Channel sounding is crucial for achieving Extremely High Throughput (EHT) and Ultra-high reliability (UHR) in next-generation Wi-Fi systems, i.e., Wi-Fi 7 and beyond. In Downlink Multi-User Multiple-Input Multiple-Output (DL MUMIMO) communications, data rate significantly deteriorates under time-varying channel with Doppler effect. Therefore, effective channel sounding mechanisms must balance the Channel State Information (CSI) overhead and CSI staleness, which is governed by the channel coherence time. Despite its critical importance, channel sounding optimization under time-varying channel conditions remains under-explored. This paper addresses this research gap by proposing a cross-layer optimization problem for the channel sounding period with the objective of maximizing data rate by considering the CSI overhead over the MAC layer and the channel capacity degradation over the PHY layer. This problem is then converted into an equivalent formulation leveraging the EHT sounding protocol, which can be solved efficiently using our proposed optimal search algorithm. Through simulations, we evaluate the baseline EHT sounding using outdated beamforming matrices and benchmark it against our proposed solution. The numerical results demonstrate that the channel sounding period optimization significantly reduces CSI overhead by up to 11% while boosting the average data rate by up to 8%.

Vehicle-to-Everything (V2X) communication faces a critical authentication dilemma: traditional public-key schemes like ECDSA provide strong security but impose 2 ms verification delays unsuitable for collision avoidance, while symmetric approaches like TESLA achieve microsecond-level efficiency at the cost of 20-100 ms key disclosure latency. Neither meets 5G New Radio (NR)-V2X's stringent requirements for both immediate authentication and computational efficiency. This paper presents SALT-V, a novel hybrid authentication framework that reconciles this fundamental trade-off through intelligent protocol stratification. SALT-V employs ECDSA signatures for 10% of traffic (BOOT frames) to establish sender trust, then leverages this trust anchor to authenticate 90% of messages (DATA frames) using lightweight GMAC operations. The core innovation - an Ephemeral Session Tag (EST) whitelist mechanism - enables 95% of messages to achieve immediate verification without waiting for key disclosure, while Bloom filter integration provides O(1) revocation checking in 1 us. Comprehensive evaluation demonstrates that SALT-V achieves 0.035 ms average computation time (57x faster than pure ECDSA), 1 ms end-to-end latency, 41-byte overhead, and linear scalability to 2000 vehicles, making it the first practical solution to satisfy all safety-critical requirements for real-time V2X deployment.

Generative summarisation models have demonstrated remarkable fluency; however, they frequently encounter hallucinations and factual inconsistencies, especially in specialised domains that involve lengthy documents. Hybrid approaches aim to alleviate these issues by integrating extractive key sentences as guidance. Nevertheless, current methods generally utilise an ‘early fusion’ strategy, which involves merely concatenating key sentences with the source text. This approach results in two significant bottlenecks: representation mismatch and position bias. This paper proposes a novel Gated Dual-Encoder Summarisation Framework: unlike unified encoders, it uses two parameter-independent encoders to separately process source text and key information, avoiding semantic confusion. Additionally, we present a Dynamic Gated Fusion Module that adaptively weighs the contributions of the source context and guidance signals through a learnable gating scalar. Experimental results on CNN/DailyMail, XSum, and PubMed demonstrate that our approach significantly outperforms state-of-the-art baselines. Specifically, on the PubMed dataset, our model achieves a ROUGE-L score of 40.05 and a FactCC score of 86.4%, outperforming the BART-Large baseline by 7.2% in factual consistency. Furthermore, our model demonstrates high robustness with a performance degradation rate (PDR) of only 2.8% under sentence shuffling tests.

Next-generation Wi-Fi 8 (IEEE 802.11bn) targets ultra-high reliability (UHR) by introducing coordinated beamforming (CoBF). In dense networks with multiple access points (APs), simultaneous downlink (DL) multi-user MIMO (MU-MIMO) transmissions from multiple APs can cause severe intra-basic service set (intra-BSS) and inter-BSS interference. CoBF aided by only partial channel state information (CSI) feedback through medium access control (MAC) layer frame exchange is envisioned to support concurrent DL transmission with mitigated physical-(PHY-)layer interference. To improve the network throughput, not only the interference mitigation algorithm design requires careful design but also the selection of optimal AP CoBF clusters is crucial for dense AP deployments. This paper presents a cross-layer solution combining PHY and MAC layer design to optimize AP cluster formation for Wi-Fi 8 CoBF. At the PHY layer, we introduce two beamforming nulling strategies: full nulling, which completely cancels all intra-BSS and inter-BSS interference when sufficient spatial degrees of freedom are available, and partial nulling, which is used under limited degrees of freedom to reduce interference as much as possible. Based on this, we formulate the cross-layer problem that aims to optimize the network throughput, to which we propose an exact linear programming (LP) optimization to determine the optimal AP cluster formation. A greedy clustering algorithm is proposed as a low-complexity alternate. Simulation results demonstrate that the proposed CoBF approach significantly mitigates interference and achieves substantial throughput gains in dense AP scenarios. Furthermore, the LP-optimized AP clustering yields the higher network throughput than the greedy heuristic and mixed integer linear programming (MILP) by up to 12% and 26%, highlighting the benefits of global optimization in terms of performance and time complexity.

Extremely High Throughput (EHT) and Ultra-high reliability (UHR) are new objectives in Next-Gen Wi-Fi, i.e., Wi-Fi 7 and beyond; however, the data rate within a periodic channel sounding round is expected to significantly deteriorate under time-varying channels with Doppler effect in Downlink Multi-User Multiple-Input Multiple-Output. Therefore, Next-Gen channel sounding must carefully balance the MAC-layer CSI overhead reduction and the PHY-layer channel capacity degradation caused by the Doppler effect for data rate maximization. Despite its critical importance, the cross-layer (PHY + MAC) Wi-Fi channel sounding optimization in time-varying channels remains under-explored. This paper addresses this research gap by proposing a cross-layer optimization problem to find the optimal EHT sounding period that maximizes the average data rate by considering both MAC-layer CSI overhead and PHY-layer channel capacity degradation. This problem is then converted into an equivalent optimization problem that can be solved efficiently using our proposed model driven optimal search algorithm with proven convexity. Afterwards, we introduce a data driven Transformer-based partial CSI prediction framework to alleviate CSI staleness without introducing extra CSI overhead, which further enhances the average data rate. Through simulations, we evaluate the baseline EHT sounding protocol that always uses outdated partial CSI, and then benchmark the baseline against our proposed hybrid data and model driven approach. The numerical results demonstrate that integrating Transformer-based partial CSI prediction with the optimal channel sounding period significantly reduces CSI overhead by up to 25.2%, while increasing the average throughput by up to 30.9%.

Legged locomotion is not just about mobility; it also encompasses crucial objectives such as energy efficiency, safety, and user experience, which are vital for real-world applications. However, key factors such as battery power consumption and stepping noise are often inaccurately modeled or missing in common simulators, leaving these aspects poorly optimized or unaddressed by current sim-to-real methods. Hand-designed proxies, such as mechanical power and foot contact forces, have been used to address these challenges but are often problem-specific and inaccurate. In this paper, we propose a data-driven framework for fine-tuning locomotion policies, targeting these hard-to-simulate objectives. Our framework leverages real-world data to model these objectives and incorporates the learned model into simulation for policy improvement. We demonstrate the effectiveness of our framework on power saving for quadruped locomotion, achieving a significant 24–28% net reduction in total power consumption from the battery pack at various speeds. In essence, our approach offers a versatile solution for optimizing hard-to-simulate objectives in quadruped locomotion, providing an easy-to-adapt paradigm for continual improving with real-world knowledge. Project page https://hard-to-sim.github.io/.

The IEEE 802.11 standards, culminating in IEEE 802.11be (Wi-Fi 7), have significantly expanded bandwidth capacities from 20 MHz to 320 MHz, marking a crucial evolution in wireless access technology. Despite these advancements, the full potential of these capacities remains largely untapped due to inefficiencies in channel management, in particular, the underutilization of secondary (non-primary) channels when the primary channel is occupied. This paper delves into the Non-Primary Channel Access (NPCA) protocol, initially proposed by the IEEE 802.11 Ultra-High Reliability (UHR) group, aimed at addressing these inefficiencies. Our research not only proposes an analytical model to assess the throughput of NPCA in terms of average throughput but also crucially identifies that the overhead associated with the NPCA protocol is significant and cannot be ignored. This overhead often undermines the effectiveness of the NPCA, challenging the assumption that it is invariably superior to traditional models. Based on these findings, we have developed and simulated a new hybrid model that dynamically integrates the strengths of both legacy and NPCA models. This model overall outperforms the existing models under all channel occupancy conditions, offering a robust solution to enhance throughput efficiency.

Robust locomotion control depends on accurate state estimations. However, the sensors of most legged robots can only provide partial and noisy observations, making the estimation particularly challenging, especially for external states like terrain frictions and elevation maps. Inspired by the classical Internal Model Control principle, we consider these external states as disturbances and introduce Hybrid Internal Model (HIM) to estimate them according to the response of the robot. The response, which we refer to as the hybrid internal embedding, contains the robot’s explicit velocity and implicit stability representation, corresponding to two primary goals for locomotion tasks: explicitly tracking velocity and implicitly maintaining stability. We use contrastive learning to optimize the embedding to be close to the robot’s successor state, in which the response is naturally embedded. HIM has several appealing benefits: It only needs the robot’s proprioceptions, i.e., those from joint encoders and IMU as observations. It innovatively maintains consistent observations between simulation reference and reality that avoids information loss in mimicking learning. It exploits batch-level information that is more robust to noises and keeps better sample efficiency. It only requires 1 hour of training on an RTX 4090 to enable a quadruped robot to traverse any terrain under any disturbances. A wealth of real-world experiments demonstrates its agility, even in high-difficulty tasks and cases never occurred during the training process, revealing remarkable open-world generalizability.

The evolution of the IEEE 802.11 standards marks a significant throughput advancement in wireless access technologies, progressively increasing bandwidth capacities from 20 MHz in the IEEE 802.11a to up to 320 MHz in the latest IEEE 802.11be (Wi-Fi 7). However, the increased bandwidth capacities may not be well exploited due to inefficient bandwidth utilization on multiple channels. This issue typically occurs when the primary channel is busy, secondary channels (also known as non-primary channels) are prevented from being utilized even if they are idle, thereby wasting the available bandwidth. This paper investigates the fundamentals of the Non-Primary Channel Access (NPCA) protocol that was defined in IEEE 802.11 Ultra-High Reliability (UHR) group to cope with the above issue. We develop a novel analytical model to assess NPCA protocol performance in terms of the average throughput and delay. Via simulation, we verify that the NPCA network outperforms the legacy network by increasing at least 50% average throughput while reducing at least 40% average delay.

IEEE 802.11be (Wi-Fi 7) introduces a new concept called multi-link operation (MLO), which allows multiple Wi-Fi interfaces in different bands (2.4, 5, and 6 GHz) to work together to increase network throughput, reduce latency, and improve spectrum reuse efficiency in dense overlapping networks. To make the most of MLO, this paper proposes a new data-driven resource allocation algorithm for the 11be network with the aid of an access point (AP) controller. To maximize network throughput, a network topology optimization problem is formulated for 11be network, which is solved by exploiting the totally unimodular property of the bipartite graph formed by the connection between AP and station (STA) in Wi-Fi networks. Subsequently, a proportional fairness algorithm is applied for radio link allocation, network throughput optimization considering the channel condition, and the fairness of the multi-link device (MLD) data rate. The performance of the proposed algorithm on two main MLO implementations -multi-link multi-radio (MLMR) with simultaneous transmission and reception (STR), and the interplay between multiple nodes employing them are evaluated through cross-layer (PHY-MAC) data rate simulation with PHY abstraction.

Understanding and characterizing the vulnerability of urban infrastructures, which refers to the engineering facilities essential for the regular running of cities and that exist naturally in the form of networks, is of great value to us. Potential applications include protecting fragile facilities and designing robust topologies, etc. Due to the strong correlation between different topological characteristics and infrastructure vulnerability and their complicated evolution mechanisms, some heuristic and machine assisted analysis fall short in addressing such a scenario. In this paper, we model the interdependent network as a heterogeneous graph and propose a system based on graph neural network with reinforcement learning, which can be trained on real-world data, to characterize the vulnerability of the city system accurately. The presented system leverages deep learning techniques to understand and analyze the heterogeneous graph, which enables us to capture the risk of cascade failure and discover vulnerable infrastructures of cities. Extensive experiments with various requests demonstrate not only the expressive power of our system but also transferring ability and necessity of the specific components. All source codes and models including those that can reproduce all figures analyzed in this work are publicly available at this link: https://github.com/tsinghua-fib-lab/KDD2023-ID546-UrbanInfra.

Sidelink in cellular networks enables direct exchange of data packets between devices without the need for network infrastructure, resulting in various benefits, including communication in out-of-coverage areas and possible decrease in latency by a considerable extent. Thus, sidelink is a favorable choice for applications like public safety communications and Vehicle-to-Everything (V2X) communications. As 4G Long Term Evolution (LTE) advanced to 5G New Radio (NR) under the Third Generation Partnership Project (3GPP), several new features were introduced to sidelink, such as two-stage Sidelink Control Informations (SCIs), data and control multiplexing, and feedback-based Hybrid Automatic Repeat Request (HARQ) with a configurable maximum number of transmissions. To conduct extensive NR sidelink link-level evaluations, a comprehensive simulation platform is essential. In this paper, we introduce the first publicly accessible 5G NR Link-Level Simulator (LLS) that supports major 5G NR sidelink features and complies with the 3GPP standards. This MATLAB-based simulator allows for flexible control over various Physical Layer (PHY) configurations, facilitating customized simulations on algorithm development and performance evaluations. We discuss the simulator’s structure and the sidelink features implemented in detail and evaluate the 5G NR sidelink performance using the developed simulator. Our simulation results indicate that the Block Error Rate (BLER) curves are insensitive to error-prone 2nd-stage Sidelink Control Information (SCI2) and number of Resource Blocks (RBs) allocated; however, the sidelink communication range is sensitive to the deployment environment. We also highlight the need for a careful choice of numerology, device power class and HARQ configuration to balance performance metrics for a variety of services based on 5G NR sidelink deployment scenarios.

Since the Third Generation Partnership Project (3GPP) specified 5G New Radio (NR) sidelink in Release 16, researchers have been expressing increasing interest in sidelink in various research areas, such as Proximity Services (ProSe) and Vehicle-to-Everything (V2X). It is essential to provide researchers with a comprehensive simulation platform that allows for extensive NR sidelink link-level evaluations. In this paper, we introduce the first publicly accessible 5G NR link-level simulator that supports sidelink. Our MATLAB-based simulator complies with the 3GPP 5G NR sidelink standards, and offers flexible control over various Physical Layer (PHY) configurations. It will facilitate researcher's exploration in NR sidelink with a friendly access to the key network parameters and great potential of customized simulations on algorithm developments and performance evaluations. This paper also provides several initial link-level simulation results on sidelink using the developed simulator.

Information freshness is a crucial metric for time-sensitive services such as Basic Safety Messages (BSMs) in Vehicle-to-Everything (V2X) communications to achieve high-reliability autonomous driving. However, Semi-Persistent Scheduling (SPS) algorithm under the current 5th generation (5G) New Radio (NR) standard may not fulfill the requirement of such a metric for BSMs without optimizing relevant parameters. This paper analyzes the parameters of SPS used for BSM scheduling in NR-V2X Mode 2 from an Age-of-Information (AoI) perspective, to explore the freshness of BSMs. We first present an analytical model to illustrate that Resource Reservation Interval (RRI) is the SPS parameter which significantly impacts the AoI performance. Subsequently, we investigate the expected peak AoI (PAoI) performance with respect to RRI values under different vehicle densities. A Monte Carlo simulator is then utilized to verify the results obtained in the analytical models. Numerical results show that the optimal RRI values in SPS, which minimize the expected PAoI of the vehicular network, can be obtained based on different vehicle densities accordingly.

Integrated access and backhaul (IAB) is a novel feature for extending the network coverage in 5G cellular networks, based on sharing/efficient allocation of owner’s spectrum traditionally reserved for access. However, since ultra-reliability and low latency (URLLC) requirements are a key component of 5G advanced services, provisioning such services present stringent challenges for IAB multi-hop network design. To fulfill the URLLC requirements in the IAB network, we propose a cross-layer design on routing and resource allocation under the current 3rd Generation Partnership Project (3GPP) 5G standards. We first formulate a routing problem for the IAB multi-hop network, which minimizes the latency while satisfying the reliability requirement. Subsequently, we present a reinforcement learning (RL) framework to solve the resource allocation and routing problem based on the local information of each agent (IAB node) in the environment. Afterward, we propose a novel entropy-based RL algorithm with federated learning (FL) mechanism to improve the overall performance as well as accelerate the convergence speed. Via the simulation, the proposed algorithm outperforms baseline algorithms from the latency and reliability perspective, respectively. Meanwhile, the convergence speed with the proposed algorithm also improves by using FL.

The integration of blockchain in vehicular networks is becoming the backbone for securing intelligent and autonomous vehicles. Besides security, vehicles are also able to enhance their communication performances by expanding the services with blockchain technology. In this paper, we propose a novel blockchain-empowered platoon communication scheme for vehicular safety applications. Decentralized vehicles first form into a platoon assisted by a pre-established blockchain-based security system. The platoon communication is then utilized to update the resource scheduling scheme enabled by the blockchain on the attending vehicles. In addition, to reduce the communication overhead, we further propose an adaptive block generation period (ABGP) algorithm based on the estimated resource occupancy percentage. Monte Carlo simulation is employed to compare the results between the proposed scheme and the semi-persistent scheduling (SPS) scheme specified by the current standard. Numerical results show that the proposed scheme outperforms the SPS scheme by decreasing the collision probability at least 40% while reducing the average scheduling delay by at least 30%.

5G New Radio (NR) is envisioned to efficiently support ultra-reliable low-latency communication (URLLC) for new services and applications with high reliability, availability and low latency such as factory automation and autonomous vehicles. Multi-hop Device-to-device (D2D) communication is one such means that expands D2D coverage and achieves lower latency in the mobile edge and NR sidelink. In this paper, we first analyze the URLLC requirements in 5G and the multihop D2D communication problem with perfect knowledge of the network. Subsequently, we investigate the deep reinforcement learning (DRL) algorithm for the scheduling and resource allocation problem with only local information for each node. A simulation is employed to evaluate the performance of the related algorithms. Numerical results show that the proposed DRL algorithm outperforms the greedy algorithm in terms of different relay nodes between the source and destination, and is robust to the coming or leaving of relay nodes.

Accurate channel state information (CSI) prediction and estimation are critical to the communication system to adapt to the rapid change of wireless channels. The CSI feedback from the receiver may become outdated and inaccurate due to the compression and transmission delay, especially for the multiple-input multiple-output (MIMO) system. Deep learning-based algorithms for channel prediction have been widely used, however, traditional recurrent neural network (RNN) based methods may incur unstable behavior in the dynamic system. In this paper, we propose a novel MIMO channel prediction method based on a liquid time constant (LTC) network, which provides more stable and bounded performance in the CSI prediction task. An online prediction structure is also introduced to better cope with current architecture and reduce the computational requirement on the device. Results reveal that our proposed method outperforms the traditional RNN based algorithm and auto regressive (AR) models in prediction accuracy by 10% - 40% on both simulation data and measurement data.

MOTIVATION: Untargeted mass spectrometry experiments enable the profiling of metabolites in complex biological samples. The collected fragmentation spectra are the metabolite's fingerprints that are used for molecule identification and discovery. Two main mass spectrometry strategies exist for the collection of fragmentation spectra: data-dependent acquisition (DDA) and data-independent acquisition (DIA). In the DIA strategy, all the metabolites ions in predefined mass-to-charge ratio ranges are co-isolated and co-fragmented, resulting in multiplexed fragmentation spectra that are challenging to annotate. In contrast, in the DDA strategy, fragmentation spectra are dynamically and specifically collected for the most abundant ions observed, causing redundancy and sub-optimal fragmentation spectra collection. Yet, DDA results in less multiplexed fragmentation spectra that can be readily annotated. RESULTS: We introduce the MS2Planner workflow, an Iterative Optimized Data Acquisition strategy that optimizes the number of high-quality fragmentation spectra over multiple experimental acquisitions using topological sorting. Our results showed that MS2Planner increases the annotation rate by 38.6% and is 62.5% more sensitive and 9.4% more specific compared to DDA. AVAILABILITY AND IMPLEMENTATION: MS2Planner code is available at https://github.com/mohimanilab/MS2Planner. The generation of the inclusion list from MS2Planner was performed with python scripts available at https://github.com/lfnothias/IODA_MS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this paper, we focus on the performance of vehicle-to-vehicle (V2V) communication adopting the Dedicated Short Range Communication (DSRC) application in periodic broadcast mode. An analytical model is studied and a fixed point method is used to analyze the packet delivery ratio (PDR) and mean delay based on the IEEE 802.11p standard in a fully connected network under the assumption of perfect PHY performance. With the characteristics of V2V communication, we develop the Semi-persistent Contention Density Control (SpCDC) scheme to improve the DSRC performance. We use Monte Carlo simulation to verify the results obtained by the analytical model. The simulation results show that the packet delivery ratio in SpCDC scheme increases more than 10% compared with IEEE 802.11p in heavy vehicle load scenarios. Meanwhile, the mean reception delay decreases more than 50%, which provides more reliable road safety.

Multi-focus image fusion in wireless visual sensor networks (WVSN) is a process of fusing two or more images to obtain a new one which contains a more accurate description of the scene than any of the individual source images. In this letter, we propose an efficient algorithm to fuse multi-focus images or videos using discrete cosine transform (DCT) based standards in WVSN. The spatial frequencies of the corresponding blocks from source images are calculated as the contrast criteria, and the blocks with the larger spatial frequencies compose the DCT presentation of the output image. Experiments on plenty of pairs of multi-focus images coded in Joint Photographic Experts Group (JPEG) standard are conducted to evaluate the fusion performance. The results show that our fusion method improves the quality of the output image visually and outperforms the previous DCT based techniques and the state-of-art methods in terms of the objective evaluation.

Inthis paperwe presentan approach foroptical vehicle positioning (e.g. fork lifters). Our approach is motivated by planar marker detection systems like ARTag or ARToolkit, in which poses of planarmarkers relative to the camera can be determined. In contrast toexisting optical positioning systems(e.g. SkyTrax), we mount cameras on the ceiling and passive (non-electronic) planar markers on the top of the vehicles. The absenceof complexelectronic components on the high stressed vehicles enablesrapid process integration, which is particularly important forrental vehicles. We have evaluated our method keeping the most important user requirementscoverage, costsand accuracyunder consideration for three intra-logistic scenarios a) zone monitoring with zone precise positioning, b) storage aisle monitoring with storage place precise positioning and, c) complete driving rangemonitoringwith maximumprecision.