arXiv subjects

Ali Al Khansa

Publications and source records attributed to Ali Al Khansa.

5 recordsLinked to original sources

arXiv2026

Scalable Extended-Target Handover in Distributed Integrated Sensing and Communication

Distributed integrated sensing and communication (DISAC) networks require multi-target tracking methods whose per-node computation and inter-base-station communication remain bounded as both the network and target populations grow. Existing multisensor fusion methods provide a strong theoretical foundation, but scalability is seldom treated as the primary design objective. We develop a grouped-measurement belief-propagation multi-target tracking (MTT) method and combine it with a proposed event-triggered target-handover protocol. When a tracked target is predicted to become observable at another base station, the owner transfers a predicted track message while retaining its local copy. Under bounded local workload and neighbor degree, we show that all variants of the proposed handover methods have network-size-independent per-node loads. Simulations demonstrate that handover reduces boundary track loss relative to uncoordinated processing and approaches coordinated-processing accuracy with lower communication overhead.

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arXiv2026

Bridging the Data Gap: Digital Twin as a New Paradigm for AI-based Radio Sensing

We present a methodology that places a 3D digital twin (DT) of the environment as the main enabler behind the development of radio sensing at scale. The DT acts as a world model, providing geometry, materials, and transmitter/receiver placements to a ray-tracing engine that generates time-indexed channel impulse responses (CIRs) for large numbers of plausible scenes (moving people and objects, layout variants, seasonal/weather conditions, etc). From these synthetic sequences, we train a sequential neural network that maps CIR time series to spatial occupancy estimates, enabling device-free localization (DFL) without instrumented targets. We posit that sensing is best approached as an environment-conditioned learning problem: rather than seeking a single global model, we advocate training or fine-tuning local models specialized to a site-specific DT. As a first experiment, we introduce a novel State Space Model architecture, trained and evaluated across multiple room geometries. The localization performances obtained demonstrate the potential of the approach.

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arXiv2025

Adaptive Dual-Windowing Strategies for Multi-Target Detection in OFDM ISAC

In Orthogonal Frequency Division Multiplexing (OFDM) Integrated Sensing and Communication (ISAC) systems, a key challenge is balancing sidelobe attenuation and resolution for multi-target detection scenarios. While windowing functions are typically used to manage this trade-off, state-of-the-art methods rely on a single, fixed window followed by a predefined detection strategy (e.g., Binary Successive Target Cancellation (BSTC) (low complexity) or Coherent Successive Target Cancellation (CSTC) (high performance)). This paper proposes a novel dual-window periodogram-based algorithm that leverages two complementary windows: one optimized for resolution and the other for sidelobe suppression. Then, a low-complexity detection algorithm (e.g., BSTC) is applied to both, and a decision mechanism compares the outputs. When results align, the resolution-optimized estimates are directly used; otherwise, high performance algorithm (e.g., CSTC) is triggered to resolve ambiguities. This adaptive approach dynamically balances the detection performance and the complexity, addressing limitations in existing fixed strategies. The Numerical results confirm that the proposed method achieves high performance while reducing the complexity, especially at a high Signal to Noise Ratio (SNR).

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arXiv2025

Learning to Count Targets from Dual-Window: A CNN Approach for OFDM ISAC

Integrated Sensing and Communication (ISAC) with Orthogonal Frequency Division Multiplexing (OFDM) waveforms is a key enabler for next-generation wireless systems. Recent studies show that Convolutional Neural Networks (CNNs) can estimate the number of targets from two-dimensional (2D) range-Doppler periodogram maps, yet accuracy often degrades as scenes become denser. One significant factor is the classical resolution-sidelobe attenuation trade-off, which limits performance when targets are weak or closely spaced. While windowing is routinely applied to shape this trade-off, the choice is typically static. This paper proposes a new CNN method that uses two windowed range-Doppler periodograms and learns to fuse complementary views: one window optimized for resolution and one window optimized for sidelobe suppression. The design explicitly targets the resolution-sidelobe attenuation trade-off by exposing the model to complementary windowed maps and letting it learn when each is most informative. Numerical experiments show consistent gains over single-window CNN baselines, with better scaling in target density and greater robustness across different noise levels.

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arXiv2025

Dynamic Power Allocation in OFDM ISAC for Time of Arrival Estimation

Resource allocation in Integrated Sensing and Communication (ISAC) systems is critical for balancing communication and sensing performance. This paper introduces a novel dynamic power allocation strategy for Orthogonal Frequency Division Multiplexing (OFDM) ISAC systems, optimizing communication capacity while adhering to Peak Side-lobe Level (PSL) and sensing accuracy constraints, particularly for Time of Arrival (ToA) estimation. Unlike conventional methods that address either PSL or accuracy in isolation, our approach dynamically allocates power to satisfy both constraints. Additionally, it prioritizes communication when sensing performance is insufficient, avoiding any loss in communication capacity. Numerical results validate the importance of considering both sensing constraints and demonstrate the effectiveness of the proposed dynamic power allocation strategy.

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