Computationally efficient approaches for blind adaptive beamforming in SIMO-OFDM systems
Bibliographic record
Abstract
In orthogonal frequency division multiplexing (OFDM) based transmissions over single-input multiple-output (SIMO) wireless channels, adaptive beamforming can be employed at the receiver side to combat the effect of directional interference. To save channel bandwidth, there is strong incentive to use blind algorithms that attempt to restore properties of the transmitted digital signals. Among these, the recursive least-squares constant modulus algorithm (RLS-CMA) is of considerable interest due to its fast convergence and good interference cancelation properties. However, since a distinct copy of the RLS-CMA must be run on each individual sub-carrier in OFDM applications, this approach may entail considerable computations. In this paper, we investigate frequency interpolation schemes to reduce the computational complexity of the SIMO-OFDM beamforming system based on the RLS-CMA. These approaches, which exploit the coherence bandwidth of the broadband wireless channels, divide the sub-carriers into several contiguous groups and apply the RLS-CMA to a selected subcarrier in each group; the weight vectors at other frequencies are then obtained by interpolation. We show through simulations that an M-fold reduction in complexity can be achieved where M, the number of sub-carriers in each interpolation group, depends on the characteristics of the radio channel and OFDM system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".