A Two‐Stage Genetic Algorithm for Joint Optimization of PAPR and PSLR for Precise Signal Reconstruction in OFDM‐ISAC Systems

ABSTRACT

In multi‐base integrated sensing and communication (ISAC) scenarios, weak signals, multipath fading and interference severely degrade radar signal reconstruction and waveform design, posing significant challenges to simultaneously achieving reliable communication and high‐accuracy sensing. To address these challenges, we propose a multi‐objective genetic algorithm‐based ISAC waveform design that jointly optimizes the peak‐to‐average power ratio (PAPR) and the peak sidelobe ratio (PSLR). To enhance the robustness of ISAC signal reconstruction under adverse channel conditions, we exploit the error‐correction capability of forward error correction (FEC) in orthogonal frequency division multiplexing (OFDM) communication systems, which enables reliable recovery of corrupted subcarriers. Building upon this insight, we design an OFDM‐ISAC waveform employing reserved subcarriers and FEC coding, and formulate a joint PAPR‐PSLR optimization model under Bose–Chaudhuri–Hocquenghem coding constraints. Owing to the inherent nonlinearity and nonconvexity of the resulting optimization problem, we further develop a two‐stage genetic algorithm‐based optimization framework to jointly optimize PAPR and PSLR. The proposed approach preserves the QAM characteristics of OFDM waveforms, enabling accurate signal reconstruction at the receiver even in the presence of interference, thereby improving sensing performance. Extensive simulation results validate the effectiveness of the proposed method.