Accurate and timely channel state information (CSI) is fundamental for efficient link adaptation. However, challenges such as channel aging, user mobility, and feedback delays significantly impact the performance of adaptive modulation and coding (AMC). This paper proposes and evaluates two CSI prediction frameworks applicable to both time division duplexing (TDD) and frequency division duplexing (FDD) systems. The proposed methods operate in the effective signal to interference plus noise ratio (SINR) domain to reduce complexity while preserving predictive accuracy. A comparative analysis is conducted between a classical Wiener filter and state-of-the-art deep learning frameworks based on gated recurrent units (GRUs), long short-term memory (LSTM) networks, and a delayed deep neural network (DNN). The evaluation considers the accuracy of the prediction in terms of mean squared error (MSE), the performance of the system, and the complexity of the implementation regarding floating point operations (FLOPs). Furthermore, we investigate the generalizability of both approaches under various propagation conditions. The simulation results show that the Wiener filter performs close to GRU in terms of MSE and throughput with lower computational complexity, provided that the second-order statistics of the channel are available. However, the GRU model exhibits enhanced generalization across different channel scenarios. These findings suggest that while learningbased solutions are well-suited for TDD systems where the base station (BS) handles the computation, the lower complexity of classical methods makes them a preferable choice for FDD setups, where prediction occurs at the power-constrained user equipment (UE).

翻译：准确且及时的通道状态信息（CSI）是实现高效链路自适应的基础。然而，通道老化、用户移动性和反馈延迟等挑战显著影响了自适应调制与编码（AMC）的性能。本文提出并评估了两种适用于时分双工（TDD）和频分双工（FDD）系统的CSI预测框架。所提方法在有效信干噪比（SINR）域中运行，以在保持预测精度的同时降低复杂度。研究对经典维纳滤波器与基于门控循环单元（GRU）、长短期记忆（LSTM）网络和延迟深度神经网络（DNN）的先进深度学习框架进行了比较分析。评估从均方误差（MSE）衡量的预测精度、系统性能以及浮点运算（FLOPs）相关的实现复杂度等方面展开。此外，我们探究了两种方法在不同传播条件下的泛化能力。仿真结果表明，在可获得通道二阶统计量的前提下，维纳滤波器在MSE和吞吐量方面表现接近GRU，且计算复杂度更低。然而，GRU模型在不同通道场景中展现出更强的泛化能力。这些发现表明，虽然基于学习的解决方案适用于基站（BS）负责计算的TDD系统，但经典方法的较低复杂度使其成为功率受限的用户设备（UE）执行预测的FDD场景中的更优选择。