Rate-splitting multiple access (RSMA) has appeared as a powerful transmission and multiple access strategy for multi-user multi-antenna communications.
The proposed validation strategies, which are based on the dynamical systems properties of chaotic time series, are shown to outperform the state-of-the-art validation strategies.
Security is achieved thanks to physical layer security mechanisms, namely MIMO beamforming and Artificial Noise (AN).
For this purpose, fiber-optical sensors can be directly integrated into the needle tip.
Recent works propose neural network- (NN-) inspired analog-to-digital converters (NNADCs) and demonstrate their great potentials in many emerging applications.
Nevertheless, it is impractical to achieve a perfect acquisition of the local models in wireless communication due to noise, which also brings serious effects on federated learning.
We assume access to one or more, potentially black box, stochastic "oracle" predictive functions, each of which maps from input (e. g., protein sequences) design space to a distribution over a property of interest (e. g. protein fluorescence).
We consider the problem of optimally designing a body wireless sensor network, while taking into account the uncertainty of data generation of biosensors.