Framework links random signals to practical integrated sensing and communication systems
A DSP Framework for ISAC with random signals: From Physical Transceiver to Periodic Models
Information Theory
Summary
Using random communication signals for both sending data and sensing the environment is tricky because the math models don't clearly connect to real digital hardware. The authors developed a detailed framework that shows how signals are shaped, transmitted, and processed digitally from the physical device to the models used for sensing and communication. They explain how to represent these signals and their processing so that both data decoding and target detection can be done efficiently. They tested their framework by estimating distances to targets and measuring communication errors over difficult channels.
What this means in practice
- •For wireless communication engineers: Design receivers that jointly decode data and detect targets using a consistent signal processing model connecting physical transmission and sensing.
- •For radar system developers: Implement sensing algorithms that interpret reflected communication signals with cyclic prefix and pulse shaping to estimate target ranges precisely.
Authors
Junjie Chen
Abstract
Communication-centric integrated sensing and communication~(ISAC) directly employs random data-bearing communication signals for both information transmission and environmental sensing. Existing studies generally describe pulse-shaped transmission in continuous time or directly adopt periodic discrete-time sensing models, leaving the digital signal processing~(DSP) operations connecting the physical transceiver and the periodic models unclear. This paper develops a DSP-oriented transceiver framework for single-antenna communication-centric ISAC. A common transmitter is constructed through modulation, cyclic-prefix~(CP) insertion, upsampling, and pulse shaping. For communication reception, the physical transceiver chain is reduced to a symbol-rate equivalent linear channel, which becomes circulant after CP removal and thereby supports frequency-domain equalization. For sensing reception, a high-rate reference waveform is extracted from the transmitted waveform and equivalently represented by circular pulse shaping. CP removal at the receiver then converts physical target delays into circular shifts of this reference waveform, leading to a periodic matched-filtering model and the corresponding range profile. Numerical results validate the developed framework through target range estimation and the symbol error rate performance of pulse-shaped CP-OFDM over frequency-selective Rayleigh fading channels.