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IEEE 802.11be -Extremely High Throughput-, commercially known asWireless-Fidelity (Wi-Fi) 7 is the newest IEEE 802.11 amendment that comes toaddress the increasingly throughput hungry services such as Ultra HighDefinition (4K/8K) Video and Virtual/Augmented Reality (VR/AR). To do so, IEEE802.11be presents a set of novel features that will boost the Wi-Fi technologyto its edge. Among them, Multi-Link Operation (MLO) devices are anticipated tobecome a reality, leaving Single-Link Operation (SLO) Wi-Fi in the past. Toachieve superior throughput and very low latency, a careful design approachmust be taken, on how the incoming traffic is distributed in MLO capabledevices. In this paper, we present a Reinforcement Learning (RL) algorithmnamed Multi-Headed Recurrent Soft-Actor Critic (MH-RSAC) to distribute incomingtraffic in 802.11be MLO capable networks. Moreover, we compare our results withtwo non-RL baselines previously proposed in the literature named: Single LinkLess Congested Interface (SLCI) and Multi-Link Congestion-aware Load balancingat flow arrivals (MCAA). Simulation results reveal that the MH-RSAC algorithmis able to obtain gains in terms of Throughput Drop Ratio (TDR) up to 35.2% and6% when compared with the SLCI and MCAA algorithms, respectively. Finally, weobserved that our scheme is able to respond more efficiently to high throughputand dynamic traffic such as VR and Web Browsing (WB) when compared with thebaselines. Results showed an improvement of the MH-RSAC scheme in terms of FlowSatisfaction (FS) of up to 25.6% and 6% over the the SCLI and MCAA algorithms.

RL meets Multi-Link Operation in IEEE 802.11be: Multi-Headed Recurrent Soft-Actor Critic-based Traffic Allocation