Improved likelihood ratio statistic‐based cooperative spectrum sensing for cognitive radio

Patel, Dhaval; Soni, Brijesh; López-Benítez, Miguel

doi:10.1049/iet-com.2019.0862

Cited by 15 publications

(7 citation statements)

References 38 publications

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“…Imperfect knowledge of the noise variance leads to a concept called signal to noise ratio (SNR) wall [9]. Moreover, non-parametric goodness of fit (GoF) tests based sensing schemes like Anderson Darling test [10], Kolmogorov-Smirnov test [11], likelihood, and improved likelihood ratio based sensing [12], [13] among many others, are also proposed in the literature.…”

Section: A Current State Of the Art And Motivationmentioning

confidence: 99%

Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics

2020

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The cognitive radio (CR) network consists of primary users (PUs) and secondary users (SUs). The SUs in the CR network senses the spectrum band to opportunistically access the white space. Exploiting the white spaces helps to improve the spectrum efficiency. Owing to the excellent learning ability of machine learning/deep learning framework, many works in the recent past have applied shallow/deep multi-layer perceptron approach for spectrum sensing. However, the multi-layer perceptron networks are not well suited for time-series data due to the absence of memory elements. On the other hand, long short-term memory (LSTM) network, an improved version of Recurrent neural network is well suited for time-series data. In this paper, we propose an LSTM based spectrum sensing (LSTM-SS), which learns the implicit features from the spectrum data, for instance, the temporal correlation (i.e., the correlation between the present and past timestamp).Moreover, the CR systems also exploits the PU activity statistics to improve the CR performance. In this context, we compute the PU activity statistics like on and off period duration, duty cycle and propose the PU activity statistics based spectrum sensing (PAS-SS) to enhance the sensing performance. The proposed sensing schemes are validated on the spectrum data of various radio technologies acquired using an experimental test-bed setup. The proposed LSTM-SS scheme is compared with the state of the art spectrum sensing techniques. Experimental results indicate that the proposed schemes has improved detection performance and classification accuracy at low signal to noise ratio regimes. We notice that the improvement achieved is at the cost of longer training time and a nominal increase in execution time.

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Section: A Current State Of the Art And Motivationmentioning

confidence: 99%

Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics

2020

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“…As described in (25), the weight is the decision ability when the decision ability is better than 𝛾 u ; for some extreme MUs, when the received result 1 arrived at the DFC, but the presence ability is too poor, and 0 may be more reliable. At this time, the weight (j 1 i )∕j 1 is designed for 0, but for the convenience of calculation, we use the weight 1 − (j 1 i )∕j 1 for 1 to rural area), the SU is only allowed to submit 1 via the reporting channel, otherwise, the DFC defaults to the SU's reporting result as 0, as shown in Figure 5.…”

Section: Weight Evaluationmentioning

confidence: 99%

“…The idea of weight is also used by 24 Simpson and Yichuang in Reference 24, which a weighted averaging factor is determined by the credibility and dissociability of the SU sensing data evidence. An improved likelihood ratio statistic‐based CSS scheme was proposed by Patel et al in Reference 25 to mitigate the poor detection at low SNR regime and misdetections arising due to sudden drops in signal energy and the generalized Byzantine attack. To solve the blind problem where Byzantines make the DFC incapable of deciding the presence of the PU (the DFC will be unable to decide on a particular decision and the performance at the DFC can be no better than just a random guess of the state of channel, that is, a critical value of 50% of Byzantine attackers can completely blind the DFC), Wu et al proposed a sequential detection approach to reuse Byzantine data in Reference 26.…”

Section: Introductionmentioning

confidence: 99%

Robust and efficient cooperative spectrum sensing against probabilistic hard Byzantine attack

Chen

Gan

et al. 2021

Trans Emerging Tel Tech

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Cognitive radio (CR) is regarded as a powerful technology to solve the problem of spectrum shortage and underutilization. As a key function of CR technology, cooperative spectrum sensing (CSS) allows secondary users (SUs) to detect the primary user (PU)'s signal so that they identify and opportunistically access the available spectrum. However, the openness of CSS paradigm makes cognitive radio networks (CRNs) suffer from Byzantine attack, thereby undermining the premise of CR framework. To this aim, we formulate a probabilistic hard Byzantine attack model, in which malicious users (MUs) can conduct various attack strategies, and make an in‐depth investigation on the blind scenario. On the one hand, in order to ensure the robustness of CSS, a method to evaluate the reliability of the secondary user (SU)'s sensing result based on the channel status detection is proposed and an innovative weight coefficient is considered to selectively utilize the sensing information from MUs. On the other hand, we design a sequential fusion method based on reputation value (RV) and differential mechanism, with the aim of improving the efficiency of CSS. According to above methods and mechanism, the weighted differential sequential symbol (WDS2) algorithm is designed, which integrates the weight evaluation into sequential method to make the global decision for CSS. Finally, compared to the existing various data fusion algorithms, simulation results show that the proposed WDS2 not only defends against various Byzantine attacks to secure the robustness of CSS, but also requires less samples in support of an accurate global decision to improve the efficiency of CSS.

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“…Many spectrum sensing techniques are used for an effective sensing of spectrum for utilizing the frequency band [8][9][10]. Although these spectrum sensing techniques are used for allocating the spectrum bands, capturing the primary user activities under environmental noise factors still remains to be challenge [11][12][13][14]. The primary user's statistical information can be useful in the CR for an effective prediction of spectrum occupancy mechanism and optimization of system performance with improved spectral efficiency.…”

Section: Introductionmentioning

confidence: 99%

Optimized Deep Learning Model for Effective Spectrum Sensing in Dynamic SNR Scenario

Arunachalam¹,

SureshKumar²

2023

Computer Systems Science and Engineering

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The main components of Cognitive Radio networks are Primary Users (PU) and Secondary Users (SU). The most essential method used in Cognitive networks is Spectrum Sensing, which detects the spectrum band and opportunistically accesses the free white areas for different users. Exploiting the free spaces helps to increase the spectrum efficiency. But the existing spectrum sensing techniques such as energy detectors, cyclo-stationary detectors suffer from various problems such as complexity, non-responsive behaviors under low Signal to Noise Ratio (SNR) and computational overhead, which affects the performance of the sensing accuracy. Many algorithms such as Long-Short Term Memory (LSTM), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) play an important role in designing intelligent spectrum sensing techniques due to the excellent learning ability of deep learning frameworks, but still require improvisation in terms of sensing accuracy under dynamic environmental conditions. This paper, we propose the novel and hybrid CNN-Cuttle-Fish Optimized Long Short Term Memory (COLSTM), an improved version of LSTM that is well suited for the dynamic changes of environmental SNR with less computational overhead and complexity. The proposed COLSTM based spectrum sensing technique exploits the various statistical features from spectrum data of PU to improve the sensing efficiency. Furthermore, the addition of shuttle-fish optimization in LSTM has reduced the computational overhead and complexity which in turn enhanced the sensing performances. The proposed methodology is validated on spectrum data acquired using RaspberryPi-RTLSDR experimental test-beds. The proposed spectrum sensing technique and the existing classical spectrum sensing techniques are compared. Experimental results show that the proposed scheme has shown the brighter enhancement of performance under different SNR environments. Further, the improvised performance has been achieved at low complexity and low computational overhead when compared with the other existing LSTM networks.

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Improved likelihood ratio statistic‐based cooperative spectrum sensing for cognitive radio

Cited by 15 publications

References 38 publications

Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics

Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics

Robust and efficient cooperative spectrum sensing against probabilistic hard Byzantine attack

Optimized Deep Learning Model for Effective Spectrum Sensing in Dynamic SNR Scenario

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