To enhance the efficiency and quality of communications, it is crucial to employ digital pre-distortion (DPD) technology for linearizing the power amplifiers (PAs) in terahertz/mmWave transceivers. Previous studies have shown that training DPD models within the direct learning architecture (DLA) framework yields superior results, as the Transformer-based PA behavioral model in this framework can directly compute the inverse function. Owing to resource-constrained deployment environments, DPD models trained via DLA typically rely on alternative lightweight models—such as long short-term memory (LSTM) networks. However, in scenarios where only DLA is applicable, lightweight DPD models often suffer from limited linearization performance. To mitigate this limitation, drawing inspiration from related work on iterative learning control (ILC), we propose a simple yet effective cross-architecture knowledge distillation method in the DLA framework (dubbed CAKDDLA). In this method, the lightweight DPD model is trained using two sources: the PA behavioral model and input-output knowledge distilled from a teacher model. To fully verify the proposed method’s effectiveness, extensive experiments conducted on a 1-GHz dataset show that our approach outperforms baseline methods in terms of error vector magnitude (EVM) and adjacent channel leakage ratio (ACLR), while also raising the upper bound of model performance.

To design reliable and efficient terahertz/mmWave transceivers, accurate power amplifier (PA) behavioral modeling is essential. In terahertz/millimeter wireless communication, the bandwidth will be 1 GHz or even more than 1 GHz, where PAs exhibit strong non-linearity and strong memory effects. This necessitates a powerful model capable of capturing long-term signal dependencies while managing strong non-linearity. To this end, we propose multi-scale augmented Transformer (MSAformer), which combines the ability of long short-term memory (LSTM) to capture complex sequence patterns with the self-attention mechanism’s dynamic attention adjustment, allowing it to effectively capture the intricate relationships between PA input and output signals. To fully validate the methods’ effectiveness, we collected and analyzed signals from the physical platform of the D-band system and set the bandwidth from 1 GHz to 4 GHz. Extensive behavioral modeling experiments on the collected datasets demonstrate that our method outperforms the existing methods in terms of normalized mean square error (NMSE). Further application in DPD scenarios proves that our method can effectively help improve the linearization performance of lightweight DPD models in terms of error vector magnitude (EVM) and adjacent channel leakage ratio (ACLR).

Low earth orbit (LEO) satellite communication system is vital due to its global coverage and low latency. To meet higher data rates, orthogonal frequency division multiplexing (OFDM) technology is recommended for adoption. In this paper, we analyze the nonlinear behavior of high-power amplifier (HPA) at Ka and Q/V frequency bands in wideband OFDM-based satellite communication system. A real satellite PA testing platform is constructed. Experimental results reveal that in wideband OFDM-based systems with high peak-to-average power ratio (PAPR), the conventional power back-off (PBO) method is no longer effective, particularly in mitigating in-band imbalance. Furthermore, we propose a low-complexity digital predistortion (DPD) scheme for satellite communication system. Experimental results demonstrate the robust performance of the proposed DPD.

Large-scale antenna arrays in millimeter wave (mmWave) systems are the cornerstone for next-generation Internet of Things (IoT) infrastructure. However, wideband mmWave systems suffer from frequency-dependent channel responses, known as beam squint. With beam squint effect, the far-field over-the-air (OTA) signal may exhibit frequency selective fading, rendering ineffective digital predistortion (DPD) design based on an OTA feedback structure. Therefore, existing mmWave DPD feedback architectures need to be examined carefully. In this article, we analyze the impact of beam squint on DPD and propose a proper DPD feedback architecture. To the best of our knowledge, this is the first work to address DPD in wideband mmWave systems with beam squint. Our results indicate that, with beam squint, the nonlinearity observed at the far-field OTA side differs from that at the power amplifier (PA) output side. We demonstrate that the far-field OTA received signal is no longer suitable as the feedback signal for DPD estimation. Moreover, we propose an effective DPD scheme for mmWave systems with beam squint. In this scheme, analog beamforming coefficients are fixed for DPD identification. Compared to existing solutions, hardware complexity of the proposed DPD scheme is much reduced. Numerical results validate the effectiveness of the proposed DPD scheme.

To meet the demand for high-speed and high-quality communication in next 6G satellite communication, it is very necessary and urgent to study the behavioral modeling of 6G satellite communication power amplifiers (PAs). In satellite communication, PAs face the situation of high dynamic and wide bandwidth and exhibit strong nonlinearity and strong memory effects. In this case, we need to study Transformer architectures that can better handle long sequence data and further explore the inherent characteristics of the PA signal data. In this article, we propose a behavioral modeling method of PAs named augmented real-valued time-delay transformer (ARVTDform). ARVTDform is an augmented transformer-based method, which can capture long-range dependencies between the PA signal data and has powerful nonlinear modeling capabilities. To simulate the working status of the satellite PAs, we set up two physical platforms and collect and analyze twelve datasets. To the best of our knowledge, this is the first time real satellite data has been used for behavioral modeling. Extensive experiments on the collected datasets further demonstrate that our transformer-based method is more suitable for handing PAs with strong nonlinearity and strong memory effects in terms of normalized mean square error (NMSE). Finally, we discuss the major challenge and list the potential future work that may contribute to the sustained development of high-performance transceivers.