A millimeter wave network for billions of things
Bibliographic record
Abstract
With the advent of the Internet of Things (IoT), billions of new connected devices will come online, placing a huge strain on today's WiFi and cellular spectrum. This problem will be further exacerbated by the fact that many of these IoT devices are low-power devices that use low-rate modulation schemes and therefore do not use the spectrum efficiently. Millimeter wave (mmWave) technology promises to revolutionize wireless networks and solve spectrum shortage problem through the usage of massive chunks of high-frequency spectrum. However, adapting this technology presents challenges. Past work has addressed challenges in using mmWave for emerging applications, such as 5G, virtual reality and data centers, which require multiple-gigabits-per-second links, while having substantial energy and computing power. In contrast, this paper focuses on designing a mmWave network for low-power, low-cost IoT devices. We address the key challenges that prevent existing mmWave technology from being used for such IoT devices. First, current mmWave radios are power hungry and expensive. Second, mmWave radios use directional antennas to search for the best beam alignment. Existing beam searching techniques are complex and require feedback from access points (AP), which makes them unsuitable for low-power, low-cost IoT devices. We present mmX, a novel mmWave network that addresses existing challenges in exploiting mmWave for IoT devices. We implemented mmX and evaluated it empirically.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".