Cluster based coordinated beamforming and power allocation for MIMO heterogeneous networks
Bibliographic record
Abstract
Coordinated intercell interference management is essential in dense heterogeneous networks with limited backhaul capacity. This paper proposes a cluster-based hierarchical cooperative transmission and resource allocation scheme with proportionally fair objective in a cellular network where both the macro base station (BS) and the small cell access-points (SCAs) are equipped with multiple antennas and share the entire available bandwidth. As the first step, SCAs form clusters based on their pairwise distances where each cluster comprised of adjacent SCAs which are potentially strong interferers. Clustering enables intra-cluster coordinated transmission and inter-cluster coordinated resource allocation. Specifically, SCAs within each cluster form a network multiple-input multiple-output (MIMO) system, share the users' data symbols, and cancel intra-cluster interference via zero-forcing spatial multiplexing. Further, a distributed power control scheme is devised for the purpose of mitigating inter-cluster interference without exchanging users' data signals. We show that clustering facilitates intra-cluster coordination by enabling data exchange and channel training with reasonable backhaul communication within each cluster. We also show that the proposed inter-cluster power control scheme can further improve the network-wide utility.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".