Energy-Efficient 200 GHz Power Amplifier Design for 5G-IoT Smart-Grid Edge Communication
Bibliographic record
Abstract
With the rapid development of fifth-generation mobile communication (5G) and the Internet of Things (IoT), the communication demands of smart grids are evolving toward higher frequencies, broader bandwidths, and greater energy efficiency. Future smart grid nodes require not only stable power control but also high-speed, low-latency data interaction in distributed environments. Consequently, radio frequency frontend circuits operating in the millimeter-wave and sub-terahertz (sub THz) frequency bands have emerged as a critical technology. However, existing 200 GHz power amplifiers (PAs) in CMOS and SiGe technologies typically achieve only 10-15 dB gain with PAE below 5%, limiting their use in energy-constrained smartgrid edge nodes. Therefore, a design that combines broadband gain and high energy efficiency under compact integration is required. This paper designs and simulates a broadband power amplifier operating at 200 GHz to support edge communication in 5G and IoT driven smart grids. The amplifier achieves a gain exceeding 20 dB, a 3 dB bandwidth of 40 GHz, and input output matching better than −10 dB. By adopting a dual parallel structure and Wilkinson power combiner, the amplifier delivers approximately 6 dB of power gain, achieving 10 mW output power and over 10% power added efficiency (PAE) under a 2 V supply voltage. Simulation results demonstrate stable performance ($K>1, \mu>1$) across the 1 GHz-400 GHz frequency range, validating its potential application in future 5G smart grids and IoT communication nodes.
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) | 0.000 | 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 | 0.001 |
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".