Performance Analysis of Massive Multi-input and Multi-output with Imperfect Channel State Information
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Bibliographic record
Abstract
The type and state of the fading channel directly affect the performance of massive multi-input and multi-output (MIMO) system. For example, the small and large fading (SSF and LSF) of the channel have a great impact on the sum-rate of the system. However, the channel state information (CSI) is far from perfect, making it difficult to analyze the sum-rate of massive MIMO systems with uniform user distribution. To solve the problem, this paper proposes three scheduling algorithms, namely, semi-orthogonal user scheduling (SUS), random user scheduling (RUS), and distance-dependent user scheduling (DUS). The three algorithms were adopted to schedule different number of users (8, 10 and 12), based on the maximum signalto-noise ratio (SNR) with changing number of base station antennas, number of active users, etc. The zero forcing (ZF) precoding was employed to improve the sum-rate, and the highly scattering Rayleigh fading channel was considered for both SSF and LSF, in the light of user locations. Under imperfect CSI and additional noise, the DUS achieved higher sum-rate than the other algorithms. The research results shed new light on the use of massive MIMO systems for 5G applications with high sum-rate requirements.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it