A Survey of Millimeter-Wave Communication: Physical-Layer Technology Specifications and Enabling Transmission Technologies
Bibliographic record
Abstract
Millimeter-wave (mmWave) frequency bands, which offer abundant underutilized spectral resources, have been explored and exploited in the past several years to meet the requirements of emerging wireless services highlighted by high data rates, ultrareliability, and ultralow delivery latency. Yet, the unique characteristics of mmWave, e.g., continuous wide bandwidth, large path, and penetration losses, along with hardware constraints, call for innovative technologies for mmWave communication. Recently, an extensive amount of work on mmWave communication has been carried out by researchers and practitioners from both academia and industry, and various technologies have been developed for mmWave communication systems to fulfill the full potential of mmWave frequency bands. In this article, we present a comprehensive survey of the standardization of mmWave communication, the latest progress and outcomes of the research on mmWave communication technologies, and the emerging applications of mmWave communication. In particular, we provide a timely and in-depth summary of the state-of-the-art technology specifications of mmWave communication with an emphasis on the physical (PHY) layer. Then, we elaborate on a number of well-established or promising antenna architectures in mmWave communication systems and investigate the enabling PHY layer transmission technologies. Finally, we show some existing and emerging applications of mmWave communication and discuss the potential open research issues.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".