Deep Neural Network (DNN)-based physical layer techniques are attracting considerable interest due to their potential to enhance communication systems. However, most studies in the physical layer have tended to focus on the application of DNN models to wireless communication problems but not to theoretically understand how does a DNN work in a communication system. In this paper, we aim to quantitatively analyze why DNNs can achieve comparable performance in the physical layer comparing with traditional techniques and their cost in terms of computational complexity. We further investigate and also experimentally validate how information is flown in a DNN-based communication system under the information theoretic concepts.

Recently, deep neural network (DNN)-based physical layer communication techniques have attracted considerable interest. Although their potential to enhance communication systems and superb performance have been validated by simulation experiments, little attention has been paid to the theoretical analysis. Specifically, most studies in the physical layer have tended to focus on the application of DNN models to wireless communication problems but not to theoretically understand how does a DNN work in a communication system. In this paper, we aim to quantitatively analyze why DNNs can achieve comparable performance in the physical layer comparing with traditional techniques, and also drive their cost in terms of computational complexity. To achieve this goal, we first analyze the encoding performance of a DNN-based transmitter and compare it to a traditional one. And then, we theoretically analyze the performance of DNN-based estimator and compare it with traditional estimators. Third, we investigate and validate how information is flown in a DNN-based communication system under the information theoretic concepts. Our analysis develops a concise way to open the “black box” of DNNs in physical layer communication, which can be applied to support the design of DNN-based intelligent communication techniques and help to provide explainable performance assessment.

Abstract In the linear minimum mean square error (LMMSE) estimation for orthogonal frequency division multiplexing (OFDM) systems, the channel correlation function (CCF) is required. Some methods have been proposed to calculate the CCF. Instead of providing a novel method to obtain the CCF, a scheme is developed for the estimator to select among different CCFs. In this paper, an enhanced LMMSE estimation is proposed that is able to select the best‐matched CCF within a candidate set. To this end, a parameter comparison scheme is proposed, in which the possible channel statistics for the LMMSE estimation can be evaluated using the sampled noise MSE. Analytical expressions are thus derived to indicate the accuracy of the proposed scheme. Furthermore, fuzzy bound is provided as the performance metric, which reflects the resolution of the parameter comparison scheme. As an example of application, the enhanced LMMSE method is used with the block pilot pattern in the OFDM systems, and the possible CCF candidates for typical scenarios are presented. The complexity of the estimator is also analyzed and a simplified parameter comparison algorithm is proposed to reduce the complexity. Finally, the theoretical analysis and performance comparison are demonstrated by simulation experiments.

In this paper, we devise a highly efficient machine learning-based channel estimation for orthogonal frequency division multiplexing (OFDM) systems, in which the training of the estimator is performed online. A simple learning module is employed for the proposed learning-based estimator. The training process is thus much faster and the required training data is reduced significantly. Besides, a training data construction approach utilizing least square (LS) estimation results is proposed so that the training data can be collected during the data transmission. The feasibility of this novel construction approach is verified by theoretical analysis and simulations. Based on this construction approach, two alternative training data generation schemes are proposed. One scheme transmits additional block pilot symbols to create training data, while the other scheme adopts a decision-directed method and does not require extra pilot overhead. Simulation results show the robustness of the proposed channel estimation method. Furthermore, the proposed method shows better adaptation to practical imperfections compared with the conventional minimum mean-square error (MMSE) channel estimation. It outperforms the existing machine learning-based channel estimation techniques under varying channel conditions.

Recently, machine learning-based channel estimation has attracted much attention. The performance of machine learning-based estimation has been validated by simulation experiments. However, little attention has been paid to the theoretical performance analysis. In this paper, we investigate the mean square error (MSE) performance of machine learning-based estimation. Hypothesis testing is employed to analyze its MSE upper bound. Furthermore, we build a statistical model for hypothesis testing, which holds when the linear learning module with a low input dimension is used in machine learning-based channel estimation, and derive a clear analytical relation between the size of the training data and performance. Then, we simulate the machine learning-based channel estimation in orthogonal frequency division multiplexing (OFDM) systems to verify our analysis results. Finally, the design considerations for the situation where only limited training data is available are discussed. In this situation, our analysis results can be applied to assess the performance and support the design of machine learning-based channel estimation.

This paper investigates the air-to-air channel model for Unmanned Aerial Vehicle (UAV) communication links. Although the 3-D ellipsoid model is widely used for characterization of wireless channels, present studies only include the angular distribution of scatterers while the power distribution is absent. Besides, all scatterers are assumed homogeneous in existing work, which is inaccurate for low-altitude UAVs. For above-mentioned problems, our work has two main contributions. First, a precise description of statistical characteristics of receiving power in both delay and direction of arrival (DoA) is provided based on the current 3-D ellipsoid model. Second, we extend the original model to a composite model including two independent ellipsoid models. Apart from surrounding scatterers, obtrusive objects like skyscrapers are specially considered in our model as far clusters. These far clusters could cause distinctive rays with excessive delay even from a distance and influence the statistical characteristics of the channel evidently. Numerical results show that the occurrence of far clusters increases the spreads of delay and DoA.