Opportunistic splitting algorithm for underlay cognitive radio networks

Tanab, Manal El; Fahmy, Yasmine; Khairy, Mohamed M.

doi:10.1109/icufn.2014.6876738

Cited by 8 publications

(6 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This technique is a generalization of the one introduced in [17] for the MIMO case. In the attachment phase, each SU selects a single transmit antenna in the competition for a certain RB.…”

Section: A Minimum Interferencementioning

confidence: 96%

“…The proposed algorithm consists of two steps (i) subchannel attachment and (ii) sub-channel assignment [17]. In the attachment step, users define the RB for which they will compete.…”

Section: Mimo Cr-osa Descriptionmentioning

confidence: 99%

“…Let N denote the estimate of the number of attached users to each RB. A reasonable estimate of N is its mean value μ N which is calculated as [17]:…”

Section: A Minimum Interferencementioning

confidence: 99%

“…In that we introduce two different antenna selection techniques; (i) minimum interference at the primary user, and (ii) maximum secondary-to-primary channel gain ratio. The minimum interference technique was originally introduced in [17] for the single-input single-output (SISO) system and generalized here for the MIMO model. On the other hand, the maximum ratio is a novel technique where both the SU and PU channel gains are considered in the resource allocation problem.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

On the Distributed Resource Allocation of MIMO Cognitive Radio Networks

Tanab¹,

Hamouda²,

Fahmy³

2014

2015 IEEE Global Communications Conference (GLOBECOM)

Self Cite

View full text Add to dashboard Cite

One of the key challenges of cognitive radio networks is the design of efficient resource allocation schemes. Here we propose a distributed resource allocation algorithm for an underlay multiple-input multiple-output (MIMO) cognitive radio network. To reduce the complexity and cost, we introduce two different antenna selection techniques to allow the secondary communication via a single radio frequency (RF) chain. We show that the proposed distributed algorithm approaches the centralized performance while reducing the delay and overhead. Moreover, the simulation results compare the performance of the two proposed techniques showing their merits and demerits. The results show that the proposed algorithm achieves high throughput with low complexity.

show abstract

“…This technique is a generalization of the one introduced in [17] for the MIMO case. In the attachment phase, each SU selects a single transmit antenna in the competition for a certain RB.…”

Section: A Minimum Interferencementioning

confidence: 96%

“…The proposed algorithm consists of two steps (i) subchannel attachment and (ii) sub-channel assignment [17]. In the attachment step, users define the RB for which they will compete.…”

Section: Mimo Cr-osa Descriptionmentioning

confidence: 99%

“…Let N denote the estimate of the number of attached users to each RB. A reasonable estimate of N is its mean value μ N which is calculated as [17]:…”

Section: A Minimum Interferencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the Distributed Resource Allocation of MIMO Cognitive Radio Networks

Tanab¹,

Hamouda²,

Fahmy³

2014

2015 IEEE Global Communications Conference (GLOBECOM)

Self Cite

View full text Add to dashboard Cite

show abstract

“…The authors in [46] proposed two heuristic distributed RA algorithms based on opportunistic splitting. A heuristic packet transmission scheduling technique was developed in [47] that depends on measured link and interference conditions at CR nodes.…”

Section: Heuristicmentioning

confidence: 99%

Resource allocation strategies in cognitive radio D2D communication networks

Sultana¹

2021

Preprint

View full text Add to dashboard Cite

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.

show abstract