arXiv subjects

Liu Cao

Publications and source records attributed to Liu Cao.

At least 19 recordsLinked to original sources

arXiv2026

Dex-X: Learning Visual-Tactile Dexterous Manipulation From Human Videos with Simulated Interaction

Human videos are an abundant source of dexterous manipulation behaviors, but they lack tactile information that is crucial for contact-rich interaction. This raises a fundamental question: can robots learn deployable visual-tactile dexterous manipulation policies from human video demonstrations without robot-side data collection? We present DEX-X, a framework for learning visual-tactile dexterous manipulation from human videos through simulation. Our key insight is that simulation can serve as a tactile completion engine. Given monocular human demonstrations, DEX-X reconstructs hand-object interactions in simulation, where physically grounded contact dynamics provide tactile supervision unavailable in the original videos. Leveraging this recovered tactile information, we train visual-tactile dexterous manipulation policies and distill them into deployable policies operating on point-cloud observations and tactile sensing. We demonstrate zero-shot sim-to-real transfer on a dexterous hand-arm platform across diverse grasping and contact-rich tool-use tasks. The teacher policy achieves 65.9% average success across six task categories in simulation, while the distilled visual-tactile policy achieves 93% success on real-world cube picking and 53% on the challenging table-cleaning task. Zero-shot generalization to unseen object geometries is also observed on object-picking tasks. Our results suggest that simulated interaction is a key bridge between human videos and deployable dexterous manipulation policies, providing the missing physical supervision needed for scalable robot skill learning from Internet-scale human video data.

cs.RO↗

arXiv2026

PHY-Layer Modeling and Throughput-Driven Adaptation for Batteryless V2X Networks

Passive overlay communication for batteryless devices is an important enabling capability for next-generation vehicle-to-everything (V2X) networks. However, enabling reliable passive payload delivery without occupying additional spectrum remains challenging, since overlay signaling must be embedded into short and time-varying vehicular packets while preserving the decodability of the legacy host transmission. This paper investigates a packetized batteryless V2X overlay architecture in which a dedicated short-range communications (DSRC)-based packet simultaneously carries conventional V2X data and a passive overlay payload. A compact PHY-layer model is developed to characterize the coupled effects of attenuation depth, embedded-bit rate, and legacy modulation and coding scheme (MCS) on host-link and passive-link reliability, as well as packet-level embedding feasibility. We then formulate a sum-throughput maximization problem that jointly accounts for the legacy packet error rate and passive decoding error rate. We further propose a multi-agent reinforcement learning (MARL)-based adaptive parameter-selection method. Simulation results show that the proposed MARL controller achieves stable convergence and improves the average throughput by 15\%, demonstrating the effectiveness of throughput-driven PHY adaptation for batteryless V2X overlay communications.

eess.SY↗

arXiv2026

Data-Driven Batteryless Channel Sounding for Wi-Fi 8-Inspired Downlink MU-MIMO

Batteryless overlays couple passive throughput to Wi-Fi sounding overhead and channel state information (CSI) aging. This paper investigates channel sounding for ultra-high reliability (UHR) operation in a Wi-Fi 8/IEEE 802.11bn-inspired downlink multi-user multiple-input multiple-output (MU-MIMO) system with a batteryless passive overlay. We optimize the post-sounding transmission interval to maximize the aggregate throughput of the active Wi-Fi and passive links, while jointly accounting for sounding overhead, CSI aging, modulation and coding scheme (MCS), passive attenuation, and passive data rate. A packet-level cross-layer model evaluates the cycle-average throughput, and a data-driven search identifies the optimal interval under different operating conditions. Simulations demonstrate that passive overlay reshapes the conventional sounding tradeoff: depending on the MCS and passive-link configuration, the additional passive throughput may or may not compensate for the associated Wi-Fi reliability loss, causing the optimal interval to shift. The results provide design guidance for reliable and low-power MU-MIMO WLANs.

cs.IT↗

arXiv2026

RIS-Aided E2E Multi-Path Uplink Transmission Optimization for 6G Time-Sensitive Services

The Access Traffic Steering, Switching, and Splitting (ATSSS) defined in the latest 3GPP Release 19 enables traffic flow over the multiple access paths to achieve the lower-latency End-to-end (E2E) delivery for 6G time-sensitive services. However, the existing E2E multi-path operation often falls short of more stringent QoS requirements for 6G time-sensitive services. This work proposes a Reconfigurable Intelligent Surfaces (RIS)-aided E2E multi-path uplink (UL) transmission architecture that explicitly accounts for both radio link latency and N3 backhaul latency, via the coupled designs of the UL traffic-splitting ratio, transmit power, receive combining, and RIS phase shift under practical constraints to achieve the minimum average E2E latency. We develop an alternating optimization framework that updates the above target parameters to be optimized. The simulations were conducted to compare the effectiveness of the proposed E2E optimization framework that lowers the average E2E latency up to 43% for a single user and 32% for the whole system compared with baselines in our prior work [1].

eess.SY↗

arXiv2025

A Lightweight Coordinate-Conditioned Diffusion Approach for 6G C-V2X Radio Environment Maps

Transmitter vehicles that broadcast 6G Cellular Vehicle-to-Everything (C-V2X)-based messages, e.g., Basic Safety Messages (BSMs), are prone to be impacted by PHY issues due to the lack of dynamic high-fidelity Radio Environment Map (REM) with dynamic location variation. This paper explores a lightweight diffusion-based generative approach, the Coordinate-Conditioned Denoising Diffusion Probabilistic Model (CCDDPM), that leverages the signal intensity-based 6G V2X Radio Environment Map (REM) from limited historical transmitter vehicles in a specific region, to predict the REMs for a transmitter vehicle with arbitrary coordinates across the same region. The transmitter vehicle coordinate is encoded as a smooth Gaussian prior and fused with the Gaussian noise through a lightweight two-channel conditional U-Net architecture. We demonstrate that the predicted REM closely matches the statistics and structure of ground-truth REM while exhibiting the improved stability and over other widely applied generative AI approaches. The resulting predictor enables rapid and scenario-consistent REM with arbitrary transmitter coordinates, which thereby supports more efficient 6G C-V2X communications where transmitter vehicles are less likely to suffer from the PHY issues.

cs.NI↗

arXiv2025

SALT-V: Lightweight Authentication for 5G V2X Broadcasting

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.

cs.CR↗

arXiv2025

LLP-V2X: Low Latency-Power Vehicular Networking Towards 6G V2X

The trade-off between energy and latency budgets is becoming significant due to the more stringent QoS requirements in 6G vehicular networks. However, comprehensively studying the trade-off between energy and latency budgets for 6G vehicular network with new Vehicle-to-Everything (V2X) features is still under-explored. This paper proposes a novel multi-hop, multi-path vehicular networking that jointly optimizes vehicular traffic splitting across candidate routes and per-link transmit power to achieve low-latency and low-power communications. Afterwards, we formalize two complementary problem formulations (minimum latency and minimum power) based on the proposed 6G V2X architecture and provide sufficient conditions. The performance of the proposed scheme is evaluated via well-designed simulations. Based on these theories, we design algorithm (LLP MHMP Scheduler) that switches on demand between a fixed-power minimum-latency mode and a fixed-latency minimum-power mode.

cs.NI↗

arXiv2025

EPFL-REMNet: Efficient Personalized Federated Digital Twin Towards 6G Heterogeneous Radio Environment

Radio Environment Map (REM) is transitioning from 5G homogeneous environments to B5G/6G heterogeneous landscapes. However, standard Federated Learning (FL), a natural fit for this distributed task, struggles with performance degradation in accuracy and communication efficiency under the non-independent and identically distributed (Non-IID) data conditions inherent to these new environments. This paper proposes EPFL-REMNet, an efficient personalized federated framework for constructing a high-fidelity digital twin of the 6G heterogeneous radio environment. The proposed EPFL-REMNet employs a"shared backbone + lightweight personalized head" model, where only the compressed shared backbone is transmitted between the server and clients, while each client's personalized head is maintained locally. We tested EPFL-REMNet by constructing three distinct Non-IID scenarios (light, medium, and heavy) based on radio environment complexity, with data geographically partitioned across 90 clients. Experimental results demonstrate that EPFL-REMNet simultaneously achieves higher digital twin fidelity (accuracy) and lower uplink overhead across all Non-IID settings compared to standard FedAvg and recent state-of-the-art methods. Particularly, it significantly reduces performance disparities across datasets and improves local map accuracy for long-tail clients, enhancing the overall integrity of digital twin.

cs.NI↗

arXiv2025

Fine-Tuning Hard-to-Simulate Objectives for Quadruped Locomotion: A Case Study on Total Power Saving

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/.

cs.RO↗

arXiv2024

Revisiting Multi-User Downlink in IEEE 802.11ax: A Designers Guide to MU-MIMO

Downlink (DL) Multi-User (MU) Multiple Input Multiple Output (MU-MIMO) is a key technology that allows multiple concurrent data transmissions from an Access Point (AP) to a selected sub-set of clients for higher network efficiency in IEEE 802.11ax. However, DL MU-MIMO feature is typically turned off as the default setting in AP vendors' products, that is, turning on the DL MU-MIMO may not help increase the network efficiency, which is counter-intuitive. In this article, we provide a sufficiently deep understanding of the interplay between the various underlying factors, i.e., CSI overhead and spatial correlation, which result in negative results when turning on the DL MU-MIMO. Furthermore, we provide a fundamental guideline as a function of operational scenarios to address the fundamental question "when the DL MU-MIMO should be turned on/off".

cs.NI↗

arXiv2024

Optimized Non-Primary Channel Access Design in IEEE 802.11bn

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.

cs.NI↗

arXiv2024

Non-Primary Channel Access in IEEE 802.11 UHR: Comprehensive Analysis and Evaluation

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.

cs.NI↗

arXiv2023

Hybrid Internal Model: Learning Agile Legged Locomotion with Simulated Robot Response

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.

cs.RO↗

arXiv2023

IEEE 802.11be Network Throughput Optimization with Multi-Link Operation and AP Coordination

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.

cs.NI↗

arXiv2023

Codebook-based Uplink Transmission Enhancement in 5G Advanced: Sub-band Precoding

The transformative enhancements of fifth-generation (5G) mobile devices bring about new challenges to achieve better uplink (UL) performance. Particularly, in codebook-based transmission, the wide-band (WB) precoding and the legacy UL codebook may become main bottlenecks for higher efficient data transmission. In this paper, we investigate the codebook-based UL single-layer transmission performance using fully coherent antenna ports in the context of sub-band (SB) precoding. We analyze the SB precoder selection criteria and design an UL codebook used for SB precoding by increasing the number of relative phase shifts of each port. Via link-level simulations, we verify that the UL SB precoding can improve up to 2 dB performance gain in terms of the block error rate (BLER) compared with the UL WB precoding which is the current UL precoding scheme. We also show that UL performance gain is sensitive to the SB size selection as well as the relative phase shift diversity.

cs.IT↗

arXiv2023

Toward 6G Sidelink Reliability: MAC PRR Modeling for NR Mode 2 SPS and ns-3 Validation

5G New Radio (NR) Sidelink (SL) Mode 2 has enabled decentralized, infrastructure-less direct communications which is evolving to serve reliability-critical services in 6G SL. Particularly, the channel access in NR SL Mode 2 relies on the Sensing-based Semi-Persistent Scheduling (SPS) whose key features significantly influence the packet reception ratio (PRR). While SPS has been widely studied, existing analytical models typically abstract or omit several NR-specific SPS features that are standardized in the 3rd Generation Partnership Project (3GPP), limiting their ability to explain how SPS parameters shape MAC collision dynamics and PRR. This paper develops an analytical MAC-layer PRR model for broadcast NR SL mode 2 by explicitly modeling SPS-driven MAC collision events. The model captures (i) Collisions caused by simultaneous resource reselection and (ii) Persistent collisions induced by resource keeping across resource reservation intervals (RRIs). Based on the event-level characterization, we derive closed-form expressions for the steady-state MAC collision probability and PRR. We further extend the analysis to incorporate under-explored SPS features, including the duplicate transmissions per RRI and the minimum resource-availability requirement for reselection, and quantify their impact on PRR in under-saturated regimes. The analytical results are validated using ns-3 simulations based on the 5G-LENA framework, showing close agreement under under-saturation and revealing deviations as the system approaches saturation. The proposed model provides mechanistic insight and design guidance of tuning the SPS parameters to improve 6G SL reliability.

cs.NI↗

arXiv2023

Detecting Vulnerable Nodes in Urban Infrastructure Interdependent Network

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.

cs.LG↗

arXiv2022

Latency-aware End-to-end Multi-path Data Transmission for URLLC Services

5th Generation Mobile Communication Technology (5G) utilizes the Access Traffic Steering, Switching, and Splitting (ATSSS) rule to enable multi-path data transmission, which is currently being standardized. Recently, the 3rd Generation Partnership Project (3GPP) SA1 and SA2 have been working on the multi-path solution for possible improvement from different perspectives. However, the existing 3GPP multi-path solution has some limitations on ultra-reliable low-latency communication (URLLC) traffic in terms of reliability and latency requirements. In order to capture the potential gains of multi-path architecture in the context of URLLC services, this paper proposes a novel traffic splitting technique that can more efficiently enjoy the benefit of multi-path architecture in reducing user equipment (UE) uplink (UL) end-to-end (E2E) latency. In particular, we formulate an optimization framework that minimizes user's UL E2E latency via the joint optimization on the ratio of traffic assigned to each path and their corresponding transmit power. The performance of the proposed scheme is evaluated via well-designed simulations.

cs.NI↗