Peiyuan Si

Peiyuan Si, Wenhan Yu, Jun Zhao et al.

Metaverse is expected to create a virtual world closely connected with reality to provide users with immersive experience with the support of 5G high data rate communication technique. A huge amount of data in physical world needs to be synchronized to the virtual world to provide immersive experience for users, and there will be higher requirements on coverage to include more users into Metaverse. However, 5G signal suffers severe attenuation, which makes it more expensive to maintain the same coverage. Unmanned aerial vehicle (UAV) is a promising candidate technique for future implementation of Metaverse as a low-cost and high-mobility platform for communication devices. In this paper, we propose a proximal policy optimization (PPO) based double-agent cooperative reinforcement learning method for channel allocation and trajectory control of UAV to collect and synchronize data from the physical world to the virtual world, and expand the coverage of Metaverse services economically. Simulation results show that our proposed method is able to achieve better performance compared to the benchmark approaches.

Peiyuan Si, Jun Zhao, Kwok-Yan Lam et al.

In this paper, we aim to explore the use of uplink semantic communications with the assistance of UAV in order to improve data collection effiicency for metaverse users in remote areas. To reduce the time for uplink data collection while balancing the trade-off between reconstruction quality and computational energy cost, we propose a hybrid action reinforcement learning (RL) framework to make decisions on semantic model scale, channel allocation, transmission power, and UAV trajectory. The variables are classified into discrete type and continuous type, which are optimized by two different RL agents to generate the combined action. Simulation results indicate that the proposed hybrid action reinforcement learning framework can effectively improve the efficiency of uplink semantic data collection under different parameter settings and outperforms the benchmark scenarios.

Liangxin Qian, Wenhan Yu, Peiyuan Si et al.

Optimization problems in communication networks and information systems often contain coupled multiplicative or fractional terms, such as sum-of-products, sum-of-ratios, and logarithmic product-ratio structures. These problems are generally non-convex and difficult to solve, which motivates the development of tractable transformation and approximation techniques. In this paper, we propose an inequality-based transform framework for handling multiplicative and fractional terms involving an arbitrary number of coupled functions. The proposed framework is built upon the harmonic-mean, geometric-mean, arithmetic-mean, and quadratic-mean inequalities, and yields lower-bound and upper-bound surrogates for product-type terms. We derive the corresponding auxiliary-variable updates in closed form and show that the constructed surrogates are tight and first-order consistent at the current iterate. Based on these properties, we develop a class of successive approximation (SA) methods for sum-of-products/ratios minimization and maximization problems. When the transformed surrogate is convex for minimization or concave for maximization, the proposed method reduces to a standard successive convex approximation (SCA) method. When such convexity or concavity is not guaranteed, we further develop gradient-based SA variants and establish their sublinear convergence to an $ε$-stationary point under standard smoothness and boundedness assumptions. We also discuss extensions to logarithmic product-ratio objectives and non-convex constraints. Numerical studies and application examples, including transmit-energy minimization, age-of-information minimization, semantic utility maximization, reliability-aware routing, cooperative edge caching, and product-loss learning, demonstrate the versatility and effectiveness of the proposed transform framework.

Renyang Liu, Wei Zhou, Jinhong Zhang et al.

Recently, Graph Neural Networks (GNNs), including Homogeneous Graph Neural Networks (HomoGNNs) and Heterogeneous Graph Neural Networks (HeteGNNs), have made remarkable progress in many physical scenarios, especially in communication applications. Despite achieving great success, the privacy issue of such models has also received considerable attention. Previous studies have shown that given a well-fitted target GNN, the attacker can reconstruct the sensitive training graph of this model via model inversion attacks, leading to significant privacy worries for the AI service provider. We advocate that the vulnerability comes from the target GNN itself and the prior knowledge about the shared properties in real-world graphs. Inspired by this, we propose a novel model inversion attack method on HomoGNNs and HeteGNNs, namely HomoGMI and HeteGMI. Specifically, HomoGMI and HeteGMI are gradient-descent-based optimization methods that aim to maximize the cross-entropy loss on the target GNN and the $1^{st}$ and $2^{nd}$-order proximities on the reconstructed graph. Notably, to the best of our knowledge, HeteGMI is the first attempt to perform model inversion attacks on HeteGNNs. Extensive experiments on multiple benchmarks demonstrate that the proposed method can achieve better performance than the competitors.

Peiyuan Si, Liangxin Qian, Jun Zhao et al.

Unmanned aerial vehicles (UAVs) are promising for providing communication services due to their advantages in cost and mobility, especially in the context of the emerging Metaverse and Internet of Things (IoT). This paper considers a UAV-assisted Metaverse network, in which UAVs extend the coverage of the base station (BS) to collect the Metaverse data generated at roadside units (RSUs). Specifically, to improve the data collection efficiency, resource allocation and trajectory control are integrated into the system model. The time-dependent nature of the optimization problem makes it non-trivial to be solved by traditional convex optimization methods. Based on the proposed UAV-assisted Metaverse network system model, we design a hybrid framework with reinforcement learning and convex optimization to {cooperatively} solve the time-sequential optimization problem. Simulation results show that the proposed framework is able to reduce the mission completion time with a given transmission power resource.