Effective Digital PIM Cancellation in Multi-Band MIMO FDD Radios in 5G and Beyond
Bibliographic record
Abstract
This paper investigates advanced digital cancellation techniques for passive intermodulation (PIM) in multi-band, multiple-input multiple-output (MIMO), frequency-division duplexing transceivers targeting 5 G and future wireless systems. A novel, unifying PIM signal model is proposed, applicable to multi-band radios with arbitrary number of transmit and receive antennas, enabling systematic generation of basis functions across all relevant intermodulation frequencies. Building on this framework, a comparative evaluation is conducted on three state-of-the-art nonlinear models—the memory polynomial, generalized memory polynomial, and Volterra—using real-world radio frequency measurements from commercial 5 G New Radio single- and dual-band units. The analysis highlights trade-offs between modeling accuracy and computational complexity, revealing limitations of existing PIM cancellation (PIMC) techniques. Results show that digital PIMC achieves up to 12–14 dB of suppression, underscoring its potential as a hardware-agnostic solution for effective PIM mitigation in next-generation MIMO radios.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".