# Probabilistic Constellation Shaping for OFDM-Based ISAC Signaling

arXiv: https://arxiv.org/abs/2310.18090 · HTML: https://mlsanity.com/paper/2310.18090/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2023.

## Built on by (most cited first, 1 of 1)

- [Short-Length Code Designs for Integrated Sensing and Communications: A Deep Learning Approach](https://mlsanity.com/paper/2609.33605/) (2026)

## Abstract (arXiv)

Integrated Sensing and Communications (ISAC) has garnered significant attention as a promising technology for the upcoming sixth-generation wireless communication systems (6G). In pursuit of this goal, a common strategy is that a unified waveform, such as Orthogonal Frequency Division Multiplexing (OFDM), should serve dual-functional roles by enabling simultaneous sensing and communications (S&C) operations. However, the sensing performance of an OFDM communication signal is substantially affected by the randomness of the data symbols mapped from bit streams. Therefore, achieving a balance between preserving communication capability (i.e., the randomness) while improving sensing performance remains a challenging task. To cope with this issue, in this paper we analyze the ambiguity function of the OFDM communication signal modulated by random data. Subsequently, a probabilistic constellation shaping (PCS) method is proposed to devise the probability distributions of constellation points, which is able to strike a scalable S&C tradeoff of the random transmitted signal. Finally, the superiority of the proposed PCS method over conventional uniformly distributed constellations is validated through numerical simulations.