Fractional Frequency Reuse (FFR) Scheme for Inter-Cell Interference (ICI) Mitigation in Multi-Relay Multi-Cell OFDMA Systems
Bibliographic record
Abstract
In next-generation wireless networks, high data rates, improvement of Spectral Efficiency (SE), meeting the Quality of Service (QoS) requirements, increasing Energy Efficiency (EE), reducing cost, and lower complexity of cellular networks systems have become essential design metrics. Use of resource allocation and interference mitigation techniques achieve these targets, especially for users located in the cell edge region. FFR schemes with relays are used to minimize the impact of ICI in Orthogonal Frequency Division Multiple Access (OFDMA) cellular networks and to enhance the QoS. The frequency patterns (sets) in the FFR scheme are designed such that the interference from adjacent cells is minimized. Instead of using the cell system with Frequency Reuse Factor (FRF) of 1 TRF \pmb=1), FRF \pmb=3, and FRF \pmb=(1,3), this paper proposes new frequency patterns for FRF \pmb=(1,7/3) using (7,3,1) difference set and FRF \pmb=(1,7/4) using (7,4,2) difference set to provide an enhancement of the system performance in terms of SE, EE, Signal-to-Interference plus Noise Ratio (SINR), Cumulative Distribution Function (CDF) of SINR of the received signal, and Outage Probability (OP). Simulation results show that the proposed schemes have more significant ICI reduction for cell edge users compared to those of previous work.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".