Joint Subcarrier and Power Allocation for Cooperative Communications in LTE-Advanced Networks
Why this work is in the frame
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Bibliographic record
Abstract
This paper considers an LTE-Advanced cooperative cellular network where a Type II relay station (RS) is deployed to enhance the cell-edge throughput and to extend the coverage area. To better exploit the existing resources, the RS and the eNodeB (eNB) transmit in the same channel (In-Band) with decode-and-forward relaying strategy. For such a network, this paper proposes joint Orthogonal Frequency Division Multiplexing (OFDM) subcarrier and power allocation schemes to optimize the downlink multi-user transmission efficiency. Firstly, an optimal power dividing method between eNB and RS is proposed to maximize the achievable rate on each subcarrier. Based on this result, we show that the optimal joint resource allocation scheme for maximizing the overall throughput is to allocate each subcarrier to the user with the best channel quality and to distribute power in a water-filling manner. Since QoS provision is one of the major design objectives in cellular networks, we further formulate a lexicographical optimization problem to maximize the minimum rate of all users while improving the overall throughput. A sufficient condition for optimality is derived. Due to the complexity of searching for the optimal solution, we propose an efficient, low-complexity suboptimal joint resource allocation algorithm, which outperforms the existing suboptimal algorithms that simplify the joint design into separate allocation. Both theoretical and numerical analyses demonstrate that our proposed scheme can drastically improve the fairness as well as the overall throughput.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it