Radio resource allocation for orthogonal frequency division multiple access-based underlay cognitive radio networks utilising weighted ergodic rates

Dashti, Mina; Azmi, Paeiz; Navaie, Keivan

doi:10.1049/iet-com.2011.0822

Cited by 11 publications

(5 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a CR, the PU is prioritised; thus, its QoS should be ensured at all times. Methods that protect the PU from harmful interference include the average interference power constraint [14,27,28] and the peak interference power constraint [29,30]. In fact, the advantage of the average interference power constraint, as shown in [27], is that it provides not only higher ergodic throughput for the secondary system but also better protection for the primary system.…”

Section: System Modelmentioning

confidence: 99%

Optimal resource allocation for cooperative orthogonal frequency division multiplexing‐based cognitive radio networks with imperfect spectrum sensing

et al. 2015

View full text Add to dashboard Cite

Section: System Modelmentioning

confidence: 99%

Optimal resource allocation for cooperative orthogonal frequency division multiplexing‐based cognitive radio networks with imperfect spectrum sensing

et al. 2015

View full text Add to dashboard Cite

“…Since there are many advantages of flexible spectrum scheduling of orthogonal frequency division multiplexing (OFDM) technology, OFDM has been widely introduced to CRNs [11][12][13]. In [11], for an OFDM-based multi-hop CRN, a cross-layer optimization design is proposed to address both aggregate utility maximization and energy consumption minimization.…”

Section: Introductionmentioning

confidence: 99%

“…In [11], for an OFDM-based multi-hop CRN, a cross-layer optimization design is proposed to address both aggregate utility maximization and energy consumption minimization. In [12], radio resource allocation in an underlay CRN based on OFDMA is studied to maximize the sum capacity of the secondary service and to find the optimal allocated power, subcarrier, and rate across all subcarriers and different SUs. In [13], the authors propose a robust ergodic resource allocation (ERA) scheme in the framework of an OFDM-based underlay heterogeneous network to maximize the average sum rate while guaranteeing macro network interference requirements with any desired high probability.…”

Section: Introductionmentioning

confidence: 99%

Power control for cognitive relay networks with sensing uncertainties

Zhao

2016

J Wireless Com Network

View full text Add to dashboard Cite

Power control (PC) is a key solution to enable spectrum sharing between secondary users (SUs) and primary users (PUs). However, previous research lacks sensing uncertainties for the status of PUs. In this article, we focus on the PC problem for a cognitive relay network under the spectrum sensing uncertainties to minimize the total bit error rate (BER) of SUs under the constraints of maximum transmit power budgets, signal-to-interference-and-noise ratio (SINR) constraints, and interference requirements to provide protection for PUs. We first formulate the interference model by taking sensing uncertainties into account, while the worst-channel-state-information (worst-CSI) PC algorithm is introduced to limit the BER of SUs, which only needs to operate the algorithm in one link whose CSI is worst. And a cooperative spectrum sensing (CSS) strategy is considered to optimize the sensing performance. To deal with the optimization problem, the original min-max BER problem is converted into an equivalent max-min SINR problem solved by Lagrange dual decomposition method. Finally, simulation results are presented to indicate that our proposed algorithm can obtain good BER performance and guarantee quality of service of PU.

show abstract

“…Due to the advantages of underlay spectrum sharing model, many power control schemes for underlay CRNs are proposed in . In a multiuser network, an admission control scheme based on geometric programming(GP) is proposed to solve joint rate and power allocation problem under quality of service (QoS) and IT constraints .…”

Section: Introductionmentioning

confidence: 99%

“…In , considering an orthogonal frequency‐division multiple access based CRN, a novel power allocation algorithm based on water‐filling principle is proposed to maximize transmission rate of SUs. In , under the same network model, resource allocation algorithm is proposed to maximize sum capacity of SUs under maximum allowable transmission power constraint and averaged interference constraint. However, both QoS requirement of every SU and interference from nonorthogonal subcarrier are ignored.…”

Section: Introductionmentioning

confidence: 99%

Distributed power control for multiuser cognitive radio networks with quality of service and interference temperature constraints

Zhao

2014

Wireless Communications

View full text Add to dashboard Cite

One of the most challenging problems in dynamic resource allocation for cognitive radio networks is to adjust transmission power of secondary users (SUs) while quality of service needs of both SUs and primary users (PUs) are guaranteed. Most power control algorithms only consider interference temperature constraint in single user scenario while ignoring the interference from PUs to SUs and minimum signal to interference plus noise ratio (SINR) requirement of SUs. In this paper, a distributed power control algorithm without user cooperation is proposed for multiuser underlay CNRs. Specifically, we focus on maximizing total throughput of SUs subject to both maximum allowable transmission power constraint and SINR constraint, as well as interference temperature constraint. To reduce the burden of information exchange and computational complexity, an average interference constraint is proposed. Parameter range and convergence analysis are given for feasible solutions. The resource allocation is transformed into a convex optimization problem, which is solved by using Lagrange dual method. In computer simulations, the effectiveness of our proposed scheme is shown by comparing with distributed constrained power control algorithm and Nash bargaining power control game algorithm.

show abstract

Radio resource allocation for orthogonal frequency division multiple access-based underlay cognitive radio networks utilising weighted ergodic rates

Cited by 11 publications

References 27 publications

Optimal resource allocation for cooperative orthogonal frequency division multiplexing‐based cognitive radio networks with imperfect spectrum sensing

Optimal resource allocation for cooperative orthogonal frequency division multiplexing‐based cognitive radio networks with imperfect spectrum sensing

Power control for cognitive relay networks with sensing uncertainties

Distributed power control for multiuser cognitive radio networks with quality of service and interference temperature constraints

Contact Info

Product

Resources

About