arXiv subjects

Sihua Shao

Publications and source records attributed to Sihua Shao.

16 recordsLinked to original sources

arXiv2026

Sensing-Based Communication: A Non-Orthogonal Passive Reflection Framework for ISAC

While integrating uplink without disrupting the primary downlink remains a critical challenge for existing reflective communication systems, this paper proposes a non-orthogonal passive integrated sensing and communication (ISAC) mechanism termed Sensing-based Communication. Originating from and validated within an optical retro-reflective system, this mechanism possesses an underlying wave-agnostic architecture with the potential to be generalized across other physical modalities. It leverages incident downlink signals for parasitic modulation via passive tags. By relying solely on fundamental on-off keying (OOK) reflection, the tag unifies the reflective sensing channel and the uplink communication channel, and simultaneously achieves both sensing and duplex communication. Consequently, our approach introduces uplink capability without sacrificing the downlink data rate or consuming additional spectrum resources. At the receiver, we design a joint decoding architecture combining cross-correlation computation and successive interference cancelation (SIC) to fully exploit the degrees of freedom in broadband signals for multi-user interference suppression. We derive closed-form expressions for the interference and bit error rate (BER) assuming zero-mean Gaussian signals. Using an experimental testbed, we validate the pivotal theoretical finding, the scaling relationship of SINR with signal bandwidth and cross-correlation time--where linear regression yields a strong goodness-of-fit with $R^2 = 0.9688$. Furthermore, evaluations under noise-like orthogonal frequency division multiplexing (OFDM) waveforms exhibit high consistency between theoretical derivations and simulation data ($R^2 = 0.9372$, mean absolute percentage error, $\text{MAPE} = 14\%$), confirming the model's accuracy and waveform generalizability.

cs.IT↗

arXiv2026

Tags Helping Tags: Coordination-Free Power Boosting for Battery-less Backscatter Networks

Battery-less backscatter sensor nodes powered by radio-frequency wireless power transfer are attractive for large or hard-to-reach deployments, but their range is typically limited by the forward power-transfer link rather than by communication sensitivity. We present a method for improving power delivery, and thus range, in dense tag deployments by having nearby tags act as helpers: each helper terminates its antenna in purely reactive impedances, reflecting the power beacon's signal toward a target tag so that it combines constructively with the direct line-of-sight path and increases the power the target harvests. On a hardware testbed, helpers using discrete impedance control are shown to increase the target tag's harvested power by over 50% above an all-absorbing baseline, while randomly set helper impedances give no reliable benefit, and are sometimes even detrimental. We further propose and implement a local algorithm that lets each helper tune its own impedance using only measurements of the target's packet rate, without channel estimation or coordination between helpers, and we show that it brings the target's packet rate close to that of an independently tuned configuration. These results suggest that independent, passive sensor nodes can cooperatively extend each other's power-transfer range with minimal added infrastructure.

eess.SY↗

arXiv2026

A Quad Beam Variable Height Antenna System for Reliable Underground Mine Tunnel Communications

Mine accidents can damage the wired backbone supporting underground wireless networks, forcing rescue teams to deploy temporary mesh nodes to maintain communication with the surface. Although higher carrier frequencies can support bandwidth-intensive applications such as live video, multimode propagation within mine tunnels produces severe, spatially localized fading. Movable antennas can mitigate these fades by adjusting antenna position; however, moving all antennas on a multi-link relay node simultaneously may improve one link at the expense of another. This paper introduces QuadBeam, a compact mesh node antenna system comprising four independently positionable 2.4~GHz patch antennas mounted on the side faces of a central hub, together with a 900~MHz monopole for backup voice communication. A preliminary prototype incorporates one motor-driven patch mount and three manually adjustable mounts. Bench measurements show interelement coupling below $-46$~dB between patches on adjacent faces. Measurements at five carrier frequencies from 2.40~GHz to 2.48~GHz in the Edgar Experimental Mine reveal 19~dB to 26~dB received-power variations across transmitter and receiver separations of 8.3~m to 18.3~m. Adjusting the receiver height over approximately 20~cm yields received-power improvements of up to 22~dB. Repositioning neighboring patches and removing or reinstalling the monopole produce no noticeable change in the active link. These results demonstrate the potential of independently positioned antennas to alleviate spatial fading in underground mesh communications.

eess.SY↗

arXiv2026

Spatial-Temporal Hybrid Path Planning for Multi-UAV Methane Emission Rate Quantification

Accurate emission-rate quantification of methane point sources is essential for inventory verification and leak detection. UAV-borne sensing provides high-resolution, near-source measurements, but existing quantification methods mostly follow a predefined trajectory and rely on a single UAV. Building on a source receptor matrix (SRM) estimator and empirical observability Gramian (EMGR)-based path planning, this paper proposes a spatial-temporal hybrid path planning strategy for multi-UAV methane emission rate quantification. Measurements are time-averaged, and the forward Gaussian plume model is calibrated against a filament-based plume simulator (MOABS) to reduce model mismatch. Cooperating UAVs first spread out spatially to locate the source quickly, then switch to sequentially retracing a leader trajectory with a time lag to reduce quantification variance. Simulations show that the spatial stage localizes the source markedly faster than a single-UAV baseline, the temporal stage yields the lowest variance but converges slowly, and the proposed hybrid strategy converges substantially faster than the purely temporal strategy while matching its steady-state accuracy, with a narrower error band than the purely spatial strategy. A window-length study further shows that longer time-averaging windows exhibit diminishing returns, justifying the balance between spatial coverage and temporal averaging.

eess.SY↗

arXiv2026

Multi-Agent Reinforcement Learning for UAV-Based Chemical Plume Source Localization

Undocumented orphaned wells pose significant health and environmental risks to nearby communities by releasing toxic gases and contaminating water sources, with methane emissions being a primary concern. Traditional survey methods such as magnetometry often fail to detect older wells effectively. In contrast, aerial in-situ sensing using unmanned aerial vehicles (UAVs) offers a promising alternative for methane emission detection and source localization. This study presents a robust and efficient framework based on a multi-agent deep reinforcement learning (MARL) algorithm for the chemical plume source localization (CPSL) problem. The proposed approach leverages virtual anchor nodes to coordinate UAV navigation, enabling collaborative sensing of gas concentrations and wind velocities through onboard and shared measurements. Source identification is achieved by analyzing the historical trajectory of anchor node placements within the plume. Comparative evaluations against the fluxotaxis method demonstrate that the MARL framework achieves superior performance in both localization accuracy and operational efficiency.

eess.SY↗

arXiv2025

Retroreflective Optical ISAC for 6G: Technologies, Applications and Future Directions

Integrated sensing and communication (ISAC) has emerged as a key technological paradigm for sixth generation (6G) mobile networks, aiming to unify sensing and communication in a spectrally efficient and hardware lightweight manner. Radio frequency ISAC (RF-ISAC) is constrained by spectrum crowding, limited sensing resolution, and susceptibility to electromagnetic interference. In contrast, optical ISAC (O-ISAC) leverages the large bandwidth and short wavelength of optical carriers and is regarded as an important complement to RF-ISAC. However, conventional O-ISAC relies on natural optical reflections from target surfaces, which generate weak echoes that are highly dependent on surface materials, thereby limiting the achievable sensing range and sensing accuracy. This article introduces retroreflective optical ISAC (RO-ISAC), which alleviates these limitations by equipping targets with compact retroreflective modules. These modules return incident light approximately back to the source over a useful range of incidence angles, forming a well controlled double pass optical path with strong and stable echoes, and thereby further unlocking the application potential of O-ISAC. The conceptual architecture of RO-ISAC is presented together with the underlying retroreflection mechanism. Key enabling technologies for RO-ISAC systems are discussed, including channel modeling, waveform design, bidirectional transmission, and multi-target sensing and communication, with representative details and experimental validation. The suitability of RO-ISAC is analyzed in indoor, aerial, underwater, and satellite scenarios, and challenges and research directions related to mobility, cooperative networking, intelligent operation, and sustainable deployment are outlined, pointing toward robust and scalable RO-ISAC deployment in future 6G networks.

eess.SP↗

arXiv2024

Dynamic Client Clustering, Bandwidth Allocation, and Workload Optimization for Semi-synchronous Federated Learning

Federated Learning (FL) revolutionizes collaborative machine learning among Internet of Things (IoT) devices by enabling them to train models collectively while preserving data privacy. FL algorithms fall into two primary categories: synchronous and asynchronous. While synchronous FL efficiently handles straggler devices, it can compromise convergence speed and model accuracy. In contrast, asynchronous FL allows all devices to participate but incurs high communication overhead and potential model staleness. To overcome these limitations, the semi-synchronous FL framework introduces client tiering based on computing and communication latencies. Clients in different tiers upload their local models at distinct frequencies, striking a balance between straggler mitigation and communication costs. Enter the DecantFed algorithm (Dynamic client clustering, bandwidth allocation, and local training for semi-synchronous Federated learning), a dynamic solution that optimizes client clustering, bandwidth allocation, and local training workloads to maximize data sample processing rates. Additionally, DecantFed adapts client learning rates according to their tiers, addressing the model staleness problem. The algorithm's performance shines in extensive simulations using benchmark datasets, including MNIST and CIFAR-10, under independent and identically distributed (IID) and non-IID scenarios. DecantFed outpaces FedAvg and FedProx in terms of convergence speed and delivers a remarkable minimum 28% boost in model accuracy compared to FedProx.

cs.DC↗

arXiv2022

ABODE-Net: An Attention-based Deep Learning Model for Non-intrusive Building Occupancy Detection Using Smart Meter Data

Occupancy information is useful for efficient energy management in the building sector. The massive high-resolution electrical power consumption data collected by smart meters in the advanced metering infrastructure (AMI) network make it possible to infer buildings' occupancy status in a non-intrusive way. In this paper, we propose a deep leaning model called ABODE-Net which employs a novel Parallel Attention (PA) block for building occupancy detection using smart meter data. The PA block combines the temporal, variable, and channel attention modules in a parallel way to signify important features for occupancy detection. We adopt two smart meter datasets widely used for building occupancy detection in our performance evaluation. A set of state-of-the-art shallow machine learning and deep learning models are included for performance comparison. The results show that ABODE-Net significantly outperforms other models in all experimental cases, which proves its validity as a solution for non-intrusive building occupancy detection.

cs.LG↗

arXiv2022

Backhaul-aware Drone Base Station Placement and Resource Management for FSO-based Drone-assisted Mobile Networks

In drone-assisted mobile networks, Drone-mounted Base Stations (DBSs) are responsively and flexibly deployed over any Places of Interest (PoI), such as sporadic hotspots and disaster-struck areas, where the existing mobile network infrastructure is unable to provide wireless coverage. In this paper, a DBS is an aerial base station to relay traffic between a nearby Macro Base Station (MBS) and the users. In addition, Free Space Optics (FSO) is applied as the backhauling solution to significantly increase the capacity of the backhaul link between an MBS and a DBS. Most of the existing DBS placement solutions assume the FSO-based backhaul link provides sufficient link capacity, which may not be true, especially when a DBS is placed far away from an MBS (e.g., > 10 km in disaster-struck areas) or in a bad weather condition. In this paper, we formulate a problem to jointly optimize bandwidth allocation and DBS placement by considering the FSO-based backhaul link capacity constraint. A Backhaul awaRe bandwidth allOcAtion and DBS placement (BROAD) algorithm is designed to efficiently solve the problem, and the performance of the algorithm is demonstrated via extensive simulations.

cs.NI↗

arXiv2021

Optimal Discrete Constellation Inputs for Aggregated LiFi-WiFi Networks

In this paper, we investigate the performance of a practical aggregated LiFi-WiFi system with the discrete constellation inputs from a practical view. We derive the achievable rate expressions of the aggregated LiFi-WiFi system for the first time. Then, we study the rate maximization problem via optimizing the constellation distribution and power allocation jointly. Specifically, a multilevel mercy-filling power allocation scheme is proposed by exploiting the relationship between the mutual information and minimum mean-squared error (MMSE) of discrete inputs. Meanwhile, an inexact gradient descent method is proposed for obtaining the optimal probability distributions. To strike a balance between the computational complexity and the transmission performance, we further develop a framework that maximizes the lower bound of the achievable rate where the optimal power allocation can be obtained in closed forms and the constellation distributions problem can be solved efficiently by Frank-Wolfe method. Extensive numerical results show that the optimized strategies are able to provide significant gains over the state-of-the-art schemes in terms of the achievable rate.

cs.IT↗

arXiv2018

A Hybrid RF-VLC System for Energy Efficient Wireless Access

In this paper, we propose a new paradigm in designing and realizing energy efficient wireless indoor access networks, namely, a hybrid system enabled by traditional RF access, such as WiFi, as well as the emerging visible light communication (VLC). VLC facilitates the great advantage of being able to jointly perform illumination and communications, and little extra power beyond illumination is required to empower communications, thus rendering wireless access with almost zero power consumption. On the other hand, when illumination is not required from the light source, the energy consumed by VLC could be more than that consumed by the RF. By capitalizing on the above properties, the proposed hybrid RF-VLC system is more energy efficient and more adaptive to the illumination conditions than the individual VLC or RF systems. To demonstrate the viability of the proposed system, we first formulate the problem of minimizing the power consumption of the hybrid RF-VLC system while satisfying the users requests and maintaining acceptable level of illumination, which is NP-complete. Therefore, we divide the problem into two subproblems. In the first subproblems, we determine the set of VLC access points (AP) that needs to be turned on to satisfy the illumination requirements. Given this set, we turn our attention to satisfying the users' requests for real-time communications, and we propose a randomized online algorithm that, against an oblivious adversary, achieves a competitive ratio of $\log(N)\log(M)$ with probability of success $1 - \frac{1}{N}$, where $N$ is the number of users and $M$ is the number of VLC and RF APs. We also show that the best online algorithm to solve this problem can achieve a competitive ratio of $\log(M)$. Simulation results further demonstrate the advantages of the hybrid system.

cs.NI↗

arXiv2017

Indoor Localization Using Visible Light Via Fusion Of Multiple Classifiers

A multiple classifiers fusion localization technique using received signal strengths (RSSs) of visible light is proposed, in which the proposed system transmits different intensity modulated sinusoidal signals by LEDs and the signals received by a Photo Diode (PD) placed at various grid points. First, we obtain some {\emph{approximate}} received signal strengths (RSSs) fingerprints by capturing the peaks of power spectral density (PSD) of the received signals at each given grid point. Unlike the existing RSSs based algorithms, several representative machine learning approaches are adopted to train multiple classifiers based on these RSSs fingerprints. The multiple classifiers localization estimators outperform the classical RSS-based LED localization approaches in accuracy and robustness. To further improve the localization performance, two robust fusion localization algorithms, namely, grid independent least square (GI-LS) and grid dependent least square (GD-LS), are proposed to combine the outputs of these classifiers. We also use a singular value decomposition (SVD) based LS (LS-SVD) method to mitigate the numerical stability problem when the prediction matrix is singular. Experiments conducted on intensity modulated direct detection (IM/DD) systems have demonstrated the effectiveness of the proposed algorithms. The experimental results show that the probability of having mean square positioning error (MSPE) of less than 5cm achieved by GD-LS is improved by 93.03\% and 93.15\%, respectively, as compared to those by the RSS ratio (RSSR) and RSS matching methods with the FFT length of 2000.

stat.ML↗

arXiv2016

Pixelated VLC-backscattering for Self-charging Indoor IoT Devices

Visible light communication (VLC) backscatter has been proposed as a wireless access option for Internet of Things (IoT). However, the throughput of the state-of-the-art VLC backscatter is limited by simple single-carrier pulsed modulation scheme, such as on-off keying (OOK). In this paper, a novel pixelated VLC backscatter is proposed and implemented to overcome the channel capacity limitation. In particular, multiple smaller VLC backscatters, switching on or off, are integrated to generate multi-level signals, which enables the usage of more advanced modulation schemes than OOK. Based on experimental results, rate adaptation at different communication distances can be employed to enhance the achievable data rate. Compared to OOK, the data rate can be tripled when 8-PAM is used at 2 meters. In general, $n$-fold throughput enhancement is realized by utilizing $n$ smaller VLC backscatters while incurring negligible additional energy using the same device space as that of a single large backscatter.

cs.IT↗

arXiv2015

Delay Analysis of Hybrid WiFi-LiFi System

Heterogeneous wireless networks are capable of effectively leveraging different access technologies to provide a wide variety of coverage areas. In this paper, the coexistence of WiFi and visible light communication (VLC) is investigated as a paradigm. The delay of two configurations of such heterogeneous system has been evaluated. In the first configuration, the non-aggregated system, any request is either allocated to WiFi or VLC. While in the second configuration, the aggregated system, each request is split into two pieces, one is forwarded to WiFi and the other is forwarded to VLC. Under the assumptions of Poisson arrival process of requests and the exponential distribution of requests size, it is mathematically proved that the aggregated system provides lower minimum average system delay than that of the non-aggregated system. For the non-aggregated system, the optimal traffic allocation ratio is derived. For the aggregated system, an efficient solution for the splitting ratio is proposed. Empirical results show that the solution proposed here incurs a delay penalty (less than 3\%) over the optimal result.

cs.NI↗

arXiv2015

Joint Link Scheduling and Brightness Control for Greening VLC-based Indoor Access Networks

Demands for broadband wireless access services is expected to outstrip the spectrum capacity in the near-term - "spectrum crunch". Deploying additional femotocells to address this challenge is cost-inefficient, due to the backhaul challenge and the exorbitant system maintenance. According to an Alcatel-Lucent report, most of the mobile Internet access traffic happens indoor. Leveraging power line communication and the available indoor infrastructure, visible light communication (VLC) can be utilized with small one-time cost. VLC also facilitates the great advantage of being able to jointly perform illumination and communications, and little extra power beyond illumination is required to empower communications, thus rendering wireless access with small power consumption. In this study, we investigate the problem of minimizing total power consumption of a general multi-user VLC indoor network while satisfying users' traffic demands and maintaining an acceptable level of illumination. We utilize the column generation method to obtain an $ε$-bounded solution. Several practical implementation issues are integrated with the proposed algorithm, including different configurations of light source and ways of resolving the interference among VLC links. Through extensive simulations, we show that our approach reduces the power consumption of the state-of-art VLC-based scheduling algorithms by more than 60\% while maintaining the required illumination.

cs.NI↗

arXiv2015

Design of a visible-light-communication enhanced WiFi system

Visible light communication (VLC) has wide unlicensed bandwidth, enables communication in radio frequency (RF) sensitive environments, realizes energy-efficient data transmission, and has the potential to boost the capacity of wireless access networks through spatial reuse. On the other hand, WiFi provides more coverage than VLC and does not suffer from the likelihood of blockage due to the light of sight (LOS) requirement of VLC. In order to take the advantages of both WiFi and VLC, we propose and implement two heterogeneous systems with Internet access. One is the hybrid WiFi-VLC system, utilizing unidirectional VLC channel as downlink and reserving the WiFi back-channel as uplink. The asymmetric solution resolves the optical uplink challenges and benefits from the full-duplex communication based on VLC. To further enhance the robustness and increase throughput, the other system is presented, in which we aggregate WiFi and VLC in parallel by leveraging the bonding technique in Linux operating system. Online experiment results reveal that the hybrid system outperforms the conventional WiFi for the crowded environments in terms of throughput and web page loading time; and also demonstrate the further improved performance of the aggregated system when considering the blocking duration and the distance between access point and user device.

cs.NI↗