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As the number of antenna elements increases in massive multiple-input multiple-output-based radios such as fifth generation mobile technology (5G), designing true multi-band base-station transmitter, with efficient physical size, power consumption and cost in emerging cellular frequency bands up to 10 GHz, is becoming a challenge. This demands a hard integration of radio components, particularly the radio's digital application-specific integrated circuits (ASIC) with high performance multi-band data converters. In this work, a novel radio frequency digital-to-analog converter (RF DAC) solution is presented, that is also capable of monolithic integration into today's digital ASIC due to its digital-in-nature architecture. A voltage-mode conversion method is used as output stage, and configurable mixing logic is employed in the data path to create a higher frequency lobe and utilize the output signal in the first or the second Nyquist zone. This 12-bit RF DAC is designed in a 22 nm FDSOI CMOS process, and shows excellent linearity performance for output frequencies up to 10 GHz, with no calibration and no trimming techniques. The achieved linearity performance is able to fulfill the high requirements of 5G base-station transmitters. Extensive Monte-Carlo analysis is performed to demonstrate the performance reliability over mismatch and process variation in the chosen technology.

Optimization problem formulation for semi-digital FIR digital-to-analog converter (SDFIR DAC) is investigated in this work. Magnitude and energy metrics with variable coefficient precision are defined for cascaded digital RD modulators, semi-digital FIR filter, and Sinc roll-off frequency response of the DAC. A set of analog metrics as hardware cost is also defined to be included in SDFIR DAC optimization problem formulation. It is shown in this work, that hardware cost of the SDFIR DAC, can be significantly reduced by introducing flexible coefficient precision while the SDFIR DAC is not over designed either. Different use-cases are selected to demonstrate the optimization problem formulations. A combination of magnitude metric, energy metric, coefficient precision and analog metrics are used in different use cases of optimization problem formulation and solved to find out the optimum set of analog FIR taps. A new method with introducing the variable coefficient precision in optimization procedure was proposed to avoid non-convex optimization problems. It was shown that up to 22% in the total number of unit elements of the SDFIR filter can be saved when targeting the analog metric as the optimization objective subject to magnitude constraint in pass-band and stop-band.

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A digital-to-RF converter (DRFC) architecture for IQ modulator is proposed in this paper. The digital-RF converter utilizes the mixer DAC concept but a discrete-time oscillatory signal is applied to the digital-RF converter instead of a conventional continuous-time LO. The architecture utilizes a low pass Σ∆ modulator and a semi-digital FIR filter. The digital Σ∆ modulator provides a single-bit data stream to a current-mode SDFIR filter in each branch of the IQ modulator. The filter taps are realized as weighted one-bit DACs and the filter response attenuates the out-of-band shaped quantization noise generated by the Σ∆ modulator. To find the semi-digital FIR filter response, an optimization problem is formulated. The magnitude metrics in out-of-band is set as optimization constraint and the total number of unit elements required for the DAC/mixer is set as the objective function. The proposed architecture and the design technique is described in system level and simulation results are presented to support the feasibility of the solution.

In this work, we present an analytical study of aliasing image spurs problem in digital-RF modulators. The inherent finite image rejection ratio of this types modulators is conceptually discussed. A pulse amplitude modulation (PAM) model of the converter is used in the theoretical discussion. Behavioral level simulation of the digital-RF converter model is included. Finite image rejection is a limiting issue in this architecture, and Digital-IF mixing is used to alleviate the problem which is also reviewed and simulated.

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