RESPEC: A Super-Resolution Algorithm for Multi-Target Sensing in OFDM-ISAC Systems

The increasing demand for integrated sensing and communication (ISAC) in sixth generation (6 G) mobile networks calls for advancements in sensing parameter estimation technologies. Orthogonal frequency division multiplexing (OFDM) is the core technology of the fifth generation (5G) system with excellent resilience to multi-path fading and is gaining popularity as an ISAC waveform. However, the performance of traditional range/velocity (r/v) estimation algorithms, e.g., multiple signal classification (MUSIC), is restricted by the resolution defined by the bandwidth and symbol duration of OFDM, especially in multi-target scenarios. To overcome this issue, we proposed a REsidual network based SPEctra Calibration (RESPEC) algorithm. It improved the multi-target sensing accuracy by residual network that resists gradient vanishment to calibrate the spectra generated with the two-dimensional MUSIC (2D-MUSIC). Simulation results demonstrated the superiority in r/v estimation accuracy of RESPEC in multi-target scenarios, compared to the traditional 2D-MUSIC algorithm and other benchmarks. The results also exposed a trade-off between neural network depth and communication bandwidth for range estimation.