An Investigation of Wideband Hybrid Precoding Techniques for 6G THz Massive MIMO Systems
Bibliographic record
Abstract
For next-generation applications like holographic communication, immersive extended reality, and dense Internet of Things (IoT) deployments, sixth-generation (6G) wireless networks operating at terahertz (THz) frequencies promise extremely high speeds of data, immense connections, and very little latency. In this paper, a detailed overview of hybrid precoding architecture has been provided which encompasses analog digital decomposition, beamspace modelling, and wideband aspect of implementation. In order to justify above techniques, we simulate the DFT codebook based hybrid precoder and compare its performance with completely digital SVD precoding and the random analog baseline. In a 64x16 MIMO system using four data streams to transmit over a sparse multiple path channel the DFT codebook hybrid precoder has a average spectral efficiency of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$6.34 \text{bps} / \text{Hz}$</tex> at 20 dB SNR whilst the fully digital SVD benchmark is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$29.55 \text{bps} / \text{Hz}$</tex> and the random baseline is 16.12 bps/Hz. The DFT codebook hybrid scheme has demonstrated a 78 percent performance difference as compared to the fully digital bound, but it has a large improvement on the random analog beamforming because it is using structured spatial codebook and less RF chain. The findings show that hybrid precoding is an effective compromise of hardware cost and complexity with respect to spectral efficiency and it is therefore a viable choice in next generation wideband massive MIMO systems.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 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".