Bibliographic record
Abstract
There are three parts to this thesis. In the first part, we study the channel estimation problem in frequency-selective multi-user (MU) multi-cell massive multiple-input multiple-output (MIMO) systems, where, a time-domain semi-blind channel estimation technique is proposed. Compared to frequency-domain, the time-domain channel estimation requires fewer parameters be estimated. Importantly, the time-domain estimation has enough samples for an accurate channel estimate. Given this many samples in the time-domain, the proposed channel estimation technique obtains a better estimate of the channel. Here, there is no assumption on orthogonality of users' channels, knowledge of large-scale fading coefficients, and the orthogonality between the training symbols of the users in all cells. The second part of the thesis studies the channel estimation problem in correlated massive MIMO systems with a reduced number of radio-frequency (RF) chains. Leveraging the knowledge of channel correlation matrices, we propose to estimate the channel entries in its eigen-domain. Due to the limited number of RF chains, channel estimation is typically performed in multiple time slots. Using the minimum mean squared error (MMSE) criterion, the optimal precoder and combiner in each time slot are aligned to transmitter and receiver eigen-directions, respectively. Meanwhile, the optimal power allocation for each training time slots is obtained via a waterfilling-type expression. In the final part, we study the downlink channel estimation for frequency-division-duplex (FDD) massive MIMO systems. Acquiring downlink channel state information in these systems is challenging due to the large training and feedback overhead. Motivated by the partial reciprocity of uplink and downlink channels, we first estimate the frequency-independent channel parameters, i.e., the path gains, delays, angles-of-arrivals (AoAs) and angles-of-departures (AoDs), via uplink training, since these parameters are common in both uplink and downlink. Then, the frequency-specific channel parameters are estimated via downlink training using a very short training signal. To efficiently estimate the channel parameters in the uplink, the underlying distribution of the channel parameters is incorporated as a prior into our estimation algorithm. This distribution is captured using deep generative models (DGMs). The proposed channel estimation technique significantly outperforms the conventional channel estimation techniques in practical ranges of signal-to-noise ratio (SNR).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".