You Need Less Pilot and DCI: A Novel Detector for 5G NR System

Abstract

With the growing demand for large-scale interconnection of electronic devices, efficient utilization of limited spectrum resources has become increasingly important. Cognitive radio, which communicates by sensing the electromagnetic environment, significantly enhances spectrum utilization and offers a promising solution. Dynamic spectrum access and automatic modulation classification (AMC) are two key technologies in cognitive radio, attracting more attention. However, most existing AMC methods face limitations under flexible time-frequency resource allocation, particularly in identifying high-order modulation signals at low signal-to-noise ratios. To address this issue, we propose a multitask learning network called downlink control information network (DCI-Net), and apply it to the existing fifth-generation (5G) new radio (NR) communication system. The network aims to detect the effective time-frequency resources of the signal while identifying its modulation type. Furthermore, to further reduce the dependence of noncooperative communication receivers on pilot signals, we introduce a data-assisted channel estimation (CE) algorithm. This algorithm combines the identified modulation type with the equalized signal to generate pseudo-pilots, which are then used for signal demodulation in subsequent time slots. Notably, this method only requires the transmission of pilot signals in the first time slot, thereby reducing the receiver’s dependence on pilot signals. Simulation and over-the-air (OTA) test results demonstrate that the proposed multitask learning network significantly improves the accuracy of signal modulation recognition, while the data-assisted CE algorithm outperforms the least squares method in static environments.