Indoor wireless connectivity remains a complex challenge, particularly in dense environments and underground facilities. Although modern Wi-Fi technologies (such as Wi-Fi 6, Wi-Fi 7, and future iterations) offer higher throughput, they are constrained by the limitations of unlicensed spectrum, client-driven connection decisions, and channel management complexities that make it difficult to ensure consistent Quality of Service (QoS). Mobile Network Operator (MNO) signals—especially those using mid- and high-band 5G frequencies—face significant attenuation through walls and other building materials, resulting in weak or inconsistent indoor coverage.An MNO-based Distributed Antenna System (DAS) can extend outdoor cellular coverage indoors by amplifying and redistributing the MNO’s signal; however, its performance remains limited by the capacity and bandwidth of the external MNO network. In contrast, a Neutral Host (NH) system operating in the Citizens Broadband Radio Service (CBRS) spectrum allows multiple MNOs to share a single private cellular infrastructure. This approach reduces deployment costs while enhancing coverage and capacity across enterprise and public spaces. This paper evaluates and compares four primary indoor connectivity solutions—MNO, Wi-Fi, Neutral Host (NH), and Distributed Antenna System (DAS)—by analyzing key performance indicators, including signal strength (RSRP, RSSI), Signal-to-interference ratio plus noise (SINR), and throughput. The results highlight the strengths and limitations of each technology, offering guidance on their optimal use across various indoor deployment scenarios.

Channel sounding is crucial for achieving Extremely High Throughput (EHT) and Ultra-high reliability (UHR) in next-generation Wi-Fi systems, i.e., Wi-Fi 7 and beyond. In Downlink Multi-User Multiple-Input Multiple-Output (DL MUMIMO) communications, data rate significantly deteriorates under time-varying channel with Doppler effect. Therefore, effective channel sounding mechanisms must balance the Channel State Information (CSI) overhead and CSI staleness, which is governed by the channel coherence time. Despite its critical importance, channel sounding optimization under time-varying channel conditions remains under-explored. This paper addresses this research gap by proposing a cross-layer optimization problem for the channel sounding period with the objective of maximizing data rate by considering the CSI overhead over the MAC layer and the channel capacity degradation over the PHY layer. This problem is then converted into an equivalent formulation leveraging the EHT sounding protocol, which can be solved efficiently using our proposed optimal search algorithm. Through simulations, we evaluate the baseline EHT sounding using outdated beamforming matrices and benchmark it against our proposed solution. The numerical results demonstrate that the channel sounding period optimization significantly reduces CSI overhead by up to 11% while boosting the average data rate by up to 8%.

Vehicle-to-Everything (V2X) communication faces a critical authentication dilemma: traditional public-key schemes like ECDSA provide strong security but impose 2 ms verification delays unsuitable for collision avoidance, while symmetric approaches like TESLA achieve microsecond-level efficiency at the cost of 20-100 ms key disclosure latency. Neither meets 5G New Radio (NR)-V2X's stringent requirements for both immediate authentication and computational efficiency. This paper presents SALT-V, a novel hybrid authentication framework that reconciles this fundamental trade-off through intelligent protocol stratification. SALT-V employs ECDSA signatures for 10% of traffic (BOOT frames) to establish sender trust, then leverages this trust anchor to authenticate 90% of messages (DATA frames) using lightweight GMAC operations. The core innovation - an Ephemeral Session Tag (EST) whitelist mechanism - enables 95% of messages to achieve immediate verification without waiting for key disclosure, while Bloom filter integration provides O(1) revocation checking in 1 us. Comprehensive evaluation demonstrates that SALT-V achieves 0.035 ms average computation time (57x faster than pure ECDSA), 1 ms end-to-end latency, 41-byte overhead, and linear scalability to 2000 vehicles, making it the first practical solution to satisfy all safety-critical requirements for real-time V2X deployment.

Next-generation Wi-Fi 8 (IEEE 802.11bn) targets ultra-high reliability (UHR) by introducing coordinated beamforming (CoBF). In dense networks with multiple access points (APs), simultaneous downlink (DL) multi-user MIMO (MU-MIMO) transmissions from multiple APs can cause severe intra-basic service set (intra-BSS) and inter-BSS interference. CoBF aided by only partial channel state information (CSI) feedback through medium access control (MAC) layer frame exchange is envisioned to support concurrent DL transmission with mitigated physical-(PHY-)layer interference. To improve the network throughput, not only the interference mitigation algorithm design requires careful design but also the selection of optimal AP CoBF clusters is crucial for dense AP deployments. This paper presents a cross-layer solution combining PHY and MAC layer design to optimize AP cluster formation for Wi-Fi 8 CoBF. At the PHY layer, we introduce two beamforming nulling strategies: full nulling, which completely cancels all intra-BSS and inter-BSS interference when sufficient spatial degrees of freedom are available, and partial nulling, which is used under limited degrees of freedom to reduce interference as much as possible. Based on this, we formulate the cross-layer problem that aims to optimize the network throughput, to which we propose an exact linear programming (LP) optimization to determine the optimal AP cluster formation. A greedy clustering algorithm is proposed as a low-complexity alternate. Simulation results demonstrate that the proposed CoBF approach significantly mitigates interference and achieves substantial throughput gains in dense AP scenarios. Furthermore, the LP-optimized AP clustering yields the higher network throughput than the greedy heuristic and mixed integer linear programming (MILP) by up to 12% and 26%, highlighting the benefits of global optimization in terms of performance and time complexity.

Extremely High Throughput (EHT) and Ultra-high reliability (UHR) are new objectives in Next-Gen Wi-Fi, i.e., Wi-Fi 7 and beyond; however, the data rate within a periodic channel sounding round is expected to significantly deteriorate under time-varying channels with Doppler effect in Downlink Multi-User Multiple-Input Multiple-Output. Therefore, Next-Gen channel sounding must carefully balance the MAC-layer CSI overhead reduction and the PHY-layer channel capacity degradation caused by the Doppler effect for data rate maximization. Despite its critical importance, the cross-layer (PHY + MAC) Wi-Fi channel sounding optimization in time-varying channels remains under-explored. This paper addresses this research gap by proposing a cross-layer optimization problem to find the optimal EHT sounding period that maximizes the average data rate by considering both MAC-layer CSI overhead and PHY-layer channel capacity degradation. This problem is then converted into an equivalent optimization problem that can be solved efficiently using our proposed model driven optimal search algorithm with proven convexity. Afterwards, we introduce a data driven Transformer-based partial CSI prediction framework to alleviate CSI staleness without introducing extra CSI overhead, which further enhances the average data rate. Through simulations, we evaluate the baseline EHT sounding protocol that always uses outdated partial CSI, and then benchmark the baseline against our proposed hybrid data and model driven approach. The numerical results demonstrate that integrating Transformer-based partial CSI prediction with the optimal channel sounding period significantly reduces CSI overhead by up to 25.2%, while increasing the average throughput by up to 30.9%.

The IEEE 802.11 standards, culminating in IEEE 802.11be (Wi-Fi 7), have significantly expanded bandwidth capacities from 20 MHz to 320 MHz, marking a crucial evolution in wireless access technology. Despite these advancements, the full potential of these capacities remains largely untapped due to inefficiencies in channel management, in particular, the underutilization of secondary (non-primary) channels when the primary channel is occupied. This paper delves into the Non-Primary Channel Access (NPCA) protocol, initially proposed by the IEEE 802.11 Ultra-High Reliability (UHR) group, aimed at addressing these inefficiencies. Our research not only proposes an analytical model to assess the throughput of NPCA in terms of average throughput but also crucially identifies that the overhead associated with the NPCA protocol is significant and cannot be ignored. This overhead often undermines the effectiveness of the NPCA, challenging the assumption that it is invariably superior to traditional models. Based on these findings, we have developed and simulated a new hybrid model that dynamically integrates the strengths of both legacy and NPCA models. This model overall outperforms the existing models under all channel occupancy conditions, offering a robust solution to enhance throughput efficiency.

Wi-Fi has been widely used in commercial facilities for wireless connectivity. However, the evolving network traffic and performance requirements, especially for mission-critical applications demanding millisecond-level latency, have highlighted limitations in Wi-Fi’s traditional channel access protocol, Carrier Sense Multiple Access (CSMA), and the challenges of unlicensed spectrum. These issues result in decreased reliability and connectivity disruptions for critical applications. Moreover, seamless roaming and mobility are typically challenging in Wi-Fi networks. The introduction of private networks using Fourth Generation Long-Term Evolution (4G LTE) and Fifth Generation New Radio (5G NR) protocols has enhanced application reliability through scheduled access and interference-free spectrum usage. These deployments can be tailored for both outdoor and indoor environments. This study examines actual field deployments and their test results in environments like oil refineries, warehouses, outdoor parking areas, and distribution centers. We compare actual measurements with a theoretical model to validate the performance enhancements and understand the practical implications for real-world applications. We analyzed performance factors such as signal strength (Reference Signal Received Power (RSRP) heat maps), coverage, packet latency, and mobility. This research provides insights into the practical performance of private networks and their suitability for supporting mission-critical applications in diverse settings.

The evolution of the IEEE 802.11 standards marks a significant throughput advancement in wireless access technologies, progressively increasing bandwidth capacities from 20 MHz in the IEEE 802.11a to up to 320 MHz in the latest IEEE 802.11be (Wi-Fi 7). However, the increased bandwidth capacities may not be well exploited due to inefficient bandwidth utilization on multiple channels. This issue typically occurs when the primary channel is busy, secondary channels (also known as non-primary channels) are prevented from being utilized even if they are idle, thereby wasting the available bandwidth. This paper investigates the fundamentals of the Non-Primary Channel Access (NPCA) protocol that was defined in IEEE 802.11 Ultra-High Reliability (UHR) group to cope with the above issue. We develop a novel analytical model to assess NPCA protocol performance in terms of the average throughput and delay. Via simulation, we verify that the NPCA network outperforms the legacy network by increasing at least 50% average throughput while reducing at least 40% average delay.

IEEE 802.11be (Wi-Fi 7) introduces a new concept called multi-link operation (MLO), which allows multiple Wi-Fi interfaces in different bands (2.4, 5, and 6 GHz) to work together to increase network throughput, reduce latency, and improve spectrum reuse efficiency in dense overlapping networks. To make the most of MLO, this paper proposes a new data-driven resource allocation algorithm for the 11be network with the aid of an access point (AP) controller. To maximize network throughput, a network topology optimization problem is formulated for 11be network, which is solved by exploiting the totally unimodular property of the bipartite graph formed by the connection between AP and station (STA) in Wi-Fi networks. Subsequently, a proportional fairness algorithm is applied for radio link allocation, network throughput optimization considering the channel condition, and the fairness of the multi-link device (MLD) data rate. The performance of the proposed algorithm on two main MLO implementations -multi-link multi-radio (MLMR) with simultaneous transmission and reception (STR), and the interplay between multiple nodes employing them are evaluated through cross-layer (PHY-MAC) data rate simulation with PHY abstraction.

Sidelink in cellular networks enables direct exchange of data packets between devices without the need for network infrastructure, resulting in various benefits, including communication in out-of-coverage areas and possible decrease in latency by a considerable extent. Thus, sidelink is a favorable choice for applications like public safety communications and Vehicle-to-Everything (V2X) communications. As 4G Long Term Evolution (LTE) advanced to 5G New Radio (NR) under the Third Generation Partnership Project (3GPP), several new features were introduced to sidelink, such as two-stage Sidelink Control Informations (SCIs), data and control multiplexing, and feedback-based Hybrid Automatic Repeat Request (HARQ) with a configurable maximum number of transmissions. To conduct extensive NR sidelink link-level evaluations, a comprehensive simulation platform is essential. In this paper, we introduce the first publicly accessible 5G NR Link-Level Simulator (LLS) that supports major 5G NR sidelink features and complies with the 3GPP standards. This MATLAB-based simulator allows for flexible control over various Physical Layer (PHY) configurations, facilitating customized simulations on algorithm development and performance evaluations. We discuss the simulator’s structure and the sidelink features implemented in detail and evaluate the 5G NR sidelink performance using the developed simulator. Our simulation results indicate that the Block Error Rate (BLER) curves are insensitive to error-prone 2nd-stage Sidelink Control Information (SCI2) and number of Resource Blocks (RBs) allocated; however, the sidelink communication range is sensitive to the deployment environment. We also highlight the need for a careful choice of numerology, device power class and HARQ configuration to balance performance metrics for a variety of services based on 5G NR sidelink deployment scenarios.

Since the Third Generation Partnership Project (3GPP) specified 5G New Radio (NR) sidelink in Release 16, researchers have been expressing increasing interest in sidelink in various research areas, such as Proximity Services (ProSe) and Vehicle-to-Everything (V2X). It is essential to provide researchers with a comprehensive simulation platform that allows for extensive NR sidelink link-level evaluations. In this paper, we introduce the first publicly accessible 5G NR link-level simulator that supports sidelink. Our MATLAB-based simulator complies with the 3GPP 5G NR sidelink standards, and offers flexible control over various Physical Layer (PHY) configurations. It will facilitate researcher's exploration in NR sidelink with a friendly access to the key network parameters and great potential of customized simulations on algorithm developments and performance evaluations. This paper also provides several initial link-level simulation results on sidelink using the developed simulator.

In this study, we present the first measurement study of commercial IEEE 802.11 Wi-Fi wireless local-area network (WLAN) and 3GPP 5G LAN (i.e., CBRS) networks in terms of latency, packet drop, and throughput. As the number of downlink and uplink traffic increases in the WLAN network, we observed significant number of packet drops and increased latency on the delay sensitive traffic, especially in mobility test cases where mobile devices roam from one Access Point (AP) to another AP. On the other hand, in both static and mobility test cases, packet drop rate of 5G LAN is consistently low at the desired target level due to interference free spectrum channel and efficient allocation of radio resources at MAC scheduling. Also, in the 5G LAN network, the Celona Micro-slicing feature guarantees minimum latency in a consistent manner across different test conditions. During the capacity experiments, we observed that, as the load increases, 5G LAN is able to guarantee fair allocation of radio resource among the connected users compared to WLAN system. In the end, we ran test with realistic enterprise use-case applications such as Zoom, Bar-code scanning, VOIP, and Camera to observe and analyze the user experience in WLAN and 5G LAN systems.

Information freshness is a crucial metric for time-sensitive services such as Basic Safety Messages (BSMs) in Vehicle-to-Everything (V2X) communications to achieve high-reliability autonomous driving. However, Semi-Persistent Scheduling (SPS) algorithm under the current 5th generation (5G) New Radio (NR) standard may not fulfill the requirement of such a metric for BSMs without optimizing relevant parameters. This paper analyzes the parameters of SPS used for BSM scheduling in NR-V2X Mode 2 from an Age-of-Information (AoI) perspective, to explore the freshness of BSMs. We first present an analytical model to illustrate that Resource Reservation Interval (RRI) is the SPS parameter which significantly impacts the AoI performance. Subsequently, we investigate the expected peak AoI (PAoI) performance with respect to RRI values under different vehicle densities. A Monte Carlo simulator is then utilized to verify the results obtained in the analytical models. Numerical results show that the optimal RRI values in SPS, which minimize the expected PAoI of the vehicular network, can be obtained based on different vehicle densities accordingly.

5G New Radio (NR) is envisioned to efficiently support both enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC). Due to the severe constraint of URLLC traffic, the time resources used by eMBB are further divided into mini-slots. From the media access control (MAC) layer scheduling perspective, the resource allocation occurs on different timescales, and URLLC traffic is pre-emptively overlapped at the mini-slot timescale to obtain the strict requirements of URLLC (achieve 99.999% reliability within 1 ms latency). In this article, we analyze the multiplexing of eMBB and URLLC traffic in 5G downlink transmission, with the dual objectives of maximizing eMBB utility like proportional fairness for eMBB users while satisfying URLLC constraints. We formulate the resource allocation problem in each mini-slot as the integer programming (IP) problem with two solutions: 1) convex relaxation; 2) greedy algorithm. The simulation results show that our algorithms have a higher utility of eMBB users while satisfying the URLLC users' latency and reliability requirements than using basic round robin. The performance comparison between the two algorithms is extensively studied, and the choice of two algorithms in practice can be decided by the trade-off analysis investigated in this article.

A novel bidirectional computation task model has emerged as an important use case of 5G. For example, interactive AR/VR gaming service needs to render the live scene by jointly computing user features such as 3D positions and video data generated from the Internet. In this article, we consider the bidirectional computation task model, where each task is served via three mechanisms, i.e., local computing with local caching, local computing without local caching, and computing at the mobile edge computing server. To minimize the average bandwidth, we formulate the joint caching and computing optimization problem under the latency, cache size and average power constraints. In the homogeneous scenario, we derive the optimal policy and analytical expression for the minimum bandwidth. In the heterogeneous scenario, to reduce the computation complexity of the NP-hard problem, we relax some constraints of the original problem and propose a Lagrangian relaxation (LR) suboptimal solution, which may be infeasible. We then reformulate the original problem as an auxiliary problem based on the LR solution and solve this via Concave-Convex Procedure (CCCP), which outputs feasible local optimal solution. Simulation has shown that LR-based algorithms outperform the baselines including greedy and CCCP algorithms in the bandwidth performance and time efficiency.

In this paper, we focus on the performance of vehicle-to-vehicle (V2V) communication adopting the Dedicated Short Range Communication (DSRC) application in periodic broadcast mode. An analytical model is studied and a fixed point method is used to analyze the packet delivery ratio (PDR) and mean delay based on the IEEE 802.11p standard in a fully connected network under the assumption of perfect PHY performance. With the characteristics of V2V communication, we develop the Semi-persistent Contention Density Control (SpCDC) scheme to improve the DSRC performance. We use Monte Carlo simulation to verify the results obtained by the analytical model. The simulation results show that the packet delivery ratio in SpCDC scheme increases more than 10% compared with IEEE 802.11p in heavy vehicle load scenarios. Meanwhile, the mean reception delay decreases more than 50%, which provides more reliable road safety.

With the advent of the modern mobile traffic, e.g., online gaming, augmented reality delivery and etc., a novel bidirectional computation task model where the input data of each task consists of two parts, one generated at the mobile device in real-time and the other originated from the Internet proactively, is emerging as an important use case of 5G. In this paper, for ease of analytical analysis, we consider the homogeneous bidirectional computation task model in a mobile edge network which consists of one mobile edge computing (MEC) server and one mobile device, both enabled with computing and caching capabilities. Each task can be served via three mechanisms, i.e., local computing with local caching, local computing without local caching and computing at the MEC server. To minimize the average bandwidth, we formulate the joint caching and computing optimization problem under the latency, cache size and average power constraints. We derive the closed-form expressions for the optimal policy and the minimum bandwidth. The tradeoff among communications, computing and caching is illustrated both analytically and numerically, which provides insightful guideline for the network designers.

Carrier sensing multiple access/collision avoidance (CSMA/CA) is the backbone MAC protocol for IEEE 802.11 networks. However, tuning the binary exponential back-off (BEB) mechanism of CSMA/CA in user-dense scenarios so as to maximize aggregate throughput still remains a practically essential and challenging problem. In this paper, we propose a new and enhanced multiple access mechanism based on the application of deep reinforcement learning (DRL) and Federated learning (FL). A new Monte Carlo (MC) reward updating method for DRL training is proposed and the access history of each station is used to derive a DRL-based MAC protocol that improves the network throughput vis-a-vis the traditional distributed coordination function (DCF). Further, federated learning (FL) is applied to achieve fairness among users. The simulation results showcase that the proposed federated reinforcement multiple access (FRMA) performs better than basic DCF by 20% and DCF with request-to-send/clear-to-send (RTS/CTS) by 5% while guaranteeing the fairness in user-dense scenarios.