Power Allocation for Adaptive-Connectivity Wireless Networks Under Imperfect CSI
Bibliographic record
Abstract
In this paper, we propose a power allocation algorithm to guarantee quality of experience (QoE) while considering the impact of imperfect channel state information (CSI) in multi-connectivity multiple-input multiple-output (MIMO) systems. Multi-connectivity is a promising solution for wireless technologies to fulfill ever-increasing demands for QoE via massive MIMO. However, the performance of MIMO systems mostly depends on the accuracy of the channel estimation algorithm that provides CSI. Under imperfect CSI scenarios, increasing transmit power to satisfy QoE for one user exacerbates interference with others, thus leading to a tradeoff between power consumption and user satisfaction. To guarantee QoE while minimizing transmission power under imperfect CSI, the effect of imperfect CSI is quantified, and a closed form signal-to-interference-plus-noise ratio (SINR) is derived. Then, a two-stage algorithm categorizes UEs into two groups: those that only need single connectivity and those that need dual connectivity (i.e., adaptive connectivity). After classification, transmission power is allocated to guarantee QoE via a power control strategy that minimizes the transmission power. By comparing performance with a single connectivity–based algorithm and fixed multi-connectivity–based algorithms, we show that our proposed algorithm not only satisfies all the UEs in the system but also consumes less transmission power than the benchmarks.
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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".