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Estimasi Rapat Spektral Daya Berbasiskan Compressive Sampling Dyonisius Dony Ariananda
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 4: November 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2295.66 KB)

Abstract

This paper focus on spectrum sensing based on power spectral density (PSD) reconstruction from sub-Nyquist-rate samples. In the existing works on PSD reconstruction from sub-Nyquist-rate samples, the resulting system of linear equations (SLE) is generally overdetermined, which allows the PSD reconstruction using least-squares (LS). Note that there is a lower bound for the achievable sampling rate ensuring that the resulting SLE is overdetermined. This paper aims for a further sampling rate reduction, which results in an underdetermined SLE. However, when the resulting SLE is underdetermined, the LS method cannot be used to reconstruct PSD and additional constraints are required. Under this circumstance, a sparsity assumption (which is applicable for some applications) can be applied on the PSD. The use of the orthogonal matching pursuit (OMP) and the least absolute shrinkage and selection operator (LASSO) algorithms to reconstruct the PSD for the case of underdetermined SLE is examined. The simulation study shows that if an appropriate regularization parameter is used, the quality of the PSD reconstructed using LASSO is only slightly below the one produced using Nyquist-rate sampling. From the detection point of view, the PSD reconstructed using LASSO can accurately locate the occupied frequency band when the user signal power is sufficiently high compared to the noise power. Meanwhile, OMP can be used only in the noiseless scenario. These results indicate that the sampling rate alleviation up to a very low rate is possible while maintaining the quality of the spectrum sensing results at the acceptable level.
A Microstrip Antenna Design Using an Heuristic Algorithm I Made Adhi Wiryawan; Maria Veronica Astrid Wahyuningtyas; Anugerah Galang Persada; Dyonisius Dony Ariananda
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 4, No 1 (2020): March 2020
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijitee.56343

Abstract

Microstrip antennas have several advantages. Some of them are that they have a compact shape and small dimensions. Moreover, they are also easy to be fabricated and easily connected as well as integrated with other electronic devices. Currently, designing antennas conventionally is limited by time, energy, and experience as well as expertise. As an alternative, a way to design antennas with revolutionary methods is developed using algorithms and computing. Algorithm design techniques can overcome limitations and automatically find practical solutions that usually take a long time to discover. The particle swarm optimization algorithm and a genetic algorithm can find solutions from microstrip antennas. Objective functions play an essential role in heuristic algorithms. With a proper objective function, simulation results are obtained on the particle swarm optimization algorithm with a return loss value of -47.837, VSWR of 1.0083, and impedance of 46.805 Ω. In contrast, the genetic algorithm obtains return loss of -16.157 dB, impedance of 50.233 Ω, and VSWR of 1.3687.
Kinerja Energy Detection Spectrum Sensing untuk Cognitive Radio Menggunakan GNU Radio Hudaya Muna Putra; Sigit Basuki Wibowo; Dyonisius Dony Ariananda; Wahyu Dewanto
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 11 No 3: Agustus 2022
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v11i3.3757

Abstract

The increasing number of wireless communication applications has led to spectrum scarcity problems. On the other hand, the current system in allocating the spectrum frequency is inefficient. To mitigate this issue, a cognitive radio (CR) system is proposed. CR is a smart radio that is able to sense the environment, locate the spectrum holes, and adapt its transmission parameter to exploit the existing spectrum holes. This underlines the importance of the spectrum sensing module to enable the operation of the CR system. The objective of the spectrum sensing module is to achieve the best utility from the available spectrum frequency. CR system is implemented in the unlicensed secondary users allowed to rent the spectrum currently not used by primary users (PU). In this paper, energy-detection-based spectrum sensing is implemented on the GNU Radio platform. We first implement the power spectral density (PSD) estimation method based on the periodogram by exploiting the Embedded Python block facility on the GNU Radio. Next, we implement the spectrum sensing decision module in the GNU Radio, which compares the PSD estimate of the PU signals corrupted by noise with a threshold. The PU signal is simulated as a bandpass random process occupying a particular frequency band. The spectrum sensing decision module is developed to allow the computation of the probability of detection (PD) and the probability of false alarm (PFA), which is performed by exploiting the Embedded Python block. One indicator to evaluate the performance of the spectrum sensing module is the receiver operating characteristic curve based on the computed PD and PFA on the GNU Radio. We evaluate the performance of the spectrum sensing for different SNRs and thresholds. The result shows that the energy-detection-based spectrum sensing is able to locate the existence of the PU when the signal-to-noise ratio (SNR) is sufficiently high.