Behavioral modeling and FPGA implementation of digital predistortion for RF and microwave power amplifiers

With the high interest in digital modulation techniques which are very sensitive to the PA nonlinearity, modern wireless communication systems require the usage of linearization techniques to improve the linear behavior of the RF power amplifier. The powerful and cheap digital processing technology makes the digital predistortion (DPD) a competitive candidate for the linearization of the PA. This thesis introduces the basic principle of DPD, its implementation on FPGA and the adaptive DPD system. The linearization of 4 PAs with DPD technique has been introduced: for the hybrid class AB PA operating at 2.6 GHz with a WiMAX testing signal, 33.7 dBm average power, 29.6 % drain efficiency, 13 dB ACPR and 9 dB NMSE improvement have been obtained; for the hybrid Doherty PA operating at 3.4 GHz with an I/Q testing signal, 35.0 dBm average power, 36.8 % drain efficiency, 12 dB ACPR and 13 dB NMSE improvement have been obtained; for the MMIC class AB PA operating at 7 GHz with an I/Q testing signal, 29.4 dBm average power, 25.7 % drain efficiency, 12 dB ACPR and 12 dB NMSE improvement have been obtained; for the two-stage PA operating at 24 GHz with an I/Q testing signal, 23.5 dBm average power, more than 14.0 % drain efficiency, 11 dB ACPR and 11 dB NMSE improvement have been obtained. The DPD algorithm has been implemented on FPGA with two methods based on LUT and a direct structure with only adders and multipliers. The block RAM on the FPGA board is chosen as the table in the LUT methods. The linearization performance for these three methods is similar. The test PA is the hybrid Doherty PA mentioned above and the test signal is the I/Q signal with 7.4 dB PAPR. 35.1 dBm average power, 36.8 % efficiency, 11 dB ACPR and 11 dB NMSE improvement have been obtained. The cost of logic resources for the direct structure method is the largest with 1,172 flip-flops, while the number of flip-flops for the two LUT methods are 263 and 583, respectively. A new adaptive algorithm has been proposed in this thesis for the adaptive DPD system. This new algorithm improves the performance in extracting the model parameters in complex number domain. With the experimental data from a combined class AB PA, the final accuracy of the model extracted by the new algorithm has been improved from -20 dB to about -40 dB and the converge speed is faster

Joint compensation of I/Q impairments and PA nonlinearity in mobile broadband wireless transmitters

The main focus of this thesis is to develop and investigate a new possible solution for compensation of in-phase/quadrature-phase (I/Q) impairments and power amplifier (PA) nonlinearity in wireless transmitters using accurate, low complexity digital predistortion (DPD) technique. After analysing the distortion created by I/Q modulators and PAs together with nonlinear crosstalk effects in multi-branch multiple input multiple output (MIMO) wireless transmitters, a novel two-box model is proposed for eliminating those effects. The model is realised by implementing two phases which provide an optimisation of the identification of any system. Another improvement is the capability of higher performance of the system without increasing the computational complexity. Compared with conventional and recently proposed models, the approach developed in this thesis shows promising results in the linearisation of wireless transmitters. Furthermore, the two-box model is extended for concurrent dual-band wireless transmitters and it takes into account cross-modulation (CM) products. Besides, it uses independent processing blocks for both frequency bands and reduces the sampling rate requirements of converters (digital-to-analogue and analogue-to-digital). By using two phases for the implementation, the model enables a scaling down of the nonlinear order and the memory depth of the applied mathematical functions. This leads to a reduced computational complexity in comparison with recently developed models. The thesis provides experimental verification of the two-box model for multi-branch MIMO and concurrent dual-band wireless transmitters. Accordingly, the results ensure both the compensation of distortion and the performance evaluation of modern broadband wireless transmitters in terms of accuracy and complexity

Machine-Learning-Aided Trajectory Prediction and Conflict Detection for Internet of Aerial Vehicles

As exploitation of low and medium airspace for air traffic management (ATM) is gaining more attention, aerial vehicles' security issues pose a major challenge to the air-ground-integrated vehicle networks (AGIVNs). Traditional surveillance technology lacks the capacity to support the intensive ATM of the future. Therefore, an advanced automatic-dependent surveillance-broadcast (ADS-B) technique is applied to track and monitor aerial vehicles in a more effective manner. In this article, we propose a grouping-based conflict detection algorithm based on the preprocessed ADS-B data set, and analyze the experimental results and visualize the detected conflicts. Then, in order to further improve flight safety and conflict detection, the trajectories of the aerial vehicles are predicted based on machine learning-based algorithms. The results are fed into the conflict detection algorithm to execute conflict prediction. It was shown that the trajectory prediction model using long short-term memory (LSTM) can achieve better prediction performance, especially when predicting the long-term trajectory of aerial vehicles. The conflict detection results based on the trajectory prediction methods show that the proposed scheme can make it possible to detect whether there would be conflicts within seconds

Machine Learning in Digital Signal Processing for Optical Transmission Systems

The future demand for digital information will exceed the capabilities of current optical communication systems, which are approaching their limits due to component and fiber intrinsic non-linear effects. Machine learning methods are promising to find new ways of leverage the available resources and to explore new solutions. Although, some of the machine learning methods such as adaptive non-linear filtering and probabilistic modeling are not novel in the field of telecommunication, enhanced powerful architecture designs together with increasing computing power make it possible to tackle more complex problems today. The methods presented in this work apply machine learning on optical communication systems with two main contributions. First, an unsupervised learning algorithm with embedded additive white Gaussian noise (AWGN) channel and appropriate power constraint is trained end-to-end, learning a geometric constellation shape for lowest bit-error rates over amplified and unamplified links. Second, supervised machine learning methods, especially deep neural networks with and without internal cyclical connections, are investigated to combat linear and non-linear inter-symbol interference (ISI) as well as colored noise effects introduced by the components and the fiber. On high-bandwidth coherent optical transmission setups their performances and complexities are experimentally evaluated and benchmarked against conventional digital signal processing (DSP) approaches. This thesis shows how machine learning can be applied to optical communication systems. In particular, it is demonstrated that machine learning is a viable designing and DSP tool to increase the capabilities of optical communication systems

Millimetre-Wave Fibre-Wireless Technologies for 5G Mobile Fronthaul

The unprecedented growth in mobile data traffic, driven primarily by bandwidth rich applications and high definition video is accelerating the development of fifth generation (5G) mobile network. As mobile access network evolves towards centralisation, mobile fronthaul (MFH) architecture becomes essential in providing high capacity, ubiquitous and yet affordable services to subscribers. In order to meet the demand for high data rates in the access, Millimetre-wave (mmWave) has been highlighted as an essential technology in the development of 5G-new radio (5G-NR). In the present MFH architecture which is typically based on common public radio interface (CPRI) protocol, baseband signals are digitised before fibre transmission, featuring high overhead data and stringent synchronisation requirements. A direct application of mmWave 5G-NR to CPRI digital MFH, where signal bandwidth is expected to be up to 1GHz will be challenging, due to the increased complexity of the digitising interface and huge overhead data that will be required for such bandwidth. Alternatively, radio over fibre (RoF) technique can be employed in the transportation of mmWave wireless signals via the MFH link, thereby avoiding the expensive digitisation interface and excessive overhead associated with its implementation. Additionally, mmWave carrier can be realised with the aid of photonic components employed in the RoF link, further reducing the system complexity. However, noise and nonlinearities inherent to analog transmission presents implementation challenges, limiting the system dynamic range. Therefore, it is important to investigate the effects of these impairments in RoF based MFH architecture. This thesis presents extensive research on the impact of noise and nonlinearities on 5G candidate waveforms, in mmWave 5G fibre wireless MFH. Besides orthogonal frequency division multiplexing (OFDM), another radio access technology (RAT) that has received significant attention is filter bank multicarrier (FBMC), particularly due to its high spectral containment and excellent performance in asynchronous transmission. Hence, FBMC waveform is adopted in this work to study the impact of noise and nonlinearities on the mmWave fibre-wireless MFH architecture. Since OFDM is widely deployed and it has been adopted for 5G-NR, the performance of OFDM and FBMC based 5G mmWave RAT in fibre wireless MFH architecture is compared for several implementations and transmission scenarios. To this extent, an end to end transmission testbed is designed and implemented using industry standard VPI Transmission Maker® to investigate five mmWave upconversion techniques. Simulation results show that the impact of noise is higher in FBMC when the signal to-noise (SNR) is low, however, FBMC exhibits better performance compared to OFDM as the SNR improved. More importantly, an evaluation of the contribution of each noise component to the overall system SNR is carried out. It is observed in the investigation that noise contribution from the optical carriers employed in the heterodyne upconversion of intermediate frequency (IF) signals to mmWave frequency dominate the system noise. An adaptive modulation technique is employed to optimise the system throughput based on the received SNR. The throughput of FBMC based system reduced significantly compared to OFDM, due to laser phase noise and chromatic dispersion (CD). Additionally, it is shown that by employing frequency domain averaging technique to enhance the channel estimation (CE), the throughput of FBMC is significantly increased and consequently, a comparable performance is obtained for both waveforms. Furthermore, several coexistence scenarios for multi service transmission are studied, considering OFDM and FBMC based RATs to evaluate the impact inter band interference (IBI), due to power amplifier (PA) nonlinearity on the system performance. The low out of band (OOB) emission in FBMC plays an important role in minimising IBI to adjacent services. Therefore, FBMC requires less guardband in coexistence with multiple services in 5G fibre-wireless MFH. Conversely, OFDM introduced significant OOB to adjacent services requiring large guardband in multi-service coexistence transmission scenario. Finally, a novel transmission scheme is proposed and investigated to simultaneously generate multiple mmWave signals using laser heterodyning mmWave upconversion technique. With appropriate IF and optical frequency plan, several mmWave signals can be realised. Simulation results demonstrate successful simultaneous realisation of 28GHz, 38GHz, and 60GHz mmWave signals