Low-rank channel estimation for mm-Wave multiple antenna systems using joint spatio-temporal covariance matrix

Proceedings of: 2019 IEEE International Conference on Communications Workshops (ICC Workshops), 20-24 May 2019, Shanghai, China.Millimeter-Wave (mm-Wave) and very large multiple antenna systems (VLMAS) are two key technologies in the deployment of Fifth Generation (5G) mobile communication systems. In order to exploit all the benefits of VLMAS, spatial and temporal (ST) features must be estimated and exploited to compute the precoding/decoding matrices. In the literature, a practical channel estimation approach is proposed by assuming that the spatial features are completely unknown, leading to non-parametric estimation in which antenna array calibration is not required. Additionally, when the signal-to-noise ratio (SNR) is not so high, a low-rank (LR) version of the estimated channel is proposed that provides better performance than the full-rank (FR) one in terms of bias-variance trade-off in the mean squared error (MSE). However, previous work assumes that spatial and temporal characteristics of the channel can be estimated separately. Then, the performance is degraded in realistic channels. In this paper, we propose an alternative way to characterize the FR estimated channel using a joint ST covariance matrix, combined with a low-complexity semi-parametric spatial response and delay estimation technique. Moreover, we propose an automatic rank-selector (ARS) based on the MSE in order to provide the best LR channel estimation for each scenario. Numerical results show that the proposed technique outperforms existing approaches in the literature.This work has been partly funded by projects TERESA-ADA (TEC2017-90093-C3-2-R)(MINECO/AEI/FEDER, UE) and the fellowship of University Carlos III of Madrid under the program "Ayudas para la Movilidad del Programa Propio de Investigacion" granted to K. Chen-H

Channel estimation techniques for next generation mobile communication systems

Mención Internacional en el título de doctorWe are witnessing a revolution in wireless technology, where the society is demanding new services, such as smart cities, autonomous vehicles, augmented reality, etc. These challenging services not only are demanding an enormous increase of data rates in the range of 1000 times higher, but also they are real-time applications with an important delay constraint. Furthermore, an unprecedented number of different machine-type devices will be also connected to the network, known as Internet of Things (IoT), where they will be transmitting real-time measurements from different sensors. In this context, the Third Generation Partnership Project (3GPP) has already developed the new Fifth Generation (5G) of mobile communication systems, which should be capable of satisfying all the requirements. Hence, 5G will provide three key aspects, such as: enhanced mobile broad-band (eMBB) services, massive machine type communications (mMTC) and ultra reliable low latency communications (URLLC). In order to accomplish all the mentioned requirements, it is important to develop new key radio technologies capable of exploiting the wireless environment with a higher efficiency. Orthogonal frequency division multiplexing (OFDM) is the most widely used waveform by the industry, however, it also exhibits high side lobes reducing considerably the spectral efficiency. Therefore, filter-bank multi-carrier combined with offset quadrature amplitude modulation (FBMC-OQAM) is a waveform candidate to replace OFDM due to the fact that it provides extremely low out-ofband emissions (OBE). The traditional spectrum frequencies range is close to saturation, thus, there is a need to exploit higher bands, such as millimeter waves (mm-Wave), making possible the deployment of ultra broad-band services. However, the high path loss in these bands increases the blockage probability of the radio-link, forcing us to use massive multiple-input multiple-output (MIMO) systems in order to increase either the diversity or capacity of the overall link. All these emergent radio technologies can make 5G a reality. However, all their benefits can be only exploited under the knowledge and availability of the channel state information (CSI) in order to compensate the effects produced by the channel. The channel estimation process is a well known procedure in the area of signal processing for communications, where it is a challenging task due to the fact that we have to obtain a good estimator, maintaining at the same time the efficiency and reduced complexity of the system and obtaining the results as fast as possible. In FBMC-OQAM, there are several proposed channel estimation techniques, however, all of them required a high number of operations in order to deal with the self-interference produced by the prototype filter, hence, increasing the complexity. The existing channel estimation and equalization techniques for massive MIMO are in general too complex due to the large number of antennas, where we must estimate the channel response of each antenna of the array and perform some prohibitive matrix inversions to obtain the equalizers. Besides, for the particular case of mm-Wave, the existing techniques either do not adapt well to the dynamic ranges of signal-to-noise ratio (SNR) scenarios or they assume some approximations which reduce the quality of the estimator. In this thesis, we focus on the channel estimation for different emerging techniques that are capable of obtaining a better performance with a lower number of operations, suitable for low complexity devices and for URLLC. Firstly, we proposed new pilot sequences for FBMC-OQAM enabling the use of a simple averaging process in order to obtain the CSI. We show that our technique outperforms the existing ones in terms of complexity and performance. Secondly, we propose an alternative low-complexity way of computing the precoding/postcoding equalizer under the scenario of massive MIMO, keeping the quality of the estimator. Finally, we propose a new channel estimation technique for massive MIMO for mm-Wave, capable of adapting to very variable scenarios in terms of SNR and outperforming the existing techniques. We provide some analysis of the mean squared error (MSE) and complexity of each proposed technique. Furthermore, some numerical results are given in order to provide a better understanding of the problem and solutions.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Antonia María Tulino.- Secretario: Máximo Morales Céspedes.- Vocal: Octavia A. Dobr