5G Positioning and Mapping with Diffuse Multipath

5G mmWave communication is useful for positioning due to the geometric connection between the propagation channel and the propagation environment. Channel estimation methods can exploit the resulting sparsity to estimate parameters(delay and angles) of each propagation path, which in turn can be exploited for positioning and mapping. When paths exhibit significant spread in either angle or delay, these methods breakdown or lead to significant biases. We present a novel tensor-based method for channel estimation that allows estimation of mmWave channel parameters in a non-parametric form. The method is able to accurately estimate the channel, even in the absence of a specular component. This in turn enables positioning and mapping using only diffuse multipath. Simulation results are provided to demonstrate the efficacy of the proposed approach

Spatial channel characterization for smart antenna solutions in FDD wireless networks

This paper introduces a novel metric for determining the spatial decorrelation between the up- and down-link wireless bearers in frequency division duplex (FDD) networks. This metric has direct relevance to smart or adaptive antenna array base-station deployments in cellular networks, which are known to offer capacity enhancement when compared to fixed coverage solutions. In particular, the results presented were obtained from field trial measurement campaigns for both urban and rural scenarios, with the observations having a direct impact on the choice of down-link beamforming architecture in FDD applications. Further, it is shown that significant spatial decorrelation can occur in urban deployments for bearer separations as small as 5 MHz. Results are presented in terms of both instantaneous characteristics as well as time averaged estimates, thus facilitating the appraisal of smart antenna solutions in both packet and circuit switched network

Scattering Center Extraction and Recognition Based on ESPRIT Algorithm

Inverse Synthetic Aperture Radar (ISAR) generates high quality radar images even in low visibility. And it provides important physical features for space target recognition and location. This thesis focuses on ISAR rapid imaging, scattering center information extraction, and target classification. Based on the principle of Fourier imaging, the backscattering field of radar target is obtained by physical optics (PO) algorithm, and the relation between scattering field and objective function is deduced. According to the resolution formula, the incident parameters of electromagnetic wave are set reasonably. The interpolation method is used to realize three-dimensional (3D) simulation of aircraft target, and the results are compared with direct imaging results. CLEAN algorithm extracts scattering center information effectively. But due to the limitation of resolution parameters, traditional imaging can’t meet the actual demand. Therefore, the super-resolution Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm is used to obtain spatial target location information. The signal subspace and noise subspace are orthogonal to each other. By combining spatial smoothing method with ESPRIT algorithm, the physical characteristics of geometric target scattering center are obtained accurately. In particular, the proposed method is validated on complex 3D aircraft targets and it proves that this method is applied to most scattering mechanisms. The distribution of scattering centers reflects the geometric information of the target. Therefore, the electromagnetic image to be recognized and ESPRIT image are matched by the domain matching method. And the classification results under different radii are obtained. In addition, because the neural network can extract rich image features, the improved ALEX network is used to classify and recognize target data processed by ESPRIT. It proves that ESPRIT algorithm can be used to expand the existing datasets and prepare for future identification of targets in real environments. Final a visual classification system is constructed to visually display the results