WLC16-4: Low-Complexity Turbo Equalization for Alamouti Space-Time Block Coded EDGE Systems
Bibliographic record
Abstract
Enhanced Data Rates for Global Evolution (EDGE) based systems are expected to facilitate the same services as third generation WCDMA systems. This goal is achieved through physical layer enhancements to increase the data rate and spectral efficiency. Thus, it has become important to incorporate the recent advances in the physical layer. Recently, space-time block coding (STBC) has evolved as an effective transmit diversity technique. Convolutional channel coding is employed in EDGE systems. The combination of channel coding and STBC can be used to achieve high throughput over hostile wireless channels. On the other hand, turbo equalization can be employed in channel coded broadband wireless systems to further enhance the radio link performance. EDGE-based systems employ 8PSK high- level modulation. Hence, maximum a posteriori (MAP)-based soft equalization is not suitable due to high complexity. Therefore, in this paper, we propose a low-complexity MMSE-based turbo equalization scheme for Alamouti space-time (ST) block coded multiple-input multiple-output (MIMO) systems. In the proposed iterative receiver, widely linear (WL) processing is used to exploit the rotational variance of the ST block coded transmit signal. Equalization and ST block decoding are jointly carried out at each iteration using the a priori information delivered by the convolutional channel decoder from the previous iteration. The simulation results demonstrate that high performance improvement can be obtained using the proposed iterative scheme in comparison to the non-iterative equalization. Due to the low-complexity, the proposed scheme is highly attractive for future EDGE-based systems with Alamouti STBC.
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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 | 0.000 | 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 | 0.000 | 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".