Energy-Efficient Power Allocation for HARQ Systems
Bibliographic record
Abstract
Reliability of data transmission is a fundamental problem in wireless communications. Fading in wireless channels causes the signal strength to vary at the receiver and this results in loss of data packets. To improve the reliability, automatic repeat request (ARQ) schemes were introduced. However these ARQ schemes suffer from a reduction in the throughput. To address the throughput reduction, conventional ARQ schemes were combined with forward error correction (FEC) schemes to develop hybrid-ARQ (HARQ) schemes. For improving the reliability of data transmission, HARQ schemes are included in the wireless standards like LTE, LTE-Advanced and WiMAX. Conventional HARQ systems use the same transmission power in different ARQ rounds. However this is not optimal in terms of minimizing the average energy spent for successful transmission of a data packet. In this book chapter, the recent research results related to HARQ systems are reviewed first. Next, optimal resource allocation in HARQ systems with a limit on the maximum number of allowed transmissions for a data packet is considered in the next part. Specifically, the problem of minimizing the rate-outage probability under a constraint on average energy consumption per data packet for both incremental redundancy (IR)-based and Chase combining (CC)-based HARQ systems is considered. Towards solving the optimization problems, the expressions for rate-outage probability of both IR-HARQ and CC-HARQ systems in i.i.d. Rayleigh fading channels is provided. Methods to solve the optimization problems using nonlinear optimization techniques are discussed. To reduce the complexity of finding a solution, the rate-outage probability expressions are approximated, using which, the non-convex optimization problems are converted into geometric programming problems (GPPs), for which the closed-form solutions are derived. Illustrative and analytical results show that the proposed power allocation provides significant gains in energy savings over the traditional equal power allocation transmission, and the closed-form GPP solution can provide a performance close to that of the exact method for smaller values of rate-outage probability.
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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 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".