Hybrid beamforming and DFT-based channel estimation for millimeter wave MIMO systems
Bibliographic record
Abstract
Millimeter wave (mmWave) communication systems are considered to be a promising technology enabling gigabit-per-second data rates, as the mmWave band offers large transmission bandwidth. However, precoding in mmWave systems cannot be performed in digital domain, due to the large number of radio frequency chains required. Deploying a hybrid precoding transceiver architecture, where mmWave precoding is divided among digital and analog processing domains, is an attractive and cost-efficient alternative. Yet, the design of near-optimal hybrid precoders is a non-trivial optimization problem. In this paper, we consider transmit precoding and receive combining in mmWave systems with large antenna arrays in a partially-connected structure, then we make use of projection algorithms to greatly simplify the design problem of digital baseband and analog radio frequency precoders into two sub-optimization problems whose optimal solutions can be found. We also develop a channel estimation algorithm to estimate mmWave channel parameters via a codebook of beamforming vectors obtained through the discrete Fourier transform design. The results illustrate that the system using the proposed projection hybrid precoding and channel estimation algorithms approaches the spectral efficiency achievable when perfect channel knowledge is available.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".