1. Optimisation of Radio Spectrum Usage
Engineering
Advanced
Monte-
Carlo
Analysis
Tool
For
Optimisation of Radio Spectrum Usage
Artūras Medeišis
European Radiocommunications Office

2. Outline: Spectrum sharing: means and rules
Worst-case vs. Statistical analysis
Monte-Carlo statistical analysis
SEAMCAT as spectrum optimisation tool
October 2006

3. Need for spectrum sharing:
There are no more “empty” spectrum
Proposed new systems have to find way of “sharing” with some of existing systems
Thus the need for spectrum engineering and optimisation:
to find which existing radio systems are easiest to share with, and then
determine the “sharing rules”
October 2006

4. Sharing methods: Spacing radio systems in frequency
Using the gaps between existing channels
Spacing geographically
Using the gaps between intended deployment areas (e.g. cities vs. rural areas)
Time sharing
Exploiting different work time (day vs. night)
Working at different power levels
E.g. “underlay” spectrum use by UWB
October 2006

5. Sharing implementation:
Agile (cognitive) radio systems require minimum sharing rules as they could be adapting dynamically
Simple example: finding free channel in a given geographic area
Traditional rigid-design radio system will require precisely defined sharing rules
Maximum transmit power, guard-bands to existing systems, etc
October 2006

6. Defining the sharing rules:
Analytical analysis, usually by worst-case approach:
Minimum Coupling Loss (MCL) method, to establish rigid rules for minimum “separation”
Statistical analysis of random trials:
The Monte-Carlo method, to establish probability of interference for a given realistic deployment scenario
October 2006

7. MCL principle: The stationary worst-case is assumed
Wanted
Signal
Victim
Interferer
Dmin, or minimum frequency separation for D=0
However such worst-case assumption will not be permanent during normal operation and therefore sharing rules might be unnecessarily stringent – spectrum use not optimal!
October 2006

8. Monte-Carlo principle:
Repeated random generation of interferers and their parameters (activity, power, etc…)
Wanted
Signal
t=t0
Victim
t=ti
t=t1
Active
Interferer
Inactive
Interferer
After many trials not only unfavourable, but also favourable cases will be accounted, the resulting rules will be more “fair” – spectrum use optimal!
October 2006

9. Monte-Carlo assumptions:
User will need to define the distributions of various input parameters, e.g.:
How the power of interferer varies (PControl?)
How the interferer’s frequency channel varies
How the distance between interferer and victim varies, and many others
Number of trials has to be sufficiently high (many 1000s) for statistical reliability:
Not a problem with modern computers
October 2006

10. SEAMCAT: Spectrum Engineering Advanced Monte-Carlo Analysis Tool
An open software tool for analysing various scenarios for co-existence of radio systems
Developed in co-operation by European administrations and industry between , latest updated in 2005
Free download from
October 2006

11. Using SEAMCAT:
User defines a scenario, describing mutual positioning of two systems, both in geographical domain…
…as well as many other parameters:
October 2006

12. Scenario parameters: Positioning of two systems in frequency Powers
Masks
Activity
Etc.
October 2006

13. SEAMCAT event generator:
Random generation of transceivers
Link budget
Signal values
October 2006

14. How event generator works:
Succession of snapshots…
dRSS
Snapshot#
iRSS WT
1) Calculate d, Ptx, GaTx, GaRx, L IT
2) Calculate dRSSi WT VR
1) Calculate d, Ptx, GaTx, GaRx, L VR
2) Calculate iRSSi
1) Calculate d, Ptx, GaTx, GaRx, L IT
2) Calculate received signal, if PC, adjust Ptx WR WR
October 2006

15. Result of simulations:
Vectors for useful and interfering signals:
October 2006

16. Evaluating interference:
By comparing signal instances:
- For each random event:
Desired signal value (dBm)
C/Itrial > C/Itarget?
Interfering signal (dBm)
Interference (dB)
Noise Floor (dBm)
- If C/Itriali >C/Itarget: “good” event
- If C/Itriali <C/Itarget: “interfered”
- Finally, after cycle of Nall events:
Overall Pinterference= 1- (Ngood/Nall)
October 2006

17. Result of simulations:
One value of probability of interference,
Or, as function of one of input parameters
October 2006

18. As a result:
Deployment of radio systems would be evaluated in realistic situations, so the “averaging” of real-life interference could be estimated with statistical precision
Impact of input parameters may be evaluated more precisely, therefore the sharing rules will be more optimal
As a result, all this leads to statistically proven optimal exploitation of spectrum
October 2006

19. SEAMCAT-3 (2005):
CDMA:
October 2006

20. SEAMCAT-3:
Interference into CDMA as capacity loss:
October 2006

21. Conclusions:
Sharing rules are important element of spectrum optimisation process
Unless some intelligent interference avoidance is implemented in radio systems, the careful choice of sharing conditions is the only means for achieving successful co-existence and optimal spectrum use
Statistical tools, as SEAMCAT, could be a simple yet powerful tool for such analysis
October 2006

22. Thank you! For further info: www.ero.dk/seamcat www.seamcat.org
October 2006