ComNet: Combination of Deep Learning and Expert Knowledge in OFDM Receivers (1810.09082v1)

Abstract: In this article, we propose a model-driven deep learning (DL) approach that combines DL with the expert knowledge to replace the existing orthogonal frequency-division multiplexing (OFDM) receiver in wireless communications. Different from the data-driven fully connected deep neural network (FC-DNN) method, we adopt the block-by-block signal processing method that divides the receiver into channel estimation subnet and signal detection subnet. Each subnet is constructed by a DNN and uses the existing simple and traditional solution as initialization. The proposed model-driven DL receiver offers more accurate channel estimation comparing with the linear minimum mean-squared error (LMMSE) method and exhibits higher data recovery accuracy comparing with the existing methods and FC-DNN. Simulation results further demonstrate the robustness of the proposed approach in terms of signal-to-noise ratio and its superiority to the FC-DNN approach in the computational complexities or the memory usage.

• The paper introduces ComNet, a hybrid OFDM receiver that combines deep neural networks with traditional signal processing for enhanced channel estimation and signal detection.
• It demonstrates lower bit-error rates and faster convergence than conventional methods and fully-connected DNN models, even under nonlinear conditions.
• The model-driven approach leverages expert initializations to optimize computational efficiency and reliability in wireless communications.

ComNet: Integrating Deep Learning with Expert Knowledge for OFDM Receivers

The paper entitled "ComNet: Combination of Deep Learning and Expert Knowledge in OFDM Receivers" introduces an innovative methodology for enhancing orthogonal frequency-division multiplexing (OFDM) receivers through a model-driven approach that intertwines deep learning (DL) with expert knowledge. This approach challenges the conventional reception models that treat OFDM receivers as black boxes and rely solely on data-driven deep learning solutions. The authors propose ComNet, a system that integrates traditional signal processing techniques and deep neural networks (DNNs), structured into two cascading subnets: channel estimation and signal detection.

ComNet Architecture and Implementation

The ComNet architecture is a departure from the fully-connected deep neural network (FC-DNN) methodologies previously employed, which face limitations in terms of explainability and computational efficiency. Instead of replacing the entire OFDM receiver with a deep learning model, ComNet utilizes domain knowledge by adopting traditional solutions as initialization points for deep learning networks. This hybrid approach is structured into two main subnets:

1. Channel Estimation Subnet (CE): This subnet begins with a least-square (LS) based estimation, which is refined using a DNN called LS_RefineNet. The CE subnet utilizes conventional methods to initialize its parameters, leading to improvements in channel estimation accuracy compared to linear minimum mean-squared error (LMMSE).
2. Signal Detection Subnet (SD): The SD subnet receives input from zero-forcing (ZF) detection and refines the estimates using either fully connected neural networks (FC-SD) or bi-directional long short-term memory (BiLSTM-SD) networks. These networks facilitate more robust data recovery in nonlinear scenarios and adapt through minimized weights alterations driven by specific cost functions.

Numerical Results and Comparative Analysis

The paper furnishes an extensive comparative analysis of ComNet against FC-DNN-based receivers and traditional LMMSE-MMSE methods across three scenarios: linear, cyclic prefix removal, and clipping. The robustness of ComNet, especially in nonlinear cases like CP removal and signal clipping where traditional methods falter, underscores its utility. The simulation results showcased superior bit-error rate (BER) performance for ComNet, notably achieving lower BERs even under signal-to-noise ratio (SNR) mismatches, indicating the stability of the model.

Importantly, ComNet exhibits faster convergence, requiring fewer computational epochs and parameters compared to FC-DNN, thereby illustrating advantages in computational efficiency and reduced memory usage.

Implications and Future Directions

The findings have implications for practical wireless communications, particularly in enhancing the reliability and efficiency of signal processing within OFDM systems. By leveraging expert domain knowledge within DL architectures, the model-driven approach elucidates more transparent and predictable system behavior.

Looking forward, this amalgamation of model-driven DL techniques in wireless communications underscores potential future applications in physical layers, offering deeper insights and novel feature creation opportunities that promise accelerated training processes and efficacious deployment. The paper highlights the prospect of adapting these principles more broadly, potentially informing enhanced methodologies in AI-driven communications technologies.

In conclusion, the ComNet framework signifies a meaningful advancement in the evolution of DL application within telecommunications, promising enhanced performance while maintaining the interpretability through the marriage of established communication protocols with advanced machine learning innovations.