Nokia Technologies OY (20240297695). SPATIAL QUANTIZATION FOR SPHERICAL COVERAGE OF USER EQUIPMENT simplified abstract
Contents
SPATIAL QUANTIZATION FOR SPHERICAL COVERAGE OF USER EQUIPMENT
Organization Name
Inventor(s)
SPATIAL QUANTIZATION FOR SPHERICAL COVERAGE OF USER EQUIPMENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240297695 titled 'SPATIAL QUANTIZATION FOR SPHERICAL COVERAGE OF USER EQUIPMENT
The present disclosure pertains to wireless communications, specifically focusing on spatially quantizing a spherical coverage of a User Equipment (UE) and utilizing this quantized coverage in beamforming at the UE and a network node in a wireless communication system.
- UE transmits location information and antenna parameter measurements to the network node.
- Network node obtains a signal model based on UE location information and antenna parameter measurements.
- Signal model guides the transmission of reference signals from the network node to the UE, optimizing signal reception.
- Key Features and Innovation:**
- Spatial quantization of spherical coverage for beamforming optimization. - Utilization of machine learning algorithms for signal model generation. - Enhanced transmission of reference signals based on spatial sector suitability.
- Potential Applications:**
- 5G and beyond wireless communication systems. - IoT devices requiring optimized signal reception. - Mobile networks with high user density.
- Problems Solved:**
- Inefficient signal transmission in wireless communication systems. - Lack of optimized beamforming techniques for UE devices. - Difficulty in determining the most suitable spatial sector for signal reception.
- Benefits:**
- Improved signal reception and network efficiency. - Enhanced user experience with better connectivity. - Increased network capacity and coverage.
- Commercial Applications:**
Optimized beamforming techniques can be utilized in telecommunications infrastructure, IoT networks, and mobile devices to improve signal reception and network performance, potentially leading to increased customer satisfaction and loyalty.
- Questions about Wireless Communications:**
1. How does spatial quantization of spherical coverage improve beamforming in wireless communication systems? 2. What are the potential challenges in implementing machine learning algorithms for signal model generation in wireless networks?
Original Abstract Submitted
the present disclosure relates generally to the field of wireless communications and, in particular, to techniques for spatially quantizing a spherical coverage of a user equipment (ue) and using the spatially quantized spherical coverage in beamforming at the ue and a network node in a wireless communication system. to this end, the ue transmits, to the network node, location information of the ue and antenna parameter measurements which are taken at least one time in at least one spatial section of the spatially quantized spherical coverage. the network node obtains a signal model based on the ue location information and the antenna parameter measurements. the signal model may be obtained by using a data processing algorithm, e.g., a machine learning algorithm. the signal model is indicative of how reference signals are to be transmitted from the network node to the ue. by so doing, it is possible to optimize the transmission of the reference signals from the network node to the ue, since the network node knows which of the spatial sectors of the spatially quantized spherical coverage of the ue is most suitable for their reception.
