Bibliographic record
Abstract
Introduction The spectrum between 30 and 300 GHz is referred to as the millimeter wave (mmWave) band because the wavelengths for these frequencies are in the range from about one to ten millimeters. The Federal Communications Commission (FCC) has allocated the 57–64 GHz mmWave band for general unlicensed use, opening the door to supporting high data rate wireless applications over the 7 GHz unlicensed band. Given the spectrum deficiency and network densification of cellular systems, how to use the mmWave band to support various machine/human-to-machine/human communications is critically important for fifth generation (5G) cellular systems. Millimeter wave can be applied to both outdoor and indoor wireless communications. mmWave together with massive multiple-input multiple-output (MIMO) is a promising candidate for 5G outdoor transmission, as discussed in Chapter 15. For indoor uses, mmWave communication has many salient features, listed below, and it is highly desirable for 5G femtocell communications. This chapter focuses on the indoor femtocell scenario. First, mmWave can achieve very high data rates (up to multi-Gbps), so it can enable many killer applications such as high-definition and interactive streaming services, and the Internet of Things. These applications require not only a high data rate but also stringent quality-of-service (QoS) requirements in terms of delay, jitter, and loss. Second, mmWave can coexist well with other wireless communication systems, such as the existing cellular systems, Wi-Fi (IEEE 802.11), and ultra-wideband (UWB) systems, because of the large frequency difference. Third, oxygen absorption has its peak at 60 GHz, so the transmission and interference ranges of mmWave communication are small, which allows very dense deployment of mmWave-based femtocells. In addition, the fact that the mmWave signal degrades significantly when passing through walls and over distance is helpful for ensuring security of the content. The special channel characteristics and features of mmWave communication pose new challenges regarding how to coordinate mmWave transmissions to achieve high spatial reuse and guarantee the QoS. In the following, given the unique characteristics of mmWave communications and of the appropriate multiplexing technologies and network architectures for mmWave-based femtocells, we discuss the key opportunities and challenges in resource management of mmWave-based wireless networks, and introduce an appropriate scheduling solution to explore the spatial multiplexing gain in mmWave networks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".