Optimization for Signal Transmission and Reception in a Macrocell of Heterogeneous Uplinks and Downlinks
Bibliographic record
Abstract
Internet-of-things (IoT) applications continue to drive advancements in serving as many heterogeneous low-latency downlinks and uplinks as possible within a constrained communication bandwidth. Full-duplexing (FD) transceivers have been introduced to implement simultaneous signal transmission and reception (STR) over the entire available frequency band. However, both inter-link interference and FD loop-interference are hardly suppressed to a necessary level for the effectiveness of FD-based STR even for microcells. This paper proposes an alternative STR technique per one time-slot for macrocells, where a fraction of a time-slot is used for downlinks and the remaining complementary fraction of the time-slot is used for uplinks. Thus, STR over the entire available bandwidth can be implemented in a way with no loop interference. Furthermore, another approach of using a fraction of the available bandwidth for downlinks and the remaining complementary fraction of the bandwidth for uplinks over the whole time-slot is also proposed. The problem of both downlink and uplink beamforming to maximize the energy efficiency of such heterogeneous networks subject to the quality-of-service in terms of downlink and uplink throughput is examined for all three possible STRs. Numerical results demonstrate the advantages of the time-fraction-wise STR and bandwidth-fraction-wise STR over the FD-based STR, where the time-fraction-wise STR is not only the best in serving the same numbers of downlinks and uplinks but also is capable of serving many more downlinks and uplinks with a higher energy efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".