Beijing University of Posts and Telecommunications

Wanli Ni

Ping Zhang

Hui Tian

Gaofeng Nie

Wen Jie Wang

Shaoshuai Fan

Xue Rong

Shilin Tao

A wireless federated learning (FL) framework and a resource optimization method are provided to resolve a problem that FL is not suitable for many hardware-constrained Internet of Things (IoT) devices with a small amount of computing resources. In the framework, users with sufficient computing resources upload locally trained model parameters to a base station, and users with limited computing resources only need to send training data to the base station. The base station performs data training and model aggregation to obtain a global model. In this way, the users with limited computing resources and the users with sufficient computing resources cooperatively train the global model. To improve a data transmission rate and reduce an aggregation error of FL, a non-convex optimization problem is constructed to jointly design user transmit power and a reception strategy of the base station, and solves the problem through a successive convex approximation (SCA) method.

Wireless federated learning framework and resource optimization method

A multi-functional reconfigurable intelligence surface (MF-RIS) integrating signal reflection, refraction and amplification and energy harvesting and an application thereof are provided. The MF-RIS can support wireless signal reflection, refraction and amplification and energy harvesting on one surface, to amplify, reflect, or refract a signal through harvested energy, and further enhance effective coverage of wireless signals. When a signal model of the MF-RIS constructed in the present disclosure is applied to a multi-user wireless network, a non-convex optimization problem of jointly designing operation modes and parameters that include BS transmit beamforming, and different components and a deployment position of the MF-RIS is constructed with an objective of maximizing a sum rate (SR) of a plurality of users in an MF-RIS-assisted non-orthogonal multiple access network. Then, an iterative optimization algorithm is designed to effectively solve the non-convex optimization problem, to maximize the SR of the plurality of users.

Multi-functional reconfigurable intelligence surface integrating signal reflection, refraction and amplification and energy harvesting and application thereof

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Beijing, China

Wanli Ni