Massive MIMO has emerged as a cornerstone technology for 5G-Advanced and future 6G networks, yet its practical deployment remains limited by hardware cost and power consumption. Switch-based architectures, which share a small number of RF chains among many antenna elements, provide a scalable alternative, but create a new bottleneck: only a subset of antennas is observable at any given moment, leaving the channel state of the remaining elements unknown. Lacking this information prevents the system from exploiting advanced physical-layer functions such as digital beamforming or multi-stream MIMO. In this paper, we present ARGUS, a generative channel reconstruction framework that infers the CSI of unobserved antennas from partial observations. The key idea is that all antenna responses are governed by the same underlying wireless propagation environment, enabling the task to be formulated as a generative inference problem. We employ a variational autoencoder to capture the latent spatial structure and reconstruct unobserved channels through sampling. Extensive experiments show that our reconstructed CSI incurs less than 2.5% achievable rate loss, and real-world measurements demonstrate a more than 90% antenna-selection match rate, confirming the practicality of the proposed approach.

Reconfigurable Intelligent Surfaces (RISs) have emerged as cost-effective technologies for improving wireless signal transmission. Conventional RIS designs, however, face challenges such as bulkiness, high production costs, limited scalability, and complex installation due to their reliance on wired connections. In this work, we introduce MetaMosaic, a novel RIS platform that repurposes 920 MHz RFID tags into battery-free unit cells, enabling an affordable, scalable, and flexible one-bit phase-modulated RIS. The system is engineered for compatibility with 2.4 GHz Wi-Fi communications while being controlled at 920 MHz. Our design incorporates two central innovations: the transformation of commercial RFID tags into functional unit cells and the development of a tailored neural radiance field to guide efficient reconfiguration. To further enhance global search capability and support multi-hotspot alignment, we extend the system with a genetic algorithm (GA)-based optimization strategy. Compared with the vanilla MetaMosaic, the GA-based MetaMosaic achieves an additional 3.1 dB signal strength improvement and enables simultaneous enhancement for up to five target points. Extensive testing across ten diverse environments demonstrates that MetaMosaic consistently boosts signal strength, with a mean gain of 19 dB over non-RIS setups. This outperforms current leading RIS systems by a 3-fold improvement.

In industrial systems, monitoring micro-scale vibrations is crucial for assessing the operational health of equipment. Existing solutions typically rely on battery-powered sensors or are constrained by LoS requirements. To address these limitations, we propose Vibro-Stethos, a micro-scale,sub-herz vibration sensing system based on battery-free tags. It innovatively utilizes the resistance characteristic of JFET in the ohmic region to convert the voltage generated by PZT vibrations into antenna’s impedance, enabling the modulation of vibration information into the backscatter signal. An integrated RFID chip enhances identifiability and controllability. Additionally, a GCN-based vibration fault recognition model ensures accurate identification of vibration states regardless of tag placement. Experimental results demonstrate that Vibro-Stethos achieves an amplitude error of 2μm and a frequency error of 0.1Hz, with a sensing range of up to 5 meters. It can also recognize seven types of vibration states with 89.3% accuracy and has proven robust and effective in real plant deployments.

Large language models (LLMs) have shown dominant performance in various language tasks, including code-writing, machine translation, and semantic comprehension. With prompt engineering, LLMs can also comprehend complex tasks and translate them into executable code. These powers offer great potential for controlling the motion of robots. In this paper, we focus on leveraging the ability of LLMs, prompt engineering, and predefined robot action APIs to facilitate high-level motion planning for quadruped robots. With LLMs, we enable the robot to autonomously plan and execute sophisticated actions based on the comprehension of effective prompts. Through various experiments and evaluations, we demonstrate the effectiveness and adaptability of our approach in handling intricate motion tasks. Our research contributes to the advancement of intelligent robotics and paves the way for more versatile quadruped robots in real-world scenarios.

Within the burgeoning 6G wireless network landscape, there is an intensified push toward achieving all-encompassing accessibility through integrated solutions spanning a multitude of domains. Notwithstanding recent advancements, the conventional relay-centric communication paradigms grapple with scalability and optimal performance issues. In this paper, we introduce MeAnt ---a versatile IoT platform uniquely architected to foster seamless cross-medium communication by leveraging the compact design of piezoelectric-based mechanical antennas (Piezo-MAs). By capitalizing on the propagation attributes of medium-frequency radios emitted from Piezo-MAs, MeAnt promises communication across diverse environments such as air, water, soil, concrete, and even biological tissue, all while maintaining a compact antenna footprint. Moreover, in light of challenges such as potential interference from AM broadcasts and the intrinsic unidirectional nature of Piezo-MAs, we have developed a finely crafted full-stack communication protocol. Comprehensive tests underscore the system's proficiency, demonstrating a penetration depth of up to 10 m in cross-medium environments and realizing a throughput of 8.7 kbps.

Growing interest in wireless sensing, a cornerstone of the Artificial Intelligence of Things (AIoT), stems from its ability to gauge target states through nearby wireless signals. However, the escalating count of AIoT nodes escalates redundant data flow and exacerbates energy usage in AI cloud infrastructures. This amplifies the urgency for machine learning techniques that function in proximity to, or directly within, sensors. In light of this, we present the Radio-Frequency Neural Network (RFNN), a novel architecture that uses cost-effective transmissive intelligent surfaces to mimic the functions of a traditional neural network near (or in) sensors, transforming sensory nodes into intelligent terminals primed for machine learning. We first devised a unique training algorithm to mitigate the issues arising from unmodelable error-backward propagation; secondly, we incorporated contrastive learning to address the issue of blind labels stemming from environmental uncertainties. Our RFNN prototype, resonating at a 5 GHz WiFi bandwidth, has been honed across nine varied sensing tasks. The rigorous evaluation shows that it achieves a mean accuracy of 91.5% while consuming only 67.2 μJ of energy. This positions RFNN as a match in inferencing prowess to its electronic neural network counterparts but with significantly diminished energy demands.

Cross-Technology Communication (CTC) is an emerging technology that enables physical-layer direct communication from a WiFi sender to other Internet of Things (IoT) receivers via waveform emulation. Previous research has primarily relied on the reverse engineering to identify the suitable WiFi payloads capable of emulating waveforms similar to desired IoT packets (e.g., ZigBee). However, this approach has several limitations, including irreversibility, scalability challenges, symbol misalignment, and an over-reliance on empirical methods. In this work, we present XiTuXi, a one-size-fits-all solution to automatically achieve the CTC by taking advantage of the neural machine translation (NMT). Inspired by the task comparability between CTC and homophony-based cross-linguistic communication, we employ a well-known NMT model called Transformer to learn the rationale behind translating bit sequences for CTC without human intervention. Specifically, we introduce forward engineering as a solution to tackle the challenge of acquiring training datasets. By utilizing XiTuXi, we effortlessly achieved CTC across 30 protocol combinations (including 802.11b, g, n, ax, ah → Zig-Bee, Bluetooth, LoRa, and Sigfox), ultimately freeing experts from the tedious tasks they faced previously.

Wireless sensing has attracted considerable attention because it can sense the state of the targets by analyzing the surrounding wireless signals, which has become the key role of the artificial intelligence of things (AIoT). As the number of sensory nodes increases, large amounts of redundant data are exchanged between sensory terminals and the AI cloud. To process such large amounts of data efficiently and decrease power consumption, a machine-learning approach that operates close to or inside sensors must be developed. To this end, we present the radio-frequency neural network (RFNN), a physical neural network taking advantage of a group of transmissive intelligent surfaces (i.e., metasurfaces) to mimic the computations of a fully-connected neural network. The design is spurred by the capability of RFNNs to perform expensive multiplication and additions at the speed of light, with ultra-low power consumption. We prototype RFNN at 5 GHz for WiFi sensing regarding nine wireless sensing tasks. Extensive evaluations demonstrate the comparably equivalent inference ability as the conventional electronic neural networks while consuming less energy.

Cross-Technology Communication (CTC) is an emerging technology that enables physical-layer direct communication from a WiFi sender to other Internet of Things (IoT) receivers via waveform emulation. The previous works use the reverse engineering to find the appropriate WiFi payload that can emulate the waveform similar to the desired IoT packet in the format of the IoT protocol (e.g., ZigBee). Unfortunately, the reverse engineering approach suffers from many limitations, such as being non-reversible and unscalable, misaligning symbols, and over-relying on empiricism. In this work, we present XiTuXi, a one-size-fits-all solution to automatically achieve the CTC by taking advantage of the neural machine translation (NMT), inspired by the task comparability between CTC and homophony-based cross-linguistic communication. We employ a well-known NMT model called Transformer to learn the bit-sequence to bit-sequence translation rationale behind the CTC without human intervention. Particularly, we introduce the forward engineering to address the dilemma of acquiring training datasets. By using XiTuXi, we achieved the CTC with 30 protocol combinations (ie., 802.11b, g, n, ax, ah Å ZigBee, Bluetooth, LoRa, and Sigfox) effortlessly, which ultimately liberates the experts from previous tedious tasks.

Given the increasing number of building collapse tragedies nowadays (e.g., Florida condo collapse), people gradually recognize that long-term and persistent structural health monitoring (SHM) becomes indispensable for civilian buildings. However, current SHM techniques suffer from high cost and deployment difficulty caused by the wired connection. In this work, we collaborate with experts from civil engineering to create a type of promising self-sensing concrete by introducing a novel functional filler, called EcoCapsule-a battery-free and miniature piezoelectric backscatter node. We overcome the fundamental challenges in in-concrete energy harvesting and wireless communication to achieve SHM via EcoCapsules. We prototype EcoCapsules and mix them with other raw materials (such as cement, sand, water, etc) to cast the self-sensing concrete, into which EcoCapsules are implanted permanently. We tested EcoCapsules regarding real-world buildings comprehensively.