Deep Learning-Based Adaptive Beamforming for Interference Cancellation in V2I Scenarios
Bibliographic record
Abstract
The fifth-generation (5G) mobile networks rely on advanced smart antenna systems to achieve high accuracy and low latency.In vehicular-to-infrastructure (V2I) scenarios, adaptive beamforming methods play a critical role in enhancing network throughput.This paper proposes a deep learning-based adaptive beamforming technique using long shortterm memory (LSTM) networks as beamformers to determine complex weights for the antenna array, thereby mitigating interference in multiuser environments.Unlike conventional minimum variance distortionless beamformers (MVDR) that require knowledge of the direction of arrival (DoA) for the desired signal, the proposed LSTMbased beamformer estimates the desired signal in the presence of interference and noise without DoA knowledge.The LSTM network is trained to predict the angles between user equipment (UE) and roadside units (RSU) using complex time series input data, resulting in a beamformed output.Simulation results demonstrate that the proposed LSTM-based beamforming approach achieves comparable performance in terms of throughput, making it a promising solution for interference cancellation in 5G V2I scenarios.
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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 | 0.000 | 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 | 0.000 | 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".