Sequence Design via Semidefinite Programming Relaxation and Randomized Projection

Wideband is a booming technology in the field of wireless communications. The receivers in wideband communication systems are expected to cover a very wide spectrum and adaptively extract the parts of interest. The literature has focused on mixing the input spectrum to baseband using a pseudorandom sequence modulation and recovering the received signals from linearly independent measurements by parallel branches to mitigate the pressures from required extreme high sampling frequency. However, a pseudorandom sequence provides no rejection for the strong interferers received together with weak signals from distant sources. The interferers cause significant distortion due to the nonlinearity of the subsequent amplifier and mask the weak signals. In this dissertation, we optimize the modulation sequences with a specific spectrum shape to mitigate interferers while preserving messages; the sequences have binary entries to simplify hardware implementation. Though the resulting sequence design problems are NP-hard, we solve them approximately by semidefinite relaxation and randomized projection. First, we formulate the design algorithm for a single spectrally shaped binary sequence base on a randomized convex optimization method. We analyze the performance of the algorithm in obtaining binary sequences and show its advantages compared with method available in the literature. And, we show a comparison between the proposed sequence design method with the exhaustive approaches when feasible. Additionally, we propose several custom sequence scoring functions that allow for an improved selection of binary sequences for message preservation and interference rejection. Second, we propose an algorithm to design a multi-branch set of binary sequences one by one by introducing the constrains on the orthogonality between pairs of sequences. Numerical results show the proposed algorithm obtains sequences with a small search size compared with the exhaustive search. Finally, we extend the randomized method to multi-branch sequence design. In order to avoid the unstable performance and high complexity of designing multi-branch sequence iteratively, the whole branch sequences will be obtained directly via matrix randomized projection from the relaxed problems

PAPR Reduction Solutions for 5G and Beyond

The latest fifth generation (5G) wireless technology provides improved communication quality compared to earlier generations. The 5G New Radio (NR), specified by the 3rd Generation Partnership Project (3GPP), addresses the modern requirements of the wireless networks and targets improved communication quality in terms of for example peak data rates, latency and reliability. On the other hand, there are still various crucial issues that impact the implementation and energy-efficiency of 5G NR networks and their different deployments. The power-efficiency of transmitter power amplifiers (PAs) is one of these issues. The PA is an important unit of a communication system, which is responsible from amplifying the transmit signal towards the antenna. Reaching high PA power-efficiency is known to be difficult when the transmit waveform has a high peak-to-average power ratio (PAPR). The cyclic prefix (CP)-orthogonal frequencydivision multiplexing (OFDM) that is the main physical-layer waveform of 5G NR, suffers from such high PAPR challenge. There are generally many PAPR reduction methods proposed in the literature, however, many of these have either very notable computational complexity or impose substantial inband distortion. Moreover, 5G NR has new features that require redesigning the PAPR reduction methods. In line with these, the first contribution of this thesis is the novel frequencyselective PAPR reduction concept, where clipping noise is shaped in a frequencyselective manner over the active passband. This concept is in line with the 5G NR, where aggressive frequency-domain multiplexing is considered as an important feature. Utilizing the frequency-selective PAPR reduction enables the realization of the heterogeneous resource utilization within one passband. The second contribution of this thesis is the frequency-selective single-numerology (SN) and mixed-numerology (MN) PAPR reduction methods. The 5G NR targets utilizing different physical resource blocks (PRBs) and bandwidth parts (BWPs) within one passband flexibly. Yet, existing PAPR reduction methods do not exploit these features. Based on this, novel algorithms utilizing PRB and BWP level control of clipping noise are designed to meet error vector magnitude (EVM) limits of the modulations while reducing the PAPR. TheMNallocation has one critical challenge as inter numerology interference (INI) emerges after aggregation of subband signals. Proposed MN PAPR reduction algorithm overcomes this issue by cancelling INI within the PAPR reduction loop, which has not been considered earlier. The third contribution of this thesis is the proposal of two novel non-iterative PAPR reduction methods. First method utilizes the fast-convolution filteredOFDM (FC-F-OFDM) that has excellent spectral containment, and combines it with clipping. Moreover, clipping noise is also allocated to guard bands by filter passband extension (FPE) and clipping noise in out-of-band (OOB) regions is essentially filtered through FC filtering. The second method is the guard-tone reservation (GTR) which is applied to discrete Fourier transform-spread-OFDM (DFT-s-OFDM). Uniquely, GTR estimates the time domain peaks in data symbol domain before inverse fast Fourier transform (IFFT), and uses guard band tones for PAPR reduction. The fourth contribution of the thesis is the design of two novel machine learning (ML) algorithms that improve the drawbacks of frequency-selective PAPRreduction. The first ML algorithm, PAPRer, models the nonlinear relation between the PAPR target and the realized PAPR value. Then, it auto-tunes the optimal PAPR target and this way minimizes the realized PAPR. The second ML algorithm, one-shot clipping-and-filtering (OSCF), solves the complexity problem of iterative clipping and filtering (ICF)-like methods by generating proper approximated clipping noise signal after running only one iteration, leading to very efficient PAPR reduction. Finally, an over-arching contribution of this thesis is the experimental validation of the performance benefits of the proposed methods by considering realistic 5GNR uplink (UL) and downlink (DL) testbeds that include realistic PAs and associated hardware. It is very important to confirm the practical benefits of the proposed methods and, this is realized with the conducted experimental work