Soft Input Soft Output MMSE-SQRD Based Turbo Equalization for MIMO-OFDM Systems under Imperfect Channel Estimation
Bibliographic record
Abstract
In this paper, a turbo equalization scheme for MIMO-OFDM systems under imperfect channel estimation based on soft-input soft-output (SISO) minimum mean-square error (MMSE) sorted QR decomposition (SQRD) is proposed. A turbo structure consists of a SISO detector and a SISO decoder where extrinsic information is exchanged between the two SISO modules. Turbo equalization schemes are preferable in practical communication systems due to their good performance and acceptable computational complexity. MMSE-SQRD based SISO detection derives from SISO MMSE detection, and successive interference cancellation (SIC) is performed using a posteriori information obtained from previous detected symbols. Compared to SISO MMSE detection, MMSE-SQRD based SISO detection is of low complexity but has significant bit error rate (BER) performance enhancement. However, the derivation of the MMSE-SQRD based SISO detection scheme is under perfect knowledge of channel information at receivers. When channel estimation errors are presented, it has been pointed out that the system performance will degrade. In this paper, we studied this practical issue, and proposed the SISO MMSE-SQRD based turbo equalization under imperfect channel estimation. We first model the channel estimation error as added random Gaussian noise over the channel estimation matrix; based on that, we rederive the SISO MMSE detection for the data, and then redefine the extended channel matrix and receive vector by taking into account of channel estimation errors; after that, the SQRD algorithm is adjusted in accordance; MMSE-SQRD based data detection algorithm is finally performed. Numerical simulation results show that the proposed SISO MMSE-SQRD based turbo equalization for MIMO-OFDM systems under imperfect channel estimation outperforms the traditional MMSE based SISO detection with imperfect channel estimation in terms of BER performance and computational complexity.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".