Efficient Mutual Interference Minimization and Power Allocation for OFDM-Based Cognitive Radio
Bibliographic record
Abstract
The ever-increasing demand for precious radio spectrum along with the inefficient usage of licensed band has led to the advent of the cognitive radio (CR) technology, which aims to provide opportunistic spectrum usage to unlicensed users and thus lead to the co-existence and interference control problem among heterogeneous systems. In this paper, an interference minimization and subcarrier power allocation approach for orthogonal frequency division multiplexing (OFDM)-based cognitive network is proposed. A transmission power negotiation signaling between CR transmitter and receiver is established through the use of encoded cyclic prefix (CP). Therefore, mutual interference to primary and other cognitive users can be minimized with reduced unnecessary transmission power. In addition, system performance of the receiver can be guaranteed in the process of mutual interference minimization. Beside this, subcarrier power allocation profile can be chosen from a set of predefined profiles and can be sent at the same time without additional signaling link and extra delay to the network. The transceiver structure and control signal encoding and decoding algorithm are investigated. The performance of the proposed signaling links are also analyzed and evaluated through simulations in different channel scenarios. In addition, interference minimization technique is validated through the simulation of probability density function (PDF) and then in a cognitive radio network with randomly distributed nodes to assess the overall perceived interference.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".