Deep learning optimizes wireless signals for communication and sensing
Learning-Aided Short Code Design for ISAC based on MIMO-OFDM
Information Theory
Summary
Communications systems often need to send data reliably while also sensing the environment around them, like detecting targets. This paper shows how deep learning can design wireless signals that balance good data transmission with accurate sensing using a common multi-antenna, multi-frequency setup. The authors build special transformer-based encoders and decoders that directly create signals tuned for both goals, and their method outperforms traditional approaches. They also analyze how the designed signals change depending on whether the focus is more on communication or sensing.
What this means in practice
- •For wireless communication engineers: Design waveforms that jointly improve data transmission reliability and environment sensing accuracy in short-packet MIMO-OFDM systems.
- •For radar system developers: Tailor integrated signals that offer a practical balance between communication quality and target ranging precision using learning-based methods.
Authors
Mingcheng Nie, Shuangyang Li, Geng Wang, Peng Cheng, Shenghong Li, Chang Liu, Giuseppe Caire, Yonghui Li
Abstract
This paper proposes a deep learning (DL)-based coded waveform design for integrated sensing and communications (ISAC), enabling flexible trade-offs between communication reliability and ranging accuracy in short-block transmissions. The proposed scheme is built upon a practical multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) architecture, where the communication channel state information and the angles of the static targets are assumed available at the transmitter. A transformer-based transmitter encodes input information bits directly into ISAC transmit waveforms to jointly optimize the bit error rate (BER) performance and the delay modified Cramer-Rao bound (MCRB). A corresponding transformer-based receiver is adopted at the communication side to recover the transmitted information bits. We further examine the learned codewords for communication-oriented and sensing-oriented designs, revealing that a balanced ISAC waveform naturally exhibits an intermediate structure between these two extremes. Numerical results illustrate these codeword structures and demonstrate that the proposed design provides substantial trade-off gains over conventional schemes based on standard channel coding and modulation.