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Pooi-Yuen Kam

Publications and source records attributed to Pooi-Yuen Kam.

12 recordsLinked to original sources
arXiv2026

Bayesian Bandit Beamforming with Implicit Channel Learning for RIS under Hybrid Near/Far-Field Propagation

Reconfigurable intelligent surfaces (RISs) can improve high-frequency wireless links by shaping the propagation environment, but their passive architecture makes channel acquisition costly. Conventional estimate-then-optimize methods usually require pilot overhead that scales with the number of reflecting elements, which is undesirable under short coherence times and hybrid near-/far-field propagation. This paper proposes the Bayesian bandit framework for RIS phase-shift configuration with implicit channel learning. The method updates a Gaussian posterior of the cascaded channel from one scalar pilot observation per slot and uses Thompson sampling to balance channel estimation and beamforming gain. We derive a Bayesian regret decomposition that connects Bayesian received-power regret to posterior uncertainty contraction, and further establish a conditional sublinear Bayesian-regret guarantee. To exploit sparse hybrid-field propagation, we develop an energy-focusing angle-distance dictionary and a sparse Bayesian learning (SBL)-based Thompson-sampling algorithm with warm-started hyperparameter refinement. Simulations show that the proposed policies approach the perfect-channel state information (CSI) benchmark in the line-of-sight (LOS)-dominant setting within 10 time block and improve transmission efficiency over the considered baselines in multipath and Rayleigh fading scenarios.

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arXiv2026

Privacy-Preserving Federated Radio Map Learning for Wireless Digital Twins via Adaptive Noise Allocation

Radio maps provide a foundational data layer for wireless digital twins, and federated learning offers a natural framework for their distributed construction without centralizing raw radio environment data. However, the exchanged client model updates may still leak transmitter-location information, even when the underlying measurement data are never shared. Existing noise-based privacy defenses inject perturbation either uniformly across all uploaded coordinates or according to a fixed static rule, thereby ignoring the architecture-specific structure of this leakage. This paper proposes a budget-constrained adaptive noise allocation mechanism that redistributes a fixed perturbation budget across transmitter-sensitive upload groups identified from the two-stage RadioUNet architecture. The proposed method uses low-dimensional upload statistics to dynamically adjust group-wise noise scales and is integrated locally before client upload transmission. We evaluate the framework on a federated radio map learning task under a unified noise multiplier, comparing it against uniform and structure-aware baselines using reconstruction mean squared error and transmitter localization error as metrics. Results show that adaptive allocation achieves the strongest privacy protection while maintaining the best reconstruction quality among all noise-based defenses under a matched perturbation budget.

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arXiv2026

Robust Semantic Transmission for Low-Altitude UAVs: Predictive Channel-Aware Scheduling and Generative Reconstruction

Unmanned aerial vehicle (UAV) downlink transmission facilitates critical time-sensitive visual applications but is fundamentally constrained by bandwidth scarcity and dynamic channel impairments. The rapid fluctuation of the air-to-ground (A2G) link creates a regime where reliable transmission slots are intermittent and future channel quality can only be predicted with uncertainty. Conventional deep joint source-channel coding (DeepJSCC) methods transmit coupled feature streams, causing global reconstruction failure when specific time slots experience deep fading. Decoupling semantic content into a deterministic structure component and a stochastic texture component enables differentiated error protection strategies aligned with channel reliability. A predictive transmission framework is developed that utilizes a split-stream variational codec and a channel-aware scheduler to prioritize the delivery of structural layout over reliable slots. Experimental evaluations indicate that this approach achieves a 5.6 dB gain in peak signal-to-noise (SNR) ratio over single-stream baselines and maintains structural fidelity under significant prediction mismatch.

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arXiv2026

Physics-Aware Tensor Reconstruction for Radio Maps in Pixel-Based Fluid Antenna Systems

The deployment of pixel-based antennas and fluid antenna systems (FAS) is hindered by prohibitive channel state information (CSI) acquisition overhead. While radio maps enable proactive mode selection, reconstructing high-fidelity maps from sparse measurements is challenging. Existing physics-agnostic or data-driven methods often fail to recover fine-grained shadowing details under extreme sparsity. We propose a Physics-Regularized Low-Rank Tensor Completion (PR-LRTC) framework for radio map reconstruction. By modeling the signal field as a three-way tensor, we integrate environmental low-rankness with deterministic antenna physics. Specifically, we leverage Effective Aerial Degrees-of-Freedom (EADoF) theory to derive a differential gain topology map as a physical prior for regularization. The resulting optimization problem is solved via an efficient Alternating Direction Method of Multipliers (ADMM)-based algorithm. Simulations show that PR-LRTC achieves a 4 dB gain over baselines at a 10% sampling ratio. It effectively preserves sharp shadowing edges, providing a robust, physics-compliant solution for low-overhead beam management.

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arXiv2025

Geometry-Aligned Differential Privacy for Location-Safe Federated Radio Map Construction

Radio maps that describe spatial variations in wireless signal strength are widely used to optimize networks and support aerial platforms. Their construction requires location-labeled signal measurements from distributed users, raising fundamental concerns about location privacy. Even when raw data are kept local, the shared model updates can reveal user locations through their spatial structure, while naive noise injection either fails to hide this leakage or degrades model accuracy. This work analyzes how location leakage arises from gradients in a virtual-environment radio map model and proposes a geometry-aligned differential privacy mechanism with heterogeneous noise tailored to both confuse localization and cover gradient spatial patterns. The approach is theoretically supported with a convergence guarantee linking privacy strength to learning accuracy. Numerical experiments show the approach increases attacker localization error from 30 m to over 180 m, with only 0.2 dB increase in radio map construction error compared to a uniform-noise baseline.

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arXiv2025

Noncoherent Detection of Constant-Envelope Signals for Mobile Edge Applications -- Optimum Detectors and Intelligent Decision Rule

Constant-envelope signals are widely used in mobile edge applications and wireless communication systems for their hardware-friendly design, energy efficiency, and reliability. However, reliable detection with simple, power-efficient receivers remains challenging. Coherent methods offer superior performance but require complex synchronization, increasing complexity and power use. Noncoherent detection is simpler, avoiding synchronization, but traditional approaches rely on in-phase and quadrature-phase (IQ) demodulators for signal magnitudes and assume energy detectors without theoretical justification. This paper proposes a framework for optimal detection using a bandpass-filter envelope-detector (BFED) with Bayes criterion and generalized likelihood ratio test (GLRT) under unknown amplitudes. Using modified Bessel function approximations, we show the optimal detector shifts based on SNR: in the low-SNR regime, we rigorously prove for the first time that the well-known energy detector (ED) is the Bayesian-optimal solution, thus providing a firm theoretical foundation for its widespread use; in high-SNR regimes, a novel amplitude detector (AD) compares estimated amplitude to noise deviation, leading to a simple yet optimal detection strategy. For unknown SNR, a reliability-based intelligent decision (RID) rule adaptively selects detectors, leveraging their strengths across SNR ranges. Simulations confirm energy and amplitude detectors minimize errors in their domains, with RID providing robust gains. The proposed framework provides a rigorous theoretical foundation and enables low-complexity implementations for resource-constrained, interference-limited mobile edge applications, including wireless sensor networks (WSNs) and Internet of Things (IoT) systems.

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arXiv2025

Sensing, Detection and Localization for Low Altitude UAV: A RF-Based Framework via Multiple BSs Collaboration

The rapid growth of the low-altitude economy has resulted in a significant increase in the number of Low, slow, and small (LLS) unmanned aerial vehicles (UAVs), raising critical challenges for secure airspace management and reliable trajectory planning. To address this, this paper proposes a cooperative radio-frequency (RF) detection and localization framework that leverages existing cellular base stations. The proposed approach features a robust scheme for LSS target identification, integrating a cell averaging-constant false alarm rate (CA-CFAR) detector with a micro-Doppler signature (MDS) based recognition method. Multi-station measurements are fused through a grid-based probabilistic algorithm combined with clustering techniques, effectively mitigating ghost targets and improving localization accuracy in multi-UAV scenarios. Furthermore, the Cramer-Rao lower bound (CRLB) is derived as a performance benchmark and reinforcement learning (RL)-based optimization is employed to balance localization accuracy against station resource usage. Simulations demonstrate that increasing from one to multiple BSs reduces the positioning error to near the CRLB, while practical experiments further verify the framework's effectiveness. Furthermore, our RL-based optimization can find solutions that maintain high accuracy while minimizing resource usage, highlighting its potential as a scalable solution for ensuring airspace safety in the emerging low-altitude economy.

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arXiv2017

Background Radiation Cancellation for Free-Space Optical Communications with IM/DD

Besides atmospheric turbulence and pointing errors which cause the signal intensity fluctuation, background radiation also impairs the free-space optical intensity-modulation / direct-detection link performance by introducing a noisy photocurrent component in the receiver. Methods such as adopting some specific optics systems, using pilot symbols or line codes to estimate the background information and cancel it accordingly, and pulse-position modulation (PPM), can be adopted to mitigate the impact of background radiation. However, purely depending on the optics system, the background radiation can only be reduced, but not completely cancelled; and the use of any of pilot symbols, line codes and PPM reduces the system spectral efficiency drastically. In this paper, based on the generalized likelihood ratio test (GLRT) principle, we develop a Viterbi-type trellis-search sequence receiver (the GLRT sequence receiver) that can estimate the unknown channel gain and the background radiation simultaneously, and detect the data sequence accordingly. This receiver requires very few pilot symbols, and therefore, does not significantly reduce the bandwidth efficiency. Its error performance can approach that of detection with perfect information of the channel state and the background radiation, as the observation window length used for forming the decision metric increases. Since a Viterbi-type trellis-search algorithm is adopted, the search complexity is very low and is independent of the observation window size. However, the search complexity of the GLRT sequence receiver grows exponentially with the modulation order. To further simplify the implementation, we derive a more efficient decision-feedback symbol-by-symbol receiver which retains the same error performance as that of the GLRT sequence receiver.

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arXiv2015

Efficient Symbol Detection for the FSO IM/DD System with Automatic and Adaptive Threshold Adjustment: The Multi-level PAM Case

To detect M-ary pulse amplitude modulation signals reliably in an FSO communication system, the receiver requires accurate knowledge about the instantaneous channel attenuation on the signal. We derive here an optimum, symbol-by-symbol receiver that jointly estimates the attenuation with the help of past detected data symbols and detects the data symbols accordingly. Few pilot symbols are required, resulting in high spectral efficiency. Detection can be performed with a very low complexity. From both theoretical analysis and simulation, we show that as the number of the detected data symbols used for estimating the channel attenuation increases, the bit error probability of our receiver approaches that of detection with perfect channel knowledge.

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arXiv2015

Robust Data Detection for the Photon-Counting Free-Space Optical System with Implicit CSI Acquisition and Background Radiation Compensation

Since atmospheric turbulence and pointing errors cause signal intensity fluctuations and the background radiation surrounding the free-space optical (FSO) receiver contributes an undesired noisy component, the receiver requires accurate channel state information (CSI) and background information to adjust the detection threshold. In most previous studies, for CSI acquisition, pilot symbols were employed, which leads to a reduction of spectral and energy efficiency; and an impractical assumption that the background radiation component is perfectly known was made. In this paper, we develop an efficient and robust sequence receiver, which acquires the CSI and the background information implicitly and requires no knowledge about the channel model information. It is robust since it can automatically estimate the CSI and background component and detect the data sequence accordingly. Its decision metric has a simple form and involves no integrals, and thus can be easily evaluated. A Viterbi-type trellis-search algorithm is adopted to improve the search efficiency, and a selective-store strategy is adopted to overcome a potential error floor problem as well as to increase the memory efficiency. To further simplify the receiver, a decision-feedback symbol-by-symbol receiver is proposed as an approximation of the sequence receiver. By simulations and theoretical analysis, we show that the performance of both the sequence receiver and the symbol-by-symbol receiver, approach that of detection with perfect knowledge of the CSI and background radiation, as the length of the window for forming the decision metric increases.

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arXiv2014

Efficient Direct Detection of M-PAM Sequences with Implicit CSI Acquisition for The FSO System

Compared to on-off keying (OOK), M-ary pulse amplitude modulation (M-PAM, M>2) is more spectrally efficient. However, to detect M-PAM signals reliably, the requirement of accurate channel state information is more stringent. Previously, for OOK systems, we have developed a receiver that requires few pilot symbols and can jointly detect the data sequence and estimate the unknown channel gain implicitly. In this paper, using the same approach, we extend our previous work and derive a generalized receiver for M-PAM systems. A Viterbi-type trellis-search algorithm coupled with a selective-store strategy is adopted, resulting in a low implementation complexity and a low memory requirement. Therefore, the receiver is efficient in terms of energy, spectra, implementation complexity and memory. Using theoretical analysis, we show that its error performance approaches that of maximum likelihood detection with perfect knowledge of the channel gain, as the observation window length increases. Also, simulation results are presented to justify the theoretical analysis.

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