Robust MSE-Based Transceiver Optimization in MISO Downlink Cognitive Radio Network.
ABSTRACT The concurrent transmission of cognitive radios (CRs) and primary users (PUs) can achieve high spectral efficiency, provided that the interference power to each PU is limited below a certain threshold. Joint optimization of the precoding and the equalization is widely used to exploit the spatial diversity to mitigate the interference. However, its performance is sensitive to the inevitable imperfect channel state information (CSI). This paper studies the robust transceiver optimization in multiple-input and single-output downlink CR network, aiming at minimizing the worst-case per-user mean square error. The channel uncertainty model is assumed to be ellipsoidal bounded for both channel matrices and short-term channel covariance matrices. Under the uncertainty models for both cases of CSI, we first derive the strict upper bound or equivalent conditions for the constraints, then reformulate the original optimization problem into the semi-definite programming problems which can be efficiently solved. Simulation results demonstrate that the proposed schemes effectively mitigates the performance degradation due to the imperfect CSI. Their robustness with respect to the interference constraint is also validated, which is regarded as the critical part in the design of CR network.
Conference Proceeding: Robust downlink beamforming in multiuser MISO Cognitive Radio Networks[show abstract] [hide abstract]
ABSTRACT: This paper is concerned with robust downlink beamforming in a multiuser Multi-Input Single-Output (MISO) Cognitive Radio Network (CR-Net) in which multiple Primary Users (PUs) coexist with multiple Secondary Users (SUs). It is assumed that the Channel State Information (CSI) for all relevant channels is not perfectly known. We design the linear precoder matrix to minimize the transmit power of the SU-Transmitter (SU-Tx) and simultaneously targeting a lower bound on the received Signal-to-Interference-plus-Noise-Ratio (SINR) for the SU's, while requiring an upper bound on the Interference-Power (IP) at the PUs. The method used is based on a worst case design scenario through which the performance metrics of the design are immune to variations of the channels. Three approaches based on convex programming are proposed. Finally, simulation results are provided to evaluate the robustness of the proposed approaches.Personal, Indoor and Mobile Radio Communications, 2009 IEEE 20th International Symposium on; 10/2009
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ABSTRACT: This paper studies the robust beamforming design for a multi-antenna cognitive radio (CR) network, which transmits to multiple secondary users (SUs) and coexists with a primary network of multiple users. We aim to maximize the minimum of the received signal-to-interference-plus-noise ratios (SINRs) of the SUs, subject to the constraints of the total SU transmit power and the received interference power at the primary users (PUs) by optimizing the beamforming vectors at the SU transmitter based on imperfect channel state information (CSI). To model the uncertainty in CSI, we consider a bounded region for both cases of channel matrices and channel covariance matrices. As such, the optimization is done while satisfying the interference constraints for all possible CSI error realizations. We shall first derive equivalent conditions for the interference constraints and then convert the problems into the form of semi-definite programming (SDP) with the aid of rank relaxation, which leads to iterative algorithms for obtaining the robust optimal beamforming solution. Results demonstrate the achieved robustness and the performance gain over conventional approaches and that the proposed algorithms can obtain the exact robust optimal solution with high probability.IEEE Transactions on Signal Processing 01/2010; · 2.81 Impact Factor
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ABSTRACT: A spectrum sharing based cognitive radio (CR) communication system, which consists of a secondary user (SU) having multiple transmit antennas and a single receive antenna and a primary user (PU) having a single receive antenna, is considered. The channel state information (CSI) on the link of the SU is assumed to be perfectly known at the SU transmitter (SU-Tx). However, due to loose cooperation between the SU and the PU, only the channel mean and/or covariance (partial CSI) of the link between the SU-Tx and the PU is available at the SU-Tx. With the partial CSI and a prescribed transmit power constraint, our design objective is to determine the transmit signal covariance matrix that maximizes the rate of the SU while keeping the interference power to the PU below a threshold with high probability. This problem, termed the robust cognitive beamforming problem, can be naturally formulated as a semi-infinite programming (SIP) problem with infinitely many constraints. The SIP problem is transformed into a finite constrained optimization problem and yields to a simple analytical solution, which is developed from a geometric perspective. As an alternative, a much more complex approach based on an interior point algorithm is provided. Simulation examples are presented to validate the effectiveness of the proposed algorithms.IEEE Transactions on Wireless Communications 07/2008; · 2.42 Impact Factor