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This article presents a piecewise neural network (NN) with dynamic sparsity for modeling and linearization of radio frequency (RF) power amplifiers (PAs). A mixture of experts NN (MENN) approach is employed to combine several smaller real-valued time-delay NNs (RVTDNNs) by means of a gating NN. Furthermore, we complement the MENN framework with top- $K$ sparse gating, such that only a subset of experts is activated during each sample inference, reducing the computational complexity at run time. An end-to-end training approach is presented, to optimize the gating alongside with specializing the expert NNs, enabling the experts to collaborate. We experimentally investigate the scaleability of the proposed model in terms of modeling accuracy and linearization performance, as well as run time and model complexity, using RF measurements with two different gallium-nitride Doherty PAs at 1.8 and 3.5 GHz, respectively. Our experiments confirm a significant reduction in run-time complexity due to the sparse gating, with only a small penalty on accuracy, linearization capability and scaleability. Furthermore, the proposed approach is shown to offer favorable complexity-performance trade-offs, outperforming the existing state-of-the-art.

In this article, we comprehensively investigate the potential of the digital polar radio transmitter architecture for multi-user massive multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) downlink system. In terms of throughput performance, we derive a lower bound for the average sum-rate achievable with Gaussian signaling inputs and zero-forcing (ZF) precoding based on Bussgang decomposition. By diagonal approximation, we derive an approximate, yet accurate, model for the distortion caused by uniform polar quantization, which can be used to evaluate the corresponding sum-rate in closed form. To assess the power efficiency, we provide power consumption models with realistic parameters and values for the quantized polar and Cartesian transmitters, based on state-of-the-art integrated circuit (IC) designs and measurements. Extensive numerical results demonstrate that the proposed quantized polar transmitter can enable excellent performance in terms of average sum-rate, symbol error rate (SER), and out-of-band (OOB) emission level, compared to the Cartesian architecture. Furthermore, the power consumption comparisons show that the digital polar transmitter can save more than 36% in the energy consumption under 64-antenna setting in typical 5G enhanced mobile broadband use cases, thus making it highly appealing for future power-efficient massive MIMO transmitter implementations.

In this article, a novel digital predistortion (DPD) solution for fully digital multiple-input–multiple-output (MIMO) transmitters (TXs) is proposed. Opposed to classical DPD solutions that operate at TX chain or antenna level, the proposed DPD operates at the stream or beam level, and hence, its complexity is proportional to the number of spatially multiplexed streams or users rather than to the number of antennas. In addition, the proposed beam-level DPD operates in the frequency domain (FD), which makes it possible to provide flexible frequency-dependent linearization of the transmit waveforms. This feature is very well suited to the linearity requirements applicable at the 5G new radio (NR) frequency-range 2 (FR2), where the inband quality requirements commonly limit the feasible TX power and can also vary significantly within the channel bandwidth depending on the utilized data modulation and coding schemes of the different frequency-multiplexed users. Altogether, the proposed solution enables a large reduction in the computational complexity of the overall DPD system, and its performance is demonstrated and verified through both experimental and simulation-based results.