HFC-Hosted MEC Control for 5G Small Cell QoS Congestion
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Solution Overview
Problem
Existing 5G small cell devices lack effective monitoring and management systems to handle high data rates and latency-sensitive services, particularly in hybrid fiber coaxial (HFC) infrastructure, leading to network congestion and inefficiencies.
Innovation Solution
Implementing mobile edge computing (MEC) techniques hosted by HFC devices to monitor and adjust the operation parameters of 5G small cell devices, including transmission power and antenna azimuth, based on quality of service (QoS) data, using machine learning and AI to optimize network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If 5G small cell devices are deployed in HFC infrastructure, then network capacity and data rates are improved, but network congestion and management complexity increase
Solution Approach 1:
The patent introduces a HFC device as an intermediary that hosts mobile edge computing (MEC) functions. This MEC-hosting HFC device acts as a mediator between the small cell devices and the core network, providing surveillance, monitoring, and management capabilities. The MEC platform collects QoS data from small cells, processes it using machine learning algorithms, and adjusts operating parameters accordingly, thereby reducing the management burden on the core network while maintaining high network capacity.
Solution Approach 2:
The patent segments the network management functions by distributing MEC capabilities to individual HFC devices that serve specific groups of small cell devices. Each HFC device independently monitors and manages its connected small cells, dividing the overall management complexity into manageable segments rather than requiring centralized control of all small cells through the core network.
2Quantity of substance
If small cell density is increased to support more IoT devices, then network coverage and connectivity are improved, but network congestion and contention increase
Solution Approach 1:
The patent implements a feedback mechanism where the MEC platform continuously collects QoS data from small cell devices, analyzes it using machine learning algorithms, and automatically adjusts operating parameters such as transmission power and antenna azimuth. This closed-loop feedback system optimizes network performance in real-time, preventing congestion and contention by proactively adjusting parameters based on current network conditions, thereby supporting higher small cell density without proportionally increasing contention.
3Reliability
If manual monitoring and adjustment of small cell parameters is performed, then network performance can be optimized, but operational time and resource consumption increase
Solution Approach 1:
The patent enables the network to self-optimize through automated MEC-based monitoring and control. The MEC platform continuously collects QoS data from small cell devices, processes it using machine learning algorithms, and automatically adjusts operating parameters without human intervention. This self-service capability maintains reliable network performance optimization while eliminating the time loss associated with manual monitoring and parameter adjustment.
Solution Approach 2:
The patent dynamically changes operating parameters such as transmission power and antenna azimuth based on real-time QoS data analysis. The MEC platform uses machine learning to determine optimal parameter values and automatically applies adjustments, replacing static manual configuration with dynamic parameter optimization that adapts to changing network conditions, thereby maintaining high reliability without requiring continuous operational time investment.
Data Source
AI summary
An architecture for surveillance of hybrid fiber coaxial (HFC) 5th generation (5G) Long Term Evolution (LTE) small cell devices using mobile edge computing techniques hosted by a HFC device is disclosed. A method can comprise receiving first data representing a quality of service value associated with a small cell device of a first group of 5G small cell devices; retrieving second data representing a historical quality of service value associated with a second group of 5G small cell devices; as a function of the first data and the second data, generating a change value for the small cell device; and based on the change value, facilitating an adjustment in an operation of the small cell device.