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.

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.