Recursive Waterfilling for Wireless Links With Energy Harvesting Transmitters
Bibliographic record
Abstract
Energy harvesting is often used for green communications. The problem of power allocation is then to maximize the throughput, taking into account the fact that channel conditions and energy sources are time varying. In particular, for the constraints of the target problem, besides the allocated power values being nonnegative, the successively harvested energy sum leads to the triangle coefficient matrix of the power sum constraints. In this paper, we propose a geometric waterfilling (GWF) algorithm in place of the conventional waterfilling (CWF) algorithm for power allocation with a sum power constraint. We then recursively apply the GWF as a functional block to sequentially solve the power allocation problem for energy harvesting transmission in a fading channel. This algorithm is referred to as RGWF. The proposed RGWF is further extended to solving the minimization of the transmission completion time (referred to as RGWFn) by inserting a condition to check if the preset information transmission data bits are achieved. Since RGWF is defined by recursion and along natural progress of time, we can compute a family of solutions for subprocesses from epoch 1 to epoch k, for k = 1, ..., K, where K is the index of the final epoch for the entire process. Thus, RGWF can be utilized for efficiently carrying out the computation of RGWFn. RGWF and RGWFn belong to dynamical recursive algorithms. Compared with the existing results in the open literature, the proposed algorithms have distinguished features: 1) They provide the exact optimal solutions via efficient finite computation under the recursive category, and 2) the optimality of the proposed algorithms is strictly proven. Numerical examples are provided to illustrate the procedures to obtain the optimal power allocation by using the proposed algorithms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 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".