Synchronized Distributed Processing In A Communications Network

Overview

This patent details a system for decentralizing artificial intelligence and machine learning capabilities within a communications network. Instead of relying on a massive, centralized data center for processing and decision-making, this architecture moves the intelligence to the network's edge. The core idea is to use a distributed model where local edge nodes (like cell towers or local servers) make and validate predictions based on real-time, local data. This creates a more agile, efficient, and context-aware network that can respond quickly to localized conditions.

The Problem

Traditional AI-driven network management systems are heavily centralized. They collect vast amounts of data from across an entire network, store it in a central data lake, and use powerful, resource-intensive models to make predictions (e.g., for network traffic, security threats, or user behavior). This approach has several major drawbacks:

High Cost: Maintaining massive, perpetual storage for a nationwide network's data is prohibitively expensive.
Lack of Local Context: A centralized model trained on data from an entire state or country cannot effectively adapt to unique local conditions, such as microclimates, specific geographic traffic patterns (e.g., a dense city vs. a rural area), or sudden local events.
Inflexibility: These large, static models are slow to adapt to new or sudden shifts in network behavior, as recent data is only a tiny fraction of the massive historical dataset used for training.
Impracticality for Edge Deployment: The high computational power (e.g., GPUs) and large data requirements of centralized models make them impossible to deploy directly on resource-constrained edge nodes.

The Solution

The patent proposes a distributed architecture that pushes intelligence to the edge. The key components of the solution are:

Edge Node Processing: Each edge node receives data directly from local clients (e.g., mobile phones, IoT devices).
Local Prediction Generation: The edge node first generates a quick prediction based on the local data, often using a simple, efficient rules-based engine.
Localized Validation: The prediction is then fed into a validator module—a lightweight, fine-tuned machine learning model that also runs on the edge node. This model is trained specifically for the nuances of its local environment.
Function Activation: If the validator module confirms the prediction is valid, the edge node activates a corresponding network function (e.g., blocking a malicious IP address, caching a popular video locally, or adjusting network resources).

This creates a synchronized but distributed system where each part of the network can think for itself, while still being part of a coordinated whole.

Why It Matters

This shift from a centralized to a distributed intelligence model has significant implications for building next-generation networks:

Improved Performance and Lower Latency: By processing data and making decisions locally at the edge, the system can respond much faster, which is critical for applications like autonomous driving, real-time analytics, and edge computing.
Greater Accuracy and Relevance: Models that are fine-tuned for local conditions are more accurate and effective than a one-size-fits-all central model.
Increased Agility: The system can adapt quickly to real-time changes in the local environment without needing to retrain a massive central model.
Cost Efficiency: It eliminates the need for huge, centralized data lakes and the associated storage and computational costs.
Scalability: The architecture is inherently scalable, as new edge nodes can be added to the network without overloading a central brain.

Relevance Beyond Telecommunications

The concept of pushing intelligence to the edge and using locally-tuned models has powerful applications across many industries:

Smart Grids and Utilities: Local substations or even individual transformers can act as edge nodes. They could predict energy demand in a specific neighborhood, detect faults in real-time, and optimize power distribution locally, making the grid more resilient and efficient without waiting for commands from a central control center.
Retail and Logistics: An individual store or warehouse can be an edge node. A local AI model could analyze real-time sales data and in-store foot traffic to dynamically adjust pricing, manage inventory, and personalize promotions for that specific location, reacting much faster than a system relying on batch data sent to a corporate headquarters.
Smart Cities: Traffic intersections equipped with edge processing can analyze local vehicle and pedestrian flow to optimize signal timing in real-time. This is far more effective than a city-wide model, as it can instantly adapt to localized events like accidents, construction, or public gatherings.
Industrial IoT and Manufacturing: On a factory floor, individual machines or production lines can be edge nodes. They can use locally-trained models to predict maintenance needs, detect production defects, and optimize their own performance without sending massive streams of sensor data to a central cloud, reducing latency and improving operational efficiency.

This architecture is ideal for any system where real-time, context-aware decisions are critical and where conditions can vary significantly from one location to another.

Technical Details

The architecture distinguishes between a central architecture and a distributed architecture. While a central system may still exist for overarching coordination, the core of the invention lies in the functionality of the distributed edge nodes.

Key components at the edge include:

Edge Node (260): A network node that communicates directly with client devices (e.g., a base station). It is the primary location for the distributed processing.
User Plane (206): Part of the edge node that handles initial, fast processing. It often contains a rules database derived from past predictions, allowing it to handle common, known events without engaging more complex models.
Control Plane (208): Part of the edge node that contains the intelligent components:
- Predictor: A machine learning model that processes observations that don't match the rules in the user plane.
- Validator Module: A crucial component, similar to a tuner in a centralized system but critically different because it is fine-tuned for local nuances. It validates the predictions made by the predictor or the rules engine before an action is taken.
Cross-Pollination: The system allows for learnings and successful responses from one edge node to be shared with others, enabling the entire distributed network to evolve and improve over time without centralized retraining.

This approach avoids the need to wholesale move a large, computationally expensive model to the edge. Instead, it uses a hybrid system of fast rules and a lightweight, localized validator to achieve intelligent, real-time processing.

Status: Issued

Application Number: 16/399,844

Patent Number: 10979307

Filing Date: 2019-04-30

Issue Date: 2021-04-13

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