Huiqin Du

Joint Transceiver Beamforming in MIMO Cognitive Radio Network Via Second-Order Cone Programming

This paper considers the spectrum sharing multiple- input-multiple-output (MIMO) cognitive radio network, in which multiple primary users (PUs) coexist with multiple secondary users (SUs). Joint transceiver cognitive beam former design is introduced to minimize the transmit power of the SU base station (SBS) while simultaneously targeting lower bounds on the received signal-to-interference-plus-noise ratio (SINR) for the SUs and imposing upper limits on the interference temperature to the PUs. With the perfect knowledge of all links, the optimal secondary transceiver beam former is achieved iteratively. Due to the limited cooperation between SBS and PUs, perfect information of primary links may not be available at SBS which could lead to severe interference to the PUs. Robust designs are developed against the uncertainties in the primary links by keeping the interference to the PU below a prespecifled threshold with high probability. Simulation results are presented to validate the effectiveness of the proposed algorithms that minimizes the total transmit power and simultaneously guarantees quality-of-service (QoS) of both SUs and PUs.

A Probabilistic Constraint Approach for Robust Transmit Beamforming With Imperfect Channel Information

Transmit beamforming (or precoding) is a powerful technique for enhancing performance of wireless multiantenna communication systems. Standard transmit beamformers require perfect channel state information at the transmitter (CSIT) and are sensitive to errors in channel estimation. In practice, such errors are inevitable due to finite feedback resources, quantization errors and other physical constraints. Hence, robustness has become a crucial issue recently. Among two popular robust designs, the stochastic approach exploits channel statistics and optimizes the average system performance while the maximin approach considers errors as deterministic and optimizes the worst case performance. The latter usually leads to a very conservative design against extreme (but rare) conditions which may occur at a very low probability. In this paper, we propose a more flexible approach that maximizes the average signal-to-noise ratio (SNR) and takes the extreme conditions into account using the probability with which they may occur. Simulation results show that the proposed beamformer offers higher robustness against channel estimation errors than several popular transmit beamformers.

Reweighted Nuclear Norm Approach for Interference Alignment

Managing uncoordinated interference becomes a substantial problem for heterogeneous networks, since the unplanned interferences from the femtos cannot be coordinately aligned with that from the macro/pico base stations (BSs). Due to the uncoordinated interference, perfect interference alignment (IA) may be not attained. In order to achieve linear capacity scaling by IA, we follow the rank-constrained rank minimization (RCRM) framework which minimizes the rank of the interference subspace with full rank constraint on the direct signal space. Considering that the sum of log function can obtain low-rank solutions to linear matrix inequality (LMI) problems for positive semidefinite matrices, we introduce sum of log function as an approximation surrogate of the rank function. To minimize the concave function, we implement a Majorization-Minimization (MM) algorithm and develop a reweighted nuclear norm minimization algorithm with a weight matrix introduced. Moreover, considering the practical available signal-to-noise ratio (SNR), a mixed approach is developed to further improve the achievable sum rate in low-to-moderate SNR region. Simulation results show that the proposed algorithm considerably improves the sum rate performance and achieves the highest multiplexing gain than the recently developed IA approaches for various interference channels.

Robust joint signal and interference alignment for MIMO cognitive radio network

In this work, a robust joint signal and interference alignment is designed for cognitive multiple-input multiple-output (MIMO) interference channel in which single primary link coexists with multiple secondary links. Considering channel imperfections and finite signal-to-noise ratio (SNR) in practice, we propose a worst-case joint design to not only minimize the leakage of interference signals but also ensure the desired signal falling into the orthogonal complement of interference subspace. Under the assumption of the norm-bounded channel uncertainties, the underlying problem is recast to convex form via semidefinite programming (SDP), and the optimum solutions are obtained iteratively. Simulation results reveal the effectiveness of the proposed joint design, and robustness of the worst-case design against channel uncertainties.

Robust joint signal and interference alignment in cognitive radio networks with ellipsoidal channel state information uncertainties

The authors propose a distributed robust joint signal and interference alignment design for multiple-input-multiple-output cognitive radio (CR) networks where single primary link coexists with multiple secondary links. Considering two practical challenges of interference alignment: imperfect channel state information (CSI) and finite signal-to-noise ratio, the proposed scheme aims to minimise both the leakage of interference signals and that of the desired signals, while maintaining interference to the primary user below a permissible level. Under the assumption of the ellipsoidal CSI uncertainties, the joint worst-case optimisation problem is decomposed and reformulated as semi-definite programming form by using S -lemma, orthogonal relaxation and semi-definite relaxation. Simulation results verify the effectiveness of the joint design, and robustness of the worst-case design against channel uncertainties.

Robust Utility Maximization and Admission Control for a MIMO Cognitive Radio Network

This paper considers a multiple-input–multiple-output (MIMO) cognitive radio network, in which a single primary user (PU) coexists with multiple secondary users (SUs). Standard cognitive radio techniques require perfect channel state information (CSI) and are sensitive to the errors in channel estimation. In practice, such errors are inevitable due to finite feedback resources, quantization errors, and other physical constraints. As a result, robustness has become a crucial issue. Under the assumption of norm-bounded channel imperfections, we develop a robust energy-aware design that strives to admit the maximum number of served SUs and enhance the transmit rate of the secondary links while using the least transmit power while imposing the upper limit of the interference temperature to the PU. A utility function is introduced to achieve the multi-objective function by dealing with the tradeoff between maximizing the transmit rate and minimizing the transmit power. Instead of using the deterministic target, the signal-to-interference-and-noise ratio (SINR) target becomes one of the ongoing optimization variables, which is dynamically adapted with the channel condition under the pre-specified bit-error-rate (BER) requirement, and acts as admission control implicitly, i.e., the cognitive link is not allowed to transmit as long as its SINR falls to zero. Due to its nonconvexity and NP-hardness, the underlying problem is reformulated in a semidefinite programming (SDP) form and solved via an iterative bisection search approach. Simulation results are provided to validate the robustness and efficiency of the proposed scheme.

Joint admission control and beamforming with adaptive modulation for cognitive radio network

This paper considers the spectrum sharing multiple-input multiple-output (MIMO) cognitive radio network, in which single primary user (PU) coexists with multiple secondary users (SUs). Joint beamforming and admission control with adaptive modulation technique is designed to maximize the number of the serviced SUs and the transmit rate of the secondary links while imposing upper limits on the interference temperature to the PU by using the least transmit power. In the proposed cross-layer design, the SINR target is dynamically adapted with channel condition and bit-error rate (BER) requirement, and works as admission control implicitly, that is, the cognitive link is dropped as long as its SINR requirement is equal to zero. With the pre-fixed receiver, the multi-objective optimization problem is NP-hard, which is further transformed into single problem by introducing a utility function. The optimum solutions are achieved via second-order cone programming (SOCP) with an iterative bisection search method. Simulation results show that the proposed scheme achieves desirable spectrum and power efficiency, and the maximum number of the total active SUs are intelligently selected.

Transmit Beamforming in MIMO Cognitive Radio Network via Semidefinite Programming

Transmit beamforming is designed for multiple-input multiple-output (MIMO) cognitive radio network in which single primary user (PU) coexists with single secondary user (SU). The proposed cognitive beamforming minimizes the transmit power of the SU while limiting the interference temperature to PU and achieving the signal-to-interference-plus-noise ratio (SINR) target at SU. With perfect knowledge of channel information and receive beamforming pattern of the PU, the optimization problem is formulated as quadratically constrained quadratic programming (QCQP), and the optimal solution is obtained by using semidefinite programming relaxations (SDR). A probabilistic approach is developed by guaranteeing the interference temperature below a pre-specified threshold with high probability for the case when no knowledge of receive beamforming vector of the PU is available at the SU transmitter (SU-Tx). Simulation results demonstrate the effectiveness of the proposed approach in both scenarios.

Securing Cognitive Radio with a Combined Approach of Beamforming and Cooperative Jamming

The authors consider secret communication through a relay-assisted cognitive interference channel in which primary and secondary base stations (SBSs), respectively, communicate with the primary and secondary receivers (PU-Rx and SU-Rx) in the presence of multiple eavesdroppers. An SBS is allowed to transmit simultaneously with the primary base station (PBS). They propose three cooperative jamming (CJ) schemes based on the available channel state information at the base stations and the relay. The proposed CJ schemes are designed to create additional interference in the direction of eavesdroppers without creating any interference to the PU-Rx and the SU-Rx. A combined approach of beamforming and zero-forcing precoding is developed at the PBS, the SBS and the relay to cancel out jamming interference at the PU-Rx and the SU-Rx. The secrecy rate of SU-Rx is calculated with the constraint of maintaining interference temperature at the PU-Rx under a certain threshold. Compared with the direct transmission schemes that are available in the literature, the authors’ results show that the approach which combined beamforming and CJ significantly improves the secrecy rate of the cognitive interference channel.

A robust interference alignment technique for the MIMO interference channel with uncertainties

Interference alignment (IA) is a transmission technique for exploiting all available degrees of freedom in the frequency- or time-selective interference channel with an arbitrary number of users, which can achieve linear capacity scaling. However, standard approach requires perfect channel state information (CSI) and is sensitive to inevitable channel imperfection, which motivates us to consider the robust design. In this work, a robust interference alignment approach is designed for multiple-input multiple-output (MIMO) interference channel to handle channel imperfections. Based on the norm-bounded error model, the proposed robust design minimizes the interference signals that spill out the interference subspace. The underlying problem is recast to semidefinite programming (SDP) form by using Schur complement theorem, and the optimum solutions are obtained iteratively. Simulation results reveal the robustness of the worst-case design against channel uncertainties.