A Comparative Study of Deep Learning and Iterative Algorithms for Joint Channel Estimation and Signal Detection
arxiv(2023)
摘要
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.
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关键词
channel estimation,deep learning,signal detection,iterative algorithms
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