A Novel Scheme for Separate Training of Deep Learning-Based CSI Feedback Autoencoders

In this paper, we introduce a novel scheme for the separate training of deep learning-based autoencoders used for Channel State Information (CSI) feedback. Our distinct training approach caters to multiple users and base stations, enabling independent and individualized local training. This ensures the more secure processing of data and algorithms, different from the commonly adopted joint training method. To maintain comparable performance with joint training, we present two distinct training methods: separate training decoder and separate training encoder. It’s noteworthy that conducting separate training for the encoder can pose additional challenges, due to its responsibility in acquiring a compressed representation of underlying data features. This complexity makes accommodating multiple pre-trained decoders for just one encoder a demanding task. To overcome this, we design an adaptation layer architecture that effectively minimizes performance losses. Moreover, the flexible training strategy empowers users and base stations to seamlessly incorporate distinct encoder and decoder structures into the system, significantly amplifying the system’s scalability. .

New Relationship for Estimation of Wave Overtopping on Vertical Walls

Soft computing tools in the form of combination of multiple nonlinear regression and M5′ model tree were used for estimation of overtopping rate at the vertical coastal structures. For reliable and precise estimation of overtopping rate, the experimental data available in the database CLASH were used. The dimensionless overtopping rate was estimated in terms of conventional dimensionless parameters including the relative crest freeboard Rc/Hs, seabed slope tanθ, deep water wave steepness S（om）, surf similarity ξ（om） and local relative water depth ht/Hs. The accuracy of the new model was compared with other existing models and also evaluated with some field measurements. The results indicated that the model presented in this paper is more accurate than other existing models. With statistical parameters, it is shown that the accuracy of predictions in the new model is better than that of other models.

Finite Convergence of On-line BP Neural Networks with Linearly Separable Training Patterns

In this paper we prove a finite convergence of online BP algorithms for nonlinear feedforward neural networks when the training patterns are linearly separable.