Digital Predistortion in Large-Array Digital Beamforming Transmitters

In this article, we propose a novel digital predistortion (DPD) solution that allows to considerably reduce the complexity resulting from linearizing a set of power amplifiers (PAs) in single-user large-scale digital beamforming transmitters. In contrast to current state-of-the art solutions that assume a dedicated DPD per power amplifier, which is unfeasible in the context of large antenna arrays, the proposed solution only requires a single DPD in order to linearize an arbitrary number of power amplifiers. To this end, the proposed DPD predistorts the signal at the input of the digital precoder based on minimizing the nonlinear distortion of the combined signal at the intended receiver direction. This is a desirable feature, since the resulting emissions in other directions get partially diluted due to less coherent superposition. With this approach, only a single DPD is required, yielding great complexity and energy savings.Comment: 8 pages, Accepted for publication in Asilomar Conference on Signals, Systems, and Computer

Spectral Efficiency of Mixed-ADC Massive MIMO

We study the spectral efficiency (SE) of a mixed-ADC massive MIMO system in which K single-antenna users communicate with a base station (BS) equipped with M antennas connected to N high-resolution ADCs and M-N one-bit ADCs. This architecture has been proposed as an approach for realizing massive MIMO systems with reasonable power consumption. First, we investigate the effectiveness of mixed-ADC architectures in overcoming the channel estimation error caused by coarse quantization. For the channel estimation phase, we study to what extent one can combat the SE loss by exploiting just N << M pairs of high-resolution ADCs. We extend the round-robin training scheme for mixed-ADC systems to include both high-resolution and one-bit quantized observations. Then, we analyze the impact of the resulting channel estimation error in the data detection phase. We consider random high-resolution ADC assignment and also analyze a simple antenna selection scheme to increase the SE. Analytical expressions are derived for the SE for maximum ratio combining (MRC) and numerical results are presented for zero-forcing (ZF) detection. Performance comparisons are made against systems with uniform ADC resolution and against mixed-ADC systems without round-robin training to illustrate under what conditions each approach provides the greatest benefit.Comment: To appear in IEEE Transactions on Signal Processin

Spatial Characteristics of Distortion Radiated from Antenna Arrays with Transceiver Nonlinearities

The distortion from massive MIMO (multiple-input--multiple-output) base stations with nonlinear amplifiers is studied and its radiation pattern is derived. The distortion is analyzed both in-band and out-of-band. By using an orthogonal Hermite representation of the amplified signal, the spatial cross-correlation matrix of the nonlinear distortion is obtained. It shows that, if the input signal to the amplifiers has a dominant beam, the distortion is beamformed in the same way as that beam. When there are multiple beams without any one being dominant, it is shown that the distortion is practically isotropic. The derived theory is useful to predict how the nonlinear distortion will behave, to analyze the out-of-band radiation, to do reciprocity calibration, and to schedule users in the frequency plane to minimize the effect of in-band distortion

Impact of Spatial Filtering on Distortion from Low-Noise Amplifiers in Massive MIMO Base Stations

In massive MIMO base stations, power consumption and cost of the low-noise amplifiers (LNAs) can be substantial because of the many antennas. We investigate the feasibility of inexpensive, power efficient LNAs, which inherently are less linear. A polynomial model is used to characterize the nonlinear LNAs and to derive the second-order statistics and spatial correlation of the distortion. We show that, with spatial matched filtering (maximum-ratio combining) at the receiver, some distortion terms combine coherently, and that the SINR of the symbol estimates therefore is limited by the linearity of the LNAs. Furthermore, it is studied how the power from a blocker in the adjacent frequency band leaks into the main band and creates distortion. The distortion term that scales cubically with the power received from the blocker has a spatial correlation that can be filtered out by spatial processing and only the coherent term that scales quadratically with the power remains. When the blocker is in free-space line-of-sight and the LNAs are identical, this quadratic term has the same spatial direction as the desired signal, and hence cannot be removed by linear receiver processing

Single Digital Predistortion Technique for Phased Array Linearization

In this paper, we present a novel and effective linearization technique for nonlinear phased array antennas. For large phased arrays, linearization of the array using a single digital predistortion (DPD) is inevitable since one digital path is upconverted and feeds several RF transmission paths, each of which is connected to a power amplifier (PA) and an antenna element. However, a critical issue is that the PA characteristics can vary considerably within an array. Thus, linearizing individual PAs with one DPD is rather challenging. We formulate and solve an optimization problem that corresponds to jointly minimizing the maximum residuals between the input to the array and the output of individual PAs. We demonstrate that the proposed technique outperforms state-of-the-art linearization solutions while retaining the linear gain of the array

Modeling and Linearization of MIMO RF Transmitters

Multiple-input multiple-output (MIMO) technology will continue to play a vital role in next-generation wireless systems, e.g., the fifth-generation wireless networks (5G). Large-scale antenna arrays (also called massive MIMO) seem to be the most promising physical layer solution for meeting the ever-growing demand for high spectral efficiency. Large-scale MIMO arrays are typically deployed with high integration and using low-cost components. Hence, they are prone to different hardware impairments such as crosstalk between the transmit antennas and power amplifier (PA) nonlinearities, which distort the transmitted signal. To avert the performance degradation due to these impairments, it is essential to have mechanisms for predicting the output of the MIMO arrays. Such prediction mechanisms are mandatory for performance evaluation and, more importantly, for the adoption of proper compensation techniques such as digital predistortion (DPD) schemes. This has stirred a considerable amount of interest among researchers to develop new hardware and signal processing solutions to address the requirements of large-scale MIMO systems. In the context of MIMO systems, one particular problem is that the hardware cost and complexity scale up with the increase of the size of the MIMO system. As a result, the MIMO systems tend to be implemented on a chip and are very compact. Reduction of the cost by reducing the bill of material is possible when several components are eliminated. The reuse of already existing hardware is an alternative solution. As a result, such systems are prone to excessive sources of distortion, such as crosstalk. Accordingly, crosstalk in MIMO systems in its simplest form can affect the DPD coefficient estimation scheme. In this thesis, the effect of crosstalk on two main DPD estimation techniques, know as direct learning algorithm (DLA) and indirect learning algorithm (ILA), is studied. The PA behavioral modeling and DPD scheme face several challenges that seek cost-efficient and flexible solutions too. These techniques require constant capture of the PA output feedback signal, which ultimately requires the implementation of a complete transmitter observation receiver (TOR) chain for the individual transmit path. In this thesis, a technique to reuse the receiver path of the MIMO TDD transceiver as a TOR is developed, which is based on over-the-air (OTA) measurements. With these techniques, individual PA behavioral modeling and DPD can be done by utilizing a few receivers of the MIMO TDD system. To use OTA measurements, an on-site antenna calibration scheme is developed to individually estimate the coupling between the transmitter and the receiver antennas. Furthermore, a digital predistortion technique for compensating the nonlinearity of several PAs in phased arrays is presented. The phased array can be a subset of massive MIMO systems, and it uses several antennas to steer the transmitted signal in a particular direction by appropriately assigning the magnitude and the phase of the transmitted signal from each antenna. The particular structure of phased arrays requires the linearization of several PAs with a single DPD. By increasing the number of RF branches and consequently increasing the number of PAs in the phased array, the linearization task becomes challenging. The DPD must be optimized to results in the best overall linear performance of the phased array in the field. The problem of optimized DPD for phased array has not been addressed appropriately in the literature. In this thesis, a DPD technique is developed based on an optimization problem to address the linearization of PAs with high variations. The technique continuously optimizes the DPD coefficients through several iterations considering the effect of each PA simultaneously. Therefore, it results in the best optimized DPD performance for several PAs. Extensive analysis, simulations, and measurement evaluation is carried out as a proof of concept. The different proposed techniques are compared with conventional approaches, and the results are presented. The techniques proposed in this thesis enable cost-efficient and flexible signal processing approaches to facilitate the development of future wireless communication systems