The approach of applying deep learning-based object detectors to wideband spectrograms for signal detection, classification, and localization has garnered increasing interest. However, the diversity of signal bandwidths and durations results in significant variations in the scales and aspect ratios of signal bounding boxes within spectrograms. These characteristics pose the anchor mismatch problem for the anchor-based detectors in existing methods, leading to inaccurate time-frequency localization and suboptimal detection performance. This letter proposes a novel signal detector that employs a concise anchor-free paradigm instead of anchors to detect signals. Furthermore, a coarse-grained classification to fine-grained regression strategy rather than direct regression is adopted to achieve more accurate time-frequency localization information. Experimental results demonstrate that the proposed detector outperforms the deep learning-based baselines.

The spectrogram-based wideband signal detection framework has garnered increasing attention in various wireless communication applications. However, the front-end spectrograms in existing methods suffer from visual and informational deficiencies. This paper proposes a novel multichannel enhanced spectrogram (MCE spectrogram) to address these issues. The MCE spectrogram leverages additional channels for both visual and informational enhancement, highlighting signal regions and features while integrating richer recognition information across channels, thereby significantly improving feature extraction efficiency. Moreover, the back-end networks in existing methods are typically transferred from original object detection networks. Wideband signal detection, however, exhibits task-specific characteristics, such as the inherent signal-to-noise ratio (SNR) attribute of the spectrogram and the large variations in shapes of signal bounding boxes. These characteristics lead to issues like inefficient task adaptation and anchor mismatch, resulting in suboptimal performance. To tackle these challenges, we propose an SNR-aware detection network that employs an anchor-free paradigm instead of anchors for signal detection. Additionally, to address the impact of the SNR attribute, we design a trainable gating module for efficient feature fusion and introduce an auxiliary task branch to enable the network to capture more discriminative feature representations under varying SNRs. Experimental results demonstrate the superiority of the MCE spectrogram compared to those utilized in existing methods and the state-of-the-art performance of our SNR-aware Net among comparable detection networks.

Deep learning-based object detectors have become increasingly critical in spectrogram-based wideband multi-signal detection, recognition, and time-frequency localization. Current methods primarily evolve from anchor-based detectors and keypoint-based anchor-free detectors. However, these detectors encounter limitations such as anchor mismatch and inefficient adaptation of detection paradigms to signal characteristics, leading to suboptimal performance. This letter proposes a novel anchor-free method that leverages a center frequency axis learning mechanism, which facilitates the capture of more robust features. Furthermore, a sample-weighting training strategy is proposed to recalibrate the contribution of samples with varying feature representation capabilities to the loss, thereby improving network training efficiency and performance. Experimental results demonstrate the superiority of the proposed method compared to existing state-of-the-art wideband signal detectors.

Aiming to boost the throughput performance in mobile networks, network-coded multiple access (NCMA) has been proposed as a new framework in non-orthogonal multiple access (NOMA). Normally, user-centric design is adopted in NCMA to allocate the same power to different users. However, devices and the corresponding messages may have different quality-of-service (QoS) requirements (such as delay or throughput) due to the potential content diversity in Internet of Things (IoT) networks. In this paper, we present a power-diverse NCMA (PD-NCMA) system. In particular, a two-stage framework in NCMA downlink is adopted. Based on the throughput requirement of content, messages are given different levels, and content-centric clustering is solved dynamically in the first stage. Then, power allocation in each cluster is realized in the second stage. Numerical results demonstrate the feasibility of the proposed PD-NCMA, and the throughput of PD-NCMA significantly outperforms the traditional NCMA system.

The wideband signal detection framework which applies deep learning-based object detection networks to wideband spectrograms for joint signal detection, classification, and time-frequency localization has attracted growing interest. However, it remains an open question whether this approach can effectively distinguish PSK/QAM modulation signals with visually confusing time-frequency characteristics in spectrograms. This paper proposes a phase-aware spectrogram (PA spectrogram) to answer this question positively. Furthermore, previous state-of-the-art networks designed for wideband signal detection mainly employ anchor-based and direct localization regression-based detection paradigms. Nevertheless, the diversity of signal transmission parameters results in significantly varying scales and aspect ratios of signal bounding boxes in spectrograms as well as large regression variance for localization. These factors result in the anchor mismatch problem and learning difficulty of direct regression, ultimately leading to suboptimal detection performance and inaccurate time-frequency localization. To address these problems, we propose a novel detection network based on the dual-granularity cooperative localization, termed DGCL-Net, which employs a concise anchor-free paradigm and a coarse-grained classification plus fine-grained regression strategy to achieve more accurate time-frequency localization. Experiments on synthetic and real signal datasets demonstrate the effectiveness of the PA spectrogram and the superiority of the DGCL-Net compared to other baseline networks.