Joint Power Control and Beamforming for Secondary Spectrum Sharing

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

doi:10.1109/vetecf.2007.329

Cited by 10 publications

(2 citation statements)

References 9 publications

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“…As an effective interference suppression technique, joint beamforming and power allocation has been widely used in the traditional communication systems with multi-antenna [3]- [6], and has been attached great importance to in CR [7]- [10]. In this paper, we consider a cognitive network (CN) wherein single-input multi-output multiple access channels (SIMO-MAC) are assumed.…”

Section: Introductionmentioning

confidence: 99%

Joint Beamforming and Power Allocation for Throughput Maximization in Cognitive Radio

Yang¹,

Wang²,

Wu³

2010

Frequenz

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We consider the total throughput maximization problem involving joint beamfoming and power allocation in the cognitive radio network (CN), wherein the single-input multi-output multiple access channels are assumed. Subject to two sets of constraints: the interference constraint of primary user (PU) and the peak power constraints of cognitive users (CUs), a joint beamforming and power allocation algorithm for total throughput maximization in CN is proposed, which is implemented by alternating iteration of MMSE-DFE beamforming and a best response based iterative power allocation algorithm, we note it as MMSE-BR. Compared with the existing algorithm, the proposed MMSE-BR can achieve the optimal throughput under various interference situation, and does not have limit on the number of CU. The optimality of MMSE-BR algorithm is detailedly proved in theorem and corroborating numerical results are presented to present the comparisons with the existing algorithm.

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Section: Introductionmentioning

confidence: 99%

Joint Beamforming and Power Allocation for Throughput Maximization in Cognitive Radio

Yang¹,

Wang²,

Wu³

2010

Frequenz

View full text Add to dashboard Cite

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“…In this scenario, the channel between the secondary transmit antennas and the primary receive antennas must be known. Studies where the cognitive rates are maximized subject to primary user communication guarantees (such as maximum average interference power constraints) are considered in [53,54,115,[117][118][119]. The scenarios considered in these papers can be considered as an interference-avoiding scheme if the tolerable interference at the primary receivers is set to zero, other-wise it falls under the interference-controlled paradigm we look at in the following subsection.…”

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confidence: 99%

Cognitive Radio: From Theory to Practical Network Engineering

Hossain

Devroye

et al. 2009

New Directions in Wireless Communications Research

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IntroductionUnder utilization of radio spectrum in traditional wireless communications systems [30], along with the increasing spectrum demand from emerging wireless applications, is driving the development of new spectrum allocation policies for wireless communications. These new spectrum allocation policies, which will allow unlicensed users (i.e., secondary users) to access the radio spectrum when it is not occupied by licensed users (i.e., primary users) will be exploited by the cognitive radio (CR) technology. Cognitive radio will improve spectrum utilization in wireless communications systems while accommodating the increasing amount of services and applications in wireless networks. A cognitive radio transceiver is able to adapt to the dynamic radio environment and the network parameters to maximize the utilization of the limited radio resources while providing flexibility in wireless access [45]. The key features of a CR transceiver include awareness of the radio environment (in terms of spectrum usage, power spectral density of transmitted/received signals, wireless protocol signaling) and intelligence. This intelligence is achieved through learning for adaptive tuning of system parameters such as transmit power, carrier frequency, and modulation strategy (at the physical layer), and higher-layer protocol parameters.Implementation of a cognitive radio will be based on the concept of dynamic spectrum access (DSA). Through DSA, frequency spectrum can be shared among primary users and cognitive radio users (i.e., secondary users) in a dynamically changing radio environment. There are two major flavors 2 E. Hossain, L. Le, N. Devroye, and M. Vu of dynamic spectrum access: dynamic licensing (for dynamic exclusive use of radio spectrum) and dynamic sharing (for coexistence) [3,120]. Dynamic sharing can be of two types: horizontal spectrum sharing and vertical spectrum sharing. In the former case, all users/nodes have equal regulatory status while in the latter case all users/nodes do not have equal regulatory status (i.e., there are primary users and secondary users) and secondary users opportunistically access the spectrum without negatively affecting the primary users' performance.In this chapter, we focus on vertical spectrum sharing in a cognitive radio network. In particular, we outline the recent information theoretic advances pertaining to the limits of such networks. Information theory provides an ideal framework as well as tools and metrics for analyzing the fundamental limits of communication. The limits obtained provide benchmarks for the operation of cognitive networks, allowing researchers and engineers to gauge the efficiency of any practical network and guide their design. Spectrum sensing is one of the major functions of a cognitive radio the goal of which is to determine the activity of licensed user by periodically observing signals on the target frequency bands. We discuss some theoretical results on the effect of side information (e.g., spatial locations of the users, transmission probabil...

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Ergodic capacity analysis for SIMO cognitive fading channel in spectrum sharing environment

Yang

Wang

et al. 2010

J. Electron.(China)

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In this paper, we consider the spectrum sharing Cognitive Radio (CR) system, wherein Single-Input Multi-Output (SIMO) cognitive fading channel is assumed. Subject to the average interference constraint of primary user, the ergodic capacity of cognitive user involving joint beamforming and power control is analyzed. We derive the optimal joint beamforming and power control strategy and deduce the general integral expression for ergodic capacity. Furthermore, under different channel fadings including Raleigh fading, Nakagami-m fading and Lognormal shadowing fading, the specific expressions of ergodic capacity for SIMO cognitive channel are exhibited. Most of the expressions are presented in closed-form or with some special integral functions. Large amount of simulations are conducted to corroborate our theoretical results.

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Joint Power Control and Beamforming for Secondary Spectrum Sharing

Cited by 10 publications

References 9 publications

Joint Beamforming and Power Allocation for Throughput Maximization in Cognitive Radio

Joint Beamforming and Power Allocation for Throughput Maximization in Cognitive Radio

Cognitive Radio: From Theory to Practical Network Engineering

Ergodic capacity analysis for SIMO cognitive fading channel in spectrum sharing environment

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