1. A Distributed Approach for Planning Radio Communications David Kidner 1, Ian Fitzell 2, Phillip Rallings 3, Miqdad Al Nuaimi 2 & Andrew Ware 3 University of Glamorgan 1 School of Computing 2 School of Electronics 3 Division of Mathematics & Computing Pontypridd, Rhondda Cynon Taff WALES, U.K. CF37 1DL e-mail: dbkidner@glam.ac.uk

2. Geocomputation’99 July 25 th - 28 th Scope Spatial Data Problems & Site Selection From Sight: Visibility Analysis To Sound: Radio Field Planning Topographic Modelling Parallel Solutions Results & Conclusions

3. Geocomputation’99 July 25 th - 28 th Optimal Site Selection & Planning Site selection (or location allocation) dates back to the 1950s and 60s However, the availability of spatial data and GIS (including spatial modelling and analysis) opens up greater challenges –More efficient and effective analysis –Environmentally-acceptable solutions –Optimal solutions

4. Geocomputation’99 July 25 th - 28 th From Sight: Visibility Analysis... GIS applications may require visibility functions for –minimising visual intrusion e.g. contentious developments such as wind farms –maximising the field-of-view e.g. radar or missile sites Massive workloads, compounded by very high resolution datasets

8. Geocomputation’99 July 25 th - 28 th To Sound: Radio Field Planning Path loss models describe the signal attenuation between the transmitter and receiver as a function of the propagation distance and other parameters related to the terrain profile and its surface features. Role of radio planning engineer is critical –increased deregulation & network providers –limited radio spectrum

9. Geocomputation’99 July 25 th - 28 th Radio Field Planning Point-to-point links are generally straightforward –milliseconds to seconds Broadcast Coverages (to a field-of-view) –minutes to hours Optimal Transmitter Locations –hours to days to weeks

10. Geocomputation’99 July 25 th - 28 th Radio Path Planning

11. Geocomputation’99 July 25 th - 28 th Radio Communications Planning

12. Geocomputation’99 July 25 th - 28 th Topographic Modelling Topographic Data Quality and Accuracy –will greatly improve application performance –Satellite Imagery Clutter categories (dense urban, suburban, vegetation, water features) –Aerial Photography (including heights) –Existing Mapping –LiDAR Data Structures?

13. Geocomputation’99 July 25 th - 28 th Airborne Laser Scanning - LiDAR (Cardiff)

14. Geocomputation’99 July 25 th - 28 th LiDAR Very high resolution (1 or 2m as a DEM) With or Without Clutter Accurate Cheap

16. Geocomputation’99 July 25 th - 28 th Managing Complex Data

17. Geocomputation’99 July 25 th - 28 th Complex Features Elevated Features Vegetation Roof Ridges

18. Geocomputation’99 July 25 th - 28 th Proposed 3D Standard for Topographic Data (for Radio Planning)

19. Geocomputation’99 July 25 th - 28 th Design Issues for Parallel Algorithms Sometimes difficult to recognise parallel aspects of a task –If it takes 1 woman 9 months to produce a baby, how long will it take 2 women ? –Some things are inherently sequential How do we split up the tasks ? –Data or Task Parallelism? How do we store the data ? –Shared or Distributed Memory Architecture?

20. Geocomputation’99 July 25 th - 28 th The Need for a Parallel Radio Broadcast Algorithm Determining an optimal transmitter location

21. Geocomputation’99 July 25 th - 28 th Previous Work Based on TRANSPUTERS (a distributed memory architecture, specifically designed for parallel processing) Very good at transferring information between processors, but little processing power and limited memory Transputers failed to capture the share of the processor market that they should have!

22. Geocomputation’99 July 25 th - 28 th Parallel Workstation Cluster Advances in the field of networks & operating systems have provided organisations with a valuable non- specialised, general purpose parallel processing resource. Cluster computing can scale to provide a very large parallel machine and specialised hardware can be made available to all machines. Each individual machine would also have total and independent control of its own resources (e.g. memory, disk, etc.)

23. Geocomputation’99 July 25 th - 28 th Current State of Play We have looked at a Data Parallel approach on what is essentially a Distributed Memory architecture. Looked at numerous STATIC & DYNAMIC approaches to the allocation of data. –Blocks, Quadrants, Octants, Rows/Columns, Individual Points, etc.

24. Geocomputation’99 July 25 th - 28 th Speed-up = elapsed time of a uniprocessor elapsed time of the multiprocessors Efficiency = speed-up * 100 number of processors Parallel Implementation Comparison Indicators

25. Geocomputation’99 July 25 th - 28 th Test Data (520 Possible Transmitter Locations)

26. Geocomputation’99 July 25 th - 28 th Speed-Up Performance

27. Geocomputation’99 July 25 th - 28 th Relative Efficiency

28. Geocomputation’99 July 25 th - 28 th Summary Phenomenal Results! Distributed cluster architecture is ideally suited for spatial data processing Dynamic partitioning is consistently superior to static partitioning –the variability of terrain can seriously affect load-balancing –small workloads are superior, provided communication overheads can be minimised.