Joint Hybrid Precoder and Combiner for Wideband Millimeter-Wave Massive MIMO Systems
Bibliographic record
Abstract
Hybrid precoding and combining is a key technique to provide an appropriate antenna gain in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. In wideband mmWave channels, the analog precoder and combiner is designed in the time domain and remain unchanged over the whole bandwidth. In contrast, digital precoders and combiners are optimized on a per-subcarrier basis which makes the resultant problem very difficult. To solve this problem, we combine the well-known turbo-equalizer with the tabu-search (TS)-algorithm developed in artificial intelligence and propose TS-based joint hybrid precoding and combining scheme to intelligently search the near-optimum pair of hybrid precoder and combiner. Specifically, our scheme consists of two key steps. At first, a base station (BS) and a mobile station (MS) develop the turbo-like (TL)-joint search by using the idea of iterative information exchange between them. Then, to find out the near-optimum pair of hybrid precoder and combiner in each iteration of the TL-joint search, the TS-algorithm is employed. Simulation results are shown to verify the significant sum-rate performance of the proposed solution with low-complexity compared to some existing solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".