Adaptive Network Planning for 5G Small Cell Site Selection
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Solution Overview
Problem
The manual process of network site selection in 5G communication systems is time-consuming and inefficient due to the high density of small cell sites, which often lack sufficient backhaul, and is affected by clutter, requiring a more automated and adaptive method for network planning to meet the demands of high throughput, low latency, and cost-effectiveness.
Innovation Solution
An automated adaptive network planning method that generates a list of proposed active sites based on calculated likelihoods of meeting network coverage goals, using a simulation engine and site selection module to prioritize sites, and dynamically update plans according to site availability and backhaul requirements, incorporating 2D and 3D path loss models and geospatial building models to optimize wireless network deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual network site selection process is used, then flexibility in negotiating site availability and backhaul can be achieved, but the process becomes extremely time-consuming and requires hundreds of re-plans
Solution Approach 1:
The system performs preliminary automated calculations of coverage likelihood for all potential sites before site acquisition negotiations begin. By pre-computing the probability that each site will meet network coverage goals using propagation models and clutter data, the planning system establishes a prioritized list of preferred sites. This preliminary action enables the acquisition team to negotiate with sites in order of importance, reducing the number of iterative re-plans needed while maintaining flexibility to substitute alternative sites if negotiation fails.
Solution Approach 2:
The system implements feedback loops where site acquisition outcomes (available/unavailable, backhaul capacity) are fed back into the automated planning system. When sites become unavailable or backhaul proves insufficient, the system automatically recalculates coverage likelihoods for alternative sites and generates updated plans. This feedback mechanism maintains adaptability while reducing manual intervention time, as the system learns from each negotiation outcome and automatically adjusts the site selection strategy.
2Reliability
If traditional cell towers with fiber optic backhaul are used, then reliable backhaul connection is achieved, but deployment cost and complexity increase significantly for high density small cell sites
Solution Approach 1:
The system changes the parameter of backhaul capacity requirements by calculating and prioritizing sites based on their likelihood of meeting network coverage goals. Instead of uniformly requiring high-capacity fiber optic backhaul at all sites, the automated system identifies sites where alternative backhaul solutions (wireless backhaul, fixed wireless access) may be sufficient based on coverage probability calculations. This parameter change enables deployment at lower cost and complexity for sites where full fiber backhaul is not critical, while maintaining reliability at high-priority sites.
3Adaptability or versatility
If site substitution with nearby locations is performed, then deployment flexibility is improved, but coverage quality deteriorates due to strong clutter effects on 5G signals
Solution Approach 1:
The system replaces manual judgment of site suitability with automated electromagnetic propagation modeling. Using 3D building models, clutter databases, and path loss models, the system calculates the probability that each potential site (including substitutes) will achieve required coverage. This mechanical substitution of automated calculation for manual assessment ensures that even when site substitution is necessary, the alternative sites are selected based on quantitative coverage probability rather than arbitrary proximity, thereby maintaining coverage quality while preserving deployment flexibility.
Data Source
AI summary
A method for automatic adaptive network planning includes receiving a first list which includes a plurality of potential sites. A set of the network coverage goals and one or more models substantially related to the network coverage are received. A wireless network coverage map is generated for each site based on the received model(s). The coverage map includes a plurality of locations within a corresponding coverage area. For each location and for each site the likelihood of the network coverage goals being realized is calculated using the generated wireless network coverage map. A second list of proposed active sites is automatically generated. The second list includes a subset of the sites included in the first list based on the calculations performed for each location. The second list of the proposed active sites substantially meets the set of network coverage goals.