Spectrum Leasing for OFDM-Based Cognitive Radio Networks

Toroujeni, Seyed Mahdi Mousavi; Sadough, Seyed Mohammad Sajad; Ghorashi, Seyed Ali

doi:10.1109/tvt.2012.2231102

Cited by 25 publications

(24 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For spectrum leasing, PUs who own the spectrum can possibly lease a part of the spectrum to CUs for appropriate remuneration and can exploit the existence of CUs to enhance their performance and QoS. In counterpart, CUs can use the leased spectrum for their own transmission by performing decentralized power control [12], [13]. On the other hand, for time leasing, PUs can lease a portion of their time for CUs' transmission.…”

Section: B Overlay Cognitive Radio: Literature Reviewmentioning

confidence: 99%

Joint Bandwidth and Power Allocation for MIMO Two-Way Relays-Assisted Overlay Cognitive Radio Systems

Alsharoa

Ghazzai

Yaacoub³

et al. 2015

IEEE Trans. Cogn. Commun. Netw.

View full text Add to dashboard Cite

Section: B Overlay Cognitive Radio: Literature Reviewmentioning

confidence: 99%

Joint Bandwidth and Power Allocation for MIMO Two-Way Relays-Assisted Overlay Cognitive Radio Systems

Alsharoa

Ghazzai

Yaacoub³

et al. 2015

IEEE Trans. Cogn. Commun. Netw.

View full text Add to dashboard Cite

“…And with the help of the information theory, there has been extensive research on how to guarantee the benefits of spectrum-leasing based CRN via optimally allocate the wireless resources among PUs and SUs [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18], and a good summary of the state of the art is provided in [3,4]. Thereinto, the cooperation among PUs and SUs was commonly modeled by the convex optimization problem [5,6] where the globally optimal resource allocation is analytically derived, or by employing the widely used economical concepts, such as a Stackelberg game [2], a Nash Bargaining game [7][8][9], contract theory [10,11], auction theory [12,13], matching theory [14], etc. For example, aiming to optimally calculate the design parameters of the primary and secondary networks, [5] has considered the problem of maximizing the smaller of secondary transceiver average rates, while preserving a minimum rate for the primary pair.…”

Section: Introductionmentioning

confidence: 99%

“…Lu et al [6] studies the joint optimization of the set of subcarriers used for cooperation, subcarrier pairing, and power allocation such that the transmission rate of the secondary system is maximized, while helping the primary system to achieve its target rate. Toroujeni et al [12] proposes an auction framework in which the PU tries to find the optimum amount of resources (in both time and frequency) leased to SUs, whereas each SU's goal is to find the optimum power level to maximize its own profit. Inspired by the matching approach, in [14], the network is modeled as a matching market, where each PU puts forward a proposal representing a combination of relay power and spectrum access time to attract SUs, while each SU maximizes its utility by selecting the most suitable PU.…”

Section: Introductionmentioning

confidence: 99%

Resource allocation for spectrum-leasing based CRN with delay-sensitive traffic

Yin

Yang

et al. 2017

J Wireless Com Network

View full text Add to dashboard Cite

In this paper, cooperative resource allocation strategies are characterized for a spectrum-leasing based cognitive radio network (CRN), where the primary system leases the licensed band to the secondary system for a fraction of time in exchange for the secondary user (SU) acting as relay. Here, both amplify-and-forward (AF) and decode-and-forward (DF) relay protocols are considered. Considering the delay-sensitive traffic in CRN, the proposed strategies ensure delay provisioning for both primary user (PU) and SU with multiple system design objectives. In particular, we propose a multi-objective optimization framework, which incorporates two important system design objectives: the average sum power minimization and the leased time minimization. By integrating information theory with the concept of effective capacity, the adopted multi-objective optimization problem is recast as a convex optimization one via employing weighting method and sequentially solved by applying the Lagrangian dual method. It is shown that the global optimal solution of the original problem is characterized by a Pareto set which provides a quantitative insight into the tradeoff between the transmit power and leased time. Moreover, to learn the statistics of the wireless channels on the fly, we also put forward a stochastic iterative algorithm to achieve the optimal power and time allocation by employing the stochastic optimization theory. Numerical results not only reveal the nontrivial tradeoff among the considered conflicting system design objectives but also demonstrate that the proposed strategies perform better in saving wireless resources than existing resource allocation policies for different Quality-of-Service (QoS) exponent sets, especially when the delay requirement is strict.

show abstract

“…As a summary, those previous researches can be divided into two categories. As for the first category, the spectrum leasing problem in CRN was investigated by employing the widelyused economical concepts [2,3]. For example, [2] proposed an auction framework in which an iterative and negotiationbased approach was suggested for spectrum leasing.…”

Section: Introductionmentioning

confidence: 99%

“…As for the first category, the spectrum leasing problem in CRN was investigated by employing the widelyused economical concepts [2,3]. For example, [2] proposed an auction framework in which an iterative and negotiationbased approach was suggested for spectrum leasing. The second category, the Lagrangian dual decomposition is adopted to solve such kind of spectrum leasing problem, in which the globally optimal resource allocation can be found [4,5].…”

Section: Introductionmentioning

confidence: 99%

Joint Fair Resource Allocation for Opportunistic Spectrum Sharing in OFDM-based Cognitive Radio Networks

Ma¹,

Ma²,

Zhang³

2015

Proceedings of the 8th International Conference on Mobile Multimedia Communications

View full text Add to dashboard Cite

This paper considers a cooperative Orthogonal Frequency Division Multiplexing (OFDM)-based cognitive radio network, where the primary system leases a fraction of its subcarriers to the secondary system in exchange for the secondary users (SUs) acting as decode-and-forward relays. Our aim is to determine an fair resource allocation strategy among the primary users and SUs as so to maximize the network capacity. To this end, a network utility maximization optimization problem of power, subcarrier allocation and relay selection is formulated based on a class of α-fair utility. This problem is solved by applying the lagrangian dual method and a joint fair resource allocation policy at the SUs is derived in a closed-form expression. Moreover, a novel stochastic algorithm is developed to approach the optimal policy by dynamically learning the intended wireless channels. Simulation results demonstrate that both primary and secondary systems can benefit from the proposed resource allocation policy.

show abstract

Spectrum Leasing for OFDM-Based Cognitive Radio Networks

Cited by 25 publications

References 14 publications

Joint Bandwidth and Power Allocation for MIMO Two-Way Relays-Assisted Overlay Cognitive Radio Systems

Joint Bandwidth and Power Allocation for MIMO Two-Way Relays-Assisted Overlay Cognitive Radio Systems

Resource allocation for spectrum-leasing based CRN with delay-sensitive traffic

Joint Fair Resource Allocation for Opportunistic Spectrum Sharing in OFDM-based Cognitive Radio Networks

Contact Info

Product

Resources

About