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.

This paper studies the cross-layer challenges and performance of Hybrid Beamforming (HBF) and Multi-User Multiple-Input Multiple-Output (MU-MIMO) in 5G millimeter wave (mmWave) cellular networks with full-stack TCP/IP traffic and MAC scheduling. While previous research on HBF and MU-MIMO has focused on link-level analysis of full-buffer transmissions, this work reveals the interplay between HBF techniques and the higher layers of the protocol stack. To this aim, prior work on the full-stack evaluation of mmWave cellular networks has been extended by including the modeling of MU-MIMO and HBF. Our results reveal novel relations between the networking layers and the HBF MU-MIMO performance at the physical layer. Particularly, throughput can be increased in 5G networks by means of Space Division Multiple Access (SDMA). However, in order to achieve such benefits it is necessary to take into account certain trade-offs and the implementation complexity of a full-stack HBF solution.

Recent advances in the physical layer have demonstrated the feasibility of in-band wireless full-duplex which enables a node to transmit and receive simultaneously on the same frequency band. While the full-duplex operation can ideally double the spectral efficiency, the network-level gain of full-duplex in large-scale networks remains unclear due to the complicated resource allocation in multi-carrier and multi-user environments. In this paper, we consider a single-cell full-duplex OFDMA network which consists of one full-duplex base station (BS) and multiple full-duplex mobile nodes. Our goal is to maximize the sum-rate performance by jointly optimizing subcarrier assignment and power allocation considering the characteristics of full-duplex transmissions. We develop an iterative solution that achieves local Pareto optimality in typical scenarios. Through extensive simulations, we demonstrate that our solution empirically achieves near-optimal performance and outperforms other resource allocation schemes designed for half-duplex networks. Also, we reveal the impact of various factors such as the channel correlation, the residual self-interference, and the distance between the BS and nodes on the full-duplex gain.

In this article, we investigate 4G network architecture and consider two underlying layers: PHY and MAC. We compare two models of wireless access network: pure all-IP and subnet based networks. The pure all-IP model is simple and cost-efficient but causes implementation issues of mobility management and resource coordination. In contrast, the subnet based network enables layer 2 and layer 3 handoffs to be executed independently, deploying several access points under an access router. Further, to handle various cases efficiently according to traffic class and mobility, we present an advanced model of a hierarchical cellular system that combines multiple access techniques of OFDMA and FH-OFDMA with microcells and macrocells. Finally, as an integrated approach to support diverse QoS requirements, we consider an IP-triggered resource allocation strategy (ITRAS) that exploits IntServ and DiffServ of the network layer to interwork with channel allocation and multiple access of MAC and PHY layers, respectively. These cross layer approaches shed light on designing a QoS support model in a 4G network that cannot be handled properly by a single layer based approach

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.

This paper studies the cross-layer challenges and performance of Hybrid Beamforming (HBF) and Multi-User Multiple-Input Multiple-Output (MU-MIMO) in 5G millimeter wave (mmWave) cellular networks with full-stack TCP/IP traffic and MAC scheduling. While previous research on HBF and MU-MIMO has focused on link-level analysis of full-buffer transmissions, this work reveals the interplay between HBF techniques and the higher layers of the protocol stack. To this aim, prior work on the full-stack evaluation of mmWave cellular networks has been extended by including the modeling of MU-MIMO and HBF. Our results reveal novel relations between the networking layers and the HBF MU-MIMO performance at the physical layer. Particularly, throughput can be increased in 5G networks by means of Space Division Multiple Access (SDMA). However, in order to achieve such benefits it is necessary to take into account certain trade-offs and the implementation complexity of a full-stack HBF solution.

Recent advances in the physical layer have demonstrated the feasibility of in-band wireless full-duplex which enables a node to transmit and receive simultaneously on the same frequency band. While the full-duplex operation can ideally double the spectral efficiency, the network-level gain of full-duplex in large-scale networks remains unclear due to the complicated resource allocation in multi-carrier and multi-user environments. In this paper, we consider a single-cell full-duplex OFDMA network which consists of one full-duplex base station (BS) and multiple full-duplex mobile nodes. Our goal is to maximize the sum-rate performance by jointly optimizing subcarrier assignment and power allocation considering the characteristics of full-duplex transmissions. We develop an iterative solution that achieves local Pareto optimality in typical scenarios. Through extensive simulations, we demonstrate that our solution empirically achieves near-optimal performance and outperforms other resource allocation schemes designed for half-duplex networks. Also, we reveal the impact of various factors such as the channel correlation, the residual self-interference, and the distance between the BS and nodes on the full-duplex gain.

In this article, we investigate 4G network architecture and consider two underlying layers: PHY and MAC. We compare two models of wireless access network: pure all-IP and subnet based networks. The pure all-IP model is simple and cost-efficient but causes implementation issues of mobility management and resource coordination. In contrast, the subnet based network enables layer 2 and layer 3 handoffs to be executed independently, deploying several access points under an access router. Further, to handle various cases efficiently according to traffic class and mobility, we present an advanced model of a hierarchical cellular system that combines multiple access techniques of OFDMA and FH-OFDMA with microcells and macrocells. Finally, as an integrated approach to support diverse QoS requirements, we consider an IP-triggered resource allocation strategy (ITRAS) that exploits IntServ and DiffServ of the network layer to interwork with channel allocation and multiple access of MAC and PHY layers, respectively. These cross layer approaches shed light on designing a QoS support model in a 4G network that cannot be handled properly by a single layer based approach

Orthogonal frequency-division multiple access (OFDMA) systems are considered promising candidates for implementing next-generation wireless communication systems. They provide multiple channels that can be accessed via random access schemes. However, traditional random access schemes could result in an excessive amount of access delay. To address this issue, we develop a fast retrial scheme that is based on slotted Aloha and exploits the structure of OFDMA. A salient feature of this scheme is that when collisions occur instead of retrials occuring randomly in time, they occur randomly in frequency, i.e., the scheme randomly selects the subchannels for retrial. To further achieve fast access, retrials are designed to follow the 1-persistent type, i.e., no exponential backoff. To achieve the maximum throughput, we limit the maximum number of allowed retrials according to the load condition. We also consider the issue of designing for an appropriate reuse factor for random access channels in order to overcome the intercell interference problem in OFDMA multicell environments. Our finding is that full sharing, i.e., a reuse factor of one, performs best for given random access channels. Through analysis and simulation, we confirm that our fast retrial algorithm has the advantage of high throughput and low access delay, and the full sharing policy for random access channels shows high throughput as well as low collision.

Distracted driving contributes to fatal crashes worldwide. To address this, researchers are using Driver Activity Recognition (DAR) with Impulse Radio Ultra-Wideband (IR-UWB) radar, which offers advantages like interference resistance, low-power use, and privacy. However, two challenges limit its adoption: the lack of large-scale, real-world UWB datasets on diverse distracted driving behaviors, and the difficulty in adapting fixed-input Vision Transformers (ViTs) to UWB radar data with non-standard dimensions. This work tackles both challenges. We present the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ALERT</i> dataset, containing 10,220 radar samples of seven distracted driving activities in real driving conditions. We also propose the Input-Size-Agnostic Vision Transformer <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(ISA-ViT)</i>, a framework designed for radar-based DAR. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ISA-ViT</i> resizes UWB data to fit ViT input requirements while preserving radar-specific information like Doppler shifts and phase data. By adjusting patches and using pre-trained positional embedding vectors (PEVs), <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ISA-ViT</i> avoids the limitations of simple resizing. Additionally, a domain fusion strategy combines range and frequency domain features, enhancing classification accuracy. Comprehensive experiments demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ISA-ViT</i> achieves a 22.68% higher accuracy compared to the existing ViT method in UWB-based DAR. By publicly releasing the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ALERT</i> dataset and detailing our input-size-agnostic strategy, this work paves the way for more robust and scalable distracted driving detection systems in real-world scenarios.

Interest in Direct Current Power Line Communication (DC-PLC) is growing as industries seek to reduce wiring complexity and cost by combining power and data over a single medium. A programmable DC-PLC testbed is developed to enable power transfer and bidirectional communication over a shared DC power line bus. The system employs a master–slave scheme in half-duplex mode under a flexible MAC protocol, using Voltage Polarity Modulation (VPM) for downlink and Current Amplitude Modulation (CAM) for uplink. Implemented with low-cost microcontrollers, the master and slave are designed as modular units for easy connection and expansion. The platform achieves approximately 100 kbps throughput in both directions and demonstrates reliable operation with a simple polling-based MAC protocol, highlighting its potential for future DC-PLC research and applications.

High-quality wireless communication is paramount to coordinating flights and missions effectively on a micro-UAV (a.k.a. drone). However, most cellular antennas are optimized for users on the ground and have not been planned for aerial devices. Many have studied cellular communication quality, but few works explore the impact of altitude during a flight. Through real-world experiments using an actual drone, we demonstrate significant connectivity dynamics in the air for both 4G LTE and 5G NR, and reveal that fixed-coordinate flights cannot maintain high quality connectivity in response to those dynamics. To address this problem, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ASCEND</i>, a reinforcement learning-based 3D altitude selection scheme that maintains high-quality connectivity during a flight over a planned 2D path without requiring prior training. We evaluate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ASCEND</i> at multiple real-world locations to demonstrate a notable increase in expected throughput and a reduction in the proportion of low-quality legs during a flight mission.

For real-time video analytics, the inference trigger frame selection problem is crucial. While continuous video streams offer rich data, processing every frame is computationally intensive. Thus, modern systems strategically analyze the frames to choose frames for inference, i.e., inference trigger frames (ITF). Between ITFs, they use tracking to leverage temporal coherence. Consequently, the ITF selection problem functions as the cornerstone technology for video analytics in terms of both accuracy and efficiency. Additional to selecting the critical frames for system accuracy, a major requirement for the ITF selection algorithms is ‘efficiency.’ ITF selection algorithms should be able to extract the informative frames while imposing minimal computational overhead to the analytics pipeline. While current solutions, such as ‘frame differencing’ methods, provide promising results, they still fall short of fully addressing this issue.We point out that using frame difference value as the metric can mislead the re-detection necessity, thus falling short in choosing the optimal frame sets for inference. We introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FrameBoost</i>, an intelligent ITF selection method that adopts aggregate tracking error (ATE) as its metric, leveraging object-wise IoU predictions. We study the key factors controlling the IoU prediction performance and propose a lightweight solution that effectively addresses the aforementioned limitations. Through extensive evaluations measuring both accuracy and computational efficiency, we demonstrate that FrameBoost delivers superior performance compared to existing approaches.