One of the key challenges in wideband spectrum sensing is that it has to be performed with extremely low latency and high accuracy over a large band to detect tiny spectrum holes. Motivated by this, we introduce a parallel convolutional neural network (ParallelCNN)-based wideband spectrum sensing approach, which aims to detect tiny spectrum holes with high sensing accuracy and low latency. The ParallelCNN contains two parallel CNNs, where the I/Q samples are split into two parts and processed concurrently by the two CNNs. Afterwards, the outputs of the two CNNs are combined to improve spectrum sensing performance. We extensively evaluate the accuracy, latency and generality performance of ParallelCNN. Performance evaluations indicate that the ParallelCNN approach outperforms other deep learning (DL)-based approaches such as the DeepSense and TCN, in terms of a higher precision, a higher recall and a lower latency, under a wide range of signal-to-noise (SNR) levels. In addition, the ParallelCNN can be generalized to different protocols such as LTE-M uplink transmissions, 5G NR downlink transmissions and WiFi transmissions.

In cognitive radio (CR), compressed spectrum sensing (CSS) is an attractive technology for detecting spectrum holes over a large band with a sub-Nyquist sampling rate. Traditional compressed spectrum (CS) reconstruction methods exhibit poor reconstruction performance and high complexity, due to the unknown and dynamically changing sparsity order of the observed signal, the hand-crafted optimization parameters involved in solvers, and dozens or hundreds of iterations. To cope with these challenges, we propose a wideband signal CS reconstruction algorithm by merging the iterative shrinkage thresholding algorithm (ISTA) and deep learning (DL) through deep unfolding and refer to it as the ISTA deep reconstruction network (ISTADR-Net). The parameters in ISTADR-Net (e.g., regularization and step size parameters) are learned end-to-end rather than being adjusted manually. Next, we propose a convolutional neural network (CNN)-based wideband spectrum sensing (WSS) network (CNNWSS-Net), which makes full use of the sparse characteristics of the reconstructed signal in the frequency domain to improve the WSS performance. The simulation results show that the proposed CSS scheme combining ISTADR-Net and CNNWSS-Net works well in low and high compression ratio scenarios, low and high signal-to-noise ratio (SNR) regions. Compared to the traditional CSS methods and other DL-based CSS methods, the proposed CSS scheme yields superior performance in terms of the probability of detection and the probability of false alarm. Furthermore, the computational complexity and computation time of the proposed CSS scheme are reduced compared to the traditional CSS methods.

Deep learning (DL) has exhibited immense potential across several domains, including image classification, speech recognition, and language translation, among others. Furthermore, it has been effectively employed in the domain of wireless communications, addressing intricate scenarios that involve dealing with uncertain channel models, high-speed data transmissions, and precise processing demands. To this end, we present CRNN-ResNet, a DL-based architecture to replace the carrier frequency offset compensation, in-phase and quadrature mismatch correction, channel estimation, equalization, and soft demapping modules of the orthogonal frequency division multiplexing (OFDM) receiver. We propose a novel residual neural network (ResNet) architecture that alternately stacks convolutional and bidirectional long short-term memory (BiLSTM) layers. This approach leverages the state prediction capabilities of BiLSTM and the feature extraction capabilities of convolutional layers to enhance the performance of receivers. Simulation results show that CRNN-ResNet achieves comparable performance to the ideal linear minimum mean square error (LMMSE) method and outperforms the conventional approximate LMMSE (ALMMSE) method and other known DL-based receivers under various non-ideal factors including radio frequency impairments, multipath fading, and Doppler frequency shift. Furthermore, CRNN-ResNet is more robust than other DL-based receivers when the cyclic prefix is omitted. We also demonstrate that CRNN-ResNet has lower computational complexity or time consumption compared to the other DL-based receivers.

In this letter, we introduce a novel blind equalization algorithm based on the convolutional neural network (CNN), to improve the bit error rate (BER) performance of equalizers against the multipath fading effect and nonlinear distortion. In contrast to existing neural network (NN)-based blind equalization algorithms, the proposed algorithm performs equalization and soft demapping jointly, which allows it to obtain soft bits directly from received data. In addition, the input preprocessing module is used to reorganize the received data to fully exploit the feature information of the received signals. Afterwards, the two-dimensional (2D) convolutions can be operated on a three-dimensional (3D) input array obtained by input preprocessing to recover the transmitted bits. Simulation results show that the proposed algorithm outperforms other CNN-based blind equalization algorithms, in terms of BER performance, under a wide range of channel models, signal-to-noise (SNR) levels and modulation schemes. Strikingly, the BER obtained by the proposed algorithm approaches the theoretical BER of multiple quadrature amplitude modulation (M-QAM) in linear channels.

In this paper, we investigate the spectrum access and power control problem in a heterogeneous cellular and vehicular communication network comprising both cellular links and vehicle-to-vehicle (V2V) links. We formulate the spectrum access and power control problem as a fully cooperative multi-agent reinforcement learning task, where each V2V link receives an identical reward at each time step. This strategy caters to the heterogeneous Quality-of-Service requirements, including the high reliability and low latency requirements for safety-centric applications, as well as the high transmission rate requirements for entertainment applications. Consequently, we design a multi-criterion objective reward function that accounts for the heterogeneous traffic demands. Subsequently, we introduce a robust multi-agent proximal policy optimization algorithm with a centralized training and decentralized execution framework. The proposed approach employs global observation to learn policies for each V2V agent during training, while allowing each agent to take an action based on its local observation during distributed execution phase. Furthermore, we consider imperfect spectrum sensing and outdated channel state information (CSI) in the work. Henceforth, a critic network incorporating long short-term memory layers is developed to effectively leverage the global observation, thereby mitigating the impact of outdated CSI and sensing errors. Extensive experiment results demonstrate the superiority of the proposed method comparing to other existing methods in terms of transmission success rate of cellular links, average capacity for entertainment applications of V2V links, and payload delivery rate for safety-centric applications of V2V links. Furthermore, we demonstrate the robustness of the proposed method against variations in V2V payloads and SINR thresholds.

In this paper, we study the two-dimensional direction of arrival (2D-DOA) estimation problem in a switching uniform circular array (SUCA), which means performing 2D-DOA estimation with a reduction in the number of radio frequency (RF) chains. We propose a covariance matrix completion algorithm for 2D-DOA estimation in a SUCA. The proposed algorithm estimates the complete covariance matrix of a fully sampled UCA (FUCA) from the sample covariance matrix of the SUCA through a neural network. Afterwards, the MUSIC algorithm is performed for 2D-DOA estimation with the completed covariance matrix. We conduct Monte Carlo simulations to evaluate the performance of the proposed algorithm in various scenarios; the performance of 2D-DOA estimation in the SUCA gradually approaches that in the FUCA as the SNR or the number of snapshots increases, which means that the advantages of a FUCA can be preserved with fewer RF chains. In addition, the proposed algorithm is able to implement underdetermined 2D-DOA estimation.