Joint power control and beamforming for cognitive radio networks

Islam, Habibul; Liang, Ying-Chang; Hoang, Anh Tuan

doi:10.1109/twc.2008.061003

Cited by 156 publications

(94 citation statements)

References 11 publications

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“…Power control for OSA in TV bands is investigated in [11,12], where the primary users (TV broadcast) transmit all the time and spatial (rather than temporal) spectrum opportunities are exploited by secondary users.…”

Section: Related Workmentioning

confidence: 99%

Power control in cognitive radio networks: how to cross a multi-lane highway

Ren

Zhao

Swami

2009

IEEE J. Select. Areas Commun.

105

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We consider power control in cognitive radio networks where secondary users identify and exploit instantaneous and local spectrum opportunities without causing unacceptable interference to primary users. We qualitatively characterize the impact of the transmission power of secondary users on the occurrence of spectrum opportunities and the reliability of opportunity detection. Based on a Poisson model of the primary network, we quantify these impacts by showing that (i) the probability of spectrum opportunity decreases exponentially with respect to the transmission power of secondary users, where the exponential decay constant is given by the traffic load of primary users; (ii) reliable opportunity detection is achieved in the two extreme regimes in terms of the ratio between the transmission power of secondary users and that of primary users. Such analytical characterizations allow us to study power control for optimal transport throughput under constraints on the interference to primary users. Furthermore, we reveal the difference between detecting primary signals and detecting spectrum opportunities, and demonstrate the complex relationship between physical layer spectrum sensing and MAC layer throughput. The dependency of this PHY-MAC interaction on the application type and the use of handshake signaling such as RTS/CTS is illustrated.

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Section: Related Workmentioning

confidence: 99%

Power control in cognitive radio networks: how to cross a multi-lane highway

Ren

Zhao

Swami

2009

IEEE J. Select. Areas Commun.

105

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“…According to the descriptions in [1], the power control has an effective impact on the probability of bit error rate. In [2] joint beamforming and power control using weighted least square algorithm have been performed. A collaborative beamforming technique was proposed in [3], in which randomly distributed nodes in a network cluster form an antenna array and beamform data to a faraway destination without each node exceeding its power constraint.…”

Section: Introductionmentioning

confidence: 99%

Bio-inspired distributed beamforming for cognitive radio networks in non-stationary environment

Derakhshan-Barjoei

Dadashzadeh

Razzazi

et al. 2011

IEICE Electron. Express

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Distributed power control by beamforming approach in cognitive radio networks requires a precise analysis of the impacts of the transmission parameters, tolerable interference and guarantees the quality of service of both the primary users and secondary users. In this paper, we propose an improved performance to solve the constrained nonlinear multi-object optimization and coherent power assignment by beamforming problem based on particle swarm optimization. This method is invoked to solve the constrained nonlinear optimization problem in order to reach to maximum capacity. A numerical study is performed to show the convergence behavior of the proposed algorithm and the efficiency of the proposed technique with a dynamic cost function.

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“…This has facilitated migration of research results on power control, transmit beamforming, and scheduling from the cellular to the cognitive regime [13], [12], [32]. An uplink beamforming and power control scenario where the objective is to maximize the sum rate of the secondary users under interference constraints on the primary users has been considered in [32].…”

mentioning

confidence: 99%

“…Explicit user admission is not needed in a sum-rate context. A downlink beamforming scenario for the secondary users is considered in [13], under SINR constraints on the primary and secondary users. Infeasibility and user selection issues were not dealt with in [13].…”

mentioning

confidence: 99%

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Joint Power and Admission Control for Ad-Hoc and Cognitive Underlay Networks: Convex Approximation and Distributed Implementation

Mitliagkas

Sidiropoulos

Swami

2011

IEEE Trans. Wireless Commun.

109

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Abstract-Power control is important in interference-limited cellular, ad-hoc, and cognitive underlay networks, when the objective is to ensure a certain quality of service to each connection. Power control has been extensively studied in this context, including distributed algorithms that are particularly appealing in adhoc and cognitive settings. A long-standing issue is that the power control problem may be infeasible, thus requiring appropriate admission control. The power and admission control parts of the problem are tightly coupled, but the joint optimization problem is NP-hard. We begin with a convenient reformulation which enables a disciplined convex approximation approach. This leads to a centralized approximate solution that is numerically shown to outperform the prior art, and even yield close to optimal results in certain cases -at affordable complexity. The issue of imperfect channel state information is also considered. A distributed implementation is then developed, which alternates between distributed approximation and distributed deflationreaching consensus on a user to drop, when needed. Both phases require only local communication and computation, yielding a relatively lightweight distributed algorithm with the same performance as its centralized counterpart.

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Joint power control and beamforming for cognitive radio networks

Cited by 156 publications

References 11 publications

Power control in cognitive radio networks: how to cross a multi-lane highway

Power control in cognitive radio networks: how to cross a multi-lane highway

Bio-inspired distributed beamforming for cognitive radio networks in non-stationary environment

Joint Power and Admission Control for Ad-Hoc and Cognitive Underlay Networks: Convex Approximation and Distributed Implementation

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