Joint channel estimation and signal detection (JCESD) in wireless communication systems is a crucial and challenging task, especially since it inherently poses a nonlinear inverse problem. This challenge is further highlighted in low signal-to-noise ratio (SNR) scenarios, where traditional algorithms often perform poorly. Deep learning (DL) methods have been investigated, but concerns regarding computational expense and lack of validation in low-SNR settings remain. Hence, the development of a robust and low-complexity model that can deliver excellent performance across a wide range of SNRs is highly desirable. In this paper, we aim to establish a benchmark where traditional algorithms and DL methods are validated on different channel models, Doppler, and SNR settings. In particular, we propose a new DL model where the backbone network is formed by unrolling the iterative algorithm, and the hyperparameters are estimated by hypernetworks. Additionally, we adapt a lightweight DenseNet to the task of JCESD for comparison. We evaluate different methods in three aspects: generalization in terms of bit error rate (BER), robustness, and complexity. Our results indicate that DL approaches outperform traditional algorithms in the challenging low-SNR setting, while the iterative algorithm performs better in high-SNR settings. Furthermore, the iterative algorithm is more robust in the presence of carrier frequency offset, whereas DL methods excel when signals are corrupted by asymmetric Gaussian noise.

翻译：无线通信系统中的联合信道估计与信号检测（JCESD）是一项关键且具有挑战性的任务，尤其因其本质上构成非线性逆问题而加剧难度。在低信噪比场景下，这种挑战更为突出，传统算法往往表现不佳。尽管已对深度学习方法展开研究，但其计算开销大且缺乏低信噪比环境下的验证问题依然存在。因此，开发一种能在宽信噪比范围内兼具优异性能与低复杂度的鲁棒模型具有重要意义。本文旨在建立基准框架，在不同信道模型、多普勒频移及信噪比设置下验证传统算法与深度学习方法。具体而言，我们提出了一种新型深度学习模型，其主干网络通过展开迭代算法构建，超参数由超网络估计。此外，我们将轻量级DenseNet适配至JCESD任务作为对比。我们从误码率泛化性能、鲁棒性和复杂度三个维度评估不同方法。结果表明：在具有挑战性的低信噪比场景下，深度学习方法优于传统算法；而在高信噪比场景下，迭代算法表现更优。此外，存在载波频率偏移时迭代算法更具鲁棒性，而当信号受非对称高斯噪声污染时深度学习方法更具优势。