Uplink Achievable Rate and Power Allocation in Cooperative LTE-Advanced Networks
Bibliographic record
Abstract
This paper studies the achievable rate and power allocation to improve the uplink (UL) spectrum efficiency in a Long-Term Evolution Advanced (LTE-A) cooperative cellular network with the deployment of Type-II in-band decode-and-forward (DF) relay stations (RSs). The physical-layer UL transmission technology is based on single-carrier frequency-division multiple access (SC-FDMA) with frequency-domain equalization (FDE). Different from the downlink (DL) orthogonal FDMA system, signals on all subcarriers in the SC-FDMA system are transmitted sequentially rather than in parallel; thus, the user's achievable rate is not simply the summation of the rates on all allocated subcarriers. Moreover, each user equipment (UE) device has its own transmission power constraint instead of a total power constraint at the base station in the DL case. Therefore, the UL resource allocation problem in the LTE-A system is more challenging. To this end, we first derive the achievable rates of the SC-FDMA system with two commonly used FDE techniques, namely, zero-forcing (ZF) equalization and minimum-mean-square-error (MMSE) equalization, based on the joint superposition coding for cooperative relaying. We then propose optimal power allocation schemes among subcarriers at both the UE and RS to maximize the overall throughput of the system. Both theoretical analysis and numerical results demonstrate that our proposed power allocation schemes can drastically improve system throughput.
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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 | 0.001 |
| 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".