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Security is a critical concern in shared spectrum environments. In additional to degrading service, attacks can influence the market interactions between competing service providers (SPs). This paper investigates these interactions by considering two SPs engaged in Cournot competition while utilizing both proprietary and shared spectrum, with shared spectrum available in either licensed or open-access forms. Additionally, we assume the presence of an attacker whose objective is to deny service to one or more of the shared bands for a fraction of the time, consequently reducing the overall total revenue. We analyze the optimal forms of attacks under different attacker objectives and their repercussions on the resulting market equilibrium. Utilizing these analyses, we compare the impacts of various spectrum sharing approaches (licensed and open access) and differing amounts of spectrum holdings of the two providers. 

Networks that provide agents with access to a common database of the agents' actions enable an agent to easily learn by observing the actions of others, but are also susceptible to manipulation by “fake” agents. Prior work has studied a model for the impact of such fake agents on ordinary (rational) agents in a sequential Bayesian observational learning framework. That model assumes that ordinary agents do not have an ex-ante bias in their actions and that they follow their private information in case of an ex-post tie between actions. This paper builds on that work to study the effect of fake agents on the welfare obtained by ordinary agents under different ex-ante biases and different tie-breaking rules. We show that varying either of these can lead to cases where, unlike in the prior work, the addition of fake agents leads to a gain in welfare. This implies that in such cases, if fake agents are absent or are not adequately present, an altruistic platform could artificially introduce fake actions to effect improved learning. 

Observational learning models seek to understand how distributed agents learn from observing the actions of others. In the basic model, agents seek to choose between two alternatives, where the underlying value of each alternative is the same for each agent. Agents do not know this value but only observe a noisy signal of the value and make their decision based on this signal and observations of other agents’ actions. Here, instead we consider a scenario in which the choices faced by an agent exhibit a negative externality so that value of a choice may decrease depending on the history of other agents selecting that choice. We study the learning behavior of Bayesian agents with such an externality and show that this can lead to very different outcomes compared to models without such an externality. 

We consider a model in which two competing wireless service providers with licensed spectrum may pool a portion of their spectrum to better exploit statistical multiplexing. Given an amount of pooled spectrum, the providers engage in Cournot competition. We study the impact of pooling spectrum on the outcome of this competition and show that the gains from multiplexing are dissipated due to the competition among the providers. 

In this paper, we study a fresh data acquisition problem to acquire fresh data and optimize the age-related performance when strategic data sources have private market information. We consider an information update system in which a destination acquires, and pays for, fresh data updates from a source. The destination incurs an age-related cost, modeled as a general increasing function of the age-of-information (AoI). The source is strategic and incurs a sampling cost, which is its private information and may not be truthfully reported to the destination. To this end, we design an optimal (economic) mechanism for timely information acquisition by generalizing Myerson's seminal work. The goal is to minimize the sum of the destination's age-related cost and its payment to the source, while ensuring that the source truthfully reports its private information and will voluntarily participate in the mechanism. Our results show that, under some distributions of the source's cost, our proposed optimal mechanism can lead to an unbounded benefit, compared against a benchmark that naively trusts the source's report and thus incentivizes its maximal over-reporting. 

Cellular Vehicle-to-Everything (C-V2X) networks are increasingly adopted by automotive original equipment manufacturers (OEMs). C-V2X, as defined in 3GPP Release 14 Mode 4, allows vehicles to self-manage the network in absence of a cellular base-station. Since C-V2X networks convey safety-critical messages, it is crucial to assess their security posture. This work contributes a novel set of Denial-of-Service (DoS) attacks on C-V2X networks operating in Mode 4. The attacks are caused by adversarial resource block selection and vary in sophistication and efficiency. In particular, we consider "oblivious" adversaries that ignore recent transmission activity on resource blocks, "smart" adversaries that do monitor activity on each resource block, and "cooperative" adversaries that work together to ensure they attack different targets. We analyze and simulate these attacks to showcase their effectiveness. Assuming a fixed number of attackers, we show that at low vehicle density, smart and cooperative attacks can significantly impact network performance, while at high vehicle density, oblivious attacks are almost as effective as the more sophisticated attacks. 

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 the 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.