arXiv subjects

Dengke Wang

Publications and source records attributed to Dengke Wang.

5 recordsLinked to original sources

arXiv2026

A Distributed Microphone Network for Real-Time Gunshot Detection and Localization in GNSS-Challenged Large Areas

This paper presents a scalable distributed acoustic surveillance system for real-time gunshot detection and localiza tion in large-area, GNSS-challenged environments. The system consists of multiple low-cost MEMS microphone array units (MAUs) deployed as distributed anchors. Each MAU performs edge-level sound-event detection using a lightweight Sound De tection and Echo Suppression (SDES) algorithm, followed by a TinyML-based classifier for gunshot identification. For detected gunshots, an enhanced Generalized Cross-Correlation (eGCC) al gorithm estimates intra-array time differences of arrival (TDoAs) with improved robustness under low signal-to-noise ratio and re verberant conditions. Angle-of-arrival (AoA) estimates obtained from individual MAUs are fused through a modified SCO-EV localization framework to determine the gunshot position in a global coordinate system. A misplacement calibration (MC) algorithm compensates for platform orientation errors, improv ing angular consistency and localization accuracy in practical field conditions. Extensive experiments conducted over coverage areas ranging from 4,985 m2 to 49,063 m2 demonstrate reliable operation across varying spatial scales. The proposed framework achieves a Matthews Correlation Coefficient (MCC) of 0.70 for gunshot recognition and average localization errors below 6.5 m over 25,000 m2 , while maintaining errors under 15 m at larger scales. The results demonstrate the practical potential of the proposed system for distributed gunshot surveillance in GNSS challenged large areas.

eess.SP↗

arXiv2026

Logit-Aware MIMO AirComp for Distributed Mixture-of-Experts LLM Inference over Wireless Edge Networks

Distributed mixture-of-experts (MoE) inference is a promising architecture for deploying large language models (LLMs) at wireless edge networks because sparse experts can be placed across coordinated base stations (BSs), while the anchor node and user equipment (UE) can offload LLM inference tasks to BSs. The communication bottleneck is the MoE aggregation, where the anchor BS must recover a weighted sum of selected expert outputs before each decoding step. Over-the-air computation (AirComp) is well matched to this operation because the wireless multiple-access channel naturally superposes simultaneous transmissions. However, conventional AirComp minimizes communication distortion, whereas MoE aggregation errors have unequal impact on LLM outputs. We propose a logit-aware MIMO AirComp framework that estimates local logit sensitivity as block-level weights and jointly optimizes receive combiners and BS precoders under per-BS power constraints. We also develop an alternating algorithm that protects decoding decisions from aggregation perturbations. Using OpenCompass, we evaluate Qwen3-30B-A3B-Instruct-2507-FP8 on GSM8K and ARC-Challenge. At 30 dB aggregation SNR, perturbed Qwen3 retains 99.3% of clean GSM8K accuracy and 94.8% of clean ARC-Challenge accuracy. In a direct ARC-Challenge closed-loop audit over 295 examples, SW-AirComp achieves 43.73% accuracy at 30 dB and 26.78% at 20 dB, outperforming unweighted, matched-filter, and zero-forcing AirComp. In the wireless simulator, SW-AirComp reduces decision-relevant aggregation distortion; at 20 dB, its final weighted-sum mean-squared error is 53.5% lower than unweighted AirComp under the same channels, samples, and sensitivity weights. Logit-RMSE and logit-gap diagnostics further show that the gain comes from reducing output-logit perturbation and lowering the risk of top-token changes.

cs.IT↗

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.

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arXiv2022

On the Performance of Non-Terrestrial Networks to Support the Internet of Things

The advent of the Internet of Things (IoT) era, where billions of devices and sensors are becoming more and more connected and ubiquitous, is putting a strain on traditional terrestrial networks, that may no longer be able to fulfill service requirements efficiently. This issue is further complicated in rural and remote areas with scarce and low-quality cellular coverage. To fill this gap, the research community is focusing on non-terrestrial networks (NTNs), where Unmanned Aerial Vehicles (UAVs), High Altitude Platforms (HAPs) and satellites can serve as aerial/space gateways to aggregate, process, and relay the IoT traffic. In this paper we demonstrate this paradigm, and evaluate how common Low-Power Wide Area Network (LPWAN) technologies, designed and developed to operate for IoT systems, work in NTNs. We then formalize an optimization problem to decide whether and how IoT traffic can be offloaded to LEO satellites to reduce the burden on terrestrial gateways.

cs.NI↗

arXiv2021

The Potential of Multilayered Hierarchical Nonterrestrial Networks for 6G: A Comparative Analysis Among Networking Architectures

6th generation (6G) communication research is currently focusing on non-terrestrial networks (NTNs) to promote ubiquitous and ultra-high-capacity global connectivity. Specifically, multi-layered hierarchical networks, i.e., the orchestration among different aerial/space platforms, including Low and High Altitude Platforms (LAPs and HAPs), and satellites co-operating at different altitudes, currently represent one the most attractive technological options to solve coverage and latency constraints associated with the NTN paradigm. However, there are still several issues to be resolved for proper network design. In this work, we evaluate the performance of different multi-layered non-terrestrial configurations, and provide guidelines on the optimal working point(s) for which it is possible to achieve a good compromise between improved system flexibility and network performance, with respect to a baseline standalone deployment.

cs.NI↗