Internet of Things (IoT) has emerged as a key application domain in modern wireless communication systems, where its effectiveness heavily relies on the accuracy of channel state information (CSI). The deployment of large-scale reconfigurable intelligent surfaces (RIS) makes near-field effects non-negligible, bringing great challenges to channel estimation in RIS-assisted IoT scenarios. Existing near-field estimation methods often suffer from modeling errors due to Fresnel approximation or excessive computational complexity arising from dense polar-domain sparse representation. In this paper, we propose a novel RIS-aided near-field channel estimation framework tailored for IoT environments. We first present an accurate sparse channel model that captures the true spherical wavefront propagation and thus eliminates the modeling inaccuracies introduced by Fresnel approximation. Leveraging this model, we further incorporate IoT environmental context to derive a novel dimensionality-reduced sparse representation. Subsequently, we devise a fast sparse Bayesian learning (SBL)-based sparsity recovery scheme with coarse off-grid refinement, and embed generalized approximate message passing (GAMP) to significantly reduce computational complexity. Simulation results demonstrate that the proposed method achieves high estimation accuracy with reduced complexity, owing to the dimensionality-reduced sparse representation and the fast GAMP-embedded SBL framework. These advantages make it highly suitable for RIS-assisted IoT systems.

Joint channel estimation and data detection can enhance the performance of both tasks, improve communication efficiency, and reduce pilot overhead. However, this has been rarely explored in the context of reconfigurable intelligent surface (RIS)-assisted OFDM systems due to the complexity of handling multiple coupling effects between various data sequences and reflection coefficients. The recently proposed solution is tailored for purely phase modulations, whereas modern 5G/6G systems predominantly use quadrature amplitude modulation (QAM). To overcome this limitation, the paper proposes an auxiliary Bayesian learning approach for joint channel estimation and data detection with RIS-assisted OFDM systems, which integrates advanced techniques to offer robustness, improved performance, and reduced computational complexity. The novelties of the proposed method are threefold: i) present an auxiliary Bayesian learning framework to probabilistically link pilot and data information, effectively formulating the common sparsity pattern while avoiding potential modeling errors introduced by modulation schemes; ii) derive a hybrid approximate message passing (AMP) approach for efficient execution of Bayesian inference, with various approximation strategies seamlessly integrated to balance computational complexity and approximation accuracy; and iii) embed a novel rearrangement strategy to streamline multilayer message extraction, facilitating efficient message passing among sub-modules and subsequent parameter learning. Simulation results demonstrate its superiority.

Electroencephalography (EEG) signals record the electrical activity of the brain and have significant applications in neuroscience and medicine. However, accurately reconstructing EEG signals has been a challenge due to potential signal missing and noise interference during signal acquisition. The paper exploits the underlying structure of EEG signals and presents an efficient method for reconstructing EEG signals based on local graph signal smoothness (LGS-based). Firstly, we introduce the concept of local graph signal smoothness according to distinct functional areas of the cerebral cortex. Then, considering the graph that does not properly represent similar relationships between signals will have a negative impact on the reconstruction performance, we propose a joint graph learning and EEG signal reconstruction optimization method. Since it is not jointly convex, we utilize the alternating direction method of multipliers (ADMM) to solve it. In experiments, it is shown that the proposed LGS-based method outperforms benchmark methods in EEG signal reconstruction. Additionally, the proposed method achieves a higher signal-to-noise ratio (SNR) as well as a smaller normalized mean square error (RMSE).

Traditional machine learning techniques have achieved great success in improving data-rate performance and reducing latency in millimeter wave (mmWave) communications. However, these methods still face two key challenges: (i) their reliance on large-scale paired data for model training and tuning, which limits performance gains and makes beam predictions outdated, especially in multi-user mmWave systems with large antenna arrays, and (ii) meta-learning (ML)-based beamforming solutions are prone to overfitting when trained on a limited number of tasks. To address these issues, we propose a memristorbased meta-learning (M-ML) framework for predicting mmWave beam in real time. The M-ML framework generates optimal initialization parameters during the training phase, providing a strong starting point for adapting to unknown environments during the testing phase. By leveraging memory to store key data, M-ML ensures the predicted beamforming vectors are wellsuited to episodically dynamic channel distributions, even when testing and training environments do not align. Simulation results show that our approach delivers high prediction accuracy in new environments, without relying on large datasets. Moreover, MML enhances the model's generalization ability and adaptability.

ABSTRACT We propose an efficient image compression scheme leveraging Vision Mamba and dynamic convolution, addressing the limitations of existing methods, such as failure to capture long‐range pixel dependencies and high computational complexity. Our approach improves both global and local information learning with reduced computational cost. Experimental results on the Kodak, Tecnick and CLIC datasets show that our model achieves competitive performance with lower algorithm complexity. Our code is available on: https://github.com/Lynxsx/ICVM .

Accurate disparity estimation in light field (LF) imaging remains challenging due to the narrow baseline between adjacent sub-aperture images (SAIs) and the occlusion effect. Existing learning-based methods suffer from degraded performance in complex scenarios owing to the scarcity of high-quality and diverse training data. To address this limitation, we propose a Texture and Occlusion-oriented Light Field dataset (TO-LF) containing 78 carefully curated images. Unlike the widely used HCI 4D LF benchmark, TO-LF not only provides more training samples but also introduces a more challenging test set with complex occlusions and significant textureless regions. Furthermore, we present a viewpoint-selective sub-pixel cost volume construction method (VS-Sub), which extends disparity labels to the subpixel level for denser cost volumes, and employs dynamic dilated convolutions to differentiate between occluded and non-occluded viewpoints. Comprehensive experiments demonstrate that our framework achieves state-of-the-art (SOTA) performance in disparity estimation.

As an aperture-divided computational imaging system, microlens array (MLA) -based light field (LF) imaging is playing an increasingly important role in computer vision. As the trade-off between the spatial and angular resolutions, deep learning (DL) -based image super-resolution (SR) methods have been applied to enhance the spatial resolution. However, in existing DL-based methods, the depth-varying defocus is not considered both in dataset development and algorithm design, which restricts many applications such as depth estimation and object recognition. To overcome this shortcoming, a super-resolution task that reconstructs all-in-focus high-resolution (HR) LF images from low-resolution (LR) LF images is proposed by designing a large dataset and proposing a convolutional neural network (CNN) -based SR method. The dataset is constructed by using Blender software, consisting of 150 light field images used as training data, and 15 light field images used as validation and testing data. The proposed network is designed by proposing the dilated deformable convolutional network (DCN) -based feature extraction block and the LF subaperture image (SAI) Deblur-SR block. The experimental results demonstrate that the proposed method achieves more appealing results both quantitatively and qualitatively.

Abstract In this paper, an error recovery (ER) detection based on the lattice reduction (LR) algorithm with adaptive iteration is presented for uplink massive multiple‐input multiple‐output (MIMO) systems to obtain an improved bit error rate (BER) performance, especially when the number of active users increases. In the proposed algorithm, as the result of LR pre‐processing, a larger diagonally dominant index (DDI) is used to measure the diagonal dominance of gram matrix for the initialisation of the ER detection. In addition, the larger DDI could be further used to realise adaptive iteration, which helps to strike a trade‐off between the BER performance and the computational complexity. Moreover, two properties of the DDI are derived, and the simulation results show that the larger DDI leads to better detection performance, and it could be widely applied to linear elimination and MIMO detection algorithms.

In this study, the authors propose a generalisedswitching protocol (GSP) for increasing the flexibility of energy harvesting (EH) protocol and enhancing the security of amplify‐and‐forward (AF) multi‐antenna relaying system. The proposed GSP‐based relaying [generalised switching relaying (GSR)] system is established with target node assisted interference and EH technologies. In phase I, while the source transmits certain signal to the relay, the destination transmits an artificial noise to interfere the passive eavesdropper. In this phase, the relay harvests energy from the two nodes. In phase II, the relay node processes the two different received signals, which are from the source and destination, then AF them by utilising the harvested energy. The authors also derive a new analytical formula for the proposed protocol on ergodic secrecy capacity. The effect of the number of relay antennas N , the source transmitted power , the destination transmitted power and EH efficiency factors and on ergodic secrecy capacity are investigated. The simulation results show that as N , , , and increase, the ergodic secrecy capacity of GSR system increases accordingly. Moreover, the GSR protocol can provide higher ergodic secrecy capacity than both time switching relaying and powersplitting relaying protocols.

We first generalize the extended orthogonal space time block codes (EO-STBCs). Then, based on the generalized EO-STBCs (GEO-STBCs) and channel state information (CSI), the feedback bits are generated at the receiver. By using the feedback bits from the legitimate receiver, we propose a new advantage distillation scheme for multiple-input multiple-output (MIMO) wiretap channels.

Progressive edge-growth (PEG) algorithm construction builds a Tanner graph, or equivalently a parity-check matrix, for an LDPC code by establishing edges between the symbol nodes and the check nodes in an edge-by-edge manner and maximizing the girth in a greedy fashion. This approach is simple but the complexity of the PEG algorithm scale is O(nm), where n is the number of symbol nodes and m is the number of check nodes. We deal with this problem by construct a base matrix Hbof size mb× nbwith the PEG algorithm and simultaneously expand this base matrix into a parity-check matrix H of size mx n via the the Chinese remainder theorem (CRT), where m ≫ mband n ≥ nb. The size of the base matrix is expanded without decreasing the girth. For convenience, the PEG and CRT combined algorithm is referred to as the PEG-CRT algorithm in this paper. Since a smaller matrix is constructed with the PEG algorithm and the complexity of the CRT computation is negligible compared to the PEG algorithm, the complexity of the whole code construction process is reduced. Furthermore, the proposed algorithm has a potential advantage of saving storage space by storing a smaller matrix Hband expanding it to H "on-the-fly" in hardware. The expanded matrix H preserves the important properties of base matrix such as large girth, flexible code rate and low density. The complexity analysis shows that the complexity of the PEG-CRT algorithm does not grow with the code length n. Simulation results show that compared with the PEG LDPC codes of length nb, the expanded PEG-CRT LDPC codes have better bit error rate (BER) performance with the iterative decoding. It is also shown that compared with PEG LDPC codes of length n, which constructed with higher complexities, the PEG-CRT codes have similar BER performance.

In this paper, we propose a new method to transmit one more information bit by using orthogonal Space-Time Block Codes (STBC) for 4 antennas. Using two different STBCs matrices transmit one additional bit to achieve high rate-9/8. To maintain full rank and full diversity for the coding gain matrix, a new STBC code with full rate and full diversity is proposed in this letter. In order to implement a fast Maximum-likelihood (ML) decoding, a property of Frobenius norms of received signals is considered to reduce its complexity by checking the non-null values of the Frobenius at the transmitter side. Simulation results show that this method achieves better bit error rate (BER) performance and throughputs in the high SNR region without losing diversity gain.

Progressive edge-growth (PEG) algorithm construction builds the Tanner graph for an LDPC code by establishing edges between the symbol nodes and the check nodes in an edge-by-edge manner and maximizing the local girth in a greedy fashion. This approach is simple but the computational complexity of the PEG algorithm scale as O(nm), where n is the number of symbol nodes and m is the number of check nodes. We deal with this problem by first construct a base LDPC code of length n1with the PEG algorithm and then extend this LDPC code into an LDPC code of length n, where n ≥ n1, via the the chinese remainder theorem (CRT). This method increase the code length of an LDPC code generated with the PEG algorithm, without decreasing its girth. Due to the code length reducing in the PEG construction step, the computational complexity of the whole code construction process is reduced. Furthermore, the proposed algorithm have a potential advantage by storing a small parity-check matrix of a base code and extending it “on-the-fly” in hardware.

This paper presents methods to the construction of regular and irregular low-density parity-check (LDPC) codes based on Euclidean geometries. Codes constructed by these methods are quasi-cyclic. The degree distributions of proposed LDPC codes can be optimized by the curve fitting approach in the extrinsic information transfer (EXIT) charts. Simulation results show that these codes perform very well with the iterative decoding.

In this paper, the issue of capacity enhancement and power allocation has been presented using multi-antenna relay system. The multi-antenna relay system is constructed by a selective decode-and-forward (SDF) and an incremental-decode-and-forward (IDF) protocol. These two protocols are jointly introduced in a cooperative relay network. We have derived the issues of outage capacity and power allocation for each protocol and demonstrated that they can outperform high capacity and allocated 90% power. All relay input signals are combined, re-encoded and re-transmitted to the destination. We have proposed the multi-antenna relay with the joint protocol relay system which efficiently enhances the capacity and power allocation of the cooperative networks.

This paper introduces a class of Semi-Random (SR) Low-Density Parity-Check (LDPC) codes and a class of Quasi-Cyclic (QC) LDPC codes. Both of them are derived from Multiple Serially Concatenated Multiple Parity-Check (M-SC-MPC) codes and interleavers, therefore, have low encoding complexities. These two codes are called M-SR-LDPC codes and M-QC-LDPC codes, respectively, in this paper. The M-SR-LDPC codes are designed with the modified Progressive Edge-Growth (PEG) algorithm and the M-QC-LDPC codes are designed with the existing cycle-condition formula. Consequently, the proposed codes have large girths. Simulation results show that our codes perform very well over the AWGN channel with iterative decoding.

Progressive edge-growth (PEG) algorithm is known to construct low-density parity-check (LDPC) codes at finite code lengths with large girths. A simple variation of the PEG algorithm, linear-encoding PEG (LPEG) algorithm, can be applied to generate linear encodable LDPC codes. This paper presents a modified LPEG algorithm to construct fast encodable LDPC codes with a certain girth constraint. The presented fast encodable LDPC codes facilitate the parallel encoding processes for parity bit generations. Therefore, their encoding time is much shorter than that of LPEG codes.

In, the authors have presented a detailed analysis on the problem of distributed space-time coding employing Amplify-and-Forward (AF) protocol for a synchronized wireless relay networks, where maximum likelihood (ML) decoding is employed. In this paper, an iterative turbo-like decoder is introduced to improve system performance by allocating interleavers at each relay node and using multiuser concept. Due to different overall channel gains consisting source-relay link and relay-destination link, ordering and canceling techniques of interferences are considered in our design. Simulation results show an accepted improvement in the high SNR region.

This letter presents an approach to the construction of non-binary low-density parity-check (LDPC) codes based on alpha-multiplied circulant permutation matrices and hyperplanes of two different dimensions in Euclidean geometries. Codes constructed by this method have large girth and high binary column weight when the order of Galois field is high. Simulation results show that these codes perform very well with fast Fourier transform (FFT) based sum-product algorithm (SPA).