The exponential growth of wireless devices and stringent reliability requirements of emerging applications demand fundamental improvements in distributed channel access mechanisms for unlicensed bands. Current Wi-Fi systems, which rely on binary exponential backoff (BEB), suffer from suboptimal collision resolution in dense deployments and persistent fairness challenges due to inherent randomness. This paper introduces a multiagent reinforcement learning framework that integrates artificial intelligence (AI) optimization with legacy device coexistence. We first develop a dynamic backoff selection mechanism that adapts to real-time channel conditions through access deferral events while maintaining full compatibility with conventional CSMA/CA operations. Second, we introduce a fairness quantification metric aligned with enhanced distributed channel access (EDCA) principles to ensure equitable medium access opportunities. Finally, we propose a centralized training decentralized execution (CTDE) architecture incorporating neighborhood activity patterns as observational inputs, optimized via constrained multi-agent proximal policy optimization (MAPPO) to jointly minimize collisions and guarantee fairness. Experimental results demonstrate that our solution significantly reduces collision probability compared to conventional BEB while preserving backward compatibility with commercial Wi-Fi devices. The proposed fairness metric effectively eliminates starvation risks in heterogeneous scenarios.

IEEE 802.11be (Wi-Fi 7) introduces Multi-Link Operation (MLO) as a While MLO offers significant parallelism and capacity, realizing its full potential in guaranteeing strict delay bounds and optimizing Quality of Service (QoS) for diverse, heterogeneous traffic streams in complex multi-link scenarios remain a significant challenge. This is largely due to the limitations of static Enhanced Distributed Channel Access (EDCA) parameters and the complexity inherent in cross-link traffic management. To address this, this paper investigates the correlation between overall MLO QoS indicators and the configuration of EDCA parameters and Acess Catagory (AC) traffic allocation among links. Based on this analysis, we formulate a constrained optimization problem aiming to minimize the sum of overall packet loss rates for all access categories while satisfying their respective overall delay violation probability constraints. A Genetic Algorithm (GA)-based MLO EDCA QoS optimization algorithm is designed to efficiently search the complex configuration space of AC assignments and EDCA parameters. Experimental results demonstrate that the proposed approach’s efficacy in generating adaptive MLO configuration strategies that align with diverse service requirements. The proposed solution significantly improves delay distribution characteristics, and enhance QoS robustness and resource utilization efficiency in high-load MLO environments.

With the growing demand for next-generation wireless networks and the rapid development of AI, traditional Wi-Fi MAC design challenges to manage the increasingly complex network optimization, and the machine learning-integrated MAC (MLMAC) initiative we work on is expected to contribute to the future intelligent network. This paper studies deep reinforcement learning (DRL)-empowered Wi-Fi distributed channel access (DCA) strategy, introducing the concept of network access entropy to effectively quantify DCA chaotic degree and facilitate ML-MAC performance analysis. We focus on the centralized training decentralized execution (CTDE) paradigm, e.g. the algorithm QMIX, in the multiagent reinforcement learning (MARL) framework and formulate decentralized-partially observable Markov decision process (DECPOMDP) in DCA cooperation network. We consider the tradeoff between total and individual rewards and first introduce wait decision counter (WDC) as part of the DCA agent observation, enabling deep neural network (DNN) to achieve better performance and convergence stability. Extensive simulation results on our MLMAC protocol stack platform with NS3 and PyTorch demonstrate the strategy's superiority over CSMA/CA under both unsaturated and saturated traffic, as well as the advantage revelation of i) trained-network generalization; ii) dynamic access robustness; iii) protocol heterogeneity fairness.

In this paper, we design a novel orthogonal time frequency space modulation based differential chaos shift keying (OTFS-DCSK) transceiver for reliable information transmissions. In this design, we exploit the orthogonal time frequency space (OTFS) modulation which is a two-dimensional modulation designed in the delay-Doppler domain, to combat multiplicative channel fading. Besides, since the OTFS-DCSK signal matrix is rank-1, we propose to utilize a singular vector decomposition (SVD) aided detection at the receiver to suppress the additive white Gaussian noise (AWGN) to further enhance the reliability. Thanks to the diversity obtained from OTFS modulation and the noise-suppression achieved by the SVD aided detection, the proposed OTFS-DCSK transceiver significantly improves the reliability performance, particular in the high-mobility wireless communications over the dual-dispersive channels caused by multipath propagation effects and Doppler shift effects. Simulation results validate the proposed OTFS-DCSK system outperforms the benchmark systems over AWGN, multipath and dual-dispersive fading channels.