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Corner-truncated patch antenna with parasitic elements and circular feed slot for S-band CubeSat applications
(2025-12-01)
Hemachai, Thanaphon
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Dentri, Sitthichai
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Janpangngern, Pisit
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Phakphisut, Watid
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Torrungrueng, Danai
This study presents a corner-truncated patch antenna with symmetrically loaded parasitic elements and a circular feed slot designed for CubeSat S-band communication applications. The antenna design evolves through successive stages, integrating corner truncations, a slit-ring structure, and segment-circular parasitic patches to enhance impedance matching and polarization performance. In its final configuration, a dual-stacked arrangement with inter-substrate spacing and a vertical capacitive feed further improves current distribution symmetry and broadens the operational bandwidth. Parametric analysis validates the effectiveness of each antenna design refinement, demonstrating improvements in impedance bandwidth, axial ratio bandwidth, and gain performance. The fabricated prototype achieves a wide impedance bandwidth from 1.65 GHz to 2.70 GHz, fully encompassing the CubeSat uplink (2.025–2.110 GHz) and downlink (2.200–2.290 GHz) frequency ranges. It maintains an axial ratio below 3 dB across 1.97 GHz to 2.32 GHz, ensuring efficient circular polarization. Additionally, a stable gain of approximately 7.50 dBic at 2.025 GHz supports reliable communication with ground stations. The combination of compact structure, low profile, and wideband circular polarization makes the proposed antenna a promising candidate for CubeSat communication systems. The novelty of this research lies in the integration of a corner-truncated patch, symmetrically loaded segment-circular parasitic elements, and a circular slit-ring capacitive feed within a dual-stacked substrate configuration to achieve wideband circular polarization and stable unidirectional radiation.
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A circularly polarized tunable frequency circular patch antenna for S-band space base application
(2025-05-01)
Waranon, Likhit
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Dentri, Sitthichai
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Phakphisut, Watid
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Krairiksh, Monai
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Phongcharoenpanich, Chuwong
This research proposes a unidirectional circularly polarized tunable frequency circular patch antenna scheme for S-band uplink satellite communications. In the antenna scheme, the circular radiating patch is enclosed by 4×8 rectangular-shaped auxiliary elements along the +x, −x, +y, and −y−axes. To shift the resonant frequency of the antenna to lower frequencies, the auxiliary elements on the four axes are uniformly shorted. There are nine shorting configurations (configurations A – I) corresponding to the S-band uplink chain (2.025 GHz to 2.110 GHz). Besides, the circular polarization is switchable between right- (RHCP) and left-hand circular polarization (LHCP) by relocating the feed point from the +y axis for RHCP to +x axis for LHCP. Switching mechanisms dynamically reconfigure the antenna by modifying its electrical length to support multiple frequency bands, all of which maintain circular polarization. Since the proposed antenna scheme is intended for the S-band uplink frequency of 2.06 GHz, measurements are carried out with configuration F of the antenna scheme. The measured impedance bandwidth (|S<inf>11</inf>|≤-10 dB) and axial ratio bandwidth (AR<3 dB) are 6.2% and 4.49%, with the maximum gain of 6.54 dBic for RHCP and LHCP. The radiation pattern is of unidirectionality. Furthermore, the proposed antenna scheme meets the qualification sine vibration and random vibration test requirements. The proposed antenna scheme is thus suitable for S-band uplink satellite communications. The novelty of this research lies in the use of the auxiliary elements of various shorting configurations to achieve frequency tunability; and the alteration of the feed point location to switch between RHCP and LHCP.
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Deep Learning Aided Robust RSRP Prediction in Cellular Networks
(2024-01-01)
Wongphatcharatham, Tanutsorn
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Phakphisut, Watid
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Jaruvitayakovit, Tanun
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Boonkajay, Amnart
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Huang, Jiajia
We propose a transfer learning enhanced hybrid model for robust reference signal received power (RSRP) prediction. The hybrid model comprises an expected RSRP estimation based on transmit power, 3-D antenna gain models, path loss, and a deep learning (DL) for predicting an error from ground-truth measurement. The DL architecture consists of regression neural network (NN) and convolutional neural network (CNN). Besides cell site configuration and the long-term evolution (LTE) measurement report from user equipments (UEs), the expected RSRP and geospatial data e.g. building percentage and clutter index are considered. Since trained model may not perform well in new environment, it requires tedious work and long time to collect data at a new cell site. Therefore, we use transfer learning (TL) to apply the trained model to the other areas, which have differences in environment information and antenna configurations, by transferring the knowledge acquired from trained model. The results of the trained area show that root mean square error (RMSE) and mean absolute error (MAE) are approximately 2.92 and 2.01, respectively. For the other area, TL have improved MAE approximately 1 to 2.
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Efficient Design of Read Voltages and LDPC Codes in NAND Flash Memory Using Density Evolution
(2023-01-01)
Duangthong, Chatuporn
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Phakphisut, Watid
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Wardkein, Paramote
Low-density parity-check (LDPC) codes play an important role in the reliability enhancement of commercial NAND flash memory. Unfortunately, due to the requirement of the reading speed of NAND flash memory, the LDPC decoder will not obtain precise soft information to achieve high error-correcting capability. In this work, we use a density evolution (DE) algorithm to reveal the decoding threshold of the LDPC decoder affected by the read voltages. We propose the efficient design of read voltages so that the LDPC decoder has the lowest decoding threshold. Therefore, this method can guarantee that the designed read voltages are suitable for a given LDPC code. Moreover, since we found that the designed read voltages are related to the structure of the LDPC code, the joint design of the read voltages and LDPC code is then proposed to achieve the capacity of NAND flash memory. The simulation results demonstrate that our proposed design significantly improves the frame error rate (FER) performance of NAND flash memory.
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Development of High Efficient LDPC Encoder for Deep Space Applications
(2023-01-01)
Srisupha, Thanat
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Wongsa, Anusorn
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Duangthong, Chatuporn
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Phakphisut, Watid
In this paper, we present the design and implementation of a high efficient low-density parity-check (LDPC) encoder for deep space applications. The proposed encoder utilizes the generator matrix of CCSDS LDPC codes to simplify the encoding process and reduces the complexity of hardware implementation. The proposed encoder has two types. The first type aims to design a low-complexity architecture and flexibility. The second type presents high throughput architecture, allowing the user to choose the appropriate type according to their usage condition. The results of FPGA synthesis show that the proposed LDPC encoders achieve flexibility, low complexity, and high throughput.
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Multi-Agent Deep Q-Learning for Antenna Tilt Optimization in Wireless Networks
(2023-01-01)
Wongphatcharatham, Tanutsorn
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Phakphisut, Watid
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Puttarak, Nattakan
The configuration of an antenna installed at a base station involves the quality of communication in wireless networks. For example, at each transmitter, the antenna tilt must be optimized such that the desired and undesired receivers obtain the highest and lowest signal strength, respectively. In this work, we propose to use multi-agent deep Q-learning to optimize the antenna tilt. Our channel model includes the three-dimensional antenna gain, the Ericsson path loss model, and the digital elevation model (DEM). Our simulation indicates that multiagant deep Q-learning provides good signal quality.
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Design and Implementation of Forward-Backward Processing Unit for LDPC Decoder
(2023-01-01)
Wongsa, Anusorn
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Phakphisut, Watid
This paper presents an efficient scalable architecture of node processing in low-density parity-check decoder. The node processing unit is based on the forward-backward algorithm. The proposed design relies on the cyclic shifting operation of scalable vectors. The number of nodes can be simply scaled without an increase of operator. Our design supports run-time scalability with a low hardware resource and high maximum operating frequency. The result of FPGA-based synthesis shows that, for every 1 degree increase, the number of logic elements increases at near linear rate roughly 47 elements, as well as the number of registers linearly increases at 48 registers. The clock cycles also linearly increase by 2 clock cycles per degree.
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Reference Signal Received Power Prediction Using Convolutional Neural Network with Residual Loss
(2023-01-01)
Ngenjaroendee, Thearrawit
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Phakphisut, Watid
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Wijitpornchai, Thongchai
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Areeprayoonkij, Poonlarp
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Jaruvitayakovit, Tanun
In this paper, LTE measurement reports collected from user equipments are used to generate the residual loss, which can represent the loss value of each grid. The residual loss and geospatial data are used in the learning process of convolutional neural network (CNN). We also use the site configuration and three-dimensional antenna pattern. Thus, the neural network and convolutional neural network are proposed to construct deep learning to predict the reference signal received power (RSRP) in Bangkok, Thailand. The results show that residual loss can improve the efficiency of prediction.
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5G Channel Coding Tool: Learning and Performance Evaluation
(2023-01-01)
Mueadkhunthod, Krittiyaporn
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Wongsa, Anusorn
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Srisupha, Thanat
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Duangthong, Chatuporn
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Puntsri, Kidsanapong
The 5G channel coding tool is presented to explain channel coding in 5G New Radio (NR). Our tool consists of six modules such as information generator, channel encoder, modulator, noise generator, demodulator, and channel decoder. The channel encoder and modulator are developed according to 3GPP TS 38.212 and 3GPP TS 38.211 technical specifications, respectively. The 5G channel coding tool provides a graphical user interface (GUI) in which users can study the block diagrams of downlink and uplink process. In addition, our 5G channel coding tool can be used to evaluate the bit error rate performance of 5G channel coding in additive white Gaussian noise (AWGN) and fading channels.
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Integer programming-based non-uniform window decoding schedules for spatially coupled low-density parity-check codes
(2022-10-01)
Khittiwitchayakul, Sirawit
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Phakphisut, Watid
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Supnithi, Pornchai
Spatially coupled low-density parity-check (SC-LDPC) codes generally use a window decoding scheme, which is known to yield a near-optimal decoding, compared to full block decoding. Recently, a non-uniform schedule has been proposed to eliminate unnecessary updates of variable nodes within a window: this schedule is generated based on the behaviour of variable node updates analysed by density evolution. Here, the authors present a new non-uniform schedule based on integer programming, whereby the objective functions and constraints are derived from a protograph-based extrinsic information transfer chart. Our design is more flexible than the previous design, because the integer programming-based design allows reduction of update numbers and performance losses through the constraints function, whereas the previous design requires observation of variable node update behaviour. The authors report the performance of their designs of non-uniform schedules in additive white Gaussian noise (AWGN) and inter-symbol interference (ISI) channels. Particularly, in the ISI channel, the authors’ non-uniform schedules are designed with cooperative decoding between a Bahl-Cocke-Jelinek-Raviv (BCJR) detector and an SC-LDPC decoder.

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