A Study of Convergence Modem Design for Giga Internet Service over CATV Network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
본 논문에서는 CATV 동축케이블 망에서 초고속인터넷 서비스를 제공하기 위한 네트워크 융복합의 새로운 기술을 제안하고, 1Gbps급 전송속도를 갖는 케이블 집선장치와 모뎀을 제작하였다. 이 기술은 기존 DOCSIS 규격의 결합(Bonding) 기술에 비해 제작비용을 낮출 수 있을 뿐만 아니라 채널 대역폭 조절에 따라 데이터 속도도 가변이 용이한 구조로 설계되었다. 실험결과에 따르면, 128QAM 컨볼루션 코드 레이트를 1/2, 2/3, 3/4, 5/6, 7/8로 변화시켰을 때, 데이터는 에러율이 0 상태에서 최대 299Mbps까지의 전송속도를 나타냈으며, 256QAM에서는 <TEX>$10^{-5}$</TEX>이내의 에러율 상태에서 342Mbps의 전송속도를 나타내었다. 본 논문의 결과를 활용하여, 채널 대역폭 200MHz를 확보하고 채널 상태에 따라 변조율과 부호율을 조정하면, 1Gbps 이상 전송속도가 가능하고, 기존 DOCSIS방식 보다 성능과 가격 면에서 경쟁력을 가질 것으로 기대된다. This paper aims to propose a novel technology of network convergence to provide ultra high speed internet services over CATV networks, by which a CMC(cable modem concentrator) and CM(cable modem) of 1Gbps level are designed. This technology not only lowers the production cost in comparison to the existing bonding technology with DOCSIS specification but also enables the adjustment of data speed based on the channel bandwidth. According to the experiments, when convolutional code rate with 128QAM is changed to 1/2, 2/3, 3/4 and 7/8, the data recorded the maximum transmission speed of up to 299 Mbps at the zero error rate. As the convolutional code rates with 256QAM is increased, it showed 334Mbps at the error rate of <TEX>$10^{-5}$</TEX>. Based on the findings of this paper, if we secure the channel bandwidth of 200MHz and adjust the modulation order of QAM and the convolution code rate depending on the channel status, we can get the transmission speed of more than 1Gbps, which is much more competitive in its function and price than the existing technology based on DOCSIS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it