Threshold Energy Detection and Its Optimization in Cognitive Wireless Sensor Networks

Cooperative spectrum sensing (CSS) is a very important technique in cognitive wireless sensor networks, but the channel and multipath affect the sensing performance. For improving the sensing performance, this paper incorporates a modified double-threshold energy detection (MDTED) and the location and channel information to improve the clustering cooperative spectrum sensing (CCSS) algorithm. Within each cluster, the cognitive node with the best channel quality to the fusion center (FC) is chosen as the cluster head (CH), and each node uses the MDTED. The detective information is sent to CH, and CH makes the decision of the cluster. The decision information is sent to FC by each CH, and FC uses the “or” rule to fuse all clusters’ decision information and makes a final decision. Since MDTED needs to transfer large traffic and occupy channel widely, this paper further optimizes the improved algorithm. Ensuring the detection performance, the cognitive nodes participating in the sensing are properly reduced. Simulation results show that the detecting accuracy of the improved algorithm is higher than conventional CSS, and the improved algorithm can also significantly improve collaborative sensing ability. For the optimization of cognitive nodes’ number, the detection probability of the network can be obviously increased.

A cluster-based sequential cooperative spectrum sensing scheme utilizing reporting framework for cognitive radios

The cognitive radio (CR) technology is being considered as a novel approach for improving the spectrum utilization by detecting and using the vacant spectrum bands of licensed users. Cooperative spectrum sensing (CSS) is the most promising method for offering more reliable sensing performance compared to individual sensing. However, in a large CR network, CSS requires a huge number of sensing result transmissions from cognitive radio users (CUs) to the fusion center (FC), which can result in overhead traffic of the control channel and lack of power consumption of the CUs. In this paper, we propose a cluster-based sequential cooperative spectrum sensing (SCSS) scheme utilizing a reporting framework to reduce the number of direct reports from CUs to FC while keeping similar sensing performance as the conventional CSS. By using the proposed scheme, the power consumption in the sensing process and the overhead of the control channel can be significantly reduced. Simulation proves the efficiency of the proposed scheme. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

SNR-based weighted cooperative spectrum sensing in cognitive radio networks

Cooperative spectrum sensing (CSS) is an approach to confront fading environment. However, in conventional cooperative spectrum sensing (CCSS), the difference among the secondary users (SUs) is ignored when SUs suffer from different fading. In this article, a signal-to-noise ratio (SNR)-based weighted spectrum sensing scheme is proposed to improve the sensing performance. The sensing performance of the weighted spectrum sensing scheme is then derived. Considering the minor contribution of the SUs with small weighted factor, a selective CSS scheme is proposed, where SUs with low SNR are not selected into cooperative spectrum sensing. The simulation results confirm the analytical results. And the performance of weighted scheme is better than that of conventional schemes. In the case where the SNR of SUs are randomly distributed, the performance of selective scheme is almost the same as the weighted scheme while the number of cooperative SUs is reduced to save the consumption of system resource in cooperation with little additional complexity.