A Novel Low-Overhead Channel Sounding Protocol for Downlink Multi-User MIMO in IEEE 802.11ax WLAN

Nabetani, Toshihisa; Madhavan, Narendar; Mori, Hiroki; Aoki, Tsuguhide

doi:10.1587/transcom.2017ebp3106

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…Moreover, multiple D2D pairs meeting the interference conditions can share the same RU. Since the downlink transmission can be scheduled directly by AP and operated easily [29], this paper only focuses on the uplink data transmission. We also make an assumption that the network is in the saturated condition, i.e., all nodes in the network always have packets to send.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

Enabling Device‐to‐Device (D2D) Communication for the Next Generation WLAN

Wang

Lei

Shang

2021

Wireless Communications and Mobile Computing

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Device-to-device (D2D) communication technology is widely acknowledged as an emerging candidate to alleviate the wireless traffic explosion problem for the next generation wireless local area network (WLAN) IEEE 802.11ax. In this paper, we integrate D2D communication into IEEE 802.11ax for maximizing the spectrum efficiency. Due to the spectrum scarcity, the number of available resource units (RUs) is typically less than D2D pairs and stations (STAs), which makes the management of spectrum resources more complex. To tackle this issue, we design an efficient resource management algorithm for uplink random access and resource allocation in D2D-enabled 802.11ax, which provides more channel access and resource reuse opportunities for STAs and D2D pairs. Specifically, we propose an enhanced back-off mechanism and derive the optimal contention window (CW) by a theoretical model, which improves the uplink random access efficiency. To tackle the complex interference problem in RU scheduling phase, we develop an efficient and low-complexity resource allocation algorithm based on the maximal independent set (MIS), which can schedule multiple D2D pairs to share the same RU with the specific STA. Simulation results demonstrate that the proposed algorithm significantly improves the network performance in terms of system throughput, collision rate, complete time, and channel utilization.

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Section: The Proposed Algorithmmentioning

confidence: 99%

Enabling Device‐to‐Device (D2D) Communication for the Next Generation WLAN

Wang

Lei

Shang

2021

Wireless Communications and Mobile Computing

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“…In IEEE 802.11ax, the multiplexing of data, control, and management frames are made possible through these technologies. As a result, the significant system throughput improvement can be expected in dense environments [1]. Since the launch of the TG, the authors were actively involved in the IEEE 802.11ax standardization activities, and the standard was published as the latest wireless LAN standard in May 2021.…”

Section: Introductionmentioning

confidence: 99%

Full Duplex for IEEE 802.11 Wireless LANs

Nabetani¹,

Harada²

2022

Preprint

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<p>With the development of wireless LANs, they are expected to have wide applications not only in traditional home but also in industrial applications. Therefore, the next generation of IEEE 802.11 wireless LAN standard demands higher throughput and lower latency. Full duplex (FD) technology, which allows a device to simultaneously transmit and receive signals on the same channel by cancelling self-interference, is one of the promising solutions to meet those requirements. Feasibility studies for applying FD technology to wireless LAN systems started in IEEE 802.11 standardization. In this article, we provide an overview of the FD mechanism and the technical problems for the realization. Furthermore, we provide the direction of the FD system for wireless LANs in IEEE 802.11 standardization. In addition, we discuss the expected low latency effect of the FD mechanism on the wireless LAN systems based on the direction in IEEE 802.11 standardization.</p>

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“…Although these previous efforts have emphasized the importance of an intelligent backoff scheme to the performance guarantee of 802.11ax networks, their fixed adjustment pattern or limited adaptivity when increasing and resetting the CW still induces performance degradation and cannot optimize the system output. To satisfy the diverse requirements of dense WLANs, prospective HEW is anticipated to autonomously access the best channel resources with the assistance of sophisticated wireless channel condition inference to control channel collisions [21]. Such intelligence is possible with the introduction of deep learning techniques into the Wi-Fi 6 systems.…”

Section: Introductionmentioning

confidence: 99%

Contention Resolution in Wi-Fi 6-Enabled Internet of Things Based on Deep Learning

Chen

Balasubramaniam

et al. 2021

IEEE Internet Things J.

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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.

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A Novel Low-Overhead Channel Sounding Protocol for Downlink Multi-User MIMO in IEEE 802.11ax WLAN

Cited by 6 publications

References 8 publications

Enabling Device‐to‐Device (D2D) Communication for the Next Generation WLAN

Enabling Device‐to‐Device (D2D) Communication for the Next Generation WLAN

Full Duplex for IEEE 802.11 Wireless LANs

Contention Resolution in Wi-Fi 6-Enabled Internet of Things Based on Deep Learning

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