Diversity Combining of Signals with Different Modulation Levels in Cooperative Relay Networks
Bibliographic record
Abstract
In digital cooperative relaying, signals from the source-destination and relay-destination links are combined at the destination to achieve spatial diversity. These signals do not necessarily belong to the same modulation scheme due to the varying channel qualities of the two links. In this paper, we present novel and low complexity schemes for diversity combining of signals with different modulation levels. We start by developing the optimum solution as a maximum likelihood detector (MLD). Due to its high complexity, we propose two other receiver structures that we refer to as soft-bit maximum likelihood detector (SBMLD) and soft-bit maximum ratio combiner (SBMRC). The proposed schemes are simple bit-by-bit detectors and only 0.3 dB inferior to the MLD in performance. The SBMLD provides only marginal performance gain over SBMRC through the computation of the conditional probability density functions of the soft-bits. Consequently, the SBMRC is a more attractive and practical solution. The performance of SBMRC is compared to that of selection combining which is the current approach in the literature for combining signals with different modulations. The SBMRC, along with its simplicity, outperforms selection combining by almost 2 dB without bandwidth loss or the need for extra channel state information. The SBMRC scheme can be viewed as a more general form of the classical maximum ratio combiner (MRC).
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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 | 0.001 |
| 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".