A Reduced-Complexity Load-Modulated MIMO Transmitter Readily Scalable in 5G Massive MIMO Transmitters
Bibliographic record
Abstract
This brief presents the design of a four-branch reduced-complexity load-modulated MIMO transmitter. The transmitter is designed to operate at 3 GHz. The transmitter can generate 64-QAM constellations at the input of the radiating elements. The waveforms of the 64-QAM constellations are generated by changing the impedance parameters of the load modulator circuits connected to antennas. At the same time, the output of the oscillator is kept constant. Thus, a single RF chain can drive the entire transmitter where the power amplifier amplifies a constant signal and does not need to operate in the back-off region. A four-way Wilkinson power divider is used to split the power to the four branches of the transmitter. Microstrip patch antennas have been connected to the load modulators to transmit the desired outputs. RF isolators absorb reflections from each load modulator, allowing branches to generate desired constellations independently and maintaining the system matched at the input all the time. Advanced Design System (ADS) has been used to design, simulate, and fully characterize the transmitter. The proposed transmitter does not require mixers and digital-to-analog converters (DACs). The transmitter has been fabricated, and over-the-air transmission of 64-QAM signals has been tested. The architecture can be scaled to larger array sizes, making it suitable for adoption in 5G massive MIMO systems. High cost, energy, and RF-complexity savings can be achieved if the transmitter is scaled to a larger number of branches.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".