A rate‐maximizing spectrum sharing algorithm for cognitive radio networks with generic resource constraints

Abstract

Algorithms for spectrum sharing over resource‐limited cognitive radio networks are often designed to solve specific problems. This means that a certain algorithm deals specifically with a certain limited resource, and is not suitable for other resources. This limitation violates the software‐defined networking philosophy, where a scheme has to be reprogrammable to cope with different limited resources that can dynamically arise depending on network conditions. In this work, we investigate the problem of spectrum sharing in resource‐limited cognitive radio networks. Specifically, we introduce a novel spectrum sharing algorithm that is compatible with software‐defined networks, in the sense that it can be reprogrammed to support multiple constraints on resources of different types. A main feature of the proposed scheme is that computations could be distributed across multiple processing units to reduce computational complexity on each unit. In addition, the proposed algorithm is equipped with a fairness scheme. Simulation results demonstrate the efficacy of the proposed scheme.

Cross‐layer perspective for channel assignment in cognitive radio networks: A survey

“…It was also ensured that considering fairness, if any SU is assigned a low channel gain subcarrier, then the minimum desired sum rate is also achieved. Halloush et al proposed a rate‐maximizing fair spectrum sharing algorithm for CRN. Main focus of the authors in the proposed work is to support multiple constraints on various types of resources.…”

On seamless and high‐bandwidth connectivity for cognitive multi‐unmanned aerial vehicle‐assisted networks

“…Cognitive radio (CR) technology can be a choice for solving spectrum shortage problems 7,8 . CR technology has been effectively used in mobile sensor networks, 9 wireless mesh networks, 10 Internet of things (IoT), 11 and cellular networks 12 .…”

Machine Learning/AI for IoT, M2M, and Computer Communication

“…The ninth paper, by Zangiabady et al, “Self‐adaptive online virtual network migration in network virtualization environments,” leverages reinforcement learning to minimize the cost associated with migrating virtual network resource from one to another. The 10th paper, “A rate‐maximizing spectrum sharing algorithm for cognitive radio networks with generic resource constraints” by Halloush et al, proposed a spectrum sharing scheme for CRNs that is compatible with SDN and reduces computational complexity by applying distributed computing.…”