An Efficient Precoder Design for Multiuser MIMO Cognitive Radio Networks with Interference Constraints

Nguyen, Van‐Dinh; Tran, Le-Nam; Shin, Oh‐Soon; Farrell, Ronan

doi:10.1109/tvt.2016.2602844

Cited by 21 publications

(18 citation statements)

References 49 publications

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“…11 compares the sum rate performance of the proposed PSBSS to that of the spectrum underlay, opportunistic spectrum access, and spectrum underlay with zero-forcing beammforming (ZFBF) [15]. For the spectrum underlay with ZFBF in [15], the secondary BS places null spaces at the beamforming vector of each SU to cancel co-channel interference. Since a perfect CSI has been assumed in [15], thus to ensure a fair comparison between those models, we solve the proposed Algorithm 1 by assuming no channel uncertainty (i.e., ǫ s = ǫ p = 0).…”

Section: Numerical Resultsmentioning

confidence: 99%

“…For the spectrum underlay with ZFBF in [15], the secondary BS places null spaces at the beamforming vector of each SU to cancel co-channel interference. Since a perfect CSI has been assumed in [15], thus to ensure a fair comparison between those models, we solve the proposed Algorithm 1 by assuming no channel uncertainty (i.e., ǫ s = ǫ p = 0). In Fig.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…Depending on the power usage, the spectral efficiency problem has been studied with a sum power constraint [14], [17], [19] and per-antenna power constraints [15], [16]. However, the minimum rate requirements for all SUs were not addressed in [13]- [16], [18], [19], [28], although such rate constraints are crucial to resolving the so-called user fairness.…”

Section: A Related Workmentioning

confidence: 99%

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Cooperative Prediction-and-Sensing-Based Spectrum Sharing in Cognitive Radio Networks

Nguyen

Shin

2018

IEEE Trans. Cogn. Commun. Netw.

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Abstract-This paper proposes prediction-and-sensingbased spectrum sharing, a new spectrum-sharing model for cognitive radio networks, with a time structure for each resource block divided into a spectrum prediction-and-sensing phase and a data transmission phase. Cooperative spectrum prediction is incorporated as a sub-phase of spectrum sensing in the first phase. We investigate a joint design of transmit beamforming at the secondary base station (BS) and sensing time. The primary design goal is to maximize the sum rate of all secondary users (SUs) subject to the minimum rate requirement for all SUs, the transmit power constraint at the secondary BS, and the interference power constraints at all primary users. The original problem is difficult to solve since it is highly nonconvex. We first convert the problem into a more tractable form, then arrive at a convex program based on an inner approximation framework, and finally propose a new algorithm to successively solve this convex program. We prove that the proposed algorithm iteratively improves the objective while guaranteeing convergence at least to local optima. Simulation results demonstrate that the proposed algorithm reaches a stationary point after only a few iterations with a substantial performance improvement over existing approaches.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Numerical Resultsmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

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Cooperative Prediction-and-Sensing-Based Spectrum Sharing in Cognitive Radio Networks

Nguyen

Shin

2018

IEEE Trans. Cogn. Commun. Netw.

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“…We consider the downlink transmissions in a CR network, where a secondary BS equipped with N t transmit antennas serves K single-antenna SUs in the presence of M singleantenna PUs. It is assumed that all SUs are allowed to share the same bandwidth with the PUs for transmission [6], [7]. The channel vectors from the secondary BS to the k-th SU and m-th PU are represented by h k ∈ C Nt×1 , k ∈ K {1, 2, · · · , K} and g m ∈ C Nt×1 , m ∈ M {1, 2, · · · , M }, respectively.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

Joint Beamforming and Antenna Selection for Sum Rate Maximization in Cognitive Radio Networks

Nguyen

et al. 2017

IEEE Commun. Lett.

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This letter studies joint transmit beamforming and antenna selection at a secondary base station (BS) with multiple primary users (PUs) in an underlay cognitive radio multiple-input single-output broadcast channel. The objective is to maximize the sum rate subject to the secondary BS transmit power, minimum required rates for secondary users, and PUs' interference power constraints. The utility function of interest is nonconcave and the involved constraints are nonconvex, so this problem is hard to solve. Nevertheless, we propose a new iterative algorithm that finds local optima at the least. We use an inner approximation method to construct and solve a simple convex quadratic program of moderate dimension at each iteration of the proposed algorithm. Simulation results indicate that the proposed algorithm converges quickly and outperforms existing approaches.Index Terms-Antenna selection, cognitive radio, nonconvex programming, sum rate, transmit beamforming.

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“…Symmetry 2017, 9, 247 2 of 13 To significantly improve spectrum utilization in MIMO networks, cognitive radio has been widely considered by previous studies [15,16], in which [15] considered a linear precoder design for an underlay cognitive radio MIMO broadcast channel and [16] considered the power allocation for spectrum sharing in cognitive radio networks.…”

Section: Introductionmentioning

confidence: 99%

Precoding Design and Power Allocation in Two-User MU-MIMO Wireless Ad Hoc Networks

et al. 2017

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Abstract:In this paper, we consider the precoding design and power allocation problem for multi-user multiple-input multiple-output (MU-MIMO) wireless ad hoc networks. In the first timeslot, the source node (SN) transmits energy and information to a relay node (RN) simultaneously within the simultaneous wireless information and power transfer (SWIPT) framework. Then, in the second timeslot, based on the decoder and the forwarding (DF) protocol, after reassembling the received signal and its own signal, the RN forwards the information to the main user (U1) and simultaneously sends its own information to the secondary user (U2). In this paper, when the transmission rate of the U1 is restricted, the precoding, beamforming, and power splitting (PS) transmission ratio are jointly considered to maximize the transmission rate of U2. To maximize the system rate, we design an optimal beamforming matrix and solve the optimization problem by semi-definite relaxation (SDR), considering the high complexity of implementing the optimal solution. Two sub-optimal precoding programs are also discussed: singular value decomposition and block diagonalization. Finally, the performance of the optimization and sub-optimization schemes are compared using a simulation.

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An Efficient Precoder Design for Multiuser MIMO Cognitive Radio Networks with Interference Constraints

Cited by 21 publications

References 49 publications

Cooperative Prediction-and-Sensing-Based Spectrum Sharing in Cognitive Radio Networks

Cooperative Prediction-and-Sensing-Based Spectrum Sharing in Cognitive Radio Networks

Joint Beamforming and Antenna Selection for Sum Rate Maximization in Cognitive Radio Networks

Precoding Design and Power Allocation in Two-User MU-MIMO Wireless Ad Hoc Networks

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