The inherent flexibility of small unmanned aerial vehicles (UAVs) enables their deployment across various emerging markets. Unauthenticated UAVs pose a significant threat if they intrude into aviation-sensitive areas. To address this issue, deep learning (DL)-based radio frequency fingerprint identification (RFFI) has been developed as a promising approach for identifying illegal UAVs. However, these commonly used DL-based methods demand high computation and storage requirements, which are not suitable for the deployment of RFFI. In this paper, we propose an efficient and low-complexity RFFI method for UAV identification. Specifically, we design a lightweight backbone network consisting of lightweight multi-scale convolution (LMSC) blocks that can significantly reduce the model size and enhance the feature extraction ability. The simulation results indicate that our proposed UAV RFFI method outperforms other state-of-the-art and popular DL-based RFFI methods in terms of both identification performance and complexity. The identification accuracy surpasses that of all other methods at low signal-to-noise ratios (SNRs) and achieves nearly 100% accuracy at high SNRs. To further enhance model efficiency, we employ data truncation in our experimental simulations, demonstrating that a sample length of 2000 is sufficient to retain high identification performance. Additionally, we incorporate the Mixup regularization strategy, which improves accuracy without increasing the complexity, especially as sample length decreases.

Radio frequency fingerprint identification (RFFI) is regarded as one of the most promising techniques for managing and regulating Internet of Things (IoT) devices. This technology analyzes the unique electromagnetic signals emitted by wireless devices to enable precise identification and authentication. Most existing RFFI methods focus on RF signals collected in specific scenarios. However, in real-world applications, signals are often collected at different times or from varying deployment locations, leading to differences between the training and testing distributions. The study of RFFI methods under these conditions remains underexplored. To address this gap, this paper introduces a cross-domain RFFI framework centered on adaptive semantic augmentation (ASA). The framework integrates a computationally efficient multi-resolution spectrogram decomposition strategy with a feature-sensitive multi-scale network. The ASA method enhances RFFI accuracy in cross-domain settings by linearly interpolating between two distinct semantic features to create new semantics for further identification. The proposed approach leverages two-dimensional discrete wavelet transform (2D-DWT) to decompose the raw spectrogram into four sub-bands, followed by a multi-scale network to extract critical semantic features for the ASA method. Simulation results show that the proposed ASA method significantly improves Unmanned Aerial Vehicle (UAV) identification performance, achieving accuracies of 93.05% and 98.90% on two different cross-domain datasets, respectively, outperforming existing data augmentation (DA) methods. Furthermore, generalizability validation demonstrates that the proposed method performs outstandingly across other Internet of Things (IoT) applications.

Polar codes have become increasingly popular recently because of their capacity achieving property. In this paper, a memory efficient stage-combined belief propagation (BP) decoder design for polar codes is presented. Firstly, we briefly reviewed the conventional BP decoding algorithm. Then a stage-combined BP decoding algorithm which combines two adjacent stages into one stage and the corresponding belief message updating rules are introduced. Based on this stage-combined decoding algorithm, a memory-efficient polar BP decoder is designed. The demonstrated decoder design achieves 50% memory and decoding latency reduction in the cost of some combinational logic complexity overhead. The proposed decoder is synthesized under TSMC 45nm Low Power CMOS technology. It achieves 0.96 Gb/s throughput with 14.2mm2 area when code length N=216 which reduces 51.5% decoder area compared with the conventional decoder design.

Linear feedback shift register (LFSR) has been widely applied in BCH and CRC encoding. In order to increase the system throughput, the parallelization of LFSR is usually needed. Previously, a technique named state-space transformation was presented to reduce the complexity of parallel LFSR architectures. Exhaustive searches are performed to find good transformation matrix candidates. This brief proposes a new technique for construction of the transformation matrix together with a more efficient searching algorithm. The realization results indicate that the proposed architecture outperforms the prior arts, improving the hardware efficiency by around 35% and the corresponding searching algorithm finds the desirable transformation matrix much faster.

Low-density parity-check (LDPC) code, a very promising near-optimal error correction code (ECC), is being widely considered in next generation industry standards. The VLSI implementation of high-speed LDPC decoder remains a big challenge. This paper presents the construction of a new class of implementation-oriented LDPC codes, namely shift-LDPC codes. With girth optimization, this kind of codes can perform as well as computer generated random codes. More importantly, the decoder can be efficiently implemented to obtain very high decoding speeds. In addition, more than 50% of message memory can be generally saved over conventional partially parallel decoder architectures. We demonstrate the benefits of the proposed techniques with an application-specific integrated circuit (ASIC) design (in 0.18-mum CMOS) for a 8192-bit regular LDPC code, which can achieve 5 Gb/s throughput at 15 iterations.

The explosive growth of maritime vessels imposes high demands on the security of automatic identification system (AIS). Radio frequency fingerprinting identification (RFFI) as a physical-layer authentication method offers a new perspective on security solutions for wireless communication systems. However, RFFI for AIS satellite component suffers from poor performance under low signal-to-noise ratio (SNR) scenarios. To this end, this paper proposes a data-driven RFFI method with high performance and resilience for AIS satellite links. The bivariate variational mode decomposition (VMD) enables adaptive signal decomposition for effective denoising. The raw I/Q samples after bivariate VMD are directly taken as input to the RF fingerprinting feature extraction network without any transformation process. The complex-valued neural network used for feature extraction provides a superior representation capability compared to traditional real-valued neural networks. Furthermore, channel attention mechanisms are embedded in the feature extraction network to shed light on the correlation between channels within the signal. The following integrated spatial attention mechanisms are employed to reveal the correlations between signals. Numerical results show that the proposed RFFI method can achieve over 90.37% accuracy on real-world datasets with SNRs greater than 6 dB, and achieve a balance between complexity and performance.