Conventional single-atom hybrid beamforming has limited representation capability in near-field extremely large-scale multiple-input multiple-output (XL-MIMO) systems because of spherical-wave propagation and spatial non-stationarity. A dual-resolution rate-oriented hybrid beamforming (DR²-HBF) algorithm is proposed. The algorithm constructs local objectives from the spectral-efficiency gradient, jointly represents each beam using two atoms and a constrained complex coefficient, and performs each update through two-stage Top-K candidate screening, hardware projection, and validation based on the original rate objective. Simulation results show that the proposed algorithm improves spectral efficiency under identical constant-modulus hardware constraints and enhances statistical robustness under imperfect channel state information. Results obtained with amplitude-and-phase-adjustable architectures further indicate that performance gains should be evaluated together with hardware cost.
To address the susceptibility of audio-based voice interaction to noise under low signal-to-noise ratio conditions and the privacy concerns of vision-based approaches, the recognition of speech commands based on millimeter wave radar sensing vocal-cord vibration was studied. To overcome the challenges posed by weak radar vibration signals and interference from surrounding objects, a multi-antenna beamforming-based signal enhancement method was developed, together with a frequency-domain energy analysis mechanism that removed abnormal components caused by moving objects, thereby extracting stable vibration features. To improve cross-user recognition performance, a few-shot transfer framework, mmFSNet, was designed to adapt the classifier with a small number of target-user samples, enhancing the system’s generalization capability. The system yields an average accuracy of 97.3% over 10 users and 11 speech command categories, with performance variations kept within 5% under changes in distance, angle, and environment. When fine-tuned with only three samples from a new user, the recognition accuracy exceeds 80%. The results demonstrate that the proposed method ensures robust speech command recognition in complex environments and provides effective adaptability to unseen users.
In data-driven electricity theft detection methods, feature extraction plays a critical role in detection accuracy. To address this issue, a novel approach was proposed by integrating a temporal 2D variation modeling network (TimesNet) with a long short-term memory (LSTM) network to fuse time-frequency features. In this method, long-term temporal dependencies in the time domain and periodic patterns in the frequency domain were jointly modeled. An optimization strategy combining class-weighted focal loss was introduced to effectively mitigate the adverse impact of class imbalance on model performance. Furthermore, a multi-scale InceptionBlock combined with an LSTM architecture was designed to enhance the model’s capacity to capture complex electricity consumption patterns. Experimental validation on public datasets demonstrated that the proposed method achieved an F1-score of 0.90 and an area under the curve-receiver operating characteristic (AUC-ROC) of 0.97, representing improvements of 10% and 5%, respectively, over the classical CNN-LSTM model. The results indicate that the proposed method effectively balances low false alarm rates with high detection rates.
With the rapid evolution of 6G wireless communication technologies, smart wearable devices, empowered by ultra-low latency and high-speed connectivity, have been widely deployed, continuously enhancing users' quality of life. However, real-time data interactions over public channels expose the security and privacy of user data to severe threats. To address this challenge, an efficient anonymous authentication protocol tailored for 6G-enabled smart wearable scenarios was proposed. By integrating cryptographic hash functions with physical unclonable functions (PUF), the proposed protocol achieved mutual authentication among smart wearable devices, users, and the remote server, while ensuring the secure generation of session keys. Security analysis demonstrated that the protocol exhibits strong robustness against various known attacks. Furthermore, performance comparisons indicated that, compared with existing representative schemes, the proposed protocol provided more comprehensive security properties and functional support while maintaining low overhead.
With the advancement of satellite remote sensing data acquisition technologies, satellite remote sensing images have experienced explosive growth. However, existing satellite-ground communication technologies are constrained by channel bandwidth, making it challenging to achieve efficient transmission of massive remote sensing images. To address this issue, an adaptive coding and decoding method tailored for satellite remote sensing image transmission was proposed. This method comprised five modules: a global-local feature extraction module, a key semantic feature selection module, a joint source-channel coding module, a joint source-channel decoding module, and an analysis module. Specifically, the global-local feature extraction module leveraged multi-directional Mamba blocks and residual blocks to extract global contextual features and local detail features, respectively. The key semantic feature selection module adaptively filtered critical features based on the channel state signal-to-noise ratio (SNR). The joint source-channel encoding-decoding module transmitted only essential information to minimize redundancy. Experimental results on the NWPU-RESISC45 dataset demonstrate that, compared with state-of-the-art methods, the proposed method achieves higher transmission accuracy and robustness under varying channel types and SNR conditions.
