Probability Map Beam Search for 5G mmWave Latency
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Solution Overview
Problem
Current beam searching methods in 5G mmWave systems face challenges with high latency in scanning all beam directions and decreased sensitivity due to increased dimensionality, especially when UE mobility causes channel de-correlation, necessitating frequent recalculations of beamforming solutions.
Innovation Solution
The implementation of probability maps to prioritize beam search directions based on historical data, reducing scanning latency and increasing sensitivity by focusing on subsets of the beamspace where UEs are most likely to be found, and tracking both primary and secondary beams to maintain communication even when main beams deteriorate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all beam directions are scanned to ensure complete coverage, then detection reliability is improved, but scanning latency increases significantly
Solution Approach 1:
The system performs preliminary actions by maintaining historical beam direction data and probability maps in advance. When a new UE enters the cell, the network node queries pre-stored probability maps that indicate likelihoods of primary and secondary beam directions, avoiding the need to scan all beam directions from scratch and thus reducing scanning latency while maintaining detection reliability.
Solution Approach 2:
The system uses feedback mechanisms by continuously updating probability maps based on historical beam direction data from previous UEs. These updated probability maps provide feedback guidance for searching new UEs, allowing the system to focus scanning efforts on high-probability directions and thereby reduce overall scanning time while maintaining comprehensive detection capability.
2Productivity
If beam searching focuses on high-probability directions to reduce latency, then scanning speed is improved, but detection sensitivity decreases
Solution Approach 1:
The system pre-calculates and stores probability maps indicating the likelihood of primary and secondary beam directions before new UEs arrive. This preliminary preparation allows the search to focus on high-probability directions without sacrificing detection sensitivity, as the probability maps are comprehensive and updated based on historical data from multiple UEs.
Solution Approach 2:
The system performs partial searching by focusing on high-probability beam directions identified through probability maps, rather than exhaustively scanning all possible directions. This partial action achieves sufficient detection sensitivity for practical purposes while dramatically reducing scanning latency, balancing productivity and measurement precision.
3Reliability
If comprehensive beam searching is performed to maintain connectivity, then system reliability is improved, but computational complexity increases
Solution Approach 1:
The system maintains pre-computed probability maps that store historical beam direction data and likelihoods. When searching for new UEs, the system queries these pre-prepared maps rather than performing complex real-time calculations, significantly reducing computational complexity while maintaining system reliability through comprehensive beam direction coverage.
Solution Approach 2:
The probability maps serve as an intermediary between historical data and current search operations. Instead of directly computing beam directions from raw historical data during each search, the system uses the probability maps as a mediator that pre-organizes and summarizes this information, reducing computational complexity while preserving reliability.
4Measurement precision
If probability maps are maintained and updated for all UEs, then beam search accuracy is improved, but memory requirements increase
Solution Approach 1:
The system maintains a universal probability map that serves multiple UEs simultaneously, rather than maintaining separate maps for each UE. This probability map stores aggregated historical beam direction data and likelihoods that can be queried by any new UE entering the cell, achieving high beam search accuracy while minimizing memory requirements through shared data structures.
Data Source
AI summary
A method for generating a probability map for a cell served by a network node is provided. The method includes generating a first probability map (P1) indicating a likelihood of primary beam directions (step 602). Generating the first probability map includes recording a first direction of a first user equipment (UE), the first direction indicating a direction of a first beam (e.g., a first set of antenna weights (a.k.a., precoding vector)) associated with the first UE when the first UE appears in the cell served by the network node. The method further includes generating a second probability map (P2) indicating a joint likelihood of primary and secondary beam directions (step 604). Generating the second probability map includes recording a second direction of a second beam associated with the first UE when the first UE switches from the first beam to the second beam, the second direction being recorded in association with the first direction.