116 research outputs found
Throughput Analysis of IEEE 802.11bn Coordinated Spatial Reuse
Multi-Access Point Coordination (MAPC) is becoming the cornerstone of the
IEEE 802.11bn amendment, alias Wi-Fi 8. Among the MAPC features, Coordinated
Spatial Reuse (C-SR) stands as one of the most appealing due to its capability
to orchestrate simultaneous access point transmissions at a low implementation
complexity. In this paper, we contribute to the understanding of C-SR by
introducing an analytical model based on Continuous Time Markov Chains (CTMCs)
to characterize its throughput and spatial efficiency. Applying the proposed
model to several network topologies, we show that C-SR opportunistically
enables parallel high-quality transmissions and yields an average throughput
gain of up to 59% in comparison to the legacy 802.11 Distributed Coordination
Function (DCF) and up to 42% when compared to the 802.11ax Overlapping Basic
Service Set Packet Detect (OBSS/PD) mechanism
IEEE 802.11ax: challenges and requirements for future high efficiency wifi
The popularity of IEEE 802.11 based wireless local area networks (WLANs) has increased significantly in recent years because of their ability to provide increased mobility, flexibility, and ease of use, with reduced cost of installation and maintenance. This has resulted in massive WLAN deployment in geographically limited environments that encompass multiple overlapping basic service sets (OBSSs). In this article, we introduce IEEE 802.11ax, a new standard being developed by the IEEE 802.11 Working Group, which will enable efficient usage of spectrum along with an enhanced user experience. We expose advanced technological enhancements proposed to improve the efficiency within high density WLAN networks and explore the key challenges to the upcoming amendment.Peer ReviewedPostprint (author's final draft
INSPIRE: Distributed Bayesian Optimization for ImproviNg SPatIal REuse in Dense WLANs
WLANs, which have overtaken wired networks to become the primary means of
connecting devices to the Internet, are prone to performance issues due to the
scarcity of space in the radio spectrum. As a response, IEEE 802.11ax and
subsequent amendments aim at increasing the spatial reuse of a radio channel by
allowing the dynamic update of two key parameters in wireless transmission: the
transmission power (TX_POWER) and the sensitivity threshold (OBSS_PD). In this
paper, we present INSPIRE, a distributed solution performing local Bayesian
optimizations based on Gaussian processes to improve the spatial reuse in
WLANs. INSPIRE makes no explicit assumptions about the topology of WLANs and
favors altruistic behaviors of the access points, leading them to find adequate
configurations of their TX_POWER and OBSS_PD parameters for the "greater good"
of the WLANs. We demonstrate the superiority of INSPIRE over other
state-of-the-art strategies using the ns-3 simulator and two examples inspired
by real-life deployments of dense WLANs. Our results show that, in only a few
seconds, INSPIRE is able to drastically increase the quality of service of
operational WLANs by improving their fairness and throughput
An optimization of network performance in IEEE 802.11ax dense networks
The paper focuses on the optimization of IEEE 802.11ax dense networks. The results were obtained with the use of the NS-3 simulator. Various network topologies were analyzed and compared. The advantage of using MSDU and MPDU aggregations in a dense network environment was shown. The process of improving the network performance for changes in the transmitter power value, CCA Threshold, and antenna gain was presented. The positive influence of BSS coloring mechanism on overal network efficiency was revealed. The influence of receiver sensitivity on network performance was determined
Lyapunov Optimization-Based Latency-Bounded Allocation Using Deep Deterministic Policy Gradient for 11ax Spatial Reuse
With the growing demand for wireless local area network (WLAN) applications that require low latency, orthogonal frequency-division multiple access (OFDMA) has been adopted for uplink and downlink transmissions in the IEEE 802.11ax standard to improve the spectrum efficiency and reduce the latency. In IEEE 802.11ax WLANs, OFDMA resource allocation that guarantees latency, called latency-bounded resource allocation, is more challenging than that in cellular networks because severe unmanaged interference from overlapping basic service sets is enhanced due to the concurrent-transmission mechanism newly employed in IEEE 802.11ax. To improve the downlink OFDMA resource allocation with the unmanaged interference caused by IEEE 802.11ax concurrent transmissions, we propose Lyapunov optimization-based latency-bounded allocation with reinforcement learning (RL). We focus on the transmission-queue size for each station (STA) at the access point that determines the STA latency. Using Lyapunov optimization, we formulate the resource-allocation problem with the queue-size constraints in a form that can be solved using RL (i.e., a Markov decision process) and prove the upper bound of the queue size. Our simulation results demonstrated that the proposed method, which uses an RL algorithm with a deep deterministic policy gradient, satisfied the queue-size constraints. This means that the proposed method met the latency requirements, while some baseline methods failed to meet them. Furthermore, the proposed method achieved a higher fairness index than the baseline methods
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