A Channel-Estimation and Data-Detection Scheme for Multiuser MIMO-CDMA Systems in Fading Channels
Bibliographic record
Abstract
In this paper, we examine the effect of channel estimation errors on the performance of multiple-input-multiple-output (MIMO) systems that employ code-division multiple-access (CDMA) transmission. Channel estimation based on training techniques has widely been considered throughout the literature. However, employing these training techniques in MIMO-CDMA systems degrades system performance due to multiuser interference. This degradation is clear as the diversity advantage of the MIMO system diminishes with the increased level of interference. As a remedy to this problem, we propose a channel-estimation and data-detection scheme based on the superimposed training technique for space-time spreading systems. The proposed scheme enhances the performance of the space-time system by eliminating the interference effect from both the channel and data estimates using two decorrelators: channel and data decorrelators. We investigate the performance of the proposed estimation technique considering an asynchronous CDMA uplink transmission over frequency-selective slow-fading channels. In particular, we analyze the bit-error-rate (BER) performance of the multiuser system with two-transmit-antenna and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i> -receive-antenna configuration over Rayleigh fading channels. Compared with other conventional estimation techniques, our results show that the proposed estimation technique is more robust to channel-estimation errors. Furthermore, both simulations and analytical results indicate that full system diversity is achieved.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".