The multi-link operation (MLO) is a new feature proposed to be part of the IEEE 802.11be Extremely High Throughput (EHT) amendment. Such feature represents a paradigm shift towards multi-link communications, as nodes will be allowed to transmit and receive data over multiple radio interfaces concurrently. To make it possible, the 802.11be Task Group has proposed different modifications in regards to nodes' architecture, transmission operation, and management functionalities. This article reviews such changes and tackles the question of how traffic should be distributed over multiple links, as it is still unresolved. To that end, we evaluate different load balancing strategies over the active links. Results show that in high load, dense and complex scenarios, implementing congestion aware load balancing policies to significantly enhance next-generation WLAN performance using MLO is a must.

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The multi-link operation (MLO) is a new feature proposed to be part of the IEEE 802.11be Extremely High Throughput (EHT) amendment. Through MLO, access points and stations will be provided with the capabilities to transmit and receive data from the same traffic flow over multiple radio interfaces. However, the question on how traffic flows should be distributed over the different interfaces to maximize the WLAN performance is still unresolved. To that end, we evaluate in this article different traffic allocation policies, under a wide variety of scenarios and traffic loads, in order to shed some light on that question. The obtained results confirm that congestion-aware policies outperform static ones. However, and more importantly, the results also reveal that traffic flows become highly vulnerable to the activity of neighboring networks when they are distributed across multiple links. As a result, the best performance is obtained when a new arriving flow is simply assigned entirely to the emptiest interface.

The multi-link operation (MLO) is a key feature of the next IEEE 802.11be Extremely High Throughput amendment. Through its adoption, it is expected to enhance users' experience by improving throughput rates and latency. However, potential MLO gains are tied to how traffic is distributed across the multiple radio interfaces. In this paper, we introduce a traffic manager atop MLO, and evaluate different high-level traffic-tolink allocation policies to distribute incoming traffic over the set of enabled interfaces. Following a flow-level approach, we compare both non-dynamic and dynamic traffic balancing policy types. The results show that the use of a dynamic policy, along with MLO, allows to significantly reduce the congestion suffered by traffic flows, enhancing the traffic delivery in all the evaluated scenarios, and in particular improving the quality of service received by video flows. Moreover, we show that the adoption of MLO in future Wi-Fi networks improves also the coexistence with non-MLO networks, which results in performance gains for both MLO and non-MLO networks.

Next generation of wireless local area networks (WLANs) will operate in dense, chaotic and highly dynamic scenarios that in a significant number of cases may result in a low user experience due to uncontrolled high interference levels. Flexible network architectures, such as the software-defined networking (SDN) paradigm, will provide WLANs with new capabilities to deal with users' demands, while achieving greater levels of efficiency and flexibility in those complex scenarios. On top of SDN, the use of machine learning (ML) techniques may improve network resource usage and management by identifying feasible configurations through learning. ML techniques can drive WLANs to reach optimal working points by means of parameter adjustment, in order to cope with different network requirements and policies, as well as with the dynamic conditions. In this paper we overview the work done in SDN for WLANs, as well as the pioneering works considering ML for WLAN optimization. Finally, in order to demonstrate the potential of ML techniques in combination with SDN to improve the network operation, we evaluate different use cases for intelligent-based spatial reuse and dynamic channel bonding operation in WLANs using Multi-Armed Bandits.