• Journal of Broadcast Engineering
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• v.18 no.4
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• pp.572-588
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• 2013

In this paper, 4K-UHDTV or 8K-UHDTV and UHD-3DTV that the next generation broadcasting implementation and the possibility of direct receiving environment construction is analyzed on the terrestrial broadcasting. Particularly, we investigated the possibility by analyzing the previous and related works with regard to UHDTV transmission by DVB-T2 that is one of the best commercialized transmission mode. In order that the UHDTV broadcasting succeeds once again after completion of digital terrestrial switch over at the end of 2012, the ultra high resolution image transfer is important. However, the direct, the indoor and ubiquitous receiving environment is important in not only TV but also the personal type multimedia terminal in the sense of UHDTV service penetration. Therefore, in this paper, by using SFN and high error-correcting mode in DVB-T2 standard, the efficient frequency utilization and effective reception environment construction is illustrated. Particularly, SFN network constitution by 2 mutually different frequencies including the VHF bandwidth and UHF band, and etc. is shown. And the method that builds the free wireless receive environment by using SFN low power radio repeater and for home use gap filler is proposed. And the effect and frequency amount required are presented, when UHDTV broadcasting use 10MHz bandwidth.

• 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.