The classic multi-hop wireless network optimization problem has recently re-attracted many attentions due to some emerging applications such as wireless mesh network, space-air ground integrated networks, and 5G/6G integrated access and backhaul systems. The key issue in multi-hop wireless network optimization is the interference management through scheduling. The fundamental NP-hardness of this problem is that there are exponentially many possible independent sets (ISs), but only a small number of them will be scheduled in the optimal solution (termed as the scheduling structure). The existing literature on approximation algorithms are mainly along the direction of searching the ISs in a heuristic manner. With the capability of machine learning (ML) algorithms in supporting big data analytics, in this paper, we propose a two-stage self-supervised learning framework that can explore the scheduling structure from historical optimization instances, and such knowledge is then exploited to solve a new instance with greatly reduced computational overhead. At the exploring stage, we develop dimension reduction techniques for effective scheduling structure classification in a high-dimensional vector space. At the exploiting stage, we design an innovative structure criticalness indicator (SCI)-based IS selection algorithm that can robustly lead to close-to-optimal approximation of the average achievable throughput with roughly constant complexity. Furthermore, we also contribute a geometric canonical representation (GCR) system to equip the ML-assisted optimization framework with a topology-aware applicability. The proposed topology-aware ML (TAML) framework exhibits good generalizability across different network topologies and flow demands.

The era of artificial intelligence generated content (AIGC) has emerged to offer the benefits of personalized content to meet the needs of different users. A majority of generative AI models require computationally intensive training and/or finetuning with large datasets, which make resource-constrained devices, such as mobile edge devices (MEDs) to seldom participate in the training process. In addition, MEDs are not willing to share their private data with a central cloud server to train the models. Distributed machine learning (ML) approaches, such as federated learning (FL) and split learning (SL), offer some benefits in achieving data privacy, as well as model convergence for balanced data distributions. However, in practical settings where the datasets of MEDs are imbalanced, both FL and SL suffer poor performance. This motivates us to propose collaborative split federated learning (CSFL) for AIGC. Our CSFL approach supports resource cognition in which MEDs participate based on their available resources. We demonstrate that CFSL achieves good performance in terms of model convergence speed and training latency, while preserving data privacy when training a denoising diffusion probabilistic model for AIGC.

Age of information (AoI) is a crucial metric in modern communication systems, quantifying the information freshness at the receiver side. This study proposes a novel and general approach utilizing stochastic hybrid systems (SHS) for AoI analysis and minimization in carrier sense multiple access (CSMA) networks. Specifically, we consider a practical networking scenario where multiple nodes contend for transmission through a standard CSMA-based medium access control (MAC) protocol, and the tagged node under consideration uses a small transmission buffer for a low AoI. We for the first time develop an SHS-based analytical model for this finite-buffer transmission system over the CSMA MAC. Moreover, we develop a creative method to incorporate the collision probability into the SHS model, with background nodes having heterogeneous traffic arrival rates. This new model enables us to analytically find the optimal sampling rate to minimize the AoI of the tagged node in a wide range of practical networking scenarios. Our analysis reveals insights into buffer size impacts when jointly optimizing throughput and AoI. The SHS model is cast over an 802.11-based MAC to examine the performance, with comparison to ns-based simulation results. The accuracy of the modeling and the efficiency of optimal sampling are convincingly demonstrated.

Reasoning under conditions of uncertainty is important in many areas where posterior knowledge depends on prior likelihood estimation from data. Distributed computing can provide a leverage for enhancing Bayesian network (BN) structure learning while keeping data private to users. We propose a decentralized learning framework based on distributed computing of BNs in local sites. Our method yields a higher fitness score (FS) for BNs in local sites, preserves local data privacy and significantly reduces the computation cost by 69.09% in comparison with a centralized approach which yields a low FS score, and does not keep user data private.

A significant challenge in 802.11p based vehicular ad hoc networks (VANETs) is that the cooperative awareness messages (CAMs) tend to experience collisions. In this paper, we propose an adaptive CAM messaging algorithm based on the emerging methodology of the age of information (AoI). Our objective is to minimize an age-penalty function in a trajectory prediction application. In our design, each vehicle will compute a local penalty which serves as an indicator on whether the CAM messaging frequency is appropriate for its mobility status; and at the same time, calculates an appropriate penalty associated with all its neighbors which serves as an indicator regarding the impact of network congestion on the trajectory prediction quality. The aggregated penalty score integrating both the local and neighboring parts will be used to adaptively control the CAM sending frequency. We are to present simulation results demonstrating that our adaptive messaging method can indeed mitigate network congestion while meet the driving safety requirements.