To enhance user experience, IEEE 802.11be introduces Multi-Link Operation (MLO), enabling improved throughput performance by simultaneously utilizing two or more links. To mitigate In-Device Coexistence (IDC) interference, Multi-Link Devices (MLDs) can employ Non-Simultaneous Transmit and Receive (NSTR) MLO. The performance of NSTR MLO is highly influenced by environmental factors such as channel occupancy, making it essential to devise and adopt NSTR MLO algorithms that align with specific user environments. This paper proposes several NSTR MLO algorithms for U-MAC packet scheduling (specifically, OPA) and for L-MAC packet transmission (specifically, EATX, SATX-C, SATX-A, and ATX), and provides a comprehensive experimental evaluation of various NSTR MLO algorithms to identify their optimal performance across diverse conditions. Using a MATLAB-based in-house MLO simulator, we assess the performance of NSTR MLO algorithms under different environmental scenarios. The evaluation reveals that NSTR MLO with Alternating Transmission (ATX) is well-suited for most cases, while NSTR MLO with Start-time Aligned Transmission (SATX) is optimal in environments with low channel occupancy and bursty traffic arrivals. Moreover, the results show that the proposed OPA algorithm for U-MAC significantly enhances the performance of an MLD using SATX under high channel occupancy environments.

Mobile Augmented Reality (MAR) has emerged as a versatile technology applied across diverse domains such as entertainment, industry, education, etc. Despite its great potential, MAR has not proliferated due to the lack of stringent latency guarantee, which is crucial for AR’s quality-of-experience. To fill the gap, this paper proposes HAWA, a WLAN AP-coordinated AR framework, designed to reliably upperbound the motion-to-photon latency of AR users via fully-orchestrated channel access and AR traffic scheduling in the edge-assisted AR network. Based on HAWA, we formulate an admission control problem with which AR devices can be maximally admitted while maintaining the required latency performance. For this, we present rigorous and complete analytical steps to derive the problem’s essential components. Then, our extensive evaluations show the accuracy of the analysis and the dynamics of AR admission control under various AR scenarios.

This paper proposes a high-performance wireless communication method for Hyperloop systems using the HE11 mode in a dielectric-lined circular waveguide. The TE11 mode, used in previous studies, exhibits significant attenuation and higher-order mode interference at 3.5 GHz, while the TE01 mode provides low loss but suffers from a narrow bandwidth, limiting its practical applicability. The HE11 mode addresses the limitations of both modes by offering low loss and a wide bandwidth of up to 4.5 GHz. CST simulations were conducted based on a circular waveguide with a diameter of 3.3 m, confirming stable propagation of the HE11 mode at 3.5 GHz. In addition, mathematical analysis showed that mode dispersion is minimized when the curvature radius exceeds 23.5 km. Furthermore, to simulate realistic Hyperloop conditions, a 1/52 scaled model experiment was performed at 52 times the target frequency, experimentally validating the feasibility of HE11 mode propagation.

This paper investigates the impact of tube curvature on electromagnetic (EM) wave propagation in the Hyperloop communication environment. The Hyperloop tube can be modeled as a circular waveguide, whose geometric curvature may affect intra-tube signal propagation. To unveil the true impact of tube curvature, we have conducted EM simulations to analyze the propagation characteristics of the TE11 mode under two configurations: a perfectly straight tube, and a curved tube with bend radius R = 500m and bending angle θ = 90°. The obtained results show that TE11 exhibits stable propagation in the straight tube, whereas fluctuating received signal power in the curved counterpart. Such findings reveal how much tube curvature affects EM wave propagation and provide practical insights and guidelines for the tube design and intra-tube communication systems.

Wi-Fi 7 introduces Multi-link operation (MLO) to enhance throughput and latency performance compared to legacy Wi-Fi standards. MLO enables simultaneous transmission and reception through multiple links, departing from conventional single-link operations (SLO). To fully exploit MLO's potential, it is essential to investigate Wi-Fi 7's coexistence performance with legacy Wi-Fi devices. Existing approaches, however, have overlooked some crucial aspects of MLO, necessitating the development of a standards-compliant analytical framework to model the actual channel access mechanism of MLO. Therefore, this paper tries to fill the gap by proposing a set of novel Markov chains (MC) to accurately model the MLO operation aligned with multi-link backoff behaviors specified by the standard. Specifically, we design two separate MCs for AP and non-AP multi-link devices (MLD) respectively, based on which transmit and collision probabilities are derived under the saturated traffic condition. Then, we also derive closed-form expressions for the throughput of various device types in the coexistence scenario between Wi-Fi 7 and legacy Wi-Fi, including AP MLD, non- AP MLD, and legacy devices. To validate the accuracy of our proposed models, we developed an ns-3 based simulator by implementing both STR(simultaneous transmission and reception) and NSTR(non-STR) based MLO operations. Our ns-3 based extensive simulations have demonstrated that the proposed analytic model provides accurate estimates on the per device throughput performance, while also revealing the dynamics of inter-WLAN coexistence scenarios.

This paper examines the influence of EM dispersion on the propagation of OFDM-modulated signals inside the metallic Hyperloop tube, which is caused by the waveguide-like nature of the tube. Intra-tube EM dispersion results in varying propagation speeds among different frequency components, inducing non-uniform propagation delays across OFDM subcarriers. By modeling the Hyperloop channel experiencing such frequency-dependent propagation delays, we show that significant and varying phase rotations are imposed on the OFDM symbols, despite subcarrier orthogonality being still preserved. This suggests OFDM-based intra-tube wireless propagation should consider a method to compensate the aforementioned effect, to avoid any communication performance degradation. Specifically, our analysis emphasizes the necessity of a phase compensation mechanism for reliable OFDM-based Hyperloop communications, and provides a foundation for future work on the performance assessment and compensation strategies.

The future 6G network necessitates its RAN architecture to guarantee rigorous service- and user-centric performances, while seamlessly integrating communication, computing, and AI in the combined domains of cloud, edge, and user terminals. Open RAN (O-RAN) offers a promising platform to meet such requirements by enabling open interfaces, data-driven control, and the orchestration of heterogeneous resources. This paper examines the principal challenges and key research directions in O-RAN based radio resource management (RRM). We first review deep learning based RRM approaches and their applicability within the O-RAN near-RT and non-RT RIC frameworks. Next, we highlight the limitations of current 5G interfaces and discuss potential enhancements to facilitate the joint optimization of radio and O-Cloud resources. Finally, we outline future research directions, including channel prediction based RRM, user-centric scheduling, and service provider oriented optimization. These insights indicate that O-RAN, when integrated with intelligent RRM, can form a foundational basis for realizing the service-driven performant 6G network.

The coexistence between Wi-Fi and the cellular technology in the unlicensed spectrum has been a significant research topic for several years. To investigate their coexistence performance, ns-3, one of the most popular network simulators, introduced the LTE-LAA project in 2016 [1], but its modules have not been updated in later versions of ns-3. Moreover, ns-3 does not yet support Wi-Fi 7’s NSTR(Non-Simultaneous Transmit and Receive) MLO(Multi-Link Operation) features. Therefore, there is an urgent need to update both LTE-LAA and Wi-Fi modules of ns-3 accordingly. This paper presents a methodology to modify ns-3 to address the aforementioned need, so as to enable the ns-3 simulator to accurately evaluate coexistence scenarios between Wi-Fi MLO and LTE-LAA.

We propose to use a HE11 mode in Hyperloop communication systems to overcome bandwidth and loss limitations when a TE11 mode is used. By using dielectric-coated circular waveguides and shifting the operating frequency to 865 MHz, the HE11 mode enables significantly improved communication performance in terms of loss, bandwidth, and efficiency, as demonstrated for a 3.3 m diameter Hyperloop system.

RAN-agnostic communications can identify intrinsic features of the unknown signal without any prior knowledge, with which incompatible RANs in the same unlicensed band could achieve better coexistence performance than today’s LBT-based coexistence. Blind modulation identification is its key building block, which blindly identifies the modulation type of an incompatible signal without any prior knowledge. Recent blind modulation identification schemes are built upon deep neural networks, which are limited to single-carrier signal recognition thus not pragmatic for identifying spectro-temporal OFDMA signals whose modulation varies with time and frequency. Therefore, this paper proposes RiSi, a semantic segmentation neural network designed to work on OFDMA’s spectrograms, that employs flattened convolutions to better identify the grid-like pattern of OFDMA’s resource blocks. We trained RiSi with a realistic OFDMA dataset including various channel impairments, and achieved the modulation identification accuracy of 86% on average over four modulation types of BPSK, QPSK, 16-QAM, 64-QAM. Then, we enhanced the generalization performance of RiSi by applying domain generalization methods while treating varying FFT size or varying CP length as different domains, showing that thus-generalized RiSi can perform reasonably well with unseen data.

Federated learning (FL) is a decentralized AI mechanism suitable for a large number of devices like in smart IoT. A major challenge of FL is the non-IID dataset problem, originating from the heterogeneous data collected by FL participants, leading to performance deterioration of the trained global model. There have been various attempts to rectify non-IID dataset, mostly focusing on manipulating the collected data. This paper, however, proposes a novel approach to ensure data IIDness by properly clustering and grouping mobile IoT nodes exploiting their geographical characteristics, so that each FL group can achieve IID dataset. We first provide an experimental evidence for the independence and identicalness features of IoT data according to the inter-device distance, and then propose Dynamic Clustering and Partial-Steady Grouping algorithms that partition FL participants to achieve near-IIDness in their dataset while considering device mobility. Our mechanism significantly outperforms benchmark grouping algorithms at least by 110 times in terms of the joint cost between the number of dropout devices and the evenness in per-group device count, with a mild increase in the number of groups only by up to 0.93 groups.

Hyperloop is a futuristic transfortation system to carry passengers and frieght in the vehicle called ‘pod’, which travels at the speed of 1,200 km/h within a near-vacuum ‘tube’. To ensure safety and to provide Internet connectivity to passengers, wireless communications is an essential building block whose development necessitates evaluating and enhancing intra-tube wireless channel capacity. In this regard, this paper presents an in-depth investigation of per-pod downlink channel capacity, leveraging our previously-developed novel evaluation methodology on intra-tube electromagnetic (EM) propagation characteristics. Specifically, we first show that metallic pods (as considered in most Hyperloop proposals) cannot satisfy the per-passenger capacity requirement for on-board Internet service. As a remedy, we propose a double-layered pod structure to enhance the channel capacity, consisting of an EM absorbing outer layer and a metallic inner layer enclosing the passenger cabin. Our intensive evaluations revealed that double-layered pods achieve per-pod channel capacity large enough to support 4K UHD video streaming for all the passengers, thanks to the outer layer absorbing the power of interference signals. Furthermore, we present how to maximize the channel capacity via an optimized thickness of the EM absorbing layer and the choice of the best EM mode for communications.

Wi-Fi 7 introduces Multi-link operation (MLO) to enhance throughput and latency performance compared to previous Wi-Fi standards. MLO enables simultaneous transmission and reception through multiple links, departing from conventional single-link operations (SLO). To fully exploit MLO’s potential, it is essential to investigate Wi-Fi 7’s coexistence performance with legacy Wi-Fi devices. Existing approaches, however, have overlooked some crucial aspects of MLO, necessitating the development of a standards-compliant analytical framework to model the actual channel access mechanism of MLO. Therefore, this research aims to addresses this gap by constructing an accurate Markov chain model for MLO aligned with the backoff behaviors specified by the standard, and by deriving closed-form expressions for the throughput of each device type in the Wi-Fi 7 and legacy Wi-Fi coexistence scenario.

—Smart vehicles require constantly running heavy vehicular computations with their limited computation/energy resources. 5G vehicular networks have potential to resolve the issue, by letting the vehicular tasks offloaded to 5G mobile edge computing (MEC) servers. To better support vehicular computation offloading, this paper proposes a road-side 5G infrastructure consisting of multiple millimeter-wave (mmWave) small-cell base stations (BSs) and a cellular mid-band based macro-cell BS where each BS is equipped with an MEC server. Then, the vehicles with mmWave/mid-band dual interfaces can decide which BS to choose for offloading. We propose a decentralized offloading decision mechanism where each vehicle tries to minimize the time-energy joint cost with three choices: local computing, offloading to a small-cell MEC, offloading to a macro-cell MEC. In particular, we model the problem as an ordinal potential game, derive its potential function to ensure the existence of and finite-time convergence to a Nash equilibrium (NE), analyze its Price-of-Anarchy, and develop an iterative offloading decision update algorithm. In doing so, we also consider slicing the global game into multiple non-overlapping smaller games and running them in parallel, to investigate the best slicing strategy. Our extensive simulations show the game’s real-time convergence to an NE, reveal the NE’s near-optimal performance, and present the efficacy of the proposed game slicing.

Hyperloop is a next-generation transportation system which conveys people and freight in a vehicle called a pod , traveling through a near-vacuum tube at a near-sonic speed of 1,200 km/h. Its concept has been proposed in 2013 by Elon Musk of SpaceX, as a mass transit system connecting distant cities [1] . SpaceX sponsored the annual Hyperloop pod competition in 2016–2019, where each team builds a prototype pod to run in a mile-long test track. There also have been industrial activities to develop Hyperloop, such as Virgin Hyperloop One reaching 387 km/h in 2017 and Hyperloop Transportation Technologies building a test track since 2018.

UAM (Urban Air Mobility) is future aerial mobility for passengers and cargo, usually based on eVTOLs (electric vertical take-off and landing aircrafts). UAM communications is a key enabling technology of UAM systems and services, to ensure efficient and safe navigation and to provide network access to passengers. Traditional terrestrial networks, however, are not reliable to fulfill the requirements of UAM since they have not been designed to support airborne mobile users in the three-dimensional space. In the meantime, 6G communications is considered as a good match to UAM due to its vision to cover ground, air, and space. Hence, this paper overviews major challenges of UAM communications and their existing approaches, and then provides future research directions for 6G-based UAM communications.

Millimeter-wave (mmWave) networks are conventionally considered to bear a fundamental coverage limitation, due to the directional beams and limited field-of-view (FoV) of the phased array antennas. In this paper, we explore an array of phased arrays (APA) architecture, which aggregates co-located phased arrays with complementary FoVs to approximate WiFi-like omni-directional coverage. We found that straightforwardly activating all the arrays may even hamper network performance. To fully exploit the APA's potential, we propose X-Array, which jointly selects the arrays and beams, and applies a dynamic co-phasing mechanism to ensure different arrays' signals enhance each other. X-Array also incorporates a link recovery mechanism to identify alternative arrays/beams that can efficiently recover the link from outage. We have implemented X-Array on a commodity 802.11ad APA radio. Our experiments demonstrate that X-Array can approach omni-directional coverage and maintain high performance in spite of link dynamics.

Low latency networking is gaining attention to support futuristic network applications like the Tactile Internet with stringent end-to-end latency requirements.In realizing the vision, cut-through (CT) switching is believed to be a promising solution to significantly reduce the latency of today's store-andforward switching, by splitting a packet into smaller chunks called flits and forwarding them concurrently through input and output ports of a switch.Nevertheless, the end-to-end latency performance of CT switching has not been well studied in heterogeneous networks, which hinders its adoption to general-topology networks with heterogeneous links.To fill the gap, this paper proposes an end-to-end latency prediction model in a heterogeneous CT switching network, where the major challenge comes from the fact that a packet's end-to-end latency relies on how and when its flits are forwarded at each switch while each flit is forwarded individually.As a result, traditional packet-based queueing models are not instantly applicable, and thus we construct a method to estimate per-hop queueing delay via M/G/c queueing approximation, based on which we predict end-to-end latency of a packet.Our extensive simulation results show that the proposed model achieves 3.98-6.05%90th-percentile error in end-to-end latency prediction.

The prosperity of IEEE 802.11-based Wi-Fi networks aggravates cross-technology interference to IEEE 802.15.4-enabled ZigBee networks widely deployed to enable various Internet-of-Things applications. To make ZigBee communication reliable and robust even in a dense Wi-Fi environment, taming Wi-Fi interference in ZigBee networks especially from the perspective of physical layer is of paramount importance. In this context, this work takes aim to design a novel Wi-Fi interference-resilient ZigBee decoder called PolarScout, which separates collided ZigBee signal samples out of Wi-Fi interference to bootstrap ZigBee data decoding. Unlike several existing solutions which need clear signal preamble, tremendous signal strength difference between ZigBee and Wi-Fi, and Wi-Fi interference recognition in prior to ZigBee decoding, PolarScout aims at direct ZigBee decoding in a more generic and challenging case where Wi-Fi interference features a wide range of power levels and arises within a ZigBee packet at an arbitrary position. At the heart of PolarScout lies a subtle shell-shaping technique which harnesses a customized sample sequence to smooth the shell of corrupted signal samples. PolarScout then refers to the resulting shell to recover each contaminated ZigBee sample. Experimental results validate the superiority of PolarScout and its resilience to a wide range of Wi-Fi interference types.

Virtual Reality (VR) applications require low latency transmission of high-definition video frames to support immersive user experience without incurring side-effects like motion sickness. In this paper, we claim that state-of-the-art MU-MIMO user selection schemes fail to achieve the aforementioned goal due to their focus on building the best MU-MIMO group with maximal throughput. To fill the gap, we propose two new scheduling schemes to effectively reduce the user-experienced latency in WLAN-based VR systems. Via extensive simulations, we compare the performance of our proposed schedulers with that of Guidepost, a well-known MU-MIMO scheduler, to show the need of considering inter-group dependency in user scheduling to best support VR service provisioning. The latency enhancement by the proposed algorithms is revealed as up to 23.2% in the average sum airtime and up to 36.9% in the maximum sum airtime.