Performance Enhancement of IEEE 802.11AX in Ultra-Dense Wireless Networks

IEEE 802.11ax, which is one emerging WLAN standard, aims at providing highly efficient communication in ultra-dense wireless networks. However, due to a large number of stations (STAs) in dense deployment scenarios and diverse services to be supported, there are many technical challenges to be overcome. Firstly, the potential high packet collision rate significantly degrades the network efficiency of WLAN. In this thesis, we propose an adaptive station (STA) grouping scheme to overcome this challenge in IEEE 802.11ax using Uplink OFDMA Random Access (UORA). In order to achieve optimal utilization efficiency of resource units (RUs), we first analyze the relationship between group size and RU efficiency. Based on this result, an adaptive STA grouping algorithm is proposed to cope with the performance fluctuation of 802.11ax due to remainder stations after grouping. The analysis and simulation results demonstrate that our adaptive grouping algorithm dramatically improves the performance of both the overall system and each STA in the ultra-dense wireless network. Meanwhile, due to the limited RU efficiency of UORA, we adopt the proposed grouping scheme in the Buffer State Report (BSR) based two-stage mechanism (BTM) to enhance the Uplink (UL) Multi-user (MU) access in 802.11ax. Then we propose an adaptive BTM grouping scheme. The analysis results of average RU for each STA, average throughput of the whole system and each STA are derived. The numerical results show that the proposed adaptive grouping scheme provides 2.55, 413.02 and 3712.04 times gains in throughput compared with the UORA grouping, conventional BTM, and conventional UORA, respectively. Furthermore, in order to provide better QoS experience in the ultra-dense network with diverse IoT services, we propose a Hybrid BTM Grouping algorithm to guarantee the QoS requirement from high priority STAs. The concept of ``QoS Utility is introduced to evaluate the satisfaction of transmission. The numerical results demonstrate that the proposed Hybrid BTM grouping scheme has better performance in BSR delivery rate as well as QoS utility than the conventional BTM grouping

An OFDMA-based Hybrid MAC Protocol for IEEE 802.11ax

Two types of MAC mechanisms i.e., random access and reservation could be adopted for OFDMA-based wireless LANs. Reservation-based MAC is more appropriate than random access MAC for connection-oriented applications as connectionoriented applications provide strict requirements of traffic demands. On the other hand, random access mechanism is a preferred choice for bursty traffic i.e., data packets which have no fixed pattern and rate. As OFDMA-based wireless networks promise to support heterogeneous applications, researchers assume that applications with and without traffic specifications will coexist. Eventually, OFDMA-based wireless LAN will deploy hybrid MAC mechanisms inheriting traits from random access and reservation. In this article, we design a new MAC protocol which employs one kind of hybrid mechanism that will provide high throughput of data as well as maintains improved fair access policy to the medium among the terminals. The protocol works in two steps, where at step 1 sub-channels are approximately evenly distributed to the terminals and at step 2 terminals within in a subchannel will contend for medium randomly if the total number of terminals of the system is larger than the number of sub-channels. The details of the protocol is illustrated in the paper and we analyze the performance of our OFDMA-based multi-channel hybrid protocol using comprehensive computer simulations. Simulation results validate that our proposed protocol is more robust than the conventional CSMA/CA protocol in terms of throughput, collision reduction and fair access. In addition, the theoretical analysis of the saturation throughput of the protocol is also evaluated using an existing comprehensive model

Contention resolution in wi-fi 6-enabled internet of things based on deep learning

Internet of Things (IoT) is expected to vastly increase the number of connected devices. As a result, a multitude of IoT devices transmit various information through wireless communication technology, such as the Wi-Fi technology, cellular mobile communication technology, low-power wide-area network (LPWAN) technology. However, even the latest Wi-Fi technology is still ready to accommodate these large amounts of data. Accurately setting the contention window (CW) value significantly affects the efficiency of the Wi-Fi network. Unfortunately, the standard collision resolution used by IEEE 802.11ax networks is nonscalable; thus, it cannot maintain stable throughput for an increasing number of stations, even when Wi-Fi 6 has been designed to improve performance in dense scenarios. To this end, we propose a CW control strategy for Wi-Fi 6 systems. This strategy leverages deep learning to search for optimal configuration of CW under different network conditions. Our deep neural network is trained by data generated from a Wi-Fi 6 simulation system with some varying key parameters, e.g., the number of nodes, short interframe space (SIFS), distributed interframe space (DIFS), and data transmission rate. Numerical results demonstrated that our deep learning scheme could always find the optimal CW adjustment multiple by adaptively perceiving the channel competition status. The finalized performance of our model has been significantly improved in terms of system throughput, average transmission delay, and packet retransmission rate. This makes Wi-Fi 6 better adapted to the access of a large number of IoT devices. © 2014 IEEE

TIME AND FREQUENCY CHARACTERISTIC OF 802.11AX

Wireless Local Area Network (WLAN) is a wireless communication system that connects two or more devices using radio frequency to form a local network (LAN). WLAN is used inside buildings (e.g., apartment, campus, train station). Unlike Ethernet, The first WLAN standard, namely IEEE 802.11b, was released in the 1990s. Maximum achievable data rate is 11 Mbps for 802.11b. The most recent version is 802.11ax with maximum throughput of 3.46 Gbps. This thesis reports network throughput, jitter, and delay performance of IEEE 802.11ax experimentally measured and analyzed under various conditions (SNR, window size and packet length). It also gives an empirical model to simulate the behavior of 802.11ax network throughput and jitter

Comparison between Different Channel Coding Techniques for IEEE 802.11be within Factory Automation Scenarios

This paper presents improvements in the physical layer reliability of the IEEE 802.11be standard. Most wireless system proposals do not fulfill the stringent requirements of Factory Automation use cases. The harsh propagation features of industrial environments usually require time retransmission techniques to guarantee link reliability. At the same time, retransmissions compromise latency. IEEE 802.11be, the upcoming WLAN standard, is being considered for Factory Automation (FA) communications. 802.11be addresses specifically latency and reliability difficulties, typical in the previous 802.11 standards. This paper evaluates different channel coding techniques potentially applicable in IEEE 802.11be. The methods suggested here are the following: WLAN LDPC, WLAN Convolutional Codes (CC), New Radio (NR) Polar, and Long Term Evolution (LTE)-based Turbo Codes. The tests consider an IEEE 802.11be prototype under the Additive White Gaussian Noise (AWGN) channel and industrial channel models. The results suggest that the best performing codes in factory automation cases are the WLAN LDPCs and New Radio Polar Codes.This work was supported in part by the Basque Government under Grant IT1234-19, in part by the PREDOC under Grant PRE2019_099407, and in part by the Spanish Government through project PHANTOM (MCIU/AEI/FEDER, UE) under Grant RTI2018-099162-B-I00