• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.89-95
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• 2020

In this paper, we present Beamforming (BF) Training (BFT) for asymmetric links in IEEE 802.11ay. IEEE 802.11ay introduced BFT for asymmetric links that aims to increase the BFT success probability for Station (STA) with insufficient link budget to communicate with an Access Point (AP). BFT for asymmetric links utilizes directional BFT allocation to avoid the usage of quasi-omni pattern at the AP side, and thus to increase STA's BFT success rate. However, there are no publicly available simulation tools supporting IEEE 802.11ay. For these reasons, we present in this paper an implementation of BFT for asymmetric links in ns-3 with its novel techniques such as Training RX (TRN-R) subfield and BFT allocation. We then evaluate by simulation the performance of BFT for asymmetric links.

• Electronics and Telecommunications Trends
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• v.31 no.5
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• pp.21-30
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• 2016

현재 이동/무선통신 환경은 사람과 사람 간의 통신 서비스 형태뿐만 아니라 IoT 통신 서비스로 점차 확대되어가고 있으며, Wi-Fi, Bluetooth 중심의 근거리 무선통신뿐만 아니라 더욱 통신영역이 좁아진 근접통신의 필요성이 대두되고 있다. 이에 따라 근접통신 기술 개발이 지속적으로 이루어지고 있으며, 특히 기기 간의 대용량 데이터 교환이 증가함에 따라 기가급의 속도를 제공하는 초고속 근접통신 기술들이 활발하게 개발되고 있다. IEEE 802에서는 주변 기기들간의 직접(Point-to-Point: P2P) 통신을 지원하는 802.15.3e 초고속 근접통신 기술을 개발하고 있으며, 또한, 802.11ad의 후속 표준으로 개발이 시작된 802.11ay의 Usage Model에도 근접통신이 포함되어 있다. 본고에서는 이러한 초고속 근접통신 기술동향에 대해 기술하고자 한다.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.13-20
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• 2022

IEEE 802.11ay Wi-Fi is the next generation wireless technology and operates in mmWave band. It supports the MU-MIMO (Multiple User Multiple Input Multiple Output) transmission in which an AP (Access Point) can transmit multiple data streams simultaneously to multiple STAs (Stations). To this end, the AP should perform MU-MIMO beamforming training with the STAs. For efficient MU-MIMO beamforming training, it is important for the AP to estimate signal strength measured at each STA at which multiple beams are used simultaneously. Therefore, in the paper, we propose a deep learning-based link quality estimation scheme. Our proposed scheme estimates the signal strength with high accuracy by utilizing a deep learning model pre-trained for a certain indoor or outdoor propagation scenario. Specifically, to estimate the signal strength of the multiple concurrent beams, our scheme uses the signal strengths of the respective single beams, which can be obtained without additional signaling overhead, as the input of the deep learning model. For performance evaluation, we utilized a Q-D (Quasi-Deterministic) Channel Realization open source software and extensive channel measurement campaigns were conducted with NIST (National Institute of Standards and Technology) to implement the millimeter wave (mmWave) channel. Our simulation results demonstrate that our proposed scheme outperforms comparison schemes in terms of the accuracy of the signal strength estimation.