Doppler Spread Estimation Based on Machine Learning for an OFDM System

In this paper, we propose a Doppler spread estimation approach based on machine learning for an OFDM system. We present a carefully designed neural network architecture to achieve good performance in a mixed-channel scenario in which channel characteristic variables such as Rician K factor, azimuth angle of arrival (AOA) width, mean direction of azimuth AOA, and channel estimation errors are randomly generated. When preprocessing the channel state information (CSI) collected under the mixed-channel scenario, we propose averaged power spectral density (PSD) sequence as high-quality training data in machine learning for Doppler spread estimation. We detail intermediate mathematical derivatives of the machine learning process, making it easy to graft the derived results into other wireless communication technologies. Through simulation, we show that the machine learning approach using the averaged PSD sequence as training data outperforms the other machine learning approach using the channel frequency response (CFR) sequence as training data and two other existing Doppler estimation approaches.

Characterizing the Impact of Doppler Effects on Body-Centric LoRa Links with SDR

Long-range, low-power wireless technologies such as LoRa have been shown to exhibit excellent performance when applied in body-centric wireless applications. However, the robustness of LoRa technology to Doppler spread has recently been called into question by a number of researchers. This paper evaluates the impact of static and dynamic Doppler shifts on a simulated LoRa symbol detector and two types of simulated LoRa receivers. The results are interpreted specifically for body-centric applications and confirm that, in most application environments, pure Doppler effects are unlikely to severely disrupt wireless communication, confirming previous research, which stated that the link deteriorations observed in a number of practical LoRa measurement campaigns would mainly be caused by multipath fading effects. Yet, dynamic Doppler shifts, which occur as a result of the relative acceleration between communicating nodes, are also shown to contribute to link degradation. This is especially so for higher LoRa spreading factors and larger packet sizes.

Improving the Performance of MIMO Fading Channel Simulators Using New Parameterization Method: تحسين أداء نموذج محاكاة الخفوت للأقنية MIMO باستخدام خوارزميات جديدة لحساب المعاملات

The research aims to modeling and simulation of fading channels for providing necessary tool to develop communication systems. The modeling and simulation of MIMO fading channels is studied for both isotropic- and non-isotropic scattering cases for one-ring model, so in this context, three methods for MIMO simulation channel models are studied: Extended Method of Exact Doppler Spread (EMEDS), Modified Method of Equal Areas (MMEA) and Method Lp-Norm (LPNM). Three new methods are also suggested to design the simulation model of MIMO channel: Modified Extended Method of Exact Doppler Spread (MEMEDS), New Modified Method of Equal Areas (NMMEA) and Modified Lp-Norm method (MLPNM). The performance of each proposed method is compared with the original method by comparing the statistical properties ACF, 2D-CCF and CF of both reference and simulation models.

3D Air-Ground Channel Model Reconstruction Based on the Experimental Data and Q-D Method

This paper proposes to use the Q-D (Quasi-Deterministic) method for reconstructing the angular and frequency characteristics of the 3D Air-to-Ground (A-G) channel from the available experimental data. This method allows to expand the applicability of tapped delay line (TDL) channel models for performance investigation of the aviation radio systems with directional antennas and multi-element antenna arrays. The developed 3D A-G channel models also give opportunity to take into account Doppler spread impact on the communication system performance.