Reinforcement learning for performance improvement in cooperative spectrum sensing

Cognitive radio (CR) can provide a promising solution to the spectrum scarcity issue for upcoming wireless communication technologies. Cooperative spectrum sensing (CSS) is generally adopted to improve spectrum utilization and minimize interference to primary users (PUs). The performance of CSS is significantly affected by imperfect reporting channels, and it is an easy target for Byzantine attackers. This paper studies CSS under imperfect reporting channels and Byzantine attacks. We have considered centralized CSS with a hard combining fusion rule. The binary symmetric channel (BSC) is used to model the imperfect reporting channels, and a centralized independent probabilistic small scale attack model is chosen to model Byzantine attackers. We first analyze the traditional CSS (T-CSS) under the imperfect reporting channel and the Byzantine attack. The performance of T-CSS is found to be greatly affected in the considered scenarios. We propose a reinforcement learning-based algorithm to detect cooperating secondary users (CSUs) experiencing weak reporting channels and Byzantine attackers. Generally, in literature, the detected malicious users (MUs) are removed to improve the performance. However, in case there are CSUs with weak reporting channels, the genuine CSUs may be detected as Byzantine attackers and hence removed. To overcome this issue, we propose a weighted sum-based CSS (WS-CSS) algorithm that can improve the CSS performance under weak reporting channels and attacks from Byzantine attackers. It is demonstrated using plots that the proposed WS-CSS algorithm significantly improves the CSS performance.     

Cooperative spectrum sensing protocols and evaluation with IEEE 802.15.4 devices

Spectrum Sensing is one of the important tasks for wireless devices. By sensing the spectrum, wireless devices sense their radio environment and perform spectrum access accordingly to reduce collisions. Due to radio propagation effects and inherent noise in the measurements, performance of todays wireless technologies with individual spectrum sensing cannot solve the hidden node problem. Cooperative sensing is seen as a way to improve the performance of wireless devices improving the radio bandwidth utilization and minimizing interference among wireless devices. To the best of our knowledge, this is the first work which provides protocols for cooperative sensing and presents experimental results with IEEE 802.15.4 devices. We present and implement protocols and applications for primary, secondary and cooperative users with a dedicated control channel. Thereby, the secondary user receiver serves as a first cooperative node in the system which reduces collisions between primary and secondary users. We evaluate the system performance with receiver sensing and additional cooperative nodes. We also propose a mechanism to extend the protocol for multiple secondary users sharing the same control channel. Based on the evaluations, we also provide recommendations for usage of cooperative sensing with focus on IEEE 802.15.4.     

Single decision reporting for cooperative spectrum sensing under erroneous feedback channels with Byzantine attack

Spectrum sensing based on a single user suffers from low detection performance due to fading, shadowing, and hidden node problems. Cooperative spectrum sensing (CSS) is thought to be a potential method to overcome these issues and improve detection performance in determining the available spectrum in cognitive radio (CR). However, CSS suffers in case of erroneous reporting channels, and it is also susceptible to Byzantine attacks by malicious users (MUs). In this paper, we first analyze the traditional CSS under erroneous feedback channels. And then, we extend the analysis to include erroneous reporting channels in the presence of the Byzantine attack. We propose a single decision reporting (SD-R) algorithm immune to erroneous reporting channels. The proposed algorithm also improves the performance under the Byzantine attack. With the proposed algorithm, MUs can attempt only false alarm attacks, whereas the miss detection attack is not possible. An extensive analysis is carried out, and the plots are shown to prove the superiority of the proposed algorithm.     

Performance evaluation of improved double-threshold energy detector over Rayleigh-faded sensing and imperfect reporting channels

Cognitive radio (CR) has been viewed as a promising solution to spectrum scarcity. In order to design a reliable CR system, many improvements have been proposed to enhance spectrum sensing performance of secondary users (SUs) in a CR network (CRN). Sensing reliability and transmission throughput of SUs are two important performance criteria, which should be optimized to enhance signal protection of primary user (PU) as well as spectrum utilization rate. In this paper, we consider Rayleigh-faded sensing channels and SUs use improved energy detector (IED) to make their local decisions. The final decision is made in a fusion center (FC) through the cooperative spectrum sensing (CSS) scheme with erroneous reporting channels. We show that the improved double-threshold energy detector (IDED) outperforms the conventional energy detector (CED) in terms of the total error rate. Furthermore, we evaluate the transmission throughput of the CRN through various ED schemes with detection constraints over both perfect and imperfect reporting channels. We show that the IDED has the highest achievable throughput among different ED schemes over imperfect reporting channels.     

5G-based wideband cognitive radio system design with cooperative spectrum sensing

Since the 5G bandwidth is very large, there are a large number of non-continuous idle spectrum in 5G communication. In this paper, we have designed transmitter and receiver of a 5G-based wideband cognitive radio (CR) system with cooperative spectrum sensing, in order to improve transmission performance and avoid interference signals. Each CR user marks the spectrum availability for getting the sub-basis function through doing Inverse Fast Fourier Transform (IFFT) with the product of spectrum marker vector and random phase vector. The cooperative spectrum sensing can be realized by cascading the sub-basis functions of all the users. Multiple access of the CR system is also proposed to access much non-continuous idle spectrum. The simulation results have shown that the proposed CR system can avoid the interference effectively and outperform the spread spectrum system obviously.     

Joint optimal fair cooperative spectrum sensing and transmission in cognitive radio

Since the sensing power consumption of cooperative spectrum sensing (CSS) will decrease the throughput of secondary users (SU) in cognitive radio (CR), a joint optimal model of fair CSS and transmission is proposed in this paper, which can compensate the sensing overhead of cooperative SUs. The model uses the periodic listen-before-transmission method, where each SU is assigned a portion of channel bandwidth, when the primary user (PU) is estimated to be free by the coordinator. Then, a joint optimization problem of local sensing time, number of cooperative SUs, transmission bandwidth and power is formulated, which can compensate the sensing overhead of cooperative SUs appropriately through choosing suitable compensating parameter. The proposed optimization problem can be solved by the Polyblock algorithm. Simulation results show that compared with the traditional model, the total system throughput of the fairness cooperation model decreases slightly, but the total throughput of the cooperative SUs improves obviously.     

TCVQ-SVM algorithm for narrowband spectrum sensing

Spectrum sensing is viewed as the basic and crucial technology for cognitive radio. To improve the accuracy of spectrum sensing in low signal to noise ratio (SNR), this paper presents an efficient TCVQ-SVM method based on machine learning for narrowband spectrum sensing. Firstly, trace of covariance matrix and variance of quadratic covariance matrix (TCVQ) is extracted as feature vectors and combined as training samples of spectrum sensing. Then, the classification model can be achieved by training samples based on support vector machine (SVM), which can avoid setting threshold and adjusting classification hyperplane by its self-learning ability. Lastly, the result of spectrum sensing can be obtained. By utilizing trace and variance as input features of SVM, the algorithm can make full use of the eigenvalue difference and structure characteristic of the received signal, and at the same time, achieve good performance in low SNR. Theoretical analysis reveals that the proposed method has low computational complexity. Simulation results and experiments on the hardware platform illustrate that the proposed algorithm is effective and robust.     

基于改进混合蛙跳算法的认知无线电协作频谱感知

提出了一种改进的混合蛙跳算法（shuffled frog leaping algorithm,SFLA）,并提出了基于改进SFLA的认知无线电协作频谱感知方法,通过仿真对改进SFLA算法性能与传统SFLA算法性能进行了比较,并对本文提出的基于改进SFLA的协作感知方法与已有的基于修正偏差因子（modified deflection coefficient,MDC）的协作感知方法性能进行了比较.结果表明改进SFLA算法性能优于传统SFLA;基于改进SFLA的协作感知方法比MDC方法能获得更大的检测概率,验证 关键词： 认知无线电 频谱感知 混合蛙跳算法     

A cost-effective approach for spectrum sensing using beamforming

Spectrum sensing (SS) is one of the principal challenges on which the mobile communication is based on. Identifying the available frequency bands, also called white spaces, is the main issue. A novel blind approach for SS in the narrowband context is proposed in order to improve the signal detection. Considering a channel with its angle of arrival (AoA), we use beamforming technique to exploit the maximum and minimum angular energy. Both theoretical developments of the threshold and performance analysis are developed. To validate our contribution, the analytical results of the performance developed in this paper are compared with those from simulation. A comparison of state-of-the-art SS method using the eigenvalue decomposition is provided which brings an interesting trade-off between complexity and performance. Finally, simulation results considering the probability of misdetection under very low signal-to-noise ratio (SNR) are presented.     

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

The massive growth in mobile users and wireless technologies has resulted in increased data traffic and created demand for additional radio spectrum. This growing demand for radio spectrum has resulted in spectrum congestion and mandated the need for coexistence between radar and interfering communication emitters. To address the aforementioned issues, it is critical to review existing policies and evaluate new technologies that can utilize spectrum in an efficient and intelligent manner. Cognitive radio and cognitive radar are two promising technologies that exploit spectrum using dynamic spectrum access techniques. Additionally, introducing the bio-inspired concept ‘metacognition’ in a cognitive process has shown to increase the effectiveness and robustness of the cognitive radio and cognitive radar system. Metacognition is a high-order thinking agent that monitors and regulates the cognition process through a feedback and control process called the perception–action cycle. Extensive research has been done in the field of spectrum sensing in cognitive radio and spectral coexistence between radar and communication systems. This paper provides a detailed classification of spectrum sensing schemes and explains how dynamic spectrum access strategies share the spectrum between radar and communication systems. In addition to this, the fundamentals of cognitive radio, its architecture, spectrum management framework, and metacognition concept in radar are discussed. Furthermore, this paper presents various research issues, challenges, and future research directions associated with spectrum sensing in cognitive radar and dynamic spectrum access strategies in cognitive radar.     

Secure collaborative cognitive radio based on chaotic modulation and compressive sensing

Cognitive radio (CR) is a wireless technology that is used to overcome the spectrum scarcity problem. CR includes several stages, spectrum sensing is the first stage in the CR cycle. Traditional spectrum sensing (SS) techniques have many challenges in the wideband spectrum. CR security is an important problem, since when an attacker from outside the network access the sensing information this produces an increase in sensing time and reduces the opportunities for exploiting vacant band. Compressive sensing (CS) is proposed to capture all the wideband spectrum at the same time to solve the challenges and improve the performance in the traditional techniques and then one of the traditional SS techniques are applied to the reconstructed signal for detection purpose. The sensing matrix is the core of CS must be designed in a way that produces a low reconstruction error with high compression. There are many types of sensing matrices, the chaotic matrix is the best type in terms of security, memory storage, and system performance. Few works in the literature use the chaotic matrix in CS based CR and these works have many challenges: they used sample distance in the chaotic map to generate a chaotic sequence which consumes high resources, they did not take into consideration the security in reporting channel, and they did not measure their works using real primary user (PU) signal of a practical application under fading channel and low SNR values. In this paper, we propose a chaotic CS based collaborative scenario to solve all challenges that have been presented. We proposed a chaotic matrix based on the Henon map and use the differential chaotic shift keying (DCSK) modulation to transmit the measurement vector through the reporting channel to increase the security and improve the performance under fading channel. The simulation results are tested based on a recorded real-TV signal as PU and Compressive Sampling Matching Pursuit (CoSaMP) recovery algorithm under AWGN and TDL-C fading channels in collaborative and non-collaborative scenarios. The performance of the proposed system has been measured using recovery error, mean square error (MSE), derived probability of detection (Pdrec), and sensitivity to initial values. To measure the improvement introduced by the proposed system, it is evaluated in comparison with selected chaotic and random matrices. The results show that the proposed system provides low recovery error, MSE, with high Pdrec, security, and compression under SNR equal to −30 dB in AWGN and TDL-C fading channels as compared to other matrices in the literature.     

Cooperative spectrum sensing based on a modified shuffled frog leaping algorithm in 5G network

The explosion of different types of wireless communications is leading to an impending spectrum famine. As a result, spectrum sensing has gained increasing interest from governments, industry and regulators. In this paper, a novel approach for cooperative spectrum sensing is proposed based on a modified shuffled frog leaping algorithm (SFLA). This approach is to fuse the perceived results of multiple nodes, and to improve the detection reliability. Simulations are used to compare the performance of the modified SFLA to the conventional one. The performance of the proposed cooperative spectrum sensing method based on the modified SFLA and that of the cooperative spectrum sensing method using modified deflection coefficient (MDC) are also compared. Results show that the proposed SFLA outperforms the traditional SFLA, and the proposed cooperative spectrum sensing method based on the modified SFLA gives higher miss detection probability than the MDC-based method, which validates the effectiveness of the modified SFLA-based cooperative sensing method.     

A tree-structured DFT filter bank based spectrum detector for estimation of radio channel edge frequencies in cognitive radios

Cognitive Radio (CR) aims to provide efficient spectrum utilization in spectrum scarce wireless environments. One of the key CR functionalities is the spectrum sensing, which allows CRs to monitor the electromagnetic spectrum and detect unused bands of spectrum. Wideband spectrum sensing needs to be employed for better spectrum opportunity detection and interference avoidance both in the case of commercial and military applications. Accurate sensing needs to be employed for blocker detection in commercial systems such as LTE for the design of transmit/receive path. In military radios, the challenge lies in the robust detection of the location of the center frequencies and bandwidths of individual radio channels in the wideband input signal. In this paper, an energy detector based on tree-structured discrete Fourier transform based filter bank (TDFTFB) is proposed for detecting the edges of the channels in the spectrum. The proposed method is compared with the conventional wavelets based method for complexity and performance. The design example and simulations show that the gate count resource utilization of the proposed detection scheme is 22.9% lesser than the wavelets method at the cost of a slight degradation (0.5%) in detection accuracy. Over-the-air tests performed using Universal Software Radio Peripheral 2 (USRP2) and MATLAB/SIMULINK showed that the present method is not input specific whereas the conventional wavelet based approach depends on the spectral location of the input.     

Spectrum sensing for OFDM signals using pilot induced cyclostationarity in the presence of cyclic frequency offset

In this paper, the problem of spectrum sensing of OFDM signals for cognitive radios is considered. It is proposed to detect the cyclostationary features introduced in an OFDM signal due to inter-pilot correlation. The performance of the proposed detector is derived and verified in case of AWGN channels. It is observed that the performance of cyclostationary detectors relies on the knowledge of the exact value of the cyclic frequency of the signal of interest. However, an offset in the cyclic frequency may arise due to several reasons. Therefore, for the proposed detector to perform reliably, there is a need to estimate the cyclic frequency offset. The Cramer–Rao bound for the cyclic frequency offset (CFO) estimator is derived, and based on it, two algorithms to estimate and compensate for the CFO are proposed. Simulation results are then used to study the performance of the proposed detection technique under Rayleigh fading both in the presence and the absence of CFO. The performance of the proposed system model is also studied under fast fading, and an alternative test statistic is proposed.     

Robust detection analysis of linear cooperative spectrum sensing for cognitive radio networks

Robust detection is employed in this work to cope with uncertainties on the channel gains and the noise power levels in a cognitive radio system based on linear cooperative spectrum sensing. The minimum number of samples required to achieve given false-alarm and missed-detection probabilities is derived as a function of the system parameter uncertainty levels and the nominal SNRs. A lower bound to the received symbol energy required to achieve reliable system operation is derived. This lower bound extends the concept of SNR wall to the case of a cooperative CR system with multiple secondary users. Then, a symmetric CR system scenario is investigated analytically and by numerical simulations. Simple asymptotic results are obtained in this case to relate the minimum number of samples required and the system parameters.     

Outlier-aware cooperative spectrum sensing in cognitive radio networks

This paper considers the problem of cooperative spectrum sensing in cognitive radio networks (CRN). Communication in CRNs may be disrupted due to the presence of malicious secondary users (SU) or channel impairments such as shadowing. This paper proposes a spatio-frequency framework that can detect and track malicious users and anomalous measurements in CRNs. The joint problem of spectrum sensing and malicious user identification is posed as an optimization problem that aims to exploit the sparsity inherent to both, spectrum occupancy and malicious user occurrence. Proposed scheme obtains improved performance by utilizing node location information, and can handle missing or inaccurate location information, and noisy SU reports. A distributed block-coordinate descent-based algorithm is proposed that is shown to outperform the state-of-the-art PCA-based approach, and is flexible enough to defeat a variety of attacks encountered in SU networks. An online algorithm, that can handle incorporate multiple SU readings sequentially and adapt to time-varying channels, primary user, and malicious user activity, is also proposed and shown to be consistent. Simulation results demonstrate the efficacy of the proposed algorithms.     

Spectrum mobility in cognitive radio network using spectrum prediction and monitoring techniques

The spectrum mobility during data transmission is an integral part of the cognitive radio network (CRN) which is conventionally two types for instance reactive and proactive. In the reactive approach, the cognitive user (CU) switches its communication after the emergence of the primary user (PU), where the detection of emergence of PU relies either on spectrum sensing and/or monitoring. Due to certain limitations of the reactive approach such as: (1) loss at least one packet on the emergence of PU and (2) resource (bandwidth) wastage if the periodic sensing is used for mobility, the researchers have introduced the concept of proactive spectrum mobility. In this approach, the emergence of PU is predicted on the bases of pre-available spectrum information, and switching is performed before true emergence of the PU, in order to avoid even the single packet loss. However, the imperfect spectrum prediction is a major milestone for the proactive spectrum mobility. Recently, due to introduction of the spectrum monitoring simultaneous to the data transmission, the reactive approach has come into lime-light again, however, it suffers from the ‘single packet loss’ and ‘imperfect spectrum monitoring’ issues. Therefore in this paper, we have exploited the spectrum monitoring and prediction techniques, simultaneously for the spectrum mobility, in order to enhance the performance of cognitive radio network (CRN). In the proposed strategy, the decision results of the spectrum prediction and monitoring techniques are fused using AND and OR fusion rules, for the detection of emergence of PU during the data transmission. Further, the closed-form expressions of the resource wastage, achieved throughput, interference power at PU and data-loss for the proposed approaches as well as for the prediction and monitoring approaches are derived. Moreover, the simulation results for the proposed approaches are presented and validation is performed by comparing the results with prediction and monitoring approach. In a special case, when the prediction error is zero, the graphs of all metric values overlies the spectrum monitoring approach, which further validates the proposed approach.     

Blockchain based Scalable model for Secure Dynamic Spectrum Access

Conventional Cooperative spectrum sensing techniques either suffer from single point of failure attack or lack in providing incentives to users which makes them incompatible for Wireless Service Provider (WSP). We propose a dynamic spectrum access framework for WSP which gives prominence to automated sensing and sharing with the use of blockchain. In this system, the opportunity of spectrum access is first examined by sensor nodes and the access right is then allocated to the users when their transactions to WSP are authenticated in a decentralized manner. Apart from using blockchain as a reliable platform for automatic enforcement of spectrum sensing, we propose a novel mechanism for securing our network from the threats designed primarily for Cognitive Radio Networks. In addition to this, our proposed approach enhances the scalability of blockchain networks by using the sidechains for storing data and checkpointing it onto main chain after periodic intervals of time. Extensive simulations in Octave indicate superior performance offered by our proposed model.     

Performance analysis of overlay cognitive NOMA network with imperfect SIC and imperfect CSI

In this paper, we investigate a multiple users cooperative overlay cognitive radio non-orthogonal multiple access (CR-NOMA) network in the presence of imperfect successive interference cancellation (SIC) and imperfect channel state information (CSI). In the context of cellular network, cell-center cognitive secondary users act as relays to assist transmission from the primary user (PU) transmitter to the cell-edge PU receiver via NOMA. According to the received signals between the primary transmitter and multiple cognitive secondary center users, the best cell-center cognitive SU with the maximum signal to noise ratio (SNR) is selected to transmit the PU’s signals and its own signal to cell-edge users through NOMA principle. Then, the PU cell-edge user combine the signals received from direct transmission in the first phase and relay transmission from the best cell-center cognitive SU in the second phase by selection combining (SC). To measure the performance of the system quantitatively, we derive the end-to-end outage probability and capacity for the primary and secondary networks by taking the imperfect SIC and CSI into consideration. Finally, the performance analysis is validated by the simulations, and show that serious interference caused by imperfect SIC and (or) imperfect CSI reduce the system performance.