On the Distributed Resource Allocation of MIMO Cognitive Radio Networks

Tanab, Manal El; Hamouda, Walaa; Fahmy, Yasmine

doi:10.1109/glocom.2015.7417277

Cited by 11 publications

(2 citation statements)

References 14 publications

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“…[35,36], and IEEE 802.22 wireless regional area network (WRAN) [37,38] are examples of such networks. Although most of the strategies in literature for this model are centralized, distributed schemes can also be supported [39].…”

Section: ) Centralizedmentioning

confidence: 99%

Resource allocation strategies in cognitive radio D2D communication networks

Sultana¹

2021

Preprint

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Device-to-device (D2D) communication is developed as a new paradigm to enhance net- work performance according to LTE and WiMAX advanced standards. On the other hand, cognitive radio (CR) approach provides efficient spectral usage using intelligent wireless nodes. In this thesis, a number of optimal resource allocation strategies for D2D communi- cation networks are investigated using the CR approach. As a first step, the CR approach in radio access networks is introduced. In the second step, the taxonomy of the RA process in CRNs is provided. For radio resource allocation (RRA), the most crucial task is to associate a user with a particular serving base station, to assign the channel and to allocate the power efficiently. In this thesis, a subcarrier assignment scheme and a power allocation algorithm using geometric water-filling (GWF) is presented for orthogonal frequency division multiplexing (OFDM) based CRNs. This algorithm is proved to maximize the sum rate of secondary users by allocating power more efficiently. Then, the RA problem is studied to jointly employ CR technology and D2D communication in cellular networks in terms of spectral efficiency (SE) and energy efficiency (EE). In the first case, in terms of SE, a two-stage approach is considered to allocate the radio resource efficiently where a new adaptive subcarrier allocation (ASA) scheme is designed first and then a novel power allocation (PA) scheme is developed utilizing proven GWF approach that can compute exact solution with less computation. In the second case, in terms of EE, the power allocation problem of cellular networks that co-exist with D2D communication considering both underlay and overlay CR approaches are investigated. A proven power allocation algorithm based on GWF approach is utilized to solve the EE maximization problem which results in an “exact" and “low complexity" solution.

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Section: ) Centralizedmentioning

confidence: 99%

Resource allocation strategies in cognitive radio D2D communication networks

Sultana¹

2021

Preprint

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“…The spectrum sharing scheme in the work of Lopez-Ramos et al 17 aimed at maximizing the throughput (sum-rate) of the CRN while protecting the PU through an average interference constraint. In the work of Tanab et al, 18 the authors proposed a distributed resource allocation scheme for multiple-input-multiple-output CRNs. The proposed scheme is based on antenna selection at the transmitter and receiver sides.…”

Section: Related Workmentioning

confidence: 99%

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

Halloush

Almalkawi

et al. 2019

Trans Emerging Tel Tech

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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.

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