Chance Constrained Robust Beamforming in Cognitive Radio Networks

Ma, Shuai; Sun, Dechun

doi:10.1109/lcomm.2012.112812.121829

Cited by 54 publications

(38 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For transmit beamforming, the secondary base station (BS) requires knowledge of the channels to the SUs and the PUs, which can be obtained via channel estimation. In practice, the secondary BS cannot expect to have perfect channel knowledge due to errors in the estimation or other factors, such as quantization, thus requiring a robust beamforming design in the presence of channel uncertainty [28], [29]. In addition, the perfect channel state information (CSI) of the PUs' channels is even more difficult to obtain at the secondary BS since two systems operate independently [16].…”

Section: A Related Workmentioning

confidence: 99%

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

Nguyen

Shin

2018

IEEE Trans. Cogn. Commun. Netw.

View full text Add to dashboard Cite

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.

show abstract

Section: A Related Workmentioning

confidence: 99%

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

Nguyen

Shin

2018

IEEE Trans. Cogn. Commun. Netw.

View full text Add to dashboard Cite

show abstract

“…Where 1 Λ is a diagonal matrix containing the K major eigenvalues of the matrix C % , 2 Λis the matrix containing the remaining minor eigenvalues. Then the quadratic constraint H d()Cd() θθ % represents the majority of the value outside the desired sector, and the estimate â of the desired steering vector can be given some constraint to ensure that the estimate â does not converge to the region where the interfering signals are located.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…First, adaptive beamforming is widely used in radar, sonar, wireless communication, microphone arrays, medical imaging and other fields [1][2]; Second, there is an urgent demand of the robustness of the algorithm in the process of practical application. Early robust adaptive beamforming methods such as diagonal loading method (DL) [3] and eigen-subspace method (ESB) [4] have their own drawbacks, the former drawback is that the loading factor is difficult to select, while the latter results in poor performance in the case of low signal to noise ratio.…”

Section: Introductionmentioning

confidence: 99%

An Robust Adaptive Beamforming Algortihm based on Subspace Complement

Zou¹,

Zi-fa²,

Liu³

2017

Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)

View full text Add to dashboard Cite

Abstract.A novel algorithm for robust adaptive beamforming is put forward in this paper. This algorithm use the characteristic of complement of desired signal subspace complement to build constrained model and solve the supremum, then the solution based on convex programming are given in the paper .The two characteristics of the proposed algorithm are: on one hand, this algorithm does not need imprecise steering vector of desired signal and exact antenna array geometry; on the other hand, the presented algorithm has robustness for steering vector mismatches, antenna element displacement errors and other errors, and has better performance than existing algorithms. The simulation results show the effectiveness of the proposed algorithm. Due to the little prior information is needed; therefore, it is helpful to engineering application.

show abstract

“…gwith k max Q ð Þ denotes the maximum eigenvalue of matrix Q [8]. The inequalities in (14) and (15) are of Bernsteintype, which bound the probability that the quadratic form G of complex Gaussian random variables deviates from its mean TrðQÞ.…”

Section: Bernstein-type Inequality Based Conservative Approachmentioning

confidence: 99%

“…Unfortunately, the probabilistic constraints are known as non-convex in general and thus haven't a closed-form solution. To cope with the difficulty, conservative methods such as transfer probabilistic constraints into matrix inequality are usually considered [8,9].…”

Section: Introductionmentioning

confidence: 99%

Joint Probabilistic Constrained Robust Beamforming and Antenna Selection

Jin

Yue

Liu

2015

Wireless Pers Commun

View full text Add to dashboard Cite

A novel method is proposed for joint probabilistic constrained robust beamforming and antenna selection used in cognitive radio networks. Assuming complex Gaussian distributed channel state information errors, the Bernstein-type inequalities are introduced to transform no closed-form probabilistic constrained forms into the deterministic forms. Moreover, the '1-norm is used as the closest convex approximation of '0-norm. Thus the original NP-hard optimal problem can be relaxed as a tractable convex optimization problem. A computationally efficient and near-optimal solution is obtained by an iteratively re-weighted algorithm. Simulations show that the proposed algorithm satisfies the predetermined service levels at relatively small excess transmission power in a number of transmitter reduction scenarios.

show abstract

Chance Constrained Robust Beamforming in Cognitive Radio Networks

Cited by 54 publications

References 14 publications

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

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

An Robust Adaptive Beamforming Algortihm based on Subspace Complement

Joint Probabilistic Constrained Robust Beamforming and Antenna Selection

Contact Info

Product

Resources

About