a distributed approach for planning radio communications david kidner 1, ian fitzell 2, phillip...

A Distributed Approach for Planning Radio CommunicationsDavid Kidner1, Ian Fitzell2, Phillip

Rallings3,Miqdad Al Nuaimi2 & Andrew Ware3

University of Glamorgan1School of Computing

2School of Electronics

3Division of Mathematics & Computing

Pontypridd, Rhondda Cynon Taff WALES, U.K. CF37 1DLe-mail: [email protected]

Geocomputation’99 July 25th - 28th

Scope

• Spatial Data Problems & Site Selection• From Sight: Visibility Analysis• To Sound: Radio Field Planning• Topographic Modelling• Parallel Solutions• Results & Conclusions


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


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


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


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


Radio Path Planning


Radio Communications Planning


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?


Airborne Laser Scanning - LiDAR (Cardiff)


LiDAR• Very high resolution (1 or 2m as a

DEM)• With or Without Clutter• Accurate• Cheap


Managing Complex Data


ComplexFeatures

• Elevated Features

• Vegetation

• Roof Ridges


Proposed 3D Standard for Topographic Data (for Radio Planning)


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?


The Need for aParallel Radio Broadcast Algorithm• Determining an optimal transmitter location


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!


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.)


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.


Speed-up = elapsed time of a uniprocessor elapsed time of the multiprocessors

Efficiency = speed-up * 100 number of processors

Parallel Implementation Comparison Indicators


Test Data (520 Possible Transmitter Locations)


Speed-Up PerformanceSpeed-Up Performance

0

2

4

6

8

10

12

14

16

18

20

22

24

2 4 6 8 10 12 14 16 18 20 22 24

Number of Processors

Sp

ee

d-U

p

Sight

Sound


Relative Efficiency

Relative Efficiency

92

93

94

95

96

97

98

99

100

2 4 6 8 10 12 14 16 18 20 22 24

Number of Processors

%

Sight

Sound


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.

a distributed approach for planning radio communications david kidner 1, ian fitzell 2, phillip...

Documents