Multiple-antenna wireless communications: detection and estimation with smart antennas, and space-time code design considerations
Bibliographic record
Abstract
The main theme of this thesis is wireless communications using multiple antennas. The thesis consists of four topics on smart antenna technology, its applications to direct sequence code division multiple access (DS/CDMA) communications, and multiple-input multiple-output wireless communications. The first problem under consideration is the joint estimation of direction-of-arrival (DoA), propagation delay, and complex channel gain for antenna-array DS/CDMA communications over frequency selective multipath channels. We propose a subspace based MUSIC-type estimation algorithm which utilizes the spatial smoothing preprocessing technique. The proposed algorithm essentially breaks the multipath induced coherency within the received signals and recovers the full signal subspace spanned by the dominant signal paths of all users. This allows for the use of MUSIC-type DoA and delay estimators for individual paths of a particular user. We then describe a new criterion for detecting the number of signals impinging on a uniform linear array (ULA), which exploits eigenvector information of the sample array covariance matrix and makes explicit use of the peak information of the MUSIC spectrum. In the third part we present an iterative weight matrix approximation (IWMA) algorithm. IWMA computes an approximation to the optimum weight matrix used by weighted spatial smoothing (WSS) to completely decorrelate input sources and generate a diagonal source covariance matrix. A useful observation regarding IWMA is that the generated matrix is suitable as a basis for subspace-type DoA estimation. In the last part we discuss two deterministic measures for designing linear processing space-time
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".