Di Duan

Channel estimation is crucial in 5G communication networks for optimizing transmission parameters and ensuring reliable, high-speed communication. However, the use of multiple-input and multiple-output (MIMO) and millimeter-wave (mmWave) in 5G networks presents challenges in achieving accurate estimation under strict latency requirements on resource-limited hardware platforms. To address these challenges, we propose SwiftChannel, an algorithm-hardware co-design framework that integrates a hardware-friendly deep learning-based channel estimator with a dedicated accelerator. Our approach employs a convolutional neural network enhanced with a parameter-free attention mechanism, which effectively reconstructs full-resolution spatial-frequency domain channel matrices from low-resolution least squares (LS) estimates. We further develop a multi-stage model compression pipeline combining knowledge distillation, convolution re-parameterization, and quantization-aware training, resulting in substantial model size reduction with negligible accuracy loss. The hardware accelerator, implementing the compressed model and the LS estimator on FPGA platforms using High-level Synthesis (HLS), features a fine-grained pipeline architecture and optimized dataflow strategies. Tested on a Zynq UltraScale+ RFSoC, the accelerator achieves sub-millisecond latency, providing up to 24x speed-up and over 33x improvement in energy efficiency compared to GPU-based solutions. Extensive evaluations demonstrate that the proposed design generalizes not only across various noise levels and user mobilities, but also to a variety of unseen channel profiles, outperforming state-of-the-art baselines. By unifying algorithmic innovation with hardware-aware design, our work presents a future-proof channel estimation solution for 5G MIMO systems.

Serving machine learning models on edge, mobile, and embedded devices places stringent requirements on inference latency. From operating a real enterprise service, we observed that even a fully optimized model could lead to severe violations of latency objectives when the load surges. A straightforward and mature approach is to auto-scale multiple models to balance the load. However, unlike cloud clusters, edge or mobile devices usually cannot afford to deploy multiple model replicas. Therefore, in this paper, we explore a new idea: in addition to the original model, we deploy one (or more) heterogeneous model(s) with much smaller resource overhead on the device, and perform load balancing among all models. We overcame the technical challenges posed by performance dynamics and developed InferRouter based on queuing theory. We implement and evaluate InferRouter on three real on-device inference systems, covering mobile sensing, video analytics, and natural language processing applications. Experimental results show that compared with strong baselines, InferRouter can decrease 85.2% P99 latency (5.8x faster) and improve 5.9% accuracy on the mobile workload. For a traffic video analytics task, InferRouter achieves 55.1% higher accuracy with zero deadline misses. InferRouter also shows its advantages in saving resources compared with auto-scaling and offloading approaches.

Resistance training is widely incorporated in exercise programs, including in-home fitness and rehabilitation. However, improper motion patterns and muscle stimulation can undermine the safety of the subjects, making precise monitoring essential. Existing solutions primarily focus on correcting motion patterns with difficulties assessing muscle contraction levels. In this work, we introduce MyoTrainer, which provides muscle-aware motion descriptions and personalized feedback in natural language. Taking a person's exercise video as input, MyoTrainer first utilizes pose estimation models to capture motion sequences in real-time. A GCN-Former model has been developed for fine-grained motion analysis, which includes action recognition, incorrect movement pattern detection, and muscle contraction intensity estimation. Additionally, MyoTrainer integrates fitness and physiotherapeutic domain knowledge to deliver personalized, professional feedback. Extensive evaluations show that our system outperforms existing solutions in all recognition tasks and a survey indicates 88.9% of users find the generated feedback to be beneficial.

The remarkable growth of Virtual Reality (VR) in recent years has extended its applications beyond entertainment to sectors including education, e-commerce, and remote communication. Since VR devices contain user's private information, user authentication becomes increasingly important. Current authentication systems in VR, such as password-based or static biometric-based methods, are either cumbersome to use or vulnerable to attacks such as shoulder surfing. To address these limitations, we propose Medusa3D, a challenge-response authentication system for VR based on reflexive eye responses. Unlike existing methods, reflexive eye responses are involuntary and effortless, offering a secure and user-friendly credential for authentication. We implement Medusa3D on an off-the-shelf VR and conduct evaluations with 25 participants. The evaluation results show that Medusa3D achieves 0.21% FAR and 0.13% FRR, demonstrating high security under various ocular conditions and resilience against attacks such as zero-effort attack, replay attack, and mimicry attack. A user study indicates that Medusa3D is user-friendly and well-adopted among participants.

In recent years, the Internet of Vehicles (IoV) has experienced significant growth, but the lack of effective secret key establishment remains a security concern due to the dynamic and ad-hoc nature of IoV communications. Physical layer key generation has emerged as a promising solution for establishing a pair of cryptographic keys in a lightweight and information-theoretic secure manner. However, previous works have primarily focused on legacy communication technologies, such as Wi-Fi, ZigBee, and 5 G, which are limited to short-range IoV communications. With the emergence of Long-range (LoRa) communication technology, which features long-range, low power, and extremely low data rates, new challenges arise for key generation in long-range IoV scenarios. This paper presents Vehicle-Key , a secret key generation system designed to secure LoRa-enabled IoV communications. Vehicle-Key presents an innovative scenario adaptive deep learning model that performs channel prediction and quantization concurrently while reducing the training cost through a data augmentation pipeline and enhancing the model's generalization using a domain-adaption method. Additionally, we propose a bloom filter-assisted autoencoder-based reconciliation method to significantly improve the key agreement rate. Comprehensive real-world experiments show that Vehicle-Key surpasses the State-of-the-Art, achieving a 15.26%–50.35% improvement in key agreement rate and a 9–15× increase in key generation rate. Moreover, the proposed method attains a 4.37--9.33% improvement when adapted to new scenarios with limited data sizes. A security analysis demonstrates that Vehicle-Key is resilient against several common attacks. Furthermore, we implement Vehicle-Key on a Raspberry Pi and demonstrate its ability to execute within 3.5 ms.

In this paper, we proposed F²Key, the first earable physical security system based on commercial off-the-shelf headphones. F²Key enables impactful applications, such as enhancing voiceprint-based authentication systems, reliable voice assistants, audio deepfake defense, and the legal validity of artifacts. The key idea of F²Key is to establish a stable acoustic sensing field across the user's face and embed the user's facial structures and articulatory habits into a user-specific generative model that serves as a private key. The private key can decrypt the Channel Impulse Response (CIR) profiles provided by the acoustic sensing field into an inferred spectrogram that can match the real one calculated from the corresponding speech, provided that the user's CIR-spectrogram mapping relationship is consistent with the one embedded in the generative model. Extensive experiments demonstrate that F²Key resists 99.9%, 96.4%, and 95.3% of speech replay attacks, mimicry attacks, and hybrid attacks, respectively. We discussed and evaluated F²Key from different perspectives, such as the health consideration and identical twins study, to show the practicality and reliability.