1. ADVANCED OPTIMIZATION TECHNIQUES FOR UAS COMMAND AND CONTROL (C2) COMMUNICATIONS TERRESTRIAL INFRASTRUCTURE
Dr. Erton Boci
UAS Terrestrial Infrastructure

2. Agenda
Background of C2 UAS feasibility study Radio Site Layout Design Objective Coverage Prediction Model Selection Availability of Radio Station Sites along the Pipeline Optimization of site selection using Linear Programming Optimization Results Conclusion and future work

3. Background of C2 UAS feasibility study
In early 2015, Harris Corporation. and the University of Alaska Fairbanks (UAF) conducted a feasibility study to assess the use of C-Band terrestrial based C2 communications as enabler of low altitude unmanned aircraft system (UAS) operations along the 800 mile length of the Trans- Alaska Pipeline System (TAPS)
The Technical Assessment of Infrastructure Requirements consisted of a detailed study to select an optimum solution of terrestrial radio stations that would meet the required C2 link coverage for the Alyeska Pipeline use case

4. Radio Site Layout Design Objective
For the terrestrial infrastructure selection process, we followed a well-established Service Volume (SV) design approach
SV engineering effort is the selection of radio sites and their configuration to provide the RF coverage required in offering the service(s) in predefined 3-dimensional airspaces
The site selection process includes
Selection and adaptation of an RF coverage model
Model-based solution architecting for optimal radio station siting
Complex geospatial/RF design model development and design sharing
Flight campaign for model verification / risk reduction
For this feasibility study, we focused on the first two steps of our overall site selection process

5. Coverage Prediction Model Selection
Generally, coverage prediction models can be grouped in the following three major categories
Empirical
Statistical
Deterministic
Viewshed (Radio Line-Of-Sight)
Field-Integral (CRC-Predict, Air-predict models)
Ray-Tracing
Deterministic prediction models are selected for further evaluation because they achieve a high level of accuracy at the expense of longer simulation time

6. Coverage Prediction Model Selection (2)
Viewshed coverage model was selected for this study because:
It is simplest to set up and implement
It provides excellent first order coverage estimation for most situations (cases A and D)
Case
Possible coverage combinations
Coverage Prediction Accuracy
Simple Setup
Complex RF Setup
Very Complex RF Setup
LOS ?
RF Link Closed ?
Viewshed
CRC-Predict
Ray-Tracing A1
Yes
Excellent
Good B2 No
Very poor
Fair C3 D1
1 - These cases are coverage conditions most likely encountered along the pipeline
2 - Even without radio LOS, RF link closes because of diffraction
3 - Even with radio LOS, RF link does not close, possibly because of multipath

7. Sample Coverage Analysis (100ft AGL)
Terrain
Viewshed
CRC-Predict
Case A
Case B
Case C
Case D

8. Pipeline Terrain Environment
Available terrain data for analysis
USGS high resolution terrain (75m)
STRM high resolution terrain (90m) – [Selected]
Constraints
Minimum prediction altitude 100 ft AGL
Prediction models considered
Viewshed model – [Selected]
CRC Predict model
Ray Tracing Model

9. Availability of Radio Station Sites along the Pipeline
Pump Stations (12 sites)
Assume installation of 75ft tower structures at each of the pump station locations
Existing VHF radio sites (24 sites)
Assume co-location with existing VHF radio system infrastructure
SBSS ADS-B (8 sites)
Assume co-location with SBSS ADS-B sites
FCC listed sites (141 sites)
Assume sites/structures identified in the FAA database are available for site installation

10. Example PS04 to PS05 Viewshed Analysis
Combined viewshed analysis using eight sites along PS-04 to PS-05 section of pipeline
No predicted low altitude visibility for many sections of the pipeline primarily due to terrain and lack of infrastructure
Google Earth views and viewshed analysis show agreement

11. Radio Site Optimization Approach
An initial set of 57 sites was identified as the most suitable for providing best coverage along pipeline
Give priority to sites with the closest proximity to the pipeline and with some infrastructure, especially an existing tower.
Coverage Problem Optimization Statement
Minimize the number of sites while maintaining the same coverage provided by the initial set of selected sites for the flight altitudes of interest
Coverage Problem Solution
Leverage Linear Programming techniques in search of the optimum problem solution

12. Radio Site Linear Programming Formulation
𝑗=1 𝑛 𝑐 𝑗 𝑥 𝑗
Objective function: Minimize
subjet to
where
𝑛 - number of the initial set of selected radio sites
𝑚 - number of 3D points in space for coverage evaluation
𝑥 𝑗 - the 𝑗𝑡ℎ site 0 if the site is not required and 1 if the site is required
𝑐 𝑗 - the cost associated with the deployment of 𝑗𝑡ℎ site
𝑙𝑜𝑠 𝑖𝑗 - the predicted line-of-site (LOS) from 𝑗𝑡ℎ site to the desired 3D 𝑖𝑡ℎ point in space with allowed values of 0 (no LOS) is predicted and 1 if LOS is predicted
𝑏 𝑖 - required LOS coverage at 𝑖𝑡ℎ 3D point in space with allowed values of 0 if no LOS is required, 1 if single site LOS coverage is required, … and k if k- redundant LOS coverage is required
𝑗=1 𝑛 𝑙𝑜𝑠 𝑖𝑗 𝑥 𝑗 ≥ 𝑏 𝑖 𝑖=1,…, 𝑚
𝑥 𝑗 ∈ 0,1 𝑗=1,…,𝑛

13. Radio Site Optimization Example
Assume our goal is to provide non-redundant LOS coverage to ten 3D points in space [ 𝑝 1 , 𝑝 2 , …, 𝑝 10 ] Three radio sites [ 𝑥 1 , 𝑥 2 , 𝑥 3 ] selected as the initial set Cost to acquire each of these sites is the same [ 𝑐 1 = 𝑐 2 = 𝑐 3 ]
Coverage requirements (3D points)
LOS Analysis 3D
Lat
Lon
Altitude
(ft AGL) x1 x2 x3 p1
200 1 p2
500 p3
1000 p4
1500 p5
2000 p6
2500 p7 p8 p9
p10
Linear Programming Formulation
𝑗=1 3 𝑥 𝑗
Minimize
subject to
𝑗=1 3 𝑙𝑜𝑠 𝑖𝑗 𝑥 𝑗 ≥ 𝑏 𝑖 𝑖=1,…, 10
𝑥 𝑗 ∈ 0,1 𝑗=1,2,3

14. Linear Programing “LP Solve” software
LP Solve is a linear programing solver written in C and available on both Linux and Windows It solves linear programming, mixed-integer programming (MIP), and semi-continuous and special ordered sets problems
LP Formulation using LPSolve software
LPSolve Solution
Based on the LPSolve solution, only two sites, 𝑥 1 𝑎𝑛𝑑 𝑥 3 , are needed to achieve the RLOS coverage provided by the initial set of three sites 𝑥 1 , 𝑥 2 , 𝑥 3 .

15. Pipeline LP Optimization Parameters
Total of 18,295 sample points were used along the pipeline with an average sample distance of 185m Out of ~300 sites along the pipeline, we selected 57 best candidate sites based on the proximity of the site to the pipeline and removal of sites in clustered areas The altitude floor of the SV along the pipeline was selected to be 100ft AGL with the ceiling set to 3000ft AGL. Coverage predictions were generated per site per 100ft of altitude increase
Linear Programming Formulation per altitude
𝑗=1 57 𝑥 𝑗
Minimize
𝑗=1 57 𝑙𝑜𝑠 𝑖𝑗 𝑥 𝑗 ≥ 𝑏 𝑖 𝑖=1,…, 18925
subject to
𝑥 𝑗 ∈ 0,1 𝑗=1,…,57

16. Data Storage and Processing
Viewshed predictions for each pipeline geo sample location were stored in MongoDB database MongoDB is a flexible and scalable NoSQL document-oriented database MongoDB Map-Reduce was used to query and generate the LP Solve source file
Sample record stored in MongoDB
Pipeline sample point location
Radio site information
Predicted complex viewshed value representing the height the point should be raised or lowered to make it just visible from the radio station location

17. Pipeline Radio Site Optimization Results
Optimized number of sites given the 57 sites as the initial set

18. Sensitivity Analysis
Percentage C2 System Coverage Provided by each Site over a Range of Altitudes of Operational Interest

19. Conclusion
We described the use of linear programming optimization techniques, as the bases of our technical assessment and infrastructure requirements derivation for the C-Band terrestrial based C2 communications
The site optimization results were used to evaluate various optimal and near-optimal infrastructure solutions
Future enhancements
Account for coverage scenarios that require redundant coverage so to improve the overall performance of the operational system
Account for site acquisition cost and formulate the LP problem so to minimize the overall cost of the proposed solution
Use RF predictions instead of viewshed predictions