Browsing by Subject "5G"
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Item Open Access 5G PDSCH: performance analysis of DMRS and PTRS designs for channel and phase noise estimation in MM-WAVE(Bilkent University, 2021-08) Pekcan, Doğan KutayThe mm-Wave is one of the main enablers for the performance requirements of 5G. Although it provides communication systems with huge bandwidth and data rates, it also has some disadvantages as the carrier frequencies can significantly exceed 6 GHz and go up to 300 GHz. For example, there are significant challenges such as propagation loss and severe phase noise (PN). The PN can be observed in two parts: common phase error (CPE) and inter-carrier interference (ICI). In the literature, there are algorithms for the estimation and compensation of PN for OFDM-based systems. We apply both CPE and ICI compensation algorithms for 5G PDSCH at the carrier frequency of 70 GHz. Detailed performance analysis is performed for demodulation reference signal (DMRS) based channel estimation and phase-tracking reference signal (PTRS) based PN estimation. We observe the effects of different reference signal parameters in 5G for each PN compensation algorithm. For this purpose, we use up-to-date power spectral density (PSD) models for PN modeling and show uncoded bit error rate (BER) graphs obtained via extensive simulations for MATLAB's tapped delay line (TDL) channels. We also analyze the system performance under very high Doppler, where PTRS based channel estimation is compared with DMRS based channel estimation.Item Open Access Fair user scheduling for downlink power domain NOMA(Bilkent University, 2019-06) Topçuoğlu, ZaferNon-Orthogonal Multiple Access (NOMA) has been proposed as a new radio access technique in which multiple users (the case of user pairs or triples are covered in this thesis) are allowed to use the wireless channel simultaneously in a way to improve the overall system capacity. A bucket-based Temporal Fair Scheduling algorithm (TFS) has been proposed in the literature for Orthogonal Multiple Access (OMA) systems. In this thesis, we extend this existing work to downlink power domain NOMA by which user pairs or triples are to be scheduled with the goal of maximizing system capacity under temporal fairness constraints. The effectiveness of the proposed fair user scheduling algorithm for NOMA is validated with simulations in which the effects of transmit power and coverage radius of the base station, as well as the number of users are thoroughly studied.Item Open Access High-power and low-loss SPDT switch design using gate-optimized GaN on SiC HEMTs for S-band 5G T/R modules(Bilkent University, 2022-07) Ertürk, VolkanRadio frequency (RF) switches are one the fundamental components of modern communication systems. They enable the routing of high-frequency signals into different transmission paths. Therefore, they play a crucial role in transceiver (T/R) modules. Especially, 5G technology creates a demand for compact switches with high power handling, high isolation, and low insertion loss. GaN on SiC high electron mobility transistor (HEMT) technology stands out with its exceptional electrical and thermal characteristics among other semiconductor technologies. However, switch performance is limited by selected topology and transistor capability. Notably, the T-gate dimensions of the HEMTs directly affect the small-signal and large-signal performance of the switch. This study focuses on designing a single-pole double-throw (SPDT) monolithic microwave integrated circuit (MMIC) switch using gate-optimized HEMT in AlGaN/GaN on SiC technology. The foot length of the gate is varied from 200 nm to 250 nm, and the head length is varied from 500 nm to 750 nm in the T-gate structure to optimize the RF performance. An asymmetric SPDT switch using transistor with 500 nm head length and 250 nm foot length is designed to demonstrate transistor performance. The switch achieved an insertion loss of better than 0.85 dB throughout the 3.2–3.8 GHz bandwidth. The low-noise path can handle 25 W power level, while the high-power path can withstand up to 50 W of RF power at 1 dB compression level. The isolation performance is about 25 dB, while the return loss of the switch is better than 12 dB. The switch occupies a chip area of 2 x 2.2 mm2.Item Open Access Offloading deep learning powered vision tasks from UAV to 5G edge server with denoising(Institute of Electrical and Electronics Engineers, 2023-06-20) Özer, S.; İlhan, H. E.; Özkanoğlu, Mehmet Akif; Çırpan, H. A.Offloading computationally heavy tasks from an unmanned aerial vehicle (UAV) to a remote server helps improve battery life and can help reduce resource requirements. Deep learning based state-of-the-art computer vision tasks, such as object segmentation and detection, are computationally heavy algorithms, requiring large memory and computing power. Many UAVs are using (pretrained) off-the-shelf versions of such algorithms. Offloading such power-hungry algorithms to a remote server could help UAVs save power significantly. However, deep learning based algorithms are susceptible to noise, and a wireless communication system, by its nature, introduces noise to the original signal. When the signal represents an image, noise affects the image. There has not been much work studying the effect of the noise introduced by the communication system on pretrained deep networks. In this work, we first analyze how reliable it is to offload deep learning based computer vision tasks (including both object segmentation and detection) by focusing on the effect of various parameters of a 5G wireless communication system on the transmitted image and demonstrate how the introduced noise of the used 5G system reduces the performance of the offloaded deep learning task. Then solutions are introduced to eliminate (or reduce) the negative effect of the noise. Proposed framework starts with introducing many classical techniques as alternative solutions, and then introduces a novel deep learning based solution to denoise the given noisy input image. The performance of various denoising algorithms on offloading both object segmentation and object detection tasks are compared. Our proposed deep transformer-based denoiser algorithm (NR-Net) yields state-of-the-art results in our experiments.Item Open Access Performance of edge windowing for OFDM under non-linear power amplifier effects(IEEE, 2017) Göken, Çağrı; Dizdar, OnurEdge windowing is a windowing technique for Orthogonal Frequency Division Multiplexing (OFDM) signals based on the idea of using shorter cyclic prefix (CP) and longer window lengths at the edge subcarriers while keeping the symbol length fixed. In this study, we investigate the performance of OFDM signals with edge windowing under non-linear power amplifier (PA) effects by observing out-of-band (OOB) emission characteristics, average error vector magnitude (EVM) and coded block error rate (BLER) performance. We explore whether the possible gains over conventional windowing in the presence of PA is possible. We show that the edge windowing can still provide improvements over conventional windowing in terms of OOB emission suppression under various PA models at the expense of increased average EVM, whereas the channel coding substantially mitigates the performance loss due to inter-symbol and inter-carrier interference (ISI-ICI) effects arising as a result of shorter CP length at the edge subcarriers.Item Open Access Zone based GLRT for detecting physical random access channel signals in 5G(Bilkent University, 2019-12) Tütüncüoğlu, FeridunIn LTE/5G systems, the random access channel (RACH) process occurs during the boot-up phase. As channel state information is not available at this stage, detecting several devices with high performance presents a challenging problem. In particular, servicing many devices simultaneously can get difficult when a large number of user equipment and machines exist in the network. The problem can become more dramatic as the number of user equipment increases around the world. In the literature, power delay profile (PDP) is proposed as a decision metric for this problem. The use of this metric handles many cases with satisfactory performance and low complexity; however, it does not lead to optimal detection performance. In this thesis, we address this issue with a generalized likelihood ratio test (GLRT) based approach and propose detectors with high detection performance. We also derive an ideal detector that provides an upper bound on the detection probability. Via extensive RACH simulations, it is shown that improvements in detection performance can be achieved by the proposed approach in various scenarios.