Next-generation Wireless Local Area Networks Research Articles

Adaptive Multilink Channel Access Under Cross-Link Interference in Next-Generation WLANs

Adaptive Multilink Channel Access Under Cross-Link Interference in Next-Generation WLANs

Eight-Port Tapered-Edged Antenna Array With Symmetrical Slots and Reduced Mutual-Coupling for Next-Generation Wireless and Internet of Things (IoT) Applications

A compact and low-cost eight-port (2x4 configuration) tapered-edged antenna array (TEAA) with symmetrical slots and reduced mutual-coupling is presented in this paper using the inset-feed technique. The 8-port TEAA is designed and simulated using CST microwave studio, fabricated using the flame-resistant (FR4) substrate having a dielectric constant (εr) = 4.3 and thickness (h) = 1.66mm and characterized using Keysight technologies vector network analyzer (VNA). The designed 8-port TEAA operates at the 5.05-5.2GHz frequency band. Various performance design parameters, like return-loss, bandwidth, gain, 2D/3D radiation patterns, surface current distributions, and isolation-loss, are briefly studied, and the results are summarized. The eight-port TEAA has featured the bandwidth/ gain characteristic of 195MHz/10.25dB, 3dB beam-width of 52.8o, and excellent mutual-coupling (high isolation-loss) of less than -20dB, respectively. The 8-port TEAA is proposed and characterized to work for next-generation high-throughput WLANs like IEEE 802.11ax (WiFi-6E), Internet-of-Things (IoT), and the upcoming 5G wireless communication systems.

Co-NOMA: AP Coordination Based NOMA Protocol for the Next-Generation WLANs

Co-NOMA: AP Coordination Based NOMA Protocol for the Next-Generation WLANs

Downlink AP coordination based OFDMA and NOMA protocols for the next-generation WLANs

Downlink AP coordination based OFDMA and NOMA protocols for the next-generation WLANs

A First Look at Wi-Fi 6 in Action: Throughput, Latency, Energy Efficiency, and Security

This paper presents a first-of-its-kind performance measurement of Wi-Fi 6 (IEEE 802.11ax) using real experiments. Our experiments focus on multi-client scenarios. The results reveal the impact of the new channel access mechanisms (i.e., OFDMA and TWT) on the spectrum efficiency, energy consumption, latency, and network security. (i) A comparison with the legacy CSMA/CA scheme shows that the commodity Wi-Fi 6 achieves 3× overall throughput and dramatically reduce the latency (5×) when coexisting with legacy Wi-Fi network. (ii) However, the current OFDMA implementation significantly increases the power consumption (6×), implying a design tradeoff between throughput and latency gain versus the cost of energy consumption. (iii) Finally, TWT negotiating procedure is vulnerable to various malicious attacks. We believe that our findings provide critical insights for the scheduling algorithm design, power optimization, and security protection of the next-generation WLANs.

QoS-oriented media access control using reinforcement learning for next-generation WLANs

Orthogonal frequency division multiple access (OFDMA) is introduced in IEEE 802.11ax to satisfy massive transmission demands. However, the uplink OFDMA-based random access (UORA) mechanism provides poor quality of service (QoS), and restricts channel utilization efficiency, especially in the dynamic network environment. To solve these issues, in this study, the transmission period is decoupled into a contention stage and a transmission stage, and an intelligent media access control (MAC) algorithm with QoS-guaranteed for next-generation wireless local area networks (WLANs) is presented. Specifically, we consider stochastic and various traffic in time-varying wireless communication conditions and propose two contention window (CW) optimization mechanisms based on Q-learning (QL) and a deep Q-network (DQN) (referred to as QL-MAC and DQN-MAC) for static and dynamic network scenarios, respectively. Meanwhile, the access point (AP) acts as the reinforcement learning (RL) agent and centrally optimizes the CW for all stations to eventually maximize the system throughput. Furthermore, we provide the QoS-guaranteed channel access mechanism for different priority data traffic, in which high-priority traffic can obtain more channel access opportunities in the uplink contention stage. Simulation results demonstrate that the proposed algorithms can significantly improve the network performance in terms of convergence, throughput, delay, and fairness compared to the adaptive grouping-based two-stage mechanism (BTM) and double random access QoS-oriented OFDMA MAC (DRA-OFDMA) algorithm in various scenarios.

Multi-Link Operation in IEEE 802.11be WLANs

The multi-link operation (MLO) is a new feature proposed to be part of the IEEE 802.11be Extremely High Throughput (EHT) amendment. Such feature represents a paradigm shift towards multi-link communications, as nodes will be allowed to transmit and receive data over multiple radio interfaces concurrently. To make it possible, the 802.11be Task Group has proposed different modifications in regards to nodes' architecture, transmission operation, and management functionalities. This article reviews such changes and tackles the question of how traffic should be distributed over multiple links, as it is still unresolved. To that end, we evaluate different load balancing strategies over the active links. Results show that in high load, dense and complex scenarios, implementing congestion aware load balancing policies to significantly enhance next-generation WLAN performance using MLO is a must.

Analysis of a Multi-Armed Bandit solution to improve the spatial reuse of next-generation WLANs

The next generation of WLANs will be, for the most part, ubiquitous in urban areas, densely deployed, and implementing the latest amendment of IEEE 802.11 standard, namely 802.11ax also known as Wi-Fi 6. Among the main purposes of 802.11ax is the improvement of the spatial reuse of radio channels by allowing the dynamical update of the sensitivity threshold and the transmission power at each node. In this regard, our contributions are twofold. First, we investigate the performance improvement resulting from a more efficient spatial reuse of radio channels with 802.11ax. Second, we introduce a centralized solution based on the Multi-Armed Bandit (MAB) framework and a sub-sampling technique to quickly discover an appropriate configuration of the sensitivity threshold and transmission power at each access point. We evaluate our solution with the network simulator ns-3 on different network topologies. The simulation results show the ability of our solution to quickly and robustly adjust these parameters of access points in order to significantly improve the behavior of WLANs.

Federated spatial reuse optimization in next-generation decentralized IEEE 802.11 WLANS

As wireless standards evolve, more complex functionalities are introduced to address the increasing requirements in terms of throughput, latency, security, and efficiency. To unleash the potential of such new features, Artificial Intelligence (AI) and Machine Learning (ML) are currently being exploited for deriving models and protocols from data, rather than by hand-programming. In this paper, we explore the feasibility of applying ML in next-generation Wireless Local Area Networks (WLANs). More specifically, we focus on the IEEE 802.11ax Spatial Reuse (SR) problem and predict its performance through Federated Learning (FL) models. The overview of the set of FL solutions in this work is part of the 2021 International Telecommunication Union (ITU) AI for 5G Challenge.

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance With Machine Learning

Wireless local area networks (WLANs) empowered by IEEE 802.11 (Wi-Fi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well as the existence of affordable and highly interoperable devices. The Wi-Fi community is currently deploying Wi-Fi 6 and developing Wi-Fi 7, which will bring higher data rates, better multi-user and multi-AP support, and, most importantly, improved configuration flexibility. These technical innovations, including the plethora of configuration parameters, are making next-generation WLANs exceedingly complex as the dependencies between parameters and their joint optimization usually have a non-linear impact on network performance. The complexity is further increased in the case of dense deployments and coexistence in shared bands. While classical optimization approaches fail in such conditions, machine learning (ML) is able to handle complexity. Much research has been published on using ML to improve Wi-Fi performance and solutions are slowly being adopted in existing deployments. In this survey, we adopt a structured approach to describe the various Wi-Fi areas where ML is applied. To this end, we analyze over 250 papers in the field, providing readers with an overview of the main trends. Based on this review, we identify specific open challenges and provide general future research directions.

Improving Spatial Reuse of Wireless LAN Uplink Using BSS Color and Proximity Information

With the density of wireless networks increasing rapidly, one of the major goals in next-generation wireless LANs (Local Area Networks) is to support a very dense network with a large number of closely deployed APs (Access Points) and crowded users. However, the CSMA (Carrier-Sense Multiple Access)-based medium access control of current wireless network systems suffers from significantly degraded performance when the network becomes dense. Recent WLAN (Wireless Local Area Networks) standards include measures for increasing spatial reuse such as BSS (Basic Service Set) coloring, but the schemes based on BSS coloring such as OBSS/PD (Overlapping BSS/Preamble Detection) have limitations in improving spatial reuse. In this paper, we propose a spatial reuse method for uplink which can utilize BSS color and proximity information to improve the efficiency of carrier sensing and thus spatial reuse. Specifically, through the BSS color and the proximity information, a node receiving a preamble can figure out how far the receiver of the ongoing traffic is located. This information is used to determine whether the node should aggressively start transmitting or defer its transmission to protect the ongoing transmission. Simulation results show that the proposed method outperforms existing methods in terms of throughput and fairness.

Machine learning for performance prediction of channel bonding in next-generation IEEE 802.11 WLANS

With the advent of Artificial Intelligence (AI)-empowered communications, industry, academia, and standardization organizations are progressing on the definition of mechanisms and procedures to address the increasing complexity of future 5G and beyond communications. In this context, the International Telecommunication Union (ITU) organized the First AI for 5G Challenge to bring industry and academia together to introduce and solve representative problems related to the application of Machine Learning (ML) to networks. In this paper, we present the results gathered from Problem Statement 13 (PS-013), organized by Universitat Pompeu Fabra (UPF), whose primary goal was predicting the performance of next-generation Wireless Local Area Networks (WLANs) applying Channel Bonding (CB) techniques. In particular, we provide an overview of the ML models proposed by participants (including artificial neural networks, graph neural networks, random forest regression, and gradient boosting) and analyze their performance on an open data set generated using the IEEE 802.11ax-oriented Komondor network simulator. The accuracy achieved by the proposed methods demonstrates the suitability of ML for predicting the performance of WLANs. Moreover, we discuss the importance of abstracting WLAN interactions to achieve better results, and we argue that there is certainly room for improvement in throughput prediction through ML.

ATARI: A Graph Convolutional Neural Network Approach for Performance Prediction in Next-Generation WLANs.

IEEE 802.11 (Wi-Fi) is one of the technologies that provides high performance with a high density of connected devices to support emerging demanding services, such as virtual and augmented reality. However, in highly dense deployments, Wi-Fi performance is severely affected by interference. This problem is even worse in new standards, such as 802.11n/ac, where new features such as Channel Bonding (CB) are introduced to increase network capacity but at the cost of using wider spectrum channels. Finding the best channel assignment in dense deployments under dynamic environments with CB is challenging, given its combinatorial nature. Therefore, the use of analytical or system models to predict Wi-Fi performance after potential changes (e.g., dynamic channel selection with CB, and the deployment of new devices) are not suitable, due to either low accuracy or high computational cost. This paper presents a novel, data-driven approach to speed up this process, using a Graph Neural Network (GNN) model that exploits the information carried in the deployment’s topology and the intricate wireless interactions to predict Wi-Fi performance with high accuracy. The evaluation results show that preserving the graph structure in the learning process obtains a 64% increase versus a naive approach, and around 55% compared to other Machine Learning (ML) approaches when using all training features.

Machine learning for performance prediction of channel bonding in next-generation IEEE 802.11 WLANS

With the advent of Artificial Intelligence (AI)-empowered communications, industry, academia, and standardization organizations are progressing on the definition of mechanisms and procedures to address the increasing complexity of future 5G and beyond communications. In this context, the International Telecommunication Union (ITU) organized the first AI for 5G Challenge to bring industry and academia together to introduce and solve representative problems related to the application of Machine Learning (ML) to networks. In this paper, we present the results gathered from Problem Statement~13 (PS-013), organized by Universitat Pompeu Fabra (UPF), which primary goal was predicting the performance of next-generation Wireless Local Area Networks (WLANs) applying Channel Bonding (CB) techniques. In particular, we overview the ML models proposed by participants (including Artificial Neural Networks, Graph Neural Networks, Random Forest regression, and gradient boosting) and analyze their performance on an open dataset generated using the IEEE 802.11ax-oriented Komondor network simulator. The accuracy achieved by the proposed methods demonstrates the suitability of ML for predicting the performance of WLANs. Moreover, we discuss the importance of abstracting WLAN interactions to achieve better results, and we argue that there is certainly room for improvement in throughput prediction through ML.

Joint Radio Resource Management and Link Adaptation for Multicasting 802.11ax-Based WLAN Systems

Adopting OFDMA and MU-MIMO techniques for both downlink and uplink IEEE 802.11ax will help next-generation WLANs efficiently cope with large numbers of devices but will also raise some research challenges. One of these is how to optimize the channelization, resource allocation, beamforming design, and MCS selection jointly for IEEE 802.11ax-based WLANs. In this paper, this technical requirement is formulated as a mixed-integer non-linear programming problem maximizing the total system throughput for the WLANs consisting of unicast users with multicast groups. A novel two-stage solution approach is proposed to solve this challenging problem. The first stage aims to determine the precoding vectors under unit-power constraints. These temporary precoders help re-form the main problem into a joint power and radio resource allocation one. Then, two low-complexity algorithms are proposed to cope with the new problem in stage two. The first is developed based on the well-known compressed sensing method while the second seeks to optimize each of the optimizing variables alternatively until reaching converged outcomes. The outcomes corresponding to the two stages are then integrated to achieve the complete solution. Numerical results are provided to confirm the superior performance of the proposed algorithms over benchmarks.

A Spatial Group-Based Multi-User Full-Duplex OFDMA MAC Protocol for the Next-Generation WLAN.

The Wireless Local Area Network (WLAN) has become a dominant piece of technology to carry wireless traffic for Internet of Things (IoT). The next-generation high-density WLAN scenario is very suitable for the development trend of the industrial wireless sensor network. However, in the high-density deployed WLAN scenarios, the access efficiency is low due to severe collisions, and the interference is diffused due to the scattered locations of the parallel access stations (STAs), which results in low area throughput, i.e., low spatial reuse gain. A spatial group-based multi-user full-duplex orthogonal frequency division multiple access (OFDMA) (GFDO) multiple access control (MAC) protocol is proposed. Firstly, the STAs in the network are divided into several spatial groups according to the neighbor channel sensing ability. Secondly, a two-level buffer state report (BSR) information collection mechanism based on P-probability is designed. Initially, intra-group STAs report their BSR information to the group header using low transmission power. After that, group headers report both their BSR information collected from their members and inter-group interference information to the access point (AP). Finally, AP schedules two spatial groups without mutual interference to carry on multi-user full duplex transmission on the subchannels in cascading mode. The closed-form formulas are theoretically derived, including the number of uplink STAs successfully collected by AP, the network throughput and area throughput under saturated traffic. The simulation results show that the theoretical analysis coincide with the simulation results. The system throughput of the GFDO protocol is 16.8% higher than that of EnFD-OMAX protocol.

Dynamic Channel Bonding in Spatially Distributed High-Density WLANs

In this paper, we discuss the effects on throughput and fairness of dynamic channel bonding (DCB) in spatially distributed high-density wireless local area networks (WLANs). First, we present an analytical framework based on continuous-time Markov networks (CTMNs) for depicting the behavior of different DCB policies in spatially distributed scenarios, where nodes are not required to be within the carrier sense range of each other. Then, we assess the performance of DCB in high-density IEEE 802.11ac/ax WLANs by means of simulations. We show that there may be critical interrelations among nodes in the spatial domain–even if they are located outside the carrier sense range of each other–in a chain reaction manner. Results also reveal that, while always selecting the widest available channel normally maximizes the individual long-term throughput, it often generates unfair situations where other WLANs starve. Moreover, we show that there are scenarios where DCB with stochastic channel width selection improves the latter approach both in terms of individual throughput and fairness. It follows that there is not a unique optimal DCB policy for every case. Instead, smarter bandwidth adaptation is required in the challenging scenarios of next-generation WLANs.

An Effective Wide-Bandwidth Channel Access in Next-Generation WLAN-Based V2X Communications

As Intelligent Transport System (ITS) applications are diversified and amount of ITS data increases, high throughput and reliability are required in next-generation V2X communications. In order to meet such increased throughput and reliability requirements, IEEE 802.11bd, the next-generation V2X communication standard, has commenced standard development. One of the main features of IEEE 802.11bd is a 20-MHz bandwidth transmission. In this paper, a novel wide-bandwidth channel access scheme in next-generation Wireless Local Area Network (WLAN)-based vehicular communications is proposed. The proposed scheme is designed to provide fairness with other ITS devices and channel efficiency considering adjacent channel interference. By using the proposed scheme, through extensive simulations, it is verified that, while satisfying the fairness requirement with other ITS devices, the channel access delay of wide-bandwidth packet transmission can be optimized.

Analyzing the Impact of the Coexistence With IEEE 802.11ax Wi-Fi on the Performance of DSRC Using Stochastic Geometry Modeling

The proliferation of high data-rate applications and services in next-generation WLANs, as well as the significant increase in the number of Wi-Fi devices, is triggering the search for more bandwidth. For this, the Federal Communications Commission (FCC) is considering a proposal to permit devices, such as those that use the IEEE 802.11 (Wi-Fi) protocol, to operate in the 5.9 GHz band allocated to the intelligent transportation system (ITS). However, the nature of these bands poses a challenge since ITS dedicated short range communications (DSRC) networks use them to share safety-critical messages and may be subject to severe interference from other coexisting transmissions. We discuss the impact of coexistence of Wi-Fi networks on the performance of DSRC through an analytical framework based on stochastic geometry. We consider the recent IEEE 802.11ax Wi-Fi standard, and analyze uplink and downlink transmissions, with voice and background traffic being the highest and lowest priority traffic, respectively. We also validate the fixed contention window size of the derived stochastic geometry model using NS-3 simulations. The proposed framework can serve to increase awareness among regulatory bodies of the extent to which coexistence will adversely affect the performance of DSRC.

Performance analysis of multi-channel MAC with single transceiver for the next generation WLAN

As the amount of mobile traffic grows dramatically, the high-density Wi-Fi networking that proposed in the next generation WLAN (NGW) has attracted an increasing attention. However, some new challenges on the medium access control (MAC) layer design are emerging, for example, the multichannel hidden terminal (MHT) and the vicious circle problems. To overcome the challenges, a distributed multichannel MAC protocol based on multiple step reliable channel reservation is proposed, called m-RCR. Under the proposed m-RCR protocol, each node pair can reserve multiple transmission opportunities on data channels using only a single half-duplex transceiver, meanwhile, the control channel (CCH) is reserved to rebroadcast the channel reservation information (CRI). An analytical model is developed by extending the two-dimensional Markov process to describe the node behaviors on the CCH, and the usage of data channels is characterized as the M/M/K queueing model, where we consider the packet arrivals as a Poisson process. Furthermore, the throughput of m-RCR protocol is derived, and the performance optimization is also presented. Without loss of generality, some practical implementation methods are discussed. By conducting extensive simulations on NS-2 simulator, our analytical model is validated and the significant improvement of our proposed m-RCR protocol is demonstrated.