Probability Forecast-Based Resource Allocation in Communications Networks
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Solution Overview
Problem
Communications networks face challenges in allocating limited resources for asset delivery opportunities due to incomplete or inaccurate information, uncertainty about when asset delivery opportunities will occur, and the need to optimize resource allocation in real-time to maximize value and revenue.
Innovation Solution
A resource allocation process that uses intelligent predictions and valuations to determine the likelihood and value of asset delivery opportunities within specific time windows, allowing for dynamic allocation of resources based on calculated probabilities and values, implemented through a computer-based platform that considers historical data and current network conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If resources are allocated based on first-come-first-served or opportunistic basis, then resource allocation is simple to implement, but resource utilization efficiency and revenue maximization are compromised
Solution Approach 1:
The system performs preliminary actions by calculating probability forecasts for multiple potential asset delivery opportunities in advance, before actual resource allocation decisions are required. This allows the system to pre-evaluate and rank multiple ADOs based on their likelihood of occurrence and expected value, so that when resources need to be allocated, the decision can be made quickly by selecting from pre-ranked opportunities rather than reacting to opportunities as they arise.
Solution Approach 2:
The system implements dynamic resource allocation by continuously updating probability forecasts and resource availability as new information becomes available. The allocation strategy adapts to changing conditions by recalculating which ADOs should receive resources based on current probability assessments and resource constraints, rather than following a static allocation rule.
2Reliability
If resources are allocated to support all possible asset delivery opportunities, then complete coverage is achieved, but resource constraints are violated and resource utilization efficiency decreases
Solution Approach 1:
The system applies local quality by allocating resources selectively to specific ADOs based on their individual characteristics and probability forecasts, rather than applying a uniform allocation strategy to all opportunities. Each ADO is evaluated on its own merits with respect to probability of occurrence, expected value, and resource requirements, allowing high-probability, high-value opportunities to receive resources while lower-priority opportunities forgo resources.
Solution Approach 2:
The system changes parameters by using probability forecasts as a key parameter to guide resource allocation decisions. Instead of allocating resources based on fixed rules or equal distribution, the allocation is adjusted dynamically according to the calculated probability that each ADO will occur and its expected value, optimizing the balance between reliability and resource utilization.
3Measurement precision
If resource allocation decisions are made without probability forecasts, then decision-making is faster and simpler, but allocation accuracy and revenue optimization are reduced
Solution Approach 1:
The system performs preliminary calculations of probability forecasts and expected values for multiple ADOs in advance, before actual resource allocation decisions are required. This pre-computation allows the system to have allocation decisions ready quickly when needed, as the heavy computational work of probability assessment has already been performed.
Solution Approach 2:
The system skips time-consuming manual analysis and direct observation approaches by using automated probability forecasting models that rapidly assess multiple ADOs. Rather than manually evaluating each opportunity or waiting for certain conditions to be observed, the system rushes through the assessment process using computational models that provide quick, accurate probability estimates.
Data Source
AI summary
A system (1000) is disclosed including a resource allocation optimization (RAO) platform (1002) for optimizing the allocation of resources in network (1004) for delivery of assets to user equipment devices (UEDs) (1012). The RAO platform (1002) determines probabilities that certain asset delivery opportunities (ADOs) will occur within a selected time window and uses these probabilities together with information concerning values of asset delivery to determine an optimal use of asset deliveries. In this regard, the RAO platform (1004) received historical data from repository (1014) that facilitates calculation of probabilities that ADOs will occur. Such information may be compiled based on asset delivery records for similar network environments in the recent past or over time.