Beijing University of Posts and Telecommunications

Xiaojun Jing

Hao Zhang

Wei Yang

Ronghui Zhang

Hai Huang

Junsheng Mu

Zexuan Jing

Yanxi Xie

Yuanhao Cui

The present disclosure relates to a distributed trusted sensing method and system for an integrated communication, sensing, and computation network, and relates to the field of wireless sensing technologies. First, a global model and an initial global parameter are transmitted to each edge node. Each edge node performs local training by using local data, to obtain a local model parameter, broadcasts the local model parameter through a corresponding miner, then assigns a weight to each local model parameter, to calculate a global parameter, and updates the global parameter through aggregation iteration. In the present disclosure, calculation is performed by using computation resources and data resources of each distributed edge node, thus saving the overall communication and computation resources.

Distributed trusted sensing method and system for integrated communication, sensing and computation network

The embodiment of the present application provides a method and apparatus for spectrum prediction in a cognitive radio network. The method comprises: receiving a target access request from a secondary user; obtaining real-time occupancy information of bands through a subspace filtering-based spectrum sensing approach; updating a spectrum resource database of radio environment maps (REM) according to the real-time occupancy information of hands; establishing a prediction model for bands occupied by primary users according to the updated spectrum resource database of radio environment maps (REM); and allowing an access of the secondary user to a target band according to the prediction model for bands occupied by primary users. The method and apparatus provided by embodiments of the present application allow a secondary user to have a targeted spectrum access. The spectrum utilization efficiency of the radio network can thus be improved.

Frequency spectrum prediction method and apparatus for cognitive wireless network

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Beijing, China

Xiaojun Jing