Spreading Code Assignment Strategies for MIMO-CDMA Systems Operating in Frequency-Selective Channels
Bibliographic record
Abstract
Code Division Multiple Access (CDMA) and multiple input multiple output- (MIMO-) CDMA systems suffer from multiple access interference (MAI) which limits the spectral efficiency of these systems. By making these systems more power efficient, we can increase the overall spectral efficiency. This can be achieved through the use of improved modulation and coding techniques. Conventional MIMO-CDMA systems use fixed spreading code assignments. By strategically selecting the spreading codes as a function of the data to be transmitted, we can achieve coding gain and introduce additional degrees of freedom in the decision variables at the output of the matched filters. In this paper, we examine the bit error rate performance of parity bit-selected spreading and permutation spreading under different wireless channel conditions. A suboptimal detection technique based on maximum likelihood detection is proposed for these systems operating in frequency selective channels. Simulation results demonstrate that these code assignment techniques provide an improvement in performance in terms of bit error rate (BER) while providing increased spectral efficiency compared to the conventional system. Moreover, the proposed strategies are more robust to channel estimation errors as well as spatial correlation.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".