Papers for

wireless communication teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

CNN-Transformer improves angle detection accuracy below one degree

CNN-Transformer with Sigmoid Label for High-Resolution Sub-Degree DOA Estimation

Abstract: This paper addresses the grid quantization error of deep-learning (DL)-based direction-of-arrival (DOA) estimation. Classification- based DOA methods discretize the continuous angular space into a grid, so that with a peak-based read-out, the accuracy and resolution are limited by the grid step and by the width of the label peak. To address this issue, we propose a sigmoid label that encodes the sources' DOAs as the sum of angle-shifted monotonic sigmoid functions. Differentiating the output of a CNN-Transformer network trained with the sigmoid label produces sharp spectral peaks at the source DOAs. Combined with a moderately refined grid step of 0.25 degrees, the CNN-Transformer with the proposed sigmoid label attains sub-degree accuracy and a 1-degree resolution, breaking the limit of the grid quantization error. Under the unified CNN-Transformer backbone, we compare the proposed Sigmoid label with the Gaussian label, the 0-1 hard label, and the binary cross-entropy (BCE) baseline. Numerical results demonstrate that the Sigmoid label attains the highest accuracy and superior resolution compared to other labels.

Fri 9 OctArtificial Intelligence
The gist
Estimating the direction from which sounds or signals come is often done by dividing the space into fixed steps, but this limits how precisely you can tell the direction. The authors created a new label style using sigmoid functions that better captures slight shifts in angle. By training a neural network combining CNN and Transformer components with this label, they achieve more precise direction estimates than before, going below one degree of error. Their method works better than previous labeling styles on this task.
Open → 2610.13433v1

Short-Time Fourier transform zeros reveal noise characteristics clearly

Zeros of the Spectrogram of Colored Noise

Abstract: We study the expected number of zeros of the Short-Time Fourier Transform (STFT) with a Gaussian window for signals degraded by complex Gaussian colored noise. We provide an exact formula for this quantity and investigate its asymptotic behavior. From a computational perspective, we propose an algorithmic method to recover a smoothed power spectral density (PSD) directly from the spatial distribution of the transform's zeros. Our results formalize the commonly-held heuristic that detection algorithms based on spectrogram zeros, though designed for white noise, also perform adequately under moderately colored noise.

Wed 16 SeptInformation Theory
The gist
Signals often get noisy, making it hard to analyze them. This paper looks at a specific way to study signals called the Short-Time Fourier Transform and focuses on where this transform hits zero when noise is added. The authors found an exact formula for how often these zeros appear when the noise has some color (not just random white noise). They also show how these zeros can help recover information about the noise's power, guiding better noise detection even when the noise is not perfectly random.
Open → 2609.18689v1