Blind Signal Separation in MIMO OFDM Systems Using ICA and Fractional Sampling
Bibliographic record
Abstract
This paper addresses the problem of Blind Signal Separation (BSS) as it pertains to Multiple Input Multiple Output (MIMO) systems utilizing Orthogonal Frequency Division Multiplexing (OFDM). In systems with N subcarriers affected by frequency selective channels, when sampling the received signals at the Nyquist rate, the original BSS is transformed into a set of N standard Independent Component Analysis (ICA) problems. In this paper, fractional sampling is employed to increase the number of received signals and improve diversity at the receiver. The up-sampling of the OFDM frames is analyzed in the frequency domain with an up-sampling factor of 2. This doubles the number of ICA problems which provide N new solutions to aid in the recovery of the original data symbols. The additional solutions using equal gain combining improve signal-to-noise ratio (SNR) and therefore the data recovery. To achieve convergence of the ICA algorithm for the over-sampled data symbols, a specialized rotation of constellations on adjacent subcarriers is introduced. The simulation results demonstrate effectiveness of the overall system in its resiliency to inter-carrier interference and the AWGN channel.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".