Deepfakes have emerged as a significant threat to online communications, enabling nearly indistinguishable impersonation of executives, public figures, and trusted contacts during video calls. While state-of-the-art deepfake detection models can achieve high accuracy offline, deploying them in real-time video conferencing systems remains challenging: the added computation quickly violates interactive latency budgets and greatly limits scalability. Our empirical analysis reveals that video decoding and frame movement dominate the detection pipeline, together accounting for approximately 86.6% of per-frame processing time.

Conventional capacitive touchscreens in mobile devices often malfunction in the presence of moisture, dust, or gloved hands, leading to unreliable user interactions. To overcome these limitations, we present TouchAI, a multi-modal touch detection and localization system that leverages built-in inertial sensors and audio interfaces. Running as a background process, TouchAI constantly monitors inertial measurements for potential touch input and then activates acoustic sensing only upon detected events, thus reducing power consumption and privacy concerns associated with continuous audio recording. We introduce a novel event identification method that distinguishes touch-start and touch-end instances, providing precise timing for data sampling. For fine-grained touch localization, TouchAI employs a lightweight Transformer-based classification model to capture spatiotemporal features from combined acoustic-inertial signals. Experiments on commercial smartphones demonstrate true positive rates of 95.3% and 99.1% for touch detection and event identification, respectively, at false positive rates only less than 3%. Ultimately, TouchAI achieves up to 97.0% and 86.5% localization accuracy on 4×2 and 6×3 touch input grids, respectively, confirming its practicality and effectiveness as an alternative interface under touchscreen malfunction scenarios.

The latest wireless local area networks (WLANs) including IEEE 802.11be have become increasingly sensitive to synchronization errors, as per stringent requirements in pursuit of high-end performance. In particular, stemming from common clock mismatch between transceivers, carrier and sampling frequency offsets (CFO and SFO) jointly induce time/frequency-wise phase rotations whose detrimental impact grows with wider channel bandwidth. This paper presents a comprehensive study into the coupled CFO and SFO impairments in emerging WLANs, with up to 320 MHz wideband operation. For better understanding of their joint effect, we introduce a useful concept of effective CFO (eCFO) referring to individual frequency drift per subcarrier. Our in-depth analysis and scrutiny into existing receiver designs reveal that wideband WLANs cannot stick to conventional synchronization mechanisms. To tackle this situation, we propose enhanced approaches for initial estimation and compensation of both frequency offsets, exploiting the frequency-dependent nature of eCFO. Extensive hardware-benchmarked simulations demonstrate the effectiveness and robustness of our strategies in coping with a variety of frame reception across wideband scenarios. Ultimately, the proposed receiver design achieves near optimal end-to-end performance by mitigating the impairments, even under the most challenging operation regime.

Recent advancements in Integrated Sensing and Communication (ISAC) have enabled human activity recognition (HAR) based on channel state information (CSI) from Wi-Fi signals, as a privacy-conscious and cost-effective alternative to conventional vision-based applications. With smart TV platforms increasingly crucial for home automation, Wi-Fi CSI-based HAR offers seamless integration into existing infrastructure. However, in real-world home environments, the close proximity between Wi-Fi AP and smart TV often leads to dominant Line-of-Sight (LOS) signal path, limiting multipath channel diversity and thus the HAR accuracy. To address this, we present HActiFi, an advanced HAR framework that adaptively controls CSI sampling to ensure reliable sensing without overloading the network. Our smartphone-assisted CSI injection method effectively restores recognition accuracy, and adaptive sampling dynamically optimizes throughput, ensuring robust HAR functionality while preserving streaming quality. These findings confirm that Wi-Fi CSI sensing is a scalable and privacy-conscious alternative to vision-based HAR, making it viable for real-world smart home environments.

There have been long efforts to refine Wi-Fi carrier sensing (CS) for more aggressive channel access, in pursuit of enhanced network performance. To this end, the recent 802.11ax amendment introduced a preamble detection (PD)-based spatial reuse, allowing concurrent transmissions between adjacent links via adjustable sensitivity levels. Against these conventional ideas, this paper presents a different perspective: Wi-Fi devices already have excessive transmission (TX) opportunities in practice, even without detecting each other under certain scenarios. We shed light on CS anomalies relevant to undetected preambles, which not only cause adjacent devices to transmit concurrently but are also triggered by the new PD-based mechanism, ultimately disrupting its intended operations. Our testbed experiments and in-depth scrutiny reveal the dominant impact of these anomalies on overall network behaviors. Based on these insights, we present two comprehensive frameworks, REFRAIN and AdOPT, to fully exploit TX opportunities enabled by the anomalies and PD-based mechanism respectively, for practical spatial reuse. Prototypes using commercialWi-Fi devices and NI USRP show the feasibility and effectiveness of our approaches. Extensive simulation results further demonstrate that REFRAIN and AdOPT achieve up to 1.94× and 1.61× higher average throughput, only with reduced transmission attempts by half, highlighting their potential to elevate network capacity and efficiency in practical Wi-Fi networks.

For emerging high-density Wi-Fi networks, there have been plenty of studies that claim the need for more aggressive channel access to enhance spatial reuse. Against those previous ideas, this paper presents a different perspective that existing Wi-Fi devices already have excessive transmission opportunities, even without protecting each other in certain scenarios. We shed light on an anomaly within actual carrier sensing (CS) behaviors, which makes some neighboring devices become blind to each other and transmit simultaneously, due to undetected preambles. Through experimental study and analysis, we reveal both sides of the anomaly heavily affecting the overall network performance. Based on the observations, we design REFRAIN, a standard-compliant PHY/MAC framework, which copes with and further exploits the anomaly for better spatial reuse. Our prototype using NI USRP and commercial Wi-Fi devices shows the feasibility and effectiveness of our approach, while extensive simulation results demonstrate that REFRAIN achieves up to 1.57× higher average throughput by promoting valid transmissions, without modifying the 802.11 CS specification at all.

Wi-Fi devices become increasingly susceptible to mutual interference in congested network scenarios. If a device starts to receive a new frame in the presence of concurrent interference signal, its preamble reception can be seriously damaged, which spoils the channel estimation process. The resulting corrupted channel state information (CSI) causes persistent decoding errors throughout the data payload. This paper presents a comprehensive study into the impact of corrupted CSI on Wi-Fi performance. Through experimental study and link-level analysis, we verify that actual receiver performance is highly dependent on CSI acquisition at the preamble, which has never been reflected by any physical layer abstraction models. We develop a realistic model that reflects the impact of corrupted CSI on overall frame reception. Applying our model to ns-3 simulator, we revisit network performance in interference-prone scenarios, to assess consistency with real-world Wi-Fi systems.

LTE-Unlicensed (LTE-U) supports LTE downlink operation in 5GHz unlicensed bands where Wi-Fi has been a traditional user for a long time. To achieve fair coexistence with Wi-Fi, LTE-U employs carrier sense adaptive transmission (CSAT), but it does not guarantee perfectly fair coexistence. Therefore, many studies have dealt with unfair coexistence problems of Wi-Fi and LTE-U. However, in this paper, our experiment results show that a Wi-Fi station not only suffers from unfair coexistence but also wastes energy and air time when it coexists with LTE-U. To cope with this problem, we propose AWARE, a station-driven adaptive Wi-Fi power save operation coexisting with LTE-U, which detects LTE-U and adjusts Wi-Fi power state adaptively. We implement AWARE on a commercial 802.11n device, and our evaluation shows that a Wi-Fi station achieves almost the same throughput while reducing its power consumption by up to 33% and enhancing the throughput of neighbor Wi-Fi stations by up to 43%.

This paper sheds light on the necessity for transmit power control of 802.11 clear-to-send (CTS) frame for better spatial reuse in densely deployed wireless local area networks (WLANs). We first study the effect of the interference caused by CTS frames through both experiments and numerical analysis. Based on the analysis, we raise a limited spatial reuse problem caused by RTS/CTS mechanism and establish a guideline for controlling CTS transmit power to solve the problem. Next, a standard-compliant CTS transmit power control method, namely Quiet CTS (QCTS), is proposed. Through extensive simulations and prototyping using both software-defined radio and commercial devices, we demonstrate that QCTS enhances the average network throughput by up to 50% by enabling more simultaneous data transmissions.

State-of-the-art IEEE 802.11ac supports wide bandwidth operation, which enables aggregating multiple 20 MHz channels up to 160 MHz bandwidth, as a key feature to achieve high throughput. In this paper, our experiment results reveal various situations where bandwidth adaptation without changing the receiver's baseband bandwidth, called operating channel width (OCW), leads to poor reception performance due surprisingly to time-domain interference not overlapping with the incoming desired signal in frequency domain. To cope with this problem, we develop RECONN, a standard-compliant and receiver-driven OCWadaptation scheme with ease of implementation. Our prototype implementation in commercial 802.11ac devices shows that RECONN achieves up to 1.85× higher throughput by completely eliminating time-domain interference. To our best knowledge, this is the first work to discover the time-domain interference problem, and to develop OCW adaptation scheme in 802.11ac system.

With increasing demand for wireless connectivity, IEEE 802.11 WLANs have become ubiquitous and continue to grow in number. This leads to high density of basic service sets with the significant co-channel interference (CCI) among them. This paper sheds light on the CCI caused by 802.11 MAC ACK frames, which has been less studied than the CCI caused by data frames. Based on stochastic geometry analysis, we propose Quiet ACK (QACK), a dynamic transmit power control algorithm for ACK frames. Fine-grained transmit power adjustment is enabled by CCI detection and CCI power estimation in the middle of a data frame reception. Our prototype using software-defined radio shows the feasibility and performance gain of QACK, i.e., 1.5x higher throughput than the legacy 802.11 WLANs. The performance of QACK is further evaluated in more general WLAN environments via extensive simulations using ns-3.

Antenna selection, a cost-effective way to enhance network performance, has been employed in a limited manner in wireless local area networks (WLANs) due to the lack of channel information at the transmitter. In this paper, a practical antenna selection system without using channel information is proposed. We first describe the practical issues of antenna selection system for infrastructure-based WLANs, and then, analyze its characteristics through an extensive measurement study. Based on that, we propose antenna selection algorithms of access point (AP) for both (1) unicast transmission and (2) multicast transmission and reception. The proposed algorithms are comparatively evaluated using prototype implementation in a commercial AP. It is demonstrated that the proposed algorithms achieve up to 34% throughput gain and 54% frame error rate reduction for unicast and multicast transmissions, respectively.