[Paper Review] Optimization of Spectrum Allocation and Subsidization in Mobile Communication Services
This paper proposes a data subsidy scheme where regulators offer spectrum price discounts to mobile network operators (MNOs) in exchange for providing a fixed data volume free to users. Using a two-stage Cournot-Bertrand competition model, the study finds that this scheme increases user welfare without reducing MNO profits, and the social gain exceeds the regulator’s subsidy cost, even in duopoly and general market settings.
Mobile traffic explosion causes spectrum shortage and polarization of data usage among users, which will eventually decrease user welfare in mobile communication services. Governments around the world are planning to make more spectrum available for mobile broadband use, and the key policy issue is to find an efficient spectrum allocation method that will improve user welfare. In this paper, we propose a data subsidy scheme where the regulator offers a spectrum price discount to mobile network operators (MNOs) in return for imposing the responsibility of providing a predefined data amount to users free of charge. To analyze the subsidy effect, we adopt the two-stage approach of Cournot and Bertrand competition, and find a Nash equilibrium of the competition. An interesting observation is that the increase in user welfare does not involve MNO profit loss and the increasing amount is higher than the regulator's expenses for implementing the data subsidy scheme. The most of the paper is for the duopoly competition, which is extended to the general case, finally.
Motivation & Objective
- Address the growing spectrum shortage and unequal data usage in mobile networks.
- Improve user welfare in mobile communication services amid rising mobile traffic.
- Design a policy mechanism that efficiently allocates spectrum while maintaining MNO incentives.
- Analyze the impact of a data subsidy scheme on market outcomes under competitive conditions.
- Evaluate whether the social benefits of the subsidy exceed its implementation costs.
Proposed method
- Formulates a two-stage game model combining Cournot competition in spectrum allocation and Bertrand competition in pricing.
- Models the regulator’s role in offering spectrum price discounts to MNOs conditional on providing free data to users.
- Derives the Nash equilibrium of the game to analyze strategic interactions among MNOs and the regulator.
- Extends the duopoly model to a general n-firm competition setting to assess scalability.
- Uses comparative statics to evaluate welfare, profit, and cost implications under the subsidy scheme.
- Analyzes the trade-off between regulator spending and user welfare gains through equilibrium outcomes.
Experimental results
Research questions
- RQ1How does a spectrum price discount subsidy to MNOs affect user welfare in mobile networks?
- RQ2Can the proposed subsidy scheme increase user welfare without reducing MNO profits?
- RQ3Is the total social gain from improved user welfare greater than the regulator’s cost of implementing the subsidy?
- RQ4How do the results scale from a duopoly to a general oligopoly market structure?
- RQ5What is the equilibrium outcome under the combined Cournot-Bertrand competition framework with the subsidy?
Key findings
- The data subsidy scheme increases user welfare without causing profit losses for mobile network operators.
- The improvement in user welfare exceeds the total cost incurred by the regulator for implementing the spectrum price discount.
- In the duopoly model, the equilibrium outcome shows that MNOs pass on part of the subsidy benefit to users through free data allocation.
- The general equilibrium analysis confirms that the social gains from the subsidy are sustainable across multiple competitors.
- The model demonstrates that the regulator can achieve higher social welfare at a lower fiscal cost than the welfare gains realized.
- The results are robust under both Cournot and Bertrand competition stages, indicating strategic stability of the subsidy mechanism.
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This review was created by AI and reviewed by human editors.