Throughput and cost‐efficient interference cancelation strategies for the downlink of spectrum‐sharing Long Term Evolution heterogeneous networks
Bibliographic record
Abstract
Abstract In a heterogeneous network (HetNet), small cells such as femtocells considered in this work are deployed jointly with macrocells. This new cells' layer, when added to the network, generates interference, which could hamper neighboring macro‐user equipment (MUE) and femto‐user equipment (FUE) transmissions. In fact, this interference results in degradation of the network performance. In this paper, we propose a downlink interference cancelation (DL‐IC) strategy for spectrum‐sharing Long Term Evolution (LTE) HetNet. This DL‐IC strategy aims to reduce the interference impact on users by optimizing their received signal to interference plus noise ratio (SINR) using new utility functions for both FUEs and MUEs. These utility functions allow relaxation of the cancelation ratios in order to reduce implementation complexity while maximizing SINR, QoS, and throughput. We support by different system‐level simulations that both global network performance and user experience in terms of total throughput and received SNR or link‐level throughput, respectively, are significantly enhanced. Throughput gains achievable by the new DL‐IC strategy can reach as much as 200%against a homogeneous LTE network without IC along with an extra 48%per additional femtocell base station against a basic spectrum‐sharing LTE HetNet without IC. These performance figures are shown to surpass those achieved by interference avoidance techniques using either power or frequency resource allocation. Copyright © 2014 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".