Joint Sensing and Semantic Communications with Multi-Task Deep Learning
Yalin E. Sagduyu Tugba Erpek Aylin Yener Sennur Ulukus
This paper explores the integration of deep learning techniques for joint
sensing and communications, with an extension to semantic communications. The
integrated system comprises a transmitter and receiver operating over a
wireless channel, subject to noise and fading effects. The transmitter employs
a deep neural network, namely an encoder, for joint operations of source
coding, channel coding, and modulation, while the receiver utilizes another
deep neural network, namely a decoder, for joint operations of demodulation,
channel decoding, and source decoding to reconstruct the data samples. The
transmitted signal serves a dual purpose, supporting communication with the
receiver and enabling sensing. When a target is present, the reflected signal
is received, and another deep neural network decoder is utilized for sensing.
This decoder is responsible for detecting the target's presence and determining
its range. All these deep neural networks, including one encoder and two
decoders, undergo joint training through multi-task learning, considering data
and channel characteristics. This paper extends to incorporate semantic
communications by introducing an additional deep neural network, another
decoder at the receiver, operating as a task classifier. This decoder evaluates
the fidelity of label classification for received signals, enhancing the
integration of semantics within the communication process. The study presents
results based on using the CIFAR-10 as the input data and accounting for
channel effects like Additive White Gaussian Noise (AWGN) and Rayleigh fading.
The results underscore the effectiveness of multi-task deep learning in
achieving high-fidelity joint sensing and semantic communications.