Automatic modulation recognition plays a crucial role in the domain of electromagnetic situational awareness. Early recognition methods predominantly relied on expert experience and prior knowledge, demanding a high level of professional background and experience from practitioners, and usually underperformed in complex signal environments. In recent years, the continual development of deep learning (DL) technologies has introduced solution ideas to address the challenge of modulation recognition in complex electromagnetic environments. However, DL methods heavily depend on large volumes of high-quality labeled data and face challenges in real electromagnetic environments with limited samples. To fully leverage the respective strengths of expert knowledge in the radio domain and data-driven approaches, this article proposes a hybrid-driven neural network (HDNet) framework for radio signal recognition. HDNet integrates deep features extracted through data-driven methods with manual features extracted based on expert knowledge, aiming to enhance recognition performance in few-shot scenarios. Experimental results on both simulated and real measured datasets demonstrate that HDNet achieves high-recognition accuracy and robustness.

Automatic modulation classification (AMC) plays an increasingly crucial role in intelligent spectrum management and dynamic spectrum access, which can effectively support the reallocation of low-utilization spectrum resources in wireless communication systems. While deep learning approaches have been widely employed in AMC, most deep learning-based AMC methods focus on signal classification as a singular task. Therefore, this paper proposes a multi-task learning-based method for radio signal recognition aimed at enhancing AMC performance. This method utilizes the designed multi-task collaborative learning network (MCLNet) model to achieve complementary gains across different tasks. By sharing parameters, it enhances the learning capability of crucial signal features, thereby acquiring more discriminative signal features and improving classification accuracy. Experimental results demonstrate that the proposed method outperforms other benchmark models on two benchmark datasets and exhibits greater performance gains in few-shot scenarios.

Many TCP-based services, such as social media and e-commerce platforms, depend on Load Balancer(LB) or Network Address Translation(NAT) gateways to manage end-user connections. In this paper, we conduct an in-depth study on the prevalence and impact of half-closed TCP connections in public cloud LB gateways. Our trace data analysis reveals that, on average, these connections account for 9.26% of new connections and consume 16.14% more memory resources compared to fully active connections. Through a series of experiments, we investigate the role of popular HTTP libraries in causing half-closed connections and identify specific implementation patterns that contribute to this behavior. To address this, we propose feasible approaches and demo code to reduce the number of such half-closed connections on both the gateway and client sides.

Orthogonal frequency division multiplexing (OFDM) is regarded as one of the key technologies in wireless communications, particularly in the integration of space and ground networks. Nevertheless, the performance of OFDM communication systems will degrade significantly in complex scenarios, which brings severe challenge to reliable information recovery at the receiver. To address this issue, we propose an intelligent receiving method for OFDM communication based on dual-channel convolutional neural network (DCNet) from the perspective of combining knowledge and data-driven, which introduces the domain knowledge of channel estimation to assist the stability of OFDM signal recovery. The experimental results under various simulation conditions demonstrate that the proposed method can effectively enhance the performance of information recovery in OFDM communication systems.