Chaotic waveforms, characterized by their noise-like properties and potential security advantages, have emerged as a promising solution for physical layer security (PLS) in wireless communication schemes. However, conventional differential chaos shift keying (DCSK) schemes suffer from limitations such as low data rate, energy inefficiency, and vulnerability to frequency-selective fading (FSF) channels. To address these challenges, this paper proposes a new matrix index technique for multi-carrier DCSK scheme, referred to asMIT-MC-DCSKscheme, tailored for robust and secure transmission over FSF channels. The proposed MIT-MC-DCSK scheme enables time-frequency diversity to mitigate the negative effect of fading while enhancing bit error rate (BER) performance. Theoretical BER expressions of the proposed scheme are rigorously derived over FSF channels, which are validated by extensive Monte Carlo simulations. Experimental results demonstrate that the proposed MIT-MC-DCSK scheme achieves superior BER performance and higher transmission efficiency than the state-of-the-art benchmark schemes. Furthermore, the proposed MIT-MC-DCSK scheme significantly reduces information leakage and improves secrecy capacity by leveraging matrix reconstruction technique, making it resilient against eavesdropping. Thanks to these advancements, the MIT-MC-DCSK scheme appears to be a robust candidate for secure and reliable communication in FSF scenarios.

3D NAND flash memory has attacked widespread attention due to its fast speed, high endurance, and strong reliability. However, its reliability decreases as program and erase time increases. To tackle this problem, current researches mostly employ machine learning models to predict flash memory failure, but there lacks the consideration of using the inter-layer difference and page type difference characteristics in flash memory chips to help failure prediction. Based on the internal characteristic of inter-layer difference and page type difference, two failure prediction algorithms are proposed in the paper, corresponding to the Standard1 and Standard2. For Standard1, an attention focused failure prediction (AFFP) algorithm is proposed. To predict the failure of the entire block, the proposed AFFP algorithm only focuses on the layer which is the most prone to failure and further predicts eight pages of the most likely failure pages within this layer. For Standard2, a low predict-frequency failure prediction (LPFFP) algorithm is proposed, which can reduce the frequency of failure prediction significantly and thus reduce the prediction overhead as much as possible. The experimental results show that, for Standard1, the AFFP algorithm can predict the block of failure accurately, and its data extraction and prediction overheads are reduced by 99.8% compared to the original algorithm, and meanwhile the F1-score exceeds 0.96. For Standard2, the LPFFP algorithm can predict the page of failure within a flash block accurately, and its F1-score exceeds 0.91 with a significant reduction in prediction overhead.

The escalating storage density of three-dimensional (3D) NAND flash memory introduces heightened channel noise, significantly degrading channel quality and exacerbating the raw bit error rate (RBER). The fixed and unchangeable code rate of the traditional global coupled low-density-parity-check (GC-LDPC) codes fails to address the performance mismatch arising from the stochastic temporal and spatial fluctuations in 3D NAND flash channels. To address the above issue efficiently, in this paper we investigate high-rate-compatible LDPC codes in 3D NAND flash memory. Firstly, we analyze the flash memory channel characteristics from the FPGA test platform and model the 3D triple-level cell (TLC) NAND flash channel. Subsequently, we propose a novel High-Rate-Compatible Algebraic GC-LDPC (HRC-A-GC-LDPC) code and conduct a comprehensive theoretical analysis of its structure and performance. Specifically, the HRC-A-GC-LDPC code features an information bit length of 4 KB and supports dynamic adjustment to multiple code rates, enabling it to match the varying characteristics of 3D NAND flash channels. This paper also examines and analyzes various construction methods for the HRC-A-GC-LDPC code corresponding to different column weights (CWs), providing insights into its design flexibility. Simulation results demonstrate that, in 3D TLC NAND flash memory, the proposed HRC-A-GC-LDPC code outperforms conventional GC-LDPC codes in both error correction capability and extended memory lifetime, validating its suitability for high-density 3D NAND flash applications.

With the rapid development of the Internet of Vehicles (IoV), the tight coupling between In-Vehicle Networks (IVN) and External Vehicle Networks (EVN) has made vehicular systems vulnerable to sophisticated cross-network attack chains. Existing Intrusion Detection Systems (IDS), however, typically operate in isolation on either IVN or EVN, and lack effective context-aware mechanisms for capturing inter-domain dependencies. To overcome this limitation, we propose CATwin-IDS, a context-aware intrusion detection framework that integrates digital twin technology with a lightweight Distilled Bidirectional Encoder Representations from Transformers (DistilBERT) model. In our design, Conditional Mutual Information (CMI) and Borderline Synthetic Minority Over-sampling Technique (Borderline-SMOTE) are applied for feature optimization and data balancing, while Temporal Self-Attention (TSA) enhances the modeling of spatiotemporal dependencies across heterogeneous traffic. The digital twin provides real-time bidirectional synchronization and a simulation environment, enabling proactive adaptation to dynamic threats. Experimental results on benchmark datasets (Car-Hacking, CICIoV2024, CICIDS2018, CICIoT2023) demonstrate that CATwin-IDS achieves higher accuracy and real-time efficiency compared with state-of-the-art methods, providing a holistic solution for securing IoV against cross-network intrusions.

D high-density NAND flash has a limited voltage window that narrows page read margins, making it vulnerable to read disturbance and triggering Read Refresh Operations (RROs). Existing methods ignore repeated voltage states caused by invalid pages on the same wordline, failing to fully utilize the voltage range. This paper proposes an Invalid Page-Aware Refresh (IPAR) scheme to achieve high-performance 3D highdensity NAND flash memory. First, IPAR merges repeated voltage states through reprogramming to widen valid page read margins and improve read endurance thresholds. Moreover, IPAR further adopts a page-level allocation strategy that contains two operations: hot-write data is placed on high-latency pages to allow more reprogramming operations, while hot-read data is allocated to low-latency pages of reprogrammed blocks, effectively mitigating read disturbance and reducing migration overhead. Trace-driven simulations based on FEMU show that IPAR reduces RROs triggers by 38.91%, write amplification by 48.08%, and average response time by 16.93% compared to baseline.

With the rapid advancement of the Internet of Vehicles (IoV), the scale of intelligent connected vehicles has expanded dramatically. Alongside this, the development of the low-altitude economy (LAE) has introduced new types of nodes, such as unmanned aerial vehicles (UAVs) operating in low-altitude airspace. This has led to an exponential increase in the demand for secure and efficient authentication between low-altitude UAVs and ground vehicles. The Chinese Remainder Theorem (CRT), characterized by its efficient computation, low storage requirements, high parallelism, and excellent real-time performance, CRT-based authentication schemes are highly compatible with the resource-constrained and low-latency requirements of IoV scenarios. However, existing CRT-based authentication schemes suffer from scalability bottlenecks. As the number of connected nodes increases, CRT-based authentication schemes exhibit a superlinear growth trend in computational overhead, posing significant challenges to their practical deployment scalability. In response to this challenge, we propose a Dynamic Group Management and Authentication Scheme based on CRT (DGMA-CRT). The proposed scheme employs dynamic grouping of vehicles to enable efficient intra-group domain key updates, avoiding the significant communication overhead caused by global domain key updates. The parameters for domain key updates depend solely on the group size, not the overall system scale, thus significantly enhancing efficiency while ensuring scalability. Furthermore, the scheme incorporates Shamir’s threshold mechanism to mitigate the risks of single-point attacks in key management and leverages elliptic curve cryptography (ECC) to establish a secure foundation for authentication. Simulation results show that DGMA-CRT, by utilizing grouped key updates and cryptographic mechanisms, significantly reduces computational overhead while maintaining robust security. Additionally, the scheme retains high efficiency as the system scales, demonstrating its suitability for large-scale IoV applications.

Rate splitting multiple access (RSMA) is a powerful multiple access technology that enables communication systems to achieve both reliable and fair data transmission by splitting and encoding user messages into common and private streams. This capability is particularly critical for space-air-ground-sea (SAGS) integrated networks, where heterogeneous nodes (e.g., satellites, UAVs, and underwater sensors) coexist with significant channel quality disparities. The common stream is formed by consolidating the diverse common messages that all users can decode, allowing the system to balance resource allocation and maintain reliable connectivity even for users with weaker channel conditions. Nevertheless, the performance of the common stream is often limited by the user with the weakest channel strength, a prevalent challenge in SAGS integrated networks with mixed near-far field communications and dynamic topology. To address this issue, this study proposes a generalized RSMA strategy to mitigate the rate limitation of common streams in RSMA systems, thereby enhancing system fairness and overall performance. The solution holds potential for crossdomain applications where strong and weak maritime/aerial users share spectrum resources. Furthermore, an algorithm is designed to optimize Max-min fairness (MMF) rate among all users. This is formulated as a non-convex optimization problem, which poses significant challenges for direct solution. To tackle this challenge, we design a low-complexity suboptimal iterative algorithm employing the successive convex approximation (SCA) method. Simulations demonstrate that the proposed generalized RSMA system outperforms traditional one-layer RSMA system and other existing counterparts, particularly in systems with weak users, by effectively enhancing the MMF rate and overall system fairness. This improvement suggests broader applicability for future integrated networks requiring unified management of heterogeneous links.

Vehicle trajectory prediction is a key technology in autonomous driving systems, playing a decisive role in ensuring autonomous driving safety and improving traffic efficiency. In complex traffic environments, the dynamic interactions between vehicles and pedestrians are extremely intricate, posing great challenges for accurate trajectory prediction. Traditional physical models exhibit obvious limitations when facing the nonlinear behaviors of traffic participants, while existing deep learning methods, although improved to some extent, still struggle to meet the practical application requirements in terms of computational efficiency and multimodal prediction capabilities. To address this, this paper proposes a novel vehicle trajectory prediction method based on Group-Rational KAN Enhanced Motion Transformer (GR-KAMT). This method combines the nonlinear modeling capability of the Kolmogorov-Arnold Network (KAN) with the attention mechanism of Transformer, effectively enhancing the trajectory prediction ability for complex scenes. Its uniqueness lies in the introduction of the efficient GR-KAN module, which demonstrates higher efficiency and accuracy in handling complex data relationships compared to traditional Multilayer Perceptrons (MLP), while supporting multimodal trajectory prediction to more comprehensively capture the behavioral patterns of traffic participants. Experiments on the NuScenes dataset demonstrate that GR-KAMT significantly outperforms existing methods in key performance indicators such as minimum average displacement error (minADE) and minimum final displacement error (minFDE), providing reliable support for safe decision-making in autonomous driving.

Edge caching is crucial for enhancing the performance of vehicular networks primarily by reducing service latency and improving data availability. However, existing research typically focuses only on unidirectional vehicle movement, which limits its application in complex scenarios, such as those in urban areas. To address this, we propose two novel edge caching strategies specifically tailored for the intricate vehicle movements and traffic signal controls at urban intersections, taking into account temporal variability of content popularity, making them more practical in the real world. The first caching strategy, based on dynamic programming (DP), is suited for scenarios with low traffic flow, providing an optimal solution and serving as a benchmark for evaluating the performance of the second strategy. This benchmark assesses how closely the second strategy approaches the optimal solution. The second strategy employs deep reinforcement learning (DRL) and is suitable for high traffic scenarios. Its performance, when compared with the DP approach in low traffic scenarios, demonstrates results that are near-optimal. Simulation outcomes indicate that the DRL strategy effectively adapts to changes in content popularity, significantly optimizing service latency and hit rates.

With the continuous improvement of data memory density, as the common solution for error correction of NAND flash, the low-density-parity-check (LDPC) code faces more challenges, such as the increasing code length requirements, the worse raw bit error rates (RBER), and the frequently varied channels. In this paper, a new (37536,33672) spatially coupled and globally coupled LDPC (SC-GC-LDPC) code is constructed to address the above challenge, which outperforms the traditional globally coupled LDPC (GC-LDPC) code with the gaps of 0.065 dB and 0.08 dB by exploiting the layered sum-product algorithm (SPA) and the layered normalized min-sum (NMS) algorithm, respectively. In the flash channel, the SC-GC-LDPC code also shows superior error correction performance than the conventional GC-LDPC codes with 25% improvement. Due to its special structure of the check matrix, an improved two-level decoding algorithm (ITLA) and an improved two-phase decoding algorithm (ITPA) are proposed in this paper. Specifically, the algorithms exploit the adjacent subcodes to correct the faulty ones, which reduces the number of check-node-updating (CNU). For the case of single subcode errors, the simulation results show that the number of CNU is reduced by ranging from 6.64% to 31.14% by applying our proposed algorithms, leading to significant improvement of throughput. Compared with the conventional algorithms, the ITLA and ITPA only show slight performance loss. Moreover, ITPA also provides high flexibility of decoding in the NAND flash memory, which can achieve the balance between the error-correction performance and the throughput.

As the number of stacked layers in 3D NAND flash memory increases, the channels experience more complex noise, leading to significant fluctuations in the error detection rate. Error-correcting codes with varying error correction capabilities (ECC) are required at different flash stages. To address this issue, we investigate multi-high-rate low-density parity-check (LDPC) codes for 3D NAND flash memory in this paper. Firstly, we mathematically model the 3D triple-level cell (TLC) NAND flash channel based on the obtained data from the test platform. Then we propose a multi-high-rate structured algebraic quasi-cyclic LDPC (MHR-SA-QC-LDPC) code. Specifically, the MHR-SA-QC-LDPC code contains the fixed number of information bits and can be adjusted to various rates according to the characteristics of NAND flash channels. Both additive and multiplicative group construction methods are analyzed in the prime field. Simulation results demonstrate that the proposed LDPC codes outperform existing benchmarks in 3D TLC NAND flash channels, improving both ECC performance and device lifetime.

In the era of big data, identity authentication in the Internet of Vehicles (IoV) is crucial for secure communications. With the diversification and expansion of IoV services, edge computing has emerged as a viable solution to effectively mitigate the high latency and scalability issues associated with traditional centralized identity authentication mechanisms. Despite the significant role that existing authentication schemes play in ensuring the security of IoV services, they still present relatively time-consuming issues during execution. To resolve this concern, we propose an efficient mutual authentication scheme, which combines the Edwards-curve Digital Signature Algorithm (EdDSA) with the Diffie-Hellman (DH) key exchange protocol, aiming to simplify the complex cryptographic computation process. To further reduce the identity authentication latency at the edge, we designed a rapid re-authentication process. Validated through simulation experiments, the proposed scheme has achieved approximately a 60.9% increase in authentication efficiency compared with the existing SEA scheme, demonstrating its effectiveness in enhancing the security and response.

The seamless integration of big data technology with the Internet of Vehicles (IoVs) has ushered in unprecedented opportunities in the fields of intelligent transportation systems, autonomous driving technology, urban planning, and personalized user services. These applications rely on vast amounts of data for training and optimization. However, traditional methods of data uploading may be subject to communication resource constraints and personal privacy security risks. In this context, Federated Learning (FL), as a distributed learning technology, demonstrates its unique advantages in protecting privacy and reducing communication burdens. Nevertheless, applying FL technology to IoVs faces numerous challenges, such as the complexity of IoV communication environments, data distribution imbalances, and potential malicious user attacks. To address these issues, we designed an IoV aggregation model framework to safeguard the security and robustness of model data, by employing model filtering strategies to counter potential malicious user attacks and utilizing model aggregation algorithms to improve the accuracy of the global model. Experimental results demonstrate that our proposed algorithm performs well in both vertical and horizontal FL, effectively mitigating various backdoor attacks, and significantly improving the accuracy and reliability of the global model.

Garbage collection (GC) is critical to improving 3D NAND flash memory space utilization. However, GC is very time-consuming for migrating valid data for error correction, which leads to a sharp decline in system performance. Copyback is an advanced command that can be used to accelerate data migration in GC. However, existing copyback-based GC scheme can not guarantee data reliability for various types of flash pages and cause a large decoding latency in the error correction process. In this paper, we first evaluate the copyback error characteristics that measure the quantitative relationships between the number of copybacks and the raw bit error rate (RBER) of upper, middle, and lower pages of triple level cell NAND flash through practical testing, respectively. Then we propose a multi-threshold copyback-based GC scheme (MCBGC). Under the specific RBER limit, the copyback thresholds of upper, middle, and lower pages are determined respectively based on the copyback error characteristics. Experimental results show that compared with the existing copyback-based GC scheme, the write latency of the proposed scheme can be improved by up to 17.7%.

In 3-D NAND flash memory, the channel is always offset due to the complicated interference from the program/erase (PE), including the data retention and layer interference, so that the channel estimation is desired. However, due to the physical structure of 3-D flash memory, there exists significant variation in channel parameters and inconsistent channel offsets among different wordlines. As a result, the process of estimating channel parameters for each individual wordline typically requires a considerable amount of computational resources, resulting in the high latency of system, which becomes a new challenge. To tackle this problem, two unsupervised learning algorithms are proposed to estimate the channel parameters, based on analyzing the error distribution in 3-D flash memory. To address the longer read latency introduced by the unsupervised learning algorithm for the channel estimation, we further propose a low-latency detection algorithm, which first detects whether the current channel needs to be updated. In the event that an update is required, the algorithm only periodically estimates the channel parameters during system idle times, resulting in a more efficient and streamlined process. Compared to the existing methods, the proposed algorithms can efficiently estimate channel parameters with lower-computational complexity. Moreover, combining with the search algorithm, a correction scheme is proposed to minimize the error between the estimated read reference voltage (RRV) and the optimal RRV in the actual device. Theoretical analysis and simulation results demonstrate that the proposed method can improve the lifetime of flash memory and reduces the number of read retries.

Low-density parity-check (LDPC) codes have been widely adopted to guarantee data reliability in 3-D NAND flash memory. However, the iterative LDPC decoding algorithm leads to high-decoding latency due to the iterative message transfer mechanism. Using a field-programmable gate array (FPGA) testbed, we first present the binary channel in NAND flash and analyze the single-symbol log-likelihood ratio (LLR) variation with the decoding iterations. Subsequently, we investigate the raw bit error ratio (RBER) characteristics of intrapage frames. To reduce the number of iterative decoding, we propose a frame feedback information aware decoding algorithm (FFIA-DA), combined with the single-symbol LLR variation and the similar error characteristics among intrapage frames. The proposed method uses the decoding feedback information of one frame to decrease the number of decoding iterations of other frames with similar RBER. Experiments show that the proposed approach can improve the decoding performance of LDPC and speed up decoding convergence.

The performance of over-the-air computation (AirComp) systems degrades due to the hostile channel conditions of wireless devices (WDs), which can be significantly improved by the employment of reconfigurable intelligent surfaces (RISs). However, the conventional RISs require that the WDs have to be located in the half-plane of the reflection space, which restricts their potential benefits. To address this issue, the novel family of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) is considered in AirComp systems to improve the computation accuracy across a wide coverage area. To minimize the computation mean-squared-error (MSE) in STAR-RIS assisted AirComp systems, we propose a joint beamforming design for optimizing both the transmit power at the WDs, as well as the passive reflect and transmit beamforming matrices at the STAR-RIS, and the receive beamforming vector at the fusion center (FC). Specifically, in the updates of the passive reflect and transmit beamforming matrices, closed-form solutions are derived by introducing an auxiliary variable and exploiting the coupled binary phase-shift conditions. Moreover, by assuming that the number of antennas at the FC and that of elements at the STAR-RIS/RIS are sufficiently high, we theoretically prove that the STAR-RIS assisted AirComp systems provide higher computation accuracy than the conventional RIS assisted systems. Our numerical results show that the proposed beamforming design outperforms the benchmark schemes relying on random phase-shift constraints and the deployment of conventional RIS. Moreover, its performance is close to the lower bound achieved by the beamforming design based on the STAR-RIS dispensing with coupled phase-shift constraints.

An$(n,k)$recoverable property array code is composed of$m\times n$arrays such that any$k$out of$n$columns suffice to retrieve all the information symbols, where$n > k$. Note that maximum distance separable (MDS) array code is a special$(n,k)$recoverable property array code of size$m\times n$with the number of information symbols being$km$. Expanded-Blaum-Roth (EBR) codes and Expanded-Independent-Parity (EIP) codes are two classes of$(n,k)$recoverable property array codes that can repair any one symbol in a column by locally accessing some other symbols within the column, where the number of symbols$m$in a column is a prime number. By generalizing the constructions of EBR and EIP codes, we propose new$(n,k)$recoverable property array codes, such that any one symbol can be locally recovered and the number of symbols in a column can be not only a prime number but also a power of an odd prime number. Also, we present an efficient encoding/decoding method for the proposed generalized EBR (GEBR) and generalized EIP (GEIP) codes based on the LU factorization of a Vandermonde matrix. We show that the proposed decoding method has less computational complexity than existing methods. Furthermore, we show that the proposed GEBR codes have both a larger minimum symbol distance and a larger recovery ability of erased lines for some parameters when compared to EBR codes. We also present a necessary and sufficient condition of enabling EBR codes to recover any$r$erased lines of a slope for any parameter$r$, which was an open problem. Moreover, we show that EBR codes can recover any$r$consecutive erased lines of any slope for any parameter$r$.

A locally repairable code (LRC) is a linear code that each code symbol can be encoded by downloading a small number (at most r) of other code symbols. In this paper, we use the parity-check matrix approach to obtain 58 classes code parameters for octonion-field optimal LRCs of distance 3. Then three optimal oction-field LRCs of distance 4 with locality r = 2 and r = 3 are given by adding linear independent set to the parity-check matrix.

Improving the repair performance of erasure code is a critical issue in order to maintain high data reliability in modern large-scale storage systems. Locally repairable codes (LRC) can improve the repair performance by locally repairing any single-node failure. In modern distributed cluster storage systems, the cross-cluster bandwidth is more scarce than the innercluster bandwidth. In this paper, we propose a well-designed placement strategy for LRC which is suitable for storing hot data. We show that our proposed placement strategy can reduce the cross-cluster bandwidth overhead in repairing node failures. Compared with the flat placement, the cross-cluster repair bandwidth can be reduced by more than 91.7% with our placement strategy in repairing one single-node failure, while the cross-cluster repair bandwidth reduction is 90.8% in repairing two-node failures.

As an attempt to tackle the low-data-rate issue of the conventional LoRa systems, we propose two novel frequency-bin-index (FBI) LoRa schemes. In scheme I, the indices of starting frequency bins (SFBs) are utilized to carry the information bits. To facilitate the actual implementation, the SFBs of each LoRa signal are divided into several groups prior to the modulation process in the proposed FBI-LoRa system. To further improve the system flexibility, we formulate a generalized modulation scheme and propose scheme II by treating the SFB groups as an additional type of transmission entity. In scheme II, the combination of SFB indices and that of SFB group indices are both exploited to carry the information bits. We derive the theoretical expressions for bit error rate (BER) and throughput of the proposed FBI-LoRa system with two modulation schemes over AWGN and Rayleigh fading channels. Simulation results not only verify the accuracy of theoretical BER and throughput analyses but also show that the proposed FBI-LoRa schemes can significantly increase the transmission throughput compared with the existing LoRa systems at the expense of a slight loss in BER performance. The proposed FBI-LoRa system is a promising alternative for high-data-rate Internet of Things (IoT) applications.

For vehicularad hocnetworks (VANETs), edge caching has attracted considerable research attention to maximize the efficiency and reliability of infotainment applications. In this article, we propose a two-layer distributed content caching scheme for VANETs by jointly exploiting the cache at both vehicles and roadside units (RSUs). Specifically, we formulate the content caching problem to minimize the overall transmission delay and cost as a nonlinear integer programming (NLIP) problem and propose an alternate dynamic programming search (ADPS)-based algorithm to solve it. In ADPS, we divide the original problem into three subproblems and then we use the dynamic programming (DP) method to solve each subproblem separately. To reduce the complexity, we further propose a cooperation-based greedy (CBG) algorithm to solve the large-scale original problem. Both numerical simulation results and experiments in the testbed show that the proposed caching scheme outperforms existed caching schemes, and the transmission delay and cost can be reduced by 10% and 24%, respectively, while the hit ratio can be increased by 30% in a practical environment, as compared to the popularity-based caching scheme.

Over-the-air computation (AirComp) has been recognized as a promising technique of enabling the fusion center (FC) to aggregate the data gleaned from massive distributed wireless devices (WDs). Nevertheless, the computational performance of AirComp is significantly affected by the potentially poor channel conditions between the WDs and FC due to physical obstacles. For mitigating this limitation, we employ reconfigurable intelligent surfaces (RISs) for enhancing the reception quality and, thus, improve the computational performance of AirComp. Moreover, the previous studies of RIS-assisted AirComp tend to rely on the real-time channel state information (CSI), leading to excessive overhead since the number of RIS elements is large. To mitigate the above issue, a mixed-timescale penalty-dual-decomposition (MTPDD) algorithm is proposed, in which the transmit power of each WD, the receive beamforming vector at the FC, and the passive beamforming matrix of the RIS are jointly optimized. We aim to minimize the average computation mean-squared error (MSE) over time with reduced signaling overhead. Specifically, at each time slot, we optimize the short-term transmit power and receive the beamforming vector based on the real-time low-dimensional CSI vectors. In contrast, in each frame, we update the long-term passive RIS beamforming matrix based on the channel statistics. Besides, we analyzed both the convergence and the computational complexity of the proposed algorithms. Simulation results verify the benefits of our proposed MTPDD beamforming algorithm. It is also shown that the performance of the MTPDD algorithm approaches that achieved by the scheme using real-time perfect CSI with reduced signal overhead.

Sharing a multiple-input multiple-output (MIMO) radar, the single platform can achieve dual functions of radar and communication (DFRC) within the same frequency spectrum, via the same transmit waveforms. In this paper, a space-time coding scheme is developed for transmit beamforming of DFRC and embedding communication information, without their cross-interference. For transmit beampattern design of DFRC, the shape approximation and integrated power approximation criteria are adopted respectively for waveforms optimization with the constant-envelope constraints of transmit waveforms and the equivalent signal in the communication direction. Based on the space-time coding scheme, the direct constellation mapping (DCM) and phase-rotation constellation mapping (PRCM) methods are proposed to embed information symbols. It turns out that the proposed space-time coding scheme for information constellation mapping can prevent missing information symbols and have better performance in bit-error rate (BER), compared to the existing information-embedding techniques. Moreover, the scheme can reduce the dependence of the communication data rate on radar pulse repetition frequency (PRF). Simulation results are presented to demonstrate the effectiveness of the proposed methods.

This paper focuses on mitigating the impact of stragglers in distributed learning system. Unlike the existing results designated for a fixed number of stragglers, we develop a new scheme calledAdaptive Gradient Coding (AGC)with flexible communication cost for varying number of stragglers. Our scheme gives an optimal tradeoff between computation load, straggler tolerance and communication cost by allowing workers to send multiple signals sequentially to the master. In particular, it can minimize the communication cost according to the unknown real-time number of stragglers in practical environments. In addition, we present aGroup AGC (G-AGC)by combining the group idea with AGC to resist more stragglers in some situations. The numerical and simulation results demonstrate that our adaptive schemes can achieve the smallest average running time.

Over-the-air computation (AirComp) has received substantial attention, given its ability to aggregate massive amounts of data from distributed wireless devices (WDs). However, the computation accuracy at the fusion center (FC) may be severely affected by receiving data corrupted by the poor channel conditions. To mitigate this issue, we consider the employment of reconfigurable intelligent surfaces (RISs) in the AirComp system considered for improving the quality of received data, and hence improve the computation accuracy. However, most previous contributions on RIS-assisted AirComp systems only employ a single RIS in the resultant single-RIS-assisted (SRIS-assisted) AirComp systems. We develop this concept further for mitigating the deleterious channel effects by conceiving a double-RIS-assisted (DRIS-assisted) AirComp system, where one of the RISs is located near the WDs and the other in the vicinity of the FC. We theoretically prove that the DRIS-assisted AirComp system outperforms its SRIS-assisted counterpart in terms of the resultant computation mean-squared-error (MSE). Furthermore, we propose a pair of algorithms for jointly optimizing the transmit power at the WDs, the receive beamforming vector at the FC, and the passive beamforming matrices at the RISs for minimizing the computational MSE. Specifically, the transmit power is updated by exploiting the Lagrange duality method, while the receive beamforming vector is optimized by utilizing the first-order optimality condition. Furthermore, a pair of techniques are developed for optimizing the passive beamforming matrices at the RISs based on semidefinite relaxation (SDR) and penalty-duality-decomposition (PDD), respectively. Both the complexity and the convergence of the proposed algorithms are analyzed. Finally, simulation results are provided for quantifying the overall performance of the resultant DRIS-assisted AirComp system.

In this paper, we address the problem of optimizing the power allocation at each time slot for energy-harvesting sensors in an IoT system, where each sensor transmits its observation via a coherent multiple access channel, and thus the observation vector received at the fusion center (FC) becomes a compressed version of the original observations. Our goal is to minimize the number of transmissions required by the high-fidelity reconstruction of the original observations in the FC. In this scenario, the power allocation, coupling with the channel effect, constitute an effective measurement matrix in compressive sensing. Because its performance decides the requirement on the number of transmissions, the goal can be achieved through constructing the measurement matrix as good as possible, or equivalently, solving the power optimization allocation problem, subject to the available energy constraints at each sensor. However, the sensors can only obtain unreliable and intermittent available energy, so that the traditional performance metric, i.e., mutual coherence (MC), of the measurement matrix cannot be directly used to guide the optimization, because an “equal-norm columns” assumption is implicitly required, but not satisfied in our scenario due to the available energy constraints. Moreover, this optimization problem is also non-convex. To overcome these obstacles, we first carry out a distortion analysis based on the generalized MC, which abandons the “equal-norm columns” assumption. The theoretical results indicate that the measurement matrix construction can be formulated as an optimization problem that not only minimizes the MC, but also minimizes the maximum and maximizes the minimum of the column norms of the effective matrix. We further transform this problem into a sequence of surrogate convex problems and iteratively find the solution. Numerical results show that the proposed framework improves the tradeoffs between reconstruction accuracy and the number of transmissions over various power allocation strategies. In some cases, where other strategies achieve a probability of exact recovery of below 0.7, the proposed framework can achieve a more than 0.9 probability.

As a spectral-efficiency technique for unequal error protection (UEP), hierarchical modulation (HM) bit-interleaved coded modulation (BICM) with iterative decoding (ID) has attracted interests in the wireless communication community. In this article, we conduct an investigation on spatially coupled (SC) protograph low-density parity-check (P-LDPC)-coded M-ary quadrature amplitude modulation (QAM) HM-BICM-ID systems. We first develop an information-theoretic methodology to calculate (log2M)/2 types of constellation-constrained average mutual information (AMI), which can be used to characterize the performance limits of different layers in the HM-BICM systems. We further propose a two-stage design approach to construct a novel type of constellations, called as structural quadrant (SQ) constellations, and develop a quadrant-based harmonic mean analysis to evaluate the nonfeedback and iterative-feedback asymptotic performance of the proposed constellations. In addition, we conceive a performance-analysis tool, referred to as multistream-based extrinsic information transfer (MS-EXIT) algorithm, for predicting the decoding thresholds of all individual coded-bit streams in the proposed SC P-LDPC-coded HM-BICM-ID systems. Simulation results not only agree well with the theoretical analyses but also indicate that the proposed SC P-LDPC-coded HM-BICM-ID systems are remarkably superior to the state-of-the-art counterparts. Thereby, the proposed SC P-LDPC-coded HM-BICM-ID systems are competent to provide diverse Quality of Service (QoS) for future wireless applications, such as 6G-enabled Internet of Things (IoT).

Code index modulated multi-carrier M-ary differential chaos shift keying (CIM-MC-M-DCSK) system not only inherits low-power and low-complexity advantages of the conventional DCSK system, but also significantly increases the transmission rate. This feature is of particular importance to Internet of Things (IoT) with trillions of low-cost devices. In particular, for e-health IoT applications, an efficient transmission scheme is designed to solve the challenge of the limited battery capacity for numerous user equipments served by one base station. In this paper, a new multiple-input-single-output simultaneous wireless information and power transfer (MISO-SWIPT) scheme for CIM-MC-M-DCSK system is proposed by utilizing orthogonal characteristic of chaotic signals with different initial values. The proposed system adopts power splitting mode, which is very promising for simultaneously providing energy and transmitting information of the user equipments without any external power supply. In particular, the new system can achieve desirable anti-multipath-fading capability without using channel estimator. Moreover, the analytical bit-error-rate expression of the proposed system is derived over multipath Rayleigh fading channels. Furthermore, the spectral efficiency and energy efficiency of the proposed system are analyzed. Simulation results not only validate the analytical expressions, but also demonstrate the superiority of the proposed system.

LoRa is a modulation technology for low power wide area networks (LPWAN) with enormous potential in 5G era. However, the performance of LoRa system deteriorates seriously in fading-channel environments. To tackle this problem, in this paper we introduce multiple-input-multiple-output (MIMO) configuration employing space-time block coding (STBC) schemes into the LoRa system to formulate an STBC-MIMO LoRa system. Then, we investigate the theoretical performance of the proposed system over Rayleigh fading channels. To this end, we derive the distribution of the decision metric for the demodulator in the proposed system. Based on the above distribution, we propose a closed-form approximate BER expression of the proposed system when perfect and imperfect channel state information (CSI) are considered. Furthermore, we analyze the diversity order and the throughput of the proposed system. The results of the diversity analysis demonstrate that the diversity order of the system in the imperfect CSI scenario with fixed channel estimate error variance is zero. However, in the imperfect CSI scenario with a decreasing channel estimate error variance and the perfect CSI scenario, the system can achieve full diversity. In addition, the results of the throughput analysis show that the throughput of the proposed system is little affected by CSI conditions. Simulation results verify the accuracy of the theoretical analysis and the excellent performance of the proposed system. Due to such superiority, the proposed STBC-MIMO LoRa system can be considered as a good scheme for LPWAN.

Expanded-Blaum-Roth (EBR) code encodes a (p - 1) × k information array into a p × p array such that any bit in a column can be recovered within the column and any k out of p columns can retrieve all (p - 1) × k information bits, where p is a prime number. In this paper, we generalize the construction of EBR code with a more flexible parameter, i.e., the number of bits stored in a column in the proposed construction can be not only a prime number but also an even number. In addition, we present an efficient encoding/decoding method for the proposed generalized EBR codes based on the LU factorization of Vandermonde matrix. We show that the proposed encoding/decoding method has less computational complexity than the existing method. Moreover, we show that the minimum symbol distance of generalized EBR codes is the same as that of EBR code for some parameters.

In this letter, we conduct an insightful study on protograph-low-density parity-check (PLDPC)-code-assisted deterministic measurement matrices for compressed sensing applications. As is well known, the recovery performance of conventional PLDPC sparse matrices (PLDPC-SM) will be dramatically degraded as the ratio of N to M increases, where M × N is the size of the matrices. To address the above issue, we propose a novel construction method to formulate a class of extended PLDPC-SM (EPLDPCSM) by intelligently inserting part of Hadamard matrices into the conventional PLDPC-SM. The proposed EPLDPC-SM not only can realize more flexible sizes with respect to the existing counterparts, but also can be amenable to lower coherence without costing more storage resources. Both coherence analyses and experiment results demonstrate that the proposed EPLDPC-SM are superior to the well-performing deterministic measurement matrices (i.e., PLDPCSM) and random matrices (i.e., random Gaussian matrices (R-GM) and random sparse matrices (R-SBM)) for various values of N/M1.

In this letter, we propose a new hyperspectral image (HSI) classification approach, called the random subspace ensemble with enhanced feature (RSE-EF), which trains several individual classifiers with enhanced spatial information. The proposed approach aims to address two common issues: the curses of the imbalanced training samples and high feature-to-instance ratio. Specifically, we first propose a similar-neighboring-sample-search (SNSS) method to address the issue of imbalanced training samples. Afterward, we generate the enhanced random subspaces (ERSs) that possess relatively lower dimensionality and more distinctive information compared with the original random subspaces (RSs) so as to alleviate the curse of high feature-to-instance ratio more effectively. Furthermore, a shallow neural network kernel-based extreme learning machine (KELM) is applied to the RSE-EF to classify image pixels. Experimental results on two public hyperspectral data sets illustrate that the proposed RSE-EF approach outperforms the state-of-the-art HSI classification counterparts.

Adaptive link selection for buffer-aided relaying can achieve significant performance gain compared with the conventional relaying with fixed transmission criterion. However, most of the existing link-selection strategies are designed based on perfect channel state information (CSI), which are very complex by requiring channel estimator. To solve this issue, in this paper, we investigate a buffer-aided differential chaos-shift-keying based simultaneous wireless information and power transfer (DCSK-SWIPT) relay system, where a decode-and-forward protocol is considered and the relay is equipped with a data buffer and an energy buffer. In particular, we propose two link-selection protocols for the proposed system based on harvested energy, data-buffer status and energy-shortage status, where the CSI is replaced by the harvested energy to avoid the channel estimation and the practical problem of the decoding cost at the relay is considered. Furthermore, the bit-error-rate (BER) and average-delay closed-form expressions of the proposed protocols are derived over multipath Rayleigh fading channels, which are validated via simulations. Finally, results demonstrate that both the proposed protocols not only provide better BER performance than the conventional DCSK system and DCSK-SWIPT relay system but also achieve better BER performance and lower average delay in comparison to the conventional signal-to-noise-ratio-based buffer-aided DCSK-SWIPT relay systems.

With the continual increase of storage density, NAND flash memory cells are particularly vulnerable to channel noise, which significantly degrades the storage reliability. To overcome this issue, low‐density parity check (LDPC) codes have been considered as a preferable choice for flash memory systems. To further improve the efficiency of the decoder, the convergence speed and the error‐rate performance are the key performance indicators for LDPC‐coded NAND flash memory. In this study, a threshold‐voltage‐drift‐aware scheduling for belief propagation (BP) decoding of LDPC‐coded NAND flash memory is introduced to improve the convergence speed and the error‐rate performance. The basic idea of the proposed scheduling is to find an appropriate updated order of variable nodes according to their corresponding locations of threshold voltages. Since the proposed scheduling updates less reliable variable nodes with higher priority, it can improve the efficiency of BP decoding. Simulation results show that the proposed scheduling not only significantly improves the convergence speed, but also obtains better error‐rate performance than the conventional serial scheduling.

As a bandwidth-efficient technique, bit-interleaved coded modulation with iterative demapping and decoding (BICM-ID) has attracted much research attention in the field of wireless communication. In this paper, we put forth a joint design of root-protograph (RP) low-density parity-check (LDPC) codes and BICM-ID, referred to as RP-based BICM-ID (RP-BICM-ID), over block-fading (BF) channels so as to boost the throughput under limited bandwidth. To preserve the full-diversity property of RP codes, we propose an efficient modulation strategy for the RP-BICM-ID system by taking the fading-block length into consideration. We also analyze the outage-probability limit of the RP-BICM-ID systems to establish the fundamental lower-limit on their word-error-rate (WER) performance. Moreover, we conceive a multi-level protograph extrinsic information transfer (ML-PEXIT) algorithm to derive the asymptotic WER and bit error rate (BER) of the RP-BICM-ID systems over BF channels. As a further insight, we develop a novel unequal-error-protection (UEP) bit-to-symbol (B2S) mapping scheme for the RP-BICM-ID systems, which gives rise to an additional performance improvement. Analyses and simulations show that the proposed RP-BICM-ID systems can not only realize desirable spectral efficiency, but also obtain near-outage-limit performance over BF channels. Therefore, the proposed RP-BICM-ID systems are very promising in achieving high-reliability and high-rate transmissions under slow-fading wireless-communication environments.

Although extreme learning machines (ELM) have been successfully applied for the classification of hyperspectral images (HSIs), they still suffer from three main drawbacks. These include: 1) ineffective feature extraction (FE) in HSIs due to a single hidden layer neuron network used; 2) ill-posed problems caused by the random input weights and biases; and 3) lack of spatial information for HSIs classification. To tackle the first problem, we construct a multilayer ELM for effective FE from HSIs. The sparse representation is adopted with the multilayer ELM to tackle the ill-posed problem of ELM, which can be solved by the alternative direction method of multipliers. This has resulted in the proposed multilayer sparse ELM (MSELM) model. Considering that the neighboring pixels are more likely from the same class, a local block extension is introduced for MSELM to extract the local spatial information, leading to the local block MSELM (LBMSELM). The loopy belief propagation is also applied to the proposed MSELM and LBMSELM approaches to further utilize the rich spectral and spatial information for improving the classification. Experimental results show that the proposed methods have outperformed the ELM and other state-of-the-art approaches.

The authors study the performance of protograph low‐density parity‐check (LDPC) codes over two‐dimensional (2D) intersymbol interference (ISI) channels in this study. To begin with, the authors propose a modified version of finite‐length (FL) extrinsic information transfer (EXIT) algorithm so as to facilitate the convergence analysis of protograph codes. Exploiting the FL‐EXIT analyses, the authors observe that the protograph codes optimised for 1D ISI channels, e.g. the 1D‐ISI protograph code, cannot maintain their advantages in the 2D‐ISI scenarios. To address this problem, the authors develop a simple design scheme for constructing a family of rate‐compatible improved protograph (RCIP) codes particularly for 2D‐ISI channels, which not only outperform the 1D‐ISI protograph code, but also are superior to the regular column‐weight‐3 code and optimised irregular LDPC codes in terms of the convergence speed and error performance. More importantly, such RCIP codes benefit from relatively lower error‐floor as well as linear encoding and fast decoding. Thanks to these advantages, the proposed RCIP codes stand out as better alternatives in comparison with other error‐correction codes for ultra‐high‐density data storage systems.

In this paper, we investigate the performance of the protograph low-density parity-check (LDPC) codes concatenated with space-time block code (STBC) over Rayleigh fading channels. We firstly extend the modified protograph extrinsic information transfer (PEXIT) algorithm in order to analyze the convergence performance of protograph codes. Based on the extended PEXIT algorithm and Gaussian approximation, we further derive the asymptotic bit-error-rate (BER) expression of protograph codes. Utilizing the PEXIT algorithm, theoretical and simulated BERs, we compare the performance of two classical protograph codes, i.e., accumulate-repeat-by-3-accumulate (AR3A) code and accumulate-repeat-by-4-jagged-accumulate (AR4JA) code, regular LDPC code, and optimized irregular LDPC codes, and illustrate that the AR3A code is superior to other three codes. Additionally, we discuss the impact of the number of receive antennas (i.e., NR) on the system performance and verify that the theoretical analyses hold as NR varies. As a result, the AR3A code stands out as a good candidate for wireless communication applications with multiple antennas.

Shuffled belief propagation (SBP), as a sequential belief propagation (BP) algorithm, speeds up the convergence of BP decoding, and maintains the least complexity of flooding BP. However, its performance is remarkably inferior to informed dynamic scheduling (IDS) BP algorithms. The authors design an informed dynamic location method, based on the residuals of variable node log‐likelihood ratio values, to reorder variable nodes of SBP to be updated. The location method significantly accelerates the convergence of SBP algorithm from two aspects: the unstable variable node with the largest residual to be updated first, and selecting the largest residual locally. Simulation results show that the proposed algorithm performs nearly the same as the best performance of IDS BP algorithms, and behaves prominently at high signal‐to‐noise ratios.

This letter considers the design of measurement matrices with low complexity, easy hardware implementation and good sensing performance for practical compressed sensing applications. We construct a class of sparse binary measurement matrices from protograph Low-density parity-check (LDPC) codes, which can satisfy these features simultaneously. The optimal performance of proposed matrices is analyzed from the mutual coherence aspect. Moreover, we obtain a sufficient condition for optimal construction of the matrices using proposed algorithm. Simulation experiments also demonstrate that orthogonal matching pursuit (OMP) algorithm performs better using the constructed matrices as compared with several state-of-the-art measurement matrices, such as random Gaussian matrices.

Non‐binary low‐density parity‐check (NB‐LDPC) codes can be directly constructed by using algebraic methods, or indirectly constructed by mapping well‐designed binary parity‐check matrices to non‐binary parity‐check matrices. Given the Tanner graph (TG) of a NB‐LDPC code, the selection of edge weights in the TG significantly affects the performance of the NB‐LDPC code. The authors introduce an edge weight distribution (EWD) parameter for the TG of NB‐LDPC codes. By utilising particle swarm optimisation (PSO), the EWD is optimised and it has been demonstrated that the optimal EWD approaches a two‐element distribution for large field size and high average variable‐node degree. With the optimised EWD, the authors construct a class of field‐compatible LDPC (FC‐LDPC) codes over GF( q ) whose parity‐check matrices only include elements 0, 1 and 2, and can be encoded and decoded over different field sizes. The simulations demonstrate that the performance of the proposed FC‐LDPC codes improves monotonically with increasing field size, and significantly outperforms that of the corresponding algebraic NB‐LDPC codes or NB‐LDPC codes generated with uniform distribution of non‐zero elements over GF( q ).

An algorithm based on the fitting of modified Extrinsic Information Transfer (EXIT) chart is proposed to calculate the thresholds of low-density parity-check (LDPC) codes optimized for the two-dimensional (2D) intersymbol interference (ISI) channels encountered in high-density magnetic recording, such as bit-patterned magnetic recording (BPMR) and two-dimensional magnetic recording (TDMR). The modified EXIT chart algorithm determines the LDPC threshold by fitting the EXIT curve of the variable node decoder (VND) combined with 2D detector (2D-DET) to the EXIT curve of the check node decoder (CND). Both the optimal Bahl-Cocke-Jelinek-Raviv (BCJR) 2D-DET and reduced-complexity Gaussian-approximated iterative row-column 2D-DET are considered.

Scheduling strategy is considered an important aspect of belief-propagation (BP) decoding of low-density parity-check (LDPC) codes because it affects the decoder's convergence rate, decoding complexity and error-correction performance. In this paper, we propose two new scheduling strategies for the BP decoding of non-binary LDPC (NB-LDPC) codes. Both the strategies are devised based on the concept of check node reliability and employ a heuristically defined threshold which can adapt to the communication channel variations. As the scheduling strategies only update a subset of the check nodes in each iteration, they result in reduced iteration cost. Furthermore, since the BP performs suboptimally for finite-length LDPC codes, especially for short-length LDPC codes, by enhancing the message propagation over the Tanner Graphs of short-length NB-LDPC codes, the new scheduling strategies can even improve the error-correction performances of BP decoding. Simulation results demonstrate that the new scheduling strategies provide good performance/complexity tradeoffs.

The simultaneous flooding scheduling is popular for Low-Density Parity-Check (LDPC) Belief Propagation (BP) decoding. Non-simultaneous sequential scheduling is superior to the flooding scheduling, and asynchronous dynamic scheduling has better FER performance than the sequential scheduling. However, all strategies encounter the trouble of locating the error variable node. This paper proposes an informed dynamic scheduling strategy, which utilizes the instability of the variable node and the residual of the variable-to-check message to locate the message to be updated first. The informed dynamic scheduling overcomes the trapping sets effectively. This paper also designs an informed dynamic scheduling strategy with adaptivity to pass more messages in parallel, which effectively postpones the influence of cycles in the Tanner graph. In some sense, the strategy lengthens cycles. Simulation results show that the two informed dynamic scheduling strategies outperform other algorithms.