Maximization of minimal throughput using genetic algorithm in MIMO underlay cognitive radio networks

Benaya, A. M.; Rosas, Ahmed A.; Shokair, Mona

doi:10.1109/nrsc.2016.7450847

Cited by 2 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors propose a three-stage heuristic to solve the sum-rate maximization problem. A power allocation scheme using genetic algorithms (GA) is proposed in (Benaya et al, 2016) for a multiple-input-multiple-output (MIMO) system in CRN with the aim to maximize the total secondary throughput. Under interference constraints of multiple SU pairs coexisting with multiple PUs pairs in an underlay spectrum sharing model, the minimal throughput among all SUs is compared with other power allocation schemes, namely, maximum-minimum-throughput-based power assignment (MMTPA) and equal power assignment (EPA).…”

Section: Related Workmentioning

confidence: 99%

Optimizing the Capacity of Cognitive Radio Networks with Power Control and Variable Spectrum Allocation

Benghabrit

Kaddour

2018

Transport and Telecommunication Journal

View full text Add to dashboard Cite

Cognitive Radio Networks (CRN) were introduced as a means to more efficiently reuse the licensed radio frequency spectrum. One of their salient features is the ability of unlicensed nodes to dynamically adapt their radio parameters according to their needs. This paper investigates the resource allocation problem in CRN by jointly considering power control and bandwidth for a set of secondary users (SU) transmitting simultaneously with a set of licensed users (PU), which transmissions must remain unaltered. The proposed allocation scheme is based on a Genetic Algorithm (GA) where the chromosome's genes represent the reconfigurable interface radio parameters, by defining genetic operations the GA is empowered to find a set of radio parameters that maximize the overall network capacity and under the physical interference model enforced to the transmissions of both PU’s and SU’s. The numerical results illustrate the prominent effect of adjusting jointly multiple radio parameters on the network capacity.

show abstract

Section: Related Workmentioning

confidence: 99%

Optimizing the Capacity of Cognitive Radio Networks with Power Control and Variable Spectrum Allocation

Benghabrit

Kaddour

2018

Transport and Telecommunication Journal

View full text Add to dashboard Cite

show abstract

“…With the increasing demand for wireless communications and the increasing requirement for environmental protection, the optimizations of system throughput and energy efficiency (EE, represented by transmission data rate per unit energy) are two important goals of MU-MIMO resource allocation algorithm. The algorithm optimizes the two goals through user selection and power allocation [2]- [4]. In MU-MIMO systems, user selection and power allocation are related to the rank of user's MIMO channel matrix, because the rank of the user's channel determines the number of space-division channels, thus affecting the user's achievable data rate given…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation Algorithm for MU-MIMO Systems With Double-Objective Optimization Under the Existence of the Rank Deficient Channel Matrix

Yan

Bonsu

et al. 2019

IEEE Access

View full text Add to dashboard Cite

This paper proposes a double-objective optimization resource allocation algorithm for the multiuser multiple-input/multiple-output (MU-MIMO) system in the general wireless environment and demonstrates the maximum number of simultaneously supportable users and the achievable bit rates of users in the general wireless environment with full rank and rank-deficient channels. The double-objective joint optimization algorithm proposed in this paper simultaneously optimizes energy efficiency and system throughput by user selection and power allocation. On this basis, the proposed algorithm guarantees the different QoS requirements of various services, including rate requirements and delay requirements. INDEX TERMS MU-MIMO, rank deficient, double-objective optimization, QoS guarantee, resource allocation.

show abstract

Maximization of minimal throughput using genetic algorithm in MIMO underlay cognitive radio networks

Cited by 2 publications

References 15 publications

Optimizing the Capacity of Cognitive Radio Networks with Power Control and Variable Spectrum Allocation

Optimizing the Capacity of Cognitive Radio Networks with Power Control and Variable Spectrum Allocation

Resource Allocation Algorithm for MU-MIMO Systems With Double-Objective Optimization Under the Existence of the Rank Deficient Channel Matrix

Contact Info

Product

Resources

About