Tongyang Xu

Falls remain a major cause of injury and death among older adults, which shows the need for reliable and non-intrusive monitoring solutions in healthcare environments. In this paper, we propose a novel Ambient Internet of Things (IoT) backscatter sensing system that utilizes a dense array of passive tags and a minimal number of reader antennas for cost-effective fall detection and localization. To fully exploit the spatial and temporal characteristics of ambient backscatter sensing data, we design a hierarchical multi-task spatio-temporal graph attention network (HM-STGAT), which jointly models the spatial relationships among tags and antennas as well as the temporal dynamics of human activities. The proposed unified framework simultaneously detects fall events and accurately estimates fall locations. We validate the proposed approach through a real-world experiment to collect a diverse dataset of fall and non-fall scenarios. Experimental results demonstrate that the proposed method achieves state-of-the-art performance in both fall detection accuracy and localization precision, highlighting its potential for practical deployment in healthcare monitoring applications.

As 6G communications advance, the demand for new services and capabilities, as defined by the international telecommunication union (ITU), is increasing. A crucial aspect of 6G advancement lies in the development of signal waveforms that can meet these demands while maintaining compatibility with existing standards. This paper explores sustainable physical layer waveform options, focusing on a balanced approach that integrates non-orthogonality with orthogonality to achieve both backward compatibility and forward innovation. Specifically, we investigate two key signal formats: single-carrier orthogonal frequency division multiplexing (SC-OFDM) (1D,2D) and single-carrier non-orthogonal frequency shaping (SC-NOFS)(1D,2D). Both can use 1D frequency and 2D time-frequency precoding, offering enhanced frequency and time diversity, simplified processing, and resilience to delay-Doppler effects. SC-NOFS(2D) further introduces advantages such as improved spectral efficiency and reduced latency, making it a strong candidate for future 6G applications. The comparative analysis highlights that SC-NOFS(2D) provides a broader range of capabilities, particularly those requiring high data rate, high mobility, low-latency communication, sustainability, and interoperability, positioning it as a versatile solution for next-generation 6G communication.

Future wireless networks are expected to deliver ultra-high throughput for supporting emerging applications. In such scenarios, conventional Nyquist signaling may falter. As a remedy, faster-than-Nyquist (FTN) signaling facilitates the transmission of more symbols than Nyquist signaling without expanding the time-frequency resources. We provide an accessible and structured introduction to FTN signaling, covering its core principles, theoretical foundations, unique advantages, open facets, and its road map. Specifically, we present promising coded FTN results and highlight its compelling advantages in integrated sensing and communications (ISAC), an increasingly critical function in future networks. We conclude with a discussion of open research challenges and promising directions.

Recent years have seen the rapid development of long-range wide area network (LoRaWAN) operating in region-specific sub-GHz frequency bands (e.g., 868 MHz in Europe and 915 MHz in North America). To achieve global deployment, LoRaWAN has been extended to operate in the globally available 2.4 GHz unlicensed band. However, this shift exposes LoRaWAN to significant interference from coexisting Wi-Fi networks, which share the same band and typically transmit at much higher power levels. To address this problem, this article presents InMAC, an interference-aware medium access control (MAC) protocol designed to improve coexistence between LoRaWAN and Wi-Fi networks. To the best of our knowledge, InMAC is the first MAC protocol specifically tailored to mitigate Wi-Fi interference for 2.4 GHz LoRaWAN. InMAC enhances LoRaWAN communication by probabilistically exploiting the silent time in Wi-Fi traffic, leveraging a Wi-Fi traffic profiling mechanism at LoRaWAN gateways and a packet length adaptation strategy at end devices. In addition to mitigating external interference from Wi-Fi, InMAC also tackles internal interference caused by signal collisions among LoRaWAN end devices. It incorporates a novel channel access mechanism based on channel activity detection (CAD), a carrier-sensing technique adapted specifically for LoRaWAN. Experimental results demonstrate that InMAC reduces both external Wi-Fi interference and internal LoRaWAN collisions, achieving up to a 111% throughput boost over existing approaches.

This paper proposes a spectrum-efficient nonorthogonal affine frequency division multiplexing (AFDM) waveform for reliable high-mobility communications in the upcoming sixth-generation (6G) mobile systems. Our core idea is to introduce a compression factor to enable controllable subcarrier overlapping in chirp-based AFDM modulation. To mitigate intercarrier interference (ICI), we introduce linear precoding at the transmitter and an iterative detection scheme at the receiver. Simulation results demonstrate that these techniques can effectively reduce interference and maintain robust bit error rate (BER) performance even under aggressive compression factors and high-mobility channel conditions. The proposed non-orthogonal AFDM waveform offers a promising solution for next-generation wireless networks, balancing spectrum efficiency and Doppler resilience in highly dynamic environments.

In this paper, we propose a unified framework for integrated sensing and communication (ISAC) that utilizes received signal strength (RSS) measurements from zero-power backscatter radio frequency identification (RFID) tags for both communication and sensing. The proposed system employs a passive tag array, which can facilitate RSS-based key generation for resilient communication and healthcare sensing for fall detection. For key generation, we propose a time-series peak pattern coding method that can achieve strong randomness and low bit mismatch rate between two users, while for fall detection, we propose both a threshold-based approach and a spatio-temporal graph neural network (ST-GNN) method. We present experiments conducted in an indoor laboratory to collect data and validate the effectiveness of the proposed methods. The results demonstrate that the ST-GNN significantly outperforms traditional threshold-based techniques, achieving over 95% accuracy in fall detection across various datasets. Moreover, our backscatter ISAC system provides a cost-effective and sustainable solution for smart healthcare environments.

Non-orthogonal signals, such as spectrally efficient frequency division multiplexing (SEFDM), enable spectral overlap, increasing bandwidth efficiency compared to orthogonal systems like orthogonal FDM (OFDM). SEFDM benefits long-haul optical fiber transmission by enhancing spectral efficiency, reducing nonlinear interference effects, and lowering power requirements. However, its detection is challenging, with maximum likelihood (ML) detection offering optimal performance but at high complexity. Iterative detectors mitigate interference efficiently and, when unfolded, resemble neural networks (NN). This work explores hybrid NN and iterative detectors, introducing Unfolding-Learn and Unfolding-Tune architectures. Results demonstrate significant performance gains, with Unfolding-Tune achieving 1.5 dB power improvement and 25% higher spectral efficiency over traditional systems.