In this paper, we develop a novel mobility-aware transformer-driven tiered structure (MASSFormer) based cooperative spectrum sensing method that effectively models the spatio-temporal dynamics of user movements. Unlike existing methods, our method considers a dynamic scenario involving mobile primary users (PUs) and secondary users (SUs) and addresses the complexities introduced by user mobility. The transformer architecture utilizes an attention mechanism, allowing the proposed method to model the temporal dynamics of user mobility by effectively capturing long-range dependencies. The proposed method first computes tokens from the sequence of covariance matrices (CMs) for each SU. It processes them in parallel using the SU-transformer to learn the spatio-temporal features at SU-level. Subsequently, the collaborative transformer learns the group-level PU state from all SU-level feature representations. The main goal of predicting the PU states at each SU-level and group-level is to improve detection performance even more. The proposed method is tested under imperfect reporting channel scenarios to show robustness. The efficacy of our method is validated with simulation results that demonstrate its higher performance compared to existing methods in terms of detection probability Pd, sensing error, and classification accuracy (CA).

Intelligent reflecting surfaces (IRS) technology has demonstrated considerable potential in enhancing wireless communication by improving signal quality and extending coverage. However, IRS-assisted systems face unique issues in channel estimation caused by their passive nature and the complexity of the channel environment. Deep learning-driven methods provide powerful tools to address complexities such as non-linearities and the high dimensionality inherent in these systems. This paper offers an extensive survey of existing channel estimation techniques in IRS-assisted systems, laying a foundation for future research. To achieve this, a comprehensive literature search was conducted across eight reputable databases and search engines, including IEEE Xplore, Google Scholar, and Scopus etc. After applying rigorous inclusion criteria, 57 key articles were identified as highly relevant, forming the basis of this review. The survey covers traditional methods, such as least squares (LS), minimum mean squared error (MMSE), and linear MMSE (LMMSE), and contrasts them with advanced approaches, including matrix decomposition, compressed sensing, and deep learning techniques. The survey then systematically categorizes the selected studies into three groups: discriminative (supervised learning), generative (unsupervised learning), and hybrid learning. This study reveals that convolutional neural networks (CNNs) are well-suited for resource-constrained or real-time applications, while transformers provide excellent adaptability and accuracy, albeit with higher computational demands. The survey concludes with insights into future research directions, emphasizing the need for improved estimation efficiency and robustness in next-generation wireless systems.

The hidden node problem is one of the most challenging issue in Cooperative Spectrum Sensing (CSS). The system models adopted by the existing Deep Learning-based spectrum sensing methods have not focused on modeling the hidden node scenario in cognitive radio networks. Further, these methods are unable to adapt to the dynamic channel conditions in the wireless environment since they have not considered the effect of fading environment. Motivated from these limitations, we propose GCN-CSS, a novel Graph Convolution Network (GCN) based cooperative spectrum sensing methodology which adapts to the dynamic changes in the Cognitive Radio Network. To the best of the author's knowledge, this is the first work to apply GCN for solving CSS problem. We have considered a practical system model which handles the dynamic channel condition i.e. SUs with multiple antennas experiencing different fading models with different fading severity. We have also catered the scenario of imperfect reporting channel between the SUs and the fusion centre along with the imperfect sensing channel to prove the robustness of the proposed model. With sufficient simulations, the superiority of the proposed methodology is proven in different dynamic scenarios of the wireless environment.

In this letter, we design a hierarchical cooperative long short-term memory (LSTM) network-based cooperative spectrum sensing (CSS) method which utilizes convolutional neural network (CNN) and LSTM network. The CNN extracts spatial features from the input covariance matrices (CMs) which are generated by sensing data of each secondary user (SU) and the sequence of spatial features corresponding to multiple sensing periods are fed into secondary user LSTM (SU-LSTM) so that the PU activity pattern at SU level can be learned. The cooperative LSTM learns the group-level PU activity pattern from all SU-level temporal feature representations. The aim of learning the PU activity pattern at SU-level and group-level is to improve the detection performance further. To demonstrate the robustness of the proposed model, the scenario of an imperfect reporting channel is taken into account. With a sufficient amount of simulations, the effectiveness of the proposed method is proven and simulation results demonstrate that the proposed method outperforms the state-of-the-art in terms of detection probability and classification accuracy.

In this paper, detection performance of various Machine learning (ML) and Deep learning (DL) algorithms based cooperative spectrum sensing (CSS) methods have been compared and analyzed. The ML algorithms are such as K-means clustering, Gaussian mixture model (GMM), support vector machine (SVM), Decision Tree (DT), and the DL architectures as artificial neural networks (ANNs) and convolutional neural networks (CNNs). To evaluate the performance of CSS methods, multi-antenna multiple secondary users (SUs) and hidden node scenarios are considered in Cognitive radio (CR) network. Such scenarios for detecting the presence of PU have not been taken into account by the system models used by the current DL-based CSS models. The fusion centre collects the SU data and computes the statistical features from sensing by adopting data fusion method. The fusion centre divides sensing data collected from all SU into two clusters and computes one-dimensional feature vector, and these features are used to train the ML classifiers. In case of DL based models, the fusion centre computes covariance matrices from the sensing data collected from each SU. These covariance matrices are fed as input to DL based CSS models. The results are showing that CNN based models outperform the ANN, and other ML based models in terms of classification accuracy and probability of detection.

With 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.

Free space optical (FSO) communication link provides high-speed data transmission rate within line of sight range for indoor as well as outdoor applications. FSO comes with high sensitivity for variation in weather condition as it reflects in the form of change of dielectric properties of medium. The uniqueness of the presented model in this paper is that it achieves 125 GBPS of data rate with QPSK modulation scheme and that too at 1550 nm of wavelength which is compatible with existing optical backbone network. In this paper, authors analyzed the performance of a FSO link with 0.6 km length and modulation scheme QPSK for different atmospheric conditions. Gamma–Gamma distribution model is employed to model the FSO channel link. Performance comparisons are recorded as bit error rate (BER) and signal to noise ratio (SNR) with help of simulation tool Optisystem13.