Method For Cognitive Radio Networks Research Articles

Adaptive approach to spectrum monitoring in cognitive radio networks through signal detection optimization

The article considers the improvement of the adaptive algorithm of the spectral monitoring method for cognitive radio networks by introducing adaptive wavelet transforms and filters. The use of adaptive Morle and Dobechy wavelet transforms, as well as adaptive Kalman, LMS, and RLS filters is proposed, which allows dynamically changing parameters depending on the conditions of the radio environment. The comparative analysis with traditional methods showed that adaptive methods significantly increase the efficiency of signal detection in conditions of low SNR values, reducing the noise level, improving the accuracy of signal detection and reducing the probability of false alarms. The results of the study confirm the perspective of using adaptive methods to increase the reliability and efficiency of spectral monitoring in real operating conditions.

A Detail Survey of Channel Access Method for Cognitive Radio Network (CRN) Applications toward 4G

A Detail Survey of Channel Access Method for Cognitive Radio Network (CRN) Applications toward 4G

A rapid coarse-grained blind wideband spectrum sensing method for cognitive radio networks

Spectrum sensing aims to sense the potential spectrum resources available in the cognitive radio environment. It is also the premise of spectrum management and spectrum sharing in cognitive radio systems. To perceive the primary user’s activity and make full use of spectrum holes, rapid detection of a broad frequency span is an essential part of cognitive radio technology. Reducing the observation time for the data collection, the data storage requirements, and hardware/software computational complexity are urgent and challenging issues in wideband spectrum sensing. High accuracy power spectral density estimation is not the primary requirement; of course, the accuracy must be controlled within the appropriate range and can support the primary user activity’s determination. This paper proposes a sub-Nyquist wideband spectrum sensing method based on compressive covariance sensing for the rapid wideband spectrum sensing. Compared with the traditional Nyquist-rate method, this method can use low-speed ADC to detect wideband signals and effectively control the observation time and computational complexity. This paper’s main contributions include: (1) developing a sub-Nyquist sampling structure based on the multi-coset sampling banks, (2) proposing a coarse-grained power spectral density estimation method for wideband spectrum sensing with short observation time and low complexity. Simulations show that the proposed method exhibits this method is suitable for fast spectral detection. At the same time, the error of spectrum analysis is basically within the acceptable range.

Deep Cognitive Perspective: Resource Allocation for NOMA-Based Heterogeneous IoT With Imperfect SIC

The Internet of Things (IoT) has attracted significant attentions in the fifth generation mobile networks and the smart cities. However, considering the large numbers of connectivity demands, it is vital to improve the spectrum efficiency (SE) of the IoT with an affordable power consumption. To improve the SE, the nonorthogonal multiple access (NOMA) technology is newly proposed through accommodating multiple users in the same spectrums. As a result, in this paper, an energy efficient resource allocation (RA) problem is introduced for the NOMA-based heterogeneous IoT. At first, we assume the successive interference cancelation (SIC) is imperfect for practical implementations. Then, based on the analyzing method for cognitive radio networks, we present a stepwise RA scheme for the mobile users and the IoT users with the mutual interference management. Third, we propose a deep recurrent neural network-based algorithm to solve the problem optimally and rapidly. Moreover, a priorities and rate demands-based user scheduling method is supplemented, to coordinate the access of the heterogeneous users with the limited radio resource. At last, the simulation results verify that the deep learning-based scheme is able to provide optimal RA results for the NOMA heterogeneous IoT with fast convergence and low computational complexity. Compared with the conventional orthogonal frequency division multiple access system, the NOMA system with imperfect SIC yields better performance on the SE and the scale of connectivity, at the cost of high power consumption and low energy efficiency.

A Real-Valued Weighted Covariance-Based Detection Method for Cognitive Radio Networks With Correlated Multiple Antennas

The weighted covariance-based detection (WCD) method for spectrum sensing (SS) can offer a reliable detection performance in the spatially correlated time-varying Rayleigh fading channel. However, it involves huge complex computations. As is well known, one complex multiplication requires two real additions and four real multiplications. To this end, we transfer the complex-valued SS problem into the real-valued SS problem and present a novel-reduced complexity WCD method in this letter, which is referred to as a real-valued WCD (RWCD) method. In particular, an asymptotic closed-form expression of the probability of detection is derived for the proposed RWCD method, which is intractable in the WCD method. Through the complexity analysis, the RWCD method can save nearly half of the computational complexity compared to the WCD method. Meanwhile, simulation results show that the proposed method achieves almost the same performance as that of the WCD method, and both outperform their competitors.

Pre-reservation based spectrum allocation for cognitive radio network

Studies on the current usage of the radio spectrum by several agencies have already revealed that a large fraction of the radio spectrum is inadequately utilized. This basic finding has led to numerous research initiatives. Cognitive radio technology is one of the key candidate technologies to solve the problems of spectrum scarcity and low spectrum utilization. However, random behavior of the primary user (PU) appears to be an enormous challenge. In this paper, a Pre-reservation based spectrum allocation method for cognitive radio network is proposed to apply a PU behavior aware joint spectrum band (SB) selection and allocation scheme. In the first step, the SB is observed in terms of PU usage statistics whereas in the second phase, a network operator (NO) using a spectrum allocation scheme is employed to allocate SBs among secondary users (SUs). We also introduce the concept of reservation and exchange functionality under the priority serving strategy in a time-varying framing process. Simulation results show that the proposed scheme outperforms existing schemes in terms of the spectrum utilization and network revenue. In addition, it helps NO to manage the spectrum on a planned basis with a systematical spectrum reservation management where the NO has the status of time slots. Moreover, SUs have an opportunity to reserve or instantly request a SB that maximizes the SUs satisfaction in terms of quality of experience.

Optimum Access Analysis of Collaborative Spectrum Sensing in Cognitive Radio Network using MRC

The performance of cognitive radio network mainly depends on the finest sensing of the presence or absence of Primary User (PU). The throughput of a Secondary User (SU) can be reduced because of the false detection of PU which causes an SU from its transmission opportunity. The factorization of the probability of correct decision is a really hard job when the special false alarm is incorporated into it. Previous works focus on collaborative sensing on the normal environment. In this paper, we have proposed a collaborative sensing method in Cognitive radio network for optimal access of PU licensed band by SU. It is shown performance analysis of energy detection through different cognitive users and conducts a clear comparison between local and collaborative sensing. In this paper, the maximal ratio combining diversity technique with energy detection has been employed to reduce the false alarm probability in the collaborative environment. The simulation result shows significant reduction of the probability of misdetection with increasing in the number of collaborative users. We also analyze that MRC scheme exhibits the best detection performance in collaborative environment.

Upgraded Spectrum Sensing Method in Cognitive Radio Network

Objective: Spectrum sensing ensures reliable detection of primary users and white spaces in the channel such that the cognitive users can utilise the unused spectrum. Method: This work concentrates on enhancement in the energy detection based spectrum sensing technique by employing wavelet packet transform in the place of Fast Fourier Transform (FFT) under different wireless channel models. Result: They are plotted using MATLAB. Receiver operating characteristics curve concludes that wavelet packet based method overrules the traditional method. Application: Free spectrum holes could be found out and efficiently utilized.

A Cognitive Radio Spectrum Sensing Algorithm to Improve Energy Detection at Low SNR

Energy detection is among the most popular spectrum sensing method for spectrum sensing due its low complexity. Unfortunately, its performance is poor at low SNR. In this paper we proposed a spectrum sensing method for cognitive radio network that improves the performance of energy detection. The proposed method based on distribution analysis using kurtosis as test statistic. This comes from the fact that distribution of received signal when a channel is occupied will be different from vacant channel. Noise tends to have a Gaussian distribution. Signal which faces multipath fading during the transmission way will have non Gaussian distribution. Sensing algorithm was tested using captured DTV signal. Result shows that our method performs well at low SNR. It achieves probability of detection of 90 % for 10 % Probability of false alarm for low SNR, below -20 dB.

Multiple Antenna Non-ideal Channel Information Interference Alignment Method in Cognitive Radio Network

Interference alignment scheme, because of non ideal channel, can cause interference to primary user increases, it is worse at the same time the user's transmission rate, aiming at this problem, this paper studies the multiple antenna system is a kind of cognitive radio interference can achieve full multiplexing gain alignment method, analyses the interference alignment method under the condition of imperfect channel state information of the optimal transmission problem, and according to this problem, put forward the iteration method of interference alignment schemes, the simulation results show that this method is compared with the previous beam forming algorithm, and can obtain more transmission rate, improve the capacity of the time the user's system.

QoS Routing Algorithm for Cognitive Radio Based on Channel Capacity and Interference

In this paper, a new QoS routing method for cognitive radio network is presented. Combining with the basic process of on-demand routing, we propose an appropriate routing algorithm which can meet the end-to-end QoS performance demanding. Difference from the existing conventional routing algorithms, the impact of two criterions: channel capacity and interference arising from intra-flow contention are took full account. Node in the cognitive radio network can select channels and switch between spectrums autonomously to meet capacity demand and avoid intra-flow contention. Simulation results show that the routing algorithm based the capacity and interference has better end-to-end QoS performance.

Hybrid Overlay/Underlay Spectrum Sharing in Cognitive Radio Networks

A hybrid overlay/underlay spectrum sharing method for cognitive radio networks based on user classification and convex optimization is proposed. Interference radii are configured for the primary receiver and each cognitive receiver. Cognitive users are divided into four groups and allocated different spectrum sharing patterns according to their distance from the primary transmitter and receiver. An optimal power allocation scheme that achieves the maximum sum rate of cognitive radio system on the premise of satisfying the interference constraint of primary receiver is acquired through the convex optimization method. Performance analysis and simulation results show that, compared with existing methods, our method leads to improved performance of achievable sum rate of cognitive users while guarantees the transmission of primary users.

Satellite-Based Spectrum Sensing for Dynamic Spectrum Sharing in Ground-Located CRNs

In the paper, we propose a novel satellite-based spectrum sensing method for cognitive radio networks (CRNs) using multibeam antennas in large-scale coverage ground areas. Our objective is to achieve the dynamic spectrum sharing easily in both temporal and geographical dimensions, such that the primary networks and the CRNs can better coexist. By simple linear transformation and energy detection at the satellite multibeam receiver, spectrum sensing can be simultaneously performed for multiple cells. This approach overcomes the limitations of the traditional sensing method, which therefore considerably improves the spectrum utilization. To verify the efficiency of the proposed approach, theoretical expressions of the average probabilities of miss detection and false alarm are derived. Simulation results show that the satellite-based spectrum sensing method is very efficient for the CRNs in large-scale scenarios under different conditions (equivalent signal-to-noise ratio, threshold, antenna beam pattern, etc.). In addition, the proposed sensing method provides a new vision on the large-scale dynamic spectrum sharing system design to effectively integrate the terrestrial and the satellite communication systems for the future CRN development.

Cooperative spectrum sensing using particle swarm optimisation

A cooperative spectrum sensing method for cognitive radio networks based on particle swarm optimisation (PSO) is proposed. Results show that the proposed PSO-based cooperative method is efficient and stable, and it results in higher detection probability than the modified deflection coefficient (MDC)-based method under the same probability of false alarm.