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1. The Economics of Bundling Content with Unlicensed Wireless Service

   https://doi.org/10.1007/978-3-030-16989-3_7  

   Zhu, Y ; Yu, H ; Berry, R. ( April 2019 , Lecture notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering)

   Adding new unlicensed wireless spectrum is a promising approach to accommodate increasing traffic demand. However, unlicensed spectrum may have a high risk of becoming congested, and service providers (SPs) may have difficulty to differentiate their wireless services when offering them on the same unlicensed spectrum. When SPs offer identical services, the resulting competition can lead to zero profits. In this work, we consider the case where an SP bundles its wireless service with a content service. We show that this can differentiate the SPs’ services and lead to positive SP profits. In particular, we study the characteristics of the contentmore »services that an SP should bundle with its wireless service, and analyze the impact of bundling on consumer surplus.« less
2. Price Competition with LTE-U and WiFi

   https://doi.org/10.1109/INFOCOM.2019.8737549  

   Wang, Xu ; Berry, Randall A. ( April 2019 , IEEE INFOCOM)

   LTE-U is an extension of the Long Term Evolution (LTE) standard for operation in unlicensed spectrum. LTE-U differs from WiFi, the predominant technology used in unlicensed spectrum in that it utilizes a duty cycle mode for accessing the spectrum and allows for a more seamless integration with LTE deployments in licensed spectrum. There have been a number of technical studies on the co-existence of LTE-U and WiFi in unlicensed spectrum In this paper, we instead investigate the impact of such a technology from an economic perspective. We consider a model in which an incumbent service provider (SP) deploys a dutymore »cycle-based technology like LTE-U in an unlicensed band along with operating in a licensed band and competes with one or more entrants that only operate in the unlicensed band using a different technology like WiFi. We characterize the impact of a technology like LTE-U on the market outcome and show that the welfare impacts of this technology are subtle, depending in part on the amount of unlicensed spectrum and number of entrants. We also investigate the impact of the duty cycle and the portion of unlicensed spectrum used by the technology.« less
3. Cross-Network Prioritized Sharing: An Added Value MVNO’s Perspective

   https://doi.org/10.1109/INFOCOM.2019.8737636  

   Zhu, Yining ; Yu, Haoran ; Berry, Randall A. ; Liu, Chang ( April 2019 , IEEE INFOCOM)

   We analyze the prioritized sharing between an added value Mobile Virtual Network Operator (MVNO) and multiple Mobile Network Operators (MNOs). An added value MVNO is one which earns added revenue from wireless users in addition to the revenue it directly collects for providing them wireless service. To offer service, an MVNO needs to contract with one or more MNOs to utilize their networks. Agreeing on such a contract requires the MNOs to consider the impact on their revenue from allowing the MVNO to enter the market as well as the possibility that other MNOs will cooperate. To further protect theirmore »customers, the MNOs may prioritize their direct customers over those of the MVNO. We establish a multi-stage game to analyze the equilibrium decisions of the MVNO, MNOs, and users in such a setting. In particular, we characterize the condition under which the MVNO can collaborate with the MNOs. The results show that the MVNO tends to cooperate with the MNOs when the band resources are limited and the added value is significant. When there is significant difference in band resources among the MNOs, the MVNO first considers cooperating with the MNO with a smaller band. We also consider the case when the users also have access to unlicensed spectrum.« less
4. Entry and Investment in CBRS Shared Spectrum

   Ghosh, A ; Berry, R ( July 2020 , 2020 18th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOPT))

   The Citizens Broadband Radio Service (CBRS) recently adopted in the U.S. enables commercial users to share spectrum with incumbent federal users. This sharing can be assisted by Environmental Sensing Capability operators (ESCs), that monitor the spectrum occupancy to determine when the use of the spectrum will not harm incumbents. An important aspect of the CBRS is that it enables two tiers of spectrum access by commercial users. The higher tier corresponds to a spectrum access (SA) firm that purchases a priority access license (PAL) in a competitive auction. The PAL holder obtains dedicated licensed access to a portion of themore »spectrum when the incumbent is not present. The lower tier, referred to as generalized Authorized Access (GAA), does not request a PAL and is similar to unlicensed access, in which multiple firms share a portion of the spectrum. Entry and investment in such a market introduces a number of new dimensions. Should an entrant bid for a PAL? How does the availability of a PAL impact their investment decisions? We develop a game-theoretic model to study these issues in which entrant SAs may bid in a PAL auction and decide on their investment levels and then compete downstream for customers.« less
5. Dynamic Pricing of Wireless Internet Based on Usage and Stochastically Changing Capacity

   Batur, Demet ; Ryan, Jennifer K. ; Zhao, Zhongyuan ; Vuran, Mehmet C. ( January 2019 , Manufacturing & service operations management)

   Problem definition: Inspired by new developments in dynamic spectrum access, we study the dynamic pricing of wireless Internet access when demand and capacity (bandwidth) are stochastic. Academic/practical relevance: The demand for wireless Internet access has increased enormously. However, the spectrum available to wireless service providers is limited. The industry has, thus, altered conventional license-based spectrum access policies through unlicensed spectrum operations. The additional spectrum obtained through these operations has stochastic capacity. Thus, the pricing of this service by the service provider has novel challenges. The problem considered in this paper is, therefore, of high practical relevance and new to themore »academic literature. Methodology: We study this pricing problem using a Markov decision process model in which customers are posted dynamic prices based on their bandwidth requirement and the available capacity. Results: We characterize the structure of the optimal pricing policy as a function of the system state and of the input parameters. Because it is impossible to solve this problem for practically large state spaces, we propose a heuristic dynamic pricing policy that performs very well, particularly when the ratio of capacity to demand rate is low. Managerial implications: We demonstrate the value of using a dynamic heuristic pricing policy compared with the myopic and optimal static policies. The previous literature has studied similar systems with fixed capacity and has characterized conditions under which myopic policies perform well. In contrast, our setting has dynamic (stochastic) capacity, and we find that identifying good state-dependent heuristic pricing policies is of greater importance. Our heuristic policy is computationally more tractable and easier to implement than the optimal dynamic and static pricing policies. It also provides a significant performance improvement relative to the myopic and optimal static policies when capacity is scarce, a condition that holds for the practical setting that motivated this research.« less