Channel Optimization Modeling and Hybrid Beamforming for Fifth-Generation Millimeter-Wave V2V Communications
Bibliographic record
Abstract
Massive multiple-input multiple-output (MIMO) systems combined with beamforming antenna array technologies is playing a vital role in 5G and beyond communication systems. 5G com- munication is characterised by high bandwidth, low latency and high reliable communication and is a key factor to enable Vehicle to Vehicle (V2V) communication. In this thesis project, we simulate one of the applications of V2V ecosystem i.e vehicles com- municating with other vehicles. For brevity, a single scenario where one vehicle is stationary and the other vehicle is either moving towards or away from the stationary vehicle is considered for experiments. Initially, a framework based on single user MIMO-OFDM hybrid beamforming system operating at mmWave frequency of 28 GHz was established. Thereafter a precoding process is established using Orthogonal Matching Pursuit (OMP) Algorithm that enables the transmitter to provide near optimum beamforming gain and directivity to the transmitted signal. Next, a simulated scenario of a receiver moving at a constant speed communicating with a transmitter is implemented. We provide simulation results of the radiation pattern emitted by the transmitter for different receiver trajectory points. Radiation pattern is analysed in terms of E-field, power and directivity. Bit error rate and RMS EVM of the received signal was evaluated and benchmarked together with Doppler frequency shift waveform to show the frequency variation in received signal strength with distance
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".