Sparse matrix-vector multiplication (SpMV) is a crucial operation in scientific computing, graph analytics, and machine/deep learning. Its performance is highly sensitive to matrix sparsity patterns, necessitating tailored program designs. This paper introduces SparseZETA, an intelligent auto-tuner that generates high-performance, machine-designed SpMV programs by directly mimicking and composing human-expert actions. To efficiently navigate the vast design space, SparseZETA reformulates auto-tuning as a behavior-cloning problem: rather than costly exploration, it directly synthesizes programs by sequentially predicting actions in a one-pass decision-making process, guided by the real-time state of the evolving, partially constructed program designs. A novel self-training mechanism further accelerates the collection of training data for the prediction models. On NVIDIA A100 (and RTX 2080 Ti) GPUs, SparseZETA achieves average speedups of 1.27×–15.66× (1.44×–19.07×) over existing auto-tuners, human-designed programs, and a sparse compiler. SparseZETA substantially reduces the human effort required to design SpMV programs, including sparse format creation and kernel implementation, cutting the design time from days or even months to an average of 82.52ms per matrix via lightweight inference on only one CPU.

Integrated sensing and communications (ISAC) is considered an innovative technology in sixth-generation (6G) wireless networks, where utilizing orthogonal frequency division multiplexing (OFDM) communication signals for sensing provides a cost-effective solution for implementing ISAC. However, the sensing performance of matched and mismatched filtering schemes can be significantly deteriorated due to the signaling randomness induced by finite-alphabet modulations with non-constant modulus, such as quadrature amplitude modulation (QAM) constellations. Therefore, improving sensing performance without significantly compromising communication capability (i.e., maintaining randomness), remains a challenging task. To that end, we propose a unified probabilistic constellation shaping (PCS) framework that is compatible with both matched and mismatched filtering schemes, by maximizing the communication rate while imposing constraints on mean square error (MSE) of sensing channel state information (CSI), power, and probability distribution. Specifically, the MSE of sensing CSI is leveraged to optimize sensing capability, which is illustrated to be a more comprehensive metric compared to the output SNR after filtering (SNRout) and integrated sidelobes ratio (ISLR). Additionally, the internal relationships among these three sensing metrics are explicitly analyzed. Building upon this, we further reveal that the normalized MSE can be interpreted as a penalty function version of the dynamic range, which is usually exploited to evaluate the behavior of delay-Doppler profiles. Finally, both simulations and field measurements validate the efficiency of proposed PCS approach in achieving a flexible S&C trade-off, as well as its credibility in enhancing 6G wireless transmission in real-world scenarios.

Sparse matrix-vector semiring computation is a key operation in sparse matrix computations, with performance strongly dependent on both program design and the features of the sparse matrices. Given the diversity of sparse matrices, designing a tailored program for each matrix is challenging. To address this, we propose SRSparse, 1 a program generator that creates tailored programs by automatically combining program designing methods to fit specific input matrices. It provides two components: the problem definition configuration , which declares the computation, and the scheduling language , which can be leveraged by an auto-tuner to specify the program designs. The two are lowered to the intermediate representations of SRSparse, the Format IR and Kernel IR , which respectively generate format conversion routine and kernel code. We evaluate SRSparse on four representative sparse kernels and three format conversion routines. For sparse kernels, SRSparse achieves median speedups over handwritten programs: COO (3.50×), CSR-Adaptive (5.36×), CSR5 (2.06×), ELL (1.63×), Gunrock (1.57×), and GraphBLAST (1.96×); over an auto-tuner: AlphaSparse (1.16×); and over a compiler: TACO (1.71×). For format conversion routines, SRSparse achieves median speedups over handwritten implementations: Intel MKL (7.60×), SPARSKIT (2.61×), CUSP (2.77×), and Ginkgo (1.74×); and over a compiler: TACO (4.04×).

The emergence of the fifth-generation (5G) New Radio (NR) technology has provided unprecedented opportunities for vehicle-to-everything (V2X) networks, enabling enhanced quality of services. However, high-mobility V2X networks require frequent handovers and acquiring accurate channel state information (CSI) necessitates the utilization of pilot signals, leading to increased overhead and reduced communication throughput. To address this challenge, integrated sensing and communications (ISAC) techniques have been employed at the base station (gNB) within vehicle-to-infrastructure (V2I) networks, aiming to minimize overhead and improve spectral efficiency. In this study, we propose novel frame structures that incorporate ISAC signals for three crucial stages in the NR-V2X system: initial access, connected mode, and beam failure and recovery. These new frame structures employ 75% fewer pilots and reduce reference signals by 43.24%, capitalizing on the sensing capability of ISAC signals. Through extensive link-level simulations, we demonstrate that our proposed approach enables faster beam establishment during initial access, higher throughput and more precise beam tracking in connected mode with reduced overhead, and expedited detection and recovery from beam failures. Furthermore, the numerical results obtained from our simulations showcase enhanced spectrum efficiency, improved communication performance and minimal overhead, validating the effectiveness of the proposed ISAC-based techniques in NR V2I networks.

In this paper, we shed light on the performance gain of integrated sensing and communications (ISAC) from the perspective of channel correlations between radar sensing and communication (S&C), namely ISAC subspace correlation. To begin with, we consider a multi-input multi-output (MIMO) ISAC system and reveal that the optimal ISAC signal is in the subspace spanned by the transmitted steering vectors of the sensing channel and the right singular matrix of the communication channel. By leveraging this result, we study a basic ISAC scenario with a single target and a single-antenna communication user, and derive the optimal waveform covariance matrix for minimizing the estimation error under a given communication rate constraint. To quantify the integration gain of ISAC systems, we define the subspace “correlation coefficient” to characterize the coupling effect between S&C channels. Finally, numerical results are provided to validate the effectiveness of the proposed approaches.

We investigate sensing-assisted beamforming for vehicle-to-infrastructure (V2I) communication by exploiting integrated sensing and communications (ISAC) functionalities at the roadside unit (RSU). The RSU deploys a massive multi-input-multi-output (mMIMO) array at mmWave. The pencil-sharp mMIMO beams and fine range-resolution implicate that the point-target assumption is impractical, as the vehicle’s geometry becomes essential. Therefore, the communication receiver (CR) may never lie in the beam, even when the vehicle is accurately tracked. To tackle this problem, we consider the extended target with two novel schemes. For the first scheme, the beamwidth is adjusted in real-time to cover the entire vehicle, followed by an extended Kalman filter to predict and track the position of CR according to resolved scatterers. An upgraded scheme is proposed by splitting each transmission block into two stages. The first stage is exploited for ISAC with a wide beam. Based on the sensed results at the first stage, the second stage is dedicated to communication with a pencil-sharp beam, yielding significant communication improvements. We reveal the inherent tradeoff between the two stages in terms of their durations, and develop an optimal allocation strategy that maximizes the average achievable rate. Finally, simulations verify the superiorities of proposed schemes over state-of-the-art methods.

A sensing-assisted predictive beamforming scheme for vehicle-to-infrastructure (V2I) communication is considered, which is built upon massive multi-input-multi-output (mMIMO) and millimeter wave (mmWave) techniques. In practical V2I networks, vehicles cannot be modeled as point targets in terms of the narrow beamwidth and high range resolution. Accordingly, the communication receiver (CR) may be beyond the beam even the vehicle is accurately tracked, which makes robust beam alignment and tracking challenging. We thus consider the extended target case, in which the beamwidth should be adjusted in real-time to cover the entire vehicle. Then an extended Kalman filtering (EKF) is presented to track the CR according to the resolved high-resolution geometry results. Finally, numerical results are provided to validate the effectiveness of the proposed approach.

Sparse Matrix-Vector multiplication (SpMV) is an essential computational kernel in many application scenarios. Tens of sparse matrix formats and implementations have been proposed to compress the memory storage and speed up SpMV performance. We develop AlphaSparse, a superset of all existing works that goes beyond the scope of human-designed format(s) and implementation(s). AlphaSparse automatically creates novel machine-designed formats and SpMV kernel implementations en-tirely from the knowledge of input sparsity patterns and hard-ware architectures. Based on our proposed Operator Graph that expresses the path of SpMV format and kernel design, AlphaS-parse consists of three main components: Designer, Format & Kernel Generator, and Search Engine. It takes an arbitrary sparse matrix as input while outputs the performance machine-designed format and SpMV implementation. By extensively evaluating 843 matrices from SuiteSparse Matrix Collection, AlphaSparse achieves significant performance improvement by 3.2 × on average compared to five state-of-the-art artificial formats and 1.5 × on average (up to 2.7×) over the up-to-date implementation of traditional auto-tuning philosophy.

Network traffic anomaly detection is an important research content in the field of network and security management. By analyzing network traffic, the health of the network environment can be intuitively evaluated. In particular, analyzing network traffic provides practical and effective guidance for identification and classification of anomaly. This paper proposes a network traffic anomaly detection method based on wavelet analysis for pcap files contain two different delay injections. The wavelet analysis can effectively extract information from the signal and is suitable for the detection of anomaly. Firstly, wavelet analysis is used to extract the waveform features, and then the support vector machine is used for classification. In particular, packet lengths in the pcap files is parsed out to form a sequence of packet lengths in chronological order. Then followed by the wavelet analysis based packet length sequence feature extraction and feature selection methods, the resulting eigenvectors are used as input features to support vector machine for training the classifier. Thus to differentiate the two types of anomaly in the mixed traffic with both normal and abnormal traffic. The qualitative and quantitative experimental results show that our approach achieves good classification results.