{"ID":2836003,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.22473","arxiv_id":"2511.22473","title":"Learning to Count Targets from Dual-Window: A CNN Approach for OFDM ISAC","abstract":"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.","short_abstract":"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, y...","url_abs":"https://arxiv.org/abs/2511.22473","url_pdf":"https://arxiv.org/pdf/2511.22473v1","authors":"[\"Ali Al Khansa\"]","published":"2025-11-27T14:03:39Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}