Massive MIMO Systems with Hardware Imperfections
Doctoral thesis, 2019

Recent years have witnessed an unprecedented explosion in mobile data traffic, due to the expansion of numerous types of wireless devices. Moreover, each device needs a high throughput to support demanding applications such as real-time video, movie streaming and games. Thus, future wireless systems have to satisfy three main requirements: 1) having a high throughput; 2) simultaneously serving many users; and 3) less energy consumption. Massive multiple-input multiple-output (MIMO) systems meet the aforementioned requirements and is nowadays a well-established technology which forms the backbone of the fifth-generation (5G) cellular communication systems. 

However, massive MIMO systems, i.e. employing hundreds or even thousands of antennas, will be a viable solution in the future only if low-cost and energy-efficient hardware is deployed. Unfortunately, low-cost, low-quality hardware is prone to hardware impairments such as in-phase and quadrature imbalance (IQI) and phase noise.  

Moreover, one of the dominant sources of power consumption in massive MIMO systems are the data converters at the BS. The baseband signal at each radio-frequency (RF) chain is generated by a pair of analog-to-digital converters (ADCs). The power consumption of these ADCs increases exponentially with the resolution (in bits) and linearly with the bandwidth. In conventional multi-antenna systems, each RF port is connected to a pair of high-resolution ADCs (e.g., 10-bit or more). For massive MIMO systems this would lead to prohibitively high-power consumption due to the large number of required ADCs. Hence, the ADC resolution must be limited to keep the power budget within tolerable levels.

In this thesis, we investigate the performance of massive MIMO systems in non-ideal hardware. We begin with by studying the impact of IQI on massive MIMO systems. Important insights are gained through the analysis of system performance indicators, such as channel estimation and achievable rates by deriving tractable approximations of the ergodic spectral efficiency.

First, a novel pilot-based joint estimator of the uplink augmented MIMO channel matrix and receiver IQI coefficients is described and then, a low-complexity IQI compensation scheme is proposed which is based on the receiver IQI coefficients' estimation and it is independent of the channel gain. 

Second, we investigate the impact of the transceiver IQI in massive MIMO considering a time division duplexing (TDD) system where we assume uplink/downlink channel reciprocity in the downlink precoding design. The uplink channel estimation accuracy and the achievable downlink rate of the regularized zero-forcing (RZF) and maximum ratio transmission (MRT) is studied when there is mismatch between the uplink and downlink channels. 

Finally, we analyze the quantization distortion in limited-precision ADCs in uplink massive MIMO systems whose channel state information (CSI) is not known a priori to transmitter and receiver. We show that even a small percentage of clipped samples at the receiver can downgrade considerably the systems performance and propose near-optimal low-complexity solutions to reconstruct the clipped signal.

achievable rate

analogue-to-digital converter

Massive MIMO

channel estimation

in-phase and quadrature imbalance

random matrix theory

EF, Hörsalsvägen 11, Chalmers
Opponent: Prof. Nikolaos Sidiropoulos, Department of Electrical and Computer Engineering, University of Virginia, USA


Nikolaos Kolomvakis

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Massive MIMO Systems with IQ Imbalance: Channel Estimation and Sum Rate Limits

IEEE Transactions on Communications,; Vol. 65(2017)p. 2382 - 2396

Journal article

IQ Imbalance in Multiuser Systems: Channel Estimation and Compensation

IEEE Transactions on Communications,; Vol. 64(2016)p. 3039-3051

Journal article

Kolomvakis, N, Ericsson, T, Coldrey, M, Viberg, M. Recontruction of Clipped Signals in Massive MIMO Systems

Subject Categories


Communication Systems

Signal Processing



Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4590



EF, Hörsalsvägen 11, Chalmers

Opponent: Prof. Nikolaos Sidiropoulos, Department of Electrical and Computer Engineering, University of Virginia, USA

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