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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".