Improving the Energy Efficiency of DFT-s-OFDM in Uplink Massive MIMO with Barker Codes
Bibliographic record
Abstract
The uplink transmission in a single cell Massive MIMO system is studied. Developing Green Communications and achieving uplink energy efficiency is one of the critical targets of 5G cellular communications. The 3GPP recommends the use of the DFT-s-OFDM as the air interface waveform for the uplink transmission in 5G cellular networks. We investigate the performance of DFT-s-OFDM and its energy efficiency during the uplink transmission. To improve its performance we propose a novel DFT-s-OFDM method that employs an adaptive length Barker code as a spreading technique for the uplink transmitted signals. To evaluate the performance of the proposed system (i.e. the system that employs DFT-s-OFDM with an adaptive length Barker code), we use the BER, and Sum-Rate capacity as the performance metrics. In particular, we investigate two different communications channel models; the i.i. d. and the correlated Rayleigh fading channels. The numerical results show that the proposed air interface waveform results in a significant improvement in the uplink energy efficiency for various signal-to-noise ratios.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".