Multi-Access Point Coordination (MAPC) and Artificial Intelligence and Machine Learning (AI/ML) are expected to be key features in future Wi-Fi, such as the forthcoming IEEE 802.11bn (Wi-Fi 8) and beyond. In this paper, we explore a coordinated solution based on online learning to drive the optimization of Spatial Reuse (SR), a method that allows multiple devices to perform simultaneous transmissions by controlling interference through Packet Detect (PD) adjustment and transmit power control. In particular, we focus on a Multi-Agent Multi-Armed Bandit (MA-MAB) setting, where multiple decision-making agents concurrently configure SR parameters from coexisting networks by leveraging the MAPC framework, and study various algorithms and reward-sharing mechanisms. We evaluate different MA-MAB implementations using Komondor, a well-adopted Wi-Fi simulator, and demonstrate that AI-native SR enabled by coordinated MABs can improve the network performance over current Wi-Fi operation: mean throughput increases by 15%, fairness is improved by increasing the minimum throughput across the network by 210%, while the maximum access delay is kept below 3 ms.

Bike-sharing systems (BSSs) are deployed in over a thousand cities worldwide and play an important role in many urban transportation systems. BSSs alleviate congestion, reduce pollution and promote physical exercise. It is essential to explore the spatiotemporal patterns of bike-sharing demand, as well as the factors that influence these patterns, in order to optimise system operational efficiency. In this study, an optimised geo-temporal graph is constructed using trip data from Moby Bikes, a dockless BSS operator. The process of optimising the graph unveiled prime locations for erecting new stations during future expansions of the BSS. The Louvain algorithm, a community detection technique, is employed to uncover usage patterns at different levels of temporal granularity. The community detection results reveal largely self-contained sub-networks that exhibit similar usage patterns at their respective levels of temporal granularity. Overall, this study reinforces that BSSs are intrinsically spatiotemporal systems, with community presence driven by spatiotemporal dynamics. These findings may aid operators in improving redistribution efficiency.

Next-generation Wi-Fi networks are looking forward to introducing new features like multi-link operation (MLO) to both achieve higher throughput and lower latency. However, given the limited number of available channels, the use of multiple links by a group of contending Basic Service Sets (BSSs) can result in higher interference and channel contention, thus potentially leading to lower performance and reliability. In such a situation, it could be better for all contending BSSs to use less links if that contributes to reduce channel access contention. Recently, reinforcement learning (RL) has proven its potential for optimizing resource allocation in wireless networks. However, the independent operation of each wireless network makes difficult -- if not almost impossible -- for each individual network to learn a good configuration. To solve this issue, in this paper, we propose the use of a Federated Reinforcement Learning (FRL) framework, i.e., a collaborative machine learning approach to train models across multiple distributed agents without exchanging data, to collaboratively learn the the best MLO-Link Allocation (LA) strategy by a group of neighboring BSSs. The simulation results show that the FRL-based decentralized MLO-LA strategy achieves a better throughput fairness, and so a higher reliability -- because it allows the different BSSs to find a link allocation strategy which maximizes the minimum achieved data rate -- compared to fixed, random and RL-based MLO-LA schemes.

Abstract: We provide a comprehensive reply to the comment written by Stefan Boettcher [arXiv:2210.00623] Conversely, we highlight the broader algorithmic development underlying our original work, and (within our original framework) provide additional numerical results showing sizable improvements over our original data, thereby refuting the comment's original performance statements. Furthermore, it has already been shown that physics-inspired graph neural networks (PI-GNNs) can outperform greedy algorithms, in particular on hard, dense instances. We also argue that the internal (parallel) anatomy of graph neural networks is very different from the (sequential) nature of greedy algorithms, and (based on their usage at the scale of real-world social networks) point out that graph neural networks have demonstrated their potential for superior scalability compared to existing heuristics such as extremal optimization. Finally, we conclude highlighting the conceptual novelty of our work and outline some potential extensions.

The main hindrance to a widespread market penetration of battery-powered electric vehicles (BEVs) has been their limited energy reservoir resulting in cruising ranges of few hundred kilometers unless one allows for recharging or switching of depleted batteries during a trip. Unfortunately, recharging typically takes several hours and battery switch stations providing fully recharged batteries are still quite rare – certainly not as widespread as ordinary gas stations. For not getting stranded with an empty battery, going on a BEV trip requires some planning ahead taking into account energy characteristics of the BEV as well as available battery switch stations. In this paper we consider very basic, yet fundamental problems for E-Mobility: Can I get from A to B and back with my BEV without recharging in between? Can I get from A to B when allowed to recharge? How can I minimize the number of battery switches when going from A to B? We provide efficient and mathematically sound algorithms for these problems that allow for the energy-aware planning of trips.