Peak to average power ratio reduction and error control in MIMO-OFDM HARQ System
Bibliographic record
Abstract
Currently, multiple-input multiple-output orthogonal frequency division multiplexing (MIMOOFDM) systems underlie crucial wireless communication systems such as commercial 4G and 5G networks, tactical communication, and interoperable Public Safety communications. However, one drawback arising from OFDM modulation is its resulting high peak-to-average power ratio (PAPR). This problem increases with an increase in the number of transmit antennas. \n \nIn this work, a new hybrid PAPR reduction technique is proposed for space-time block coding (STBC) MIMO-OFDM systems that combine the coding capabilities to PAPR reduction methods, while leveraging the new degree of freedom provided by the presence of multiple transmit chairs (MIMO). \n \nIn the first part, we presented an extensive literature review of PAPR reduction techniques for OFDM and MIMO-OFDM systems. The work developed a PAPR reduction technique taxonomy, and analyzed the motivations for reducing the PAPR in current communication systems, emphasizing two important motivations such as power savings and coverage gain. In the tax onomy presented here, we include a new category, namely, hybrid techniques. Additionally, we drew a conclusion regarding the importance of hybrid PAPR reduction techniques. \n \nIn the second part, we studied the effect of forward error correction (FEC) codes on the PAPR for the coded OFDM (COFDM) system. We simulated and compared the CCDF of the PAPR and its relationship with the autocorrelation of the COFDM signal before the inverse fast Fourier transform (IFFT) block. This allows to conclude on the main characteristics of the codes that generate high peaks in the COFDM signal, and therefore, the optimal parameters in order to reduce PAPR. We emphasize our study in FEC codes as linear block codes, and convolutional codes. \n \nFinally, we proposed a new hybrid PAPR reduction technique for an STBC MIMO-OFDM system, in which the convolutional code is optimized to avoid PAPR degradation, which also combines successive suboptimal cross-antenna rotation and inversion (SS-CARI) and iterative modified companding and filtering schemes. The new method permits to obtain a significant net gain for the system, i.e., considerable PAPR reduction, bit error rate (BER) gain as compared to the basic MIMO-OFDM system, low complexity, and reduced spectral splatter. The new hybrid technique was extensively evaluated by simulation, and the complementary cumulative distribution function (CCDF), the BER, and the power spectral density (PSD) were compared to the original STBC MIMO-OFDM signal.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".