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—We consider a decentralized wireless network with several source-destination pairs sharing a limited number of orthogonal frequency bands. Sources learn to adapt their transmissions (specifically, their band selection strategy) over time, in a decentralized manner, without sharing information with each other. Sources can only observe the outcome of their own transmissions (i.e., success or collision), having no prior knowledge of the network size or of the transmission strategy of other sources. The goal of each source is to maximize their own throughput while striving for network-wide fairness. We propose a novel fully decentralized Reinforcement Learning (RL)-based solution that achieves fairness without coordination. The proposed Fair Share RL(FSRL)solution combines: (i) state augmentation with a semiadaptive time reference; (ii) an architecture that leverages risk control and time difference likelihood; and (iii) a fairness-driven reward structure. We evaluate FSRL in more than 50 network settings with different number of agents, different amounts of available spectrum, in the presence of jammers, and in an ad-hoc setting. Simulation results suggest that, when we compare FSRL with a common baseline RL algorithm from the literature, FSRL can be up to 89.0% fairer (as measured by Jain’s fairness index) in stringent settings with several sources and a single frequency band, and 48.1% fairer on average. 

In this paper, we consider a setting in which geographically constrained “local” wireless services operate in a shared spectrum band and compete in the same market for customers who fall within their local coverage areas. When their desired coverage areas overlap, there are multiple ways that spectrum usage could be coordinated. We discuss ways in which this coordination could arise. We then characterize the market impacts of different forms of coordination via a framework of Cournot competition with congestion. Our analysis illustrates the economic trade-offs of different coordination mechanisms for local services. 

Future G networks will require more dynamic, agile support for the management of radio spectrum on a fine-grained basis. The radio access network (RAN) technologies necessary to enable Dynamic Spectrum Access (DSA) have progressed significantly over the past 20 years, but the challenges of realizing the potential for DSA requires the co-evolution of technologies, business models, and regulatory policy. This paper presents a multidisciplinary research effort to develop the building blocks needed to advance DSA. In particular, we focus on the use of standards-based Spectrum Consumption Models (SCMs) and review on-going research to incorporate SCMs in an automated management framework based on incentive-compatible, technically-sound spectrum access contracts referred to as Spectrum Access Agreements (SAAs). This paper introduces the core concepts of the SCM/SAA framework, project goals, and preliminary insights into how the framework can help improve spectrum management. The research on SCM/SAA represents a bottom-up effort to develop the techno-economic building blocks or tools to facilitate marketbased experimentation and development of DSA based spectrum sharing markets, business models, and applications. 

In order to enable the simultaneous transmission and reception of wireless signals on the same frequency, a fullduplex (FD) radio must be capable of suppressing the powerful self-interference (SI) signal emitted from the transmitter and picked up by the receiver. Critically, a major bottleneck in wideband FD deployments is the need for adaptive SI cancellation (SIC) that would allow the FD wireless system to achieve strong cancellation across different settings with distinct electromagnetic environments. In this work, we evaluate the performance of an adaptive wideband FD radio in three different locations and demonstrate that it achieves strong SIC in every location across different bandwidths.