Null-Space Based Design With Learning for RIS-Aided Wireless Information and Power Transfer
Bibliographic record
Abstract
The paper presents a novel concept for reconfigurable intelligent surfaces (RIS) aided simultaneous wireless information and power transfer (SWIPT). The concept leverages, for the first time, the degrees of freedom provided by the null-space of the channel between the access point and the information receivers (IRs) through the RIS to send an additional energy signal to the energy receivers (ERs), simultaneously with the data transmission to the IRs. As with any new concept, the first step must be to validate it and assess its advantages and potential limitations in a typical wireless system composed of a multi-antenna transmitter, multiple IRs and ERs, and a RIS of varying dimensions. The paper does exactly this by designing a SWIPT system where the proposed null-space-based (NSB) approach is implemented. This design is then used to quantify the performance of the new approach and benchmark it against prior art. In this regard, we seek to maximize the ERs' harvested power and the IRs' data rate in the designed SWIPT system, which results in a multi-objective optimization problem proper to our NSB approach. To address this optimization problem, we propose and implement two solution approaches: one leveraging the deep deterministic policy gradient (DDPG) method, and the other utilizing alternating optimization (AO). We compare both solutions in terms of system performance, showing that the DDPG consistently outperforms the AO by 2–3% in all tested cases. Additionally, benchmarking against state-of-the-art approaches demonstrates that the proposed NSB SWIPT design outperforms existing SWIPT benchmarks by 15–20%, both with and without RIS.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".