Joint Beamforming Design for Energy Efficient Wireless Communications in Heterogeneous Intelligent Connected Vehicles Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The wireless communications is studied for the intelligent connected vehicles (ICV) networks by supporting heterogeneous access with direct access of one vehicle yet the dual-hop access of another vehicle. The dual-hop access is enhanced by one dedicated vehicle as relaying vehicle with multiple antennas. The goal in this paper is to joint design of both receive beamforming and transmit beamforming at the relaying multi-antenna vehicle to suppress the interference between the two wireless receive vehicles yet match the dual-hop wireless channel as much as possible. The energy efficiency is optimized as the objective function under the constraint of the limited power over every beamforming vector. Due to the difficulty of the optimization, the principle of the signal to leakage plus noise ratio (SLNR) is introduced to the studied ICV networks and thus to obtain the analytical expression of the beamforming vectors. With the derived closed form over all the beamforming vectors, an iterative algorithm is developed for jointly optimizing all the beamforming vectors to maximize the energy efficiency of the whole system, which is guaranteed to be convergent to one local optimum. Numerical simulations show the good performances of the proposed method in ICV networks.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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