In this paper we introduce a task-oriented communication design for split
learning (SL) over a communication channel. Our approach involves the
Expressive Neural Network (ENN), a novel neural network featuring adaptive
activation functions (AAF) based on the Discrete Cosine Transform (DCT). This
architecture does not only provide better learning capabilities, but also
facilitates data transmission using the Long Range (LoRa) modulation. The
frequency nature of LoRa is adequate for the communication side of the problem,
while allowing to construct the AAFs at the receiver. Additionally, we propose
orthogonal chirp division multiplexing (OCDM) for multiple access and a
modified modulation aimed at preserving communication bandwidth. Our
experimental results demonstrate the effectiveness of this scheme, achieving
high accuracy in challenging scenarios, including low signal to noise Ratio
(SNR) and absence of channel state information (CSI) for both additive white
Gaussian noise (AWGN) and Rayleigh fading channels.Comment: Accepted in 2023 IEEE International Workshop on Computational
Advances in Multi-Sensor Adaptive Processing (CAMSAP 2023