Real-Time FPGA-Based Testbed for Evaluating Digital Predistortion in Fully Digital MIMO Transmitters
Bibliographic record
Abstract
As one of the key enabling technologies of 5G networks, massive multiple-input, multiple-output (MIMO) transmitters use many transmit chains to ensure a very high data rate and acceptable signal quality. Realizing Massive MIMO not only includes increasing antenna count but also requires proportionally more power amplifiers (PAs). Digital predistortion (DPD) is a well-established signal processing method that mitigates the non-linearities of a PA when operated near saturation. Design tradeoffs must be carefully considered to reduce the system's overall power requirements given the high PA count in MIMO systems. This implies DPD power consumption for each transmission chain must be minimized. Apart from this, larger transmission bandwidths in next-generation networks require high hardware clock rates on the order of a few gigahertz. Current hardware can satisfy clock rates of up to hundreds of megahertz. Thus, there is a need for parallelized signal processing methods to meet bandwidth requirements. \n \nThis thesis investigates and addresses some challenges for deploying massive MIMO systems by designing and building a reconfigurable digital signal processing (DSP) testbed that allows for the implementation and validation of real-time DSP algorithms including DPD, for fully digital massive MIMO transceivers. This testbed allows transmission of up to 16 fully digital transmission chains at sub-6 GHz frequencies and supports up to 120 MHz of modulation bandwidths. Finally, a low-complexity and parallelized piecewise-linear (PWL) dual-input dual-output (DISO) DPD solution is proposed for linearizing MIMO transmitters. This DPD solution is realized with a commercially available field-programmable-gate-array (FPGA).
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".