Power Scalable Beam-Oriented Digital Predistortion for Compact Hybrid Massive MIMO Transmitters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article proposes a power scalable beam-oriented digital predistortion (PSBO-DPD) architecture suitable for linearizing PAs in compact hybrid massive multiple-input multiple-output (MIMO) transmitters, without the need to implement a dedicated observation path for each PA. Based on an assumption that all PAs are similar, the feedback configuration for only one PA is sufficient to acquire the nonlinear information of PAs in a given subarray, which are driven at different power levels due to amplitude beamforming. Therefore, the PSBO-DPD resolves the deficiency of current DPD techniques in hybrid beamforming array by estimating the output signal of each PA from the only captured output signal from one PA to construct the main beam signal of the subarray. The predistorter is identified based on the estimated main beam signal and the input signal driving the subarray. To estimate the outputs of each PA efficiently, a power scalable cascade PA model is proposed to reduce the computational complexity and associated overhead in terms of cost and energy consumption. Measurements on a 4-element antenna array with up to 100 MHz bandwidth signal are carried out to validate and bench mark the proposed PSBO-DPD against the existing DPD technique in hybrid massive MIMO transmitters.
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.
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 | 0.000 |
| 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 it