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There is an urgent need to solve the problems of secure storage, reliable sharing, access control and privacy protection in medical industry. In this paper, we propose EHRChain, a blockchain-based EHR system using attribute-based and homomorphic cryptosystem to solve the above problems. First, we design a medical record storage scheme to realize secure high capacity medical data storage and reliable sharing based on blockchain technology and IPFS. Second, we propose an improved cryptographic primitive called SHDPCPC-CP-ABE. Our SHDPCPC-CP-ABE realizes the functions of semi-policy hiding and dynamic permission changing based on partial ciphertext simultaneously. Furthermore, our program achieves the neutrality of the subject of judicial identification in medical disputes and fine-grained access control of medical data. Third, our system applies an additive homomorphic cryptosystem, Paillier cryptosystem with optimized parameters on patients privacy protection during the process of the medical insurance claim. After analysis and experiment, we have proved that the SHDPCPC-CP-ABE is indistinguishable under chosen plaintext attack and takes one third of the time of CP-ABE when changing access policy. Our system has higher performance than other EHR systems based on blockchain.

Previous neural network accelerators tailored to video analysis only accept data of RGB/YUV domain, requiring decompressing the video that are often compressed before transmitted from the edge sensors. A compressed video processing accelerator can alleviate the decoding overhead, and gain performance speedup by operating on more compact input data. This work proposes a novel deep learning accelerator architecture, Alchemist, which is able to predict results directly from the compressed video bitstream instead of reconstructing the full RGB images. By utilizing the metadata of motion vector and critical blocks extracted from bitstreams, Alchemist contributes to remarkable performance speedup of 5x with negligible accuracy loss. Nevertheless, we still find that the original compressed video coded by standard algorithms such as H.264 is not suitable to be directly manipulated, due to diverse compressed structures. Although obviating the requirement to recover all RGB frames, the accelerator must parse the entire compressed video bitstream to locate reference frames and extract useful metadata. If we combine the video codec with the proposed compressed video analysis, additional optimizations can be obtained. Therefore, to cope with the mismatch between current video coding algorithms such as H.264 and neural network-based video analysis, we propose a specialized coding strategy to generate compressed video bitstreams more suitable for transmission and analysis, which further simplifies the decoding stage of video analysis and is capable of achieving significant storage reduction.

Traditional image steganography conceals secret data into the cover image and then transmits the resulting stego-image to the intended recipient via an insecure channel. The intruders try to sense the channel but cannot easily find any clues regarding the concealed data since both cover and stego images are seemingly indistinguishable. However, the key concern is the integrity of the stego image as an intruder has the ability to intercept image so as to hamper the secret communication. To address this issue, we have introduced a blockchain based framework named “Stego-chain” which utilizes an efficient steganographic scheme based on dilated Robert’s edge detection. The aim of the said scheme is to expand the edge area so as to increase the embedding payload. The resulting stego image is encrypted through a shared secret key and then broadcasted in the form of chunk of frames across the valid nodes. The receiver accepts the tampered free frames, re-constructs the stego image through the shared secret key and then recovers the secret data through extraction procedure. Simulation results ensured that the proposed scheme offers higher payload than the state-of-the-art methods with considerable distortion in the stego image.

In this paper, we propose a time-synchronization method for non-contiguous orthogonal frequency division multiplexing (NC-OFDM) systems. NC-OFDM is a modulation and multiplexing technique that is often used for cognitive radio (CR) systems. In OFDM-based CR systems, time synchronization is limited by a number of challenges: multipath channels with a high delay spread, a narrowband interference (NBI), and minimal number of subcarriers. Indeed, the proposed method addresses these problems since it does not require information on subcarriers nor the NBI center frequency; moreover, it works well on the multipath channel with a high delay spread.

Recently, semantic communication has been brought to the forefront because of its great success in deep learning (DL), especially Transformer. Even if semantic communication has been successfully applied in the sentence transmission to reduce semantic errors, existing architecture is usually fixed in the codeword length and is inefficient and inflexible for the varying sentence length. In this paper, we exploit hybrid automatic repeat request (HARQ) to reduce semantic transmission error further. We first combine semantic coding (SC) with Reed Solomon (RS) channel coding and HARQ, called SC-RS-HARQ, which exploits the superiority of the SC and the reliability of the conventional methods successfully. Although the SC-RS-HARQ is easily applied in the existing HARQ systems, we also develop an end-to-end architecture, called SCHARQ, to pursue the performance further. Numerical results demonstrate that SCHARQ significantly reduces the required number of bits for sentence semantic transmission and sentence error rate. Finally, we attempt to replace error detection from cyclic redundancy check to a similarity detection network called Sim32 to allow the receiver to reserve the wrong sentences with similar semantic information and to save transmission resources., Comment: IEEE Transactions on Communications, 2022

Protections are suggested on the basis of a current relay with two capacitors and a reed switch, which is mounted at a safe distance from a busbar inside the busduct enclosure. The relay does not need in current transformers thus saving copper, steel, and insulating materials. It differs from reed switch based analogs in the noise immunity due to signaling after two actuations of the reed switch; non-operation in the case of sealing (due to the control of duration of breaker contact); the presence of a reed switch fastening structure, and built-in test diagnostics by mean of current supply into the reed switch winding. A technique for selecting protection parameters and assessing their sensitivity is presented. The inductions of magnetic fields which affect the reed switch are calculated with the use of the simplest formulas for the shielding coefficient and of the Bio–Savart–Laplace law, where experimentally derived correcting coefficients are introduced. Examples of the calculation of the induction of protection operation and of reed switch selection are presented along with an oscillogram which confirms the cutoff operability and speed.

In this work, we study probabilistic shaping (PS) for optical wireless communications and apply the technique to wavelength division multiplexing (WDM) based visible light communication (VLC). The performance of the proposed scheme is validated with an experimental demonstration. The experimental set up uses lenses to collimate the light beam for triple LEDs. The system parameters of the WDM based VLC system are then optimised, the channel response measured, and PS based symbols are allocated to the individual orthogonal frequency division multiplexing (OFDM) subcarriers. For the channel conditions under consideration, PS resulted in a near Shannon capacity transmission rate of 10.81 Gb/s. Comparatively, the PS resulted in 25 % higher transmission rate than the widely used adaptive bit-power loading algorithm under the same channel conditions.

Privacy protection is the key to maintaining the Internet of Things communication strategy. Steganography is an important way to achieve covert communication that protects user data privacy. Steganalysis technology is the key technology to check steganography security, and its ultimate goal is extracting embedded messages. Existing methods cannot extract under known cover images. To this end, this paper proposes a method of extracting embedded messages under known cover images. First, the syndrome-trellis encoding process is analyzed. Second, a decoding path in the syndrome trellis is obtained by using the stego sequence and a certain parity-check matrix, while the embedding process is simulated using the cover sequence and parity-check matrix. Since the decoding path obtained by the stego sequence and the correct parity-check matrix is optimal and has the least distortion, comparing the path consistency can quickly filter the coding parameters, so as to determine the correct matrices, and embedded messages can be extracted correctly. The proposed method does not need to embed all possible messages for the second time, improving coding parameter recognition significantly. The experimental results show that the proposed method can identify syndrome-trellis coding parameters in stego images embedded by adaptive steganography quickly to realize embedded message extraction.

In this paper, we consider a status updating system where updates are generated at a constant rate at $K$ sources and sent to the corresponding recipients through a noise-free broadcast channel. We assume that perfect channel state information (CSI) is available at the transmitter before each transmission, and the transmitter is able to utilize the CSI information to precode the updates. Our object is to design optimal precoding schemes to minimize the summed average \emph{age of information} (AoI) at the recipients. Under various assumptions on the size of each update $B$, the number of transmit antennas $M$, and the number of receive antennas $N$ at each user, this paper identifies the corresponding age-optimal precoding and transmission scheduling strategies. Specifically, for the case when $N=1$, a round-robin based updating scheme is shown to be optimal. For the two-user systems with $N>B$ or $M\notin[N:2N]$, framed updating schemes are proven to be optimal. For other cases in the two-user systems, a framed alternating updating scheme is proven to be $2$-optimal.

We propose an innovative machine learning-based technique to address the problem of channel acquisition at the base station in frequency division duplex systems. In this context, the base station reconstructs the full channel state information in the downlink frequency range based on limited downlink channel state information feedback from the mobile terminal. The channel state information recovery is based on a convolutional neural network which is trained exclusively on collected channel state samples acquired in the uplink frequency domain. No acquisition of training samples in the downlink frequency range is required at all. Finally, after a detailed presentation and analysis of the proposed technique and its performance, the "transfer learning'' assumption of the convolutional neural network that is central to the proposed approach is validated with an analysis based on the maximum mean discrepancy metric.

We investigate state estimation of linear systems over channels having a finite state not known by the transmitter or receiver. We show that similar to memoryless channels, zero-error capacity is the right figure of merit for achieving bounded estimation errors. We then consider finite-state, worst-case versions of the common erasure and additive noise channels models, in which the noise is governed by a finite-state machine without any statistical structure. Upper and lower bounds on their zero-error capacities are derived, revealing a connection with the topological entropy of the channel dynamics. Separate necessary and sufficient conditions for bounded linear state estimation errors via such channels are obtained. These estimation conditions bring together the topological entropies of the linear system and the discrete channel.

As a new joint source-channel coding scheme, the double polar (D-Polar) codes have been proposed recently. In this letter, a novel joint source-channel decoder, namely the joint successive cancellation list (J-SCL) decoder, is proposed to improve the decoding performance of the D-Polar codes. We merge the trellis of the source polar code and that of the channel polar code to construct a compound trellis. In this compound trellis, the joint source-channel nodes represent both of the information bits and the high-entropy bits. Based on the compound trellis, the J-SCL decoder is designed to recover the source messages by combining the source SCL decoding and channel SCL decoding. The J-SCL decoder doubles the number of the decoding paths at each decoding level and then reserves the L paths with the smallest joint path-metric (JPM). For the JSC node, the JPM is updated considering both the channel decision log-likelihood ratios (LLRs) and the source decision LLRs. Simulation results show that the J-SCL decoder outperforms the turbo-like BP (TL-BP) decoder with lower complexity.

In this work, we investigate hybrid analog-digital beamforming (HBF) architectures for uplink cell-free (CF) millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. {We first propose two HBF schemes, namely, decentralized HBF (D-HBF) and semi-centralized HBF (SC-HBF). In the former, both the digital and analog beamformers are generated independently at each AP based on the local channel state information (CSI). In contrast, in the latter, only the digital beamformer is obtained locally at the AP, whereas the analog beamforming matrix is generated at the central processing unit (CPU) based on the global CSI received from all APs. We show that the analog beamformers generated in these two HBF schemes provide approximately the same achievable rates despite the lower complexity of D-HBF and its lack of CSI requirement.} Furthermore, to reduce the power consumption, we propose a novel adaptive radio frequency (RF) chain-activation (ARFA) scheme, which dynamically activates/deactivates RF chains and their connected analog-to-digital converters (ADCs) and phase shifters (PSs) at the APs based on the CSI. For the activation of RF chains, low-complexity algorithms are proposed, which can achieve significant improvement in energy efficiency (EE) with only a marginal loss in the total achievable rate., 32 pages, 5 figures

This letter analyzes the performance of uplink integrated sensing and communications (ISAC) systems where communication users (CUs) and radar targets (RTs) share the same frequency band. A non-orthogonal multiple access (NOMA) protocol is adopted in the communication procedure of the ISAC system. Novel expressions are derived to characterize the outage probability, ergodic communication rate, and sensing rate. Besides, the diversity order and high signal-to-noise ratio (SNR) slope are unveiled to gain further insights. It is found that when achieving the same communication rate, the ISAC system enjoys a higher sensing rate than the conventional frequency-division sensing and communications (FDSAC) system where CUs and RTs share isolated bands. All the results are validated by numerical simulations and are in excellent agreement., 5 pages

In this paper, we address the antenna selection (AS) problem in full-duplex (FD) cooperative non-orthogonal multiple access (NOMA) systems, where a multi-antenna FD relay bridges the connection between the multi-antenna base station and NOMA far user. Specifically, two AS schemes, namely max-$\SUu$ and max-$\SUuu$, are proposed to maximize the end-to-end signal-to-interference-plus-noise ratio at either or both near and far users, respectively. Moreover, a two-stage AS scheme, namely quality-of-service (QoS) provisioning scheme, is designed to realize a specific rate at the far user while improving the near user's rate. To enhance the performance of the QoS provisioning AS scheme, the idea of dynamic antenna clustering is applied at the relay to adaptively partition the relay's antennas into transmit and receive subsets. The proposed AS schemes' exact outage probability and achievable rate expressions are derived. To provide more insight, closed-form asymptotic outage probability expressions for the max-$\SUu$ and max-$\SUuu$ AS schemes are obtained. Our results show that while the QoS provisioning AS scheme can deliver a near-optimal performance for static antenna setup at the relay, it provides up to $12\%$ average sum rate gain over the optimum AS selection with fixed antenna setup., Comment: Submitted to IEEE Transactions on Communications

This article studies the resilient control problem for a class of unknown nonlinear systems with fading measurements under malicious denial-of-service (DoS) attacks. The system output is assumed to be transmitted through a fading channel, where the fading phenomenon is described by a Rice fading model. The strategy of the attacker is to periodically interfere with the networked channels to reduce the success rate of data transmissions. First, a dynamic linearization method along the iteration domain is introduced to convert the nonlinear system into an equivalent data-related model. Then, a model-free adaptive iterative learning control (MFAILC) scheme is presented, which is independent of model information. The convergence of the MFAILC scheme is deduced theoretically and the influence of DoS attacks and stochastic fading phenomenon on system stability are also analyzed. Finally, the effectiveness of the design is verified by a numerical simulation and a trajectory tracking example of wheeled mobile robots (WMRs).

Antenna selection is capable of reducing the hardware complexity of massive multiple-input multiple-output (MIMO) networks at the cost of certain performance degradation. Reconfigurable intelligent surface (RIS) has emerged as a cost-effective technique that can enhance the spectrum-efficiency of wireless networks by reconfiguring the propagation environment. By employing RIS to compensate the performance loss due to antenna selection, in this paper we propose a new network architecture, i.e., RIS-assisted massive MIMO system with antenna selection, to enhance the system performance while enjoying a low hardware cost. This is achieved by maximizing the channel capacity via joint antenna selection and passive beamforming while taking into account the cardinality constraint of active antennas and the unit-modulus constraints of all RIS elements. However, the formulated problem turns out to be highly intractable due to the non-convex constraints and coupled optimization variables, for which an alternating optimization framework is provided, yielding antenna selection and passive beamforming subproblems. The computationally efficient submodular optimization algorithms are developed to solve the antenna selection subproblem under different channel state information assumptions. The iterative algorithms based on block coordinate descent are further proposed for the passive beamforming design by exploiting the unique problem structures. Experimental results will demonstrate the algorithmic advantages and desirable performance of the proposed algorithms for RIS- assisted massive MIMO systems with antenna selection.

High density base station (BS) will bring the problem of frequent handover to mobile Internet of things (IoT) devices. Frequent handover will lead to quality of service problems (QoS) such as increased handover delay and reduced data transmission rate. In order to overcome the limitation of mobile IoT devices’ QoS caused by frequent handover BSs, the Multiple-Input Multiple-Output (MIMO) systems can be used to improve the channel capacity and reduce the BS density. However, because the handover decision of mobile IoT devices will affect the data rate and network stability, how to implement the handover to reduce the impact of handover on mobile IoT devices is a problem worthy of study. Therefore, this paper proposes a handover strategy based on fuzzy logic in MIMO systems, which are used to reduce the handover frequency and improve the average data rate of mobile IoT devices. Firstly, the mobile IoT devices converts the devices’s speed, the distance from the devices to the MIMO systems and the transmission power of the MIMO systems to fuzzy values. Then, the fuzzy values obtained in the first step are taken as input, and the fuzzy logic based handover algorithm is implemented through the fuzzy logic controller. Finally, according to the results of the handover algorithm and the theoretical results about the average throughput of mobile IoT devices derived in this paper, the number of antennas used to provide transmission services for IoT devices with different data rate requirements and different mobile speeds can be allocated more flexibly. Simulation results show that the proposed scheme achieves a better performance.

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.

Cost assignment plays a key role in coding performance and security of video steganography. Existing cost assignment methods (for adaptive video steganography) are designed for specific transform coefficients rather than all transform coefficients. In addition, existing video steganographic frameworks do not allow Syndrome-Trellis Codes (STCs) to modify all transform coefficients in both intra-coded and inter-coded frames at the same time. To address these limitations, in this paper, we first propose a novel video steganographic framework. Then, we give a theoretical analysis of distortion drift in both intra- and inter-coding procedures. Based on the analysis, we design a Distortion Drift-based Cost Assignment method, hereafter referred to as DDCA. DDCA considers the inner-block, inter-block and inter-frame distortion costs in order to improve the coding performance and the security of stego videos when the embedding payload is fixed. We conducted extensive experiments using two video datasets to evaluate the proposed video steganographic framework and DDCA, in terms of the coding performance and the security. Our experiments show that the proposed framework outperforms three recent state-of-the-art methods, for example the coding performance and the security of stego videos can benefit from DDCA by making full use of all nonzero transform coefficients.

We propose novel compression algorithms for time-varying channel state information (CSI) in wireless communications. The proposed scheme combines (lossy) vector quantisation and (lossless) compression. First, the new vector quantisation technique is based on a class of parametrised companders applied on each component of the normalised CSI vector. Our algorithm chooses a suitable compander in an intuitively simple way whenever empirical data are available. Then, the sequences of quantisation indices are compressed using a context-tree-based approach. Essentially, we update the estimate of the conditional distribution of the source at each instant and encode the current symbol with the estimated distribution. The algorithms have low complexity, are linear-time in both the spatial dimension and time duration, and can be implemented in an online fashion. We run simulations to demonstrate the effectiveness of the proposed algorithms in such scenarios., Comment: 12 pages, 9 figures. Accepted for publication in the IEEE Transactions on Communications

Vehicular ad hoc networks (VANETs) have attracted increasing attention in cross-regional communication. The interconnection between VANETs and satellite networks expands their communication range. To improve the transmission performance of heterogeneous networks composed of VANETs and satellite networks, this paper proposes an end-to-end transmission control scheme for satellite network-assisted VANETs, analyzes the bandwidth delay product of multihop VANETs through heterogeneous networks, and establishes a transmission control model applicable to cross-regional environments. To address the long delay and high bit error rate in heterogeneous networks, the model designs the congestion window in the slow start and congestion avoidance of the transmission scheme. The data transmission volume is increased in the slow start, and various packet losses are distinguished in congestion avoidance. The experimental results show that the scheme can achieve good transmission performance in heterogeneous networks composed of VANETs and satellite networks. The results show that VANETs data can be effectively transmitted in heterogeneous networks. Compared with the traditional transmission scheme, this simple and practical end-to-end transmission control scheme can improve speed by 83.49 kbps and reduce transmission duration time of FTP server by 4.72 x 10³ s. The results of this study provide a reference for massive data transmission in heterogeneous networks of VANETs and satellite networks.

In this paper, an IRS-aided integrated sensing and communications (ISAC) system operating in the terahertz (THz) band is proposed to maximize the system capacity. Transmit beamforming and phase-shift design are transformed into a universal optimization problem with ergodic constraints. Then the joint optimization of transmit beamforming and phase-shift design is achieved by gradient-based, primal-dual proximal policy optimization (PPO) in the multi-user multiple-input single-output (MISO) scenario. Specifically, the actor part generates continuous transmit beamforming and the critic part takes charge of discrete phase shift design. Based on the MISO scenario, we investigate a distributed PPO (DPPO) framework with the concept of multi-threading learning in the multi-user multiple-input multiple-output (MIMO) scenario. Simulation results demonstrate the effectiveness of the primal-dual PPO algorithm and its multi-threading version in terms of transmit beamforming and phase-shift design.

In uplink non-orthogonal multiple access (NOMA) channels, the existing cooperative successive interference cancellation (SIC) and power control (PC) schemes lack the capability of achieving the full capacity region, which restricts the outage performance of uplink NOMA users. For the uplink cognitive radio inspired NOMA system, we propose a new rate splitting (RS) strategy to maximize the achievable rate of the secondary user without deteriorating the primary user's outage performance. Based on the interference threshold and its own channel gain, the secondary user adaptively conducts RS, transmit power allocation and SIC, which utilizes the transmit power efficiently. The closed-form expression for the outage probability is derived for the secondary user. Numerical results show that the proposed RS scheme achieves the best outage performance for the secondary user among the existing cooperative SIC and PC schemes., Comment: 5 pages, 5 figures, accepted by IEEE Journal. arXiv admin note: substantial text overlap with arXiv:2110.02281

Most of the organizations using the cloud-based data sharing platforms are multi-group in nature. The existing directly revocable attribute-based encryption (ABE) schemes though seem to be a good fit, but they fail to provide any effective solution for secure multi-group data sharing scenarios. To bridge this gap, we first propose Revocable ABE with Verifiable Outsourced decryption (ReVO-ABE)- a directly revocable collusion-resistant ABE scheme that allows any number of user revocation and joining without affecting the secret membership keys of the nonrevoked users. Based on ReVO-ABE, we build a Dynamic Multi-Group Secure Data Sharing scheme called DMG-SDS. For operations that are exclusive to multi-groups like group merge and split can be performed without affecting the attribute secret keys or membership keys of the nonrevoked users, which is not possible with any of the existing schemes. Our proposed scheme meets the necessary security requirements, and the performance assessment shows that it has much better performance benefits when compared with most the recent competitive schemes.

Recently, substantial development is observed in the area of Internet of Vehicles owing to the application of wireless communication technologies. Majority of these technologies are based on radio frequency (RF); however, RF spectra are overly congested and regulated, and hence, insufficient to support massive data traffic in the future. In recent times, optical camera communication (OCC) that uses a light-emitting diode (LED) as a transmitter and a camera as a receiver has been deemed an excellent solution for future intelligent transportation systems. As a communication medium, OCC mostly uses visible light, the spectrum of which is vast, completely free, and unregulated. The current outdoor environment is heavily crammed with LED infrastructures, and most vehicles have built-in cameras, rendering OCC immensely promising. OCC is highly secured, supports mobility, and can achieve an excellent bit-error rate. However, the data rate obtained using OCC is not as high as that obtained using other RF-based systems; therefore, its reliability in fast-changing channels is still under research. This review article discusses the applications of the OCC system in vehicle-to-vehicle and vehicle-to-infrastructure (or vice versa) networks; to the best of our knowledge, this is the first extensive review dedicated to the above topic. Herein, we provide a general overview of OCC standardization in IEEE and ISO in recent years. Then, we explain the general principles of OCC, including channel characteristics, region of interest signaling, and modulation schemes particularly considered in vehicular communications. Additionally, we present a comprehensive overview of the effects of mobility, noise, and interference in OCC. Finally, the challenges and future opportunities in OCC are outlined.

Here, we study the problem of decoding information transmitted through unknown quantum states. We assume that Alice encodes an alphabet into a set of orthogonal quantum states, which are then transmitted to Bob. However, the quantum channel that mediates the transmission maps the orthogonal states into non-orthogonal states, possibly mixed. If an accurate model of the channel is unavailable, then the states received by Bob are unknown. In order to decode the transmitted information we propose to train a measurement device to achieve the smallest possible error in the discrimination process. This is achieved by supplementing the quantum channel with a classical one, which allows the transmission of information required for the training, and resorting to a noise-tolerant optimization algorithm. We demonstrate the training method in the case of minimum-error discrimination strategy and show that it achieves error probabilities very close to the optimal one. In particular, in the case of two unknown pure states, our proposal approaches the Helstrom bound. A similar result holds for a larger number of states in higher dimensions. We also show that a reduction of the search space, which is used in the training process, leads to a considerable reduction in the required resources. Finally, we apply our proposal to the case of the phase flip channel reaching an accurate value of the optimal error probability.

In recent years, many backdoor attacks based on training data poisoning have been proposed. However, in practice, those backdoor attacks are vulnerable to image compressions. When backdoor instances are compressed, the feature of specific backdoor trigger will be destroyed, which could result in the backdoor attack performance deteriorating. In this paper, we propose a compression-resistant backdoor attack based on feature consistency training. To the best of our knowledge, this is the first backdoor attack that is robust to image compressions. First, both backdoor images and their compressed versions are input into the deep neural network (DNN) for training. Then, the feature of each image is extracted by internal layers of the DNN. Next, the feature difference between backdoor images and their compressed versions are minimized. As a result, the DNN treats the feature of compressed images as the feature of backdoor images in feature space. After training, the backdoor attack against DNN is robust to image compression. Furthermore, we consider three different image compressions (i.e., JPEG, JPEG2000, WEBP) in feature consistency training, so that the backdoor attack is robust to multiple image compression algorithms. Experimental results demonstrate the effectiveness and robustness of the proposed backdoor attack. When the backdoor instances are compressed, the attack success rate of common backdoor attack is lower than 10%, while the attack success rate of our compression-resistant backdoor is greater than 97%. The compression-resistant attack is still robust even when the backdoor images are compressed with low compression quality. In addition, extensive experiments have demonstrated that, our compression-resistant backdoor attack has the generalization ability to resist image compression which is not used in the training process.

In most video platforms, such as Youtube, and TikTok, the played videos usually have undergone multiple video encodings such as hardware encoding by recording devices, software encoding by video editing apps, and single/multiple video transcoding by video application servers. Previous works in compressed video restoration typically assume the compression artifacts are caused by one-time encoding. Thus, the derived solution usually does not work very well in practice. In this paper, we propose a new method, temporal spatial auxiliary network (TSAN), for transcoded video restoration. Our method considers the unique traits between video encoding and transcoding, and we consider the initial shallow encoded videos as the intermediate labels to assist the network to conduct self-supervised attention training. In addition, we employ adjacent multi-frame information and propose the temporal deformable alignment and pyramidal spatial fusion for transcoded video restoration. The experimental results demonstrate that the performance of the proposed method is superior to that of the previous techniques. The code is available at https://github.com/icecherylXuli/TSAN., Comment: Accepted by AAAI2022

The rapid development of technology and the standardization of digital photography have led to an explosive growth in digital image distribution and reproduction. The enhancement of storage capacity in computer disks and advancement in networking have not been able to keep pace with the demands of handling, storing and finally transmitting huge volume of image data. Only proper image compression technologies seem to offer a solution to this challenge. The importance of digital image compression in multimedia applications has inspired extensive research all over the world. The present study recommends a newly formulated algorithm by computing Discrete Wavelet Transform (DWT) in combination with thresholding and quadtree decomposition. Findings prove that the proposed technique is at par with EZW image compression algorithm in terms of quality performance at the same bit rate, and obviates the need for employing any other conventional standard image compression techniques.

Security threats such as data forgery and leakage may occur when sharing data in cloud environments. Therefore, it is important to encrypt your data and securely access it when sharing it with other users via a cloud server. Of the various security technologies, research on secure data sharing commonly employs Key Policy Attribute-Based Encryption (KP-ABE). However, existing KP-ABE schemes generally lack ciphertext search features. Furthermore, even if a KP-ABE scheme incorporates it, the number of searches required increases markedly by the number of attributes used in the search. It in turn proportionally increases the ciphertext size. In addition, the attribute authority (AA) could be attacked, which can result in the leakage of users’ decryption keys. AA is a server that manages user attributes and decryption keys when using attribute-based encryption in a cloud environment. If the AA is curious, it can cause problems with the key escrow with the attributes and decryption (secret) key information of the users it knows. In this paper, to solve all these problems, we present a new scheme called Searchable Key-Policy Attribute-Based Encryption (SKP-ABE) for secure and efficient data sharing in the cloud. This proposed SKP-ABE scheme allows fast ciphertext search and keeps the ciphertext of constant size. The key escrow problem is solved via user key generation.

This paper investigates the covert performance for an unmanned aerial vehicle (UAV) jammer assisted cognitive radio network. In particular, the covert transmission of secondary users can be effectively protected by UAV jamming against the eavesdropping. For practical consideration, the UAV is assumed to only know certain partial channel distribution information (CDI), whereas not to know the detection threshold of eavesdropper. For this sake, we propose a model-driven generative adversarial network (MD-GAN) assisted optimization framework, consisting of a generator and a discriminator, where the unknown channel information and the detection threshold are learned weights. Then a GAN based joint trajectory and power optimization (GAN-JTP) algorithm is developed to train the MD-GAN optimization framework for covert communication, which results in the joint solution of UAV’s trajectory and transmit power to maximize the covert rate and the probability of detection errors. Our simulation results show that, the proposed GAN-JTP with a rapid convergence speed can attain near-optimal solutions of UAV’s trajectory and transmit power for the covert communication.

Covert communication is designed for hiding the subliminal communication which takes place between both of the speakers and their relationship. The traditional covert communication utilizes the centralized channel and the third-party central node or authority to distribute messages which leads to a lack of undetectability, antitraceability, and robustness. In recent years, there have been attempts to apply the blockchain to covert communication solutions, for the characteristics of blockchain such as decentralization, openness, and trustworthiness. In this paper, based on the analysis of the literature and the classification according to the hiding position, we identify four kinds of covert communication: Address Channel, Value Channel, DSA (Digital Signature Algorithm) Channel, and Script Channel, which will help inform future research agenda.

The evolution of wireless communication networks has introduced various applications that require massive device connectivity and high spectral efficiency. Non-orthogonal multiple access (NOMA) technique is one of the most promising technologies to perform efficiently data transmission. The NOMA technique can allocate the same resource block for two users by super-imposing signals. At the receiver, the signals are separated by performing successive interference cancellation (SIC) technique. For efficient data transmission, the fading and shadowing effects of channels also play a pivotal role. Many researches have considered Rayleigh, Rician, Nakagami-m, and other fading channels in various perspectives. In our paper, a system model based on a NOMA network with two users over log-normal fading distribution in the presence of channel estimation errors and SIC imperfections is proposed. The performance is analyzed in terms of outage probability and simulations are performed with the assistance of Monte-Carlo simulations. The obtained results shown the effectiveness in comparison with the traditionally used fading distributions. The same analysis is also performed in various scenarios of power allocation levels, target rates, and imperfections. The transmit SNR and power allocation of the users are important for efficient communication in any fading distribution as shown in this paper.

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 a multiple-input multiple-output (MIMO) system, or a multiuser MIMO system, it is important to acquire the accurate channel state information. In this article, a superimposed-pilot-based channel estimation scheme is proposed for multicell multiuser MIMO uplink systems. In this scheme, the pilot sequences and data sequences are superimposed, but the space generated by the pilot sequences and the space spanned by data sequences are separated far enough in order to reduce interference. Based on this scheme, the minimum mean-square error channel estimation is deduced, and the optimal power allocation between the pilots and the data signals is derived to maximize throughput. These results show that our proposed scheme can overcome the pilot contamination and achieve better performance than the traditional time-multiplexing scheme with mutually orthogonal pilot sequences for different users when the coherent time slot is limited.

We investigate joint source channel coding (JSCC) for wireless image transmission over multipath fading channels. Inspired by recent works on deep learning based JSCC and model-based learning methods, we combine an autoencoder with orthogonal frequency division multiplexing (OFDM) to cope with multipath fading. The proposed encoder and decoder use convolutional neural networks (CNNs) and directly map the source images to complex-valued baseband samples for OFDM transmission. The multipath channel and OFDM are represented by non-trainable (deterministic) but differentiable layers so that the system can be trained end-to-end. Furthermore, our JSCC decoder further incorporates explicit channel estimation, equalization, and additional subnets to enhance the performance. The proposed method exhibits 2.5 -- 4 dB SNR gain for the equivalent image quality compared to conventional schemes that employ state-of-the-art but separate source and channel coding such as BPG and LDPC. The performance further improves when the system incorporates the channel state information (CSI) feedback. The proposed scheme is robust against OFDM signal clipping and parameter mismatch for the channel model used in training and evaluation., 16 pages, 17 figures. arXiv admin note: text overlap with arXiv:2101.03909

The increasing grid data in CFD simulation has brought some new difficulties and challenges, such as high storage cost, low transmission efficiency. In order to overcome these problems, a novel method for compressing and saving the structured grid are proposed. In the present method, the geometric coordinates of the six logical domains of one grid block is saved instead of all grid vertex coordinates to reduce the size of the structured grid file when the grid is compressed. And all grid vertex coordinates are recovered from the compressed data with the use of the transfinite interpolation algorithm when the grid is decompressed. Firstly, single-block grid cases with different edge vertexes are tested to investigate the compression effect. The test results show that a higher compression ratio will be obtained on a larger grid. Secondly, further theoretical analysis is carried out to investigate the effects of parameters on grid compression. The analysis on single-block grid compression shows that the compression ratio is proportionate to the cubic root of the number of total vertexes. The highest compression ratio of single-block grid is obtained when the numbers of vertexes in three logical directions are equal. The analysis on multi-block grid compression shows that a higher compression ratio will be obtained when a larger difference of total vertexes number exists among the grid blocks. Finally, multi-blockgrids of two industrial aircraft configurations are compressed to validate the method. The compression results demonstrate that the present method has an excellent ability on structured grid compression. For a million-vertex structured grid, more than 80 percent disk space can be saved after compression.

Objective: Globally, cardiovascular diseases (CVDs) are one of the most leading causes of death. In medical screening and diagnostic procedures of CVDs, electrocardiogram (ECG) signals are widely used. Early detection of CVDs requires acquisition of longer ECG signals. It has triggered the development of personal healthcare systems which can be used by cardio-patients to manage the disease. These healthcare systems continuously record, store, and transmit the ECG data via wired/wireless communication channels. There are many issues with these systems such as data storage limitation, bandwidth limitation and limited battery life. Involvement of ECG data compression techniques can resolve all these issues. Method: In the past, numerous ECG data compression techniques have been proposed. This paper presents a methodological review of different ECG data compression techniques based on their experimental performance on ECG records of the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Results: It is observed that experimental performance of different compression techniques depends on several parameters. The existing compression techniques are validated using different distortion measures. Conclusion: This study elaborates advantages and disadvantages of different ECG data compression techniques. It also includes different validation methods of ECG compression techniques. Although compression techniques have been developed very widely but the validation of compression methods is still a prospective research area to accomplish an efficient and reliable performance.

The fundamental limits of covert communication, where a message is sent from transmitter Alice to intended recipient Bob without detection by an attentive adversary warden Willie, has been considered extensively in recent years at the physical layer. In this paper, we consider the covert throughput when the adversary is only able to observe the medium access control (MAC) layer in a wireless communication system. In particular, given that the system has a rate of λ packets per slot transmitted over n channels by allowable system users, we study the allowable rate λ_a by covert users while maintaining covertness from an attentive warden observing the channel status in a slotted ALOHA system. We characterize performance for wardens with different abilities to discern the number of packets on a given channel, ranging from simple receivers that detect only whether there was a packet present to complicated receivers that can determine the number of packets involved in any collision, and also consider intended recipients Bob with varying abilities to perform multi-packet reception. In contrast to prior work in covert communications, the application considered motivates the consideration of results for finite (often small) observation vector lengths n at the adversary.

This paper investigates physical layer security for a large-scale WSN with random multiple access, where each fusion center in the network randomly schedules a number of sensors to upload their sensed data subject to the overhearing of randomly distributed eavesdroppers. We propose an uncoordinated random jamming scheme in which those unscheduled sensors send jamming signals with a certain probability to defeat the eavesdroppers. With the aid of stochastic geometry theory and order statistics, we derive analytical expressions for the connection outage probability and secrecy outage probability to characterize transmission reliability and secrecy, respectively. Based on the obtained analytical results, we formulate an optimization problem for maximizing the sum secrecy throughput subject to both reliability and secrecy constraints, considering a joint design of the wiretap code rates for each scheduled sensor and the jamming probability for the unscheduled sensors. We provide both optimal and low-complexity sub-optimal algorithms to tackle the above problem, and further reveal various properties on the optimal parameters which are useful to guide practical designs. In particular, we demonstrate that the proposed random jamming scheme is beneficial for improving the sum secrecy throughput, and the optimal jamming probability is the result of trade-off between secrecy and throughput. We also show that the throughput performance of the sub-optimal scheme approaches that of the optimal one when facing a stringent reliability constraint or a loose secrecy constraint., Comment: accepted by the IEEE Transactions on Communications

Soft-Input Soft-Output (SISO) polar decoding algorithms, such as Belief Propagation (BP) and Soft Cancellation (SCAN) polar decoding, offer iteration capability for facilitating turbo-style detection. However, at lower Signal-to-Noise Ratios (SNRs), the performance of the BP and SCAN decoders is about 1.5 dB and 0.5 dB worse than that of the state-of-the-art hard-decision Successive Cancellation List (SCL) decoding algorithm, respectively, despite iteratively exchanging information with a Multiple Input Multiple Output (MIMO) detector. Motivated by this gap, we conceive a novel G-SCAN polar decoder, which generates both soft-decision and hard-decision outputs. This is achieved by intrinsically amalgamating a list decoder with a novel SISO decoder. These soft-decision outputs may be used for turbo-detection, but they also support the hard-decision outputs of the SCL algorithm for achieving superior block error rate (BLER) performance. As a result of these benefits, the proposed G-SCAN algorithm using a list size of L = 2 offers around 1 dB BLER gain compared to the conventional hard-decision SCL decoder relying on L = 32. Furthermore, we have carried out its Extrinsic Information Transfer (EXIT) chart analysis, and characterized the performance vs. the complexity of the proposed G-SCAN algorithm, and compared it to various soft-and hard-decision output benchmarks for a wide variety of different rate-matching modes and block lengths. Furthermore, in order to reduce the complexity of the proposed algorithm, a novel Cyclic Redundancy Check (CRC)-aided G-SCAN algorithm is also proposed, which facilitates early termination and improves the BLER performance.

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.

A general form of codebook design for code-domain non-orthogonal multiple access (CD-NOMA) can be considered equivalent to an autoencoder (AE)-based constellation design for multi-user multidimensional modulation (MU-MDM). Due to a constrained design space for optimal constellation, e.g., fixed resource mapping and equal power allocation to all codebooks, however, existing AE architectures produce constellations with suboptimal bit-error-rate (BER) performance. Accordingly, we propose a new architecture for MU-MDM AE and underlying training methodology for joint optimization of resource mapping and a constellation design with bit-to-symbol mapping, aiming at approaching the BER performance of a single-user MDM (SU-MDM) AE model with the same spectral efficiency. The core design of the proposed AE architecture is dense resource mapping combined with the novel power allocation layer that normalizes the sum of user codebook power across the entire resources. This globalizes the domain of the constellation design by enabling flexible resource mapping and power allocation. Furthermore, it allows the AE-based training to approach a global optimal MU-MDM constellations for CD-NOMA. Extensive BER simulation results demonstrate that the proposed design outperforms the existing CD-NOMA designs while approaching the single-user BER performance achieved by the equivalent SU-MDM AE within 0.3 dB over the additive white Gaussian noise channel.

The safe and reliable generation and transmission of electricity is the desirable factor for utilities which arises the need of protection equipment such as relays in the power systems. According to recent studies, the conventional relays are not able to provide the required protection to the power systems, resulting in the emergence of various artificial intelligence (AI) techniques such as (i) artificial neural network (ANN); (ii) adaptive neuro fuzzy interface system (ANFIS); and (iii) fuzzy logic based relays for the protection. This work presents the protection scheme for transmission lines using various AI based distance relays along with performance comparison of these relays with a conventional numerical distance relay (NL). The comparison analysis has been performed by generating a test model in the MATLAB/Simulink environment and comparing “response time” against the same fault occurence for all relays. The comparison analysis found that AI based relays outperformed than conventional relay in terms of response time and accuracy level against the faults.

This paper investigates the robust beamforming design in a secrecy multiple-input single-output (MISO) network aided by the intelligent reflecting surface (IRS) with simultaneous wireless information and power transfer (SWIPT). Specifically, by considering that the energy receivers (ERs) are potential eavesdroppers (Eves) and imperfect channel state information (CSI) of the direct and cascaded channels can be obtained, we investigate the max-min fairness robust secrecy design. The objective is to maximize the minimum robust information rate among the legitimate information receivers (IRs). To solve the formulated non-convex design problem in bounded and probabilistic CSI error models, we utilize the alternating optimization (AO) and successive convex approximation (SCA) methods to obtain an approximate problem. Then, an iteration-based algorithm framework was proposed, where the unit modulus constraint (UMC) of the IRS is handled by the penalty dual decomposition (PDD) method. Moreover, a stochastic SCA method is proposed to handle the outage constrained design with statistical CSI. Finally, simulation results validate the promising performance of the proposed design.

Analog beamforming (ABF) architectures for both large-scale antennas at base station (BS) and small-scale antennas at user side in millimetre wave (mmWave) channel are constructed and investigated in this paper with the aid of deep learning (DL) techniques. Transmit and receive beamformers are selected through offline training of ABF network that accepts input as channel. The joint optimization of both beamformers based on DL for maximization of spectral efficiency (SE) for massive multiple input multiple output (M-MIMO) system has been employed. This design procedure is carried out under imperfect channel state information (CSI) conditions and the proposed design of precoders and combiners shows robustness to imperfect CSI. The simulation results verify the superiority in terms of SE of deep neutral network (DNN) enabled beamforming (BF) design of mmWave massive MIMO system compared with the conventional BF algorithms while lessening the computational complexity.

In time-varying fading channels, channel coefficients are estimated using pilot symbols that are transmitted every coherence interval. For channels with high Doppler spread, the rapid channel variations over time will require considerable bandwidth for pilot transmission, leading to poor throughput. In this paper, we propose a novel receiver architecture using deep recurrent neural networks (RNNs) that learns the channel variations and thereby reduces the number of pilot symbols required for channel estimation. Specifically, we design and train an RNN to learn the correlation in the time-varying channel and predict the channel coefficients into the future with good accuracy over a wide range of Dopplers and signal-to-noise ratios (SNR). The proposed training methodology enables accurate channel prediction through the use of techniques such as teacher-force training, early-stop, and reduction of learning rate on plateau. Also, the robustness of prediction for different Dopplers and SNRs is achieved by adapting the number of predictions into the future based on the Doppler and SNR. Numerical results show that good bit error performance is achieved by the proposed receiver in time-varying fading channels. We also propose a data decision driven receiver architecture using RNNs that further reduces the pilot overhead while maintaining good bit error performance., Accepted TVT. Copyright IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Visible light communication (VLC) building on the existing illumination infrastructure utilizes light emitting diode (LED) luminaries as wireless transmitters. To achieve data rates on the order of gigabits per second in VLC systems, multiple-input multiple-output (MIMO) transmission has been identified as a key technology. In this paper, we consider a centralized network architecture where the ceiling LED luminaries act as transmitter elements of a distributed massive MIMO VLC system. To obtain the full benefits of massive MIMO, channel state information must be available at the transmitter side. Downlink (DL) channel estimation in a massive MIMO system requires huge pilot overhead in DL as well as feedback overhead in uplink. This makes the implementation of massive MIMO system infeasible if not properly designed. In our study, we address the design of massive MIMO VLC systems with a limited number of DL pilot signals. We explore different DL pilot patterns (PPs) in frequency, time, and spatial domains where interpolation is performed to obtain the global channel matrix taking advantage of the indoor environment geometry and layout of luminaries. We then present data rate results for different DL PPs under the consideration of varying number of active user equipments through Monte-Carlo simulations.