Stochastic Geometry Based Performance Characterization of SWIPT in Cell-Free Massive MIMO
Bibliographic record
Abstract
This paper investigates the integration of wireless information and power transfer (SWIPT) and cell-free massive multiple-input multiple-output (MIMO) technologies for a cell-free spatially random network, where the access points (APs) are located randomly and modeled using a Poisson point process, and the users' energy and data transfers are separated in time. For the time-division-duplexing mode of operation, the uplink channel state information is acquired locally at the distributed APs via user pilots, and the APs utilize conjugate beamforming for downlink transmissions by exploiting channel reciprocity. In addition, line-of-sight and non-line-of-sight scenarios, which arise from link blockages due to objects are considered along with the corresponding path loss and fading parameters in our performance analysis of the above system set-up. We characterize the harvested energy at a user for both linear and non-linear energy harvesting models, and derive expressions for the average achievable downlink rate for the energy harvesting users. Subsequently, we propose a multi-slot energy storing scheme, and thereby, derive the probability of a user being fully charged at any given time. The throughput and the harvested energy are investigated under different system parameters. We show that a higher mean energy can be harvested by energy users with limited impact on non-energy users through allocating a higher portion of power for the energy users. Furthermore, we reveal that increasing the AP power level has diminishing effect on the probability of being within the fully charged state.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".