Yuzhi Yang

Zirui Chen, Zhaoyang Zhang, Ziqing Xing et al.

Existing learning models often exhibit poor generalization when deployed across diverse scenarios. It is primarily due to that the underlying reference frame of the data varies with the deployment environment and settings. However, despite that data of each scenario has a distinct reference frame, its generation generally follows common underlying physical rules. Based on this understanding, this article proposes a deep learning framework named analogical learning (AL), which implicitly retrieves the reference frame information associated with a scenario and then to make accurate prediction by relative analogy with other scenarios. Specifically, we design a bipartite neural network called Mateformer. Its first part captures the relativity within multiple latent feature spaces between the input data and a small amount of embedded data from the studied scenario, while its second part uses this relativity to guide the nonlinear analogy. We apply AL to the typical multi-scenario learning problem of intelligent wireless localization in cellular networks. Extensive experiments validate AL's superiority across three key dimensions. First, it achieves state-of-the-art accuracy in single-scenario benchmarks. Second, it demonstrates stable transferability between different scenarios, avoiding catastrophic forgetting. Finally, and most importantly, it robustly adapts to new, unseen scenarios--including dynamic weather and traffic conditions--without any tuning. All data and code are available at https://github.com/ziruichen-research/ALLoc.

Xingqiu He, Chaoqun You, Yuzhi Yang et al.

Wireless Powered Mobile Edge Computing (WP-MEC) integrates mobile edge computing (MEC) with wireless power transfer (WPT) to simultaneously extend the operational lifetime and enhance the computational capability of wireless devices (WDs). In WPMEC systems, WPT and computation offloading compete for limited wireless resources, which makes their joint scheduling particularly challenging. In this paper, we investigate the energy-efficient online scheduling problem for WPMEC networks with multiple WDs and multiple access points (APs). Based on Lyapunov optimization, we develop an online optimization framework that transforms the original stochastic problem into deterministic per-slot optimization problems. To reduce computational complexity, we introduce the concept of marginal energy efficiency and derive an associated optimality condition, based on which a relax-then-adjust approach is proposed to efficiently obtain feasible solutions. For the resulting non-convex computation offloading subproblem, we analyze the structural properties of its optimal solution and transform it into an assignment problem that can be solved efficiently. We further provide theoretical performance guarantees for both the per-slot and long-term solution, establishing a fundamental trade-off between latency and energy consumption. To improve practical performance, additional mechanisms are introduced to balance the magnitudes of different queues and reduce latency without increasing energy consumption. Extensive simulation results demonstrate the effectiveness and robustness of the proposed algorithm under various system settings.