Without any doubt, Machine Learning (ML) will be an important driver of future communications due to its foreseen performance when applied to complex problems. However, the application of ML to networking systems raises concerns among network operators and other stakeholders, especially regarding trustworthiness and reliability. In this paper, we devise the role of network simulators for bridging the gap between ML and communications systems. In particular, we present an architectural integration of simulators in ML-aware networks for training, testing, and validating ML models before being applied to the operative network. Moreover, we provide insights on the main challenges resulting from this integration, and then give hints discussing how they can be overcome. Finally, we illustrate the integration of network simulators into ML-assisted communications through a proof-of-concept testbed implementation of a residential Wi-Fi network.

The WiFi landscape is rapidly changing over the last years, responding to the new needs of wireless communications. IEEE 802.11ax is the next fast-approaching standard, addressing some of todays biggest performance challenges specifically for high-density public environments. It is designed to operate at 2.4 GHz and 5 GHz bands, the latter being rapidly adopted worldwide after its inclusion in IEEE 802.11ac, and with expected growing demand in the next 10 years.This paper assesses empirically the suitability of the available IEEE 802.11ax path loss models at 5 GHz on some real testbeds and proposes a new model with higher abstraction level; i.e., without requiring from a previous in situ analysis of each considered receiver's location. The proposed TMB path loss model, used in combination with generated data sets, is able to obtain an estimation of RSSI, selected modulation and coding scheme (MCS), and number of spatial streams in function of the AP configuration and the AP-STA distance. We aim to use the model to compare IEEE 802.11ac/ax performance simulation results with experimental ones.

A short time after the official launch of WiFi 6, IEEE 802.11 working groups along with the WiFi Alliance are already designing its successor in the wireless local area network (WLAN) ecosystem: WiFi 7. With the IEEE 802.11be amendment as one of its main constituent parts, future WiFi 7 aims to include time-sensitive networking (TSN) capabilities to support low latency and ultra-reliability in license-exempt spectrum bands, enabling many new Internet of Things scenarios. This article first introduces the key features of IEEE 802.11be, which are then used as the basis to discuss how TSN functionalities could be implemented in WiFi 7. Finally, the benefits and requirements of the most representative Internet of Things low-latency use cases for WiFi 7 are reviewed: multimedia, healthcare, industrial, and transport.

No abstract

WiFi densification leads to the existence of multiple overlapping coverage areas, which allows user stations (STAs) to choose between different Access Points (APs). The standard WiFi association method makes the STAs select the AP with the strongest signal, which in many cases leads to underutilization of some APs while overcrowding others. To mitigate this situation, Reinforcement Learning techniques such as Multi-Armed Bandits can be used to dynamically learn the optimal mapping between APs and STAs, and so redistribute the STAs among the available APs accordingly. This is an especially challenging problem since the network response observed by a given STA depends on the behavior of the others, and so it is very difficult to predict without a global view of the network.In this paper, we focus on solving this problem in a decentralized way, where STAs independently explore the different APs inside their coverage range, and select the one that better satisfy its needs. To do it, we propose a novel approach called Opportunistic ε-greedy with Stickiness that halts the exploration when a suitable AP is found, then, it remains associated to it while the STA is satisfied, only resuming the exploration after several unsatisfactory association periods. With this approach, we reduce significantly the network response variability, improving the ability of the STAs to find a solution faster, as well as achieving a more efficient use of the network resources.

Will Multi-Link Operation (MLO) be able to improve the latency of Wi-Fi networks? MLO is one of the most disruptive MAC-layer techniques included in the IEEE 802.11be amendment. It allows a device to use multiple radios simultaneously and in a coordinated way, providing a new framework to improve the WLAN throughput and latency. In this paper, we investigate the potential latency benefits of MLO by using a large dataset containing 5 GHz spectrum occupancy measurements. Experimental results show that when the channels are symmetrically occupied, MLO can improve latency by one order of magnitude. In contrast, in asymmetrically occupied channels, MLO can sometimes be detrimental and increase latency. To address this case, we introduce Opportunistic Simultaneous Transmit and Receive (STR+) channel access and study its benefits. M. Carrascosa and B. Bellalta were supported by WINDMAL PGC2018-099959-B-I00 (MCIU/AEI/FEDER,UE) and Cisco. G. Geraci was supported by MINECO's Project RTI2018-101040 and by a "Ramón y Cajal" Fellowship from the Spanish State Research Agency. II. MULTI-RADIO MULTI-LINK OPERATION IEEE 802.11be considers two main channel access methods to support Multi-link Operation: Simultaneous Transmit and Receive (MLO-STR), and Non-simultaneous Transmit and 1 WACA dataset: https://github.com/sergiobarra/WACA WiFiAnalyzer. 2 The terms latency and delay are used interchangeably throughout the paper.

No abstract

Will Multi-Link Operation (MLO) be able to improve the latency of Wi-Fi networks? MLO is one of the most disruptive MAC-layer techniques included in the IEEE 802.11be amendment. It allows a device to use multiple radios simultaneously and in a coordinated way, providing a new framework to improve the WLAN throughput and latency. In this paper, we investigate the potential latency benefits of MLO by using a large dataset containing 5 GHz spectrum occupancy measurements. Experimental results show that when the channels are symmetrically occupied, MLO can improve latency by one order of magnitude. In contrast, in asymmetrically occupied channels, MLO can sometimes be detrimental and increase latency. To address this case, we introduce Opportunistic Simultaneous Transmit and Receive (STR+) channel access and study its benefits.M. Carrascosa and B.