Search

Long period digital random sequence plays an important role in reliable communications and high security scenarios. This paper improved the method of generating long period digital random sequences based on the Residue Number System (RNS) and the Chinese Remainder Theorem (CRT), and a sequence mapping method after CRT extension. This paper proves that the period of sequence after mapping will not degenerate if the modulus used in the mapping stage is coprime with the period of the original sequence. By using the parallelism of RNS, the proposed method can generate sequences at high speed with fewer hardware resources. The NIST test results show that the pass rate of each test item is above 98.40%, which meets the NIST test confidence requirements, confirming the randomness of the generated sequences. An image encryption test is given as one of the example applications of the generated sequences. On the theoretical basis, by jointly optimizing the sequence mapping and iteration procedure, a hardware implementation architecture is also presented in this paper. The implementation is based on Xilinx XC7Z020CLG484-3 FPGA and compared with the implementations of classical chaotic maps. The results show that the proposed architecture has longer sequence period with less hardware resource consumption and higher generation speed and is more general. Meanwhile, the proposed architecture has fast phase switching ability, which is about 10 clock periods. This is one of the key attributes when the sequence is used in communication systems. [ABSTRACT FROM AUTHOR]

An editorial is presented on Cybertwin supporting advanced vehicle-to-everything (V2X) applications in the future intelligent and securing vehicular 6G network. Topics include becoming the personal AI assistant and acting as a security agent through customizing the visibility of recorded data; and proposing scheme good security performances in terms of symbol error rate (SER) and secrecy outage probability.

Travelling wave based protection functions require significantly higher sampling rates than protection functions based on time-domain superimposed quantities or fundamental frequency phasors. Integrating these high sampling rates in digital substations leads to a significant increase of the communication load of process-level networks and causes high computational cost for centralized protection systems. This paper builds on a distributed signal processing approach, which allocates the filtering operations among standalone merging units (SAMU). In particular, the paper presents a decimation filter design to integrate signals with high sampling rates at the process-level. Thereby, signal sequences with high, medium and low sampling rates are provided according to the requirements of the respective protection function. The decimation filter structure is optimized with respect to time delay by the Remez exchange algorithm and with respect to computational cost by a multistage approach and a sparse filter design algorithm. In addition, the filter response is verified against the accuracy constraints defined in IEC 61869-6 and IEC 61869-13. The paper shows that it is feasible to distribute the filtering operations among SAMUs, while keeping the time delay below the maximum allowable processing delay of 2 ms. [ABSTRACT FROM AUTHOR]

Digital twin (DT) has emerged as a promising technology to create digital replicas of physical vehicles to facilitate driving in Internet of Vehicles (IoV). However, due to the uneven distribution of vehicles in the networks and the dynamics of heterogeneous wireless links, how to optimize the connection along the vehicle's trip between the vehicle and its DT to achieve the lowest cost (i.e., delay and energy consumption) is a fundamental challenge. In this paper, we investigate the joint network selection and power level allocation problem in the data synchronization issue between vehicles and DTs in the heterogeneous access networks. In order to better use the information shared by DT to facilitate the data synchronization process, we propose a learning-based heterogeneous network selection scheme in DT empowered IoV. First, we consider the data divided into blocks and model the data synchronization problem as a Markov Decision Process (MDP) to minimize the long-term cost in terms of the delay and energy consumption. Based on the information obtained from the digital twin communications, we develop an actor-critic learning approach to solve the MDP problem with the block-level state method, and obtain the optimal network selection strategy for the vehicles. In addition, we explore the capability of digital twins by applying transfer learning. The vehicles are divided into two types, i.e., learner vehicles and expert vehicles, so that the knowledge can be transferred from the expert vehicles to the learner vehicles to effectively improve the learning efficiency and decrease the data synchronization latency. Extensive experiments demonstrate that our scheme can effectively find the optimal network selection policy and achieve the fast convergence and better performance compared with existing related approaches. [ABSTRACT FROM AUTHOR]

This paper considers a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) downlink communication system where hybrid analog-digital beamforming is employed at the base station (BS). We formulate a power minimization problem by jointly optimizing hybrid beamforming at the BS and the response matrix at the RIS, under the signal-to-interference-plus-noise ratio (SINR) constraints at all users. The problem is highly challenging to solve due to the non-convex SINR constraints as well as the unit-modulus phase shift constraints for both the RIS reflection coefficients and the analog beamformer. A two-layer penalty-based algorithm is proposed to decouple variables in SINR constraints, and manifold optimization is adopted to handle the non-convex unit-modulus constraints. We also propose a low-complexity sequential optimization method, which optimizes the RIS reflection coefficients, the analog beamformer, and the digital beamformer sequentially without iteration. Furthermore, the relationship between the power minimization problem and the max-min fairness (MMF) problem is discussed. Simulation results show that the proposed penalty-based algorithm outperforms the state-of-the-art semidefinite relaxation (SDR)-based algorithm. Results also demonstrate that the RIS plays an important role in the power reduction. [ABSTRACT FROM AUTHOR]

IEC61850 is the mainstream of the development for substation automation. This paper presents a practical consideration and analysis for implementing a secure sampled measured value (SeSV) message in substation automation system. Due to the lack of security features in the standard, IEC Working Group 15 of Technical Committee 57 published IEC62351 on security for IEC61850 profiles. However, the use of authentication methods for SV based on IEC62351 standards are still not integrated, and computational capabilities and performance are not validated and tested with commercial grade equipment. Hence, this paper shows the performance of security feature enabled SeSV packets transmitted between protection and control devices by appending a message authentication code (MAC) to the extended IEC61850 packets. A prototype implementation on a low cost commodity embedded system has proved that the MAC-enabled SV message can fully secure the process bus communication in the digital substation with negligible time delay. [ABSTRACT FROM AUTHOR]

Adaptive radio techniques have been extensively studied over the past two decades. In this paper, we investigate an adaptive algorithm to attain additive white Gaussian noise (AWGN) performance in non-white channels, using multicarrier communications such as OFDM and filter-bank multicarrier (FBMC). The non-white channel can be from non-white noise, or more commonly, interference. Our proposed algorithm: spectral shaping (SS), follows a simple optimization problem to find what we term reliable subcarriers, and then per subcarrier power allocations are made in order to attain the AWGN channel bit error ratio (BER). Subcarriers that experience high interference are deactivated or chosen as guard subcarriers. After describing our analysis, we show some simulation results in two non-white interference signal examples: the Gaussian pulse shaped high-power distance measuring equipment (DME) and a classical rectangular-pulse signal. The DME example is pertinent for currently proposed aeronautical communication systems, where new multicarrier techniques, e.g., the L-band digital Aeronautical communication systems (LDACS) have been designed as an inlay approach between the high-power DME channels in L-band. Our results show that in high DME power interferences, using this adaptive technique can improve the overall reliability and attain AWGN BER performance in AWGN channel. We provide power spectral density (PSD) results to compare OFDM and FBMC based spectra when our SS algorithm is applied. Also, we provide BER vs. bit energy to noise density ratio (Eb/N0) results in the AWGN, as well as more realistic aeronautical channels to compare SS system performance. Our results show that in the presence of high power DME interference, using this adaptive technique can improve the overall reliability and enable us to attain non-interference BER performance in realistic aeronautical channels. [ABSTRACT FROM AUTHOR]

Symbol detection (SD) plays an important role in a digital communication system. However, most SD algorithms require channel state information (CSI), which is often difficult to estimate accurately. As a consequence, it is challenging for these SD algorithms to approach the performance of the maximum likelihood detection (MLD) algorithm. To address this issue, we employ both semi-supervised learning and ensemble learning to design a flexible parallelizable approach in this paper. First, we prove theoretically that the proposed algorithms can arbitrarily approach the performance of the MLD algorithm with perfect CSI. Second, to enable parallel implementation and also enhance design flexibility, we further propose a parallelizable approach for multi-output systems. Finally, comprehensive simulation results are provided to demonstrate the effectiveness and superiority of the designed algorithms. In particular, the proposed algorithms approach the performance of the MLD algorithm with perfect CSI, and outperform it when the CSI is imperfect. Interestingly, a detector constructed with received signals from only two receiving antennas (less than the size of the whole receiving antenna array) can also provide good detection performance. [ABSTRACT FROM AUTHOR]

Antenna arrays have a long history of more than 100 years and have evolved closely with the development of electronic and information technologies, playing an indispensable role in wireless communications and radar. With the rapid development of electronic and information technologies, the demand for all-time, all-domain, and full-space network services has exploded, and new communication requirements have been put forward on various space/air/ground platforms. To meet the ever increasing requirements of the future sixth generation (6G) wireless communications, such as high capacity, wide coverage, low latency, and strong robustness, it is promising to employ different types of antenna arrays (e.g., phased arrays, digital arrays, and reconfigurable intelligent surfaces, etc.) with various beamforming technologies (e.g., analog beamforming, digital beamforming, hybrid beamforming, and passive beamforming, etc.) in space/air/ground communication networks, bringing in advantages such as considerable antenna gains, multiplexing gains, and diversity gains. However, enabling antenna array for space/air/ground communication networks poses specific, distinctive and tricky challenges, which has aroused extensive research attention. This paper aims to overview the field of antenna array enabled space/air/ground communications and networking. The technical potentials and challenges of antenna array enabled space/air/ground communications and networking are presented first. Subsequently, the antenna array structures and designs are discussed. We then discuss various emerging technologies facilitated by antenna arrays to meet the new communication requirements of space/air/ground communication systems. Enabled by these emerging technologies, the distinct characteristics, challenges, and solutions for space communications, airborne communications, and ground communications are reviewed. Finally, we present promising directions for future research in antenna array enabled space/air/ground communications and networking. [ABSTRACT FROM AUTHOR]

Constrained coding is a somewhat nebulous term which we may define by either inclusion or exclusion. A constrained system is defined by a constrained set of “good” or “allowable” sequences to be recorded or transmitted. Constrained coding focuses on the analysis of constrained systems and the design of efficient encoders and decoders that transform arbitrary user sequences into constrained sequences. Constrained coding has extensively been used since the advent in the 1950s of digital storage and communication devices. They have found application in all hard disk, non-volatile memories, and optical discs, such as CD, DVD, and Blu-Ray Disc, and they are now projected for usage in DNA-based storage. We survey theory and practice of constrained coding, tracing the evolution of the subject from its origins in Shannon's classic 1948 paper to present-day applications in DNA-based data storage systems. [ABSTRACT FROM AUTHOR]

In this paper, we propose a class of high-efficiency deep joint source-channel coding methods that can closely adapt to the source distribution under the nonlinear transform, it can be collected under the name nonlinear transform source-channel coding (NTSCC). In the considered model, the transmitter first learns a nonlinear analysis transform to map the source data into latent space, then transmits the latent representation to the receiver via deep joint source-channel coding. Our model incorporates the nonlinear transform as a strong prior to effectively extract the source semantic features and provide side information for source-channel coding. Unlike existing conventional deep joint source-channel coding methods, the proposed NTSCC essentially learns both the source latent representation and an entropy model as the prior on the latent representation. Accordingly, novel adaptive rate transmission and hyperprior-aided codec refinement mechanisms are developed to upgrade deep joint source-channel coding. The whole system design is formulated as an optimization problem whose goal is to minimize the end-to-end transmission rate-distortion performance under established perceptual quality metrics. Across test image sources with various resolutions, we find that the proposed NTSCC transmission method generally outperforms both the analog transmission using the standard deep joint source-channel coding and the classical separation-based digital transmission. Notably, the proposed NTSCC method can potentially support future semantic communications due to its content-aware ability and perceptual optimization goal. [ABSTRACT FROM AUTHOR]

With the rapid development of network media, increasing research on reversible watermarking has focused on improving its robustness to resisting attacks during digital media transmission. There are some other reversible watermarking schemes that work in the encrypted domain for preserving the privacy of the cover image. The robustness of the watermarking and the privacy preserving of the cover image have become the key factors of reversible watermarking. However, there are few robust reversible watermarking schemes in the encrypted domain that could resist common attacks (such as JPEG compression, noise addition) and preserve privacy at the same time. In addition, the embedding capacity of a robust watermark and the efficiency of the encryption method must be considered. Recently, cloud computing technology has led to the rapid growth of network media, and many multimedia properties are owned by multiple parties, such as a film’s producer and multiple distributors. Multi-party watermarking has become an important demand for network media to protect all parties’ rights. In this paper, a Robust Reversible Watermarking scheme in Encrypted Image with Secure Multi-party (RRWEI-SM) based on lightweight cryptography is first proposed. Additive secret sharing and block-level scrambling are developed to generate the encrypted image. Then, the robust reversible watermarking based on significant bit Prediction Error Expansion (PEE) is performed by Secure Multi-party Computation (SMC). For applications with high robustness, a Modified RRWEI-SM is proposed by exploiting two-stage architecture. Furthermore, both the RRWEI-SM scheme and Modified RRWEI-SM scheme are separable and can be applied to multiparty copyright protection. The experimental results and theoretical analysis demonstrate here that the RRWEI-SM and the Modified RRWEI-SM are secure, robust and effective. [ABSTRACT FROM AUTHOR]

The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over siloed data centers is motivating renewed interest in the collaborative training of a shared model by multiple individual clients via federated learning (FL). To improve the communication efficiency of FL implementations in wireless systems, recent works have proposed compression and dimension reduction mechanisms, along with digital and analog transmission schemes that account for channel noise, fading, and interference. The prior art has mainly focused on star topologies consisting of distributed clients and a central server. In contrast, this paper studies FL over wireless device-to-device (D2D) networks by providing theoretical insights into the performance of digital and analog implementations of decentralized stochastic gradient descent (DSGD). First, we introduce generic digital and analog wireless implementations of communication-efficient DSGD algorithms, leveraging random linear coding (RLC) for compression and over-the-air computation (AirComp) for simultaneous analog transmissions. Next, under the assumptions of convexity and connectivity, we provide convergence bounds for both implementations. The results demonstrate the dependence of the optimality gap on the connectivity and on the signal-to-noise ratio (SNR) levels in the network. The analysis is corroborated by experiments on an image-classification task. [ABSTRACT FROM AUTHOR]

Recently, robust video transmission system that can eliminate the cliff effect in digital video transmission has attracted great interest from both academia and industry. By linearizing the whole system, robust video transmission is intrinsically scalable to channel conditions in mobile networks. However, the heterogeneity of user devices in terms of viewing resolution has not been well studied for robust video broadcast systems. In this paper, we propose a spatial scalability enabled robust video broadcast (SSRVB) system, aiming at accommodating diverse users with both heterogeneous resolutions and heterogeneous channel conditions. In SSRVB, a novel spatial decomposition method based on linear projection is first designed for robust video transmission. Then the transmission distortion minimization problem with joint subcarrier matching and power allocation is formulated. A near-optimal low-complexity subcarrier matching algorithm based on auction theory and an optimal power allocation strategy are also proposed. Furthermore, an iterative algorithm is designed to solve the problem of joint resource allocation. Simulation results demonstrate that SSRVB can achieve an average of 3 dB gain when compared with the reference schemes (i.e., ECast, MCast, discrete wavelet transform (DWT) based scheme, and scalable video coding (SVC) scheme) in terms of average peak signal-to-noise ratio under heterogeneous scenarios. [ABSTRACT FROM AUTHOR]

A word is said to be bordered if it contains a non-empty proper prefix that is also a suffix. We can naturally extend this definition to pairs of non-empty words. A pair of words $(u,v)$ is said to be mutually bordered if there exists a word that is a non-empty proper prefix of $u$ and suffix of $v$ , and there exists a word that is a non-empty proper suffix of $u$ and prefix of $v$. In other words, $(u,v)$ is mutually bordered if $u$ overlaps $v$ and $v$ overlaps $u$. We give a recurrence for the number of mutually bordered pairs of words. Furthermore, we show that, asymptotically, there are $c\cdot k^{2n}$ mutually bordered words of length- $n$ over a $k$ -letter alphabet, where $c$ is a constant. Finally, we show that the expected shortest overlap between pairs of words is bounded above by a constant. [ABSTRACT FROM AUTHOR]

Recent studies have shown that low-resolution analog-to-digital-converters and digital-to-analog-converters (ADCs and DACs) can make fully-digital beamforming more power efficient than its analog or hybrid beamforming counterpart over wide-band millimeter-wave (mmWave) channels. Inspired by this, we propose a computationally efficient fully-digital beamformer relying on low-resolution ADCs/DACs for multi-user mmWave communication networks. Both a generalized (unstructured) beamformer (GB) and a structured zero-forcing beamformer (ZFB) are proposed. For maintaining fairness among all users in the network, specifically tailored objective functions are considered under sum-power constraints, namely that of maximizing the geometric mean (GM) of users’ rate and their max-min rate. These computationally challenging beamforming design problems are tackled by developing computationally efficient steep ascent algorithms, which have the radical benefit of relying on a closed-form solution at each iteration. Moreover, to facilitate the employment of low-cost amplifiers at each antenna, the GB design problem subject to the equal-gain transmission constraint is considered, which assigns equal transmit power to each transmit antenna. The proposed algorithms promise a user-rate distribution having a reduced deviation among the user-rates, i.e., improved rate-fairness. Our extensive simulation results show an approximately upto 45% reduction for the GM-rate of a 2-bit ADC (4-bin quantization) compared to the $\infty$ -resolution ADC. [ABSTRACT FROM AUTHOR]

We present DeepWiVe, the first-ever end-to-end joint source-channel coding (JSCC) video transmission scheme that leverages the power of deep neural networks (DNNs) to directly map video signals to channel symbols, combining video compression, channel coding, and modulation steps into a single neural transform. Our DNN decoder predicts residuals without distortion feedback, which improves the video quality by accounting for occlusion/disocclusion and camera movements. We simultaneously train different bandwidth allocation networks for the frames to allow variable bandwidth transmission. Then, we train a bandwidth allocation network using reinforcement learning (RL) that optimizes the allocation of limited available channel bandwidth among video frames to maximize the overall visual quality. Our results show that DeepWiVe can overcome the cliff-effect, which is prevalent in conventional separation-based digital communication schemes, and achieve graceful degradation with the mismatch between the estimated and actual channel qualities. DeepWiVe outperforms H.264 video compression followed by low-density parity check (LDPC) codes in all channel conditions by up to 0.0485 in terms of the multi-scale structural similarity index measure (MS-SSIM), and H.265+ LDPC by up to 0.0069 on average. We also illustrate the importance of optimizing bandwidth allocation in JSCC video transmission by showing that our optimal bandwidth allocation policy is superior to uniform allocation as well as a heuristic policy benchmark. [ABSTRACT FROM AUTHOR]

Random interleavers are widely employed in digital communication systems to combat channel fading and burst errors. In applications such as grant-free access by Internet of Things (IoT) devices, accurately identifying a specific irregular interleaving pattern within an interleaver period is vital to both terminal recognition and data recovery. In this work, we investigate effective approaches for random interleaving pattern identification in Reed-Solomon (RS) coded data streams. We first propose an algorithm of low computational complexity to detect positions of code symbols belonging to the same RS codeword group (RSCG) under modest bit error rate. We further develop another low-complexity algorithm to successfully identify random interleaving patterns for RS code symbols within each RS codeword under moderate to high error rate applications. Our theoretical analysis and simulation results corroborate to demonstrate the effectiveness of our algorithms. [ABSTRACT FROM AUTHOR]

Sub-cycle algorithms aid in quick fault detection and isolation by digital relays. A new sub-cycle phase angle-based algorithm, to achieve 3 cycle Circuit Breaker (CB) operation, is proposed in this letter. Half-cycle DFT (HCDFT) is used to estimate the Instantaneous Phase Angles (IPAs) of the fundamental current and voltage. Modified IPAs (MIPAs) are derived from the IPAs. A Sub-Cycle Similarity Index (SCSI), obtained from the MIPAs, is used for Fault Detection (FD) and Fault Classification (FC). A Directional Index (DI) based process, which runs in parallel to FD and FC, is also proposed to distinguish internal and external faults. Simulation results and comparison with previous methods reveal the efficacy of the proposed algorithm in achieving high-speed protection of transmission lines. [ABSTRACT FROM AUTHOR]

The widespread proliferation of smart sensors has led to hardware that enable edge intelligence (EI) with extreme energy efficiencies. This decreases the volume of data that is transmitted to the cloud, thus reducing: 1) processing latency; 2) communication energy; and 3) network congestion. However, this comes with an added cost of computation at the edge node. The cost (energy/latency) of edge computation and the cost of communication to the cloud vary widely depending on operating conditions, which include: 1) information content in the data; 2) algorithm selection; 3) channel conditions (noise, path-loss, etc.); 4) network size, available bandwidth; and 5) resources at the cloud. This article presents a 65 nm wireless image processing SoC for real-time computation-communication trade-off on resource-constrained edge devices. The test-chip includes: 1) an all-digital, near-memory, reconfigurable, and programmable neural-network (NN)-based systolic image processor; 2) a digitally adaptive radio-frequency digital-to-analog converter (RF-DAC)-based transceiver; and 3) a mixed-signal, time-based, actor-critic (AC) neuro-controller with compute-in-memory (CIM) and in-place weight updates that provide online learning and adaptation for efficiently controlling the computation, communication blocks separately as well as jointly. The major contributions of the proposed SoC are threefold: 1) a wireless Internet of Things (IoT) SoC architecture enabling a generic computation-communication trade-off scheme; 2) a novel CIM circuit design enabling effective AC control and online learning (0.59 pJ/MAC, 0.4 pJ/update); 3) integration of programmable deep NN (DNN) accelerator (1.05 TOPS/W) and reconfigurable transceiver (184 pJ/b @ –15 dBm) supporting versatile cloud-edge collaborations; and 4) significant system-level energy efficiency improvement (5.7 $\times $) with real-time on-chip smart control enabled by seamless chip integration and AI-enabled decision-making. Furthermore, this SoC serves as a system-level IoT prototype for next-generation context-aware EI. [ABSTRACT FROM AUTHOR]

Beginning in 2030, sixth generation (6G) mobile communication is expected to play a key role by collecting billions of things, humans, and robots, resulting in zettabytes of digital information. The key features of 6G include inherent connected intelligence in the telecommunication networks with devices from different vendors. Hence, trust should be enforced among devices, sub-networks, and applications, especially for mission-critical services. Currently, trust-based intrusion detection can measure an entity's trustworthiness by collecting statistics or sending consultation messages, while it may become inefficient in the era of 6G due to the overloaded traffic and potential latency. In this article, we discuss the challenges of deploying trust-based intrusion detection in 6G, and design BCTrustFrame, a framework of blockchain-based trust management with IPFS, which aims to enhance the effectiveness of trust evaluation via the collaboration of blockchain and IPFS technology. We also perform a case study to demonstrate the viability and performance. [ABSTRACT FROM AUTHOR]

A 24 GHz self-calibrated all-digital frequency-modulated continuous-wave (FMCW) synthesizer is presented in this article. A multi-bank digitally controlled oscillator (DCO) is employed to provide adequate frequency tuning range and high frequency resolution. A full background self-calibration scheme is proposed to linearize the DCO tuning curve for highly linear and fast chirp generation. An overlap compensator based on an adaptive lookup table (LUT) is utilized to eliminate the frequency overlaps between adjacent DCO tuning bands. The compensation words in the LUT are altered by least-mean-square (LMS) algorithm for robust and precise matching. A ramp tracker, which features real-time ramp tracking with zero steady-state phase error, is employed to estimate and stabilize the frequency ramp. Implemented in 40 nm bulk CMOS, the FMCW synthesizer prototype consumes 28 mW with a 0.26 mm2 core area. The maximum chirp bandwidth is 3.2 GHz, corresponding to 13.33% of the center frequency. Measurement results show that the root-mean-square (RMS) frequency error is reduced by 1/10 with self-calibration after 2 ms convergence time. The maximum chirp slope is up to 320 MHz/ $\mu \text{s}$ with 309 kHz RMS frequency error. The minimum RMS frequency error is 7.35 kHz under 5 MHz/ $\mu \text{s}$ chirp slope, corresponding to 0.0002% of the chirp bandwidth. [ABSTRACT FROM AUTHOR]

The article reports that The last decade of the twentieth century and the first two decades of the twenty-first have witnessed a dramatic transformation of our society in lifestyle and human connectivity. In perusing my writings spanning this quarter century, I encountered both a lecture presented at the 1995 Marconi Society Symposium as well as my most recent, the 2019 Armstrong Lecture delivered at Columbia University.

The article proposes methodology to use circularly polarized (CP) ultra-wideband (UWB) signals for simultaneous measurement of orientation and distance changes between transmitter and receiver. The proposed technique uses the rotational Doppler effect on CP pulsed communication. The amplitude of a CP signal is immune to polarization misalignment in the presence of rotation; however, the phase is subjected to a frequency-invariant shift proportional to the rotation angle. This significantly distorts the pulse shape in the time domain and can be used for the measurement of the rotated angle. By combining the technique with the well-known localization capability of UWB systems, one can precisely measure not only the distance but also the orientation. This is demonstrated by both numerical and experimental studies presented in this article. [ABSTRACT FROM AUTHOR]

This work addresses the nonfragile $H_{\infty }$ controller design problem for a class of discrete-time uncertain nonlinear systems. The norm-bounded uncertainty is contained in the nonlinear plant, which is described by the well-known Takagi–Sugeno (T–S) fuzzy model. The controller gain perturbation is also considered. When the input signal is transmitted from the controller to the system through the digital communication channels, it will be quantized by a static quantizer. The main attention is to design the nonfragile $H_{\infty }$ state-feedback controller for the closed-loop quantized uncertain T–S fuzzy system. By introducing some auxiliary scalars and the fuzzy basis-dependent Lyapunov function approach, sufficient conditions are established in the form of linear matrix inequalities (LMIs). The construction for the nonfragile $H_{\infty }$ controller can be completed by solving these LMIs. In the end, the applicability and effectiveness of the proposed method have been illustrated by two simulation examples. [ABSTRACT FROM AUTHOR]

Fully digital transmitters (DTXs) have the potential of replacing analog-intensive transmitter (TX) line-ups in future massive multiple-input and multiple-output (mMIMO) systems since they hold the promise of higher system integration level and energy efficiency. DTX operation so far has been limited to low RF output powers. This article introduces a concept that enables high-power DTX operation. A DTX demonstrator targeting both high output power and high efficiency is realized as a proof of concept. It is based on a custom ${V_{T}}$ -shifted laterally-diffused MOS (LDMOS) technology, which is utilized to implement a segmented high-power output stage operated in class-BE. A low-voltage high-speed 40-nm CMOS controller drives the individual output stage segments at gigahertz rates. Measurements show the promising results for the proposed high-power DTX concept and provide valuable lessons for future DTX implementations. [ABSTRACT FROM AUTHOR]

This article presents a signal generator and a digital beamforming receiver (DBR) with high phase control resolution and high beam control resolution, respectively. The signal generator is designed based on a direct digital synthesizer (DDS) and a phase lock loop (PLL). In the DDS–PLL signal generator, the conventional divider in PLL is replaced with a mixer, comb generator, and a doubler to utilize high phase control resolution of DDS (14 bit, 0.022°) as it is to synchronize the DDS–PLL signal generator with the entire system. In the DBR, the output signals of the DDS–PLL signal generators are used as the second local oscillator (LO) signal, and the beamforming technique is implemented through the change of the phase of the second LO signals. Also, for sophisticated beamforming, all signal generator components in the DBR are synchronized for generating same and fixed frequency and phase. Finally, the DBR has the same phase control resolution as the DDS, and the phase of each chain of the DBR can be adjusted up to 0.022° (14 bit). The DBR using the DDS–PLL signal generator is designed and fabricated. The DBR consists of a $1 \times 8$ array tapered slot antenna (TSA) part, a first and second frequency conversion part, a $1 \times 8$ data acquisition and combiner part, DDS–PLL signal generator part, and a reference signal generator part. The phase control performance of the DBR was verified by measuring the phase change of the chain in the DBR according to the DDS phase change. Also, to verify the beamforming performance of the DBR with high beam control resolution, the radiation pattern of the DBR when the angle of the main lobe was 0°, 0.2°, 0.4°, 15°, and 30° was measured in the 28 GHz band. At the main lobe, the gain error is within 1 dB and the beam angle error is within 0.2°. The simulation results are in good agreement with the measured results. A high-resolution beamforming performance is achieved in the DBR using the DDS–PLL signal generator. [ABSTRACT FROM AUTHOR]

Based on the T–S fuzzy modeling method, this article aims to deal with the problem of event-triggered integral sliding-mode attitude-tracking control for a nonlinear elastic spacecraft system with the unknown actuator dead-zone and a redundant reaction wheel over digital communication channels. Considering the effect of external disturbance and the restrictions imposed by wireless network transmission, a classical dynamic logarithmic sensor-to-controller quantizer and an event-triggered mechanism are introduced to perform the analysis and design work. The T–S fuzzy model is introduced to describe the nonlinear attitude dynamic property of the flexible spacecraft, and an integral sliding surface is employed in this article. The robustness of the closed-loop attitude-tracking control system and the finite-time reachability of the sliding-surface domain are guaranteed by the presented quantized event-triggering adaptive sliding-mode control law. Simulation results are provided to verify the feasibility of the proposed attitude-tracking control strategy. [ABSTRACT FROM AUTHOR]

The design of methods for inference from time sequences has traditionally relied on statistical models that describe the relation between a latent desired sequence and the observed one. A broad family of model-based algorithms have been derived to carry out inference at controllable complexity using recursive computations over the factor graph representing the underlying distribution. An alternative model-agnostic approach utilizes machine learning (ML) methods. Here we propose a framework that combines model-based algorithms and data-driven ML tools for stationary time sequences. In the proposed approach, neural networks are developed to separately learn specific components of a factor graph describing the distribution of the time sequence, rather than the complete inference task. By exploiting stationary properties of this distribution, the resulting approach can be applied to sequences of varying temporal duration. Learned factor graphs can be realized using compact neural networks that are trainable using small training sets, or alternatively, be used to improve upon existing deep inference systems. We present an inference algorithm based on learned stationary factor graphs, which learns to implement the sum-product scheme from labeled data, and can be applied to sequences of different lengths. Our experimental results demonstrate the ability of the proposed learned factor graphs to learn from small training sets to carry out accurate inference for sleep stage detection using the Sleep-EDF dataset, as well as for symbol detection in digital communications with unknown channels. [ABSTRACT FROM AUTHOR]