Parameter Estimation of Nonlinearities in Future Wireless Systems

Nowadays, our every-day life is immersed with wireless communications.From our hand-held cell-phones to televisions to navigation systems in cars, all and all are using wireless communications. This usage will even be enormouslyexpanded due to the introduction of the era of 5G-based Internet-of-Things(IoT) which consists wearables, sensors and more smart appliances.Orthogonal frequency division multiplexing is a very well-known commu-nication method which has been utilized in modern standards and technolo-gies due to its high spectral efficiency, simple frequency-domain equalization,and robustness against inter-symbol interference. Nevertheless, the major do-wnside of OFDM systems is the large fluctuations of the amplitudes of theirsignals causing high peak-to-average-power-ratio (PAPR). This forces the po-wer amplifier (PA) in the transmitter’s RF front-end to work in its saturationregion, hence introducing nonlinear distortion to the transmitted signal. Thisis particularly challenging in low-cost and low-power (and even low-weight)devices where a high-quality PA with a large dynamic range is not affordable,using complex digital processing techniques to mitigate the PAPR or to line-arize the PA is not computationally feasible, and introducing input back-offto change the operating point of the PA is not desirable due to decreasingthe power efficiency of the PA, which can be problematic because of the shortbattery-life. On the other hand, there are more resources available for a high-quality base station (or IoT gateway) in terms of power, budget, space and computational complexity, which motivates transferring all the complexity and cost to them and implement receiver-side nonlinearity estimation and compensation algorithms.To compensate the effects of a nonlinear PA on the transmitted signal and lastly detect them correctly, an iterative detection algorithm has been proposed in the literature. However, to use this algorithm successfully, thereceiver first needs to estimate the nonlinearity parameters. The importanceof this is more noticeable in the 5G-based Internet-of-Things networks, inwhich presumedly, numerous low-cost and low-power devices aim to transmitdata to a base station (or an IoT gateway).The focus of this thesis is on estimating the nonlinearity parameters al-ong with channel estimation, nonlinearity distortion mitigation, and symboldetection in future wireless systems deploying OFDM. In particular, we firstconsider an OFDM system with a limiter (clipper) communicating over anAWGN channel, and derive a maximum-likelihood estimator of the clippingamplitude. Next, we consider OFDM systems tranceiving over multi-pathfading channels, and propose a joint channel and clipping amplitude esti-mation algorithm using block-type frequency-domain pilots. Furthermore, we propose a new packet-frame consisting time-domain and frequency-domain pilots to separately estimate channel and clipping amplitude. After, we consider a broader types of memory less nonlinear PA models, and propose a jointestimation-detection algorithm to jointly estimate the nonlinearity parame-ters and channel and detect symbols. Finally, the joint channel and clipping amplitude estimation algorithm is extended to SIMO-OFDM systems. The performance of all of these algorithms are verified by means of simulationsQC 20180926