Primary User Signal Research Articles

Deep residual learning-based cognitive model for detection and classification of transmitted signal patterns in 5G smart city networks

Primary user (PU) signal detection or classification is a critical component of cognitive radio (CR) related wireless communication applications. In CR, the PU detection methods are mostly based on statistical models, and their detection performance heavily relies on the accuracy of assumed models. In this paper, we design a novel detector, dubbed as PU-Net, that dynamically learns the PU activity patterns in a cognitive 5G smart city, where a network of unmanned aerial vehicles (UAVs) is deployed as flying base stations to serve the Internet-of-Things (IoT) users. Unlike the traditional schemes, the PU-Net is free from signal-noise model assumptions and is leveraged through deep residual learning integrated with atrous spatial pyramid pooling (ASPP) to sense the PU's transmitted signal patterns in the network. The PU-Net detects and classifies the active and idle PU states by exploiting the multilevel spatial-temporal features in the signal and noise frames. The proposed model is trained using locally synthesized Rayleigh channel-impaired data with large variability of modulated signals and different noise floor regimes. Additionally, the PU-Net model is blind-tested and evaluated on real-world over-the-air signals and with variable-length frames and varying channel effects at secondary users (SUs). With extensive experiments, it is shown that PU-Net outperforms other benchmark detectors, obtaining an accuracy of 0.9974, with 0.9978 recall and 0.9970 precision in detecting and classifying the PU transmitted signal patterns. Correspondingly, the proposed PU-Net can be adopted for IoT/UAV-assisted communication systems in optimizing spectrum efficiency and resolving the coexistence issues in 5G and beyond networks.

Algorithm for Evaluating Energy Detection Spectrum Sensing Performance of Cognitive Radio MIMO-OFDM Systems.

Cognitive radio technology enables spectrum sensing (SS), which allows the secondary user (SU) to access vacant frequency bands in the periods when the primary user (PU) is not active. Due to its minute implementation complexity, the SS approach based on energy detection (ED) of the PU signal has been analyzed in this paper. Analyses were performed for detecting PU signals by the SU in communication systems exploiting multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) transmission technology. To perform the analyses, a new algorithm for simulating the ED process based on a square-law combining (SLC) technique was developed. The main contribution of the proposed algorithm is enabling comprehensive simulation analyses of ED performance based on the SLC method for versatile combinations of operating parameter characteristics for different working environments of MIMO-OFDM systems. The influence of a false alarm on the detection probability of PU signals impacted by operating parameters such as the signal-to-noise ratios, the number of samples, the PU transmit powers, the modulation types and the number of the PU transmit and SU receive branches of the MIMO-OFDM systems have been analyzed in the paper. Simulation analyses are performed by running the proposed algorithm, which enables precise selection of and variation in the operating parameters, the level of noise uncertainty and the detection threshold in different simulation scenarios. The presented analysis of the obtained simulation results indicates how the considered operating parameters impact the ED efficiency of symmetric and asymmetric MIMO-OFDM systems.

Machine learning for cooperative spectrum sensing and sharing: A survey

AbstractWith the rapid development of next‐generation wireless communication technologies and the increasing demand of spectrum resources, it becomes necessary to introduce learning and reasoning capabilities in cognitive radio networks (CRN). In particular, our focus is on two fundamental applications in CRNs, namely spectrum sensing (SS) and spectrum sharing. The application of machine learning (ML) techniques has added new aspects to SS and spectrum sharing. This paper offers a survey on various ML‐based algorithms in the cooperative spectrum sensing (CSS) and dynamic spectrum sharing (DSS) domain, with its emphasis on types of features extracted from primary user signal, types of ML algorithm, and performance metrics utilized for evaluation of ML algorithms. Starting with the basic principles and challenges of SS, this paper also justifies the applicability of supervised, unsupervised, and reinforcement ML algorithms in the CSS domain. The application of ML algorithms, to solve the DSS problem has also been reviewed. Finally, the survey paper is concluded with some suggested open research challenges and future directions for ML application in next‐generation communication technologies.

An effective spectrum sensing in cognitive radio networks using improved convolution neural network by glow worm swarm algorithm

AbstractSpectrum sensing ranks amongst cognitive radio's most critical and demanding activities. For radio resource networks to use idle licensed bandwidth, secondary users need to identify the primary user's signal precisely. Cognitive radio networks (CRNs) have been proposed to improve the efficiency of spectrum use to meet the increasing demand for wireless bandwidth; they allow channel communication between secondary (unlicensed) and primary (licensed) users without interruption. A secondary user in a CRN must at all times track the presence of a primary user's signal to prevent the primary user (PU) from attempting to intervene whether the statistical variance of the PU data traffic would boost the prediction outcome provided by multiple current teaching algorithms in estimating the PU's average OFF time and the need for the best adaptive learning to predict accurate PU activities. The objective of the work has been modeled to identify the PUs signal. First, the data has been created via quadrature phase shift keying modulation and quadrature amplitude modulation by convolutional neural network (CNN). A dataset with different orthogonal frequency‐division (OFDM) multiple access signals is generated. Then cyclo‐stationary functions are extracted using the accumulation process fast Fourier Transform. This research aims to use glow worm swarm optimization (GWSO) to automatically check for the optimal design of CNNs without any manual work being required. Also, k‐Nearest Neighbors (kNN) replaces Softmax, as it outperforms conventional CNN architectures with noisy signals. A comparison of the proposed classifier with another machine learning classifier, namely support vector machine, Decision tree (DT), clearly shows that SVM and DT are outperformed by the improved convolution neural network‐glow worm swarm optimization (ICNN‐GWSO) classification. Results of simulation show that the improved scheme outperforms conventional spectrum sensing systems, both in fading and non‐fading settings, where efficiency is measured using measures such as chance, the total likelihood of error, and the ability to optimize data transfer possibilities.

MBSO Algorithm For Handling Energy-Throughput Trade-Off In Cognitive Radio Networks

Abstract Cognitive Radio network (CRN) depends on opportunistic spectrum access and spectrum sensing for improving the wireless networks’ spectrum efficiency. Since throughput maximization can result in high-energy consumption, the spectrum sensing technique should address the energy-throughput trade-off. The spectrum sensing time has to be determined by the considering the residual battery energies of each secondary user (SU). The primary user (PU) interference degrades the throughput of the entire network. Hence, the transmit power level should be determined by considering the PU interference and SU battery energy. This paper proposes the multi-objective brain storm optimization (MBSO) algorithm for handling energy-throughput trade-off in CRN. In this work, the sensing time is adaptively determined based on the residual battery energy of SUs, and the transmit power is determined based on the energy level of the PU signal and the residual battery energy of the SUs. A multi-objective optimization problem is formulated in order to maximize the throughput and minimize the packet error rate (PER) and is solved by applying the MBSO algorithm. Experimental results show that MBSO attains improved throughput, higher residual energy with lesser PER. The spectrum sensing performance is enhanced with higher probability of detection.

Secrecy Analysis of Overlay Mechanism in Radio Frequency Energy Harvesting Networks with Jamming under Nakagami-m fading

Overlay mechanism offers secondary users (SUs) chances to access licensed spectrum of primary users (PUs) to improve spectrum utilization efficiency. However, these chances impose serious security challenges when wire-tappers emulate legal users (SUs/PUs) to eavesdrop secret information. Moreover, energy sources available for SUs to transmit its private information as well as assist PUs in the overlay mechanism are problematic in green communication age. This paper proposes SUs who are able to scavenge radio frequency energy in signals of PUs to meet green communication requirement and jam wire-tappers to secure both secondary and primary information. Important secrecy performance indicators of overlay mechanism in radio frequency energy harvesting networks with jamming under Nakagami-m fading are evaluated by proposed precise expressions. Various results validate these expressions and show that secrecy performances of SUs and PUs are flexibly controlled, compromised and optimized with proper selections of system parameters as well as are dramatically affected by fading severity.

Spectrum Sensing Based On Deep Learning To Increase Spectrum Utilization

This paper proposes a new spectrum sensing technique for cognitive radio systems. To determine vacancy of the spectrum, the proposed method employs the recurrent neural network (RNN), one of the popular deep learning techniques. The proposed technique determines the spectrum occupancy of the primary user (PU) by observing the received signal’s energy and any information on the PU signal characteristic is not used. To this end, the received signal’s spectrum is obtained by fast Fourier transform (FFT). This process is performed on consecutive received signals and the resulting spectrums are stacked. Finally, a 2-dimensional spectrum (or spectrogram) is made. This 2-D spectrum is cut into sensing channel bandwidths and inputted to the deep learning model to decide the channel’s occupancy. While the recently published spectrum sensing technique based on convolutional neural network (CNN) relies on an empty channel, the proposed technique does not require any empty channel. Only the channel signal of interest to sense is needed. Since spectrum sensing results is two (busy or idle), binary classification deep learning model is developed. According to the computer simulation results, the proposed method has similar performance with the conventional CNN-based method while the spectral efficiency of the proposed method is much higher than that of the existing scheme. In addition, the overall learnable parameters of the proposed deep learning model is only 2/3 of the existing method

Hybrid Detection for Cooperative Cognitive Radio Using AWT and HDWT

A lot of research shows that spectrum is not effectively used in wireless communications. There are several gaps in spectrum that are unused. Cognitive radio (CR) is a smart technique of addressing inefficient usage of spectrum resources. Spectrum sensing is a vital task in cognitive radio systems. The objective of the spectrum sensing process is to investigate the presence and absence of primary user signals in different spectrum holes. To increase the feasibility of the spectrum sensing process, it can be implemented in a cooperative mode. The cooperative spectrum sensing depends on a decision making task at a group of secondary users. One of the most popular spectrum sensing techniques is the energy detection. Unfortunately, this technique fails in low SNR scenarios to give high detection results. So, there is a need for cooperative spectrum sensing in addition to noise reduction techniques to enhance the performance of the CR system. This paper presents noise reduction techniques based on additive wavelet transform (AWT) with homomorphic decomposition and Haar discrete wavelet transform (HDWT) with homomorphic decomposition to reduce the noise prior to the decision making process. This scenario is investigated with several fusion rules that are used for the cooperative spectrum sensing with fixed-time sensing. Simulation results prove that with OR fusion rule, the proposed scenario increases the probability of detection in the range of SNR from − 20 to − 15 dB using the AWT with homomorphic decomposition. The achievable throughput increases using the HDWT with homomorphic decomposition. The AND fusion rule is not recommended with the HDWT, but the probability of detection is high with the AWT. The majority fusion rule achieves high throughput with the HDWT and a high probability of detection with the AWT.

Novel filter bank-based cooperative spectrum sensing under practical challenges for beyond 5G cognitive radios

Cognitive radio (CR) technology with dynamic spectrum management capabilities is widely advocated for utilizing effectively the unused spectrum resources. The main idea behind CR technology is to trigger secondary communications to utilize the unused spectral resources. However, CR technology heavily relies on spectrum sensing techniques which are applied to estimate the presence of primary user (PU) signals. This paper firstly focuses on novel analysis filter bank (AFB) and FFT-based cooperative spectrum sensing (CSS) techniques as conceptually and computationally simplified CSS methods based on subband energies to detect the spectral holes in the interesting part of the radio spectrum. To counteract the practical wireless channel effects, collaborative subband-based approaches of PU signal sensing are studied. CSS has the capability to relax the problems of both hidden nodes and fading multipath channels. FFT- and AFB-based receiver side sensing methods are applied for OFDM waveform and filter bank-based multicarrier (FBMC) waveform, respectively, the latter one as a candidate beyond-OFDM/beyond-5G scheme. Subband energies are then applied for enhanced energy detection (ED)-based CSS methods that are proposed in the context of wideband, multimode sensing. Our first case study focuses on sensing potential spectral gaps close to relatively strong primary users, considering also the effects of spectral regrowth due to power amplifier nonlinearities. The study shows that AFB-based CSS with FBMC waveform is able to improve the performance significantly. Our second case study considers a novel maximum–minimum energy detector (Max–Min ED)-based CSS. The proposed method is expected to effectively overcome the issue of noise uncertainty (NU) with remarkably lower implementation complexity compared to the existing methods. The developed algorithm with reduced complexity, enhanced detection performance, and improved reliability is presented as an attractive solution to counteract the practical wireless channel effects under low SNR. Closed-form analytic expressions are derived for the threshold and false alarm and detection probabilities considering frequency selective scenarios under NU. The validity of the novel expressions is justified through comparisons with respective results from computer simulations.

Detection and Defense of PUEA in Cognitive Radio Network

In wireless communication, there is an enormous increase in the number of users which results in spectrum shortage. This can be defeated through dynamic spectrum access scheme and cognitive radio is the best solution to achieve this. A cognitive radio (CR) is programmable, configurable and can dynamically use the spectrum in the wireless environment. One of the important tasks of CR is to perform spectrum sensing. Sensing done on spectrum facilitates the cognitive radio user to find the unoccupied area of the spectrum. This CR is prone to many attacks in the wireless environment like primary user emulation attack (PUEA), denial of service attack, replay attack, etc. Among them, the most important attack is PUEA. When a user mimics the primary user and acquires priority over other users to access the spectrum then it is termed as PUEA. In this work, PUEA detection and its defense techniques have been modeled to avoid the degradation of spectrum. To detect the malicious users feature detection-based sensing with double threshold has been proposed. If the detected signal falls above the upper threshold or below the lower threshold then it is considered as primary user signal, whereas if the signal falls in between the thresholds then that signal is identified as malicious user signal. The detection of PUEA cannot be accurate due to factors like false alarm and miss detection. Hence, a game model has been designed for the legitimate nodes to reach strategic defense decisions in the presence of multiple attackers. Based on the actions of the players, the defenders’ payoff is determined to obtain optimal defense decisions.

Investigative analysis of channel selection algorithms in cooperative spectrum sensing in cognitive radio networks

The proliferation of wireless mobile devices has led to a number of challenges in mobile data communication. The world is experiencinganincreasingusage of finite spectrum bands for social media and other data communication services. It is due to this high usage that the Federal Communications Commission(FCC) sought to open up some spectrum bands to be used opportunistically by secondary users (SUs). However, the coexistence of Primary Users (PUs) and SUs may cause interference which leads to wastage of spectrum resources. This study investigates the impact of interferences between PUs and SUs. To ensure higher detection of PU signal, a cooperative rule was used to decide which SU to share and makea final decision about the availability of the spectrum band. To maximize the throughput of SU, a maximum likelihood function was designed to reduce delays in searching for the next available channel for data transmission. To discover more transmission opportunities and ensuring that a good number of free channels are detected, a parallel sensing technique was employed. Matlabwas used to simulate and generate the results in a distributed cognitive radio environment. The proposed extended generalizedpredictive channel selection algorithm (EXGPCSA) outperformed otherschemes in literature in terms of throughput, service timeandprobability of detection.

Improving Spectrum Sensing Under Impact of Noise Uncertainty Factor to Detect Primary User Traffic for Cognitive Radio System

Nowadays, users of wireless communication networks are facing a challenge and a shortage in allocating the radio spectrum, as a result of the rapid and widespread development in the wireless field. A major cause of this scarcity is the reliance on the strategy and policy of distribution or fixed allocation of the radio spectrum, as it is a natural and limited source. Cognitive radio is the best system to deal with the scarcity of the radio spectrum at the present time, as the idea of this system is to allow secondary or unauthorized users to use the spectrum in times it is not used by the primary of authorized users. This use is made conditionally without affecting the original users. The spectrum sensing function is one of the most important functions of the cognitive radio system and is the essence of its work. The traditional methods have proven ineffective in sensing and detecting the dynamic primary user, due to its assumptions that either the primary user is totally present or completely absent, as well as neglecting the reuse of the spectrum again. The main objective of this research is to suggest an algorithm to improve the detection process and reduce sensing time. The efficiency of spectrum sensing is improved through the use of a dynamic threshold. The new work in this scheme is the modeling of the primary dynamic user signal called Activity period (A) which presents a more realistic modeling and dynamic threshold together. The signal-to-noise ratio was used low to some extent, this is to ensure the quality of communication services. Consequently, the simulation results clearly prove that the proposed scheme achieves and meets the sensing enhancing requirements much better than many other sensing technologies.

Deep Learning for Spectrum Sensing in Cognitive Radio

The detection of primary user signals is essential for optimum utilization of a spectrum by secondary users in cognitive radio (CR). The conventional spectrum sensing schemes have the problem of missed detection/false alarm, which hampers the proper utilization of spectrum. Spectrum sensing through deep learning minimizes the margin of error in the detection of the free spectrum. This research provides an insight into using a deep neural network for spectrum sensing. A deep learning based model, “DLSenseNet”, is proposed, which exploits structural information of received modulated signals for spectrum sensing. The experiments were performed using RadioML2016.10b dataset and the outcome was studied. It was found that “DLSenseNet” provides better spectrum detection than other sensing models.

Multi-Path Hybrid Spectrum Sensing in Cognitive Radio

Inefficient utilization of the authorized spectrum emerges cognitive radio (CR) as a hopeful technology for both present and future telecommunications. It is owing to the potency to leverage the obtainable bandwidth of other wireless communication networks and thereby increase its occupancy. The key feature for the cognitive radio system for distinguishing the blank spectrum is spectrum sensing. This paper is intended to establish a hybrid sensing model for spectrum detection in CR to enhance the sensing efficiency of traditional techniques of spectrum sensing, which consists of two parallel paths of hybrid detectors. The first path is formed from two sequential detector stages; in the first phase, energy detector is used to recognize the PU signal existence where the signal has not been identified. Maximum–Minimum Eigenvalue (MME) is used as a second stage to detect the PU signal presence. The second path consists of two parallel stage detectors employing separate ED and MME to detect the PU signal individually, the two results are gathered to make a decision, and then the final detection decision is determined based on the two paths’ detection combined results. The proposed hybrid sensing approach adopted for enhancing the sensing performance is validated with conventional methods. Simulation results show that the proposed approach outperforms various traditional and hybrid approaches in terms of maximizing the detection probability on the specified limitations on the false alarm probability, as it can increase the detection probability to 94% instead of 79% for the parallel detector at SNR = − 10 dB and Pfa = 0.1.

A Deep Neural Network Model for Hybrid Spectrum Sensing in Cognitive Radio

Spectrum sensing (SS) is an essential task of the secondary user (SU) in a cognitive radio system. SS monitors the primary user (PU) activity in order to avoid any collision with SU, as the latter should be silent when PU is active on a given channel. Hybrid SS (HSS) is one of the powerful methods used to monitor PU activity. It consists of using different detectors together to make a final decision on the PU status. In this manuscript, artificial neural networks (ANN) are used to perform HSS. Since our data is composed from the test statistics (TSs) of several detectors, thus it can be modeled as tabular. Fully connected neural networks become the most suitable ANN model. We applied cutting-edge techniques in the field of deep learning in order to get the best possible accurate neural network model in our application. These techniques boil down to: embedding, regularization, batch normalization and smart learning rate selection. With the help TSs related to several detectors, ANN is trained to distinguish between two hypotheses, $$H_0$$ : PU is absent and $$H_1$$ : PU is active. Numerical results show the effectiveness of our proposed ANN-based HSS, as it outperforms the classical ANN-based energy detector and proves its capability to detect PU signal at very low SNR.

A Multi-Objective Particle Swarm Optimization Based Algorithm for Primary User Emulation Attack Detection

The cognitive radio network (CRN) has been proposed to overcome the spectrum scarcity and the massive demand on the radio frequencies through efficient utilization and smart management of the free channels. Using a spectrum sensing process, the secondary users (SUs) share the spectrum of the primary users (PUs) without causing any interference to these latter. One of the main threats affecting the channel utilization in CRN is the primary user emulation (PUE) attack. The PUE attack utilizes a sophisticated technique to craft the same signal of a legitimate PU to gain unauthorized access to the unused channels, forcing the SUs to immediately free up vacant spectrum space, resulting in a denial of service (DoS), degradation of service, and causing a noticeable impact on CRN performance. To circumvent this attack, the anchor nodes must accurately estimate the coordinates of the PU signal source. In the literature, most of the localization of unknown signal source, in our case the PU/PUE, are based on the ranging schemes which measure the distance between the blind node and the anchors. Those anchors are aware of their location and situated in optimized positions to the signal source in order to permit an accurate PU/PUE position detection. The detection rate depends tightly on the distance separating the anchors to the signal source as well as the signal-to-noise ratio (SNR). In this paper, we demonstrate the impact of the distance on the detection error while highlighting the particle swarm optimization’s (PSO) advantage in optimizing the anchors positioning. Furthermore, we illustrate the SNR impact on the probability of detection, particularly in the situation of low SNR and the attacker in the vicinity to a real PU. The main contribution in this work is the proposition of an approach with the aim of protecting an area containing more than a single PU with a limited number of anchor nodes while providing a higher detection rate to stop any eventual PUE attack. This approach is based on a multi-objective particle swarm optimization (MOPSO) algorithm for PU/PUE position detection. It minimizes concurrently the probability of detection error related to the received signal strength (RSS)/trilateration and the SNR, with the main objective of finding all optimized positions for the mobile anchor nodes and obtains the most accurate PUE attack detection.

Multi-Dimensional Small-Scale Cooperative Spectrum Sensing Approach for Cognitive Radio Receivers

Cognitive radio (CR) is one of the most important emerging technologies that have been introduced to meet the heavy data traffic of future wireless networks. Effective implementation of CR networks strongly requires devolution of efficient spectrum sensing (SS) techniques. Recently many SS techniques based on the utilization of multiple antenna elements (MAE) and matched filtering (MF) at the CR receiver are emerged due to their high detection performance. In this paper, a multi-dimensional small-scale cooperative SS approach based on the use of MAE, MF, and DOA estimation is proposed. The multi-dimensional detection is performed by identifying both the primary user (PU) signal and its DOA, which corresponds to the temporal domain and spatial domain, respectively. The use of MAE provides many versions of the received PU signal, which considered as the backbone of many signal processing operations such as DOA estimation of PU signals. DOA estimation is based on calculating time difference of arrival (TDOA) between the output correlation signals coming from fast convolution blocks. The received PU signal versions are also used as inputs for separate MF detectors to obtain various decisions taking the advantage of the spatial diversity at the CR receiver antennas. Moreover, the estimated DOA of the PU signal is used to make a combination of the signal versions emanating from each antenna branch, using the delay and sum combiner to produce a strong signal with high signal to noise ratio (SNR). This strong signal is also used as input to a separate MF to produce an accurate decision. Finally, the principle of cooperation is achieved by combining these various decisions to obtain a reliable decision. Several simulation scenarios are carried out to verify the superiority of the proposed SS approach compared to the traditional MAE based SS technique under extremely low SNR regimes and high interference. Moreover, the simulations and theoretical closed-form expressions are highly matched together.

DQN Learning Based Defense Against Smart Primary User Emulation Attacks in Cooperative Sensing Systems

The last two decades have seen Cognitive Radio (CR) technology as an efficient mechanism to combat spectrum scarcity. Cooperative spectrum sensing (CSS) further enhances its efficiency. However, primary user emulation attack (PUEA) can affect the key purpose of CSS, by changing its fundamental model. This necessitates optimization of the CSS system facing a PUEA. In this paper, such optimization has been performed with the help of Deep Q- Network (DQN) learning. A smart-PUEA strategy, where the attack are made intelligently during the absence of the primary user (PU) signal, has been considered for investigation. First, the presence of a PUEA is modelled with the Markov chain. Then, the suppression of the damages done by the PUEA on the SU throughput has been performed in two CSS architectures: the decision fusion CSS and the data fusion CSS. In both the CSS architectures, the application of DQN learning is carried out by finding an optimal policy with the help of an action-value function. The optimal policy gives the optimal decision threshold and the optimal sensing time. As the data sets for the considered system are large, a multi-layered network is used for approximating the action-value function estimator. The reward is received in terms of maximized SU throughput. The performance assessment is done with an exhaustive search mechanism and beamforming based CSS. It is shown that the proposed algorithms outperform the existing algorithms in the achievable sum-rate and number of iterations for convergence respectively.

Probabilistic Spectrum Sensing Based on Feature Detection for 6G Cognitive Radio: A Survey

With the advent of Sixth Generation (6G) telecommunication systems already envisioned, increased effort is made to further develop current communication technologies, so they can be incorporated together with the novel ones, to deliver uninterrupted and satisfactory service for any application in every location on the ground, underwater, in the air, or in space. One such technology is Cognitive Radio (CR) which has received much attention due to its potential for increase of utilization, especially in the bands below 6 GHz. The main enabler for CR is spectrum sensing because it provides the opportunity for dynamic assessment of the radio environment to identify unused channels. This functionality has been the object of many research works for that very reason. In spite of this, the provision of accurate and fast spectrum characterization in time, frequency and space has proven to be a non-trivial task. This paper presents a detailed review of probabilistic spectrum sensing methods classified by the feature they extract from the received signal samples, to provide accurate detection of the primary user (PU) signal. The main design characteristics (such as probability of detection, robustness to noise and fading, signal/noise model assumptions, and computational complexity), strengths and weaknesses for each type are also summarized. Based on current concepts for 6G networks and applications, a framework for human-centric cognition-based wireless access is presented, which specifies the role of spectrum sensing-based CR in future networks.

DWT Based Energy Detection Spectrum Sensing Method for Cognitive Radio System

In this paper a new blind energy detection spectrum-sensing method based on Discreet Wavelet Transform (DWT) is proposed. The method utilizes the DWT sub-band to collects the received energy. The proposed method recognizes the Primary User (PU) signal from noise only signal using the differences in the collected energy in first and last sub-bands of one level DWT. The simulation results show that the proposed method achieves improved detection probability especially at low Signal to Noise Ratio (SNR) compared to Conventional Energy Detector (CED). The results also show that the proposed method has shorter sensing time and less Energy Consumption (EC) compared to CED due to using small number of processed sample. Therefore, this method is suitable for Cognitive Radio (CR) applications where only limited energy like device battery is available.