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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. 

Future G networks will require more dynamic, agile support for the management of radio frequency 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 the technologies, business models/market structures, and regulatory policy for wireless networks. This paper discusses a bottom-up, multi-disciplinary approach to 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, or Spectrum Access Agreements (SAAs). This work is being undertaken as part of the NSF National Radio Dynamic Zone (NRDZ) research initiative and this paper provides an introduction to the core concepts of the SCM/SAA framework, project goals, and preliminary insights into how the SCM/SAA can help improve spectrum management and advance R&D efforts to enable the transition to a shared spectrum future. The SCM/SAA research represents a bottom-up effort to develop the techno-economic tools to facilitate market-based experimentation and development of spectrum sharing markets, business models, and applications to complement and render more economically viable and relevant emerging DSA technologies and top-down regulatory reforms aimed at lowering spectrum sharing barriers. 

Dynamic Spectrum Access (DSA) is a key mechanism for meeting the ever-increasing demand for emerging wireless services. DSA involves managing and assigning available spectrum resources in a way that minimizes interference and allows RF coexistence between heterogeneous devices and systems. Spectrum Consumption Models (SCMs)- defined in the IEEE 1900.5.2 standard, offer a mechanism for RF devices to: (i) declare the characteristics of their intended spectrum use and their interference protection needs; and (ii) determine compatibility (non-interference) with existing devices. In this paper, we propose a novel SCM-based Spectrum Deconfliction (SD) algorithm that dynamically configures RF operational parameters (e.g., center frequency and transmission power) of a target transmitter-receiver pair aiming to minimize interference with existing devices/systems. We also propose sequential and distributed DSA methods that use the SD algorithm for assigning spectrum in large-scale networks. To evaluate the performance of our methods in terms of computation time, spectrum assignment efficiency, and overhead, we use two custom-made simulation platforms. Finally, to experimentally demonstrate the feasibility of our methods, we build a proof-of-concept implementation in the NSF PAWR COSMOS wireless testbed. The results reveal the advantages of using SCMs and their capabilities to conduct spectrum assignments in dynamic and congested communication environments. 

| The relentless demand for data in our society has driven the continuous evolution of wireless technologies to enhance network capacity. While current deployments of 5G have made strides in this direction using massive multiple-input–multiple-output (MIMO) and millimeter-wave (mmWave) bands, all existing wireless systems operate in a half-duplex (HD) mode. Full-duplex (FD) wireless communication, on the other hand, enables simultaneous transmission and reception (STAR) of signals at the same frequency, offering advantages such as enhanced spectrum efficiency, improved data rates, and reduced latency. This article presents a comprehensive review of FD wireless systems, with a focus on hardware design, implementation, cross-layered considerations, and applications. The major bottleneck in achieving FD communication is the presence of self-interference (SI) signals from the transmitter (TX) to the receiver, and achieving SI cancellation (SIC) with real-time adaption is critical for FD deployment. The review starts by establishing a system-level understanding of FD wireless systems, followed by a review of the architectures of antenna interfaces and integrated RF and baseband (BB) SI cancellers, which show promise in enabling low-cost, small-form-factor, portable FD systems. We then discuss digital cancellation techniques, including digital signal processing (DSP)- and learning-based algorithms. The challenges presented by FD phased-array and MIMO systems are discussed, followed by system-level aspects, including optimization algorithms, opportunities in the higher layers of the networking protocol stack, and testbed integration. Finally, the relevance of FD systems in applications such as next-generation (xG 

The self-interference (SI) channels in full-duplex (FD) radios have large nano-second-scale delay spreads, which poses a significant challenge in designing SI cancelers that can emulate the SI channel over wide bandwidths. Passive implementations of high delay lines have a prohibitively large form factor and loss when implemented on silicon, whereas active implementations suffer from noise and linearity penalties. In this work, we leverage time-interleaved multi-path switched-capacitor (SC) circuits to provide large wideband delays with a small form factor and low power (LP) consumption to implement RF and baseband (BB) cancelers in an FD receiver (RX). We utilize capacitor stacking to obtain passive voltage gain to compensate for the loss of these delay elements, thus permitting an increased number of interleaved paths and, hence, a higher delay. Furthermore, to reduce the RX noise figure (NF) penalty due to injecting the cancellation signal into the receiver, we introduce a novel low-noise trans-impedance amplifier (LNTA) architecture, which injects the cancellation signal into RX and also accomplishes finite impulse response (FIR) filter weighting and summation. The FD receiver is implemented in a standard 65-nm CMOS process and operates from 0.1 to 1 GHz. The RF/BB canceler delay cells have real-/complex-valued weighting with delays ranging