Uplink Bandwidth Maximization via Edge Analytics
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Solution Overview
Problem
Current 5G communication systems face limitations in uplink bandwidth, leading to traffic congestion and transmission delays, which restrict the number of device connections and impair service quality, especially in applications requiring high image quality streams.
Innovation Solution
A multi-tier architecture with AI-based load analytics that utilizes Universal Customer Premise Equipment (uCPE) gateways and edge servers, incorporating AI-awareness control systems to dynamically allocate resources and compress image streams, optimizing bandwidth utilization through models trained with Convolution Neural Networks and Recurrent Neural Networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of device connections is increased to serve more customers, then service coverage is improved, but uplink bandwidth capacity is exceeded causing traffic congestion and transmission delays
Solution Approach 1:
An analytics manager is introduced as an intermediary component that collects utilization rate data from both gateway and edge server, processes this data through trained machine learning models, and generates allocation strategies to dynamically balance the load between gateway and edge server, preventing bandwidth overload while maintaining service quality
Solution Approach 2:
The system implements dynamic resource allocation by continuously monitoring utilization rates and adjusting the allocation strategy in real-time based on current network conditions, allowing the system to adapt to varying traffic demands and maintain optimal performance
2Manufacturing precision
If image stream data rate is increased to maintain high image quality, then image quality is improved, but uplink bandwidth consumption increases causing congestion
Solution Approach 1:
The system changes the parameter of image stream data rate dynamically by adjusting the allocation strategy based on utilization rate data, allowing image quality to be maintained when bandwidth is available while reducing data rate when bandwidth is constrained, thus balancing image quality and bandwidth consumption
3Productivity
If edge server processing capacity is increased to handle more image streams, then processing capability is improved, but system complexity and deployment cost increase
Solution Approach 1:
The system segments the processing function into two parts: gateway performs initial image stream reception and basic processing, while edge server performs advanced processing when capacity is available. The analytics manager segments the decision-making by separating data collection from strategy generation and allocation, reducing overall system complexity
Solution Approach 2:
The gateway is designed with multi-functionality, serving both as a network access point and as a processing node that can handle image streams independently when edge server resources are constrained, reducing the need for additional dedicated edge server infrastructure
Data Source
AI summary
A system and method for maximizing bandwidth in an uplink for a 5G communication system is disclosed. Multiple end devices generate image streams. A gateway is coupled to the end devices. The gateway includes a gateway monitor agent collecting utilization rate data of the gateway and an image inspector collecting inspection data from the received image streams. An edge server is coupled to the gateway. The edge server includes an edge server monitor agent collecting utilization rate data of the edge server. An analytics manager is coupled to the gateway and the edge server. The analytics manager is configured to determine an allocation strategy based on the collected utilization rate data from the gateway and the edge server.