ML Beam Selection for Vehicular mmWave Communication
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Solution Overview
Problem
Current wireless communication systems, particularly those using millimeter waves, face challenges in communicating high-throughput data to moving vehicles due to directional limitations, susceptibility to blockages, and constraints related to movement, uplink-downlink time division access, and deployment inefficiencies, which hinder effective data transmission in vehicular environments.
Innovation Solution
A system and method employing beam selection supported by machine learning, where a computer-controlled radio transmitter sends test signals and receives feedback to adaptively select sectors for data transmission, optimizing communication performance by identifying threshold-exceeding probability values and maximizing data transfer rates through machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If millimeter wave radio systems are used for high data-rate communication, then data throughput is improved, but reliability deteriorates due to susceptibility to blockages and directional limitations
Solution Approach 1:
The system dynamically adapts beam patterns based on real-time channel conditions, vehicle movement, and environmental factors. The beamforming configuration changes continuously to maintain optimal communication, transitioning between different beam widths and directions as vehicles move through the network coverage area.
Solution Approach 2:
The system changes key transmission parameters including beam width, beam direction, and modulation schemes based on channel quality indicators. When channel conditions are good, narrower beams with higher order modulation are used to maximize throughput; when conditions deteriorate due to blockages, the system switches to wider beams with more robust modulation to maintain reliability.
2Adaptability or versatility
If beam-forming with multielement antenna arrays is used, then directional communication capability is improved, but device complexity increases
Solution Approach 1:
The antenna array is divided into multiple independent antenna elements that can be individually controlled and optimized. Each element contributes to forming specific beam patterns, allowing the system to create multiple directional beams simultaneously or to steer beams electronically without physically moving the entire antenna structure.
Solution Approach 2:
The same multielement antenna array serves multiple functions: it performs both beamforming for directional communication and spatial multiplexing for increased capacity. The antenna system adapts its configuration based on the required function, whether providing narrow directional beams for high-gain communication or wider coverage for initial access and handover.
3Area of stationary object
If traditional LTE networks operating at sub-6 GHz are used, then coverage area is improved, but data throughput deteriorates due to frequency limitations
Solution Approach 1:
The system transitions from relying solely on frequency dimension for capacity improvement to utilizing the spatial dimension through beamforming and spatial multiplexing. By adding the spatial dimension with multiple antenna elements and directional beams, the system achieves high throughput at millimeter wave frequencies while maintaining extended coverage through coordinated multi-point transmission and wider beam patterns when needed.
Data Source
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AI summary
In a computer-implemented method for controlling a radio transmitter (110 that communicates data to objects (o.1., o.2., o.3), the objects are associated with receivers. The radio transmitter is enabled to send directed radio signals (s, s.1, s.2., s.3) as beams into a plurality of sectors (sec.1, sec.2, sec.3). The computer selects a sub-plurality of beams and causing the radio transmitter to communicate data to the objects. The radio transmitter sends directed radio signals via the selected beams. The computer processing feedback data from feedback signals that arrive from objects that have obtained data from the radio transmitter. In repeating selecting and processing, the computer applies machine learning to select sub-pluralities of beams, according to communication performance.