Parallel Quadrature Spatial Modulation for Massive MIMO Systems With ICI Avoidance
Bibliographic record
Abstract
Despite its efficiency in conventional multiple-input multiple-output (MIMO) wireless systems, quadrature spatial modulation (QSM) becomes less efficient in massive MIMO systems since it does not adapt to the number of antennas but always uses one or two out of them. To adopt QSM in massive MIMO systems, a parallel quadrature spatial modulation (PQSM) scheme is proposed in this paper. In PQSM, the transmit (Tx) antennas are divided equally into P > 1 groups, and the bit sequence to be transmitted during a time slot is divided into P+1 parts. Then, the first part is applied to map an M-QAM complex constellation symbol while the remaining P parts of the bitstream are used to perform P QSMs in parallel. By allowing a tradeoff between the spatial modulation order and signal constellation order, PQSM enables lower bit error rate (BER) with no loss of spectral efficiency compared with QSM. For a fixed signal constellation, PQSM yields higher spectral efficiency than QSM since more selected antenna indices can carry more data bits. The algorithm pertaining to the proposed scheme is designed, and an upper bound on the average bit error rate (ABER) is derived. Moreover, to minimize the ABER, an algorithm is developed to optimize the number of Tx antenna groups and the signal constellation order. Monte-Carlo simulation results demonstrate the superiority of PQSM over generalized SM and QSM, as well as the effectiveness of the developed performance analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".