Industrial Internet of Things applications require low and deterministic delays, which is a challenging target for emerging wireless communication technologies. While the upcoming Wi-Fi 7 and Wi-Fi 8, tailored for real-time and reliable communications, are under development, this article focuses on Wi-Fi 6 and explores its capabilities to support industrial applications. This article summarizes key lessons learned during an industrial project on factory automation with Wi-Fi 6, describes and advocates a solution that satisfies quality of service requirements in industrial scenarios. To generalize the designed scheme, this article proposes a framework that maximizes the capacity of the system under reliability and latency constraints. Through extensive simulations, this article shows that the proposed scheme more than doubles the network capacity compared with the default Wi-Fi 6 implementation in terms of the number of stations served with low and deterministic packet delivery delays.

Wi-Fi is evolving to support new areas such as extended reality, Industrial Internet of Things, and environments with numerous devices. Unlike previous generations that aimed to maximize peak and user-perceived throughput, Wi-Fi 8 (also known as IEEE 802.11bn Ultra High Reliability) intends to make Wi-Fi operation more reliable, i.e., to improve performance across the full range of scenarios, particularly in worst-case conditions. Specifically, 802.11bn is striving to increase throughput at various signal to interference and noise ratio levels, diminish the 95th percentile of latency, decrease packet losses, and reduce power consumption. To attain these ambitions, Wi-Fi 8 is going to introduce a series of groundbreaking mechanisms, including distributed-tone resource units, unequal modulations, prioritized and non-primary channel access, advanced roaming features, multi-AP coordination, and so on. We present the current status of Wi-Fi 8 development with an emphasis on the targets and enabling technologies. This tutorial also discusses open research directions that require the development of new intelligent methods and algorithms.

Efficient operation of resource management algorithms in modern ultra-reliable low-latency communication (URLLC) systems requires fast and accurate estimation of the system capacity, i.e., the maximum traffic volume per second that can be served under strict quality of service requirements such as low latency and high reliability of data delivery. Queueing theory is helpful in this task; however, due to the variability of communication systems and their operating conditions, queueing theory has to operate with general distributions, i.e., to consider \(G/G/s\) queues. For such queues, obtaining a precise analytical solution is either impossible in a closed-form or computationally expensive, so approximations for \(G/G/s\) models are in demand. However, existing approximation methods fail to account for URLLC features: low (but nonzero) delay thresholds and low probabilities of exceeding these thresholds, which results in significant errors in capacity estimation. We propose and investigate closed-form approximation formulas for estimating the URLLC capacity using \(G/G/s\) queues. We analyze the accuracy of the proposed formulas and distinguish regions of acceptable capacity estimation errors. Finally, we show a specific asymptotics for the URLLC system capacity in a wide range of parameters and scenarios.

Wi-Fi standardization continues: IEEE 802.11bn (Wi-Fi 8) reached Draft 1.0 in August 2025 and is progressing through comment resolution. The wireless industry now stands at a pivotal crossroads as it begins to define Wi-Fi 9. This paper analyzes technical directions and candidate features for the post-802.11bn amendment, tentatively referred to as Wi-Fi 9. We synthesize contributions from device manufacturers, network operators, and research institutions presented to the IEEE 802.11 Wireless Next Generation Standing Committee in early 2026. The emerging consensus indicates that, rather than increasing the nominal data rates, the next generation will likely prioritize deterministic performance, reliability, and expanded application domains. The paper examines the market forces driving this transition, including AI workloads that invert traditional traffic asymmetry, industrial robotics that demand sub-millisecond latency, and immersive entertainment that requires the simultaneous satisfaction of throughput, latency, and reliability constraints. Then the paper analyzes candidate technologies spanning PHY and MAC layer transformations, coordination between multiple networks, and AI integration throughout the protocol stack. Implementation challenges, including backward compatibility tradeoffs and spectrum availability, are discussed. Finally, the paper outlines the anticipated standardization timeline targeting commercial deployment of Wi-Fi 9 in the 2030s.

Для анализа производительности беспроводных локальных и сотовых сетей, обеспечивающих сверхнадежную связь с низкими задержками, требуются методы быстрой и точной оценки эффективной емкости системы, т.е. того объема трафика, для которого удается выполнить заданные требования к надежности и времени доставки. Эти методы могут использовать теорию массового обслуживания, например, моделируя систему связи как многосерверную систему массового обслуживания G/G/s. Однако существующие методы оценки показателей производительности системы G/G/s обладают либо высокой вычислительной сложностью, либо высокой погрешностью в области малых значений задержки обслуживания, а также малых вероятностей испытать эту задержку. В статье исследуются приближенные методы оценки показателей производительности многосерверных систем G/G/s, потенциально применимых для оценки эффективной емкости системы сверхнадежной связи с низкими задержками. Предлагается метод оценки вероятности превысить ограничение на время пребывания в очереди, и численно показана его низкая ошибка. Также приведен асимптотический анализ метода, результаты которого могут быть полезны при реализации алгоритмов планирования радиоресурсов в беспроводных локальных и сотовых сетях.