On the distribution of an effective channel estimator for multi-cell massive MIMO

Accurate channel estimation is of utmost importance for massive MIMO systems to provide significant improvements in spectral and energy efficiency. In this work, we present a study on the distribution of a simple but yet effective and practical channel estimator for multi-cell massive MIMO systems suffering from pilot-contamination. The proposed channel estimator performs well under moderate to aggressive pilot contamination scenarios without previous knowledge of the inter-cell large-scale channel coefficients and noise power, asymptotically approximating the performance of the linear MMSE estimator as the number of antennas increases. We prove that the distribution of the proposed channel estimator can be accurately approximated by the circularly-symmetric complex normal distribution, when the number of antennas, M, deployed at the base station is greater than 10

Channel estimation for massive MIMO TDD systems assuming pilot contamination and frequency selective fading

Channel estimation is crucial for massive multiple-input multiple-output (MIMO) systems to scale up multi-user MIMO, providing significant improvement in spectral and energy efficiency. In this paper, we present a simple and practical channel estimator for multipath multi-cell massive MIMO time division duplex systems with pilot contamination, which poses significant challenges to channel estimation. The proposed estimator addresses performance under moderate to strong pilot contamination without previous knowledge of the inter-cell large-scale fading coefficients and noise power. Additionally, we derive and assess an approximate analytical mean square error (MSE) expression for the proposed channel estimator. We show through simulations that the proposed estimator performs asymptotically as well as the minimum MSE estimator with respect to the number of antennas and multipath coefficients

Channel estimation for massive MIMO TDD mystems assuming pilot contamination and frequency selective fading

Channel estimation is crucial for massive multiple-input multiple-output (MIMO) systems to scale up multi-user MIMO, providing significant improvement in spectral and energy efficiency. In this paper, we present a simple and practical channel estimator for multipath multi-cell massive MIMO time division duplex systems with pilot contamination, which poses significant challenges to channel estimation. The proposed estimator addresses performance under moderate to strong pilot contamination without previous knowledge of the inter-cell large-scale fading coefficients and noise power. Additionally, we derive and assess an approximate analytical mean square error (MSE) expression for the proposed channel estimator. We show through simulations that the proposed estimator performs asymptotically as well as the minimum MSE estimator with respect to the number of antennas and multipath coefficients5177331774

Channel estimation for massive MIMO TDD systems assuming pilot contamination and flat fading

Channel estimation is crucial for massive massive multiple-input multiple-output (MIMO) systems to scale up multi-user (MU) MIMO, providing great improvement in spectral and energy efficiency. This paper presents a simple and practical channel estimator for multi-cell MU massive MIMO time division duplex (TDD) systems with pilot contamination in flat Rayleigh fading channels, i.e., the gains of the channels follow the Rayleigh distribution. We also assume uncorrelated antennas. The proposed estimator addresses performance under moderate to strong pilot contamination without previous knowledge of the cross-cell large-scale channel coefficients. This estimator performs asymptotically as well as the minimum mean square error (MMSE) estimator with respect to the number of antennas. An approximate analytical mean square error (MSE) expression is also derived for the proposed estimator

Channel estimation for massive MIMO TDD systems assuming pilot contamination and flat fading

Channel estimation is crucial for massive massive multiple-input multiple-output (MIMO) systems to scale up multi-user (MU) MIMO, providing great improvement in spectral and energy efficiency. This paper presents a simple and practical channel estimator for multi-cell MU massive MIMO time division duplex (TDD) systems with pilot contamination in flat Rayleigh fading channels, i.e., the gains of the channels follow the Rayleigh distribution. We also assume uncorrelated antennas. The proposed estimator addresses performance under moderate to strong pilot contamination without previous knowledge of the cross-cell large-scale channel coefficients. This estimator performs asymptotically as well as the minimum mean square error (MMSE) estimator with respect to the number of antennas. An approximate analytical mean square error (MSE) expression is also derived for the proposed estimator201

Semi-blind Channel Estimation and Data Detection for Multi-cell Massive MIMO Systems on Time-Varying Channels

We study the problem of semi-blind channel estimation and symbol detection in the uplink of multi-cell massive MIMO systems with spatially correlated time-varying channels. An algorithm based on expectation propagation (EP) is developed to iteratively approximate the joint a posteriori distribution of the unknown channel matrix and the transmitted data symbols with a distribution from an exponential family. This distribution is then used for direct estimation of the channel matrix and detection of data symbols. A modified version of the popular Kalman filtering algorithm referred to as KF-M emerges from our EP derivation and it is used to initialize the EP-based algorithm. Performance of the Kalman smoothing algorithm followed by KF-M is also examined. Simulation results demonstrate that channel estimation error and the symbol error rate (SER) of the semi-blind KF-M, KS-M, and EP-based algorithms improve with the increase in the number of base station antennas and the length of the transmitted frame. It is shown that the EP-based algorithm significantly outperforms KF-M and KS-M algorithms in channel estimation and symbol detection. Finally, our results show that when applied to time-varying channels, these algorithms outperform the algorithms that are developed for block-fading channel models.Comment: 28 pages, 13 figures, Submitted to IEEE Trans. on Vehicular Technolog

Review of Recent Trends

This work was partially supported by the European Regional Development Fund (FEDER), through the Regional Operational Programme of Centre (CENTRO 2020) of the Portugal 2020 framework, through projects SOCA (CENTRO-01-0145-FEDER-000010) and ORCIP (CENTRO-01-0145-FEDER-022141). Fernando P. Guiomar acknowledges a fellowship from “la Caixa” Foundation (ID100010434), code LCF/BQ/PR20/11770015. Houda Harkat acknowledges the financial support of the Programmatic Financing of the CTS R&D Unit (UIDP/00066/2020).MIMO-OFDM is a key technology and a strong candidate for 5G telecommunication systems. In the literature, there is no convenient survey study that rounds up all the necessary points to be investigated concerning such systems. The current deeper review paper inspects and interprets the state of the art and addresses several research axes related to MIMO-OFDM systems. Two topics have received special attention: MIMO waveforms and MIMO-OFDM channel estimation. The existing MIMO hardware and software innovations, in addition to the MIMO-OFDM equalization techniques, are discussed concisely. In the literature, only a few authors have discussed the MIMO channel estimation and modeling problems for a variety of MIMO systems. However, to the best of our knowledge, there has been until now no review paper specifically discussing the recent works concerning channel estimation and the equalization process for MIMO-OFDM systems. Hence, the current work focuses on analyzing the recently used algorithms in the field, which could be a rich reference for researchers. Moreover, some research perspectives are identified.publishersversionpublishe

Análise de desempenho de receptores baseados em reticulados para MIMO e fastICA em sistemas MIMO cego massivos

Orientador: Gustavo FraidenraichTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Estatísticas da taxa-soma dos decodificadores Integer Forcing (IF) e outros decodificadores baseados em reticulados para sistemas de Múltipla Entrada e Múltipla Saída (MIMO) são analisadas. Duas aproximações para a taxa-soma de decodificadores lineares são derivadas. A primeira aproximação é baseada no algoritimo Gauss-Lagrange para sistemas com duas antenas no transmissor e receptor (arranjo 2x2) e canais descorrelacionados. A segunda aproximação considera um sistema com um arranjo nxn de antenas, para o caso correlacionado e descorrelacionado e é baseado no segundo teorema de Minkowiski O desempenho de decodificadores IF e Compute and Forward Transform (CFT) são analisados na presença de erro de estimação de canal. Uma aproximação para a taxa-soma média na presença de erros de estimação de canal e canais com realização fixa é derivada. Uma aproximação para a taxa-soma ergódica dos decodificadores IF na presença de canais correlacionados e descorrelacionados também é derivada. Decodificadores lineares IF atraíram atenção significativa devido ao seu potencial de atingir melhor desempenho do que outros decodificadores lineares, especialmente quando as matrizes de canal são aproximadamente singulares. No entanto, uma análise mais profunda de seu desempenho na presença de canais não determinísticos é necessária para que se possa quantificar sua vantagem em relação a decodificadores lineares clássicos e para que se possa corretamente projetar sistemas baseados nestes decodificadoresAbstract: The statistics of the sum-rate of Integer Forcing (IF) and other lattice-based Multiple Input Multiple Output (MIMO) systems are analyzed. Two approximations to the achievable sum-rate of the IF linear receiver and their respective analytical probability density functions (PDF) are derived. The first approximation is based on the Gauss-Lagrange algorithm for systems with two antennas at the transmitter and receiver (2x2 arrays) and uncorrelated channels. The second approximation considers an nxn array for both correlated and uncorrelated channels and its derivation is based on Minkowiski's second theorem. The performance of IF and Compute and Forward Transform (CFT) receivers is also analyzed under the presence of channel estimation errors. An approximation to their average sum-rate in the presence of these errors for fixed channel realizations is derived. An approximation to the Ergodic IF sum-rate for correlated and uncorrelated channels is also derived. IF linear receiver has attracted significant attention recently due to their potential to perform better than other linear receivers, especially in the presence of channel matrices that are close to singular. However, a more in-depth analysis of its performance in the presence of non-deterministic channels is necessary in order to quantify its advantage over classical linear receivers and to correctly design systems that rely on these decoders. Another contribution of this work involves blind decoding in Massive MIMO systems. We propose a variation to the fast Independent Component Analysis (fastICA) which takes into consideration the shape of the constellations to obtain better performanceDoutoradoTelecomunicações e TelemáticaDoutor em Engenharia ElétricaCAPE

Channel estimation techniques for filter bank multicarrier based transceivers for next generation of wireless networks

A dissertation submitted to Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfillment of the requirements for the degree of Master of Science in Engineering (Electrical and Information Engineering), August 2017The fourth generation (4G) of wireless communication system is designed based on the principles of cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) where the cyclic prefix (CP) is used to combat inter-symbol interference (ISI) and inter-carrier interference (ICI) in order to achieve higher data rates in comparison to the previous generations of wireless networks. Various filter bank multicarrier systems have been considered as potential waveforms for the fast emerging next generation (xG) of wireless networks (especially the fifth generation (5G) networks). Some examples of the considered waveforms are orthogonal frequency division multiplexing with offset quadrature amplitude modulation based filter bank, universal filtered multicarrier (UFMC), bi-orthogonal frequency division multiplexing (BFDM) and generalized frequency division multiplexing (GFDM). In perfect reconstruction (PR) or near perfect reconstruction (NPR) filter bank designs, these aforementioned FBMC waveforms adopt the use of well-designed prototype filters (which are used for designing the synthesis and analysis filter banks) so as to either replace or minimize the CP usage of the 4G networks in order to provide higher spectral efficiencies for the overall increment in data rates. The accurate designing of the FIR low-pass prototype filter in NPR filter banks results in minimal signal distortions thus, making the analysis filter bank a time-reversed version of the corresponding synthesis filter bank. However, in non-perfect reconstruction (Non-PR) the analysis filter bank is not directly a time-reversed version of the corresponding synthesis filter bank as the prototype filter impulse response for this system is formulated (in this dissertation) by the introduction of randomly generated errors. Hence, aliasing and amplitude distortions are more prominent for Non-PR. Channel estimation (CE) is used to predict the behaviour of the frequency selective channel and is usually adopted to ensure excellent reconstruction of the transmitted symbols. These techniques can be broadly classified as pilot based, semi-blind and blind channel estimation schemes. In this dissertation, two linear pilot based CE techniques namely the least square (LS) and linear minimum mean square error (LMMSE), and three adaptive channel estimation schemes namely least mean square (LMS), normalized least mean square (NLMS) and recursive least square (RLS) are presented, analyzed and documented. These are implemented while exploiting the near orthogonality properties of offset quadrature amplitude modulation (OQAM) to mitigate the effects of interference for two filter bank waveforms (i.e. OFDM/OQAM and GFDM/OQAM) for the next generation of wireless networks assuming conditions of both NPR and Non-PR in slow and fast frequency selective Rayleigh fading channel. Results obtained from the computer simulations carried out showed that the channel estimation schemes performed better in an NPR filter bank system as compared with Non-PR filter banks. The low performance of Non-PR system is due to the amplitude distortion and aliasing introduced from the random errors generated in the system that is used to design its prototype filters. It can be concluded that RLS, NLMS, LMS, LMMSE and LS channel estimation schemes offered the best normalized mean square error (NMSE) and bit error rate (BER) performances (in decreasing order) for both waveforms assuming both NPR and Non-PR filter banks. Keywords: Channel estimation, Filter bank, OFDM/OQAM, GFDM/OQAM, NPR, Non-PR, 5G, Frequency selective channel.CK201

Channel Estimation in Multi-user Massive MIMO Systems by Expectation Propagation based Algorithms

Massive multiple input multiple output (MIMO) technology uses large antenna arrays with tens or hundreds of antennas at the base station (BS) to achieve high spectral efficiency, high diversity, and high capacity. These benefits, however, rely on obtaining accurate channel state information (CSI) at the receiver for both uplink and downlink channels. Traditionally, pilot sequences are transmitted and used at the receiver to estimate the CSI. Since the length of the pilot sequences scale with the number of transmit antennas, for massive MIMO systems downlink channel estimation requires long pilot sequences resulting in reduced spectral efficiency and the so-called pilot contamination due to sharing of the pilots in adjacent cells. In this dissertation we first review the problem of channel estimation in massive MIMO systems. Next, we study the problem of semi-blind channel estimation in the uplink in the case of spatially correlated time-varying channels. The proposed method uses the transmitted data symbols as virtual pilots to enhance channel estimation. An expectation propagation (EP) algorithm is developed to iteratively approximate the joint a posterior distribution of the unknown channel matrix and the transmitted data symbols with a distribution from an exponential family. The distribution is then used for direct estimation of the channel matrix and detection of the data symbols. A modified version of Kalman filtering algorithm referred to as KF-M emerges from our EP derivation and it is used to initialize our algorithm. Simulation results demonstrate that channel estimation error and the symbol error rate of the proposed algorithm improve with the increase in the number of BS antennas or the number of data symbols in the transmitted frame. Moreover, the proposed algorithms can mitigate the effects of pilot contamination as well as time-variations of the channel. Next, we study the problem of downlink channel estimation in multi-user massive MIMO systems. Our approach is based on Bayesian compressive sensing in which the clustered sparse structure of the channel in the angular domain is exploited to reduce the pilot overhead. To capture the clustered structure, we employ a conditionally independent identically distributed Bernoulli-Gaussian prior on the sparse vector representing the channel, and a Markov prior on its support vector. An EP algorithm is developed to approximate the intractable joint distribution on the sparse vector and its support with a distribution from an exponential family. This distribution is then used for direct estimation of the channel. The EP algorithm requires the model parameters which are unknown. We estimate these parameters using the expectation maximization (EM) algorithm. Simulation results show that the proposed combination of EM and EP referred to as EM-EP algorithm outperforms several recently-proposed algorithms in the literature