The Channel Quality Indicator (CQI) is a fundamental component of channel state information (CSI) that enables adaptive modulation and coding by selecting the optimal modulation and coding scheme to meet a target block error rate. While AI-enabled CSI feedback has achieved significant advances, especially in precoding matrix index feedback, AI-based CQI feedback remains underexplored. Conventional subband-based CQI approaches, due to coarse granularity, often fail to capture fine frequency-selective variations and thus lead to suboptimal resource allocation. In this paper, we propose an AI-driven subcarrier-level CQI feedback framework tailored for 6G and NextG systems. First, we introduce CQInet, an autoencoder-based scheme that compresses per-subcarrier CQI at the user equipment and reconstructs it at the base station, significantly reducing feedback overhead without compromising CQI accuracy. Simulation results show that CQInet increases the effective data rate by 7.6% relative to traditional subband CQI under equivalent feedback overhead. Building on this, we develop SR-CQInet, which leverages super-resolution to infer fine-grained subcarrier CQI from sparsely reported CSI reference signals (CSI-RS). SR-CQInet reduces CSI-RS overhead to 3.5% of CQInet's requirements while maintaining comparable throughput. These results demonstrate that AI-driven subcarrier-level CQI feedback can substantially enhance spectral efficiency and reliability in future wireless networks.

Artificial intelligence (AI)-based channel state information (CSI) prediction, aimed at enhancing CSI accuracy and reducing overhead, has shown significant advancements over traditional model-based prediction methods. Despite these advantages, its practical deployment has been hindered by unreliable prediction performance due to AI instability. This study introduces a reliable AI-based CSI prediction framework by implementing a proxy-based performance monitoring mechanism. Specifically, we deploy a lightweight proxy at the user equipment (UE), trained via knowledge distillation to accommodate the UE’s limited capacities. This proxy mimics the output of the CSI prediction network at the base station (BS) side, enabling the UE to monitor the accuracy of the predicted CSI and prevent undesirable outcomes. To overcome the deployment challenges in operational systems, we detail the practical implementation procedures of our proposed method, covering both offline training and online operation phases. Simulation results show that our proxy-based monitor can achieve over 90% consistency with the CSI prediction network at the BS side and avoid over 85% of unsatisfactory prediction outcomes under various practical considerations, demonstrating remarkable generalization capabilities across different configurations.

Deep learning has been introduced to improve implicit channel state information (CSI) feedback, and it significantly outperforms codebook-based feedback methods used in existing systems. This study proposes a multi-domain correlation-aided implicit CSI feedback framework that uses deep learning. This framework retains the existing implicit feedback mechanism while introducing the aid of the multi-domain correlation property of CSI matrices to the feedback process for performance improvement. First, a time correlation-aided implicit feedback framework is proposed, where the correlation among adjacent CSI matrices is exploited to improve the CSI reconstruction accuracy. Second, to utilize the correlation between the uplink and downlink channel, the uplink channel magnitude is introduced into the CSI reconstruction process at the base station. Additionally, the framework combines the aid of time and bidirectional channel correlation properties to further enhance performance. Simulation results show that, with the aid of the multi-domain correlation property, the feedback overhead can be reduced by 75% and 85% compared to approaches without correlation utilization and Type II codebook, respectively.

Deep learning has been introduced to implicit channel state information (CSI) feedback and considerably outperforms codebook-based feedback methods adopted by existing systems. This work proposes a time correlation-aided deep learning-based implicit CSI feedback framework named Tbi-ImCsiNet. The long short-term memory network is introduced into the implicit CSI compression side and reconstruction side to extract and utilize the time correlation property among CSI matrices and improve the framework performance. Simulation results show that the proposed Tbi-ImCsiNet reduces approximately 58.3% of the feedback overhead compared with the method without time correlation utilization.