Abstract

In judging whether to allow new service rules, a regulator has to balance the interests of incumbents, new entrants and the public. The trade-off between the benefits of a new service and the risks to incumbents has to date been essentially qualitative. This short paper proposes the use of quantitative risk analysis to assess the harm that may be caused by changes in radio service rules. Risk analysis considers the likelihood-consequence combinations for multiple hazard scenarios, and complements a “worst case” analysis that considers the single scenario with the most severe consequence, regardless of its likelihood. Risk-informed interference assessment is the systematic, quantitative analysis of interference hazards caused by the interaction between radio systems. Such an assessment has three major steps: 1. Make an inventory of all significant harmful interference hazard modes. 2. Define a consequence metric to characterize the severity of hazards. 3. Assess the likelihood and consequence of each hazard mode. Quantitative risk assessment can: • provide a common currency for comparing different interference scenarios and assessments; • enhance the completeness of analysis and increase the chances of identifying unexpected harmful interference mechanisms; • provide objective information to policy decision makers balancing the benefits of a new service and its adverse technical impact on incumbents. Implementing quantitative risk assessment requires a change in culture, and will take time. A regulator should therefore start small, but start soon: • Develop know-how in the agency and the wider community, for example through a lecture series on modern risk management, and — if there is an on-going education facility like the FCC University — by offering courses to staff on statistics and risk-management. • Use quantitative risk assessment in its own work, and publish the analyses and results. • Provide quantitative guidelines on unquestionably acceptable and unacceptable interference risks, respectively, e.g. as likelihood-consequence regions on a risk chart. • Pilot this approach in selected site-specific license waiver proceedings.

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