As a general and rigid mathematical tool, wavelet theory has found many applications and is constantly developing. This article reviews the development history of wavelet theory, from the construction method to the discussion of wavelet properties. Then it focuses on the design and expansion of wavelet transform. The main models and algorithms of wavelet transform are discussed. The construction of rational wavelet transform (RWT) is provided by examples emphasizing the advantages of RWT over traditional wavelet transform through a review of the literature. The combination of wavelet theory and neural networks is one of the key points of the review. The review covers the evolution of Wavelet Neural Network (WNN), the system architecture and algorithm implementation. The review of the literature indicates the advantages and a clear trend of fast development in WNN that can be combined with existing neural network algorithms. This article also introduces the categories of wavelet-based applications. The advantages of wavelet analysis are summarized in terms of application scenarios with a comparison of results. Through the review, new research challenges and gaps have been clarified, which will serve as a guide for potential wavelet-based applications and new system designs.

With recent developments, the performance of automotive radar has improved significantly. The next generation of 4D radar can achieve imaging capability in the form of high-resolution point clouds. In this context, we believe that the era of deep learning for radar perception has arrived. However, studies on radar deep learning are spread across different tasks, and a holistic overview is lacking. This review paper attempts to provide a big picture of the deep radar perception stack, including signal processing, datasets, labelling, data augmentation, and downstream tasks such as depth and velocity estimation, object detection, and sensor fusion. For these tasks, we focus on explaining how the network structure is adapted to radar domain knowledge. In particular, we summarise three overlooked challenges in deep radar perception, including multi-path effects, uncertainty problems, and adverse weather effects, and present some attempts to solve them.

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.

This paper addresses the problem of accurately modeling the spectrum occupancy patterns of real radio communication systems, which is an essential aspect in the study of cognitive radio (CR) networks. The main drawbacks and limitations of previous works are identified, and the methodological procedures on which they rely are improved and extended. Two sophisticated measurement platforms, providing low and high time resolutions, are used to obtain extensive real-world data from a multiband spectrum measurement campaign, embracing a wide variety of spectrum bands of practical interest for CR applications. A comprehensive, systematical, and rigorous analysis of the statistical properties observed in the measurement data is then performed to find accurate models capable of capturing and reproducing, within reasonable complexity limits, the statistical properties of temporal patterns, at both short and long timescales, in real wireless systems. Innovative modeling approaches capable of simultaneously describing statistical properties at both timescales are developed as well. In summary, this paper contributes realistic and accurate time-dimension spectrum usage models for their application to the study and development of CR.

Cognitive radio (CR) has been identified as a promising solution to the so-called spectrum scarcity problem. The basic idea of this paradigm is to allow unlicensed users to access in an opportunistic and non-interfering manner some licensed bands that are temporarily unoccupied by licensed users. CR is expected to significantly increase current spectrum usage. However, before this paradigm can turn into reality, a full understanding of the dynamic usage of spectrum is required. Current spectrum utilization has already been evaluated in some measurement campaigns. However, most of them were performed in USA and therefore evaluated the American spectrum regulation and utilization. Few studies have been carried out in other places. In this context, this paper presents spectrum occupancy measurements conducted in the frequency range from 75 MHz to 3 GHz in an outdoor environment in urban Barcelona, Spain. The measurements are analyzed and compared to the official spectrum regulations. The obtained results demonstrate the existence of a significant amount of spectrum available for the deployment of future CR networks.

The existing approximations for the Gaussian Q-function have been developed bearing in mind applications that require high estimation accuracies (e.g., derivation of the error probability for digital modulation schemes). Unfortunately, the associated mathematical expressions are too complex to be easily employed in many other analytical studies, even if they do not require such a high accuracy. This letter proposes a simple, yet accurate mathematical approximation to the Gaussian Q-function. When compared to other existing approximations, the proposed model provides an adequate balance between accuracy and analytical tractability. Its simplicity enables its application over a wider range of analytical studies at reasonable accuracy levels. As an illustrative example, the proposed approximation is employed to obtain a new and simple closed-form expression for the probability of detection of an energy detector under Rayleigh fading channels.