Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems (1708.08514v1)

Abstract: This article presents our initial results in deep learning for channel estimation and signal detection in orthogonal frequency-division multiplexing (OFDM). OFDM has been widely adopted in wireless broadband communications to combat frequency-selective fading in wireless channels. In this article, we take advantage of deep learning in handling wireless OFDM channels in an end-to-end approach. Different from existing OFDM receivers that first estimate CSI explicitly and then detect/recover the transmitted symbols with the estimated CSI, our deep learning based approach estimates CSI implicitly and recovers the transmitted symbols directly. To address channel distortion, a deep learning model is first trained offline using the data generated from the simulation based on the channel statistics and then used for recovering the online transmitted data directly. From our simulation results, the deep learning based approach has the ability to address channel distortions and detect the transmitted symbols with performance comparable to minimum mean-square error (MMSE) estimator. Furthermore, the deep learning based approach is more robust than conventional methods when fewer training pilots are used, the cyclic prefix (CP) is omitted, and nonlinear clipping noise is presented. In summary, deep learning is a promising tool for channel estimation and signal detection in wireless communications with complicated channel distortions and interferences.

• The paper introduces an end-to-end deep learning method that bypasses explicit channel state estimation to directly detect OFDM symbols.
• The model, based on a 5-layer DNN, demonstrates superior performance over traditional LS and MMSE estimators even with reduced pilot usage and without cyclic prefixes.
• Extensive simulations confirm the approach's robustness against non-ideal conditions, including clipping noise and mismatches in channel parameters.

Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems: An Overview

The paper "Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems" by Hao Ye, Geoffrey Ye Li, and Biing-Hwang Fred Juang offers an in-depth analysis of leveraging deep learning approaches to handle channel estimation and symbol detection in orthogonal frequency-division multiplexing (OFDM) systems. This manuscript signifies an important advancement from the conventional methods that require explicit channel state information (CSI) estimation, aiming instead for an end-to-end approach that implicitly estimates CSI and directly recovers transmitted symbols.

Methodology

The distinct approach proposed centers around training a deep neural network (DNN) offline using simulated data based on channel statistics. Once trained, this model is employed to recover transmitted symbols without separately estimating the CSI. This paradigm shift carries several benefits, such as reducing the reliance on a large number of pilots and improving robustness against non-ideal conditions like the absence of cyclic prefixes (CP) or the presence of nonlinear clipping noise.

System Architecture

The proposed system integrates a DNN within the OFDM framework, considering the wireless channel and modulation scheme as a black box. The architecture involves two primary stages:

1. Offline Training Stage: The DNN is trained using simulated data that typifies diverse channel conditions.
2. Online Deployment Stage: The trained model is used to recover transmitted data directly from received OFDM samples.

The DNN, once trained, takes inputs from received data blocks (both pilot and data blocks) and outputs the detected symbols. The model utilized in the paper consists of five layers with varying neuron configurations, leveraging both Relu and Sigmoid activation functions.

Numerical Results

The strength of the deep learning approach is validated through extensive simulations. The results demonstrate comparable, and in some cases superior, performance to traditional methods like least-square (LS) and minimum mean-square error (MMSE) estimators under various conditions.

1. Impact of Pilot Numbers: When trained with only 8 pilots (a reduction from the typical 64), the DNN showed robustness and did not exhibit saturation at high signal-to-noise ratios (SNRs), unlike the LS and MMSE methods.
2. Impact of CP Omission: The deep learning model maintained effective performance without the CP, which traditional methods struggled to manage.
3. Clipping and Filtering Distortions: The model outperformed MMSE in scenarios where the signal experienced nonlinear clipping noise, maintaining better bit-error rates (BERs) across different clipping ratios.
4. Combined Adverse Conditions: The DNN showed superior robustness when facing compiled adversities (limited pilots, no CP, and clipping noise) compared to MMSE, although there was a noticeable gap to ideal circumstances.

Robustness Analysis

The examination of robustness covered the DNN's ability to handle mismatches between training and deployment stages. The paper indicated that variations in channel model parameters during deployment did not significantly undermine detection performance, underscoring the generalization capability of deep learning models in this context.

Implications and Future Directions

The implications of this research are multifold. Practically, the introduction of deep learning models offers improved efficiency and robustness in wireless communication systems, especially under conditions where traditional methods falter. Theoretically, this paper indicates a promising future for integrating machine learning techniques into other domains of signal processing and communications, pushing boundaries of how systems can be optimized and managed.

Future work can be expected to focus on further validating these models with real-world data, enhancing model generalization, and minimizing computing overhead for real-time deployment in wireless systems.

This paper represents a significant contribution to the evolution of deep learning applications in communication systems, providing a foundation for ongoing and future research directed at improving wireless communication reliability and efficiency.