The evolving 5G and beyond 5G (B5G) wireless technologies are envisioned to provide ubiquitous connectivity and great heterogeneity in communication infrastructure by connecting diverse devices and providing multifarious services. Recently, the Internet of Things (IoT) and unmanned aerial vehicles (UAVs) are realized as an essential component of the upcoming 5G/B5G networks, enabling enhanced communication capacity, high reliability, low latency, and massive connectivity. However, one limiting factor in the expansion of 5G/B5G technology is the finite radio spectrum, which necessitates managing the anticipated spectrum crunch for future wireless networks. One potential solution is to develop intelligent cognitive methods to dynamically optimize the use of spectrum in 5G/B5G networks to solve the imminent problem of spectrum congestion and improve radio efficiency. This paper addresses the opportunistic spectrum access (OSA) problem in the 5G/B5G cognitive radio (CR) network of IoTs and UAVs through the novel deep learning-based detector, dubbed as Deep-CRNet. The proposed detector employs residual connections with cascaded multi-kernel convolutions to identify the primary user (PU) spectrum usage by extracting the inherent multi-scale signal and noise features in the sensed transmission patterns. Thereby, Deep-CRNet intelligently learns and locates the spectrum holes so that secondary users (SUs) and PUs can dynamically share network spectrum resources. The efficacy of Deep-CRNet is validated through simulation results, where it achieved 99.74% accuracy with 99.65% precision and 99.83% recall in accurately classifying the PU status. In addition, the average correct detection probability of Deep-CRNet in the low signal-to-noise ratio ( − 20 dB to − 15 dB) range is 38.21% higher than the second best-performing detector.

Deep learning-driven opportunistic spectrum access (OSA) framework for cognitive 5G and beyond 5G (B5G) networks

The Deep Learning Frameworks like ANN and CNN are employed in the area of DOA Estimation has been a recent trend. In this paper we propose Artificial Neural Network(ANN) and Convolutional Neural Network (CNN) which can estimate DOA of a single source with a Uniform Linear Array(ULA).The performance of proposed ANN and CNN which tries classify up to 21 angles with the help of 10 sensors of ULA configuration is evaluated by metrics like training loss, training accuracy etc. 

Classification based DOA estimation using ANN and CNN Models

Adaptive nulling antenna technology is widely used to estimate the direction of arrival (DOA) with the aim of mitigating the high computational complexity issues imposed by the classical multiple signal classification (MUSIC) algorithms. However, most existing adaptive algorithms, such as the least mean square (LMS) and gradient descent total least-squares (GDTLS) algorithms, exhibit slow convergence speed and large steady-state error. This paper proposes an error-censoring recursive least squares (EC-RLS) algorithm to improve convergence while reducing computational complexity. By evaluating how the sum of weighted error squares deviates from its minimal value, it is possible to prevent unnecessary weight updating and thereby alleviate the high computational cost associated with conventional algorithms. Simulation results demonstrate the superiority of the proposed EC-RLS algorithm over comparable works, even in situations where the number of incident sources is equal to the number of elements in the array or when the direction of incident signals changes abruptly. 

Low Complexity Error-Censoring RLS Algorithm for DOA Estimation

The Deep Learning Frameworks like ANN and CNN are employed in the area of DOA Estimation has been a recent trend. In this paper we propose Artificial Neural Network(ANN) and Convolutional Neural Network (CNN) which can estimate DOA of a single source with a Uniform Linear Array(ULA).The performance of proposed ANN and CNN which tries classify up to 21 angles with the help of 10 sensors of ULA configuration is evaluated by metrics like training loss, training accuracy etc.

Spectrum sensing (SS) has been heatedly discussed due to its capacity to discover the idle registered spectrum bands, which effectively alleviates the shortage of spectrum by spectrum reuse. Energy detector (ED) is widely accepted for SS as its complexity is very low. In this paper, an adaptive sampling scheme is proposed to improve the sensing performance of ED, where the sampling point of the received signal is adaptively adjusted with the environment signal-to-noise ratio (SNR). When SNR decreases, the sensing performance can be maintained and even improved by the rise of the sampling point. When SNR increases, the improved ED is considered for idle spectrum detection. The SNR is evaluated based on the joint of convolutional neural network (CNN) and long short-term memory (LSTM) network. Both theoretical derivations and simulation experiments validate the effectiveness of the proposed scheme.

/pdf/spectrum-sensing-based-on-adaptive-sampling-of-received-j59b6ejeoc.pdf

Spectrum sensing based on adaptive sampling of received signal

The direction-of-arrival (DOA) estimation model of this brief is based on deviation compensation model with input noise, and the performance of the traditional Least mean square (LMS) adaptive algorithm shows poor performance. Instead, total least squares (TLS) algorithm is widely used in models which contains input noise. Therefore, we present TLS algorithm for DOA estimation, which is used to update weight coefficient by searching the peak of the spatial spectrum to estimate the direction of the angle of arrival. Due to the unsatisfactory DOA estimation performance on fixed step algorithms, a variable step-size gradient descent total least-squares (VSS-GDTLS) is proposed. The variable step size strategy is derived by applying the instantaneous augmented weight vector and the estimated signal power. Moreover, the convergence of the proposed algorithm is analyzed. Finally, simulation results show the superiority of the VSS-GDTLS algorithm than GDTLS and the other adaptive algorithms.

A Variable Step Size Gradient-Descent TLS Algorithm for Efficient DOA Estimation

Nowadays, high-resolution DOA estimation techniques have been widely used in many fields such as sonar, communication, radio astronomy and biomedicine etc. Unfortunately, conventional algorithms cannot achieve high precision due to the limitation of the array aperture, and usually suffer from high computational complexity. In this paper, we innovatively model the DOA estimation problem of incoherent signals as a binary classification problem and greatly reduce physical complexity by virtue of sparse arrays. We first propose a classification framework for DOA estimation by benefiting from one-bit quantification. In this framework, any classification algorithm can be exploited to estimate DOA, such as logistic regression used in this paper. And then, sparse array is considered to reduce the exceeding number of antennas. Moreover, an iterative grid refinement procedure is presented to achieve more accurate DOA estimation and further reduces the actual number of physical antennas. Simultaneously, we derive Cramer-Rao bound (CRB) for the proposed algorithm. Finally, simulations are conducted for correctness and validation and the results illustrate the significant performance and reduction of complexity in hardware and computation over the existing methods.

/pdf/classification-based-one-bit-doa-estimation-for-sparse-2fqsaxl78i.pdf

Classification-Based One-Bit DOA Estimation for Sparse Arrays

Spectrum sensing is a key technique for future intelligent networks, such as AdHoc network, wireless sensor networks. However, the existing works suffer from high computational complexity, which is a huge challenge for those networks composed of many nodes with the limited computing capabilities. In this paper, we propose a simple and novel method based on the generalized likelihood ratio test(GLRT) criterion and offer a specific algorithm based on maximum value of power spectrum calculated by multitaper spectral estimation. We first derive the statistical characteristics and probability density function of test statistic, and the closed-form expression of detection probability and false-alarm probability are obtained. Then, we carry out some simulations to verify the proposed algorithm by taking BPSK signal as an example. In the simulations, we compare the sensing performance between the proposed algorithm and the existing methods. Both theoretical analysis and simulation results demonstrate the advantages of the proposed algorithm over the existing methods.

GLRT-based spectrum sensing by exploiting Multitaper Spectral Estimation for cognitive radio network

Nowadays, high-resolution direction of arrival (DOA) estimation techniques have been widely used in many fields such as sonar, communication, radio astronomy and biomedicine etc. Especially in the future wireless communication, base station (BS) is equipped with massive antennas to perform two-dimensional (2-D) DOA estimation. Unfortunately, conventional algorithms cannot achieve high precision due to the limitation of the array aperture and usually suffer from high computational complexity. Thus, we innovatively propose a SVM-based one-bit DOA estimation algorithm for 2-D DOA estimation. The 2-D DOA estimation problem is modeled as a classification problem due to one-bit quantification. Then, an iterative refinement procedure based on grid refinement is presented to achieve more accurate DOA estimation. Finally, simulations illustrate that the proposed method outperforms the existing methods in 2-D DOA estimation, effectively avoids the process of parameter pairing and requires less antenna elements.

2-D high precision DOA estimation based on SVM

In order to solve the problem of multi-parameter joint estimation of multi-band signals in mixed-field (MF), a subNyquist sampling structure is designed and a multi-parameter joint estimation framework based on symmetric uniform linear array (ULA) is proposed. Under this framework, multiparameter estimation of MF signals can be achieved at sub- Nyquist sampling rates. The frequency, direction of arrival (DOA) and distance parameters of the MF signals are automatically paired and each step is the estimation of one-dimensional parameters, which simplifies calculation complexity while avoiding parameter pairing ambiguity.

A Low Complexity Mixed-Field Sources Parameter Estimation Framework at Sub-Nyquist Sampling Rates

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