Reconfigurable intelligent surface (RIS) is very promising for wireless networks to achieve high energy efficiency, extended coverage, improved capacity, massive connectivity, etc. To unleash the full potentials of RIS-aided communications, acquiring accurate channel state information is crucial, which however is very challenging. For RIS-aided multiple-input and multiple-output (MIMO) communications, the existing channel estimation methods have computational complexity growing rapidly with the number of RIS units N (e.g., in the order of N ² or N ³ ) and/or have special requirements on the matrices involved (e.g., the matrices need to be sparse for algorithm convergence to achieve satisfactory performance), which hinder their applications. In this work, instead of using the conventional signal model in the literature, we derive a new signal model obtained through proper vectorization and reduction operations. Then, leveraging the unitary approximate message passing (UAMP), we develop a more efficient channel estimator that has complexity linear with N and does not have special requirements on the relevant matrices, thanks to the robustness of UAMP. These facilitate the applications of the proposed algorithm to a general RIS-aided MIMO system with a larger N . Moreover, extensive numerical results show that the proposed estimator delivers much better performance and/or requires significantly less number of training symbols, thereby leading to notable reductions in both training overhead and latency.

As a promising modulation scheme for high mobility wireless communications, orthogonal time frequency space (OTFS) has received tremendous attention. In the case of bi-orthogonal waveforms, OTFS allows highly efficient detection in the delay-Doppler (DD) domain by exploiting the (circulant or sparsity) structures of the channel matrix. However, in the case of more practical waveforms, such as the rectangular waveform, the special structures do not exist. In the literature, to achieve low complexity detection, null symbols or cyclic prefixes (CPs) are frequently inserted to the OTFS signal block, which, however, leads to significant overhead. In this work, we partition the large channel matrix in the time domain into a number of small sub-matrices (blocks), and represent the system with a factor graph. Based on the graph representation, a message passing receiver is developed, where the unitary approximate message passing (UAMP) is used to handle the blocks. The proposed receiver does not require the frequent use of CPs or null symbols, while delivering promising performance.

Automatic modulation classification (AMC) algorithms based on deep learning (DL) have been widely studied in the past decade, showing significant performance advantage compared to traditional ones. However, the existing DL methods generally behave worse in computational complexity. For this, this paper proposes a lightweight convolutional neural network (CNN) for AMC task, where we design a depthwise separable convolution (DSC) residual architecture for feature extraction to prevent the vanishing gradient problem and lighten the computational burden. Besides that, in order to further reduce model complexity, global depthwise convolution (GDWConv) is adopted for feature reconstruction after the last (non-global) convolutional layer. Compared to recent works, the experimental results show that the proposed network can save approximately 70~98% model parameters and 30~99% inference time on two well-known benchmarks.

We propose a fast and near-optimal approach to joint channel-estimation, equalization, and decoding of coded single-carrier (SC) transmissions over frequency-selective channels with few-bit analog-to-digital converters (ADCs). Our approach leverages parametric bilinear generalized approximate message passing (PBiGAMP) to reduce the implementation complexity of joint channel estimation and (soft) symbol decoding to that of a few fast Fourier transforms (FFTs). Furthermore, it learns and exploits sparsity in the channel impulse response. Our work is motivated by millimeter-wave systems with bandwidths on the order of Gsamples/sec, where few-bit ADCs, SC transmissions, and fast processing all lead to significant reductions in power consumption and implementation cost. We numerically demonstrate our approach using signals and channels generated according to the IEEE 802.11ad wireless local area network (LAN) standard, in the case that the receiver uses analog beamforming and a single ADC.

We propose a novel specific emitter identification (SEI) algorithm based on amplitude probability distribution (APD). By studying the subtle differences in nonlinear distortion of power amplifiers, the proposed algorithm leverages the real signal amplitude interval probability distribution (AIPD) and analytical signal’s constellation circle interval probability distribution (CIPD) to extract the local distortion features. The simulation results on both the simulated signal and actual signal show that compared to the benchmark methods, the proposed algorithm can achieve significant performance gains with relatively low complexity and has high robustness to training set size and prior information of signal to noise ratio (SNR).

Based on the Laurent decomposition (LD) and the Rimoldi decomposition (RD) with the reduced-search technique, we propose a low-complexity continuous phase modulation (CPM) detection algorithm through the operation of the g -th power. In this way, the denominator of the modulation index is effectively equivalent to a smaller value, achieving the usage of much fewer trellis states in the Viterbi algorithm (VA). Finally, simulation results show that, compared to the optimal detection method, the proposed algorithms significantly reduce the complexity with negligible bit error rate (BER) loss.