Performance Analysis of Energy Beamforming WPCN Links With Channel Estimation Errors
Bibliographic record
Abstract
In this paper, we analyze the performance of a wireless-powered communication network that consists of a multiple-antenna hybrid access point (AP) and a single-antenna user. The AP transmits radio frequency (RF) power in the downlink (DL) using maximal ratio transmission (MRT) type energy beamforming. The user harvests it and transmits data to the AP in the uplink (UL), which is received with maximal ratio combining (MRC). Previous analyses have assumed the availability of perfect channel estimation. In contrast, we eliminate this unrealistic assumption and consider the effect of imperfect channel estimates. We first derive the distributions of the received AP signal-to-noise ratio (SNR). We then analyze the average throughput performance of delay-limited and delay-tolerant modes by evaluating the outage probability (OP) and ergodic capacity (EC). We also derive the exact bit error rates (BERs) and symbol error rates (SERs) of several digital modulations. Asymptotic performance expressions in the high SNR regime and the large antenna regime are also developed. Finally, analytical and asymptotic results are validated by Monte-Carlo simulations. The impacts of the transmit power, the energy harvesting (EH) time, the number of antennas and the efficiency of EH are investigated.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".