4G\/5G Spectrum Sharing: Efficient 5G Deployment to Serve Enhanced Mobile Broadband and Internet of Things Applications
Bibliographic record
Abstract
The fifth-generation (5G) has been developed for supporting diverse services, such as enhanced mobile broadband (eMBB), massive machine-type communication (mMTC) and ultrareliable lowlatency communication (URLLC). The latter two constitute Internet of Things (IoT) enablers. The new spectrum released for 5G deployments are primarily above 3 GHz and, unfortunately, has a relatively high path loss, which limits the coverage, especially for the uplink (UL). The high propagation loss, the limited number of UL slots in a time-division duplexing (TDD) frame, and the limited user power gravely restrict the UL coverage, but this is where bandwidth is available. Moreover, the stringent requirements of eMBB and IoT applications lead to grave 5G challenges, e.g., site planning, ensuring seamless coverage, adapting the TDD downlink (DL)/UL slot ratio and the frame structure for maintaining a low bit error rate as well as low latency, and so on. This article addresses some of those challenges with the aid of a unified spectrum-sharing mechanism, and by means of a UL/DL decoupling solution based on fourth-generation (4G)/5G frequency sharing. The key concept relies on accommodating the UL resources in a long-term evolution (LTE) frequency-division duplexing (FDD) frequency band as a supplemental UL (SUL) carrier in addition to the new radio (NR) operation in the TDD band above 3 GHz. With the advent of this concept, the conflicting requirements of high-transmission efficiency, large coverage area, and low latency can be beneficially balanced. We demonstrate that the unified 5G spectrum-exploitation mechanism is capable of seamlessly supporting compelling IoT and eMBB services.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".