A two-stream network learning a residual over an analytic prior is proposed, fusing environment and physics streams by cross-attention, and reduces RMSE by 35.3% zero-shot at unseen carriers and scenes, and by 37.6% at extrapolated 60 GHz, outperforming interpolated 10 GHz.
Abstract
Deep learning models for radio map prediction are trained and tested at the same carriers and cannot serve unseen frequencies. We propose a two-stream network learning a residual over an analytic prior, fusing environment and physics streams by cross-attention. Evaluated at the query frequency, this free-space and knife-edge prior absorbs dominant frequency scaling of pathloss, leaving a residual that varies little across bands. Across 150 ray-traced scenes and four training carriers (1.8--28 GHz), the method reduces RMSE by 35.3% zero-shot at unseen carriers and scenes, and by 37.6% at extrapolated 60 GHz, outperforming interpolated 10 GHz.
Existing radio-frequency (RF) neural fields fit each scene separately, making new-scene deployment measurement- and optimization-intensive. This work studies amortized cross-scene spatial spectrum synthesis, where a shared model learns propagation across scenes and instantiates an unseen scene from sparse target-scene...
Kang Yang, Duaa Nakshbandi, Wan Du et al.· 0 citations
Multiscale Spectral Rate-Distortion (MS-SRD) estimates the bottleneck channels required at user-supplied spatial cuts from training images and a normalized mean-squared error (NMSE) bound, without fitting a neural network. Its covariance-tail rule is exact for shared linear block-convolutional autoencoders under square...
Frequency continuation does not improve every neural representation, and better velocity recovery need not imply better prediction of unseen waveforms. We compare a velocity grid with total-variation regularization, an adapted sinusoidal coordinate network (SIREN), and a frozen image generator with a full-grid residual...
The proposed framework maintains consistent prediction performance across all untrained European cities, while the uncertainty estimation mechanism provides calibrated confidence measures that may support operational decision-making, making the proposed framework a promising approach for large-scale predictive link ada...
Salma A. El-Sayed, Micheal Ibrahim, Hadia M. Elhennawy et al.· Journal on Wireless Communic...· 0 citations
Adapting deep-learning receivers after deployment is challenging because ground-truth channel state and transmitted payload symbols are unavailable, while sparse pilots alone provide insufficient supervision. The orthogonal frequency-division multiplexing waveform, however, embeds physical structure that can provide la...
Le Cheng, Xiao-Ran Liu, Ling-Jin Kong et al.· IEEE Transactions on Wireles...· 0 citations
