Distributed Beam Selection for Cellular Interference Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cellular networks face challenges in managing interference among densely deployed base stations, leading to limited data throughput, especially near cell boundaries, due to mutual interference and the need for centralized coordination which is costly, latency-intensive, and fault-vulnerable.
Innovation Solution
A distributed method where base stations exchange local radio performance parameter information to autonomously select antenna beamforming parameters, minimizing total transmit power while ensuring quality of service, using predefined codebooks and iterative updates to optimize beamforming weights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If centralized coordination is used to manage interference among base stations, then interference management capability is improved, but network cost and complexity increase due to high-capacity backhaul requirements
Solution Approach 1:
The patent divides the centralized interference coordination function into distributed segments at each base station. Each base station independently performs interference management using local information and predefined codebooks, eliminating the need for a centralized controller and high-capacity backhaul links while still achieving coordinated beamforming effects
Solution Approach 2:
Base stations are empowered to autonomously select beamforming weights from predefined codebooks based on local radio conditions and interference measurements. This self-service capability eliminates dependency on centralized coordination, reducing backhaul requirements while maintaining effective interference management
2Adaptability or versatility
If centralized coordination is implemented for dynamic interference handling, then adaptability to varying load patterns is improved, but latency increases due to backhaul communication requirements
Solution Approach 1:
The patent pre-computes and stores beamforming weight codebooks at each base station before operation. During runtime, base stations can rapidly select appropriate beamforming weights from these pre-prepared codebooks based on current load conditions, eliminating the need for real-time centralized computation and reducing coordination latency
Solution Approach 2:
Each base station autonomously adapts to varying load patterns by independently selecting beamforming strategies from local codebooks based on observed traffic conditions, eliminating dependency on centralized control signals and achieving real-time adaptability without backhaul-induced latency
3Measurement precision
If beamforming weights are computed dynamically, then beamforming accuracy is improved, but computational complexity increases
Solution Approach 1:
Beamforming weight codebooks are pre-computed offline and stored at each base station. During operation, base stations simply retrieve and select appropriate weights from these codebooks based on current conditions, achieving high beamforming accuracy without the computational burden of real-time optimization
Solution Approach 2:
The patent transforms the continuous beamforming weight optimization problem into a discrete selection problem from predefined codebooks. This parameter quantization reduces computational complexity from continuous optimization to discrete selection while maintaining sufficient beamforming accuracy for practical applications
4Ease of manufacture
If codebook-based beamforming is used, then amplifier efficiency and computational simplicity are improved, but beamforming flexibility is reduced
Solution Approach 1:
The patent introduces dynamic selection mechanisms that allow base stations to adaptively choose from multiple codebooks or different entries within codebooks based on current radio conditions, interference levels, and traffic patterns. This dynamic adaptation restores flexibility while maintaining the implementation simplicity of codebook-based approaches
Solution Approach 2:
The system allows dynamic adjustment of codebook selection parameters such as codebook index, beam width, and beam direction based on observed conditions. This parameter variability within the codebook framework provides flexibility without requiring complex real-time weight computation, balancing simplicity and adaptability
Data Source
Figure 1
Figure 2~3
Figure 4
AI summary
A group of multiple base stations (10, 12, 14, 20) implements distributed and coordinated antenna beamforming selection to achieve increased performance. Each of the base stations in the group determines an associated optimal set of antenna beam direction parameters in a distributed manner based on local radio information exchanged between neighboring ones of the base stations. Each of the base stations transmits to one or more user equipments (UEs) (18) served by that base station using its associated optimal set of beam direction parameters. The local radio information generated by one of the base stations indicates how the transmissions of its neighbor base stations affect the performance of the base station. The performance of a base station may be measured by the difficulty or challenge in maintaining a minimum desired signal quality, e.g., a minimum SINR, for the UE served by the base station.