Advanced signal processing techniques for the modeling and linearization of wireless communication systems.

Los nuevos estándares de comunicaciones digitales inalámbricas están impulsando el diseño de amplificadores de potencia con unas condiciones límites en términos de linealidad y eficiencia. Si bien estos nuevos sistemas exigen que los dispositivos activos trabajen cerca de la zona de saturación en busca de la eficiencia energética, la no linealidad inherente puede producir que el sistema muestre prestaciones inadecuadas en emisiones fuera de banda y distorsión en banda. La necesidad de técnicas digitales de compensación y la evolución en el diseño de nuevas arquitecturas de procesamiento de señales digitales posicionan a la predistorsión digital (DPD) como un enfoque práctico. Los predistorsionadores digitales se suelen basar en modelos de comportamiento como el memory polynomial (MP), el generalized memory polynomial (GMP) y el dynamic deviation reduction-based (DDR), etc. Los modelos de Volterra sufren la llamada "maldición de la dimensionalidad", ya que su complejidad tiende a crecer de forma exponencial a medida que el orden y la profundidad de memoria crecen. Esta tesis se centra principalmente en contribuir a la rama de conocimiento que enmarca el modelado y linealización de sistemas de comunicación inalámbrica. Los principales temas tratados son el modelo Volterra-Parafac y el modelo general de Volterra para sistemas complejos, los cuales tratan la estructura del DPD y las series de Volterra estructuradas con compressed-sensing y un método para la linealización en un rango de potencias de operación, que se centran en cómo los coeficientes de los modelos deben ser obtenidos.Premio Extraordinario de Doctorado U

ワイヤレス通信のための先進的な信号処理技術を用いた非線形補償法の研究

The inherit nonlinearity in analogue front-ends of transmitters and receivers have had primary impact on the overall performance of the wireless communication systems, as it gives arise of substantial distortion when transmitting and processing signals with such circuits. Therefore, the nonlinear compensation (linearization) techniques become essential to suppress the distortion to an acceptable extent in order to ensure sufficient low bit error rate. Furthermore, the increasing demands on higher data rate and ubiquitous interoperability between various multi-coverage protocols are two of the most important features of the contemporary communication system. The former demand pushes the communication system to use wider bandwidth and the latter one brings up severe coexistence problems. Having fully considered the problems raised above, the work in this Ph.D. thesis carries out extensive researches on the nonlinear compensations utilizing advanced digital signal processing techniques. The motivation behind this is to push more processing tasks to the digital domain, as it can potentially cut down the bill of materials (BOM) costs paid for the off-chip devices and reduce practical implementation difficulties. The work here is carried out using three approaches: numerical analysis & computer simulations; experimental tests using commercial instruments; actual implementation with FPGA. The primary contributions for this thesis are summarized as the following three points: 1) An adaptive digital predistortion (DPD) with fast convergence rate and low complexity for multi-carrier GSM system is presented. Albeit a legacy system, the GSM, however, has a very strict requirement on the out-of-band emission, thus it represents a much more difficult hurdle for DPD application. It is successfully implemented in an FPGA without using any other auxiliary processor. A simplified multiplier-free NLMS algorithm, especially suitable for FPGA implementation, for fast adapting the LUT is proposed. Many design methodologies and practical implementation issues are discussed in details. Experimental results have shown that the DPD performed robustly when it is involved in the multichannel transmitter. 2) The next generation system (5G) will unquestionably use wider bandwidth to support higher throughput, which poses stringent needs for using high-speed data converters. Herein the analog-to-digital converter (ADC) tends to be the most expensive single device in the whole transmitter/receiver systems. Therefore, conventional DPD utilizing high-speed ADC becomes unaffordable, especially for small base stations (micro, pico and femto). A digital predistortion technique utilizing spectral extrapolation is proposed in this thesis, wherein with band-limited feedback signal, the requirement on ADC speed can be significantly released. Experimental results have validated the feasibility of the proposed technique for coping with band-limited feedback signal. It has been shown that adequate linearization performance can be achieved even if the acquisition bandwidth is less than the original signal bandwidth. The experimental results obtained by using LTE-Advanced signal of 320 MHz bandwidth are quite satisfactory, and to the authors’ knowledge, this is the first high-performance wideband DPD ever been reported. 3) To address the predicament that mobile operators do not have enough contiguous usable bandwidth, carrier aggregation (CA) technique is developed and imported into 4G LTE-Advanced. This pushes the utilization of concurrent dual-band transmitter/receiver, which reduces the hardware expense by using a single front-end. Compensation techniques for the respective concurrent dual-band transmitter and receiver front-ends are proposed to combat the inter-band modulation distortion, and simultaneously reduce the distortion for the both lower-side band and upper-side band signals.電気通信大学201

Neural Network DPD for Aggrandizing SM-VCSEL-SSMF-Based Radio over Fiber Link Performance

This paper demonstrates an unprecedented novel neural network (NN)-based digital predistortion (DPD) solution to overcome the signal impairments and nonlinearities in Analog Optical fronthauls using radio over fiber (RoF) systems. DPD is realized with Volterra-based procedures that utilize indirect learning architecture (ILA) and direct learning architecture (DLA) that becomes quite complex. The proposed method using NNs evades issues associated with ILA and utilizes an NN to first model the RoF link and then trains an NN-based predistorter by backpropagating through the RoF NN model. Furthermore, the experimental evaluation is carried out for Long Term Evolution 20 MHz 256 quadraturre amplitude modulation (QAM) modulation signal using an 850 nm Single Mode VCSEL and Standard Single Mode Fiber to establish a comparison between the NN-based RoF link and Volterra-based Memory Polynomial and Generalized Memory Polynomial using ILA. The efficacy of the DPD is examined by reporting the Adjacent Channel Power Ratio and Error Vector Magnitude. The experimental findings imply that NN-DPD convincingly learns the RoF nonlinearities which may not suit a Volterra-based model, and hence may offer a favorable trade-off in terms of computational overhead and DPD performance

Receiver Side Signal Processing for Nonlinear Distortion Compensation in 5G AND Beyond

Trading between transmit waveform quality and power efficiency is one of the most challenging issues in radio transmitter implementation. To this end, digital predistortion is the de-facto solution for mitigating power amplifier (PA) nonlinear distortion in cellular base-stations due to its high flexibility and good linearization performance. Theoretically, it is convenient to describe predistorter (PD) transfer function as the mathematical inverse of the PA transfer function, and PD modeling is often performed through parametric methods. Thus, an additional feedback loop is required in the system for PD model parameter estimation. PA is an analog device and DPD is a part of digital front-end, implying that PA output signal is needed to be downconverted to baseband and sampled in the parameter estimation path. Consequently, it is required to employ additional components in the feedback loop such as attenuator, downconverter, and analog-to-digital converter (ADC). In order to be able to capture higher order nonlinearities, it is necessary to perform upsampling operation, which implies that in addition to digital-to-analog converters (DACs) in the forward loop, the components in the feedback loop should support higher bandwidths than the original transmission bandwidth. Additionally, to have a good linearization performance, a high resolution ADC is required. Having an ADC/DAC that supports wide bandwidth and has high resolution is directly increasing the material cost and power consumption. When future millimeter-wave (mmWave) systems are considered, adopting DPD becomes even more complex and costly due to wider waveform bandwidths and employing active antenna arrays. Alternative to DPD, receiver based approaches, referred to as digital post-distortion (DPoD), can be utilized to mitigate the nonlinear effects of transmitter PA. Naturally, receiver side techniques do not provide any improvement in terms of out-of-band (OOB) emission issues, rather they aim to improve received signal error vector magnitude (EVM). As the radiated power at mmWave is typically EVM limited and OOB emission requirements are relaxed compared to sub-6 GHz band, DPoD can offer means for improved network energy-efficiency. Several iterative DPoD methods are proposed in the literature such as power amplifier nonlinearity cancellation (PANC), and reconstruction of distorted signals (RODS). In this thesis, we present a non-iterative computationally efficient receiver side nonlinearity mitigation technique, referred to as digital post-inverse (DPoI), along with the parameter estimation approach targeting existing 5G NR standard-compliant reference signal. The receiver EVM performance of presented approach is analyzed by using computer simulations. It is seen that DPoI can reach similar or improved performance compared to the iterative PANC method, which is chosen as a reference DPoD method. Moreover, it is shown that both DPoD methods overperform ideally linearized transmitter PA under strong nonlinear conditions, which allows higher power efficiency when receiver side techniques are employed

Experimental Demonstration and Performance Enhancement of 5G NR Multiband Radio over Fiber System Using Optimized Digital Predistortion

This paper presents an experimental realization of multiband 5G new radio (NR) optical front haul (OFH) based radio over fiber (RoF) system using digital predistortion (DPD). A novel magnitude-selective affine (MSA) based DPD method is proposed for the complexity reduction and performance enhancement of RoF link followed by its comparison with the canonical piece wise linearization (CPWL), decomposed vector rotation method (DVR) and generalized memory polynomial (GMP) methods. Similarly, a detailed study is shown followed by the implementation proposal of novel neural network (NN) for DPD followed by its comparison with MSA, CPWL, DVR and GMP methods. In the experimental testbed, 5G NR standard at 20 GHz with 50 MHz bandwidth and flexible-waveform signal at 3 GHz with 20 MHz bandwidth is used to cover enhanced mobile broad band and small cells scenarios. A dual drive Mach Zehnder Modulator having two distinct radio frequency signals modulates a 1310 nm optical carrier using distributed feedback laser for 22 km of standard single mode fiber. The experimental results are presented in terms of adjacent channel power ratio (ACPR), error vector magnitude (EVM), number of estimated coefficients and multiplications. The study aims to identify those novel methods such as MSA DPD are a good candidate to deploy in real time scenarios for DPD in comparison to NN based DPD which have a slightly better performance as compared to the proposed MSA method but has a higher complexity levels. Both, proposed methods, MSA and NN are meeting the 3GPP Release 17 requirements

Modeling and Digital Mitigation of Transmitter Imperfections in Radio Communication Systems

To satisfy the continuously growing demands for higher data rates, modern radio communication systems employ larger bandwidths and more complex waveforms. Furthermore, radio devices are expected to support a rich mixture of standards such as cellular networks, wireless local-area networks, wireless personal area networks, positioning and navigation systems, etc. In general, a "smart'' device should be flexible to support all these requirements while being portable, cheap, and energy efficient. These seemingly conflicting expectations impose stringent radio frequency (RF) design challenges which, in turn, call for their proper understanding as well as developing cost-effective solutions to address them. The direct-conversion transceiver architecture is an appealing analog front-end for flexible and multi-standard radio systems. However, it is sensitive to various circuit impairments, and modern communication systems based on multi-carrier waveforms such as Orthogonal Frequency Division Multiplexing (OFDM) and Orthogonal Frequency Division Multiple Access (OFDMA) are particularly vulnerable to RF front-end non-idealities.This thesis addresses the modeling and digital mitigation of selected transmitter (TX) RF impairments in radio communication devices. The contributions can be divided into two areas. First, new modeling and digital mitigation techniques are proposed for two essential front-end impairments in direct-conversion architecture-based OFDM and OFDMA systems, namely inphase and quadrature phase (I/Q) imbalance and carrier frequency offset (CFO). Both joint and de-coupled estimation and compensation schemes for frequency-selective TX I/Q imbalance and channel distortions are proposed for OFDM systems, to be adopted on the receiver side. Then, in the context of uplink OFDMA and Single Carrier FDMA (SC-FDMA), which are the air interface technologies of the 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) and LTE-Advanced systems, joint estimation and equalization techniques of RF impairments and channel distortions are proposed. Here, the challenging multi-user uplink scenario with unequal received power levels is investigated where I/Q imbalance causes inter-user interference. A joint mirror subcarrier processing-based minimum mean-square error (MMSE) equalizer with an arbitrary number of receiver antennas is formulated to effectively handle the mirror sub-band users of different power levels. Furthermore, the joint channel and impairments filter responses are efficiently approximated with polynomial-based basis function models, and the parameters of basis functions are estimated with the reference signals conforming to the LTE uplink sub-frame structure. The resulting receiver concept adopting the proposed techniques enables improved link performance without modifying the design of RF transceivers.Second, digital baseband mitigation solutions are developed for the TX leakage signal-induced self-interference in frequency division duplex (FDD) transceivers. In FDD transceivers, a duplexer is used to connect the TX and receiver (RX) chains to a common antenna while also providing isolation to the receiver chain against the powerful transmit signal. In general, the continuous miniaturization of hardware and adoption of larger bandwidths through carrier aggregation type noncontiguous allocations complicates achieving sufficient TX-RX isolation. Here, two different effects of the transmitter leakage signal are investigated. The first is TX out-of-band (OOB) emissions and TX spurious emissions at own receiver band, due to the transmitter nonlinearity, and the second is nonlinearity of down-converter in the RX that generates second-order intermodulation distortion (IMD2) due to the TX in-band leakage signal. This work shows that the transmitter leakage signal-induced interference depends on an equivalent leakage channel that models the TX path non-idealities, duplexer filter responses, and the RX path non-idealities. The work proposes algorithms that operate in the digital baseband of the transceiver to estimate the TX-RX non-idealities and the duplexer filter responses, and subsequently regenerating and canceling the self-interference, thereby potentially relaxing the TX-RX isolation requirements as well as increasing the transceiver flexibility.Overall, this thesis provides useful signal models to understand the implications of different RF non-idealities and proposes compensation solutions to cope with certain RF impairments. This is complemented with extensive computer simulations and practical RF measurements to validate their application in real-world radio transceivers

TRANSMISSION PERFORMANCE OPTIMIZATION IN FIBER-WIRELESS ACCESS NETWORKS USING MACHINE LEARNING TECHNIQUES

The objective of this dissertation is to enhance the transmission performance in the fiber-wireless access network through mitigating the vital system limitations of both analog radio over fiber (A-RoF) and digital radio over fiber (D-RoF), with machine learning techniques being systematically implemented. The first thrust is improving the spectral efficiency for the optical transmission in the D-RoF to support the delivery of the massive number of bits from digitized radio signals. Advanced digital modulation schemes like PAM8, discrete multi-tone (DMT), and probabilistic shaping are investigated and implemented, while they may introduce severe nonlinear impairments on the low-cost optical intensity-modulation-direct-detection (IMDD) based D-RoF link with a limited dynamic range. An efficient deep neural network (DNN) equalizer/decoder to mitigate the nonlinear degradation is therefore designed and experimentally verified. Besides, we design a neural network based digital predistortion (DPD) to mitigate the nonlinear impairments from the whole link, which can be integrated into a transmitter with more processing resources and power than a receiver in an access network. Another thrust is to proactively mitigate the complex interferences in radio access networks (RANs). The composition of signals from different licensed systems and unlicensed transmitters creates an unprecedently complex interference environment that cannot be solved by conventional pre-defined network planning. In response to the challenges, a proactive interference avoidance scheme using reinforcement learning is proposed and experimentally verified in a mmWave-over-fiber platform. Except for the external sources, the interference may arise internally from a local transmitter as the self-interference (SI) that occupies the same time and frequency block as the signal of interest (SOI). Different from the conventional subtraction-based SI cancellation scheme, we design an efficient dual-inputs DNN (DI-DNN) based canceller which simultaneously cancels the SI and recovers the SOI.Ph.D