multi-ap coordination

This work addresses the challenge in multi-access point (AP) coordinated spatial reuse (C-SR), where high coordination overhead and complex parameter optimization hinder the simultaneous achievement of high throughput and fairness. To this end, the paper proposes a two-layer multi-armed bandit (MAB) algorithm that, for the first time, introduces hierarchical reinforcement learning into the multi-AP C-SR setting. The proposed framework jointly optimizes scheduling, power control, and link adaptation through a hierarchical structure, significantly reducing coordination overhead while meeting diverse quality-of-service (QoS) requirements. System-level simulations demonstrate that the scheme not only enhances aggregate network throughput but also improves fairness in resource allocation among users, offering a robust and efficient solution for next-generation Wi-Fi systems.

This work addresses the transmit power allocation problem in IEEE 802.11bn multi-AP coordinated spatial reuse (Co-SR) by proposing a per-TXOP power optimization framework based on proportional fairness (PF). Through theoretical analysis, the original two-dimensional optimization is reduced to two one-dimensional line searches, and closed-form solutions are derived under discrete modulation and coding schemes (MCS). The study proves that in any Pareto-optimal solution, at least one AP operates at maximum transmit power, substantially reducing computational complexity. It further reveals a critical distinction between continuous and discrete rate models in shaping fairness-oriented strategies. Komondor-based simulations demonstrate that joint evaluation of dual-AP links is essential to unlock Co-SR gains, yet current 802.11bn signaling supports only selfish strategies, necessitating enhanced per-TXOP channel feedback mechanisms to achieve fair and optimal performance.

To address low spectral efficiency, severe interference, and degraded performance in dense Wi-Fi deployments for ultra-low-latency, high-throughput applications—such as cloud gaming, XR, and HD video streaming—this paper proposes Co-SR (Coordinated Spatial Reuse), a standardized, IEEE 802.11be (Wi-Fi 8)-aligned mechanism. Co-SR enables explicit inter-access-point coordination to dynamically manage concurrent transmissions and interference, achieving efficient spatial reuse within a standards-compliant framework for the first time. Evaluated on an IEEE 802.11 simulation platform against DCF-based baselines, Co-SR reduces end-to-end latency by 31%–95% in a four-AP local-area network, markedly improving reliability and QoS. The core contribution is the first standardized-conformant, deployable Co-SR protocol design, accompanied by a rigorous methodology for performance evaluation and validation.

This paper addresses the joint base station (BS)-terminal association and communication-computation resource allocation for massive sensors/actuators in industrial wireless networked control systems, aiming to minimize closed-loop control latency while ensuring system stability under stringent high-reliability low-latency communication (HRLLC) requirements, spatial multiplexing constraints of Massive MIMO, and limited edge-node resources. Method: We formulate, for the first time, a joint optimization model integrating BS-terminal association and communication-computation co-scheduling under coupled HRLLC and Massive MIMO constraints, and propose a hybrid algorithm combining alternating optimization with successive convex approximation (SCA) to tackle the resulting non-convex problem. Results: Experiments demonstrate that the proposed method improves stability margin by 37.2% over heuristic and FDMA-based baselines, significantly reduces millisecond-level control latency, and meets the dual stringent requirements of real-time performance and reliability essential for industrial control applications.

To address low spatial reuse (SR) efficiency and poor fairness in multi-AP coexistence scenarios envisioned for Wi-Fi 8 and beyond, this paper proposes a distributed online learning framework based on multi-agent multi-armed bandits (MA-MAB). It is the first to jointly optimize packet detection (PD) threshold adaptation and transmit power control within an MA-MAB paradigm. A decentralized reward-sharing mechanism enables AI-native, coordinator-free dynamic SR optimization. Evaluated on the Komondor simulation platform, the proposed approach achieves a 15% average throughput gain, a 210% improvement in minimum network throughput, and a maximum access delay of ≤3 ms. These results demonstrate substantial gains in spectral efficiency and user fairness under dense multi-AP deployments.

Existing integrated sensing and communication (ISAC) research is largely confined to single-cell or link-level scenarios, falling short of meeting the stringent requirements of low-altitude economies for highly reliable and tightly integrated air-ground cooperative capabilities. This work pioneers the integration of Coordinated Multi-Point (CoMP) techniques into heterogeneous air-ground ISAC networks by proposing a two-tier architecture: macro base stations arranged in a hexagonal lattice and pico base stations distributed according to a Poisson point process, combined with a hybrid mono-/bi-static sensing mechanism. Building upon this architecture, we develop a stochastic geometry-based analytical framework to jointly characterize communication and sensing performance, revealing the fundamental trade-off between data rate and sensing accuracy under multi-base-station cooperation. Simulations demonstrate that the proposed architecture substantially enhances spatial diversity and sensing performance, offering theoretical foundations and design guidelines for scalable and efficient ISAC deployment in low-altitude environments.

This work addresses the limited scalability of existing Wi-Fi 8 multi-AP coordination mechanisms, which support only pairwise collaboration and struggle with high signaling overhead, slow convergence, and computational complexity in dense networks—particularly under coordinated spatial reuse (Co-SR). To overcome these challenges, we propose FM4WiFi, the first end-to-end framework that introduces flow-matching generative modeling to wireless coordination. FM4WiFi employs an autoencoder to compress network state and a surrogate rate predictor to evaluate configuration quality, enabling efficient generation of complete Co-SR configurations—including power and rate control—in a single inference pass. Requiring neither real-time simulation nor a digital twin, our method achieves sub-second inference even in dense deployments with over 30 APs, matching or surpassing state-of-the-art performance while significantly enhancing scalability and practical deployability.

This study addresses the limitations of conventional optimization methods in the joint communication and control co-design for B6G networks, particularly regarding modular representation, requirements traceability, and design space analysis. To overcome these challenges, this work proposes a composition-driven methodology grounded in formal co-design theory. By introducing a compositional perspective, the proposed approach circumvents the bottlenecks inherent in joint optimization, thereby enabling the modular modeling of complex interacting subsystems and systematic exploration of the design space. The effectiveness of this methodology is validated through a wireless-assisted robotic control case study. Furthermore, this paper elucidates its complementary relationship with optimization-driven approaches. Ultimately, this research establishes a novel paradigm for cross-domain co-design within B6G scenarios, offering a rigorous framework to facilitate scalable and verifiable system integration.

This work addresses the coexistence challenge among heterogeneous services—including cellular communications, RF sensing, radio navigation, and radar localization—in the sub-6 GHz licensed shared access band under high congestion. It proposes the first unified, centrally coordinated framework enabling dynamic sharing of a common physical resource block pool across all four service types. The design jointly optimizes resource allocation by maximizing a weighted sum cellular rate subject to stringent QoS constraints on duty cycle, orthogonality, sensing signal-to-noise ratio, and Cramér–Rao lower bound for localization accuracy. The solution integrates mixed-integer nonlinear programming, alternating optimization, successive convex approximation, and a low-complexity QoS-aware greedy algorithm. Validated via ray-tracing simulations on the BostonTwin urban digital twin platform, the architecture significantly enhances spectral efficiency and cellular throughput while satisfying diverse QoS requirements, demonstrating the feasibility of spectrum-efficient reuse in civil-military integrated scenarios.

This study addresses the problem of distributed throughput optimization in dense multi-access point (Multi-AP) IEEE P802.11be networks by constructing a packet-level system model that incorporates CSMA/CA, RTS/CTS, beam training overhead, directional millimeter-wave interference, SINR-driven MCS selection, and retransmission mechanisms. The configuration optimization is formulated as a combinatorial multi-armed bandit (CMAB) problem with multiple groups. To efficiently navigate the high-dimensional discrete configuration space, the authors propose an innovative exploration strategy guided by Hadamard matrices and a grouped combinatorial Successive Accept-Reject (CSAR) algorithm. Experimental results demonstrate that the proposed approach significantly improves both aggregate and per-AP throughput across various AP densities and reduces throughput convergence time by approximately 49%.