Location‐assisted clustering and scheduling for coordinated homogeneous and heterogeneous cellular networks
Bibliographic record
Abstract
ABSTRACT The multiple‐input multiple‐output (MIMO) downlink with transmitter coordination in a cellular network is considered. The transmitters are assumed to be either neighbouring base stations (homogeneous) or a base station with a number of remote radio heads that form picocells in its coverage area (heterogeneous). In centralized coordinated transmission from a cluster of nodes, the channel state information (CSI) of users needs to be sent to a central processor for precoding and resource allocation. Real‐time CSI feedback from the users to their home base station and from the base stations to the central processor is a serious challenge from a practical point of view. In this work, efficient location‐assisted limited‐feedback schemes for homogeneous and heterogeneous cellular networks are proposed. First, a hybrid mode transmission scheme with reduced feedback requirement is proposed for a homogeneous network, in which on the basis of the location of users, some are served using a single‐cell multiuser MIMO approach and some using a network MIMO approach. Next, for a heterogeneous network, a location‐assisted clustering and scheduling scheme is proposed for the case of joint reference signals, in which multiple transmission nodes that share the reference signals cannot be distinguished from each other. We evaluate the performance of our schemes with a series of simulations. In the homogeneous scenario, we compare with the case of full CSI, and in the heterogeneous scenario, we compare with joint transmission from all nodes in a cell. Copyright © 2012 John Wiley & Sons, Ltd.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".