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Deep Learning-Based Channel Estimation With 1D CNN for OFDM Systems Under High-Speed Railway Environments
(2025-01-01)
Siriwanitpong, Aphitchaya
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Sanada, Kosuke
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Hatano, Hiroyuki
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Mori, Kazuo
;
Boonsrimuang, Pisit
In OFDM wireless communications, channel estimation performance is compromised in high-speed railway environments owing to extremely fast multipath fading and severe Doppler effect. Recently, a deep learning approach has been employed to improve the channel estimation performance, however it encounters significant challenges due to its high computational complexity. In order to deal with these challenges, this paper proposes channel estimation employing deep learning with one-dimensional convolutional neural network (1D CNN) schemes to enhance conventional least squares (LS) estimation. The first scheme provides better performance compared to conventional LS estimation. However, it is only suitable for OFDM systems with full pilot symbols, leading to decreased transmission efficiency and high complexity. In order to address those problems, the second scheme develops 1D CNN-based channel estimation employing scattered pilot symbols to enhance transmission efficiency and reduce computational complexity. In comparison to conventional LS estimation and deep learning-based channel estimation with bi-gated recurrent unit (bi-GRU), the performance evaluation demonstrates that the proposed 1D CNN-based schemes simultaneously improve channel estimation performance, transmission efficiency, and reduce computational complexity.
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Adaptive K-Repetition Transmission with Site Diversity Reception for Energy-Efficient Grant-Free URLLC in 5G NR
(2024-01-01)
Dataesatu, Arif
;
Sanada, Kosuke
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Hatano, Hiroyuki
;
Mori, Kazuo
;
Boonsrimuang, Pisit
The fifth-generation (5G) new radio (NR) standard employs ultra-reliable and low-latency communication (URLLC) to provide real-time wireless interactive capability for the internet of things (IoT) applications. To satisfy the stringent latency and reliability demands of URLLC services, grant-free (GF) transmissions with the K-repetition transmission (K-Rep) have been introduced. However, fading fluctuations can negatively impact signal quality at the base station (BS), leading to an increase in the number of repetitions and raising concerns about interference and energy consumption for IoT user equipment (UE). To overcome these challenges, this paper proposes novel adaptive K-Rep control schemes that employ site diversity reception to enhance signal quality and reduce energy consumption. The performance evaluation demonstrates that the proposed adaptive K-Rep control schemes significantly improve communication reliability and reduce transmission energy consumption compared with the conventional K-Rep scheme, and then satisfy the URLLC requirements while reducing energy consumption.
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Adaptive K-Repetition Transmission Employing Site Diversity Reception for 5G NR Uplink Grant-Free URLLC
(2023-01-01)
Dataesatu, Arif
;
Sanada, Kosuke
;
Hatano, Hiroyuki
;
Mori, Kazuo
;
Boonsrimuang, Pisit
The Internet of Things (IoT) is currently being employed for wireless real-time interactive systems with ultra-reliable and low-latency communications (URLLC) that provide high service quality for the fifth generation (5G) new radio (NR) standard. Grant-free (GF) transmission with K-Repetition (K-Rep) is a transmission technique that aims to meet the URLLC requirements. However, the K-Rep increases the number of packet transmissions, which may cause severe interference and substantial energy consumption. This paper employs site diversity reception to the GF K-Rep scheme and proposes its adaptive control mechanism to decrease the number of repeated transmissions in order to improve communication reliability and energy consumption for IoT URLLC users. According to our simulation results, the proposed site diversity reception for the K-Rep scheme can significantly improve the reliability compared with the conventional K-Rep under single-cell reception. Additionally, the proposed adaptive K-Rep control scheme can reduce the transmission energy consumption greatly, hence saving battery resource of IoT URLLC users.
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System Performance Enhancement with Energy Efficiency Based Sleep Control for 5G Heterogeneous Cellular Networks
(2022-04-01)
Dataesatu, Arif
;
Sanada, Kosuke
;
Hatano, Hiroyuki
;
Mori, Kazuo
;
Boonsrimuang, Pisit
This paper presents an improved sleep control algorithm for small base stations (SBSs) in 5G New Radio (NR) heterogeneous cellular networks (HetNets). HetNets consist of various base station tiers, including macro base stations (MBSs) and small base stations (SBSs), and have been suggested as a promising solution to enhance wireless coverage and network capacity, employing many SBSs into the MBS coverage. However, power consumption increases significantly as a result of an increase in the number of the SBSs. To solve this problem, the SBS sleep control has been proposed to reduce power consumption for the SBSs and improves energy efficiency, whereas it deteriorates system throughput compared with no sleep control system, consequently degrading the quality of service (QoS) performance at user equipments (UEs). This paper proposes an enhanced algorithm for SBS sleep control based on energy efficiency as a decision criterion for SBS operating state. From the evaluation results through computer simulation, the proposed scheme can provide improved performance for both energy efficiency and system throughput simultaneously, that is it can improve energy efficiency while maintaining almost the same system throughput as the no sleep control system. Concretely, the proposed scheme has the 14.89% improvement in energy efficiency while providing almost the same system throughput of over 99%, compared with no sleep control system

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