Nonsmooth Optimization for Efficient Beamforming in Cognitive Radio Multicast Transmission

Phan, Anh Huy; Tuan, Hoang Duong; Kha, Ha Hoang; Ngo, Duy T.

doi:10.1109/tsp.2012.2189857

Cited by 129 publications

(152 citation statements)

References 34 publications

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“…Fortunately, some effective methods have been proposed to cope with this kind of multicast beamforming problem with rank-one constraints, including semidefinite relaxation (SDR) [17,20], second-order cone programming (SOCP) [5,21], PenFun method [13,15] and so on.…”

Section: Detailed Algorithm Designmentioning

confidence: 99%

“…As f o (X) is concave, problem Q DRM 2 is minimization of a concave function over a convex set, which can be efficiently solved with CCP [13,15].…”

Section: Penfun Algorithmmentioning

confidence: 99%

“…When operating in the distributed manner, each antenna forwards its own received signal mainly focus on beamformer design at cognitive base stations, either for secondary users' data transmission with restricted transmit power [9,10] or for primary-secondary cooperation with careful designed cooperation protocols [11,12]. There are also some works paying attention to the combination of multicast transmission and CR technology [13][14][15]. However, as far as we know, most of those researches concentrate on one-hop transmission, with few papers concerning cooperative multicast, especially multicast transmission via multi-antenna relays.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, due to the channel feedback error and the feedback delay, perfect channel state information at the transmitter (CSIT) is always unavailable in practice. Unfortunately, to the best of our knowledge, most of the existing researches [4][5][6][7][13][14][15] only apply to perfect CSIT feedback model. Therefore, imperfect CSIT feedback is also considered in our research model to enhance the practicability of our study.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic constrained multicast beamforming in cognitive relay systems

et al. 2015

J Wireless Com Network

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In this paper, multicast beamforming in cognitive relay systems is investigated with the consideration of imperfect channel state information at the transmitter (CSIT). We focus at the design of the optimal signal forwarding matrix at the cognitive relay in both centralized relay mode (CRM) and distributed relay mode (DRM). The problem is formulated aiming at minimizing the total consumed power at the relay node with suitable QoS guarantee for secondary users and strict interference control for primary users. Due to the uncertainty of transmission channel gains, constraints for QoS guarantee and interference control cannot be expressed in closed forms, making it extremely difficult to solve the problem directly. To circumvent this, we first employ the Bernstein-type inequality to convert the probabilistic constraints into closed-form expressions and then present both the semi-definite relaxation (SDR) algorithm and the penalty function (PenFun) algorithm to accomplish the non-convex problem optimization. Simulation results show that CRM is more resource efficient than DRM, and the PenFun algorithm can achieve a much better solution than the SDR algorithm at the cost of complexity. Meanwhile, compared with existing schemes which do not consider the CSIT error, the proposed schemes can support a much lower outage probability and enjoy perfect interference control for primary users.

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Section: Detailed Algorithm Designmentioning

confidence: 99%

“…As f o (X) is concave, problem Q DRM 2 is minimization of a concave function over a convex set, which can be efficiently solved with CCP [13,15].…”

Section: Penfun Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Probabilistic constrained multicast beamforming in cognitive relay systems

et al. 2015

J Wireless Com Network

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“…The rank-one matrix constraints are dropped to have semidefinite relaxation (SDR) problem.To deal with the rank-more-than-one solution given by SDR, [24] proposes us-ing a randomization method after SDR. As shown in [25]- [27], the performance of such a method is inconsistent and could be poor in many cases. An approximate rank-one solution with compromised performance has been proposed in [28].…”

Section: Introductionmentioning

confidence: 99%

Beamforming Design for Wireless Information and Power Transfer Systems: Receive Power-Splitting Versus Transmit Time-Switching

Nasir

Tuan

Ngo

et al. 2017

IEEE Trans. Commun.

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Nonconvex spectral optimization algorithms for reduced‐order LPV‐LFT controllers

Shi

Tuan

Apkarian

2017

Intl J Robust & Nonlinear

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Summary A novel sequential semi‐definite programming method is developed for optimization subject to rank constraints on matrix‐valued nonlinear functions of matrix decision variables, which arise in reduced‐order linear parameter varying‐linear fractional transformational control synthesis. The global convergence of the method is easily proven without any step size control. An intensive simulation shows the clear advantage of the proposed method over the state‐of‐the‐art nonlinear matrix inequality solvers. Copyright © 2017 John Wiley & Sons, Ltd.

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Nonsmooth Optimization for Efficient Beamforming in Cognitive Radio Multicast Transmission

Cited by 129 publications

References 34 publications

Probabilistic constrained multicast beamforming in cognitive relay systems

Probabilistic constrained multicast beamforming in cognitive relay systems

Beamforming Design for Wireless Information and Power Transfer Systems: Receive Power-Splitting Versus Transmit Time-Switching

Nonconvex spectral optimization algorithms for reduced‐order LPV‐LFT controllers

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