Analysis of Massive MIMO With Hardware Impairments and Different Channel Models

Massive Multiple-Input Multiple-Output (MIMO) is foreseen to be one of the main technology components in next generation cellular communications (5G). In this paper, fundamental limits on the performance of downlink massive MIMO systems are investigated by means of simulations and analytical analysis. Signal-to-noise-and-interference ratio (SINR) and sum rate for a single-cell scenario multi-user MIMO are analyzed for different array sizes, channel models, and precoding schemes. The impact of hardware impairments on performance is also investigated. Simple approximations are derived that show explicitly how the number of antennas, number of served users, transmit power, and magnitude of hardware impairments affect performance.Comment: 5 pages, 5 figure

Space-Time Parameter Estimation in Radar Array Processing

This thesis is about estimating parameters using an array of spatially distributed sensors. The material is presented in the context of radar array processing, but the analysis could be of interest in a wide range of applications such as communications, sonar, radio astronomy, seismology, and medical diagnosis. The main theme of the thesis is to analyze the fundamental limitations on estimation performance in sensor array signal processing. To this end, lower bounds on the estimation accuracy as well as the performance of the maximum likelihood (ML) and weighted least-squares (WLS) estimators are studied. The focus in the first part of the thesis is on asymptotic analyses. It deals with the problem of estimating the directions of arrival (DOAs) and Doppler frequencies with a sensor array. This problem can also be viewed as a two-dimensional (2-D) frequency estimation problem. The ML and WLS estimators for this problem amount to multidimensional, highly non-linear optimization problems which would be expensive to solve in real-time in a radar system. Therefore, simplifications of this problem are of great interest. It is shown in this thesis that, under some circumstances, the 2-D problem decouples into 1-D problems. This means a dramatic reduction in computational complexity with insignificant loss of accuracy. The second part contains a performance analysis of the ML DOA estimator under conditions of low signal-to-noise ratio (SNR) and a small number of data samples. It is well known that the ML estimator exhibits a threshold effect, i.e. a rapid deterioration of estimation accuracy below a certain SNR. This effect is caused by outliers and is not captured by standard analysis tools. In this thesis, approximations to the mean square estimation error and probability of outlier are derived that can be used to predict the threshold region performance of the ML estimator with high accuracy. Moreover, these approximations alleviate the need for time-consuming computer simulations when evaluating the ML performance

Space-Time Parameter Estimation in Radar Array Processing

This thesis is about estimating parameters using an array of spatially distributed sensors. The material is presented in the context of radar array processing, but the analysis could be of interest in a wide range of applications such as communications, sonar, radio astronomy, seismology, and medical diagnosis. The main theme of the thesis is to analyze the fundamental limitations on estimation performance in sensor array signal processing. To this end, lower bounds on the estimation accuracy as well as the performance of the maximum likelihood (ML) and weighted least-squares (WLS) estimators are studied. The focus in the first part of the thesis is on asymptotic analyses. It deals with the problem of estimating the directions of arrival (DOAs) and Doppler frequencies with a sensor array. This problem can also be viewed as a two-dimensional (2-D) frequency estimation problem. The ML and WLS estimators for this problem amount to multidimensional, highly non-linear optimization problems which would be expensive to solve in real-time in a radar system. Therefore, simplifications of this problem are of great interest. It is shown in this thesis that, under some circumstances, the 2-D problem decouples into 1-D problems. This means a dramatic reduction in computational complexity with insignificant loss of accuracy. The second part contains a performance analysis of the ML DOA estimator under conditions of low signal-to-noise ratio (SNR) and a small number of data samples. It is well known that the ML estimator exhibits a threshold effect, i.e. a rapid deterioration of estimation accuracy below a certain SNR. This effect is caused by outliers and is not captured by standard analysis tools. In this thesis, approximations to the mean square estimation error and probability of outlier are derived that can be used to predict the threshold region performance of the ML estimator with high accuracy. Moreover, these approximations alleviate the need for time-consuming computer simulations when evaluating the ML performance

On the Impact of Hardware Impairments on Massive MIMO

Massive multi-user (MU) multiple-input multiple-output (MIMO) systems are one possible key technology for next generation wireless communication systems. Claims have been made that massive MU-MIMO will increase both the radiated energy efficiency as well as the sum-rate capacity by orders of magnitude, because of the high transmit directivity. However, due to the very large number of transceivers needed at each base-station (BS), a successful implementation of massive MU-MIMO will be contingent on of the availability of very cheap, compact and power-efficient radio and digital-processing hardware. This may in turn impair the quality of the modulated radio frequency (RF) signal due to an increased amount of power-amplifier distortion, phase-noise, and quantization noise. In this paper, we examine the effects of hardware impairments on a massive MU-MIMO single-cell system by means of theory and simulation. The simulations are performed using simplified, well-established statistical hardware impairment models as well as more sophisticated and realistic models based upon measurements and electromagnetic antenna array simulations.Comment: 7 pages, 9 figures, Accepted for presentation at Globe-Com workshop on Massive MIM

Performance Analysis of DOA Estimation in the Threshold Region

This paper presents a performance analysis of Maximum Likelihood (ML) Direction-Of-Arrival (DOA) estimation in the threshold region using a sensor array. This region is a range of Signal-to-Noise Ratios (SNR) close to the threshold, where the estimation error due to outliers starts to rise very rapidly as the SNR decreases. Approximate expressions for the probability of outlier and the mean square estimation error are derived for the case of a single signal in white Gaussian noise and a single snapshot. It is verified by simulations that the approximations predict the ML DOA estimation performance with high accuracy also at low SNR where the Cramer-Rao bound is far too optimistic

Threshold Region Performance of Deterministic Maximum Likelihood DOA Estimation of Multiple Sources

This paper presents an analysis of the threshold region performance of deterministic maximum likelihood direction of arrival estimation using sensor arrays. The threshold effect is caused by outliers and thus is not captured by standard analysis tools such as the Cramer-Rao bound and Taylor expansions, since these are local in nature. The work presented in this paper provides a global analysis that can be used to predict the threshold region performance of the maximum likelihood estimator with high accuracy. It extends previous results from the author on the single source to the multiple sources case

Threshold Region Performance of Deterministic Maximum Likelihood DOA Estimation of Multiple Sources

This paper presents an analysis of the threshold region performance of deterministic maximum likelihood direction of arrival estimation using sensor arrays. The threshold effect is caused by outliers and thus is not captured by standard analysis tools such as the Cramer-Rao bound and Taylor expansions, since these are local in nature. The work presented in this paper provides a global analysis that can be used to predict the threshold region performance of the maximum likelihood estimator with high accuracy. It extends previous results from the author on the single source to the multiple sources case

Optimization of Element Positions for Direction Finding with Sparse Arrays

Sparse arrays are attractive for Direction-Of-Arrival (DOA) estimation since they can provide accurate estimates at a low cost. A problem of great interest in this matter is to determine the element positions that yield the best DOA estimation performance. A major difficulty with this problem is to define a suitable performance measure to optimize. In this paper, a novel criterion is proposed for optimizing element positions. The ambiguity threshold of the Weiss-Weinstein Bound (WWB) is used to optimize the element positions of a sparse linear array. The array obtained from the optimization is compared with some other sparse array structures that have been proposed in the literature