The multiaccess coded caching (MACC) system, as formulated by Hachem {\it et al.}, consists of a central server with a library of $N$ files, connected to $K$ cache-less users via an error-free shared link, and $K$ cache nodes, each equipped with cache memory of size $M$ files. Each user can access $L$ neighboring cache nodes under a cyclic wrap-around topology. Most existing studies operate under the strong assumption that users can retrieve content from their connected cache nodes at no communication cost. In practice, each user retrieves content from its $L$ different connected cache nodes at varying costs. Additionally, the server also incurs certain costs to transmit the content to the users. In this paper, we focus on a cost-aware MACC system and aim to minimize the total system cost, which includes cache-access costs and broadcast costs. Firstly, we propose a novel coded caching framework based on superposition coding, where the MACC schemes of Cheng \textit{et al.} are layered. Then, a cost-aware optimization problem is derived that optimizes cache placement and minimizes system cost. By identifying a sparsity property of the optimal solution, we propose a structure-aware algorithm with reduced complexity. Simulation results demonstrate that our proposed scheme consistently outperforms the scheme of Cheng {\it et al.} in scenarios with heterogeneous retrieval costs.

This paper studies the multi-access coded caching (MACC) problem with arbitrary user-cache access topology, which extends existing MACC models that rely on highly structured and combinatorially designed topologies. We consider a MACC system consisting of a single server, $Λ$ cache-nodes, and $K$ user-nodes. The server stores $N$ equal-size files, each cache-node has a storage capacity of $M$ files, and each user-node $k\in[K]$ can access an arbitrary subset of cache-nodes $\mathcal{A}_k\subseteq[Λ]$ and retrieve the cached content stored in cache-nodes $\mathcal{A}_k$. The objective is to design a universal framework for the MACC delivery problem. Decoding conflicts among the requested packets are captured by a conflict graph, and the design of the delivery is reduced to a graph coloring problem, where achieving a lower transmission load corresponds to coloring the graph using fewer colors. Under this formulation, the classical DSatur algorithm achieves a transmission load close to the index-coding (IC) converse bound, thereby providing a practical benchmark. However, its computational complexity becomes prohibitive for large-scale graphs. To overcome this limitation, we develop a learning-driven approach using graph neural networks (GNNs) that efficiently constructs coded multicast transmissions with performance close to the theoretical bounds and generalizes across different user-cache access topologies and numbers of users. In addition, we extend the IC converse bound to MACC systems with arbitrary access topology and propose a low-complexity greedy approximation that closely matches the IC converse bound. Numerical results demonstrate that the proposed approach achieves performance close to the DSatur algorithm and the IC converse bound, while significantly reducing computational complexity, making it well-suited for large-scale MACC systems.

This paper considers a new secure gradient coding problem with uncoded groupwise keys, formalized as a (K, N, N_r, M, S) secure gradient coding model, where a user aims to compute the sum of the gradients from K datasets with the assistance of N distributed servers. We consider arbitrary heterogeneous data assignment, where each dataset is assigned to at least M servers. The user should recover the sum of gradients from the transmissions of any N_r servers. The security constraint guarantees that even if the user receives the transmitted messages from all servers, it cannot obtain any other information about the datasets except the sum of gradients. Compared to existing secure gradient coding works, we introduce a practical constraint on secret keys, namely uncoded groupwise keys, where the keys are mutually independent and each key is shared by precisely S servers. An achievable secure gradient coding scheme with uncoded groupwise keys is proposed, which is then proven to be optimal if S > M and to be order optimal within a factor of 2 otherwise.

The Fluid Antenna System (FAS) overcomes the spatial degree-of-freedom limitations of conventional static antenna arrays in wireless communications.This capability critically depends on acquiring full Channel State Information across all accessible ports. Existing studies focus exclusively on narrowband FAS, performing channel estimation solely in the spatial domain. This work proposes a channel estimation and spatial equalization framework for wideband FAS, revealing for the first time an inherent group-sparse structure in aperture-limited FAS channels. First, we establish a group-sparse recovery framework for space-frequency characteristics in FAS, formally characterizing leakage-induced sparsity degradation from limited aperture and bandwidth as a structured group-sparsity problem. By deriving dictionary-adapted group restricted isometry property, we prove tight recovery bounds for a convex ℓ1/ℓ2-mixed norm optimization formulation that preserves leakage-aware sparsity patterns. Second, we develop a descending correlation group orthogonal matching pursuit algorithm that systematically relaxes leakage constraints to reduce subcoherence. This approach enables FSC recovery with accelerated convergence and superior performance compared to conventional compressive sensing methods like OMP or GOMP. Third, we formulate spatial equalization as a mixed-integer linear programming problem, complement this with a greedy algorithm maintaining near-optimal performance. Simulation results demonstrate the proposed channel estimation algorithm effectively resolves energy misallocation and enables recovery of weak details, achieving superior recovery accuracy and convergence rate. The SE framework suppresses deep fading phenomena and largely reduces time consumption overhead while maintaining equivalent link reliability.

Integrated sensing and communication (ISAC) systems provide significant enhancements in performance and resource efficiency compared to individual sensing and communication systems, primarily attributed to the collaborative use of wireless resources, radio waveforms, and hardware platforms. This paper focuses on the bistatic ISAC systems with separated multi-receiver and one sensor. Compared to a monostatic ISAC system, the main challenge in the bistatic setting is that the information messages are unknown to the sensor and therefore they are seen as interference, while the channel between the transmitters and the sensor is unknown to the transmitters. In order to mitigate the interference at the sensor while maximizing the communication degree of freedom, we introduce two strategies, namely, blind interference alignment and topological interference management. Although well-known in the context of Gaussian interference channels, these strategies are novel in the context of bistatic ISAC. For the bistatic ISAC models with heterogeneous coherence time or with heterogeneous connectivity, the achieved ISAC tradeoff points in terms of communication and sensing degrees of freedom are characterized. In particular, we show that the new tradeoff outperforms the time-sharing between the sensing-only and the communication-only schemes. Simulation results demonstrate that the proposed schemes significantly improve the channel estimation error for the sensing task, compared to treating interference as noise at the sensor and successive interference cancellation.

This paper studies the master-worker distributed linearly separable computation problem, where the considered computation task, referred to as linearly separable function, is a generic linear map. This model includes cooperative distributed gradient coding, real-time rendering, linear transforms, etc. as special cases. The computation task on K datasets can be expressed as Kclinear combinations of K messages, where each message is the output of an individual function on one dataset. In this distributed computing model, the K datasets are assigned to N workers for computation. Due to the possible presence of stragglers, it is required that the master can obtain the desired computation task from the answers of any Nrout of N workers. The computation cost is defined as the number of datasets assigned to each worker, while the communication cost is defined as the number of codewords that should be received. The objective is to characterize the optimal tradeoff between the computation and communication costs. A common way to assign the datasets to the workers is “cyclic assignment”. This has been considered in several theoretical works, as well as gradient coding, etc. Motivated by its theoretical and practical relevance, in this paper we focus on the cyclic assignment and solve the problem by determining the optimal computation/communication cost tradeoff when N = K and order optimal within a factor of 2 otherwise. In particular, this paper proposes a new computing scheme with the cyclic assignment based on the concept of interference alignment, by treating each message which cannot be computed by a worker as an interference from this worker. The decodability of our scheme is proved for the cases Kc[K/N (Nr− m + 1) : K] and N = Nrwith m+u−1 dividing N (where u = [ KcN K ]), and is further numerically verified for N ≤ 60. Beyond the appealing order-optimality result, we also show that the proposed scheme achieves significant gains over the current state of the art in practice. Experimental results over Tencent Cloud show the reduction of whole distributed computing process time of our scheme is up to 72.8% compared to the benchmark scheme which treats the computation on Kclinear combinations as Kcindividual computations.

Existing research on reconfigurable intelligent surface (RIS) beamforming in wireless communications predominantly assumes uniform phase quantization across reflecting units. However, in practical applications, engineering challenges and design requirements often lead to non-uniform phase and bit resolution of RIS units, which significantly limits the performance potential of conventional approaches. Current optimization frameworks struggle to effectively handle this non-uniform phase discretization due to the inherent non-convexity and high-dimensional nature of the resultant beamforming problem. To address this issue, this paper pioneers the study of discrete non-uniform phase configuration in RIS-assisted multi-user communication and formulates an optimization model to rigorously define the problem. For single-user scenarios, the paper proposes a partition-and-traversal (PAT) algorithm that efficiently achieves the global optimal solution through systematic search and traversal. For larger-scale multi-user scenarios, to further balance performance and computational complexity, a parameter tuning mechanism is introduced into the PAT algorithm, enhancing its flexibility and scalability. This mechanism optimizes the search strategy, significantly reduces computational overhead, and achieves linear complexity. Numerical simulations confirm the effectiveness and superiority of the proposed PAT algorithm. Additionally, we provide a detailed analysis of the impact of non-uniform phase quantization on the system performance.

This paper proposes a promising framework of multi-functional service incorporating sensing targets (STs), information receivers (IRs), and energy receivers (ERs) in an active reconfigurable intelligent surface (RIS)-aided integrated sensing, communication, and power transfer (ISCPT) system. In the proposed system, we aim to maximize the weighted sum of the received radar signal-to-interference-plus-noise-ratio (SINR) by jointly optimizing the transmit beamforming at the multi-functional base station (MFBS), the coefficients of active RIS, and the radar receive filter coefficients. Meanwhile, the constraints of the SINR of IRs, energy harvesting (EH) requirements of ERs, the power budget for the MFBS and active RIS, and the amplification gain should be satisfied. To guarantee the generality of formulated problems, we further incorporate the self-interference effects of echo signals, multi-target echo interference, simultaneous detection of multiple STs, and a nonlinear EH model into the generalized system model. Due to the presence of echo interference and multi-target echo interference, the MFBS transmits the dedicated sensing signal with the communication to enhance the sensing performance. The formulated problem is tackled by developing an efficient alternating optimization (AO) algorithm combined with fractional programming (FP) and majorization-minimization (MM) techniques. Finally, the numerical results reveal the impact of system parameters on the sensing performance, the trade-off relationship between multiple functionalities, and the deployment strategy of RIS. The main findings are as follows: 1) Active RIS is remarkably superior to passive RIS for ISCPT systems, especially for closer to the receivers with a 40 dB performance gain. 2) Comparatively, the radar sensing SINR is more sensitive to the number of active RIS units, while the SINR of IRs is more sensitive to the number of antennas at the base station. These results demonstrate that the proposed system holds the potential for practical deployment.

This paper investigates the capabilities and effectiveness of backward localization centered on reconfigurable intelligent surfaces (RISs). In the backward sensing paradigm, the region of interest (RoI) is illuminated using a set of diverse radiation patterns. These patterns encode spatial information into a sequence of measurements, which are subsequently processed to reconstruct the RoI. We show that a single RIS can estimate the direction of arrival of incident waves by leveraging configurational diversity, and that the spatial diversity provided by multiple RISs further improves the accuracy of source localization and power estimation. The underlying structure of the sensing operator in the multi-snapshot measurement process is clarified. For single-RIS localization, the sensing operator is decomposed into a product of structured matrices, each corresponding to a specific physical process: wave propagation to and from the RIS, the relative phase offsets of elements with respect to the reference point, and the applied phase configuration of each element. A unified framework for identifying key performance indicators is established by analyzing the conditioning of the sensing operators. In the multi-RIS setting, we derive–via rank analysis–the governing law among the RoI size, the number of elements, and the number of measurements. Upper bounds on the relative error of the least squares reconstruction algorithm are derived. These bounds clarify how key performance indicators affect estimation error and provide valuable guidance for system-level optimization. Numerical experiments confirm that the trend of the relative error is consistent with the theoretical bounds. Finally, we develop a proof-of-concept prototype using universal software radio peripherals and employ a magnitude-only reconstruction algorithm tailored to the system. To the best of our knowledge, this represents the first experimental demonstration of its kind.

Transformers have significantly advanced the field of 3D human pose estimation (HPE). However, existing transformer-based methods primarily use self-attention mechanisms for spatio-temporal modeling, leading to a quadratic complexity, unidirectional modeling of spatio-temporal relationships, and insufficient learning of spatial-temporal correlations. Recently, the Mamba architecture, utilizing the state space model (SSM), has exhibited superior long-range modeling capabilities in a variety of vision tasks with linear complexity. In this paper, we propose PoseMamba, a novel purely SSM-based approach with linear complexity for 3D human pose estimation in monocular video. Specifically, we propose a bidirectional global-local spatio-temporal SSM block that comprehensively models human joint relations within individual frames as well as temporal correlations across frames. Within this bidirectional global-local spatio-temporal SSM block, we introduce a reordering strategy to enhance the local modeling capability of the SSM. This strategy provides a more logical geometric scanning order and integrates it with the global SSM, resulting in a combined global-local spatial scan. We have quantitatively and qualitatively evaluated our approach using two benchmark datasets: Human3.6M and MPI-INF-3DHP. Extensive experiments demonstrate that PoseMamba achieves state-of-the-art performance on both datasets while maintaining a smaller model size and reducing computational costs.

Radio resource management in modern cellular networks often calls for the optimization of complex utility functions that are potentially conflicting between different base stations (BSs). Coordinating the resource allocation strategies efficiently across BSs to ensure stable network service poses significant challenges, especially when each utility is accessible only via costly, black-box evaluations. This paper considers formulating the resource allocation among spectrum sharing BSs as a non-cooperative game, with the goal of aligning their allocation incentives toward a stable outcome. To address this challenge, we propose PPR-UCB, a novel Bayesian optimization (BO) strategy that learns from sequential decision-evaluation pairs to approximate pure Nash equilibrium (PNE) solutions. PPR-UCB applies martingale techniques to Gaussian process (GP) surrogates and constructs high probability confidence bounds for utilities uncertainty quantification. Experiments on downlink transmission power allocation in a multi-cell multi-antenna system demonstrate the efficiency of PPR-UCB in identifying effective equilibrium solutions within a few data samples.

The use of denoising diffusion models is becoming increasingly popular in the field of image editing. However, current approaches often rely on either image-guided methods, which provide a visual reference but lack control over semantic consistency, or text-guided methods, which ensure alignment with the text guidance but compromise visual quality. To resolve this issue, we propose a framework that integrates a fusion of generated visual references and text guidance into the semantic latent space of a frozen pre-trained diffusion model. Using only a tiny neural network, our framework provides control over diverse content and attributes, driven intuitively by the simple prompt. Compared to state-of-the-art methods, the framework generates images of higher quality while providing realistic editing effects across various benchmark datasets. The code is available at https://github.com/SadAngelF/Editing-via-Step-Wise-Alignment.

Integrated sensing and communication (ISAC) systems provide significant enhancements in performance and resource efficiency compared to individual sensing and communication systems, primarily attributed to the collaborative use of wireless resources, radio waveforms, and hardware platforms. The performance limits of a system are crucial for guiding its design; however, the performance limits of ISAC systems remain an open question. This paper focuses on the bistatic ISAC systems with dispersed multi-receivers and one sensor. Compared to the monostatic ISAC systems, the main challenge is that that the communication messages are unknown to the sensor and thus become its interference, while the channel information between the transmitters and the sensor is unknown to the transmitters. In order to mitigate the interference at the sensor while maximizing the communication degree of freedom, we introduce the blind interference alignment strategy for various bistatic ISAC settings, including interference channels, MU-MISO channels, and MU-MIMO channels. Under each of such system, the achieved ISAC tradeoff points by the proposed schemes in terms of communication and sensing degrees of freedom are characterized, which outperforms the time-sharing between the two extreme sensing-optimal and communication-optimal points. Simulation results also demonstrate that the proposed schemes significantly improve on the ISAC performance compared to treating interference as noise at the sensor.

Reconfigurable intelligent surfaces (RISs) hold significant potential for enhancing coverage and data rates in 6G wireless communication systems. While most research has concentrated on reflective RIS applications, studies on transmissive RIS (TRIS) have been largely limited to simulations or laboratory prototypes. To evaluate the real-world performance of TRIS, we developed a 256 -unit cell, 1-bit TRIS prototype operating at the 5.8 GHz frequency band. The design employs an antisymmetric configuration of two PIN diodes, achieving nearly uniform transmission amplitude with inverse phase states over a wide 20 % bandwidth. The fabricated TRIS, composed of$16 \times 16$unit cells, demonstrates effective 1-bit phase tuning with minimal insertion loss and a 3 dB transmission bandwidth exceeding 1.2 GHz. By dynamically modulating the quantized code distributions, we achieved scanning beams of$\pm 60^{\circ}$. Subsequently, we integrated the TRIS into a Wi-Fi wireless communication system and assessed its performance in a real-world wall-penetration scenario. The TRIS improved the average Signal-to-Noise Ratio (SNR) by 8 dB, with a maximum gain of 15 dB, across a 29$\mathbf{m}^{2}$indoor area. Download speeds also increased, showing a maximum improvement of 10.57 Mbps and an average gain of 8.01 Mbps. These results highlight the effectiveness of TRIS in reducing path loss, enhancing SNR, and improving WiFi performance in challenging wall-penetration environments, ultimately leading to a better user experience.

Gradient coding enables a user node to efficiently aggregate gradients computed by server nodes from local datasets, achieving low communication costs while ensuring resilience against straggling servers. This paper considers the secure gradient coding problem, where a user aims to compute the sum of the gradients from K datasets with the assistance of$N$distributed servers. The user is required to recover the sum of gradients from the transmissions of any$\mathrm{N}_{\mathrm{r}}$servers, with each dataset assigned to$N-N_{r}+m$servers. The security constraint guarantees that even if the user receives transmissions from all servers, no additional information about the datasets can be obtained beyond the sum of gradients. It has been shown in the literature that the security constraint does not increase the optimal communication cost of the gradient coding problem, provided that enough source keys are shared among the servers. However, the minimum required source key size to ensure security while achieving the optimal communication cost has been studied only for the case$m=1$. In this paper, we focus on the more general case$m \geq 1$and aim to characterize the minimum required source key size for this purpose. A new information-theoretic converse bound on the source key size and a novel achievable scheme with smartly designed assignments are proposed. Our proposed scheme outperforms the optimal scheme based on the widely used cyclic data assignment and coincides with the converse bound under specific system parameters.

Consistency Models (CMs) have shown promise for efficient one-step generation. However, most existing CMs rely on manually designed discretization schemes, which can cause repeated adjustments for different noise schedules and datasets. To address this, we propose a unified framework for the automatic and adaptive discretization of CMs, formulating it as an optimization problem with respect to the discretization step. Concretely, during the consistency training process, we propose using local consistency as the optimization objective to ensure trainability by avoiding excessive discretization, and taking global consistency as a constraint to ensure stability by controlling the denoising error in the training target. We establish the trade-off between local and global consistency with a Lagrange multiplier. Building on this framework, we achieve adaptive discretization for CMs using the Gauss-Newton method. We refer to our approach as ADCMs. Experiments demonstrate that ADCMs significantly improve the training efficiency of CMs, achieving superior generative performance with minimal training overhead on both CIFAR-10 and ImageNet. Moreover, ADCMs exhibit strong adaptability to more advanced DM variants. Code is available at \url{https://github.com/rainstonee/ADCM}.

In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted 3D direction-of-arrival (DoA) estimation system, in which a uniform planar array (UPA) RIS is deployed to provide virtual line-of-sight (LOS) links and reflect the uplink pilot signal to sensors. To overcome the mutually coupled problem between the beamforming design at the RIS and DoA estimation, we explore the separable sparse representation structure and propose an alternating optimization algorithm. The grid-based DoA estimation is modeled as a joint-sparse recovery problem considering the grid bias, and the Joint-2D-OMP method is used to estimate both on-grid and off-grid parts. The corresponding Cramér-Rao lower bound (CRLB) is derived to evaluate the estimation. Then, the beampattern at the RIS is optimized to maximize the signal-to-noise (SNR) at sensors according to the estimated angles. Numerical results show that the proposed alternating optimization algorithm can achieve lower estimation error compared to benchmarks of random beamforming design.

Human activity recognition (HAR) using radio frequency (RF) signals has attracted increasing interest due to its non-intrusive and privacy-preserving nature. However, traditional systems often suffer from multipath fading, environmental noise, and limited spatial diversity, particularly in through-the-wall scenarios. In this paper, we propose TRIS-HAR, a novel HAR system that integrates a transmissive reconfigurable intelligent surface (TRIS) with an advanced dual-stream state space model, Human intelligence Mamba (HiMamba). The TRIS actively reshapes the propagation environment by constructing deterministic quasi-line-of-sight (QLoS) paths across obstacles, significantly improving channel state information (CSI) quality. Complementing this, HiMamba leverages a lightweight structured state space architecture to jointly model temporal and spectral dynamics, enabling robust activity recognition under non-line-of-sight conditions. Extensive experiments on both public and real-world datasets demonstrate that TRIS-HAR improves recognition accuracy from 85.00% to 98.06% and maintains strong generalizability across environments. The model is also deployed on a CPU-based edge device, achieving real-time inference at 108 FPS with minimal memory cost. This work establishes a co-designed hardware-algorithm framework for RF-based HAR, offering a scalable and deployable solution for smart homes, healthcare, and next-generation pervasive sensing applications.

Accurate 3D localization is essential for realizing advanced sensing functionalities in next-generation Wi-Fi communication systems. This study investigates the potential of multistatic localization in Wi-Fi networks through the deployment of multiple cooperative antenna arrays. The collaborative gain offered by these arrays is twofold: (i) intra-array coherent gain at the wavelength scale among antenna elements, and (ii) inter-array cooperative gain across arrays. To evaluate the feasibility and performance of this approach, we develop WiCAL (Wi-Fi Collaborative Antenna Localization), a system built upon commercial Wi-Fi infrastructure equipped with uniform rectangular arrays (URAs). These arrays are driven by multiplexing embedded radio frequency (RF) chains available in standard access points or user devices, thereby eliminating the need for sophisticated, costly, and power-hungry multi-transceiver modules typically required in multiple-input and multiple-output (MIMO) systems. To address phase offsets introduced by RF chain multiplexing, we propose a three-stage, fine-grained phase alignment scheme to synchronize signals across antenna elements within each array. A bidirectional spatial smoothing MUSIC algorithm is employed to estimate angles of arrival (AoAs) and mitigate performance degradation caused by correlated interference. To further exploit inter-array cooperative gain, we elaborate on the synchronization mechanism among distributed URAs, which enables direct position determination by bypassing intermediate angle estimation. Once synchronized, the distributed URAs effectively form a virtual large-scale array, significantly enhancing spatial resolution and localization accuracy. WiCAL is validated using 3 × 4 URAs operating at the 5.2 GHz band. Experimental results demonstrate median AoA estimation errors of 1° in elevation and 1.5° in azimuth under intra-array coherent processing. For inter-array collaboration, the system achieves a median localization error of 15.6 cm using two URAs, outperforming state-of-the-art methods.

The reconfigurable intelligent surface (RIS) has garnered considerable attention due to its substantial potential in reconfiguring the electromagnetic environment. In RIS-aided communications, constrained ℓ2-norm maximization problems frequently arise due to the phase configuration requirements. This paper investigates a general discrete ℓp-norm maximization problem, with power aggregation through RIS as a specific example. We propose a mathematically concise iterative framework composed of alternating inner product maximizations, which is well-suited for addressing both ℓ1- and ℓ2-norm maximizations under either discrete or continuous uni-modular variable constraints. The iteration process is proven to be monotonically non-decreasing. Additionally, this framework exhibits a distinctive capability to mitigate performance degradation caused by discrete quantization in practical systems, which is applicable to any algorithm intended for the continuous solution. Furthermore, as an integral component of the alternating iterations framework, we present a divide-and-sort (DaS) method to tackle the discrete inner product maximization problem. In the realm of ℓ∞-norm maximization, the DaS method ensures the identification of the global optimum with polynomial search complexity. We validate the proposed methods’ effectiveness and superiority through numerical and prototype experiments. Finally, we demonstrate that the proposed framework can be extended and applied to a wide range of other engineering problems.

Reconfigurable intelligent surfaces (RISs) present significant potential for enhancing coverage and data rates in 6G wireless communication systems. While most research has focused on reflective RIS applications, studies on transmissive RIS (TRIS) have largely been limited to simulations or laboratory-scale prototypes. To evaluate the real-world performance of TRIS, we develop a 256-unit, 1-bit TRIS prototype operating at the 5.8 GHz frequency band. The design uses an antisymmetric configuration of two PIN diodes, achieving nearly uniform transmission amplitude with inverse phase states over a wide 20% bandwidth. A TRIS composed of 16 × 16 units is fabricated and validated through measurements, showing effective 1-bit phase tuning with minimal insertion loss and a 3 dB transmission bandwidth exceeding 1.2 GHz at the central frequency of 5.8 GHz. By dynamically modulating the quantized code distributions, ±90° scanning beams are achieved. We then integrate the TRIS into a wireless communication system and evaluate its performance in a real-world wall-penetration scenario. With directional antennas that are connected to the Universal Software Radio Peripheral (USRP) modules and are placed on either side of a 240 mm concrete wall, the TRIS provides a signal power gain of 19-23 dB within a ±90° beamforming range, significantly reducing path loss. Additionally, we assess its impact on a commercial Wi-Fi system, where the TRIS improves the average Signal-to-Noise Ratio (SNR) by 8 dB, with a maximum gain of 15 dB, across a 29 m2indoor area. Downlink rates also increase, with a maximum improvement of 10.57 Mbps and an average gain of 8.01 Mbps. These results highlight TRIS’s effectiveness in reducing path loss, enhancing SNR, and improving Wi-Fi performance in challenging wall-penetration environments, leading to better user experiences.

Modern machine learning (ML) models have grown to a scale where training them on a single machine becomes impractical. As a result, there is a growing trend to leverage federated learning (FL) techniques to train large ML models in a distributed and collaborative manner. These models, however, when deployed on new devices, might struggle to generalize well due to domain shifts. In this context, federated domain adaptation (FDA) emerges as a powerful approach to address this challenge. Most existing FDA approaches typically focus on aligning the distributions between source and target domains by minimizing their (e.g., MMD) distance. Such strategies, however, inevitably introduce high communication overheads and can be highly sensitive to network reliability. In this paper, we introduce RF-TCA, an enhancement to the standard Transfer Component Analysis approach that significantly accelerates computation without compromising theoretical and empirical performance. Leveraging the computational advantage of RF-TCA, we further extend it to FDA setting with FedRF-TCA. The proposed FedRF-TCA protocol boasts communication complexity that isindependentof the sample size, while maintaining performance that is either comparable to or even surpasses state-of-the-art FDA methods. We present extensive experiments to showcase the superior performance and robustness (to network condition) of FedRF-TCA.

Gait recognition with radio frequency (RF) signals enables many potential applications requiring accurate identification. However, current systems require individuals to be within a line-of-sight (LOS) environment and struggle with low signal-to-noise ratio (SNR) when signals traverse concrete and thick walls. To address these challenges, we present TRGR, a novel transmissive reconfigurable intelligent surface (RIS)-aided gait recognition system. TRGR can recognize human identities through walls using only the magnitude measurements of channel state information (CSI) from a pair of transceivers. Specifically, by leveraging transmissive RIS alongside a configuration alternating optimization algorithm, TRGR enhances wall penetration and signal quality, enabling accurate gait recognition. Furthermore, a residual convolution network (RCNN) is proposed as the backbone network to learn robust human information. Experimental results confirm the efficacy of transmissive RIS, highlighting the significant potential of transmissive RIS in enhancing RF-based gait recognition systems. Extensive experiment results show that TRGR achieves an average accuracy of 97.88% in identifying persons when signals traverse concrete walls, demonstrating the effectiveness and robustness of TRGR.

The existing methods for Reconfigurable Intelligent Surface (RIS) beamforming in wireless communication are typically limited to uniform phase quantization. However, in real world applications, the phase and bit resolution of RIS units are often non-uniform due to practical requirements and engineering challenges. To fill this research gap, we formulate an optimization problem for discrete non-uniform phase configuration in RIS assisted multiple-input single-output (MISO) communications. Subsequently, a partition-and-traversal (PAT) algorithm is proposed to solve that, achieving the global optimal solution. The efficacy and superiority of the PAT algorithm are validated through numerical simulations, and the impact of non-uniform phase quantization on system performance is analyzed.

This paper investigates a stacked intelligent metasurfaces (SIM)-assisted integrated sensing and communications (ISAC) system. An extended target model is considered, where the base station (BS) aims to estimate the complete target response matrix relative to the SIM. Under the constraints of minimum signal-to-interference-plus-noise ratio (SINR) for the communication users (CUs) and maximum transmit power, we jointly optimize the transmit beamforming at the BS and the end-to-end transmission matrix of the SIM, to minimize the Cramér-Rao bound (CRB) for target estimation. Effective algorithms such as alternating optimization (AO) and semidefinite relaxation (SDR) are employed to solve the non-convex SINR-constrained CRB minimization problem. Finally, we design and build a hardware platform for SIM, and experimentally evaluate the performance of SIM-aided communication and sensing tasks.

A fair beam allocation framework through reconfigurable intelligent surfaces (RISs) is proposed, incorporating the Max-min criterion. This framework focuses on explicit RIS beamforming functionalities through optimization. Firstly, realistic models, grounded in geometrical optics, are introduced to characterize the input/output behaviors of RISs. Then, a highly efficient algorithm is developed for Max-min optimizations involving quadratic forms. Leveraging the Moreau-Yosida approximation, we successfully reformulate the original problem and propose an iterative algorithm to obtain the optimal solution. The proposed approach exhibits excellent extensibility, making it readily applicable to address a broader class of Max-min optimization problems. Finally, numerical and prototype experiments are conducted to validate the effectiveness of the framework. With the proposed beam allocation framework and algorithm, we clarify that several crucial redistribution functionalities of RISs, such as explicit beam-splitting, fair beam allocation, and wide-beam generation, can be effectively implemented.

Recently, ray tracing has gained renewed interest with the advent of Reflective Intelligent Surfaces (RIS) technology, a key enabler of 6G wireless communications due to its capability of intelligent manipulation of electromagnetic waves. However, accurately modeling RIS-enabled wireless environments poses significant challenges due to the complex variations caused by various environmental factors and the mobility of RISs. In this paper, we propose a novel modeling approach using Neural Radiance Fields (NeRF) to characterize the dynamics of electromagnetic fields in such environments. Our method utilizes NeRF-based ray tracing to intuitively capture and visualize the complex dynamics of signal propagation, effectively modeling the complete signal pathways from the transmitter to the RIS, and from the RIS to the receiver. This two-stage process accurately characterizes multiple complex transmission paths, enhancing our understanding of signal behavior in real-world scenarios. Our approach predicts the signal field for any specified RIS placement and receiver location, facilitating efficient RIS deployment. Experimental evaluations using both simulated and real-world data validate the significant benefits of our methodology.

This paper explores the potential wireless communication applications of Reconfigurable Intelligent Surfaces (RIS) in reverberant wave propagation environments. Unlike in free space, we utilize the sensitivity to boundaries of the enclosed electromagnetic (EM) field and the equivalent perturbation of RISs. For the first time, we introduce the framework of reconfigurable boundary modulation in the cavities. We have proposed a robust boundary modulation scheme that exploits the continuity of object motion and the mutation of the codebook switch, which achieves pulse position modulation (PPM) by RIS-generated equivalent pulses for wireless communication in cavities. This approach achieves around 2 Mbps bit rate in the prototype and demonstrates strong resistance to channel's frequency selectivity resulting in an extremely low bit error rate (BER).

Reconfigurable Intelligent Surface (RIS) is envisioned to be an enabling technique in 6G wireless communications. By configuring the reflection beamforming codebook, RIS focuses signals on target receivers to enhance signal strength. In this paper, we investigate the codebook configuration for RIS-aided communication systems. We formulate an implicit relationship between user's coordinates information and the codebook from the perspective of signal radiation mechanisms, and introduce a novel learning-based method, implicit neural representations (INRs), to solve this implicit coordinates-to-codebook mapping problem. Our approach requires only user's coordinates, avoiding reliance on channel models. Additionally, given the significant practical applications of the 1-bit RIS, we formulate the 1-bit codebook configuration as a multi-label classification problem, and propose an encoding strategy for 1-bit RIS to reduce the codebook dimension, thereby improving learning efficiency. Experimental results from simulations and measured data demonstrate significant advantages of our method.

Deep equilibrium models (DEQs), as typical implicit neural networks, have demonstrated remarkable success on various tasks. There is, however, a lack of theoretical understanding of the connections and differences between implicit DEQs and explicit neural network models. In this paper, leveraging recent advances in random matrix theory (RMT), we perform an in-depth analysis on the eigenspectra of the conjugate kernel (CK) and neural tangent kernel (NTK) matrices for implicit DEQs, when the input data are drawn from a high-dimensional Gaussia mixture. We prove that, in this setting, the spectral behavior of these Implicit-CKs and NTKs depend on the DEQ activation function and initial weight variances, but only via a system of four nonlinear equations. As a direct consequence of this theoretical result, we demonstrate that a shallow explicit network can be carefully designed to produce the same CK or NTK as a given DEQ. Despite derived here for Gaussian mixture data, empirical results show the proposed theory and design principles also apply to popular real-world datasets.

Elasticity plays an important role in modern cloud computing systems. Elastic computing allows virtual machines (i.e., computing nodes) to be preempted when high-priority jobs arise, and also allows new virtual machines to participate in the computation. This paper consider the elastic computing with heterogeneous speeds under uncoded storage. In 2018, Yang et al. introduced Coded Storage Elastic Computing (CSEC) to address the elasticity using coding technology, with lower storage and computation load requirements. However, CSEC is limited to certain types of computations (e.g., linear) due to the coded data storage based on linear coding. Then Centralized Uncoded Storage Elastic Computing (CUSEC) with heterogeneous computation speeds was proposed, which directly copies parts of data into the virtual machines. In all existing works in elastic computing, the storage assignment is centralized, meaning that the number and identity of all virtual machines possible used in the whole computation process are known during the storage assignment. In this paper, we consider Decentralized Uncoded Storage Elastic Computing (DUSEC) with heterogeneous computation speeds, where any available virtual machine can join the computation which is not predicted and thus coordination among different virtual machines' storage assignments is not allowed. Under a decentralized storage assignment originally proposed in coded caching by Maddah-Ali and Niesen, we propose a computing scheme with closed-form optimal computation time. We also run experiments over MNIST dataset with Softmax regression model through the Tencent cloud platform, and the experiment results demonstrate that the proposed DUSEC system approaches the state-of-art best storage assignment in the CUSEC system in computation time.

Reconfigurable intelligent surface (RIS) has been treated as a core technique in improving wireless propagation environments for the next generation wireless communication systems. This paper proposes a new coded caching problem, referred to as Reconfigurable Intelligent Surface (RIS)-assisted multiple-antenna coded caching, which is composed of a server with multiple antennas and some single-antenna cache-aided users. Different from the existing multi-antenna coded caching problems, we introduce a passive RIS (with limited number of units) into the systems to further increase the multicast gain (i.e., degrees of freedom$(\mathbf{DoF}))$in the transmission, which is done by using RIS-assisted interference nulling. That is, by using RIS, we can ‘erase’ any path between one transmission antenna and one receive antenna. We first propose a new RIS-assisted interference nulling approach to search for the phase-shift coefficients of RIS for the sake of interference nulling, which converges faster than the state-of-the-art algorithm. After erasing some paths in each time slot, the delivery can be divided into several non-overlapping groups including transmission antennas and users, where in each group the transmission antennas serve the contained users without suffering interference from the transmissions by other groups. The division of groups for the sake of maximizing the DoF could be formulated into a combinatorial optimization problem. We propose a grouping algorithm which can find the optimal solution with low complexity, and the corresponding coded caching scheme achieving this DoF.

Device-free human activity recognition plays a pivotal role in wireless sensing. However, current systems often fail to accommodate signal transmission through walls or necessitate dedicated noise removal algorithms. To overcome these limitations, we introduce TRTAR: a device-free passive human activity recognition system integrated with a transmissive reconfigurable intelligent surface (RIS). TRTAR eliminates the necessity for dedicated devices or noise removal algorithms, while specifically addressing signal propagation through walls. Unlike existing approaches, TRTAR solely employs a transmissive RIS at the transmitter or receiver without modifying the inherent hardware structure. Experimental results demonstrate that TRTAR attains an average accuracy of 98.13% when signals traverse concrete walls.

This paper studies the beamforming optimization challenge in reconfigurable intelligent surface (RIS)-aided multiple-input single-output (MISO) systems, where the RIS phase configuration is discrete. Conventional optimization meth-ods for this discrete optimization problem necessitate resource-intensive exponential search and thus fall within the universal (NP-hard) category. We formally define this task as a discrete inner product maximization problem. Leveraging the inherent structure of this problem, we propose an efficient divide-and-sort (Da$S$) search algorithm to reach the global optimality for the maximization problem. The complexity of the proposed algorithm can be minimized to$\mathrm{O}(2^{B}N)$, a linear correlation with the count of phase discrete levels$2^{B}$and reflecting units$N$. This is notably lower than the exhaustive search complexity of$\mathcal{O}(2^{BN})$. Numerical evaluations and experiments over real prototype also demonstrate the efficiency of the proposed DaS algorithm. Finally, by using the proposed algorithm, we show that over some resolution quantization level on each RIS unit (4-bit and above), there is no noticeable difference in power gains between continuous and discrete phase configurations.

Human activity recognition (HAR) is crucial in smart homes, security, and healthcare. Existing systems are limited by insufficient spatial diversity due to the constrained number of antennas. Additionally, challenges in noise reduction and feature extraction from sensing data, particularly channel state information (CSI), affect recognition performance. This study introduces a reconfigurable intelligent surface (RIS)-assisted passive HAR (RISAR) method compatible with commercial Wi-Fi devices. RISAR leverages RIS to enhance the spatial diversity of Wi-Fi signals, capturing a broader range of spatial information. A novel high-dimensional factor model based on random matrix theory is proposed to improve noise reduction and feature extraction in the temporal domain. Furthermore, a dual-stream spatiotemporal attention network model is developed to assign variable weights to different characteristics and sequences, mimicking human cognitive processes in prioritizing essential information. Experimental results demonstrate that RISAR significantly outperforms existing HAR methods in both accuracy and efficiency, achieving an average accuracy of 97.26%. These findings highlight RISAR’s adaptability and potential as a robust activity recognition solution in real-world environments.

A fair beam allocation framework through reconfigurable intelligent surfaces (RISs) is proposed, incorporating the Max-min criterion. This framework focuses on designing explicit beamforming functionalities through optimization. Firstly, realistic models, grounded in geometrical optics, are introduced to characterize the input/output behaviors of RISs, effectively bridging the gap between the requirements on explicit beamforming operations and their practical implementations. Then, a highly efficient algorithm is developed for Max-min optimizations involving quadratic forms. Leveraging the Moreau-Yosida approximation, we successfully reformulate the original problem and propose an iterative algorithm to obtain the optimal solution. A comprehensive analysis of the algorithm’s convergence is provided. Importantly, this approach exhibits excellent extensibility, making it readily applicable to address a broader class of Max-min optimization problems. Finally, numerical and prototype experiments are conducted to validate the effectiveness of the framework. With the proposed beam allocation framework and algorithm, we clarify that several crucial redistribution functionalities of RISs, such as explicit beam-splitting, fair beam allocation, and wide-beam generation, can be effectively implemented. These explicit beamforming functionalities have not been thoroughly examined previously.

Human activity recognition (HAR) with wearables is one of the serviceable technologies in ubiquitous and mobile computing applications. The sliding-window scheme is widely adopted while suffering from the multi-class windows problem. As a result, there is a growing focus on joint segmentation and recognition with deep-learning methods, aiming at simultaneously dealing with HAR and time-series segmentation issues. However, obtaining the full activity annotations of wearable data sequences is resource-intensive or time-consuming, while unsupervised methods yield poor performance. To address these challenges, we propose a novel method for joint activity segmentation and recognition with timestamp supervision, in which only a single annotated sample is needed in each activity segment. However, the limited information of sparse annotations exacerbates the gap between recognition and segmentation tasks, leading to sub-optimal model performance. Therefore, the prototypes are estimated by class-activation maps to form a sample-to-prototype contrast module for well-structured embeddings. Moreover, with the optimal transport theory, our approach generates the sample-level pseudo-labels that take advantage of unlabeled data between timestamp annotations for further performance improvement. Comprehensive experiments on four public HAR datasets demonstrate that our model trained with timestamp supervision is superior to the state-of-the-art weakly-supervised methods and achieves comparable performance to the fully-supervised approaches.

Traditional knowledge-based situation awareness (SA) modes struggle to adapt to the escalating complexity of today’s Energy Internet of Things (EIoT), necessitating a pivotal paradigm shift. In response, this work introduces a pioneering data-driven SA framework, termed digital twin-based SA (DT-SA), aiming to bridge existing gaps between data and demands, and further to enhance SA capabilities within the complex EIoT landscape. First, we redefine the concept of digital twin (DT) within the EIoT context, aligning it with data-intensive scientific discovery paradigm (the Fourth Paradigm) so as to waken EIoT’s sleeping data; this contextual redefinition lays the cornerstone of our DT-SA framework for EIoT. Then, the framework is comprehensively explored through its four fundamental steps: digitalization, simulation, informatization, and intellectualization. These steps initiate a virtual ecosystem conducive to a continuously self-adaptive, self-learning, and self-evolving big model (BM), further contributing to the evolution and effectiveness of DT-SA in engineering. Our framework is characterized by the incorporation of system theory and Fourth Paradigm as guiding ideologies, DT as data engine, and BM as intelligence engine. This unique combination forms the backbone of our approach. This work extends beyond engineering, stepping into the domain of data science—DT-SA not only enhances management practices for EIoT users/operators, but also propels advancements in pattern analysis and machine intelligence (PAMI) within the intricate fabric of a complex system. Numerous real-world cases validate our DT-SA framework.

Physically accurate and mathematically tractable models are presented to characterize the scattering and reflection properties of reconfigurable intelligent surfaces (RISs). We describe a single metallic patch and patch array, as well as their interactions with multiple incident electromagnetic (EM) waves using continuous and discrete strategies. Our models take into account the effect of the incident and scattered angles, polarization features, and the topology and geometry of RISs. In particular, we propose a simple system of linear equations to characterize the multiple-input multiple-output (MIMO) behaviors of RISs under appropriate assumptions. This model can be used as a fundamental tool for analyzing and optimizing the performance of RIS-aided systems in the far-field regime. Using the proposed models, we identify the advantages and limitations of three typical configurations. An important discovery is that the popular phase compensation designs cannot provide complicated beam reshaping functionality. A possible solution is the simultaneous configurations of collecting area and phase shifting. Numerical simulations validate the effectiveness of the proposed configuration schemes.

Distributed Linearly Separable Computation problem under the cyclic assignment is studied in this paper. It is a problem widely existing in cooperated distributed gradient coding, real-time rendering, linear transformers, etc. In a distributed computing system, a master asks N distributed workers to compute a linearly separable function from K datasets. The task function can be expressed as Kclinear combinations of K messages, where each message is the output of one individual function of one dataset. Straggler effect is also considered, such that from the answers of each Nrworker, the master should recover the task. The computation cost is defined as the number of datasets assigned to each worker, while the communication cost is defined as the number of (coded) messages which should be received. The objective is to characterize the optimal tradeoff between the computation and communication costs. Various distributed computing scheme were proposed in the literature with a well-known cyclic data assignment, but the (order) optimality of this problem remains open, even under the cyclic assignment. This paper proposes a new computing scheme with the cyclic assignment based on interference alignment, which is near optimal under the cyclic assignment.

Rapid growth of diversity, uncertainty, and coupling effect of units in modern energy systems jointly challenges the traditional model-based situation awareness (SA) in Energy Internet of Things (EIoT). This work explores the digital twin of EIoT (EIoT-DT) and then provides a novel data-driven SA paradigm, named DT-SA, as a promising alternative. Based on the combination of the latest data technologies and machine learning algorithms, DT-SA transfers those stubborn SA challenges to digital space, and then addresses them by building a domain-specific and data-friendly digital twin (DT) model upon massive data. The established model can be quantitatively tested via iterative virtual–real interaction and, thus, be evaluated and updated through closed-loop feedback to improve its performance in the physical world. To this end, some engineering and scientific problems are raised: 1) virtual–real interaction mechanism relevant to resource flow and data flow; 2) unified modeling and analysis of heterogeneous spatial–temporal data; 3) DT configuration and evolution; and 4) domain-specific DT-SA characterization. To solve these problems, cloud-edge-terminal configuration, big data analytics (BDA), DT, and SA indicator systems are studied, respectively. Then, the random matrix theory (RMT) and overarching DT-SA framework are designed as a roadmap. Besides, some potential applications and undergoing projects on the terminal, edge, or cloud are discussed, e.g., condition assessment of equipment, digital monitoring and diagnosis of the power grid network, and EIoT construction in the smart city. Finally, some perspectives and recommendations are proposed in conclusion for future research. This research can be regarded as an efficient handbook for both energy engineering and data science, which may benefit enterprise digitization, smart city, etc.

With the development of industrial and sensing technology, sensor-based activity recognition has become a promising technology for informatics applications. However, in a typical activity recognition procedure, sensory data segmentation, usually considered a preprocess with sliding windows, rarely has been investigated and significantly affected the recognition performance. In this article, we propose a novel deep-learning method to jointly segment and recognize activities with wearable sensors. Our contributions are three-fold: First, we introduce a multistage temporal convolutional network for sample-level activity prediction to overcome the multiclass windows problem. Second, for alleviating oversegmentation errors, our model forms a multitask learning framework with a boundary prediction module to adjust the entire model’s gradients. Third, we innovatively propose a boundary consistency loss to enforce the consistency of the activity and boundary prediction. Our method shows impressive performance on three public datasets, especially achieving 16% improvement over very recently advanced competing methods with class-average F1-score on the Hospital dataset. The code of this work will be open source onhttps://github.com/xspc/Segmentation-Sensor-based-HAR.

We consider the cache-aided multiple-input single-output (MISO) broadcast channel, which consists of a server with$L$antennas and$K$single-antenna users, where the server contains$N$files of equal length and each user is equipped with a local cache of size$M$files. Each user requests an arbitrary file from library. The objective is to design a coded caching scheme based on uncoded placement and one-shot linear delivery, to achieve the maximum sum Degree-of-Freedom (sum-DoF) with low subpacketization. It was shown in the literature that under the constraint of uncoded placement and one-shot linear delivery, the maximum sum-DoF is$\min \left\{{L+\frac {KM}{N},K}\right\}$. However, previously proposed schemes for this setting incurred either an exponential subpacketization order in$K$, or required specific conditions in the system parameters$L$,$K$,$M$and$N$. In this paper, we propose a new combinatorial structure called multiple-antenna placement delivery array (MAPDA). Based on MAPDA and Latin square, the first proposed scheme achieves the maximum sum-DoF$\min \left\{{L+\frac {KM}{N},K}\right\}$with the subpacketization of$K$when$\frac {KM}{N}+L=K$. Subsequently, for the general case we propose a transformation approach to construct an MAPDA from any$g$-regular PDA (a class of placement delivery arrays for the shared link caching problem where each integer in the array occurs$g$times). When$g$-regular PDA corresponds to the Maddah-Ali and Niesen scheme, the resulting MAPDA yields the maximum sum-DoF$\min \left\{{L+\frac {KM}{N},K}\right\}$with reduced subpacketization compared to the existing schemes. The general scheme can be extended to the multiple independent single-antenna transmitters (servers) corresponding to the cache-aided interference channel proposed by Naderializadeh et al. and the scenario of transmitters equipped with multiple antennas.

As a fundamental yet challenging problem in intelligent transportation systems, point cloud registration attracts vast attention and has been attained with various deep learning-based algorithms. The unsupervised registration algorithms take advantage of deep neural network-enabled novel representation learning while requiring no human annotations, making them applicable to industrial applications. However, unsupervised methods mainly depend on global descriptors, which ignore the high-level representations of local geometries. In this paper, we propose to jointly use both global and local descriptors to register point clouds in a self-supervised manner, which is motivated by a critical observation that all local geometries of point clouds are transformed consistently under the same transformation. Therefore, local geometries can be employed to enhance the representation ability of the feature extraction module. Moreover, the proposed local descriptor is flexible and can be integrated into most existing registration methods and improve their performance. Besides, we also utilize point cloud reconstruction and normal estimation to enhance the transformation awareness of global and local descriptors. Lastly, extensive experimental results on one synthetic and three real-world datasets demonstrate that our method outperforms existing state-of-art unsupervised registration methods and even surpasses supervised ones in some cases. Robustness and computational efficiency evaluations also indicate that the proposed method applies to intelligent vehicles.

Semi-supervised learning acts as an effective way to leverage massive unlabeled data. In this paper, we propose a novel training strategy, termed asSemi-supervised Contrastive Learning (SsCL), which combines the well-known contrastive loss in self-supervised learning with the cross entropy loss in semi-supervised learning, and jointly optimizes the two objectives in an end-to-end way. The highlight is that different from self-training based semi-supervised learning that conducts prediction and retraining over the same model weights, SsCL interchanges the predictions over the unlabeled data between the two branches, and thus formulates a co-calibration procedure, which we find is beneficial for better prediction and avoids being trapped in local minimum. Towards this goal, the contrastive loss branch models pairwise similarities among samples, using the pseudo labels generated from the cross entropy branch, and in turn calibrates the prediction distribution of the cross entropy branch with the contrastive similarity. We show that SsCL produces more discriminative representation and is beneficial to semi-supervised learning. Notably, on ImageNet with ResNet50 as the backbone, SsCL achieves$\bm {60.2\%}$and$\bm {72.1\%}$top-1 accuracy with 1% and 10% labeled samples respectively, which significantly outperforms the baseline, and is better than previous semi-supervised and self-supervised methods.

How to achieve better results with fewer labeling costs remains a challenging task. In this paper, we present a new active learning framework, which for the first time incorporates contrastive learning into recently proposed one-bit supervision. Here one-bit supervision denotes a simple Yes or No query about the correctness of the model's prediction, and is more efficient than previous active learning methods requiring assigning accurate labels to the queried samples. We claim that such one-bit information is intrinsically in accordance with the goal of contrastive loss that pulls positive pairs together and pushes negative samples away. Towards this goal, we design an uncertainty metric to actively select samples for query. These samples are then fed into different branches according to the queried results. The Yes query is treated as positive pairs of the queried category for contrastive pulling, while the No query is treated as hard negative pairs for contrastive repelling. Additionally, we design a negative loss that penalizes the negative samples away from the incorrect predicted class, which can be treated as optimizing hard negatives for the corresponding category. Our method, termed as ObCP, produces a more powerful active learning framework, and experiments on several benchmarks demonstrate its superiority.

Human activity recognition (HAR) with wearables is promising research that can be widely adopted in many smart healthcare applications. In recent years, the deep learning-based HAR models have achieved impressive recognition performance. However, most HAR algorithms are susceptible to the multi-class windows problem that is essential yet rarely exploited. In this paper, we propose to relieve this challenging problem by introducing the segmentation technology into HAR, yielding joint activity segmentation and recognition. Especially, we introduce the Multi-Stage Temporal Convolutional Network (MS-TCN) architecture for sample-level activity prediction to joint segment and recognize the activity sequence. Furthermore, to enhance the robustness of HAR against the inter-class similarity and intra-class heterogeneity, a multi-level contrastive loss, containing the sample-level and segment-level contrast, has been proposed to learn a well-structured embedding space for better activity segmentation and recognition performance. Finally, with comprehensive experiments, we verify the effectiveness of the proposed method on two public HAR datasets, achieving significant improvements in the various evaluation metrics.

Modern deep neural networks (DNNs) are extremely powerful; however, this comes at the price of increased depth and having more parameters per layer, making their training and inference more computationally challenging. In an attempt to address this key limitation, efforts have been devoted to the compression (e.g., sparsification and/or quantization) of these large-scale machine learning models, so that they can be deployed on low-power IoT devices.In this paper, building upon recent research advances in the neural tangent kernel (NTK) and random matrix theory, we provide a novel compression approach to wide and fully-connected \emph{deep} neural nets. Specifically, we demonstrate that in the high-dimensional regime where the number of data points $n$ and their dimension $p$ are both large, and under a Gaussian mixture model for the data, there exists \emph{asymptotic spectral equivalence} between the NTK matrices for a large family of DNN models. This theoretical result enables ''lossless'' compression of a given DNN to be performed, in the sense that the compressed network yields asymptotically the same NTK as the original (dense and unquantized) network, with its weights and activations taking values \emph{only} in $\{ 0, \pm 1 \}$ up to scaling. Experiments on both synthetic and real-world data are conducted to support the advantages of the proposed compression scheme, with code available at https://github.com/Model-Compression/Lossless_Compression.

Recent years have witnessed the successful development of health-related Internet-of-Things (IoT) devices, i.e., electroencephalographic (EEG), paving a ground-breaking way for better understanding the functionals in our brains. Despite massive research on EEG, there lacks an effective way to interpret complex EEG signals due to the shortage of informative EEG data, the challenge in capturing sophisticated connectivity patterns of EEG signals, and unfavorable results of the inherent noise associated with data collection. In this article, novel heterogeneous-domain deep learning is proposed to address these issues. Especially, we first propose a new scheme to extract the multilevel latent features using hybrid networks and provide two pathways for reconstructing the EEG signals. As a core part of the proposed method, the scheme considers the complex dependencies among adjacent EEG channels, spatiotemporal connectivity, and signal denoising. In addition, a novel consistency regularization method is proposed to enhance information sharing among the multilevel latent feature obtained from the labeled and unlabeled EEG samples, which is beneficial for both the information transfer and accelerating the training. Finally, we provide comprehensive case studies on the Lomonosov Moscow State University EEG data set, demonstrating that the proposed methods achieve superior performance than all the competing ones over a wide range of experimental settings.

Together with the rapid development of the Internet of Things, human activity recognition (HAR) using wearable inertial measurement units (IMUs) becomes a promising technology for many research areas. Recently, deep-learning-based methods pave a new way of understanding and performing analysis of the complex data in the HAR system. However, the performance of these methods is mostly based on the quality and quantity of the collected data. In this article, we innovatively propose to build a large data set based on virtual IMUs and then address technical issues by introducing a multiple-domain deep learning framework consisting of three technical parts. In the first part, we propose to learn the single-frame human activity from the noisy IMU data with hybrid convolutional neural networks in the semisupervised form. For the second part, the extracted data features are fused according to the principle of uncertainty-aware consistency, which reduces the uncertainty by weighting the importance of the features. The transfer learning is performed in the last part based on the newly released archive of motion capture as surface shapes data set, containing abundant synthetic human poses, which enhances the variety and diversity of the training data set and is beneficial for the process of training and feature transfer in the proposed method. The efficiency and effectiveness of the proposed method have been demonstrated in the real deep inertial poser data set. The experimental results show that the proposed methods can surprisingly converge within a few iterations and outperform all competing methods.

Synchronized measurements of a large power grid enable an unprecedented opportunity to study the spatial-temporal correlations. Statistical analytics for those massive datasets start with high-dimensional data matrices. Uncertainty is ubiquitous in a future's power grid. These data matrices are recognized as random matrices. This new point of view is fundamental in our theoretical analysis since true covariance matrices cannot be estimated accurately in a high-dimensional regime. As an alternative, we consider large-dimensional sample covariance matrices in the asymptotic regime to replace the true covariance matrices. The self-adjoint polynomials of large-dimensional random matrices are studied as statistics for big data analytics. The calculation of the asymptotic spectrum distribution (ASD) for such a matrix polynomial is understandably challenging. This task is made possible by a recent breakthrough in free probability, an active research branch in random matrix theory. This is the very reason why the work of this paper is inspired initially. The new approach is interesting in many aspects. The mathematical reason may be most critical. The real-world problems can be solved using this approach, however.

Remaining useful life (RUL) prediction is of fundamental importance in reliability analysis and health diagnosis of complex industrial systems. Aiming at improving the prediction accuracy, this article proposes a normalizing flow embedded sequence-to-sequence (seq2seq) learning method to predict the RUL of an asset or a system. This method introduces a block of normalizing flow into the middle area of the familiar encoder–decoder structure of the seq2seq model. This normalizing flow enjoys the remarkable representation ability for the nonlinearity between input sequential data and outputs and enables the original seq2seq model to be more suitable for vibration signals of engines. The encoder and the decoder, which fall before and after the normalizing flow, are both built by gated recurrent units. Besides, a one-hot coding of clustering is concatenated with measurement data to indicate the frequently shifting vibration state, and a sensor selection method is designed to drop some weakly related and ineffective variables. Our method is tested and further analyzed by 2008 IEEE PHM challenge data (PHM08), of which many practical preprocessing methods are conducted. Numerous tests verify that our method outperforms other related deep learning methods for RUL estimation.

Recently, the robustification of principal component analysis (PCA) has attracted much research attention in numerous areas of science and engineering. The most popular and successful approach is to model the robust PCA problem as a low-rank matrix recovery problem in the presence of sparse corruption. With this model, the nuclear norm and 11-norm penalties are usually used for low-rank and sparsity promotion. Although the nuclear norm and 11-norm are favorable due to their convexity, they have a bias problem. In comparison, nonconvex penalties can be expected to yield better recovery performance. In this paper, we consider a formulation for robust PCA using generalized nonconvex penalties for low-rank and sparsity inducing. This nonconvex formulation is efficiently solved by a multi-block alternative direction method of multipliers (ADMM) algorithm. A sufficient condition for the convergence of this new ADMM algorithm has been derived. Furthermore, to address the important issue of nonconvex penalty selection, we have evaluated the new algorithm via numerical experiments in various low-rank and sparsity conditions. The results indicate that, “exact” recovery of the low-rank principle component can be achieved only by nonconvex regularization. MATLAB code is available at https://github.com/FWen/RPCA.git.

Recently, multi-user multiple input multiple output (MU-MIMO) systems with low-resolution digital-to-analog converters (DACs) has received considerable attention, owing to the capability of dramatically reducing the hardware cost. Besides, it has been shown that the use of low-resolution DACs enable great reduction in power consumption while maintain the performance loss within acceptable margin, under the assumption of perfect knowledge of channel state information (CSI). In this paper, we investigate the precoding problem for the coarsely quantized MU-MIMO system without such an assumption. The channel uncertainties are modeled to be a random matrix with finite second-order statistics. By leveraging a favorable relation between the multi-bit DACs outputs and the single-bit ones, we first reformulate the original complex precoding problem into a nonconvex binary optimization problem. Then, using the S-procedure lemma, the nonconvex problem is recast into a tractable formulation with convex constraints and finally solved by the semidefinite relaxation (SDR) method. Compared with existing representative methods, the proposed precoder is robust to various channel uncertainties and is able to support a MU-MIMO system with higher-order modulations, e.g., 16QAM.

The power consumption of digital-to-analog converters (DACs) constitutes a significant proportion of the total power consumption in a massive multiuser multiple-input multiple-output (MU-MIMO) base station (BS). Using 1-bit DACs can significantly reduce the power consumption. This paper addresses the precoding problem for the massive narrow-band MU-MIMO downlink system equipped with 1-bit DACs at each BS. In such a system, the precoding problem plays a central role as the precoded symbols are affected by extra distortion introduced by 1-bit DACs. In this paper, we develop a highly efficient nonlinear precoding algorithm based on the alternative direction method framework. Unlike the classic algorithms, such as the semidefinite relaxation (SDR) and squared-infinity norm Douglas-Rachford splitting (SQUID) algorithms, which solve convex relaxed versions of the original precoding problem, the new algorithm solves the original nonconvex problem directly. The new algorithm is guaranteed to globally converge under some mild conditions. A sufficient condition for its convergence has been derived. The experimental results in various conditions demonstrated that the new algorithm can achieve the state-of-the-art performance comparable with the SDR algorithm while being much more efficient (e.g., more than 300 times faster than the SDR algorithm).

Analogous deployment of phase measurement units (PMUs), the increase of data quantum and deregulation of energy market, all call for robust state evaluation in large scale power systems. Implementing model based estimators is impracticable as the complexity scale of solving the high dimension power flow equations. In this paper, we first represent massive streaming PMU data as big random matrix flow. Motivated by exploiting the variations in the covariance matrix of the massive streaming PMU data, a novel power state evaluation algorithm is then developed based on the multiple high dimensional covariance matrix test. The proposed test statistic is nonparametric without assuming a specific parameter distribution for the PMU data and of a wide range of data dimensions and sample size. Besides, it can jointly reveal the relative magnitude, duration and location of an system event. For the sake of practical application, we reduce the computation of the proposed test statistic from O(εng4) to O(ηng2) by principal component calculation and redundant computation elimination. The novel algorithm is numerically evaluated utilizing the IEEE 30-, 118-bus system and a Polish 2383-bus system and a real 34-PMU system. The case studies illustrate and verify the superiority of proposed state evaluation indicator.

This work considers the robust sparse recovery problem in compressive sensing (CS) in the presence of impulsive measurement noise. We propose a robust formulation for sparse recovery using the generalized lp-norm with 0 < p < 2 as the metric for the residual error under l1-norm regularization. An alternative direction method (ADM) has been proposed to solve this formulation efficiently. Moreover, a smoothing strategy has been used to derive a convergent method for the nonconvex case of p < 1. The convergence conditions of the proposed algorithm for both the convex and nonconvex cases have been provided. Numerical simulations demonstrated that the new algorithm can achieve state-of-the-art robust performance in highly impulsive noise.

Recent works on the universality of Empirical Spectral Density (E.S.D) of Random Matrices have provided a framework to study the asymptotic behavior of random network data. In this paper, the behavior of a wireless campus network with low received base-station transmit power is investigated. Mobile users are organized into 10 clusters of 25 mobile users each and wireless IMT-Advanced Macro channel standard is used for the channel model. The network is simulated using Optimized Network Engineering Tools (OPNET) Modeler 17.5 platform and packet drop data is collected to form the entries of a non-hermittian random matrix. Data is collected for a duration of 400s during downlink transmission and analyzed for three scenarios namely: static mobile users with shadowing vs no-shadowing, and network interference from a stealth jamming source. The result shows that for static nodes with shadowing and no-shadowing, the spectral density of packet drop covariance matrix follows Marcenko and Pastur (MP) distribution and the Ring law accurately, but deviates considerably from MP distribution when the network is jammed using a pulsed jamming source. Outliers are also observed in the Ring-Law together with a shrinkage of eigenvalue distribution towards the center of the inner-circle of the Ring-Law when jammers are introduced into the network.

This letter presents an experimental demonstration of software-defined-radio-based wireless tomography using computer-hosted radio devices called Universal Software Radio Peripheral (USRP). This experimental brief follows our vision and previous theoretical study of wireless tomography that combines wireless communication and RF tomography to provide a novel approach to remote sensing. Automatic data acquisition is performed inside an RF anechoic chamber. Semidefinite relaxation is used for phase retrieval, and the Born iterative method is utilized for imaging the target. Experimental results are presented, validating our vision of wireless tomography.

This letter presents a novel single-step approach for self-coherent tomography using semidefinite relaxation. Phase retrieval for scattered fields is not required. The general solver can be used to solve the corresponding convex optimization problem and image the target. Both man-made and experimental data is exploited to demonstrate the performance of the proposed approach. The imaging results illustrate the benefit of bringing the state-of-the-art mathematics to inverse scattering or diffraction tomography.

This paper, one in a continuing series, describes a new initiative in wireless tomography. Our goal is to combine two technologies: wireless communication and radio frequency tomography, for the close-in remote sensing. The hybrid system, including wireless communication devices for wireless tomography is proposed in this paper. Noise reduction, modified standard phase reconstruction, and imaging are exploited sequentially to perform wireless tomography in noisy environments. The performance given in this paper illustrates the significance and prospect of wireless tomography. The contributions of this paper are threefold: 1) the hybrid system provides a strong and flexible infrastructure for wireless tomography; 2) machine learning, especially nonlinear dimensionality reduction, is explored to execute noise reduction and combat the nonlinear noise effect; and 3) modified standard phase reconstruction is well achieved using the de-noised amplitude-only total fields from the simple sensors and the received accurate full-data total fields from the advanced sensors. Experimental data provided by the Institute Fresnel in Marseille, France are used to demonstrate the concept of wireless tomography and validate the corresponding algorithms.

A key feature of cognitive radio network is the intelligence of secondary users who can collaborate to improve the system performance. The collaboration in terms of channel recommendation is studied in this paper. The recommendation mechanism results in dynamics of the channel preferences of secondary users, thus causing a behavior propagation in a social network. For cognitive radio networks having a grid topology, the ergodicity of the dynamics is studied using the model of interacting particles in nonequilibrium statistical mechanics. For networks having a grid topology or being randomly deployed, mean field descriptions using ordinary differential equation are used to explicitly describe the dynamics of behavior propagation. The analytic results are demonstrated by numerical simulations.

Using signal feature as the prior knowledge can improve spectrum sensing performance. In this paper, we consider signal feature as the leading eigenvector (rank-1 information) extracted from received signal's sample covariance matrix. Via real-world data and hardware experiments, we are able to demonstrate that such a feature can be learned blindly and it can be used to improve spectrum sensing performance. We derive several generalized likelihood ratio test (GLRT) based algorithms considering signal feature as the prior knowledge under rank-1 assumption. The performances of the new algorithms are compared with other state-of-the-art covariance matrix based spectrum sensing algorithms via Monte Carlo simulations. Both synthesized rank-1 signal and real-world digital TV (DTV) data are used in the simulations. In general, our GLRT-based algorithms have better detection performances, and the algorithms using signal feature as the prior knowledge have better performances than the algorithms without any prior knowledge.

The communication for the control of distributed energy generation (DEG) in microgrid is discussed. Due to the requirement of realtime transmission, weak or no explicit channel coding is used for the message of system state. To protect the reliability of the uncoded or weakly encoded messages, the system dynamics are considered as a 'nature encoding' similar to convolution code, due to its redundancy in time. For systems with or without explicit channel coding, two decoding procedures based on Kalman filtering and Pearl's Belief Propagation, in a similar manner to Turbo processing in traditional data communication systems, are proposed. Numerical simulations have demonstrated the validity of the schemes, using a linear model of electric generator dynamic system.

There are many key problems of decision making related to spectrum occupancies in cognitive radio networks. It is known that there exist correlations of spectrum occupancies in time, space and frequency, which facilitates the decision making problems. A uniform framework, utilizing graphical models and tools, is proposed to integrate the spectrum correlations and decision making problems. Bayesian networks are used to model the probabilistic dependencies of spectrum occupancies. The statistical inference over the Bayesian network is carried out for spectrum sensing. Influence diagrams are used for cross-layer decision makings by integrating the inference result of the Bayesian network model of spectrum and the QoS requirement from upper layers. A simulated scenario of primary user network is used to generate the spectrum activities, based on which the proposed graphical framework is demonstrated to improve the performance of cognitive radio networks.

In smart grid, a home appliance can adjust its power consumption level according to the realtime power price obtained from communication channels. Most studies on smart grid do not consider the cost of communications which cannot be ignored in many situations. Therefore, the total cost in smart grid should be jointly optimized with the communication cost. In this paper, a probabilistic mechanism of locational margin price (LMP) is applied and a model for the stochastic evolution of the underlying load which determines the power price is proposed. Based on this framework of power price, the problem of determining when to inquire the power price is formulated as a Markov decision process and the corresponding elements, namely the action space, system state and reward function, are defined. Dynamic programming is then applied to obtain the optimal strategy. A simpler myopic approach is proposed by comparing the cost of communications and the penalty incurred by using the old value of power price. Numerical results show the significant performance gain of the optimal strategy of price inquiry, as well as the near-optimality of the myopic approach.

Sampling is the bottleneck for ultra-wideband (UWB) communication. Our major contribution is to exploit the channel itself as part of compressed sensing, through waveform-based pre-coding at the transmitter. We also have demonstrated a UWB system baseband bandwidth (5 GHz) that would, if with the conventional sampling technology, take decades for the industry to reach. The concept has been demonstrated, through simulations, using real-world measurements. Realistic channel estimation is also considered.

This paper investigates a multiple input single output (MISO) time reversal system for ultra-wideband (UWB) communication over inter-symbol interference (ISI) channels. Time reversal takes advantage of rich scattering environments to achieve signal focusing, which enables the use of simple receiver structures without sacrificing performance. On-off-keying (OOK) modulation and energy detection (square law) are considered for the purpose of low complexity at the receiver. This research is motivated by the need for high-data-rate wireless network with simple receive nodes. The discrete channel models and bit error rate (BER) formulas for the energy detector receiver over ISI channels are derived. Performance is evaluated based on measured data, considering practical signal waveforms. One reason to use our own measured data is that there is no proper UWB channel model for antenna array related study. Numerical results suggest that the proposed MISO time reversal system with extremely simple receiver is promising to support high data rate in severe multipath environments for robust communication in the UWB band.

This paper presents a reduced-complexity time reversal technique for ultra-wideband (UWB) communications. Time reversal takes advantage of rich scattering environments to achieve signal focusing via transmitter-side processing, which enables the use of simple receivers. The goal of this paper is to demonstrate a UWB time reversal system architecture based on experimental results and practical pulse waveform, taking into account some practical constraints, and to show feasibility of UWB time reversal. Pre-decorrelating in addition to time reversal processing is considered for a downlink multiuser configuration. Multiple transmit antennas are employed to improve the performance.

This paper first introduces per-path pulse distortion in multiuser detection for ultra-wideband communications. A new generalized RAKE structure that estimates and compensates for the pulse distortion is used. An finite-impulse response filter representation of the per-path impulse response is used and estimated. The new structure greatly improves the system performance. With four users considered in our simulations in a high-rise building environment, it is found that the average performance of the generalized RAKE using minimum mean-square error (MMSE) detection is improved over the conventional RAKE by 1.8 dB. Both synchronous and asynchronous transmission schemes for decorrelating detector and MMSE detector are considered.

The autocorrelation demodulation (ACD) has potential for use in medium-range low-cost UWB communications, however, its weak performance is a bottle neck. To improve ACD's performance, it is proposed to combine the multiple-symbol detection with ACD to form a multiple-symbol-based ACD receiver. In contrast to ordinary ACD scheme that uses symbol-by-symbol decision, the proposed structure uses block-based multiple-symbol joint decision. Performance of the new scheme is evaluated analytically for arbitrary block sizes employing Gaussian approximation approach. The accuracy of the method is verified by computer simulation. The evaluation shows that the proposed scheme offers several dB improvement in performances with only a moderate increase in complexity.

This paper deals with the time domain characteristics of UWB multipath channel while considering UWB pulse shape distortion and its impact on system performance as well as optimal receiver design. It appears that no deterministic and analytical channel model in the time domain has ever appeared in the literature, to our best knowledge. A generalized channel model and some related exact solutions for some specific geometric configurations are discussed, based on the pulse propagation mechanisms of reflection and diffraction. In addition, a system performance framework integrated with the close-form channel model expressions is also provided, corresponding to the optimal receiver structure and the concrete geometric configurations of a UWB channel. This work is very important in the design of the optimal UWB systems

Ultra-wideband (UWB) communication is becoming an important technology for future wireless personal area networks (WPANs). A critical challenge in high data rate UWB system design is that a receiver usually needs tens of micro-seconds or even tens of milliseconds to synchronize with the transmitted signals, known as the timing acquisition problem. Such a long synchronization time will cause significant overhead, since the data rate of UWB systems is expected to be very high. To address the overhead problem, we previously proposed a general framework for MAC protocols in high data rate UWB networks. In this framework, a node can aggregate multiple upper-layer packets into a larger burst frame at the MAC layer. In this paper, we analyze the unsaturated throughput performance of a burst-frame-based MAC protocol within the framework. Numerical results from the analytical method give excellent agreement with the simulation results, indicating the accuracy of our analytical method.

A critical challenge in ultra-wideband (UWB) system design is that a receiver usually needs tens of microseconds or even tens of milliseconds to synchronize with transmitted signals; this is known as the timing acquisition problem. Such a long synchronization time causes significant overhead, since the data rate of UWB systems is expected to be very high. We address the timing acquisition problem at the medium access control (MAC) layer, and propose a general framework for medium access control in UWB systems; in this framework, a transmitting node can aggregate multiple upper-layer packets into a larger burst frame at the MAC layer. Furthermore, we design a MAC protocol based on the framework, and analyze its saturation throughput performance. Compared to sending each upper-layer packet individually, which is a typical situation in exiting MAC protocols, the proposed MAC can drastically reduce the synchronization overhead. Numerical and simulation results show that the proposed MAC can significantly improve the performance of UWB networks, in terms of both throughput and end-to-end delay.

The paper represents a major step toward receiver structures under the framework of the per-path pulse distortion using more realistic channel models. We extend our previous framework to include the important phenomenon of inter-symbol interference (ISI). ISI ultimately limits the maximum achievable data rate. We find that the per-path pulse distortion has impact on this maximum achievable data rate in the presence of ISI. As examples of this discovery, we investigate the optimum receiver structure and two sub-optimum receivers with zero-forcing and minimum mean square error (MMSE) equalizers. We use the high-rise building channel model as the underlying pulse propagation model. This model captures many properties that are not available in the IEEE 802.15.4a model.

Multiple-input multiple-output (MIMO) techniques are applied to ultrawideband (UWB) systems to achieve high-rate communications over indoor wireless channels. The receiver employs a zero-forcing (ZF) scheme to separate N parallel transmitted data streams for each resolvable multipath component. A RAKE is then applied to combine the ZF paths carrying information of the same symbol to form the decision variable. Analytical error rate expression of an (N,M,L) system (N transmit antennas, M receive antennas, and L paths combined) over a pragmatic indoor log-normal fading channel is derived, which captures the diversity order by a single degree-of-freedom parameter.

This paper analyzes the impact of receive fading correlation on the error performance of a multiple-input multiple-output (MIMO) system that employs a zero-forcing detection scheme over frequency-nonselective Rayleigh fading channels. Error rate expressions as a function of the eigenvalues of the fading correlation matrix and the number of transmit and receive antennas are derived. Numerical results indicate that MIMO systems are resistant to receive fading correlation.

This paper extends our previous work mainly in three aspects. Our main efforts are made analytically although a few numerical results are used for special cases to illustrate the concepts. Our interest lies in understanding basic mechanisms and their impact on system design. First we derive some new exact and approximate formulations suitable for pulse shape and system performance analysis. Second, first time we have obtained the numerical results of the system parameters that are impacted by the mechanisms of interest. Some new physical mechanisms are unearthed based on our analytical relationships. Third, we derive the optimal receiver template for the generalized multipath model.

This paper has established a new theoretical framework that allows us to analyze the ultra-wideband system performance using the closed form formulations. A new generalized time-domain multipath channel based on the geometric theory of diffraction/uniform theory of diffraction (GTD/UTD) framework is incorporated into the system bit-error rate and signal-to-noise ratio by modifying the existing system model. Closed form expressions for important geometric configurations are derived in the time domain for the first time. Some interesting insights into the physical mechanisms and system analysis are observed through explicit expressions and numerical results.

The paper addresses a crucial point in ultra-wideband (UWB) radio wave propagation, which is the spatial-temporal resolution of scattering objects into multiple frequency-dependent scattering centers. The effect contributes to the widely observed temporal dispersion of pulse-shaped transmit signals and their distortion, respectively. Particularly the latter is explained by (multiple) diffraction of the incident wave, leading to (multiple) band-limited impulse responses with characteristic frequency content, which in turn causes signal distortion and a degradation of the signal-to-noise ratio in a correlation receiver. We presented a new approach on UWB propagation modeling and optimum design of correlation receivers.

The location dependent effect of the acoustic field received by a linear array is modeled as an extra "loss" factor in the complex spectral variable. A high-resolution algorithm combining the singular value decomposition method and the eigen-matrix pencil method is then employed to find the complex directions representing the incoming directions and the location dependent factors of multipath and multimode arrivals. Five key features (namely, noise immunity, robustness, resolution, accuracy, and physical insight) of the proposed algorithm are studied using numerical examples.

Multiple ray paths are resolved using high-resolution digital signal processing algorithms. The conventional complex channel model for wireless indoor propagation is extended to include the frequency dependence of individual rays which can be used to classify the ray arrivals and provide physical insight of the channel. This approach may be crucial to broadband CDMA systems.