MAMCA -- Optimal on Accuracy and Efficiency for Automatic Modulation Classification with Extended Signal Length

With the rapid growth of the Internet of Things ecosystem, Automatic Modulation Classification (AMC) has become increasingly paramount. However, extended signal lengths offer a bounty of information, yet impede the model's adaptability, introduce more noise interference, extend the training and inference time, and increase storage overhead. To bridge the gap between these requisites, we propose a novel AMC framework, designated as the Mamba-based Automatic Modulation ClassificAtion (MAMCA). Our method adeptly addresses the accuracy and efficiency requirements for long-sequence AMC. Specifically, we introduce the Selective State Space Model as the backbone, enhancing the model efficiency by reducing the dimensions of the state matrices and diminishing the frequency of information exchange across GPU memories. We design a denoising-capable unit to elevate the network's performance under low signal-to-noise radio. Rigorous experimental evaluations on the publicly available dataset RML2016.10, along with our synthetic dataset within multiple quadrature amplitude modulations and lengths, affirm that MAMCA delivers superior recognition accuracy while necessitating minimal computational time and memory occupancy. Codes are available on https://github.com/ZhangYezhuo/MAMCA.

Specific Emitter Identification Handling Modulation Variation with Margin Disparity Discrepancy

In the domain of Specific Emitter Identification (SEI), it is recognized that transmitters can be distinguished through the impairments of their radio frequency front-end, commonly referred to as Radio Frequency Fingerprint (RFF) features. However, modulation schemes can be deliberately coupled into signal-level data to confound RFF information, often resulting in high susceptibility to failure in SEI. In this paper, we propose a domain-invariant feature oriented Margin Disparity Discrepancy (MDD) approach to enhance SEI's robustness in rapidly modulation-varying environments. First, we establish an upper bound for the difference between modulation domains and define the loss function accordingly. Then, we design an adversarial network framework incorporating MDD to align variable modulation features. Finally, We conducted experiments utilizing 7 HackRF-One transmitters, emitting 11 types of signals with analog and digital modulations. Numerical results indicate that our approach achieves an average improvement of over 20\% in accuracy compared to classical SEI methods and outperforms other UDA techniques. Codes are available at https://github.com/ZhangYezhuo/MDD-SEI.

CILF:Causality Inspired Learning Framework for Out-of-Distribution Vehicle Trajectory Prediction

Trajectory prediction is critical for autonomous driving vehicles. Most existing methods tend to model the correlation between history trajectory (input) and future trajectory (output). Since correlation is just a superficial description of reality, these methods rely heavily on the i.i.d. assumption and evince a heightened susceptibility to out-of-distribution data. To address this problem, we propose an Out-of- Distribution Causal Graph (OOD-CG), which explicitly defines the underlying causal structure of the data with three entangled latent features: 1) domain-invariant causal feature (IC), 2) domain-variant causal feature (VC), and 3) domain-variant non-causal feature (VN ). While these features are confounded by confounder (C) and domain selector (D). To leverage causal features for prediction, we propose a Causal Inspired Learning Framework (CILF), which includes three steps: 1) extracting domain-invariant causal feature by means of an invariance loss, 2) extracting domain variant feature by domain contrastive learning, and 3) separating domain-variant causal and non-causal feature by encouraging causal sufficiency. We evaluate the performance of CILF in different vehicle trajectory prediction models on the mainstream datasets NGSIM and INTERACTION. Experiments show promising improvements in CILF on domain generalization.