With the rise of the metaverse, haptic technologies have gained significant attention, striving to enhance human-machine interactions by incorporating a sense of touch alongside audio and visual experiences. Vibrotactile signals, which deliver haptic information through vibrations, are commonly used in commercial haptic interaction devices. The IEEE and MPEG have begun efforts to standardize the data compression of these signals. However, achieving efficient compression without sacrificing fidelity continues to pose a significant challenge. Inspired by the state-of-the-art audio compression techniques, we develop a novel residual vector quantization (RVQ)-based neural vibrotactile codec (RVQ-NVC) that capitalizes on the similarities between vibrotactile and audio signals in both time and frequency domains, strategically adapting RVQ to suit vibrotactile data. The RVQ-NVC scheme consists of a fully convolutional encoder-decoder network combined with an RVQ module, trained in an end-to-end manner. We employ perceptual and adversarial loss functions to ensure the high-quality reconstruction of vibrotactile signals while enabling variable bitrate coding through structured dropout techniques. Experimental results reveal that RVQ-NVC excels in signal reconstruction at low compression ratios, maintains high perceptual quality at reduced bitrates, and supports low-latency real-time processing, making it particularly well-suited for haptic interaction applications.

Cell-free integrated sensing and communication (ISAC) systems have emerged as a promising paradigm for sixth-generation (6G) networks, enabling simultaneous high-rate data transmission and high-precision radar sensing through cooperative distributed access points (APs). Fully exploiting these capabilities requires a unified design that bridges system-level optimization with multi-target parameter estimation. This paper proposes an end-to-end graph learning approach to close this gap, modeling the entire cell-free ISAC network as a heterogeneous graph to jointly design the AP mode selection, user association, precoding, and echo signal processing for multi-target position and velocity estimation. In particular, we propose two novel heterogeneous graph learning frameworks: a dynamic graph learning framework and a lightweight mirror-based graph attention network (mirror-GAT) framework. The dynamic graph learning framework employs structural and temporal attention mechanisms integrated with a three-dimensional convolutional neural network (3D-CNN), enabling superior performance and robustness in cell-free ISAC environments. Conversely, the mirror-GAT framework significantly reduces computational complexity and signaling overhead through a bi-level iterative structure with shared adjacency. Simulation results validate that both proposed graph-learning-based frameworks achieve significant improvements in multi-target position and velocity estimation accuracy compared to conventional heuristic and optimization-based designs. Particularly, the mirror-GAT framework demonstrates substantial reductions in computational time and signaling overhead, underscoring its suitability for practical deployments.

Reconfigurable antennas possess the capability to dynamically adjust their fundamental operating characteristics, thereby enhancing system adaptability and performance. To fully exploit this flexibility in modern wireless communication systems, this paper considers a novel tri-hybrid beamforming architecture, which seamlessly integrates pattern-reconfigurable antennas with both analog and digital beamforming. The proposed tri-hybrid architecture operates across three layers: (\textit{i}) a radiation beamformer in the electromagnetic (EM) domain for dynamic pattern alignment, (\textit{ii}) an analog beamformer in the radio-frequency (RF) domain for array gain enhancement, and (\textit{iii}) a digital beamformer in the baseband (BB) domain for multi-user interference mitigation. To establish a solid theoretical foundation, we first develop a comprehensive mathematical model for the tri-hybrid beamforming system and formulate the signal model for a multi-user multi-input single-output (MU-MISO) scenario. The optimization objective is to maximize the sum-rate while satisfying practical constraints. Given the challenges posed by high pilot overhead and computational complexity, we introduce an innovative tri-timescale beamforming framework, wherein the radiation beamformer is optimized over a long-timescale, the analog beamformer over a medium-timescale, and the digital beamformer over a short-timescale. This hierarchical strategy effectively balances performance and implementation feasibility. Simulation results validate the performance gains of the proposed tri-hybrid architecture and demonstrate that the tri-timescale design significantly reduces pilot overhead and computational complexity, highlighting its potential for future wireless communication systems.

Cell-free integrated sensing and communication (ISAC) is a pivotal technology for next-generation wireless networks, where dynamic collaboration enables the full exploitation of spatial degrees of freedom (DoF) to enhance system performance. By leveraging multi-view observations and effective information sharing, cell-free ISAC systems facilitate high-precision target positioning through collaborative precoding and information fusion. To fully capitalize on the collaborative potential of cell-free ISAC, this paper introduces a novel dynamic heterogeneous graph learning framework for joint base-station (BS) mode selection, user association, transmit precoding and receive information fusion design. By incorporating structural and temporal attention mechanisms, we address the discrete, coupled decision challenges associated with BS mode selection and user association. Additionally, the graph node message-passing mechanism is employed to enable efficient precoding and processing design. Extensive simulation results demonstrate that, compared to non-collaborative ISAC systems with fixed association strategies, the proposed dynamic graph learning framework significantly improves target positioning accuracy while achieving a superior communication rate.

Orthogonal frequency-division multiplexing (OFDM) is widely considered a leading waveform candidate for integrated sensing and communication (ISAC) in 6G networks. However, the cyclic prefix (CP) used to mitigate multipath effects in communication systems also limits the maximum sensing range. Target echoes arriving beyond the CP length cause inter-symbol interference (ISI) and inter-carrier interference (ICI), which degrade the mainlobe level and raise sidelobe levels in the range-Doppler map (RDM). This paper presents a unified analytical framework to characterize the ISI and ICI caused by an insufficient CP length in multi-target scenarios. For the first time, we derive closed-form expressions for the second-order moments of the RDM under both matched filtering (MF) and reciprocal filtering (RF) processing with insufficient CP length. These expressions quantify the effects of CP length, symbol constellation, and inter-target interference (ITI) on the mainlobe and sidelobe levels. Based on these results, we further derive explicit formulas for the peak sidelobe level ratio (PSLR) and integrated sidelobe level ratio (ISLR) of the RDM, revealing a fundamental trade-off between noise amplification in RF and ITI in MF. Numerical results validate our theoretical derivations and illustrate the critical impact of insufficient CP length on sensing performance in OFDM-ISAC systems.

Reconfigurable antennas can dynamically adjust their operating characteristics, thereby enhancing adaptability and performance. To fully exploit this flexibility in modern wireless communication systems, this paper proposes a novel tri-hybrid beamforming architecture that integrates pattern-reconfigurable antennas with analog and digital beamforming. The proposed tri-hybrid architecture operates across three layers: a radiation beamformer in the electromagnetic (EM) domain for dynamic pattern alignment, an analog beamformer in the radio-frequency (RF) domain for array gain enhancement, and a digital beamformer in the baseband (BB) domain for multi-user interference mitigation. First, a comprehensive mathematical model of the tri-hybrid beamforming system is developed to provide a theoretical foundation. Then, an optimization problem is formulated to maximize the sum-rate under practical constraints, and then is solved using fractional programming (FP), a penalty-based, majorization–minimization (MM), and manifold optimization methods. Simulation results validate the performance gains achieved by the proposed tri-hybrid architecture and highlight its potential for future wireless communication systems.

Integrated sensing and communication (ISAC) technology has emerged as a key enabling technology in the sixth generation (6G) mobile communications. This paper investigates the potential of multipath exploitation for enhancing target detection in orthogonal frequency division multiplexing (OFDM)-based ISAC systems. The study aims to improve target detection performance by harnessing the diversity gain in the delay-Doppler domain. We propose a weighted generalized likelihood ratio test (GLRT) detector that effectively leverages the multi-path propagation between the base station (BS) and the target. To further enhance detection accuracy, a joint optimization framework is developed for subcarrier power allocation at the transmitter and weight coefficients of the GLRT detector. The objective is to maximize the probability of target detection while satisfying constraints on total transmit power and the communication receiver’s signal-to-noise ratio (SNR). An iterative algorithm based on the majorization-minimization (MM) method is employed to address the resulting non-convex optimization problem. Simulation results demonstrate the efficacy of the proposed algorithm and confirm the benefits of multipath exploitation for target detection in OFDM-ISAC systems under multipath-rich environments.

In this paper, we explore cooperative sensing and communication within cell-free integrated sensing and communication (ISAC) systems. Specifically, multiple transmit access points (APs) collaboratively serve multiple communication users while simultaneously illuminating a potential target, with a separate sensing AP dedicated to collecting echo signals for target detection. To improve the performance of identifying a moving target in the presence of strong interference originating from transmit APs, we employ the space-time adaptive processing (STAP) technique and jointly optimize the transmit/receive beamforming. Our goal is to maximize the radar output signal-to-interference-plus-noise ratio (SINR), subject to constraints on the communication SINR and transmit power. An efficient algorithm is developed to solve the resulting non-convex optimization problem. Simulations demonstrate significant performance improvements in target detection and validate the advantages of the proposed joint STAP and beamforming design for cell-free ISAC systems.

Over the past decades, the development of tactile sensors has gained increasing attention and has gradually become a fundamental device for robots. Especially in today's context where human-robot interaction demands are growing and the requirements for tactile perception are becoming stricter, how to enable robots to better perceive their environment has become a topic worth discussing. Tactile sensors, after years of development, have emerged in two main types: taxel-based and vision-based sensors, where the latter can provide relatively low resolution (LR) tactile patterns compared with the former. Both of them have seen significant enhancements in their tactile perception capabilities on flat and regular surfaces. However, as application scenarios expand, current flat tactile perception can no longer meet the robots' needs for multi-dimensional and complex perception capabilities. Therefore, we investigate the high-resolution (HR) reconstruction of non-planar tactile patterns captured by LR taxel-based sensors in this paper. We first develop a new dataset, where the ground truth of non-planar tactile patterns are obtained with a vision-based GelSight Mini tactile sensor, and the LR data are collected via a commercial taxel-based Xela sensor. In addition, we propose to adapt the state-of-the-art CNN- and GAN-based tactile super-resolution model of flat/planar surfaces to the non-planar scenario, and also develop a diffusion-based model for the nonplanar HR reconstruction. Experimental results confirm the efficiency of the proposed models.

Today, truly immersive multimedia systems demand the integration of emerging multi-sensorial media, which go beyond traditional audiovisual signals to include haptics, olfaction, motion capture, electroencephalograms, and other novel media forms. To effectively incorporate these modalities into cutting-edge multimedia systems, advances are needed across the entire pipeline, from processing and encoding to seamless integration. In addition, human-centric factors such as ergonomics and user experience must be considered to ensure practical implementation. Our workshop, the International Workshop on Multi-Sensorial Media and Applications (MSMA'2025), seeks to attract contributions related to multi-sensorial media systems, including system design, evaluation, coding, delivery, media analysis, multi-modal interaction, human factors, ergonomics, and related areas. By fostering collaboration among researchers, MSMA aims to bridge existing work in the field, spark innovation, and push the boundaries of multimedia technology.

Extremely large-scale antenna array (ELAA) is a key candidate technology for the sixth generation (6G) mobile networks. Nevertheless, using substantial numbers of antennas to transmit high-frequency signals in ELAA systems significantly exacerbates the near-field effect. Unfortunately, traditional hybrid beamforming schemes are highly vulnerable to ELAA near-field communications. To effectively mitigate severe near-field effect, we propose a novel dynamic hybrid beamforming architecture for ELAA systems, in which each antenna is either adaptively connected to one radio frequency (RF) chain for signal transmission or deactivated for power saving. For the case that instantaneous channel state information (CSI) is available during each channel coherence time, a real-time dynamic hybrid beamforming design is developed to maximize the achievable sum rate under the constraints of the constant modulus of phase-shifters (PSs), non-overlapping dynamic connection network and total transmit power. When instantaneous CSI cannot be easily obtained in real-time, we propose a two-timescale dynamic hybrid beamforming design, which optimizes analog beamformer in long-timescale and digital beamformer in short-timescale, with the goal of maximizing ergodic sum-rate under the same constraints. Simulation results demonstrate the advantages of the proposed dynamic hybrid beamforming architecture and the effectiveness of the developed algorithms for ELAA near-field communications.

Integrated Sensing and Communication (ISAC) has emerged as a key enabler for future wireless systems. The recently developed symbol-level precoding (SLP) technique holds significant potential for ISAC waveform design, as it leverages both temporal and spatial degrees of freedom (DoFs) to enhance multi-user communication and radar sensing capabilities. Concurrently, reconfigurable intelligent surfaces (RIS) offer additional controllable propagation paths, further amplifying interest in their application. However, previous studies have encountered substantial computational challenges due to the complexity of jointly designing SLP-based waveforms and RIS passive beamforming. In this paper, we propose a novel model-driven learning approach that jointly optimizes waveform and beamforming by unfolding the iterative alternative direction method of multipliers (ADMM) algorithm. Two joint design algorithms are developed for radar target detection and direction-of-arrival (DoA) estimation tasks in a cluttered RIS-ISAC system. While ensuring the communication quality-of-service (QoS) requirements, our objectives are: 1) to maximize the radar output signal-to-interference-plus-noise ratio (SINR) for target detection, and 2) to minimize the Cramér-Rao bound (CRB) for DoA estimation. Simulation results verify that our proposed model-driven learning algorithms achieve satisfactory communication and sensing performance, while also offering a substantial reduction in computational complexity, as reflected by the average execution time.

Integrated sensing and communication (ISAC) is widely recognized as a pivotal enabling technique for the advancement of future wireless networks. This paper aims to efficiently exploit the inherent sparsity of echo signals for the multi-input-multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) based ISAC system. A novel joint receive echo processing and transmit beamforming design is presented to achieve this goal. Specifically, we first propose a compressive sensing (CS)-assisted estimation approach to facilitate ISAC receive echo processing, which can not only enable accurate recovery of target information, but also allow a substantial reduction in the number of sensing subcarriers to be sampled and processed. Then, based on the proposed CS-assisted processing method, the associated transmit beamforming design is formulated with the objective of maximizing the sum-rate of multiuser communications while satisfying the transmit power budget and ensuring the received signal-to-noise ratio (SNR) for the designated sensing subcarriers. In order to address the formulated non-convex problem involving high-dimensional variables, an effective iterative algorithm employing majorization minimization (MM), fractional programming (FP), and the nonlinear equality alternative direction method of multipliers (neADMM) with closed-form solutions has been developed. Finally, extensive numerical simulations are conducted to verify the effectiveness of the proposed algorithm and the superior performance of the introduced sparsity exploitation strategy.

Integrated sensing and communication (ISAC) is a key enabling technique for future wireless networks owing to its efficient hardware and spectrum utilization. In this paper, we focus on dual-functional waveform design for a multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) ISAC system, which is considered to be a promising solution for practical deployment. Since the dual-functional waveform carries communication information, its random nature leads to high range-Doppler sidelobes in the ambiguity function, which in turn degrades radar sensing performance. To suppress range-Doppler sidelobes, we propose a novel symbol-level precoding (SLP)-based waveform design for MIMO-OFDM ISAC systems by fully exploiting the available temporal degrees of freedom. Our goal is to minimize the range-Doppler integrated sidelobe level (ISL) while satisfying the constraints of target illumination power, multi-user communication quality of service (QoS), and constant-modulus transmission. To solve the resulting non-convex waveform design problem, we develop an efficient algorithm using the majorization-minimization (MM) and alternative direction method of multipliers (ADMM) methods. Simulation results show that the proposed waveform has significantly reduced range-Doppler sidelobes compared with signals designed only for communications and other baselines. In addition, the proposed waveform design achieves target detection and estimation performance close to that achievable by waveforms designed only for radar, which demonstrates the superiority of the proposed SLP-based ISAC approach.

Integrated sensing and communication has been identified as an enabling technology for forthcoming wireless networks. In an effort to achieve an improved performance trade-off between multiuser communications and radar sensing, this paper considers a dynamically-partitioned antenna array architecture for monostatic ISAC systems, in which each element of the array at the base station can function as either a transmit or receive antenna. To fully exploit the available spatial degrees of freedom for both communication and sensing functions, we jointly design the partitioning of the array between transmit and receive antennas together with the transmit beamforming in order to minimize the direction-of-arrival (DOA) estimation error, while satisfying constraints on the communication signal-to-interference-plus-noise ratio and the transmit power budget. An alternating algorithm based on Dinkelbach’s transform, the alternative direction method of multipliers, and majorization-minimization is developed to solve the resulting complicated optimization problem. To reduce the computational complexity, we also present a heuristic three-step strategy that optimizes the transmit beamforming after determining the antenna partitioning. Simulation results confirm the effectiveness of the proposed algorithms in significantly reducing the DOA estimation error.

Integrated sensing and communication (ISAC) is a key enabling technology for future 6G communication systems. Recently emerged symbol-level precoding (SLP) is considered to possess substantial potential for ISAC waveform design, owing to its ability to enhance multi-user communication and radar sensing performances by simultaneously leveraging both tempo-ral and spatial design degrees of freedom (DoFs). Considering the high complexity challenges brought by existing model-driven optimization based SLP design approaches, in this paper we pro-pose a lightweight SLP-Inception-Net and efficient data-driven deep learning algorithm to solve the highly complex SLP design problem. In particular, we propose a phase-based activation function method to guarantee the equality constraints and use the soft loss to ensure the inequality constraints. Simulation results verify that our proposed deep learning algorithm achieves comparable communication and radar sensing performance to the prior optimization-based approaches, while providing a noteworthy lOOO-fold reduction in computational complexity in terms of average execution time.

Integrated sensing and communication (ISAC) is a promising technology in future wireless systems owing to its efficient hardware and spectrum utilization. In this paper, we consider a multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) ISAC system and propose a novel waveform design to provide better radar ranging performance by taking range sidelobe suppression into consideration. In specific, we aim to design the MIMO-OFDM dual-function waveform to minimize its integrated sidelobe level (ISL) while satisfying the quality of service (QoS) requirements of multi-user communications and the transmit power constraint. To achieve a lower ISL, the symbol-level precoding (SLP) technique is employed to fully exploit the degrees of freedom (DoFs) of the waveform design in both temporal and spatial domains. An efficient algorithm utilizing majorization-minimization (MM) framework is developed to solve the non-convex waveform design problem. Simulation results reveal radar ranging performance improvement and demonstrate the benefits of the proposed SLP-based low-range-sidelobe waveform design in ISAC systems.

Extremely large-scale antenna array (ELAA) is deemed as one of several key candidate technologies for the sixth generation (6G) mobile networks. Nevertheless, the near-field effect poses a significant challenge for ELAA systems as a result of employing a substantial quantity of antennas for the transmission of high-frequency signals. Furthermore, the practical implementation of ELAA by employing a hybrid beamforming framework boosts the impact of the near-field effect on the system performance. In order to effectively address this severe near-field effect, we propose a novel dynamic hybrid beamforming architecture, in which each antenna is either adaptively connected to one radio frequency (RF) chain for signal transmission, or deactivated for power saving. The dynamic hybrid beamforming design algorithm is developed to maximize the achievable sum-rate under the constraints of the constant modulus of phase shifters, non-overlapping dynamic connection network, and the total transmit power. To address the resulting complicated nonconvex design problem, we employ the fractional programming (FP) method to transform the objective function into a more tractable form and exploit the manifold-based algorithm to tackle the nonconvex constraint. The simulation results demonstrate the advantages of the proposed dynamic hybrid beamforming and the effectiveness of the FP-manifold-based algorithm in ELAA near-field communication systems.

High-resolution (HR) contact surface information is essential for robotic grasping and precise manipulation tasks. However, it remains a challenge for current taxel-based sensors to obtain HR tactile information. In this paper, we focus on utilizing low-resolution (LR) tactile sensors to reconstruct the localized, dense, and HR representation of contact surfaces. In particular, we build a Gaussian triaxial tactile sensor degradation model and propose a tactile pattern reconstruction framework based on the Kalman filter. This framework enables the reconstruction of 2-D HR contact surface shapes using collected LR tactile sequences. In addition, we present an active exploration strategy to enhance the reconstruction efficiency. We evaluate the proposed method in real-world scenarios with comparison to existing prior-information-based approaches. Experimental results confirm the efficiency of the proposed approach and demonstrate satisfactory reconstructions of complex contact surface shapes.

One goal of dexterous robotic grasping is to allow robots to handle objects with the same level of flexibility and adaptability as humans. However, it remains a challenging task to generate an optimal grasping strategy for dexterous hands, especially when it comes to delicate manipulation and accurate adjustment the desired grasping poses for objects of varying shapes and sizes. In this paper, we propose a novel dexterous grasp generation scheme called GrainGrasp that provides fine-grained contact guidance for each fingertip. In particular, we employ a generative model to predict separate contact maps for each fingertip on the object point cloud, effectively capturing the specifics of finger-object interactions. In addition, we develop a new dexterous grasping optimization algorithm that solely relies on the point cloud as input, eliminating the necessity for complete mesh information of the object. By leveraging the contact maps of different fingertips, the proposed optimization algorithm can generate precise and determinable strategies for human-like object grasping. Experimental results confirm the efficiency of the proposed scheme. Our code is available at https://github.com/wmtlab/GrainGrasp.

The past decade has witnessed the blooming of autonomous mobile robot (AMR) applications with an increasing trend from closed to open working environments with unexpected obstacles. In this context, safety becomes a critical concern in the path planning of AMRs. However, it is a challenging task to guarantee the safety while maintaining the efficiency of path planning algorithm due to the uncertainty of open environments. Therefore, we propose in this paper to categorize the working environment of AMRs into three safety levels, i.e. low-risk, medium-risk and high-risk areas, according to the real-time distance between the AMR and the nearest obstacle. In particular, we incorporate safety levels into the famous reinforcement learning algorithm, deep deterministic policy gradient (DDPG), and develop a safety-guided DDPG algorithm for the path planning of AMRs. In low-risk areas, we adopt the conventional DDPG path planning algorithm directly to guarantee the efficiency (since the safety is generally not an issue in this case). In the medium-risk areas, we design a velocity threshold adjustment method, an OU noise with bias and propose a new reward function, aiming at encouraging AMRs to perform flexible actions as early as possible in order to avoid collisions. In the high-risk areas, we re-design the reward function based on the potential collision risk and shield misleading rewards that may cause local optimum problem, so as to ensure the safety of AMRs in dangerous situations. Simulation results confirm the satisfactory performance of the proposed scheme.

Integrated sensing and communication (ISAC) has become a crucial technology in future wireless systems. The recently emerged symbol-level precoding (SLP) technique is promising for ISAC waveform design since it can provide better multi-user communication and radar sensing performance by leveraging both temporal and spatial design degrees of freedom (DoFs). Previous research has been confronted with the huge computation burden challenge brought by the SLP design. In this paper, we propose a novel model-driven deep learning based SLP design by unfolding an iterative alternative direction method of multiplier (ADMM) algorithm, in order to efficiently solve the joint ISAC waveform and reconfigurable intelligent surface (RIS) beamforming design problem for RIS assisted ISAC systems. Specifically, our goal is to maximize the radar output signal-to-interference-plus-noise ratio (SINR) while satisfying the communication quality-of-service (QoS) requirements. Simulation results verify that our proposed model-driven deep learning algorithm achieves comparable radar sensing performance to the prior optimization-based approaches, while providing a noteworthy reduction in computational complexity in terms of average execution time.

The non-linearity of power amplifiers (PAs) in multiple antenna transmitters will cause spatial distortions and beam dispersion, which may lead to significant performance degradation. In this paper, we investigate the distortion-aware beamforming design in a multiuser multiple-input single-output (MU-MISO) system. Using a typical third-order memoryless polynomial distortion model, the impact of the nonlinear PA on the performance of MU-MISO is firstly analyzed by evaluating the receive signal-to-interference-plus-noise ratio (SINR) of UEs. Then, we aim to propose a distortion-aware beamforming scheme that can effectively pre-compensate for the beam dispersion caused by nonlinear PA distortion. Our objective is to maximize the sum-rate under the constraint of the transmit power by considering the effect of nonlinear PA distortion. The complex non-convex optimization problem is efficiently solved by an alternating optimization algorithm that utilizes the fractional programming (FP), penalty-based, and majorization-minimization (MM) methods. Finally, simulation studies demonstrate the substantial performance improvement achieved by utilizing the proposed distortion-aware beamforming scheme to mitigate nonlinear PA distortion and confirm the effectiveness of the developed beamforming design algorithm.

To unleash the potential of reconfigurable intelligent surface (RIS), in this paper we propose a novel dual-functional access point (DF-AP) for enhancing energy efficiency (EE) in the cell-free network. The DF-AP can dynamically switch between transmitter and reflector modes based on the wireless environment and the communication requirement. In the transmitter mode, the DF-AP works as a RIS-based transmitter, which can directly send data to users to improve the throughput of the system and ensure the quality of service (QoS). While in the reflector mode, the DF-AP operates as a conventional RIS, which passively reflects signals towards desired directions to increase coverage in an energy-efficient way. We propose a fractional programming (FP)-manifold-heuristic method to design the active beamforming of access points (APs) and DF-APs in the transmitter mode, the passive beamforming of DF-APs in the reflector mode, and the operation mode of DF-APs to maximize the system EE. The superiority of this DF-AP architecture and the effectiveness of the proposed design algorithm are then validated by simulation.

In this paper, we explore the physical layer security (PLS) of an active reconfigurable intelligent surface (RIS) assisted multi-input single-output (MISO) system in the presence of a passive eavesdropper (Eve), whose channel is unknown to the legitimate transmitter. In this practical scenario, we first propose an artificial noise (AN) based PLS approach, which can effectively deteriorate the eavesdropping by jointly designing the disturbance signal and the active RIS. However, the AN scheme requires a vast amount of energy to emit the noise signal, which can be potentially suppressed by Eve using more antennas. To tackle this issue, we then introduce another novel reconfigurable intelligent fading (RIF) approach, which utilizes the active RIS to rapidly manipulate the propagation environment, so that Eve suffers a fast fading channel, while the legitimate user (LU) still experiences a slow fading channel. Since Eve encounters difficulties in effectively estimating her fast fading channel, she cannot perform reliable legitimate information detection in a coherent manner, thereby significantly reducing the risk of information leakage and enabling secure communications. Simulation results demonstrate the effectiveness of both proposed approaches. Moreover, the RIF scheme achieves a substantial improvement in security performance compared to the AN scheme, particularly for the cases with limited transmit power.

Owing to the promising ability of saving hardware cost and spectrum resources, integrated sensing and communication (ISAC) is regarded as a revolutionary technology for future sixth-generation (6G) networks. The mono-static ISAC systems considered in most of existing works can only achieve limited sensing performance due to the single observation angle and easily blocked transmission links, which motivates researchers to investigate cooperative ISAC networks. In order to further improve the degrees of freedom (DoFs) of cooperative ISAC networks, the transmitter-receiver selection, i.e., base station (BS) mode selection problem, is meaningful to be studied. However, to our best knowledge, this crucial problem has not been extensively studied in existing works. In this paper, we consider the joint BS mode selection, transmit beamforming, and receive filter designs for cooperative cell-free ISAC networks, where multi-BSs cooperatively serve communication users and detect targets. An efficient joint beamforming design algorithm and three different heuristic BS mode selection methods are proposed to solve the non-convex NP-hard problem. Simulation results demonstrates the advantages of cooperative ISAC networks, the importance of BS mode selection, and the effectiveness of proposed algorithms.

Reconfigurable Intelligent Surfaces (RIS) have been proposed as a revolutionary technology with the potential to address several critical requirements of 6G communication systems. Despite its powerful ability for radio environment reconfiguration, the “double fading” effect constricts the practical system performance enhancements due to the significant path loss. A new active RIS architecture has been recently proposed to overcome this challenge. However, existing active RIS studies rely on an ideal amplification model without considering the practical hardware limitation of amplifiers, which may cause performance degradation using such inaccurate active RIS mod-eling. Motivated by this fact, in this paper we first investigate the amplification principle of typical active RIS and propose a more accurate amplification model based on amplifier hardware characteristics. Then, based on the new amplification model, we propose a novel joint transmit beamforming and RIS reflection beamforming design considering the incident signal power on practical active RIS for multiuser multi-input single-output (MU-MISO) communication system. Fractional programming (FP), majorization minimization (MM) and block coordinate descent (BCD) methods are used to solve for the complex problem. Simulation results indicate the importance of the consideration of practical amplifier hardware characteristics in the joint beamforming designs and demonstrate the effectiveness of the proposed algorithm compared to other benchmarks.

Integrated sensing and communication (ISAC) is an encouraging wireless technology which can simultaneously perform both radar and communication functionalities by sharing the same transmit waveform, spectral resource, and hardware platform. Recently emerged symbol-level precoding (SLP) technique exhibits advancement in ISAC systems by leveraging the waveform design degrees of freedom (DoFs) in both temporal and spatial domains. However, traditional SLP-based ISAC systems are designed in a modular paradigm, which potentially limits the overall performance of communication and radar sensing. The high complexity of existing SLP design algorithms is another issue that hurdles the practical deployment. To break through the bottleneck of these approaches, in this paper we propose an end-to-end approach to jointly design the SLP-based dual-functional transmitter and receivers of communication and radar sensing. In particular, we aim to utilize deep learning-based methods to minimize the symbol error rate (SER) of communication users, maximize the detection probability, and minimize the root mean square error (RMSE) of the target angle estimation. Multi-layer perceptron (MLP) networks and a long short term memory (LSTM) network are respectively applied to the transmitter, communication users and radar receiver. Simulation results verify the feasibility of the proposed deep-learning-based end-to-end optimization for ISAC systems and reveal the effectiveness of the proposed neural networks for the end-to-end design.

In this paper, we investigate the integration of integrated sensing and communication (ISAC) and reconfigurable intelligent surfaces (RIS) for providing wide-coverage and ultra-reliable communication and high-accuracy sensing functions. In particular, we consider an RIS-assisted ISAC system in which a multi-antenna base station (BS) simultaneously performs multi-user multi-input single-output (MU-MISO) communications and radar sensing with the assistance of an RIS. We focus on both target detection and parameter estimation performance in terms of the signal-to-noise ratio (SNR) and Cramér-Rao bound (CRB), respectively. Two optimization problems are formulated for maximizing the achievable sum-rate of the multi-user communications under an SNR constraint for target detection or a CRB constraint for parameter estimation, the transmit power budget, and the unit-modulus constraint of the RIS reflection coefficients. Efficient algorithms are developed to solve these two complicated non-convex problems. We then extend the proposed joint design algorithms to the scenario with imperfect self-interference cancellation. Extensive simulation results demonstrate the advantages of the proposed joint beamforming and reflection designs compared with other schemes. In addition, it is shown that more RIS reflection elements bring larger performance gains for direct-of-arrival (DoA) estimation than for target detection.

Integrated sensing and communication (ISAC), which simultaneously performs sensing and communication functions within a shared frequency band and hardware platform, has emerged as a promising technology for future wireless systems. Nevertheless, the weak echo signal received by the low-sensitivity ISAC receiver significantly constrains sensing performance in scenarios involving obstructed targets. Active reconfigurable intelligent surface (RIS) has become a prospective solution by situationally manipulating the wireless propagations and amplifying the signals. In this paper, we investigate active RIS-empowered ISAC systems to enhance radar echo signal quality as well as communication performance. In particular, we focus on the joint design of the base station (BS) transmit precoding and the active RIS reflection beamforming to optimize the parameter estimation performance in terms of Cramér-Rao bound (CRB) subject to the communication users’ signal-to-interference-plus-noise ratio (SINR) requirements. An efficient algorithm based on alternating optimization, semidefinite relaxation (SDR), and majorization-minimization (MM) is proposed to solve the formulated challenging non-convex problem. Finally, simulation results validate the effectiveness of the developed algorithm and the potential of employing active RIS in ISAC systems to enhance direct-of-arrival (DoA) estimation performance.

Integrated sensing and communication (ISAC) has been envisioned as a key enabling technique for future wireless communication networks. In order to realize a better performance trade-off between multiuser communications and radar sensing, in this paper we focus on the transmit/receive antenna selection problem in mono-static ISAC systems. Unlike conventional fixed and evenly divided antenna allocation strategy, we propose to jointly optimize antenna selection and transmit beamforming according to different communication requirements and available resources. In particular, we aim to minimize the root-mean-square-error (RMSE) of direction-of-arrival (DoA) estimation, while satisfying the communication signal-to-interference-plus-noise ratio (SINR) requirements, the transmit power budget, and the inherent constraints on antenna selection. An efficient alternating algorithm based on alternative direction method of multipliers (ADMM) and majorization-minimization (MM) methods and matrix transformations/derivations is developed to solve the resulting problem. Simulation results confirm that flexible antenna allocation enables lower RMSE of DoA estimation in ISAC systems and demonstrate the effectiveness of the proposed joint antenna selection and beamforming design algorithm.

Reconfigurable intelligent surface (RIS) has the potential to significantly enhance the performance of communication systems by dynamically adjusting wireless environment. In this paper, we aim to jointly optimize RIS matrices and beam-forming at base stations (BSs) in RIS-assisted multi-cell systems. Considering the high complexity of traditional algorithms, we adopt deep reinforcement learning algorithms (DRL) for the difficult joint design task. Moreover, to overcome the performance drawbacks of centralized DRL algorithms with large state-action spaces, we propose distributed DRL algorithms based on the distributed distributional deterministic policy gradient (D4PG) method and the federated learning (FL) framework. Experimental results demonstrate that distributed DRL algorithms can considerably improve learning efficiency while reducing complexity and communication overhead. Moreover, it can be applied to various systems with satisfactory performance and has better robustness compared to the centralized DRL algorithms.

Channel state information (CSI) acquisition is a crucial but challenging issue in reconfigurable intelligent surface (RIS)-aided systems due to the passive property of RIS. In this paper, we investigate channel estimation in practical RIS-assisted multiuser orthogonal frequency division multiplexing access (OFDMA) systems. Different from prior works which assume that the RIS has an ideal reflection model (i.e., each reflecting element has constant amplitude, variable phase shift, and the same response for signals at different subcarriers), in this work the channel estimation is investigated with a practical RIS reflection model by considering the amplitude-phase-frequency relationship of the reflected signals. Aiming at enhancing the accuracy and efficiency of the channel estimation, a novel channel estimation method with pilot subcarrier allocation is developed. Simulation results demonstrate the necessity of considering practical RIS reflection model for channel estimation and the effectiveness of our proposed channel estimation method and pilot subcarrier allocation scheme.

Reconfigurable intelligent surface (RIS) has been deemed as one of potential components of future wireless communication systems because it can adaptively manipulate the wireless propagation environment with low-cost passive devices. However, due to the severe double path loss, the traditional passive RIS can provide sufficient gain only when receivers are very close to the RIS. Moreover, RIS cannot provide signal coverage for the receivers at the back side of it. To address these drawbacks in practical implementation, we introduce a novel reflection and transmission dual-functional active RIS (DF-ARIS) architecture in this paper, which can simultaneously realize reflection and transmission functionalities with active signal amplification to significantly extend signal coverage and enhance the quality-of-service (QoS) of all users. The problem of joint transmit beamforming and dual-functional active RIS design is investigated in RIS-enhanced multiuser multiple-input single-output (MU-MISO) systems. Both sum-rate maximization and power minimization problems are considered. To address their non-convexity, we develop efficient iterative algorithms to decompose them into several separate design problems, which are efficiently solved by exploiting fractional programming (FP) and Riemannian-manifold optimization techniques. Simulation results demonstrate the superiority of the proposed dual-functional active RIS architecture and the effectiveness of our proposed algorithms over various benchmark schemes.

The multimedia has achieved dominant positions in both local storage and internet bandwidth, which inevitably promotes the compression of audio, image and video information. Nowadays, the emerging haptic technology, which enhances the immersion in virtual reality and remote control, has also brought new challenges in its codec design. It is thus imperative to develop haptic codecs, including kinesthetic and vibrotactile codecs, with high efficiency and low delay. In this paper, we exploit statistical features of vibrotactile data to develop a Recurrent-Network-based Vibrotactile Codec (RNVC) with high compression efficiency and low coding delay. The proposed encoder consists of vibrotactile estimation by Gate Recurrent Unit (GRU), non-uniform quantization/compensation of residuals and an entropy encoder. In particular, the GRU-based recurrent network is utilized for its high efficiency to predict signals and low complexity to converge. The decoder consists of all counterparts of encoder. Experimental results show the proposed RNVC significantly reduces of original bitrates with negligible encoding delay, which achieves the state-of-the-art coding performance of vibrotactile signal.

Intelligent reflecting surface (IRS) has been widely considered as one of the key enabling techniques for future wireless communication networks owing to its ability of dynamically controlling the phase shift of reflected electromagnetic (EM) waves to construct a favorable propagation environment. While IRS only focuses on signal reflection, the recently emerged innovative concept of intelligent omni-surface (IOS) can provide the dual functionality of manipulating reflecting and transmitting signals. Thus, IOS is a new paradigm for achieving ubiquitous wireless communications. In this paper, we consider an IOS-assisted multi-user multi-input single-output (MU-MISO) system where the IOS utilizes its reflective and transmissive properties to enhance the MU-MISO transmission. Both power minimization and sum-rate maximization problems are solved by exploiting the second-order cone programming (SOCP), Riemannian manifold, weighted minimum mean square error (WMMSE), and block coordinate descent (BCD) methods. Simulation results verify the advancements of the IOS for wireless systems and illustrate the significant performance improvement of our proposed joint transmit beamforming, reflecting and transmitting phase-shift, and IOS energy division design algorithms. Compared with conventional IRS, IOS can significantly extend the communication coverage, enhance the strength of received signals, and improve the quality of communication links.

Dual-function radar-communication (DFRC) systems, which can efficiently utilize the congested spectrum and costly hardware resources by employing one common waveform for both sensing and communication (S&C), have attracted increasing attention. While the orthogonal frequency division multiplexing (OFDM) technique has been widely adopted to support high-quality communications, it also has great potentials of improving radar sensing performance and providing flexible S&C. In this paper, we propose to jointly design the dual-functional transmit signals occupying several subcarriers to realize multi-user OFDM communications and detect one moving target in the presence of clutter. Meanwhile, the signals in other frequency subcarriers can be optimized in a similar way to perform other tasks. The transmit beamforming and receive filter are jointly optimized to maximize the radar output signal-to-interference-plus-noise ratio (SINR), while satisfying the communication SINR requirement and the power budget. An majorization minimization (MM) method based algorithm is developed to solve the resulting non-convex optimization problem. Numerical results reveal the significant wideband sensing gain brought by jointly designing the transmit signals in different subcarriers, and demonstrate the advantages of our proposed scheme and the effectiveness of the developed algorithm.

Space-time adaptive processing (STAP) is an effective method for multi-input multi-output (MIMO) radar systems to identify moving targets in the presence of multiple interferers. The idea of joint optimization in both spatial and temporal domains for radar detection is consistent with the symbol-level precoding (SLP) technique for MIMO communication systems, that optimizes the transmit waveform according to instantaneous transmitted symbols. Therefore, in this paper we combine STAP and constructive interference (CI)-based SLP techniques to realize dual-functional radar-communication (DFRC). The radar output signal-to-interference-plus-noise ratio (SINR) is maximized by jointly optimizing the transmit waveform and receive filter, while satisfying the communication quality-of-service (QoS) constraints and the constant modulus power constraint. An efficient algorithm based on majorization-minimization (MM) and nonlinear equality constrained alternative direction method of multipliers (neADMM) methods is proposed to solve the non-convex optimization problem. Simulation results verify the effectiveness of the proposed DFRC scheme and the associate algorithm.

Deep Neural Networks (DNNs) are increasingly deployed in battery-powered and resource-constrained devices. However, the most accurate DNNs usually require millions of parameters and operations, making them computation-heavy and energy-expensive, so it is an important topic to develop energy efficient DNN models. In this paper, we present an efficient DNN training framework under energy constraint to improve the energy efficiency of DNN inference. The key idea of this research is inspired by the observation that the input data of DNNs is usually inherently sparse and such sparsity can be exploited by sparse tensor DNN accelerators to eliminate ineffectual data access and compute. Therefore, we can enhance the inference accuracy within the energy budget by strategically controlling the sparsity of the input data. We build an energy consumption model for the sparse tensor DNN accelerator to quantify the inference energy consumption from the perspective of data access and data processing. In particular, we define a metric (named sporadic degree) to characterise the influence of the number of sporadic values in the sparse input on the energy consumption of data access for the sparse tensor DNN accelerator. Based on the proposed quantitative energy consumption model, we present an efficient pooling-based input mask training algorithm to optimize the energy efficiency of DNN inference by enhancing the input sparsity and reducing the number of sporadic values in the masked input. Experiments show that compared with the state-of-the-art methods, our proposed method can achieve higher inference accuracy with lower energy consumption and storage requirement owing to higher sparsity and lower sporadic degree of the masked input.

In contrast to sophisticated means of visual su-per resolution (SR), not much work has been done in the tactile SR field. Existing tactile SR algorithms for taxel-based sensors mainly focus on enhancing the localization accuracy, and generally associate with a specific type of hardware, sometimes not applicable to generic taxel-based tactile sensors. Inspired by image SR, we investigate the tactile pattern SR in this paper, and present how to transform successful image SR schemes, e.g. Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) to serve the tactile SR. We propose two tactile SR models, i.e. TactileSRCNN and TactileSRGAN, and establish a new tactile pattern SR dataset for model learning. The ground truth of high resolution (HR) tactile patterns in the dataset is obtained via multi-sampling (i.e. overlapping reception) and registration of low resolution (LR) sensor. One key contribution of this research lies in achieving ×100 (from 3×4×4 to 40×40) times tactile pattern SR with a one-time tapping of 3-axis taxel-based sensor. Different from existing tactile SR algorithms which improves the localization accuracy of a single contact point, the proposed scheme can provide multi-point contact detection to robotic applications.

In this paper, we investigate the physical layer security of multiuser multi-input single-output (MU-MISO) communication and colocated multi-input multi-output (MIMO) radar coexistence systems, in which the strong radar signals are exploited as inherent jamming signals to disrupt mali-cious receptions. The transmit beamformers of communication and radar systems are jointly designed to ensure the secure transmission by minimizing the maximum eavesdropping signal-to-interference-plus-noise ratio (SINR) on multiple legitimate users, while satisfying the quality-of-service (QoS) of legitimate transmission, the requirement of radar target detection, and the transmit power constraints of radar and communication systems. An efficient fractional programming (FP) and semi-definite relaxation (SDR) based algorithm is proposed to solve the non-convex optimization problem. Simulation results verify the advancement of the proposed joint transmit beamforming on secure transmission for radar and communication coexistence systems and the effectiveness of the associate design algorithm.

Reconfigurable intelligent surface (RIS) is a promising solution to adaptively manipulate wireless propagation with low-cost passive devices. However, the traditional passive RIS can offer sufficient signal strength only when receivers are very close to it. Moreover, the users at the back side of it cannot be well served due to its reflective property. In this paper we introduce a novel reflection and relay dual-functional RIS architecture, which can simultaneously realize passive reflection and active relay functionalities. The problem of joint transmit beamforming and dual-functional RIS design is investigated to maximize the achievable sum-rate of a multiuser multiple-input single-output (MU-MISO) system. Based on fractional programming (FP) theory and majorization-minimization (MM) technique, we propose an efficient iterative transmit beamforming and RIS design algorithm. Simulation results demonstrate the superiority of the introduced dual-functional RIS architecture and the effectiveness of the proposed algorithm.

Owing to the unique advantages of low cost and controllability, reconfigurable intelligent surface (RIS) is a promising candidate to address the blockage issue in millimeter wave (mmWave) communication systems, consequently has captured widespread attention in recent years. However, the joint active beamforming and passive beamforming design is an arduous task due to the high computational complexity and the dynamic changes of wireless environment. In this paper, we consider a RIS-assisted multi-user multiple-input single-output (MU-MISO) mmWave system and aim to develop a deep reinforcement learning (DRL) based algorithm to jointly design active hybrid beamformer at the base station (BS) side and passive beamformer at the RIS side. By employing an advanced soft actor-critic (SAC) algorithm, we propose a maximum entropy based DRL algorithm, which can explore more stochastic policies than deterministic policy, to design active analog precoder and passive beamformer simultaneously. Then, the digital precoder is determined by minimum mean square error (MMSE) method. The experimental results demonstrate that our proposed SAC algorithm can achieve better performance compared with conventional optimization algorithm and DRL algorithm.

Reconfigurable intelligent surface (RIS) is considered as an extraordinarily promising technology to solve the blockage problem of millimeter wave (mmWave) communications owing to its capable of establishing a reconfigurable wireless propagation. In this paper, we focus on a RIS-assisted mmWave communication network consisting of multiple base stations (BSs) serving a set of user equipments (UEs). Considering the BS-RIS-UE association problem which determines that the RIS should assist which BS and UEs, we joint optimize BS-RIS-UE association and passive beamforming at RIS to maximize the sum-rate of the system. To solve this intractable non-convex problem, we propose a soft actor-critic (SAC) deep reinforcement learning (DRL)-based joint beamforming and BS-RIS-UE association design algorithm, which can learn the best policy by interacting with the environment using less prior information and avoid falling into the local optimal solution by incorporating with the maximization of policy information entropy. The simulation results demonstrate that the proposed SAC-DRL algorithm can achieve significant performance gains compared with benchmark schemes.

Dual-function radar-communication (DFRC), which can simultaneously perform both radar and communication functionalities using the same hardware platform, spectral resource and transmit waveform, is a promising technique for realizing integrated sensing and communication (ISAC). Space-time adaptive processing (STAP) in multi-antenna radar systems is the primary tool for detecting moving targets in the presence of strong clutter. The idea of joint spatial-temporal optimization in STAP-based radar systems is consistent with the concept of symbol-level precoding (SLP) for multi-input multi-output (MIMO) communications, which optimizes the transmit waveform for each of the transmitted symbols. In this paper, we combine STAP and SLP and propose a novel STAP-SLP-based DFRC system that enjoys the advantages of both techniques. The radar output signal-to-interference-plus-noise ratio (SINR) is maximized by jointly optimizing the transmit waveform and receive filter, while satisfying the communication quality-of-service (QoS) constraint and various waveform constraints including constant-modulus, similarity and peak-to-average power ratio (PAPR). An efficient algorithm framework based on majorization-minimization (MM) and nonlinear equality constrained alternative direction method of multipliers (neADMM) methods is proposed to solve these complicated non-convex optimization problems. Simulation results verify the effectiveness of the proposed STAP-SLP-based MIMO-DRFC scheme and the associate algorithms.

Abstract Computer vision (CV) technologies are assisting the health care industry in many respects, i.e., disease diagnosis. However, as a pivotal procedure before and after surgery, the inventory work of surgical instruments has not been researched with the CV-powered technologies. To reduce the risk and hazard of surgical tools’ loss, we propose a study of systematic surgical instrument classification and introduce a novel attention-based deep neural network called SKA-ResNet which is mainly composed of: (a) A feature extractor with selective kernel attention module to automatically adjust the receptive fields of neurons and enhance the learnt expression and (b) A multi-scale regularizer with KL-divergence as the constraint to exploit the relationships between feature maps. Our method is easily trained end-to-end in only one stage with few additional calculation burdens. Moreover, to facilitate our study, we create a new surgical instrument dataset called SID19 (with 19 kinds of surgical tools consisting of 3800 images) for the first time. Experimental results show the superiority of SKA-ResNet for the classification of surgical tools on SID19 when compared with state-of-the-art models. The classification accuracy of our method reaches up to 97.703%, which is well supportive for the inventory and recognition study of surgical tools. Also, our method can achieve state-of-the-art performance on four challenging fine-grained visual classification datasets.

Intelligent reflecting surface (IRS) has been regarded as a promising and revolutionary technology for future wireless communication systems owing to its capability of tailoring signal propagation environment in an energy/spectrum/ hardware-efficient manner. However, most existing studies on IRS optimizations are based on a simple and ideal reflection model that is impractical in hardware implementation, which thus leads to severe performance loss in realistic wideband/multi-band systems. To deal with this problem, in this paper we first propose a more practical and more tractable IRS reflection model that describes the difference of reflection responses for signals at different frequencies. Then, we investigate the joint transmit beamforming and IRS reflection beamforming design for an IRS-assisted multi-cell multi-band system. Both power minimization and sum-rate maximization problems are solved by exploiting popular second-order cone programming (SOCP), Riemannian manifold, minimization-majorization (MM), weighted minimum mean square error (WMMSE), and block coordinate descent (BCD) methods. Simulation results illustrate the significant performance improvement of our proposed joint transmit beamforming and reflection design algorithms based on the practical reflection model in terms of power saving and rate enhancement.

Cell-free millimeter-wave (mmWave) multiple-input multiple-output (MIMO) communications have been proposed as promising enablers for the next generation wireless networks. In this paper, we study the user association and hybrid beamforming in cell-free mmWave systems without full channel state information (CSI) acquisition. We consider a cloud radio access network (C-RAN), where multiple remote radio heads (RRHs) are distributed to communicate with users via analog beamforming, and connected to a centralized baseband unit (BBU) through fronthaul links which executes digital beamforming. We aim to jointly design user association, hybrid beamforming, and fronthaul compression with the aid of uplink training. A train-and-design framework is developed to achieve this goal. In particular, we first propose a two-stage uplink training approach to assist RRH-level design, during which the analog beamforming and user association are obtained. After that, digital beamforming and fronthaul compression are optimized at BBU based on the training results. Two performance metrics are considered in this paper, i.e. weighted sum-rate maximization and max-min fairness. Simulation results demonstrate the effectiveness of the proposed train-and-design framework for both sum-rate maximization and max-min fairness performance metrics. It is shown that the proposed algorithms can achieve comparable performance to the full-digital beamformer.

Symbol-level precoding (SLP), which converts the harmful multi-user interference (MUI) into beneficial signals, can significantly improve symbol-error-rate (SER) performance in multi-user communication systems. While enjoying symbolic gain, however, the complicated non-linear symbol-by-symbol precoder design suffers high computational complexity exponential with the number of users, which is unaffordable in realistic systems. In this paper, we propose a novel low-complexity grouped SLP (G-SLP) approach and develop efficient design algorithms for typical max-min fairness and power minimization problems. In particular, after dividing all users into several groups, the precoders for each group are separately designed on a symbol-by-symbol basis by only utilizing the symbol information of the users in that group, in which the intra-group MUI is exploited using the concept of constructive interference (CI) and the inter-group MUI is also effectively suppressed. In order to further reduce the computational complexity, we utilize the Lagrangian dual, Karush-Kuhn-Tucker (KKT) conditions and the majorization-minimization (MM) method to transform the resulting problems into more tractable forms, and develop efficient algorithms for obtaining closed-form solutions to them. Extensive simulation results illustrate that the proposed G-SLP strategy and design algorithms dramatically reduce the computational complexity without causing significant performance loss compared with the traditional SLP schemes.

Reconfigurable intelligent surface (RIS) is regarded as a promising technology with great potential to boost wireless networks. Affected by the “double fading” effect, however, conventional passive RIS cannot bring considerable performance improvement when users are not close enough to RIS. Recently, active RIS is introduced to combat the double fading effect by actively amplifying incident signals with the aid of integrated reflection-type amplifiers. In order to reduce the hardware cost and energy consumption due to massive active components in the conventional fully-connected active RIS, a novel hardware-and-energy efficient sub-connected active RIS architecture has been proposed recently, in which multiple reconfigurable electromagnetic elements are driven by only one amplifier. In this paper, we first develop an improved and accurate signal model for the sub-connected active RIS architecture. Then, we investigate the joint transmit precoding and RIS reflection beamforming (i.e., the reflection phase-shift and amplification coefficients) designs in multiuser multiple-input single-output (MU-MISO) communication systems. Both sum-rate maximization and power minimization problems are solved by leveraging fractional programming (FP), block coordinate descent (BCD), second-order cone programming (SOCP), alternating direction method of multipliers (ADMM), and majorization-minimization (MM) methods. Extensive simulation results verify that compared with the conventional fully-connected structure, the proposed sub-connected active RIS can significantly reduce the hardware cost and power consumption, and achieve great performance improvement when power budget at RIS is limited.

Intelligent reflecting surface (IRS) has been widely considered as one of key enabling techniques for the future wireless networks owing to its ability of constructing favorable propagation environment by controlling the phase shifts of reflected electromagnetic (EM) waves that impinge on the surface. While an IRS only focuses on the reflective implementation, recently emerged innovative concept of intelligent omni-surface (IOS) can provide the dual-functionality of manipulating signal reflection and transmission. Thus, an IOS can provide service coverage for both sides of it. In this paper, we consider an IOS-assisted multi-user multi-input single-output (MU-MISO) system, in which the IOS utilizes its reflective and transmissive properties to enhance the MU-MISO transmission. Our goal is to jointly optimize the transmit beamformers at base station (BS), the reflective and transmissive phase-shifts of IOS, and the reflection-to-transmission ratio of IOS to minimize the total transmit power for the MU-MISO system, subject to the signal-to-interference-plus-noise ratio (SINR) requirements of individual users. An efficient iterative algorithm is presented to solve this non-convex optimization problem. Simulation results verify the advantage of the IOS-assisted wireless communication system and the efficiency of the associate beamforming design algorithm.

This paper investigates hybrid beamforming design in cooperative millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. We focus on the sum-rate maximization problem and aim to design the hybrid beamformer to maximize the weighted achievable sum-rate subject to the constraint of maximum transmit power for all base stations (BSs) and the constant modulus of phase shifters (PSs). Due to non-convexity of the weighted sum-of-logarithmic function, we firstly transform the objective function into an equivalent form with the aid of fractional programming (FP) theory. Then, we propose an iterative hybrid beamforming algorithm, in which manifold optimization is first employed to solve analog beamformer and then a closed-form solution of digital beamformer is derived by Lagrange multiplier method. Numerical results demonstrate the remarkable advantages of proposed hybrid beamforming algorithm compared with other classical approaches.

Symbol-level precoding (SLP), which can convert the harmful multi-user interference (MUI) into beneficial signals, can significantly improve symbol error rate (SER) performance in multi-user communication systems. While enjoying symbolic gain, however, the complicated non-linear symbol-by-symbol SLP design suffers high computational complexity exponential with the number of users, which is unaffordable in realistic systems. In this paper, we propose a novel low-complexity grouped SLP (G-SLP) approach and develop an efficient design algorithm for a typical max-min fairness problem. This practical G-SLP strategy divides all users into several groups. SLP is utilized for the users within each group to convert intra-group MUI into constructive interference, meanwhile the inter-group MUI is also suppressed. In particular, we first use Lagrangian and Karush-Kuhn-Tucker (KKT) conditions to simplify the G-SLP design problem and then propose an iterative majorization-minimization (MM) based algorithm to solve it. Simulation results illustrate that the proposed G-SLP strategy dramatically reduces the computational complexity without causing significant performance loss compared with the traditional SLP scheme.

In dual-functional radar-communication (DFRC) systems, the transmit beamforming has attracted extensive attentions since it can simultaneously provide radar sensing functionality and high-rate wireless communications. Unlike the conventional linear precoding technology, in this paper we propose to employ the recently emerged symbol-level precoding technique in DFRC systems, expecting to take advantages of the multiuser interference for improving both radar and communication performance. The difference between the designed and desired beampatterns is minimized subject to the quality-of-service (QoS) requirements of communication users and the constant envelope power constraint. Some derivations are developed based on the penalty dual decomposition (PDD), majorization-minimization (MM), and block coordinate descent (BCD) methods to convert the non-convex problem into two solvable sub-problems, which are iteratively solved using efficient algorithms. Simulations illustrate the effectiveness of the symbol-level precoding in DFRC systems and the proposed algorithm.

Teleoperation systems with haptic feedback allow a human user to remotely interact with a dangerous or inac-cessible environment, perform various tasks, and perceive the haptic feedback. To ensure system stability while maintaining the best possible quality of experience (QoE), different teleoperation control schemes and haptic communication strategies need to be selected to adapt to varying network conditions and teleoperation tasks. In this paper, we propose a QoE-driven control scheme switching approach, which adaptively selects the control scheme that provides the best possible QoE for varying communication delay. A transition period is designed to moderate the artifacts during the switching phase. Haptic data reduction approaches are developed for the switching strategy to match the characteristics of each control scheme. Our experiments verify the feasibility of the proposed scheme. Subjective tests confirm that the proposed adaptive switching scheme is able to achieve a superior user QoE in contrast to a fixed control scheme in the presence of varying communication delay up to 200 ms.

Intelligent reflecting surface (IRS) is deemed as a promising and revolutionizing technology for future wireless communication systems owing to its capability to intelligently change the propagation environment and introduce a new dimension into wireless communication optimization. Most existing studies on IRS are based on an ideal reflection model. However, it is difficult to implement an IRS which can simultaneously realize any adjustable phase shift for the signals with different frequencies. Therefore, the practical phase shift model, which can describe the difference of IRS phase shift responses for the signals with different frequencies, should be utilized in the IRS optimization for wideband and multi-band systems. In this paper, we consider an IRS-assisted multi-cell multi-band system, in which different base stations (BSs) operate at different frequency bands. We aim to jointly design the transmit beamforming of BSs and the reflection beamforming of the IRS to minimize the total transmit power subject to signal to interference-plus-noise ratio (SINR) constraints of individual user and the practical IRS reflection model. With the aid of the practical phase shift model, the influence between the signals with different frequencies is taken into account during the design of IRS. Simulation results illustrate the importance of considering the practical communication scenario on the IRS designs and validate the effectiveness of our proposed algorithm.

This paper investigates hybrid beamforming for cooperative multi-user millimeter-wave (mmWave) multiple-input multiple-output (MIMO) networks. We aim to jointly design the user scheduling and hybrid beamforming to maximize the sum-rate subject to the transmit power of each base station. Due to the non-convexity of constant modulus of phase shifters and objective function, the problem is mathematically intractable. We propose a low-complexity two-step scheme, in which user scheduling and analog beamforming are first obtained to maximize the sum-beamforming-gain, followed by digital beamforming calculation based on weighted minimum-mean-square-error (wMMSE) approach. We further extend the hybrid beamforming design to dynamic sub-array architecture, where a novel antenna selection algorithm is developed. Simulation results demonstrate the effectiveness of the proposed algorithms, which can outperform other state-of-the-art approaches.

Intelligent reflecting surface (IRS) is envisioned as a revolutionary technology for future wireless communication systems since it can intelligently change radio environment and integrate it into wireless communication optimization. However, most existing works adopted an ideal IRS reflection model, which is impractical and can cause significant performance degradation in realistic wideband systems. To address this issue, we first study the dual phase- and amplitude-squint effect of reflected signals and present a simplified practical IRS reflection model for wideband signals. Then, an IRS enhanced wideband multiuser multi-input single-output orthogonal frequency division multiplexing (MU-MISO-OFDM) system is investigated. We aim to jointly design the transmit beamformer and IRS reflection for the case of using both continuous and discrete phase shifters to maximize the average sum-rate over all subcarriers. By exploiting the relationship between sum-rate maximization and mean square error (MSE) minimization, the original problem is equivalently transformed into a multi-block/variable problem, which can be efficiently solved by the block coordinate descent (BCD) method. Complexity and convergence for both cases are analyzed or illustrated. Simulation results demonstrate that the proposed algorithm can offer significant average sum-rate enhancement compared to that achieved using the ideal IRS reflection model, which confirms the importance of the use of the practical model for the design of wideband systems.

Intelligent reflecting surfaces (IRSs) have emerged as a revolutionary solution to enhance wireless communications by changing propagation environment in a cost-effective and hardware-efficient fashion. In addition, symbol-level precoding (SLP) has attracted considerable attention recently due to its advantages in converting multiuser interference (MUI) into useful signal energy. Therefore, it is of interest to investigate the employment of IRS in symbol-level precoding systems to exploit MUI in a more effective way by manipulating the multiuser channels. In this article, we focus on joint symbol-level precoding and reflecting designs in IRS-enhanced multiuser multiple-input single-output (MU-MISO) systems. Both power minimization and quality-of-service (QoS) balancing problems are considered. In order to solve the joint optimization problems, we develop an efficient iterative algorithm to decompose them into separate symbol-level precoding and block-level reflecting design problems. An efficient gradient-projection-based algorithm is utilized to design the symbol-level precoding and a Riemannian conjugate gradient (RCG)-based algorithm is employed to solve the reflecting design problem. Simulation results demonstrate the significant performance improvement introduced by the IRS and illustrate the effectiveness of our proposed algorithms.

Recently emerged symbol-level precoding techniques can exploit multi-user interference (MUI) by transforming it into constructive signals at receivers and thus contribute to symbol detection. This paper adopts this concept and aims to investigate the exploitation of MUI to enhance both physical layer security against eavesdropping and the quality of legitimate transmissions. Particularly, we consider the problem of secure symbol-level precoding in multi-user multi-input single-output (MU-MISO) wiretap systems for M-ary quadrature amplitude modulation (M-QAM) signals. Our goal is to design the symbol-level precoder to minimize the average transmit power while guaranteeing the quality of service (QoS) of all legitimate transmissions as well as ensuring security against eavesdropping. In order to tackle this unaffordable large scale problem, we propose to decompose it into several sub-problems. Then, an efficient modified Hooke-Jeeves pattern search algorithm is further utilized to solve the Lagrangian dual functions of these sub-problems. Simulation results validate the exploitation of MUI for security and illustrate the effectiveness of our proposed secure symbol-level precoding algorithm.

In multimedia services, the introduction of haptic signals provides a more immersive user experience besides of conventional audio-visual perceptions. To support synchronous streaming and display of these information, it is imperative to efficiently compress and store the haptic signals, which promotes the development and optimization of haptic codecs. In this paper, we propose an end-to-end haptic codec for high-efficiency, low-delay and perception-lossless compression of kinesthetic signal, one of two major components of haptic signals. The proposed encoder consists of amplifier, DCT, quantizer, run-length encoder and entropy encoder, while the decoder includes all counterpart modules of the encoder. In particular, all parameters of these modules are deliberately calibrated aimed at a high compression efficiency of kinesthetic information. We allow a maximal DCT length of 8 samples, in order to guarantee a maximal encoding delay of 7ms for a popular haptic simulator of 1000Hz. Incorporating the model of perception deadband, the proposed codec is capable of realizing perception-lossless kinesthetic bitsteam. Finally, we examine the proposed codec on the standard database of IEEE P1918.1.1 Haptic Codecs Task Group. Comprehensive experiments reveal that our codec outperforms its rivals with 50% bit rate reduction, improved perception quality and a negligible encoder delay.

Hybrid precoding combined with large-scale antenna arrays is considered as a key enabling technology for millimeter wave (mmWave) communications for its advantages in both reducing the number of power-hungry radio frequency (RF) chains and providing for spatial multiplexing. In this paper, we consider a dynamically sub-connected hybrid architecture with hardware-efficient low-resolution phase shifters (PSs) for a wide-band mmWave multi-user multi-input single-output orthogonal frequency division multiplexing (MU-MISO-OFDM) system. In this architecture, each RF chain is adaptively connected to a non-overlapping subarray corresponding to channel state information (CSI). Thus, multiple-antenna diversity can be fully utilized to mitigate the performance loss caused by the use of low-resolution PSs. Aiming at maximize the average sum-rate of the considered mmWave MU-MISO-OFDM system, we develop an iterative algorithm based on penalty dual decomposition (PDD) methods. Simulation results demonstrate the advantages of the considered dynamically sub-connected hybrid architecture.

This paper considers the cloud radio access network (C-RAN) based millimeter-wave (mmWave) cell-free communications, where multiple remote radio heads (RRHs) are distributed to provide reliable communication links to users via analog beamforming and connected to centralized baseband unit (BBU) which carries out digital signal processing. We aim to jointly design the user association and analog/digital hybrid beamforming along with fronthaul compression to maximize the minimum signal to interference-plus-noise ratio (SINR) among users while satisfying the fronthaul capacity constraints. To solve this difficult combinatory problem, we propose to first obtain the user association and analog beamforing to maximize the minimum beamforming gain among users. Then, given the effective baseband channel, the digital beamformer and quantization noise covariance matrix still cannot be calculated directly due to the non-convexities of objective function and fronthaul constraint. To efficiently solve this problem, we transform the objective function into convex terms based on fractional programming method and iteratively calculate the digital beamformer and quantization noise covariance matrix until convergence is achieved. Simulation results show that the proposed algorithm can achieve comparable performance to the full-digital beamforming.

Intelligent reflecting surface (IRS) is considered as an enabling technology for future wireless communication systems since it can intelligently change the wireless environment to improve the communication performance. In this paper, an IRS-enhanced wideband multiuser multi-input single-output orthogonal frequency division multiplexing (MU-MISO-OFDM) system is investigated. We aim to jointly design the transmit beamformer and the reflection of IRS to maximize the average sum-rate over all subcarriers. With the aid of the relationship between sum-rate maximization and mean square error (MSE) minimization, an efficient joint beamformer and IRS design algorithm is developed. Simulation results illustrate that the proposed algorithm can offer significant average sum-rate enhancement, which confirms the effectiveness of the use of the IRS for wideband wireless communication systems.

Intelligent reflecting surface (IRS) has emerged as a promising solution to enhance wireless communications in a low-cost and hardware-efficient fashion. Besides, symbol-level precoding (SLP) technique has attracted considerable attentions recently for its advantages in converting multiuser interference (MUI) into useful signal. In this paper, we investigate the symbol-level precoding in IRS-assisted multiuser multiple-input single-output (MU-MISO) systems to minimize the transmit power while guarantee the quality-of-service (QoS) of information transmissions. In order to solve this joint optimization problem, we develop an efficient iterative algorithm to decompose it into the precoder design and IRS design problems. To tackle the non-convex IRS design problem, we propose to use the log-sum-exp function to smooth the objective and map it into the Riemannian space, where the Riemannian conjugate gradient (RCG) algorithm is employed to solve this problem. Simulation results prove the significant performance improvement of IRS and illustrate the effectiveness of our proposed algorithm.

Analog/digital hybrid beamforming is considered as a key enabling multiple antenna technology for implementing millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications since it can reduce the number of costly and power-hungry radio frequency (RF) chains while still providing for spatial multiplexing. In this paper, we introduce a novel hybrid beamforming architecture with dynamic antenna subarrays and hardware-efficient low-resolution phase shifters (PSs) for a wideband mmWave MIMO orthogonal frequency division multiplexing (MIMO-OFDM) system. By dynamically connecting each RF chain to a non-overlapping antenna subarray via a switch network and PSs, multiple-antenna diversity can be exploited to mitigate the performance loss due to the employment of practical low-resolution PSs. For this dynamic hybrid beamforming architecture, we jointly design the hybrid precoder and combiner to maximize the average spectral efficiency of the mmWave MIMO-OFDM system. In particular, the spectral efficiency maximization problem is first converted to a mean square error (MSE) minimization problem. Then, an efficient iterative hybrid beamformer algorithm is developed based on classical block coordination descent (BCD) methods. An analysis of the convergence and complexity of the proposed algorithm is also provided. Extensive simulation results demonstrate the superiority of the proposed hybrid beamforming algorithm with dynamic subarrays and low-resolution PSs.

Mobile displays have been recognized as the major contributors to the energy consumption of contemporary mobile video services. Existing display energy reduction (DER) algorithms focus on local video/image processing by utilizing the computation resources on the mobile devices. As such, a per-device DER strategy is highly inefficient from the systematic perspective since the same computation is repeated among millions of individual mobile devices. In this new era of cloud-based video streaming, a natural question to ask is can ubiquitous cloud resources be exploited to overcome these drawbacks. It is based on this motivation that we design a paradigm-shifting strategy to integrate the display energy saving engine into cloud-based video streaming in order to simultaneously benefit massive mobile devices with a one-time video processing in the cloud. By taking full advantage of the computational and storage resources in the cloud, a family of Rate-Distortion-Display Energy (R-D-DE) profiles can be created for a given video source and for different types of mobile devices. The preliminary experimental results with OLED displays prove that we can achieve the desired DER performance through jointly managing the display energy and user experience by implementing the proposed R-D-DE integrated video encoding engine in the cloud.

Analog/digital hybrid beamforming architectures with large-scale antenna arrays have been widely considered in millimeter wave (mmWave) communication systems because they can address the tradeoff between performance and hardware efficiency compared with traditional fully-digital beamforming. Most of the prior work on hybrid beamforming focused on fully-connected architecture or partially-connected scheme with fixed-subarrays, in which the analog beamformers are usually realized by infinite-resolution phase shifters (PSs). In this paper, we introduce a novel hybrid beamforming architecture with dynamic subarrays and hardware-efficient low-resolution PSs for mmWave multiuser multiple-input single-output (MU-MISO) systems. By dynamically connecting each RF chain to a non-overlap subarray via a switch network and PSs, we can exploit multiple-antenna and multiuser diversities to mitigate the performance loss due to the use of practical low-resolution PSs. An iterative hybrid beamformer design algorithm is first proposed based on fractional programming (FP), aiming at maximizing the sum-rate performance of the MU-MISO system. In an effort to reduce the complexity, we also present a simple heuristic hybrid beamformer design algorithm for the dynamic subarray scheme. Extensive simulation results demonstrate the advantages of the proposed hybrid beamforming architecture with dynamic subarrays and low-resolution PSs compared to existing fixed-subarray schemes.

Multi-user interference (MUI) is usually considered to be a harmful component in wireless communications. However, recently emerged symbol-level precoding techniques can constructively exploit the MUI by transforming it into useful signal at the receiver and contribute to symbol detection. This paper adopts this concept and aims to investigate the exploitation of the MUI to enhance both physical layer security against eavesdropping and the quality of legitimate transmissions. Particularly, we consider the problem of secure symbol-level precoding in multi-user multi-input single-output (MU-MISO) wiretap systems. Our goal is to design the symbol-level precoder to guarantee the quality of service (QoS) of all legitimate transmissions as well as ensure security against eavesdropping. The symbol-level precoder algorithms for physical layer security are developed under different assumptions about the availability of channel state information (CSI) of the legitimate and eavesdropping channels. Extensive simulation results validate the exploitation of MUI for security and illustrate the effectiveness of our proposed secure symbol-level precoding algorithms.

The IEEE “Tactile Internet” (TI) Standards working group (WG), designated the numbering IEEE 1918.1, undertakes pioneering work on the development of standards for the TI. This paper describes the WG, its intentions, and its developing baseline standard and the associated reasoning behind that and touches on a further standard already initiated under its scope: IEEE 1918.1.1 on “Haptic Codecs for the TI.” IEEE 1918.1 and its baseline standard aim to set the framework and act as the foundations for the TI, thereby also serving as a basis for further standards developed on TI within the WG. This paper discusses the aspects of the framework such as its created TI architecture, including the elements, functions, interfaces, and other considerations therein, as well as the novel aspects and differentiating factors compared with, e.g., 5G Ultra-Reliable Low-Latency Communication, where it is noted that the TI will likely operate as an overlay on other networks or combinations of networks. Key foundations of the WG and its baseline standard are also highlighted, including the intended use cases and associated requirements that the standard must serve, and the TI's fundamental definition and assumptions as understood by the WG, among other aspects.

The Tactile Internet will enable users to physically explore remote environments and to make their skills available across distances. An important technological aspect in this context is the acquisition, compression, transmission, and display of haptic information. In this paper, we present the fundamentals and state of the art in haptic codec design for the Tactile Internet. The discussion covers both kinesthetic data reduction and tactile signal compression approaches. We put a special focus on how limitations of the human haptic perception system can be exploited for efficient perceptual coding of kinesthetic and tactile information. Further aspects addressed in this paper are the multiplexing of audio and video with haptic information and the quality evaluation of haptic communication solutions. Finally, we describe the current status of the ongoing IEEE standardization activity P1918.1.1 which has the ambition to standardize the first set of codecs for kinesthetic and tactile information exchange across communication networks.

Semantic image segmentation can be accomplished by assigning a proper object category label to each meaningful region of an image. Beyond the original bottom-up models, the use of top-down categorization information has been applied to semantic segmentation to improve performance. An excellent example of such a top-down scheme is to integrate a Conditional Random Field (CRF) model with sparse dictionary learning. However, the existing solutions merely consider the discrimination of dictionaries to obtain better sparse codes, without considering the inherent data locality characteristics. In this paper, we explore such characteristics and propose a novel semantic segmentation framework based on an innovative CRF model with locality-consistent dictionary learning. In particular, we propose two new locality-consistent dictionary learning strategies by capturing the local consistencies in the feature space and the label space. In addition, we develop a joint dictionary and a CRF model parameter learning algorithm to seamlessly integrate the proposed locality-consistent dictionary learning strategies into the CRF model. Extensive experiments are conducted with two popular databases of different traits (i.e., Graz-02 and PASCAL-CONTEXT). The simulation results confirm the efficiency of the proposed scheme, especially when training data are limited.

Multiprocessor platforms, widely adopted to realize real-time systems nowadays, bring the probabilistic characteristic to such systems because of the performance variations of complex chips. In this paper, we present a harmonic partitioned scheduling scheme with workload awareness for periodic probabilistic realtime tasks on multiprocessors under the fixed-priority preemptive scheduling policy. The key idea of this research is to improve the overall schedulability by strategically arranging the workload among processors based on the exploration of the harmonic relationship among probabilistic real-time tasks. In particular, we define a harmonic index to quantify the harmonicity among probabilistic real-time tasks. This index can be obtained via the harmonic period transformation and probabilistic cumulative worst case utilization calculation of these tasks. The proposed scheduling scheme first sorts tasks with respect to the workload, then packs them to processors one by one aiming at minimizing the increase of harmonic index caused by the task assignment. Experiments with randomly generated task sets show significant performance improvement of our proposed approach over the existing harmonic partitioned scheduling algorithm for probabilistic real-time systems.

Economical and energy-efficient analog/digital hybrid beamforming has been widely considered as a promising approach for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. While most hybrid beamforming techniques consider a fully-connected structure with a large number of phase shifters (PSs), the partially-connected structure has drawn more attention recently since it requires much less PSs and can further improve energy-efficiency. However, the impractical assumption of infinite or high resolution of PSs in existing solutions frustrates the real-world deployment of hybrid beamforming designs, and low- resolution PSs are typically adopted to reduce the hardware complexity and power consumption. In an effort to achieve maximum hardware efficiency, this paper focuses on the partially-connected architecture with one-bit (binary) PSs and considers the problem of joint hybrid precoder and combiner design for such mmWave MIMO systems. We propose to successively design the analog beamformers associated with each pair of sub- array, aiming at conditionally maximizing the spectral efficiency. A novel binary analog precoder and combiner optimization algorithm is proposed under a rank-1 approximation of the interference-included equivalent channel with polynomial complexity in the number of antennas. Then, the digital precoder and combiner are computed based on the obtained effective baseband channel to further enhance the spectral efficiency. Simulation results demonstrate the advantages of proposed hardware-efficiency hybrid precoder and combiner design.

Advances of mobile technology and global booming of “smartphone economy” promote a tremendous growth of smartphone-oriented applications with an exponential increase of mobile traffic in the past decade, resulting in expectable saturation of LTE spectrum in the next few years. Mobile-to-mobile (M2M) communications, capable of extending LTE capacity with enhanced spectrum efficiency, have been considered as a promise solution to this problem. One popular approach to deploy the M2M technology is to establish an M2M subsystem as an underlay to the current LTE network so that the M2M link can share the same radio resource with LTE regular links. Although this scheme can further explore the spectrum efficiency, it is challenging to design such an M2M subsystem without harmful interference to LTE regular users. We propose in this paper a novel proactive interference cancellation mechanism to form an interference-free underlay. The major innovation of the proposed framework is to use the codebook-based precoding technique to design appropriate precoders for eNB and M2M transmitter in order to achieve maximal reception interference suppression proactively at the transmitter side. Since the codebook-based precoding technique has already been employed in LTE, the proposed scheme is compatible to current LTE systems and applicable for real-world implementation. This is in strong contrast to several existing interference cancellation M2M schemes that highly rely on the unrealistic assumption on theoretical-oriented channel state information (CSI) feedback. Preliminary simulation results demonstrate the efficiency of the proposed scheme.

Acting like a legitimate user via sending identical pilot signals, the pilot spoofing attack launched by an active eavesdropper can disrupt the reception of the legitimate receiver and, more importantly, cause severe information leakage. Although such an attack can be detected by the recently presented random-training-aided (RTA) pilot spoofing detector with high accuracy and low complexity, the assumption that the active eavesdropper remains silent during the random phase limits the usage of RTA algorithm in practice. In addition, a natural question to ask is how to secure the data transmission after spoofing detection. Motivated by these two aspects, in this paper we first investigate the robustness of RTA pilot spoofing detector and illustrate that it can provide high detection accuracy even when the eavesdropper is active during the random phase. Then, we further propose a zero-forcing (ZF)-based secure transmission approach to protect the legitimate transmission from eavesdropping in case of the missed detection of the active eavesdropper. Simulation studies demonstrate the robustness of the RTA pilot spoofing detector and the satisfactory performance of the proposed secure transmission strategy.

Mobile display has become one of the most power-hungry components in mobile video viewing. Currently, mobile devices can reduce the display energy by performing dynamic brightness scaling (DBS) under the distortion constraint of video signals. We observe that there is a pitfall preventing current practice from systematic display energy reduction. In particular, existing objective DBS schemes lack direct connection to the subjective human perception on DBS-enabled videos, which is the key to achieving human-centered energy-experience optimization. To overcome this pitfall, we present CrowdDBS, a crowdsourced display energy reduction framework for mobile video viewing. CrowdDBS is empowered by a set of crowdsourcing studies that uncover the relationship between human perception and DBS frequency, magnitude, and temporal consistency, respectively. Motivated by the insights obtained from these studies, CrowdDBS employs a suit of designs and a DBS optimization framework to optimize the energy-experience tradeoff in mobile video viewing. Comprehensive experimental results and user evaluations under a variety of practical settings show that CrowdDBS can achieve 37 percent device energy reduction on average while guaranteeing satisfactory user experience in mobile video viewing.

Millimeter wave (mmWave) communications have been considered as a key technology for future 5G wireless networks. In order to overcome the severe propagation loss of mmWave channel, multiple-input multiple-output (MIMO) systems with analog/digital hybrid precoding and combining transceiver architecture have been widely considered in mmWave systems. However, physical layer security (PLS) in mmWave MIMO systems and the secure hybrid beamformer design have not been well investigated. In this paper, we consider the problem of hybrid precoder and combiner design for secure transmission in mmWave MIMO systems in order to protect the legitimate transmission from eavesdropping. When eavesdropper's channel state information (CSI) is known, we first propose a joint analog precoder and combiner design algorithm which can prevent the information leakage to the eavesdropper. Then, the digital precoder and combiner are computed based on the obtained effective baseband channel to further maximize the secrecy rate. Next, if prior knowledge of the eavesdropper's CSI is unavailable, we develop an artificial noise (AN)-based hybrid beamforming approach, which can jam eavesdropper's reception while maintaining the quality-of-service (QoS) of intended receiver at the pre-specified level. Simulation results demonstrate that our proposed algorithms offer significant secrecy performance improvement compared with other hybrid beamforming algorithms.

Millimeter wave (mmWave) communications have been considered as a key technology for future 5G wireless networks since it can provide orders‐of‐magnitude wider bandwidth than current cellular bands. To overcome the severe propagation loss of the mmWave channel, an economic and energy‐efficient analogue/digital hybrid precoding and combining transceiver architecture is widely used in mmWave massive multiple‐input multiple‐output (MIMO) systems. The digital precoding/combining layer offers more freedom than pure analogue one and enables multi‐stream transmission. In this study, the authors consider the problem of codebook‐based joint hybrid precoder and combiner design for multi‐stream transmission in mmWave MIMO systems. The authors propose to jointly select an analogue precoder and combiner pair for each data stream successively, which can maximise the channel gain as well as suppress the interference between different data streams. Then, the digital precoder and combiner are computed based on the obtained effective baseband channel to further mitigate the interference and maximise the sum‐rate. Both fully‐connected and partially‐connected hybrid beamforming structures are investigated. Simulation results demonstrate that the proposed algorithms exhibit prominent advantages in combating interference between different data streams and offer satisfactory performance improvements compared with the existing codebook‐based hybrid beamforming schemes.

Mobile display has been considered as the major contributor to the energy consumption of the ever-increasing mobile video services. Current practices in display energy reduction (DER) utilize local computing resources to analyze the video content before DER strategies can be applied in a per-device fashion. For a given video, same analytical computations are repeated in millions of individual devices. In this paper, we demonstrate that a paradigm shifting framework can be designed to systematically move the common DER local processing to the streaming server with the emergence of cloud-based video services. This framework has the potential to replace the massive per-device DER computation by a one-time global video processing in the cloud. To accomplish this ultimate goal in DER, a new family of video rate-distortion (R-D) profile with embedded DER strategies shall be properly generated. This family of rate-distortion-display energy (R-D-DE) profiles contains a set of common DER parameters to be directly extracted and employed by individual mobile devices to achieve desired display energy saving without repeated local computation. Performance evaluations of the proposed design are carried out to show that this family of R-D-DE profiles is indeed able to command the systematic DER design based on the intrinsic relationships among bitrate, video quality and display energy saving.

In this study, the authors consider additive spread‐spectrum (SS) data embedding in transform‐domain host data. Conventional additive SS embedding schemes use an equal‐amplitude modulated carrier to deposit one information symbol across a group of host data coefficients which act as interference to SS signal of interest. If there is a flexibility of assigning different amplitudes across symbol bits, the probability of error can be further reduced by adaptively allocating amplitude to each symbol bit based on its own host/interference. In this study, they present a novel amplitude‐adaptive SS embedding scheme. Particularly, symbol‐by‐symbol adaptive amplitude allocation algorithms are developed to compensate for the impact from the known interference. They aim at designing the SS embedding amplitude for each symbol adaptively in order to minimise the receiver bit‐error‐rate (BER) at any given distortion level. Then, optimised amplitude allocation for multi‐carrier/multi‐message embedding in the same host data is studied as well. Finally, they consider the problem of amplitude optimisation for an ideal scenario where no external noise is introduced during embedding and transmission. Extensive experimental results illustrate that the proposed amplitude‐adaptive SS embedding scheme can provide order‐of‐magnitude performance improvement over several other state‐of‐the‐art SS embedding schemes.

Display power consumption has become a major concern for both mobile users and design engineers, especially considering the prevalence of today's video-rich mobile services. The power consumption of liquid crystal display (LCD), a dominant mobile display technology, can be reduced by dynamic backlight scaling (DBS). However, such dynamic changes of screen brightness may degrade users' quality of experience (QoE) in viewing videos. How would QoE be impacted by different DBS strategies has not yet been understood clearly and thus obscures the way to achieve systematic power saving. In this paper, we take a first step to explore the QoE of DBS on smartphones and aim at maximally enhancing the display power performance without negatively impacting users' QoE. In particular, we conduct three motivational studies to uncover the inherent relationship between QoE and backlight scaling frequency, magnitude, and temporal consistency, respectively. Motivated by the findings of these studies, we design a suite of techniques to implement a comprehensive DBS strategy. We demonstrate an example application of the proposed DBS designs in a mobile video streaming system. Measurements and user evaluations show that more than 40% system power reduction, or equivalently, 20% more power savings than the non-QoE approaches, can be achieved without QoE impairment.

In this study, the authors investigate eavesdropping mitigation for wireless communications with physical‐layer security techniques. They consider a multicasting scenario with one transmitter, multiple trusted receivers and multiple eavesdroppers, each equipped with one antenna. Aiming at protecting trusted communications from data interception, they develop two novel waveform designs for eavesdropping reduction under two scenarios: (i) when the eavesdroppers’ channel state information (CSI) is known, they design the waveform and transmit energy which can minimise the maximal signal‐to‐interference‐plus‐noise ratios (SINRs) at the eavesdroppers while achieving guaranteed SINRs at the trusted receivers and (ii) when the eavesdroppers’ CSIs are not available, they adopt ‘artificial noise’ (AN) strategy aiming at generating as much disturbance to eavesdroppers as possible. A novel waveform and transmit power design is proposed which can maximise the AN's energy with assured quality‐of‐service at trusted receivers. Extensive simulation studies illustrate the efficiency of the proposed designs for eavesdropping mitigation in wireless communications.

This paper presents a novel smart downlink scheduling scheme to enhance the performance of multimedia transmission over long-term evolution (LTE) networks. LTE represents a promising framework for the next-generation broadband multimedia services because of its significantly increased data rate over 3G cellular networks. However, the current LTE scheduling scheme has largely been designed for general data traffic without adequate consideration of multimedia characteristics. Moreover, the hard handoff (HO) procedure adopted in LTE will further degrade the multimedia services when the mobile user moves from one cell to another. Even with increased data rate, current LTE systems still cannot deliver the expected quality of service (QoS) to the mobile users under various mobility scenarios, especially when the hard HO is evoked. Aiming at overcoming these major challenges, we develop in this paper a QoS-driven smart downlink scheduling scheme for enhanced multimedia transmission over LTE networks. The proposed design shall take the following QoS metrics into consideration: 1) the delay constraint of voice-over-Internet protocol flow; 2) the packet deadline of video flow; and 3) the service degradation induced by the hard HO procedure. We achieve the design objectives by creating three QoS-driven operational control modules: 1) a transmission delay control module to ensure the on-time arrival of various types of multimedia data; 2) an HO control module to warrant continuous multimedia services when the user moves across a cell boundary; and 3) a resource allocation module to strategically map the requested flows to best fit radio resource blocks. The simulation results confirm the performance gain of the proposed scheme.

This paper considers the problem of intelligent jamming attack on a MIMO wireless communication link with a transmitter, a receiver, and an adversarial jammer, each equipped with multiple antennas. We present an optimal jamming signal design, which can maximally disrupt the MIMO transmission when the transceiver adopts an anti-jamming mechanism. In particular, signal-to-jamming-plus-noise ratio (SJNR) at the receiver is used as the anti-jamming reliability metric of the legitimate MIMO transmission. The jamming signal design is developed under the most crucial scenario for the jammer where the legitimate transceiver adopt jointly designed maximum-SJNR transmit beamforming and receive filter to suppress/mitigate the disturbance from the jammer. Under this best anti-jamming scheme, we aim to optimize the jamming signal to minimize the receiver's maximum-SJNR under a given jamming power budget. The optimal jamming signal designs are developed in different cases with accordance to the availability of channel state information (CSI) at the jammer. The analytical approximations of the jamming performance in terms of average maximum-SJNR are also provided. Extensive simulation studies confirm our analytical predictions and illustrate the efficiency of the designed optimal jamming signal on disrupting MIMO communications.

We present in this paper a new device-to-device (D2D) communication scheme to create a QoS enhanced multimedia services for LTE users who are physically close to each other. This scheme has a potential to provide higher bandwidth service with reduced delay and reduced power consumption. However, a significant challenge from such D2D opportunity is to establish the direct link between nearby devices without interference to other regular LTE users. In this research, we develop a novel user selective resource allocation scheme that allows D2D links to share the same radio resources with LTE regular users while proactively avoiding the interferences with intelligent user selection. The main contribution of the proposed scheme is in the innovative design of the sub-channel selection algorithms for both D2D links and regular LTE users. These algorithms serve dual purpose: (1) providing desired QoS enhancement to multimedia consumers in D2D links while minimizing the interference to regular users; (2) maximizing the sum rate of the LTE networks considering the interference from D2D links and the fairness issue. The simulation results show that the total achievable capacity of LTE networks is dramatically enhanced by D2D communication with this new user selective resource allocation.

Multi-user multiple input multiple output (MU-MIMO) system is an excellent choice for the next generation broadband communications because of its great potential in enhancing MIMO system capacity. One major challenge of such system is the jointly optimal transceiver beamforming design that maximizes the sum capacity under a total power constraint. Existing approaches have provided convex optimization solution by exploiting the duality between transmitter and receiver. However, with high computation complexity, these approaches are ineligible of practical implementation. In this paper, we aim to provide complexity efficient transceiver beamformer and power allocation design in MU-MIMO downlink (broadcast) channels. We first develop an iterative sub-optimal algorithm based on cyclic self-SINR-maximization (CSSM) beamforming design and water-filling power allocation. Comparing to convex-optimization-based (COB) approaches, the proposed solution (i.e. CSSM-WF algorithm) have insignificant sum-rate degradation but very low computational complexity and extremely fast converging speed. Trying to improve the performance of CSSM-WF algorithm, we then introduce another algorithm denoted as efficient COB (ECOB) algorithm in which CSSM-WF algorithm is used to generate a good start point for optimal COB approaches. Simulation results prove the efficiency of both proposed algorithms.

Long-term evolution (LTE) represents a promising technique for ubiquitous multimedia communication because of its significant enhancement to the data transmission rate. However, the hard hand-off (HO) procedure standardized in LTE is a menace to multimedia quality of service (QoS), due to the service interruption introduced by the procedure. Thus, one major challenge of such a system is to design an effective scheduler that can guarantee quality-of-service (QoS) to users under various mobility scenarios, including the hard HO procedure. Existing downlink scheduling approaches do not consider the characteristics of the video sources together with the service degradation evoked by hard HO, and therefore are unable to meet the QoS requirements of multimedia consumers. In this paper, we develop a QoS-driven downlink scheduling scheme for video streaming that considers both QoS metrics of video packet deadline and hard HO service degradation, in order to guarantee the QoS requirements of multimedia consumers. It will be demonstrated that the proposed scheduler is not only able to achieve satisfactory QoS for users during HO period, but also offers fairness for both multimedia traffic and regular data traffic. Simulation results confirm the efficiency of the proposed scheme.

This paper investigates fairness and quality of service (QoS) guaranteed user scheduling scheme in multi-user MIMO broadcast channel. The main contribution of this paper is to consider characteristic of video source in user scheduling process. As a result, the proposed scheme can provide fair and satisfactory services to regular data users, while QoS of multimedia consumers is guaranteed. Since video source is delay-sensitive, in our framework, first we will estimate the transmitting time of each video packet. The estimation of transmission time serves dual purpose: one is to guarantee the transmission of any given video packet before it expires, and another one is to allow other unscheduled regular data users to access the channel when there is no pressing need to transmit any video packet for the video consumer. Simulation results show that the proposed user scheduling framework achieves high QoS performance for multimedia consumers.

Contention-based access has been the key component for 802.11 wireless networks. As the video service is becoming more popular, contention-based access has been the key limiting factor in providing satisfactory quality-of-service (QoS) for video over wireless networks, especially when the data traffic includes both real-time video and other types of traffics. A contention free burst (CFB) scheme has been developed recently [1], would result in improved video QoS, but at the expenses of best effort and background traffics. In this paper, we proposed a deadline-aware virtual contention free (DVCF) scheme for H.264 video over 802.1 le wireless networks. The main contribution of this paper is to jointly consider the characteristic of video source, 802.lie protocol, and network specifications by adjusting the parameters of 802.lie protocol. As a result, the proposed scheme (DVCF EDCA) maintains the transmission of all types of traffics provided that the deadline of transported video packets can be strictly observed. In this framework, the deadline of each video packet is estimated first as in [2]. The estimated deadline serves dual purpose: one is to guarantee the transmission of any given video packet before it expires and another one is to allow other types of data traffic to transmit when there is no pressing needs to transmit any video packet. Simulation results show that the proposed framework achieves virtually no loss for video packets and noticeable improvement in combined throughput of video and non-video traffics.

A prioritized spatial multiplexing scheme has recently been developed for the transmission of scalable video coded (SVC) video over MIMO systems. One unique feature of this scheme is its capability to channelize the sub-channels according to the importance of the individually transmitted data streams. We consider in this research a challenging task of providing high quality multimedia services over MIMO systems with fast moving terminals. With fast moving terminals many existing approaches for MIMO channel estimation and equalization cannot be applied as most of them rely on the help of periodic pilot signals. When the terminals are travelling in high speed, the undesired Doppler effects prevent the existing approaches from efficient use of pilot signals for channel estimation and equalization. Although existing blind channel estimation and equalization schemes are able to avoid the problems associated with pilot signals, these schemes cannot be directly adopted for the prioritized spatial multiplexing MIMO system for video streaming in which data streams under different modulations need to be transmitted through sub-channels. In this paper, we develop a blind constant modulus algorithm (CMA)-based channel equalization (CMACE) scheme to track the fast time-varying channel without the requirement of pilot signals. We show that the transmitted signal in each sub-channel can be modulated with different symbol constellation, and delivered at different rate according to their importance. Simulation results demonstrate that the proposed scheme is effective for channel equalization with fast moving terminal in the prioritized spatial multiplexing MIMO systems.

Spatial multiplexing MIMO wireless system is an excellent choice for next generation broadband multimedia communications because of its parallel transmission capability. A major challenge for such MIMO system is that it usually requires appropriate decomposition of both wireless channels and multimedia data bitstreams. Existing approaches to MIMO channel decomposition have been passively based on simple channel feedback. To facilitate proper match between decomposed channels with compressed video that exhibit variable priority for different layer of bitstreams, it is necessary to proactively prioritize the decomposed MIMO channels. We present in this paper a novel pre-coding scheme capable of integrating both channel and source characteristics in order to achieve the desired prioritized spatial multiplexing. This pre-coding scheme is applied to the transmission of video data compressed with H.264 Scalable Video Coding (SVC) standard. Based on the desired bit-error-rate (BER) and signal-to-noise-ratio (SNR) for each SVC video layer, the base layer video is proactively assigned the highest priority sub-channel to guarantee minimum required quality while enhancement layers are mapped to other lower priority sub-channels. Comparing with existing schemes, the proposed pre-coder has low computational complexity and is suitable for rapid hardware implementation. The experimental results demonstrate that the proposed prioritized spatial multiplexing approach can efficiently enhance the quality of SVC video streaming over MIMO systems.