Aleksandra Kijanka

Francesc Wilhelmi, Boris Bellalta, Giovanni Geraci et al.

The IEEE 802.11bn amendment defines significant modifications to the standard by establishing Ultra High Reliability (UHR) targets in Wireless Local Area Networks (WLANs). This is expected to deliver substantial enhancements over previous standards, including modes of operation that increase throughput, reduce the 95th percentile of the latency distribution, and decrease MAC Protocol Data Unit (MPDU) loss (all by at least 25%) compared to Extremely High Throughput (EHT) operations defined in the 802.11be amendment. A fundamental innovation for achieving these ambitious goals is the introduction of Multi-Access Point Coordination (MAPC), an unprecedented feature whereby APs will be able to coordinate among themselves to enhance spectrum utilization and advance towards reliability. This paper provides a comprehensive overview and analysis of this key framework. We begin by reviewing existing AP coordination solutions that precede the 802.11bn standard, which serve as a foundation for understanding the transition to the current framework. We then describe the technical 802.11bn MAPC framework as defined by the task group. A detailed overview of each candidate MAPC feature is provided, contextualized with the relevant state-of-the-art. Furthermore, we introduce Kom8ndor, an open-source Wi-Fi 8 simulation tool, to evaluate these candidate MAPC features and showcase their potential to achieve UHR goals. Finally, we outline the future of MAPC beyond 802.11bn, exploring promising directions such as coordination schemes beyond 802.11bn (e.g., Joint Transmission (JT)) and new ideas.

Francesc Wilhelmi, Boris Bellalta, Miguel Casasnovas et al.

Spatial Reuse (SR) is a cost-effective technique for improving spectral efficiency in dense IEEE 802.11 deployments by enabling simultaneous transmissions. However, the decentralized optimization of SR parameters -- transmission power and Carrier Sensing Threshold (CST) -- across different Basic Service Sets (BSSs) is challenging due to the lack of global state information. In addition, the concurrent operation of multiple agents creates a highly non-stationary environment, often resulting in suboptimal global configurations (e.g., using the maximum possible transmission power by default). To overcome these limitations, this paper introduces a decentralized learning algorithm based on regret-matching, grounded in internal regret minimization. Unlike standard decentralized ``selfish'' approaches that often converge to inefficient Nash Equilibria (NE), internal regret minimization guides competing agents toward Correlated Equilibria (CE), effectively mimicking coordination without explicit communication. Through simulation results, we showcase the superiority of our proposed approach and its ability to reach near-optimal global performance. These results confirm the not-yet-unleashed potential of scalable decentralized solutions and question the need for the heavy signaling overheads and architectural complexity associated with emerging centralized solutions like Multi-Access Point Coordination (MAPC).