Receive Soft Antenna Selection for Noise-Limited/Interference MIMO Channels
Bibliographic record
Abstract
Although the Multi-Input and Multi-Output (MIMO) communication systems provide \nvery high data rates with low error probabilities, these advantages are obtained \nat the expense of having high signal processing tasks and the hardware cost, \ne.g. expensive Analog-to-Digital (A/D) converters. The increased hardware cost \nis mainly due to having multiple Radio Frequency (RF) chains (one for each antenna \nelement). Antenna selection techniques have been proposed to lower the \nnumber of RF chains and provide a low cost MIMO system. Among them, due to a \nbeamforming capability Soft Antenna Selection (SAS) schemes have shown a great \nperformance improvement against the traditional antenna sub-set selection methods \nfor the MIMO communication systems with the same number of RF chains. \nA SAS method is basically realized by a pre-processing module which is located \nin RF domain of a MIMO system. In this thesis, we investigate on the receive \nSAS-MIMO, i.e. a MIMO system equipped with a SAS module at the receiver side, \nin noise-limited/interference channels. For a noise-limited channel, we study the \nSAS-MIMO system for when the SAS module is implemented before Low Noise \nAmplifier (LNA), so-called pre-LNA, under both spatial multiplexing and diversity \ntransmission strategies. The pre-LNA SAS module only consists of passive \nelements. The optimality of the pre-LNA SAS method is investigated under two \ndi erent practical cases of either the external or internal noise dominates. For the \ninterference channel case, the post-LNA SAS scheme is optimized based on Power \nAngular Spectrum (PAS) of the received interference signals. The analytical derivations \nfor both noise-limited and interference channels are verified via the computer \nsimulations based on a general Rician statistical MIMO channel model. The simulation \nresults reveal a superiority of the post-LNA SAS to the post-LNA SAS at any \ncondition. Moreover, using the simulations performed for the interference channels \nwe show that the post-LNA SAS is upper bounded by the full-complexity MIMO. \nSince in both above-mentioned channels, noise-limited and interference, the \nchannel knowledge is needed for the SAS optimization, in this thesis we also propose \na two-step channel estimation method for the SAS-MIMO. This channel estimation \nis based on an Orthogonal Frequency-Division Multiplexing (OFDM) MIMO system. \nTwo di erent estimators of Least-Square (LS) and Minimum-Mean-Square- \nError (MMSE) are applied. Simulation results show a superiority of the MMSE \nmethod to the LS estimator for a MIMO system simulated under the 802.16 framing \nstrategy. Moreover, a 802.11a framing based SAS-MIMO is simulated using \nMATLAB SIMULINK to verify the two-step estimation procedure. \nFurthermore, we also employ a ray-tracing channel simulation to assess di erent \nSAS configurations, i.e. realized by active (post-LNA) and/or passive (pre-LNA) \nphased array, in terms of signal coverage. In this regard, a rigorous Signal to Noise \nRatio (SNR) analysis is performed for each of these SAS realizations. The results \nshow that although the SAS method performance is generally said to be upperbounded \nby a full-complexity MIMO, it shows a better signal coverage than the \nfull-complexity MIMO.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".