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Ultrasonic signal acquisition platforms generate considerable amounts of data to be stored and processed, especially when multichannel scanning or beamforming is employed. Reducing the mass storage and allowing high-speed data transmissions necessitate the compression of ultrasonic data into a representation with fewer bits. High compression accuracy is crucial in many applications, such as ultrasonic medical imaging and nondestructive testing (NDT). In this study, we present learning models for massive ultrasonic data compression on the order of megabytes. A common and highly efficient compression method for ultrasonic data is signal decomposition and subband elimination using wavelet packet transformation (WPT). We designed an algorithm for finding the wavelet kernel that provides maximum energy compaction and the optimal subband decomposition tree structure for a given ultrasonic signal. Furthermore, the WPT convolutional autoencoder (WPTCAE) compression algorithm is proposed based on the WPT compression tree structure and the use of machine learning for estimating the optimal kernel. To further improve the compression accuracy, an autoencoder (AE) is incorporated into the WPTCAE model to build a hybrid model. The performance of the WPTCAE compression model is examined and benchmarked against other compression algorithms using ultrasonic radio frequency (RF) datasets acquired in NDT and medical imaging applications. The experimental results clearly show that the WPTCAE compression model provides improved compression ratios while maintaining high signal fidelity. The proposed learning models can achieve a compression accuracy of 98% by using only 6% of the original data.

In this paper, we propose a novel successive cancellation flip (SCF) decoding to reduce the computational complexity compared to the conventional SCF decoding by using distributed CRC bits. The proposed decoding reduces the number of estimations for information bits by early termination of decodings for failed frames of the first SC decoding, while trying to minimize the additional sorting operations. Simulation results show that the proposed SCF decoding reduces the computational complexity of repeated SC decoding at least 27% compared to the conventional SCF decoding.

Herein, we present an improved electrocardiogram (ECG) steganography for the protection of patients’ sensitive data, such as blood pressure, glucose level, lipid profile, and other personal health information. Based on the integer wavelet transform (IWT), we concealed secret bits in an ECG host using the standard deviation (SD) block, coefficient alignment technique, and least significant bit replacement. By computing the SD of a host block before bit embedding, data bits can be effectively embedded in the IWT coefficients. Simulations revealed that the payload of the proposed method is superior to that of existing techniques when the payload size is larger than 15.5 Kb, and our signal-to-noise ratio (SNR) is also better than theirs. In addition, our method is superior to other ECG steganographic methods in terms of the peak SNR and hiding capability. The proposed method not only provides good perceived quality but also protection against attacks. With its short implementation time, our proposed method can be applied in portable biometric devices or wearable electronics.

In image forensics, quantization step estimation plays a crucial role in revealing the JPEG compression history. This study focuses on the estimation for images that have been JPEG compressed and re-saved in lossless formats. Several effective methods have been developed, whereas the performance on small sized images still needs to be improved. In this paper, a novel JPEG quantization step estimation method with low complexity and high efficiency is proposed. First, for a given decompressed image, as the special shape of its DCT coefficients distribution is highly related to the quantization step, a function of the candidate step taking a similar shape is designed. Then, the quantization step is determined as the candidate leading to the maximum response of the designed function on the probability density function of DCT coefficients. The relation between the maximum response and the quantization step is verified mathematically, which provides a theoretical basis for the proposed method. In addition, to further improve the estimation performance, two fine adjustment procedures are adopted. Experimental results demonstrate that the proposed method outperforms some state-of-the-art works, especially on small sized images.

In this paper, we propose a method to estimate the mean square error (MSE) of the estimated channel for ATSC (Advanced Television Systems Committee) 3.0 systems. When combining the channel MSE and noise variance, we can better estimate the a priori LLR (log likelihood ratio) for the sum–product algorithm. The experimental results show that doing so yields better BER (bit error rate) performance in the 0 dB echo channel. The improvement in the 2-D channel estimation is about 0.2 dB. In the 1-D estimation case, the proposed approach is essential to decode codewords.

For NAND flash memory, designing a good low-density parity-check (LDPC) decoding algorithm could ensure data reliability. When the decoding algorithm is implemented in hardware, it is necessary to achieve attractive trade off between implementation complexity and decoding performance. In this paper, a novel low bit-width decoding scheme is introduced. In this scheme, the Quasi-Cyclic LDPC (QC-LDPC) is used, and the row-layered normalized min-sum algorithm is improved by restricting the amplitude of minimum and second-minimum values in each check node (CN) updating. The simulation shows that our approach achieves a lower UBER (Uncorrectable Bit Error Rate) with a negligible increase in computational complexity, especially with low precision input log-likelihood ratio (LLR).

In the recently proposed cell-free massive multiple-input and multiple-output (MIMO) networks, the capacity of fronthaul links connecting all access points (APs) and a central processing unit (CPU) is limited. In this context, taking into consideration the spatial channel correlation at the APs, this article investigates the performance of cell-free massive MIMO systems with variable-resolution quantization, i.e., each analog-to-digital converter at the APs and quantizer at the CPU use arbitrary bits for quantization. Specifically, we first introduce a technique based on linear minimum mean-square to perform channel estimation. On this basis, we then derive the closed-form expressions of achievable rates over spatially correlated Rayleigh fading channels for both uplink and downlink if maximal ratio combining and maximal ratio transmission are used at the CPU. Finally, simulation results validate our theoretical analyses and corroborate that the performance of channel estimation and achievable rates reduces as the spatial correlation strengthens. Moreover, from a statistic perspective, under the constraint of the total number of quantization bits, it is preferable to assign more bits to the AP with larger aggregated large-scale fading coefficient and lower channel correlation.

In this article, channel estimation for millimeter-wave massive multiple-input–multiple-output (MIMO) systems with hybrid beamforming is considered. With a large number of antenna elements, a progressive time delay across antenna elements incurs a change in the beam direction as a function of subcarriers in an orthogonal frequency-division multiplexing system. Considering the difference in delay across the entire array, a new channel estimation framework is proposed by using a maximum likelihood criterion as a function of the analog training beam and pilot subcarrier assignment. The resulting design achieves asymptotic orthogonality among the channel paths and minimizes the mean squared error. An extension of the proposed design to a uniform planar array is discussed. The Cramer–Rao lower bound for joint channel estimation with hybrid beamforming is derived to verify the effectiveness of the proposed design.

Building a balanced relation between image quality and the payload, the robustness of the method in facing electronic attacks and securing data, all the mentioned processes represent the main challenge in steganography. Here, a novel approach to steganography is suggested using Vigenere Cipher and Huffman Coding methods to encrypt and compress the mystery message content. This approach will raise the security and ensure the message content cannot be extracted without earlier knowledge of decrypting rules and the Huffman Dictionary Table. Later, the image is segmented into blocks, size (w*h) groups for each block and with each group having n pixels. Subsequently, the knight tour algorithm and arbitrary function are utilized to select which blocks and groups can be used to conceal the mystery digit within a specific pixel in the group randomly. This is to address the weakness of the Exploiting Modification Direction (EMD) technique that uses a serial selection to enhance the robustness of the suggested scheme. The EMD technique is then utilized to insert the mystery digits inside a specific pixel. Later, the chi-square method is employed to apply statistical attacks on the stego-image to estimate the suggested scheme robustness. The empirical outcomes show that the suggested scheme is more efficient compared to the old Steganography schemes with respect to Imperceptibility by PSNR of 55.71 dB, the Payload of 52,400 bytes and the robustness.

Application of deep learning to error control coding is gaining special attention and neural network architectures on decoding are approached to compare with conventional ones. Turbo codes conventionally use BCJR algorithm for decoding. In this paper, performances of neural Turbo decoder and deep learning-based Turbo decoder are examined. A category of sequential codes are utilized to construct the RSC (Recursive Systematic Convolutional) codes as basic elements for Turbo encoder. Sequential codes suit the requirement of memory element present in convolution codes, which act as components for Turbo encoder. Turbo decoders are constructed by two means; as neural Turbo decoder and deep learning Turbo decoder. Both structures are based on recurrent neural network (RNN) architectures. RNN architectures are preferred due to the presence of memory as a feature. BER performance of both is compared with that of a convolutional Viterbi decoder in awgn channel. Both the structures are studied for different input data-lengths and code rates.

In this paper, we consider the problem of full-duplex covert millimeter wave (mmWave) communications, where a mmWave transmitter (Alice) sends information signals to its intended receiver (Bob) covertly in the presence of a watchful warden (Willie). For covering the presence of Alice, Bob operates in the full-duplex mode and generates jamming signals with a time-varying power. We investigate the covert rate optimization for both the single data stream case and the multiple data streams case under the constraints of the detection error probability at Willie. Specifically, for the single data stream case, we analytically characterize the minimum detection error probability at Willie and establish a framework for optimizing the analog beamforming, transmit power, and analog jamming jointly. As for the case of multiple data streams, we derive a tractable lower bound of the minimum detection error probability at Willie and formulate a joint optimization of the hybrid precoder and analog jamming design problem for the maximization of the achievable covert rate. Although the joint design problem is nonconvex, we adopt the penalty decomposition technique to handle the effect of the coupling between the analog precoder and digital precoder paving the way for the development of an iterative algorithm to locate its Karush-Kuhn-Tucker (KKT) solution. Finally, we show that our proposed joint design algorithm can be adapted to handle the multi-antenna Willie scenario and simulation results show that our proposed joint design algorithms can achieve significantly better performance as compared with some benchmark schemes.

Neural Network Coding and Representation (NNR) is the first international standard for efficient compression of neural networks (NNs). The standard is designed as a toolbox of compression methods, which can be used to create coding pipelines. It can be either used as an independent coding framework (with its own bitstream format) or together with external neural network formats and frameworks. For providing the highest degree of flexibility, the network compression methods operate per parameter tensor in order to always ensure proper decoding, even if no structure information is provided. The NNR standard contains compression-efficient quantization and deep context-adaptive binary arithmetic coding (DeepCABAC) as core encoding and decoding technologies, as well as neural network parameter pre-processing methods like sparsification, pruning, low-rank decomposition, unification, local scaling and batch norm folding. NNR achieves a compression efficiency of more than 97% for transparent coding cases, i.e. without degrading classification quality, such as top-1 or top-5 accuracies. This paper provides an overview of the technical features and characteristics of NNR.

Single carrier modulation (SCM) schemes are attractive for uplink (UL) transmissions due to improved power efficiency at the user equipment (UE) transmitter compared with multi-carrier modulation schemes. In a massive MIMO scenario with SCM, the UL detection must mitigate the effects of inter-symbol interference and multi-user interference. This processing is effectively performed in the frequency domain (FD) using a minimum mean squared error (MMSE) detector when the transmission is framed with a cyclic prefix. This paper analyzes an MMSE-based detector compatible with distributed processing in a time-division duplex (TDD) system. The matrix inverses computed for the UL detection are then reused to perform multi-user precoding for the downlink (DL). We find that this scheme yields tremendous savings in computational complexity compared to commonly used zero-forcing (ZF) precoding without sacrificing any performance. Since MMSE processing introduces a bias to the estimates, we derive the scalar coefficients necessary to cancel the MMSE bias. The impact of channel estimation errors is analyzed for both the UL and DL cases in conjunction with a power-efficient approach to SCM channel estimation. Moreover, extensive simulations are performed to confirm our theoretical findings.

Detecting double JPEG compression with the same quantization matrix is a challenging task in image forensics. To address this problem, in this paper, a novel method is proposed by leveraging the component convergence during repeated JPEG compressions. Firstly, an in-depth analysis of the pipeline in successive JPEG compressions is conducted, and it reveals that the rounding/truncation errors as well as JPEG coefficients tend to converge after multiple recompressions. Based on this fact, the backward quantization error (BQE) is defined, and we find that the ratio of non-zero BQE for single compression is larger than that for double compression. Moreover, to exploit the convergence property of JPEG coefficients, a multi-threshold strategy is designed for capturing the statistics of the number of different JPEG coefficients between two sequential compressions. Finally, the statistical features of the dual components are concatenated into a 15-D vector to detect double JPEG compression. Experimental results demonstrate the efficiency of the proposed method, which outperforms some state-of-the-art schemes.

Just as rank-metric or Gabidulin codes may be used to construct rate-diversity tradeoff optimal space-time codes, a recently introduced generalization for the sum-rank metric -- linearized Reed-Solomon codes -- accomplishes the same in the case of multiple fading blocks. In this paper, we provide the first explicit construction of minimal delay rate-diversity optimal multiblock space-time codes as an application of linearized Reed-Solomon codes. We also provide sequential decoders for these codes and, more generally, space-time codes constructed from finite field codes. Simulation results show that the proposed codes can outperform full diversity codes based on cyclic division algebras at low SNRs as well as utilize significantly smaller constellations.

Built on deep networks, end-to-end optimized image compression has made impressive progress in the past few years. Previous studies usually adopt a compressive auto-encoder, where the encoder part first converts image into latent features, and then quantizes the features before encoding them into bits. Both the conversion and the quantization incur information loss, resulting in a difficulty to optimally achieve arbitrary compression ratio. We propose iWave++ as a new end-to-end optimized image compression scheme, in which iWave, a trained wavelet-like transform, converts images into coefficients without any information loss. Then the coefficients are optionally quantized and encoded into bits. Different from the previous schemes, iWave++ is versatile: a single model supports both lossless and lossy compression, and also achieves arbitrary compression ratio by simply adjusting the quantization scale. iWave++ also features a carefully designed entropy coding engine to encode the coefficients progressively, and a de-quantization module for lossy compression. Experimental results show that lossy iWave++ achieves state-of-the-art compression efficiency compared with deep network-based methods; on the Kodak dataset, lossy iWave++ leads to 17.34 percent bits saving over BPG; lossless iWave++ achieves comparable or better performance than FLIF. Our code and models are available at https://github.com/mahaichuan/Versatile-Image-Compression.

Cryptography has a role to secure an important information. Until now, many varieties of cryptographic algorithms are available in the literature. In this paper, we propose a cryptographic algorithm based on Type IVa max-plus wavelet transforms (MP-Wavelets). Encryption and decryption algorithms are constructed based on the analysis and synthesis process of Type IVa MP-Wavelets, respectively. The encryption key contains the number of channels in all levels. The encryption key is chosen such that multiplication of the number of channels in all levels is greater than or equal to the number of characters in the Plaintext. The decryption key consists of the encryption key and a sequence generated by the binary encoding of detail components. This guarantees that the decryption key is very difficult to obtain using the brute-force method. The cryptographic process involves only maximization and addition operations as main operations. The experiments and analysis show that the algorithm is a good cryptographic algorithm based on the correlation between Plaintext and Ciphertext, encryption quality, the decryption key space, cryptanalysis (Ciphertext-only attack) and security analysis (entropy analysis, key sensitivity, Plaintext sensitivity). This algorithm is also efficient in the running time, because the complexity is linear w.r.t. the number of characters in the Plaintext.

Orthogonal Frequency Division Multiplexing (OFDM) is the best choice for wireless and wired high data rate communications. The OFDM system has many advantages such as high spectral efficiency, robustness to channel fading, and immunity to impulse interference. However, it has a main drawback, which is the Peak to Average Power Ratio (PAPR). Selecting Mapping (SLM) and Partial Transmit Sequences (PTS) are the two essential techniques for reducing the PAPR; however, they need to send side information to indicate how the transmitter generates signals. In this paper, Particle Swarm Optimization (PSO)-based dummy sub-carriers are inserted with the data to reduce PAPR without transmitting side information. The incorporation of PSO-based Dummy Sub-carriers Insertion (DSI) method on this system is performed by measuring the PAPR at the IFFT output (Inverse Fast Fourier Transform) after adding 6 adaptive sub-carrier sequences to the input data of IFFT. The PSO-based dummy algorithm decides to send the data once the PAPR of this data sequence and dummy sub-carrier is under a specific threshold. Using MATLAB as a mathematical simulation tool, simulation results are presented for a standard OFDM network model. The network model has been simulated in an Additive White Gaussian Noise (AWGN) channel environment to investigate PAPR and Bit-Error Rate (BER) performance. The simulation results show that the dummy sequence based on PSO reduces PAPR down to 4 dB compared to a conventional OFDM system and down to 3 dB compared with the clipping technique for the same BER performance.

We consider the coding problem for lossy source coding with side information at the decoder, which is known as the Wyner-Ziv source coding problem. The goal of the coding problem is to find the minimum rate such that the probability of exceeding a given distortion threshold is less than the desired level. We give an equivalent expression of the minimum rate by using the chromatic number and a notion of covering of a set. This allows us to analyze the coding problem in terms of graph coloring and covering.

Rateless codes can adapt to the wireless channel conditions without accurate channel state information (CSI) at the transmitter side, avoiding CSI feedback and retransmission, and thus are a promising channel coding approach to meet the stringent requirements of ultra-reliable low-latency communications (uRLLC). This paper investigates a self-adaptive ordered statistics decoder (S-OSD) scheme for finite block length non-binary Raptor code (NBRC). Aiming at minimizing the decoding complexity, some strategies for our S-OSD scheme are designed, including the segmentation rules of most reliable basis, the generating and discarding rules of test error patterns, and the stop criteria, respectively. In addition, an upper bound of block error rates (BLER) for the NBRC under OSD is derived, which can be used to estimate the number of NBRC symbols required to successfully decode the input information via the S-OSD. Simulation results show that the complexity of our S-OSD scheme is greatly reduced comparing to the existing OSD schemes, while achieving very low BLER in the short block length regime.

Serial decoding algorithms of low-density parity-check (LDPC) code converge efficiently with low errors. Previously, a serial decoding algorithm, named a shuffled belief-propagation (SBP), was applied in turbo equalization of bit-patterned magnetic recording (BPMR) systems. With the SBP algorithm, an LDPC decoder converged twice as fast as one using conventional BP algorithms. We further improved the convergence speed of SBP by updating the messages in an adaptive order, which played a flexible role throughout decoding. We proposed two adaptive-serial algorithms for LDPC codes in turbo equalization. One updated the messages using the extrinsic loglikelihood ratio (LLR) and the result of the parity-check equation checking. The second contained an additional rule that tracked the LLR sign changes in each iteration. Both algorithms converged faster and with lower bit error rates (BERs) than the SBP and previous adaptive-serial algorithms in a BPMR system with media noise.

As an emerging non-volatile memory (NVM) technology, spin-torque transfer magnetic random access memory (STT-MRAM) has received great attention in recent years since it combines the features of low switching energy, fast write/read speed, and high scalability. However, process variation and thermal fluctuation severely affect the data integrity of STT-MRAM, resulting in both write errors and read errors. Therefore, effective error correction codes (ECCs) are necessary for correcting memory cell errors. Meanwhile, the design of channel quantizer plays a critical role in supporting error correction coding for STT-MRAM. In this work, we propose a union bound analysis which can accurately predict the word error rates (WERs) of ECCs with maximum-likelihood (ML) decoding over the quantized STT-MRAM channel. The derived bound provides a theoretical tool for comparing the performance of ECCs with different quantization schemes at very low error rate levels without resorting to lengthy computer simulations. Moreover, we also propose a new criterion to design the channel quantizer by minimizing the WERs of ECC decoding that are obtained from the union bound analysis. Numerical results show that the proposed union-bound-optimized (UBO) quantizer can achieve better error rate performance than the state-of-art quantizers for STT-MRAM.

In this paper, we address the Alamouti code in Filter-Bank Multicarrier with Offset Quadrature Amplitude Modulation (FBMC/OQAM), and propose a novel block-wise Alamouti code, in which a repeated block is designed to eliminate the imaginary interference among FBMC/OQAM symbols. The proposed approach exhibits a higher spectral efficiency than the conventional block-wise Alamouti scheme as only one column of zero symbols is used as a guard interval instead of two. Furthermore, compared to the conventional scheme, the bit error ratio is improved in the proposed block-wise Alamouti, as the assumption on channel invariance at two adjacent subcarriers is not required. Simulation results indicate that, with a significantly reduced overhead, the proposed Alamouti scheme achieves better performance over the conventional block-wise Alamouti scheme.

As the smallest structural unit of feature mapping, the convolution kernel in a deep convolution neural networks (DCNN) convolutional layer is responsible for the input channel features to output channel features. A specific convolution kernel belongs to a specific group from the perspective of the input channel, and it belongs to a specific filter from the perspective of the output channel. If the input and output channels are simultaneously considered in the pruning process, the performance of the pruning model can be further improved. This paper proposes progressive kernel pruning with salient mapping of input-output channels, introduces the concept of input-output channel saliency and defines single-port salient mapping channels and dual-port salient mapping channels. This study demonstrates that single-port salient mapping channels can ensure that each input channel signal has a relatively strong convolution kernel mapped to the output channel, and vice versa. The dual-port salient mapping channel is a channel with high feature mapping abilities from both the input and output directions. Additionally, the average mapping ability measure index is defined, which is used to control the kernel pruning process of the single-port salient mapping channel to switch to the kernel pruning process of the dual-port salient mapping channel. The experimental results and analysis show that the method proposed in this paper can be used to effectively prune a heavyweight model and a lightweight model and can obtain a better accuracy under the conditions of higher compression ratio and acceleration ratios. For example, when VGG-16 is pruned on CIFAR-10, the compression ratio and acceleration ration are 91.00 × and 15.24 × , respectively, and the classification accuracy of the model decreased slightly by 0.22%. When ResNet-101 is pruned on ImageNet, the compression ratio and acceleration ratio are 3.90 × and 3.38 × , respectively, and the classification accuracy of the model decreased slightly by 0.48%. The proposed method is significantly better than state-of-the-art methods.

The sparsity of multipaths in wideband channel has motivated the use of compressed sensing for channel estimation. In this letter, we propose an entirely different approach to sparse channel estimation. We exploit the fact that $L$ taps of channel impulse response in time domain constitute a non-orthogonal superposition of $L$ geometric sequences in frequency domain. This converts the channel estimation problem into the extraction of the parameters of geometric sequences. Notably, the proposed scheme achieves the error-free estimation of the whole bandwidth with a few pilot symbols if the excess delay is bounded to a certain value.

In this paper, a simplified successive cancellation (SC) decoding algorithm for polar codes on insertion/deletion error channels is proposed. First, the SC decoding is designed to decode polar codes on insertion/deletion channels and the joint weight distribution is derived to measure the occurrence probability of different scenarios. Some scenarios with small occurrence probability can be pruned to obtain lower decoding complexity with negligible performance loss. Inspired by this, a fixed pruning strategy (FPS) is proposed to reduce the decoding complexity, which can prune as many scenarios as possible with the given performance requirement. By exploiting the periodicity of the joint weight distribution, the upper bound of the block error rate of the pruned SC decoding is derived. Furthermore, according to the convergence of the upper bound, a dynamic self-adjusting pruning strategy is designed to further reduce the decoding complexity and improve the flexibility of the pruning algorithm. Simulation results show that the decoding complexity of the proposed pruning-based decoding algorithms is significantly reduced compared to the state-of-the-art scenario simplified SC decoding algorithm.

In this paper, we propose a deep neural network (DNN) aided codebook beamforming scheme for frequency-division-duplex (FDD) millimeter-Wave (mmWave) massive multiple-input multiple-output (MIMO) systems under multipath. Different from the time-division-duplex (TDD) systems, for FDD systems the reciprocity between the down-link (DL) and up-link (UL) channel state information (CSI) does not hold in general, requiring an extra CSI feedback stage. Based on the previous theoretical analysis and measurements, however, partial reciprocities, including the spatial directional angles and number of propagation paths, do exist for FDD mmWave systems. Given such partial reciprocities, we propose a beamforming scheme based on a predefined codebook. Furthermore, we consider the channels with multipath where multiple codewords can be selected for some mobile stations (MSs) to utilize more than one propagation paths, which improves the link reliability. We then propose the corresponding beamforming strategy that not only identifies the number of paths, but also reconstructs the individual single-path channels which are easier to deal with at the beamforming stage. To select the optimal codewords for these single-path channels with lower complexity, we then present a DNN-aided approach that predicts the optimal codewords. The integration of DNN resolves not only the issue of power leakage, but also angular ambiguity in the codebook. Simulation results demonstrate that the performance of the proposed DNN-aided approach is very close to that of an exhaustive search.

The sequence reconstruction problem, introduced by Levenshtein in 2001, considers a communication scenario where the sender transmits a codeword from some codebook and the receiver obtains multiple noisy reads of the codeword. The common setup assumes the codebook to be the entire space and the problem is to determine the minimum number of distinct reads that is required to reconstruct the transmitted codeword. Motivated by modern storage devices, we study a variant of the problem where the number of noisy reads N is fixed. Specifically, we design reconstruction codes that reconstruct a codeword from N distinct noisy reads. We focus on channels that introduce single edit error (i.e. a single substitution, insertion, or deletion) and their variants, and design reconstruction codes for all values of N. In particular, for the case of a single edit, we show that as the number of noisy reads increases, the number of redundant symbols required can be gracefully reduced from logq n+O(1) to logq logq n+O(1), and then to O(1), where n denotes the length of a codeword. We also show that these reconstruction codes are asymptotically optimal. Finally, via computer simulations, we demonstrate that in certain cases, reconstruction codes can achieve similar performance as classical error-correcting codes with less redundant symbols.

Point clouds, consisting of geometry and attributes, are an emerging media format essential to the deployment of augmented reality applications. Additionally, the large data volume of point clouds poses severe challenges for efficient storage and transmission. In this paper, we borrow the idea of end-to-end deep-network-based image/video coding and propose the first--to our best knowledge--end-to-end deep framework for compressing point cloud attributes. Specifically, we propose a point cloud lossy attribute autoencoder, which directly encodes and decodes point cloud attributes with the help of geometry, instead of voxelizing or projecting the points. In the autoencoder, we propose a second-order point convolution that improves the general point convolution in previous point-based learning methods; the proposed convolution utilizes the spatial correlations between more points and the nonlinear relationship between attribute features. We also introduce a dense point-inception block, which derives from a combination of an inception-style block and a dense block, to improve feature propagation. In addition, we devise a multiscale loss to guide the autoencoder in focusing attention on the coarse-grained points with better coverage of the entire point cloud, which makes it easier for the autoencoder to obtain better optimization of the qualities of all points. Experimental results show that our proposed framework still has a performance gap compared with the state-of-the-art algorithms in the MPEG G-PCC reference software TMC13. However, it does outperform the region-adaptive hierarchical transform with a run-length Golomb-Rice entropy coder (RAHTRLGR), which is one of the core transforms used in TMC13 without many well-designed techniques that make TMC13 what it is today. It outperforms RAHT-RLGR by 2.63 dB, 1.77 dB, and 3.40 dB on average in terms of the Bjntegaard delta peak signal-to-noise ratio (BD-PSNR) for the Y, U, and V components on the test dataset. A subjective quality comparison demonstrates the advantages of our framework in preserving more local textures and reducing blocking and color noise artifacts.

VoLTE (Voice over Long-Term Evolution) is a widely used, high-performance, and end-to-end transmission solution in 4G and 5G. However, it also provides a new foundation for the CTC (covert timing channel), which transmits data in a hidden way. Because the packet type, transmission sequence, and transmission jitter in VoLTE are strong regularity, it is challenging to build CTC. In this paper, a CTC construction method named ZM-CTC and based on zigzag matrix is proposed to make the relation between the codewords and dropped packets in a nonlinear rule. Moreover, random salt and a random field in VoLTE are introduced to enhance the undetectability of ZM-CTC. The experimental results show that the ZM-CTC with 9 bits codeword could balance the undetectability, robustness, throughput, and construction cost. The throughput of CTC is 0.88 bps, and the bit error rate of CTC is less than 1.5%.

In recent years, intelligent reflecting surface (IRS) has emerged as a promising technology for 6G due to its potential/ability to significantly enhance energy- and spectrum-efficiency. To this end, it is crucial to adjust the phases of reflecting elements of the IRS, and most of the research works focus on how to optimize/quantize the phase for different optimization objectives. In particular, the quantized phase shift (QPS) is assumed to be available at the IRS, which, however, does not always hold and should be fed back to the IRS in practice. Unfortunately, the feedback channel is generally bandwidth-limited, which cannot support a huge amount of feedback overhead of the QPS particularly for a large number of reflecting elements and/or the quantization level of each reflecting element. In order to break this bottleneck, in this letter, we propose a convolutional autoencoder-based scheme, in which the QPS is compressed on the receiver side and reconstructed on the IRS side. In order to solve the problems of mismatched distribution and vanishing gradient, we remove the batch normalization (BN) layers and introduce a denosing module. By doing so, it is possible to achieve a high compression ratio with a reliable reconstruction accuracy in the bandwidth-limited feedback channel, and it is also possible to accommodate existing works assuming available QPS at the IRS. Simulation results confirm the high reconstruction accuracy of the feedback/compressed QPS through a feedback channel, and show that the proposed scheme can significantly outperform the existing compression algorithms.

This paper proposes a new information-based distributed extended Kalman filter algorithm under dynamic quantization. Our quantization framework has the advantage of utilizing online updated quantizer’s parameters. A practical adjustment strategy is derived to ensure the availability of adaptive quantizer’s parameters for the encoder and decoder. It is proved that estimation error of the proposed algorithm is exponentially bounded in mean square under some assumptions. A numerical example concerning target tracking is presented to demonstrate the validity of the main results, in which a complex network model is used to simulate the sensor network.

In this letter, a hybrid multi-domain index modulation scheme based on spreading codes domain and beam domain is proposed. In our design, the information bit stream is divided into two parts: one for covert transmission using the index of spreading code and the other using the directional modulation to improve bit error ratio (BER) performance in the desired direction and to prevent eavesdropping. Then, based on joint boundaries and statistical theory, the average error probabilities of legitimate users and eavesdroppers are derived. Moreover, we analyze the average BER and validate through simulation results that the proposed hybrid multi-domain scheme is capable of achieving better BER performance compared to conventional coded wireless communication systems.

Due to a large number of user-uploaded images, social networks have become secure channels for covert communication. However, the JPEG recompression of social networks changes the DCT coefficients of stego images, resulting in the failure of adaptive steganography. To achieve steganography in lossy channels, robust steganography has been proposed. In this letter, the ability against JPEG recompression of robust steganography is further improved by introducing a robustness cost function. For calculating the robustness cost, a robust- ness model based on the spatial domain calculated from DCT coefficients is firstly proposed. Then the robustness cost is acquired by measuring the distance between the spatial pixels calculated from modified DCT coefficients and the robustness model adjusted spatial pixels. Combining the distortion function and the robustness cost function, the method proposed has a good performance on both robustness and security. Experimental results show that with the maximum reduction of 4.04% on security performance, the algorithm proposed has a significant improvement on robustness performance compared with state- of-the-art robust steganography.

This paper presents a ternary low-density parity-check (LDPC) error correction system for wireless electrocardiogram sensors to improve the accuracy of arrhythmia classification. The classification system is based on ternary Delta-modulated bitstreams and rotation linear kernel support vector machines, which identifies the supraventricular ectopic beat (SVEB) and the ventricular ectopic beat (VEB) over the normal heartbeats. We model errors using a ternary symmetric channel with probability parameter p and construct a variety of ternary LDPC codes with different coding rates by concatenating two-component sub-matrices to form a parity-check matrix with a quasi-cyclic structure that facilitates the hardware design. In particular, a hardware-friendly LDPC encoder circuit is proposed that leverages the highly structured parity-check matrix to perform serial generation of the parity symbols using an accumulator and a look-up table. The encoder circuits are implemented on FPGA and synthesized on ASIC using a 32 nm CMOS process. Simulation results show that the ternary LDPC codes can significantly improve classification accuracy in the presence of errors. For example, with an error probability of up to 21% in the sensor output bitstreams, the classification accuracy remains above 99% with the proposed error correction system.

As the most promising indoor positioning technology, UWB has been widely used in the field of robot positioning. However, in a complex indoor environment, UWB faces problems such as non-line-of-sight, multipath, signal attenuation and scattering. Aiming at the shortcomings of single UWB positioning, this paper proposes a mobile robot integrated navigation algorithm based on template matching VO/IMU/UWB. First, a template matching VO based on IMU assistance is designed, and then the position information calculated by the VO and the UWB position information are calculated. The difference is used as the input of the Kalman filter for filtering estimation. Finally, the estimated The position error corrects the VO position information to obtain the final positioning result of the combined system. Experiments show that the proposed integrated navigation method can significantly improve the positioning accuracy of UWB.

In this letter, we study the robust beamforming design for the reconfigurable intelligent surface (RIS)-assisted communication systems from a multi-antenna access point (AP) to a single-antenna user under imperfect channel state information (CSI), where the RIS has only a finite number of phase shifts at each element. In particular, considering the cascaded AP-RIS-user channel estimation error, we aim to jointly optimize the transmit beamforming at the AP and discrete phase shifts of RIS to minimize the transmission power of AP, subject to a signal-to-noise ratio constraint at the user. To tackle the non-convex optimization problem, we decouple it into two subproblems and propose a novel alternative optimization framework. More specifically, the robust beamforming of AP is first obtained through S-Procedure and semidefinite relaxation. Then we recast the discrete phase shift constraint at RIS into an equivalent convex one, and propose an efficient algorithm to obtain near optimal solution. Finally, the two-subproblems are iteratively optimized to obtain the joint robust beamforming. Simulation results show that the proposed scheme can approach the performance of the perfect CSI counterpart, and substantially outperforms traditional non-robust methods.

Recently, deep learning based methods have been widely used for wireless communications. In this paper, different from current researches considering full channel state information (F-CSI) feedback, the eigenvector based CSI feedback with deep learning approach is proposed, referred to as EVCsiNet, where the joint eigenvector concatenated from multiple subbands is compressed and recovered at the encoder and decoder, respectively. Simulation results verify the superiority of our schemes over CSI recovery accuracy and feedback overhead compared with conventional methods using codebooks on link-level channel models in different scenarios.

Multimedia Broadcast Multicast Service (MBMS)-dedicated cell is a Long Term Evolution (LTE) based broadcast transmission mode with both high transmission efficiency and good compatibility with LTE unicast systems. However, since the physical design of Cell Acquisition Subframe (CAS), which is the control part of an MBMS-dedicated cell transmission period, does not take into account the long delay spread channels in Medium Tower Medium Power (MTMP) and High Tower High Power (HTHP) cells, receivers would probably suffer from multipath aliasing errors in practical transmission. This paper analyzes the impact of aliasing errors on channel estimation (CHE) accuracy and proposes an Aliasing-Elimination CHE algorithm for CAS reception. In the proposed algorithm, the pre-decoded Physical Broadcast Channel (PBCH) is reconstructed and utilized to generate a complete multipath distribution within the entire OFDM symbol length. By using this full-symbol length multipath distribution, the proposed algorithm eliminates aliasing errors effectively. Simulation results and complexity analysis show that the proposed CHE algorithm achieves a significant performance improvement compared to conventional CHE algorithms in long delay spread channels, with acceptable increment in complexity.

Exponential error bounds achievable by universal coding and decoding are derived for frame-asynchronous discrete memoryless multiple access channels with two senders, via the method of subtypes, a refinement of the method of types. An empirical entropy decoder is employed. A key tool is an improved packing lemma, that overcomes the technical difficulty caused by codeword repetitions via an induction based new argument. The asymptotic form of the bounds admits numerical evaluation. This demonstrates that error exponents achievable by synchronous transmission can be superseded via controlled asynchronism, i.e. a deliberate shift of the codewords.

Channel state information (CSI) is a critical part for massive multiple-input multiple-output (MIMO) system. However, it is a big challenge to send a large amount of CSI from the receiver to the transmitter with limited channel resources. In this letter, we propose asymmetric convolution-based autoencoder framework (ACCsiNet) to handle the CSI compression and decompression problem. Specifically, asymmetric convolution block (AC-Block) is used to enhance the feature extraction ability of convolution. Further, a lightweight method is applied, which can greatly reduce the storage space at the receiver. Considering the practical deployment, multi-model fusion schemes including multi-rate and multi-scenario fusion are also discussed to strengthen the generalization ability of the network. Experimental results show that the proposed ACCsiNet can improve the NMSE and cosine similarity ρ performance, especially for outdoor scenario. The results also verify that both the lightweight and multi-model fusion schemes can reach a near-optimal performance of the proposed ACCsiNet, but further significantly reduce the parameter amount by more than 83% and 90%, respectively.

The orthogonal time frequency space (OTFS) modulation has emerged as a promising modulation scheme for high mobility wireless communications. To enable efficient OTFS detection in the delay-Doppler (DD) domain, the DD domain channels need to be acquired accurately. To achieve the low latency requirement in future wireless communications, the time duration of the OTFS block should be small, therefore fractional Doppler shifts have to be considered to avoid significant modelling errors due to the assumption of integer Doppler shifts. However there lack investigations on the estimation of OTFS channels with fractional Doppler shifts in the literature. In this work, we develop a channel estimator for OTFS with particular attention to fractional Doppler shifts, and both bi-orthogonal waveform and rectangular waveform are considered. Instead of estimating the DD domain channel directly, we estimate the channel gains and (fractional) Doppler shifts that parameterize the DD domain channel. The estimation is formulated as a structured sparse signal recovery problem with a Bayesian treatment. Based on a factor graph representation of the problem, an efficient message passing algorithm is developed to recover the structured sparse signal (thereby the OTFS channel). The Cramer-Rao Lower Bound (CRLB) for the estimation is developed and the effectiveness of the algorithm is demonstrated through simulations.

Future mobile systems have shown a growing trend towards wider frequency bands, larger antenna arrays, and more user equipment, simultaneously expanding the channel in the frequency, space, and user domains. However, the huge size of the multi-domain channel brings great challenges to the acquisition of channel state information (CSI), especially in frequency division duplex (FDD) massive multiple input multiple output (MIMO) systems. In this paper, we propose a multi-domain channel extrapolation scheme that can reconstruct the huge multi-domain channel with low pilot overhead. Specifically, information on the environment shared by multiple domains is utilized for the design of a low-complexity channel extrapolation algorithm. Moreover, we investigate the patterns of sparse pilots and antenna selection by establishing a theoretical framework for the performance analysis of the patterns. We further propose a sparse random pattern design, which can legitimately obtain a set of patterns that are suitable for channel extrapolations. Numerical results demonstrate that we can accurately extrapolate the multi-domain channel using our proposed channel extrapolation scheme and our designed sparse random patterns.

Deep learning (DL)-based channel state information (CSI) feedback algorithms for massive multiple-input multiple-output (MIMO) can provide high beamforming accuracy to improve the throughput. However, bitstream errors in the feedback process can significantly affect the performance of CSI reconstruction. In this letter, we focus on building high reconstruction accuracy CSI feedback algorithm in the presence of bitstream errors. Specifically, we firstly introduce a DL-based architecture named ATNet, which can improve at least 2.96dB Normalized Mean Square Error (NMSE) compared with the existing algorithm. Then, we propose an error correction block called ECBlock and a two-step training strategy. Compared with traditional methods, the proposed scheme can reduce the influence of quantization and bitstream errors more effectively and improve the reconstruction accuracy.

In this letter, we propose a simple yet efficient coding scheme to transmit extra bits by cyclically shifting a low-density parity-check (LDPC) coded sequence without any additional consumption in bandwidth or transmission energy. At the transmitter, the payload data is encoded into an LDPC coded sequence and then cyclically shifted under a pattern specified by the extra bits. At the decoder, the extra bits are first retrieved by estimating the number of cyclic shifts based on the hard or soft syndromes. After removing the effect of cyclic shift, the payload data is recovered by the LDPC decoder. Simulation results show that, with a 5G LDPC code of length 1910, up to 10 extra bits can be reliably transmitted with negligible influence on the reliability of the LDPC coded data.

Native 2-D low-density parity-check (LDPC) codes provide 2-D burst erasure correction capability and have promising applications in two-dimensional magnetic recording (TDMR) technology. Though carefully constructed rastered 1-D LDPC codes can provide 2-D burst erasure correction, they are not as efficient as 2-D native codes constructed for handling the 2-D span of burst erasures. Our contributions are twofold. First, we propose a new 2-D LDPC code with girth greater than 4 by generating a parity-check tensor through stacking permutation tensors of size $p\times p\times p$ along with the $i,j,k$ -axes. The permutations are achieved through circular shifts on an identity tensor along different coordinate axes in such a way that it provides a burst erasure correction capability of at least $p\times p$ . Second, we propose a fast, efficient, and scalable hardware architecture for a parallel 2-D LDPC decoder based on the proposed code construction for data storage applications. Through efficient indexing of the received messages in an RAM, we propose novel routing mechanisms for messages between the check nodes and variable nodes through a set of two barrel shifters, producing shifts along two axes. Through simulations, we show that the performance of the proposed 2-D LDPC codes matches a 1-D QC-LDPC code, with a sharp waterfall drop of three to four orders of magnitude over ~0.3 dB, for random errors over code sizes of ~32 kbits or equivalently ~ $180\times 180 2$ -D arrays. Furthermore, we prove that the proposed native 2-D LDPC codes outperform their 1-D counterparts in terms of 2-D cluster erasure correction ability. For $p=16$ and code arrays of size $48\times 48$ , we implemented the proposed design architecture on a Kintex-7 KC-705 field-programmable gate array (FPGA) kit, achieving a significantly high worst case throughput of 12.52 Gb/s at a clock frequency of 163 MHz.

This paper aims to the joint design of transmit waveform and receive filter to enhance the clutter suppression capability for airborne radar with non-uniform pulse repetition interval (NUPRI). Specifically, a multipulse echo model with NUPRI incorporating a moving point-like target and signaldependent clutter is first established. Then the two-dimensional range-Doppler plane is acquired by performing pulse compression and the windowed pulse Doppler processing on the echo. Further, the signal to interference plus noise ratio (SINR) of the interested area in the range-Doppler plane is formulated as a figure of merit to maximize along with constant modulus constraint on the waveform. To solve the resultant non-convex problem, the sequential greedy optimization (SGO) algorithm through alternately updating the transmit waveform and receive filter is proposed. Each iteration of the developed algorithm invokes coordinate descent (CD) framework and a Lagrangian based algorithm to obtain the transmit waveform and receive filter, respectively. The analytical guarantee of the monotonic SINR improvement is proved. Finally, the performance of the proposed algorithm is assessed through numerical simulations showing its capability to suppress signal-dependent clutter.

To satisfy requirements of high security and transmission efficiency in application scenarios with high image quality, an image lossless encryption and compression algorithm based on four-dimensional hyperchaos and embedded block coding with optimal truncation (EBCOT) is proposed in this paper. First, according to a character that the amplitude of the high-frequency part of the wavelet coefficient is less than the amplitude of the low-frequency part, an encryption algorithm for the wavelet coefficients is proposed to improve the security while reducing the impact on the compression performance. Second, the bit-plane coding and arithmetic coding in EBCOT Tier1 are embedded with encryption points, and the encryption process and compression process are combined to propose a secure EBCOT Tier1 code, which could further improve the security of the algorithm. Furthermore, this paper proposes a new four-dimensional hyperchaotic system, using Secure Hash Algorithm-256 (SHA-256) to generate initial values of the chaotic system, so that the algorithm could resist known plaintext and selected plaintext attacks. The results of the mean square error of all restored images are 0 and the information entropy of this algorithm is close to the theoretical value. The experimental results show that the algorithm has high security and lossless compression performance.

Cooperative communication is a key factor in 5G and beyond standards for increasing the quality of service and aids for dense networks. It works in conjugate with non-orthogonal multiple access (NOMA) to increase the connectivity and spectral efficiency. In this paper, a novel minimum variance maximum mean relay selection scheme with fixed power allocation (MVMM-FPA) is proposed for two-stage cooperative NOMA (C-NOMA) networks using decode-and-forward protocol. The outage probability analytical expressions are derived for the MVMM-FPA over Rayleigh fading channel and its performance is observed with the conventional relay selection (RS) schemes. The impact of outage performance on target rate for users and number of relays have been analyzed for the proposed scheme. Monte Carlo simulations are used to corroborate the analytical expressions of outage probability. Simulation results reveal that the MVMM-FPA RS scheme for C-NOMA offers a minimal outage probability than existing max–min RS and orthogonal multiple access schemes without sacrificing the spectral efficiency.

In this paper, we design a kind of robust rateless multi-user code, which is called spatially coupled repeat-accumulate-repeat (SC-RA-R) code, for the interleave-division multiple-access (IDMA) system. The code is constructed by serially concatenating a fixed outer spatially coupled repeat-accumulate (SC-RA) code with an adjustable inner irregular repetition code. It achieves rateless property by simply adjusting the parameters of the inner repetition code. The IDMA system with the proposed SC-RA-R multi-user codes can adapt to varying channel qualities and varying number of users via rateless property while keeping encoder and decoder implementation unchanged. Analytical extrinsic information transfer (EXIT) functions are derived to determine belief propagation (BP) decoding thresholds of SC-RA-R codes on the IDMA system. The optimization expression is also given under the constraint that repeat parameters are monotonically non-decreasing with the sum rate decreasing. The numerical results show that the maximum achievable sum rates of the SC-RA-R codes can be close to the Shannon bound for arbitrary channel qualities. Besides, our proposed SC-RA-R codes have better BP thresholds and bit-error-rate (BER) performances than the conventional optimal parallel-concatenate codes (PCC) and the optimal partially repeated spatially coupled low-density parity-check (PR-SC-LDPC) codes on IDMA systems.