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

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

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

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

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

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

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

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

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

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 1997-2002, latest updated in 2005 Free download from www.seamcat.org October 2006

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

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

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

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

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

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

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

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

SEAMCAT-3 (2005): CDMA: October 2006

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

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

Thank you! For further info: www.ero.dk/seamcat www.seamcat.org E-mail: medeisis@ero.dk October 2006