Shahram Zarei

Low-complexity linear precoding for downlink large-scale MIMO systems

In this work, we present a low-complexity linear precoding scheme for downlink large-scale multiple-input multiple-output (MIMO) systems. The proposed scheme can achieve near minimum mean square error (MMSE) precoding performance in terms of the sum rate and is based on a matrix polynomial instead of matrix inversion. Simulation results show that matrix polynomials consisting of only a few terms are sufficient to closely approach the sum rate of the classical MMSE precoder and to perform orders of magnitude better than the simple conjugate beamforming (BF) precoder. We derive exact expressions for the computational complexity of the proposed scheme in terms of the number of additions and multiplications and compare it to the complexity of the BF and MMSE precoders. Our complexity analysis shows that for large number of base station antennas N compared to the number of generated transmit symbols τ per channel estimate and large number of users K, the proposed polynomial precoder has a lower complexity than the classical MMSE precoder.

I/Q Imbalance Aware Widely-Linear Receiver for Uplink Multi-Cell Massive MIMO Systems: Design and Sum Rate Analysis

In-phase/quadrature-phase imbalance (IQI) is one of the most important hardware impairments in communication systems. It arises in the analogue parts of direct conversion transceivers and can cause severe performance losses. In this paper, IQI aware widely-linear (WL) channel estimation and data detection schemes for uplink multicell massive multiple-input multiple-output (MIMO) systems are proposed. The resulting receiver is a WL extension of the minimum mean-square-error (MMSE) receiver and jointly mitigates multiuser interference and IQI by processing the real and the imaginary parts of the received signal separately. Thereby, the IQI arising at both the base station and the user terminals is taken into account. The considered channel state information acquisition model includes the effect of pilot contamination, which is caused by the reuse of the same training sequences in neighbouring cells. We apply results from random matrix theory to derive analytical expressions for the asymptotic achievable sum rates of the proposed IQI aware and conventional IQI unaware (IQU) receivers in the large system limit. Our simulation and analytical results show that the performance of the proposed IQI aware WLMMSE (IQA-WLMMSE) receiver in a system with IQI is close to that of the MMSE receiver in an ideal system without IQI.

I/Q imbalance aware widely-linear precoding for downlink massive MIMO systems

In-phase/quadrature-phase (I/Q) imbalance is one of the most important hardware impairments in communication systems. It arises in the analog parts of direct conversion radio frequency (RF) transceivers and can cause large performance losses. In this paper, the impact of I/Q imbalance on the sum rate performance of downlink massive multiple-input multiple-output (MIMO) systems is analyzed and I/Q imbalance aware widely-linear precoding is considered. The proposed precoder is a widely-linear (WL) extension of the regularized zero-forcing (RZF) precoder and jointly mitigates multi-user interference and I/Q imbalance by processing the real and the imaginary parts of the transmit signal separately. Results from random matrix theory are used to derive analytical expressions for the achievable sum rate of the proposed precoder. Our analytical results show that if the number of base station antennas is much larger than the number of users, the proposed I/Q imbalance aware WL-RZF precoder achieves the same sum rate as the RZF precoder in an ideal system without I/Q imbalance. We also provide an analytical expression for the sum rate loss of the I/Q imbalance unaware RZF precoder compared to the ideal case without I/Q imbalance.

A low-complexity linear precoding and power allocation scheme for downlink massive MIMO systems

In this paper, we present a low-complexity linear precoding and power allocation scheme for downlink massive multiple-input multiple-output (MIMO) systems. The optimization criteria adopted for the proposed precoding and power allocation scheme are maximization of the sum rate and maximization of the minimum user rate, respectively. The presented precoder is based on a matrix polynomial and can closely approach the sum rate and the minimum user rate of the optimal regularized zero forcing (RZF) precoder, which requires a matrix inversion, and performs substantially better than the simple conjugate beamforming (BF) precoder. Our simulation results show that the proposed precoding scheme with a low order matrix polynomial can approach the sum rate and the minimum user rate of the optimal RZF precoder. We also present analytical results for the asymptotic sum rate and the asymptotic minimum user rate.

Low-Complexity Widely-Linear Precoding for Downlink Large-Scale MU-MISO Systems

In this letter, we present a widely-linear minimum mean square error (WL-MMSE) precoding scheme employing real-valued transmit symbols for downlink large-scale multi-user multiple-input single-output (MU-MISO) systems. In contrast to the existing WL-MMSE transceivers for single-user multiple-input multiple-output (SU-MIMO) systems, which use both WL precoders and WL detectors, the proposed scheme uses WL precoding only and simple conventional detection at the user terminals (UTs). Moreover, to avoid the computational complexity associated with inversion of large matrices, we modify the WL-MMSE precoder using polynomial expansion (PE). Our simulation results show that in overloaded systems, where the number of UTs is larger than the number of base station antennas, the proposed PE WL-MMSE precoder with only a few terms in the matrix polynomial achieves a substantially higher sum rate than systems employing conventional MMSE precoding. Hence, more UTs sharing the same time/frequency resources can be served in a cell. We validate our simulation results with an analytical expression for the asymptotic sum rate which is obtained by using results from random matrix theory.

A System Concept for Online Calibration of Massive MIMO Transceiver Arrays for Communication and Localization

Massive multiple-input multiple-output (MIMO) techniques are being considered for the fifth generation (5G) mobile communication systems in order to deliver high multiplexing gain. However, hardware impairments like quadrature imbalance in mixers violate the requirement for channel reciprocity and may change, e.g., with temperature or while aging. In addition, advanced wireless localization techniques and the generation of predefined beam patterns require knowledge about all antenna phase center positions and the time and phase delay of all transmit and receive channels. Thus, an efficient online compensation method is needed that scales well for very large numbers of transceiver modules. We propose to extend the transmitter with a small measurement feature at the transmitter output based on one uncalibrated power detector per module as well as a single, external four-element backscatter array for the entire matrix. These enhancements facilitate a fast and efficient iterative calibration, which recognizes and mitigates all major error sources. Beside optimal communication throughput and energy efficiency, it thereby brings localization capabilities to mobile networks as an additional major benefit. For verification, a system of multiple cost-efficient 5.8-GHz massive MIMO transceivers with 150-MHz bandwidth and a backscatter array has been implemented. Measurement results demonstrate the capability of the proposed concept to efficiently compensate major error sources as well as its robustness.

Max–Min Multicell-Aware Precoding and Power Allocation for Downlink Massive MIMO Systems

We propose a max–min multicell-aware regularized zero-forcing (MCA-RZF) precoding and power allocation scheme for downlink multicell massive multiple-input multiple-output systems. A general correlated channel model is considered, and the adopted channel state information (CSI) acquisition model includes the effects of estimation errors and pilot contamination. We use results from random matrix theory to derive deterministic equivalents for the proposed max–min power allocation in the large system limit, which solely depend on statistical CSI, but not on individual channel realizations. Our numerical results show that the proposed max–min MCA-RZF precoder achieves a substantially higher network-wide minimum rate than the MCA-RZF and the conventional RZF precoders with uniform power allocation, respectively, as well as the conventional RZF precoder with max–min power allocation.

Uplink/downlink duality in massive MIMO systems with hardware impairments

In this paper, we provide a new framework for the uplink/downlink duality in single-cell massive multiple-input multiple-output (MIMO) systems suffering from residual hardware impairments (HWIs) at the base station and the user terminals. Using the proposed duality, complex downlink optimization problems can be converted to equivalent dual uplink problems, which are easier to solve. As an example, we apply the proposed uplink/downlink duality to derive an HWI aware minimum mean square error (HWIA-MMSE) precoder, which minimizes the sum mean square error under a sum power constraint in a single-cell massive MIMO system with residual HWIs. Thereby, we use results from random matrix theory to derive an asymptotic expression for the downlink power allocation for large numbers of antennas, which only depends on the channel statistics and not on the individual channel realizations. Analytical results for the asymptotic achievable sum rate of the proposed HWIA-MMSE precoder for a large number of BS antennas are also provided. Our simulation and analytical results show that the proposed HWIA-MMSE precoder achieves a higher sum rate than the conventional regularized zero-forcing precoder for moderately large numbers of base station antennas.

I/Q imbalance aware widely-linear channel estimation and detection for uplink massive MIMO systems

In this paper, we propose an in-phase/quadrature-phase imbalance (IQI) aware widely-linear minimum mean square error (IQA-WLMMSE) receiver for uplink single-cell massive multiple-input multiple-output (MIMO) systems with imperfect channel state information (CSI). In our system model, IQI is present at the base station and user terminals and affects both channel estimation and data detection. The proposed IQA-WLMMSE receiver processes the real and imaginary parts of the received signal separately and mitigates both multiple-access interference (MAI) and IQI. Our analytical and simulation results show that the proposed IQA-WLMMSE receiver performs significantly better than the conventional IQI unaware MMSE (IQU-MMSE) receiver, and its sum rate closely approaches the sum rate of the ideal system with no IQI.

Polynomial-Expansion Multi-Cell Aware Detector for Uplink Massive MIMO Systems with Imperfect CSI

In this paper, we propose a multi-cell aware (MCA) detector for uplink multi-cell massive multiple-input multiple-output (MIMO) systems. The proposed detector exploits knowledge of the channel statistics but data exchange between different base stations over backhaul links is not required. A correlated channel model is considered and the adopted channel state information (CSI) acquisition model includes the effects of estimation errors and pilot contamination. In contrast to the conventional minimum mean square error (MMSE) detector, which mitigates only the multiple-access interference (MAI) in the target cell, the proposed detector takes the interference from neighboring cells and pilot contamination into account and therefore achieves substantially higher sum rates. Moreover, in order to reduce the computational complexity, the matrix inversion required for the MCA detector is approximated by a matrix polynomial leading to a new polynomial-expansion MCA (PEMCA) detector. Using results from random matrix theory, we derive closed-form expressions for the optimal coefficients of the matrix polynomial, which only depend on the channel statistics but not on the channel realizations. Our simulation results show that the PEMCA detector with only a few terms in the matrix polynomial achieves a considerably higher sum rate than the conventional MMSE detector while having a lower computational complexity.