Reliable and fast channel estimation is crucial for next-generation wireless networks supporting a wide range of vehicular and low-latency services. Recently, deep learning (DL) based channel estimation has been explored as an efficient alternative to conventional least-square (LS) and linear minimum mean square error (LMMSE) approaches. Most of these DL approaches have not been realized on system-on-chip (SoC), and preliminary study shows that their complexity exceeds the complexity of the entire physical layer (PHY). The high latency of DL is another concern. This paper considers the design and implementation of deep neural network (DNN) augmented LS-based channel estimation (LSDNN) for preamble-based orthogonal frequency-division multiplexing (OFDM) physical layer (PHY) on SoC. We demonstrate the gain in performance compared to the conventional LS and LMMSE approaches. Via software-hardware co-design, word-length optimization, and reconfigurable architectures, we demonstrate the superiority of the LSDNN over the LS and LMMSE for a wide range of signal-to-noise ratio (SNR), number of pilots, preamble types, and wireless channels. Further, we evaluate the performance, power, and area (PPA) of the LS and LSDNN application-specific integrated circuit (ASIC) implementations in 45 nm technology. We demonstrate that word-length optimization can substantially improve PPA for the proposed architecture in ASIC implementations.

翻译：可靠且快速的信道估计对于支持广泛的车联网和低延迟服务的下一代无线网络至关重要。近年来，基于深度学习（DL）的信道估计已被探索作为传统最小二乘（LS）和线性最小均方误差（LMMSE）方法的高效替代方案。大多数此类DL方法尚未在片上系统（SoC）上实现，初步研究表明其复杂性甚至超过整个物理层（PHY）的复杂度。DL的高延迟是另一个问题。本文研究了在SoC上为基于前导的正交频分复用（OFDM）物理层（PHY）设计和实现深度神经网络（DNN）增强的LS信道估计（LSDNN）。我们展示了相比传统LS和LMMSE方法的性能增益。通过软硬件协同设计、字长优化和可重构架构，我们证明了LSDNN在不同信噪比（SNR）、导频数量、前导类型和无线信道条件下优于LS和LMMSE的性能。此外，我们评估了基于45纳米工艺的LS和LSDNN专用集成电路（ASIC）实现的性能、功耗和面积（PPA）。我们证明，在ASIC实现中，字长优化可以显著改善所提出架构的PPA指标。