Small-Macro Cell Cooperation for HetNet Uplink Transmission: Spectral Efficiency and Reliability Analyses
Bibliographic record
Abstract
We investigate the impact of small-macro cell cooperation (SMC) in improving the spectral efficiency and reliability of uplink transmission in a heterogeneous network. We consider a network of two user equipments (UEs), a macro-cell base station (BS) and a small-cell BS. Joint SMC involves macro-to-small quantized feedback and decode-forward relaying from small to macro cell. This cooperation utilizes full-duplex transmission and intra-network spectrum sharing. We first propose a transmission scheme based on superposition block Markov encoding at each UE, coherent decode-forward relaying and sliding window decoding at the small-cell BS, and quantize-forward relaying and backward decoding at the macro-cell BS. Second, we derive the optimal macro-cell quantization to maximize the whole spectral efficiency. Third, for a certain non-fading scenario, we prove that the proposed scheme asymptotically achieves the capacity (maximum spectral efficiency) by reaching the cut-set bound as macro-cell power approaches infinity. Fourth, we formulate the outage probability over block fading channels, considering the outage events at the small and macro-cell BSs and the channel variations over different blocks. Last, we generalize the proposed scheme to an N (>2)-UE case. As macro-cell power increases, the results show that the proposed scheme achieves a full diversity order of two and outperforms all existing non-SMC schemes. These strong results suggest the utility of the proposed scheme for potential deployment in 5G cellular 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".