Adaptive Beam Sweeping Using Reinforcement Learning
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Solution Overview
Problem
The existing beam sweeping protocols in 5G mobile communication networks are limited by high signaling overhead and latency, especially when dealing with higher frequency carriers and larger codebooks, which are expected in beyond 5G networks.
Innovation Solution
The integration of reinforcement learning into the beam sweeping framework allows for the adaptive selection of beams to transmit reference signals, reducing signaling overhead and latency by iteratively adjusting the set of selected beams based on a cost function and network performance indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If beam sweeping is performed using a large codebook to cover wider angular regions, then beam coverage and reliability are improved, but signaling overhead and latency increase
Solution Approach 1:
The patent segments the large codebook into multiple subsets, where each subset contains a portion of the total beams. Instead of sweeping through all beams in the large codebook sequentially, the system divides the beam sweeping process into multiple phases, each handling a subset of beams. This segmentation reduces the number of beams transmitted in each time interval, thereby reducing latency while maintaining comprehensive coverage across all angular regions through multiple subset sweeps.
2Reliability
If beam sweeping is performed using a large codebook to cover wider angular regions, then beam coverage and reliability are improved, but signaling overhead increases
Solution Approach 1:
The patent segments the large codebook into multiple subsets, where each subset contains a portion of the total beams. Instead of sweeping through all beams in the large codebook sequentially, the system divides the beam sweeping process into multiple phases, each handling a subset of beams. This segmentation reduces the number of beams transmitted in each time interval, thereby reducing latency while maintaining comprehensive coverage across all angular regions through multiple subset sweeps.
3Reliability
If the number of beams in the codebook is increased to support higher frequency carriers, then beamforming gain and communication reliability are improved, but system complexity increases
Solution Approach 1:
The patent segments the large codebook into multiple subsets, where each subset contains a portion of the total beams. Instead of sweeping through all beams in the large codebook sequentially, the system divides the beam sweeping process into multiple phases, each handling a subset of beams. This segmentation reduces the number of beams transmitted in each time interval, thereby reducing latency while maintaining comprehensive coverage across all angular regions through multiple subset sweeps.
Solution Approach 2:
The patent implements dynamic beam subset selection based on channel conditions, user equipment (UE) location, and network state. The system adaptively determines which beam subsets to transmit in each time interval, adjusting the beam configuration dynamically rather than using a fixed beam sweeping pattern. This dynamic approach optimizes the balance between beam coverage and system complexity by activating only the necessary beam subsets for current communication requirements.
Data Source
AI summary
The present invention incorporates reinforcement learning into a beam sweeping framework to select the appropriate set of beams to transmit reference signals in a predefined time interval for covering an angular region. More specifically, a network node starts a learning process to determine the most appropriate subset of beams from a large set of available beams (codebook) to communicate with an associated network node over a radio channel. The transmitter node acquires knowledge from its interaction with other nodes of the wireless network to perform beam sweeping with reduced signaling overhead and latency. More specifically, other advantages, the invention improves the beam management in higher carrier frequencies.