Kanapathippillai Cumanan

Secrecy Rate Optimizations for a MIMO Secrecy Channel With a Cooperative Jammer

In this paper, we study different secrecy rate optimization techniques for a multiple-input-multiple-output (MIMO) secrecy channel, where a multiantenna cooperative jammer is employed to improve secret communication in the presence of a multiantenna eavesdropper. Specifically, we consider two optimization problems, namely, power minimization and secrecy rate maximization. These problems are not jointly convex in terms of the transmit covariance matrices of the legitimate transmitter and the cooperative jammer. To circumvent these nonconvexity issues, we alternatively design the transmit covariance matrix of the legitimate transmitter and the cooperative jammer. For a given transmit covariance matrix at the cooperative jammer, we solve the power minimization and secrecy rate maximization problems based on a Taylor series expansion. Then, we propose two iterative algorithms to solve these approximated problems. In addition, we develop a robust scheme by incorporating channel uncertainties associated with the eavesdropper. By exploiting S-Procedure, we show that these robust optimization problems can be formulated into semidefinite programming. Moreover, we consider the secrecy rate maximization problem based on game theory, where the jammer introduces charges for its jamming service based on the amount of the interference caused to the eavesdropper. This secrecy rate maximization problem is formulated into a Stackelberg game where the jammer and the transmitter are the leader and the follower of the game, respectively. For the proposed game, Stackelberg equilibrium is analytically derived. Simulation results have been provided to validate the convergence and performance of the proposed algorithms. In addition, it is shown that the proposed robust scheme outperforms the nonrobust scheme in terms of the achieved secrecy rate and the worst-case secrecy rate. Finally, the Stackelberg equilibrium solution has been validated through numerical results.

Secrecy Rate Optimizations for a MIMO Secrecy Channel With a Multiple-Antenna Eavesdropper

This paper studies different secrecy rate optimization problems for a multiple-input-multiple-output (MIMO) secrecy channel. In particular, we consider a scenario where a communication through a MIMO channel is overheard by a multiple-antenna eavesdropper. In this secrecy network, we first investigate two secrecy rate optimization problems: 1) power minimization and 2) secrecy rate maximization. These optimization problems are not convex due to the nonconvex secrecy rate constraint. However, by approximating this secrecy rate constraint based on Taylor series expansion, we propose iterative algorithms to solve these secrecy rate optimization problems. In addition, we provide the convergence analysis for the proposed algorithms. These iterative optimization approaches are developed under the assumption that the transmitter has perfect channel state information. However, there are practical difficulties in having perfect channel state information at the transmitter. Hence, robust secrecy rate optimization techniques based on the worst-case secrecy rate are considered by incorporating channel uncertainties. By exploiting the S-Procedure, we show that these robust optimization problems can be formulated into semidefinite programming at low signal-to-noise ratios (SNRs). Simulation results have been provided to validate the convergence of the proposed algorithms. In addition, numerical results show that the proposed robust optimization techniques outperform the nonrobust schemes in terms of the worst-case secrecy rates and the achieved secrecy rates.

SINR Balancing Technique for Downlink Beamforming in Cognitive Radio Networks

We propose a novel signal to interference and noise (SINR) balancing technique for a downlink cognitive radio network (CRN) wherein multiple cognitive users (also referred to as secondary users (SUs)) coexist and share the licensed spectrum with the primary users (PUs) using the underlay approach. The proposed beamforming technique maximizes the worst SU SINR while ensuring that the interference leakage to PUs is below specific thresholds. Due to the additional interference constraints imposed by PUs, the principle of uplink-downlink duality used in the conventional downlink beamformer design cannot be directly applied anymore. To circumvent this problem, using an algebraic manipulation on the interference constraints, we propose a novel SINR balancing technique for CRNs based on uplink-downlink iterative design techniques. Simulation results illustrate the convergence and the optimality of the proposed beamformer design.

Joint Beamforming and User Maximization Techniques for Cognitive Radio Networks Based on Branch and Bound Method

We consider a network of cognitive users (also referred to as secondary users (SUs)) coexisting and sharing the spectrum with primary users (PUs) in an underlay cognitive radio network (CRN). Specifically, we consider a CRN wherein the number of SUs requesting channel access exceeds the number of available frequency bands and spatial modes. In such a setting, we propose a joint fast optimal resource allocation and beamforming algorithm to accommodate maximum possible number of SUs while satisfying quality of service (QoS) requirement for each admitted SU, transmit power limitation at the secondary network basestation (SNBS) and interference constraints imposed by the PUs. Recognizing that the original user maximization problem is a nondeterministic polynomial-time hard (NP), we use a mixed-integer programming framework to formulate the joint user maximization and beamforming problem. Subsequently, an optimal algorithm based on branch and bound (BnB) method has been proposed. In addition, we propose a suboptimal algorithm based on BnB method to reduce the complexity of the proposed algorithm. Specifically, the suboptimal algorithm has been developed based on the first feasible solution it achieves in the fast optimal BnB method. Simulation results have been provided to compare the performance of the optimal and suboptimal algorithms.

A New Design Paradigm for MIMO Cognitive Radio with Primary User Rate Constraint

This letter studies the rate maximization problem for a cognitive (secondary) radio link communicating over the same frequency band as of a primary radio link, where both the primary and the secondary radios are equipped with multiple antennas. In contrast to the conventional interference power constraint approach to protect the primary receiver, we consider a new design paradigm for rate maximization of the secondary user (SU) under a direct constraint on the minimum primary user (PU) transmission rate subject to the SU interference. This problem is shown to be non-convex due to the newly introduced PU rate constraint. However, by approximating this rate constraint by the method of Taylor series expansion, this letter proposes an iterative algorithm to maximize the SU rate while satisfying the PUs minimum data rate requirement. The proposed scheme with the PU rate constraint is shown to outperform the conventional scheme with the interference power constraint in terms of SUs achievable rates for different target PU rates.

Robust Outage Secrecy Rate Optimizations for a MIMO Secrecy Channel

This letter investigates robust secrecy rate optimization techniques for a multiple-input-multiple-output (MIMO) wiretap channel in the presence of a multiantenna eavesdropper. In particular, two robust secrecy rate optimization problems are studied: robust power minimization with an outage secrecy rate constraint and robust secrecy rate maximization subject to the outage secrecy rate and transmit power constraints. Here, it is assumed that the legitimate transmitter has perfect channel state information (CSI) of the legitimate receiver and imperfect CSI of the eavesdropper. Both robust problems are not convex in terms of transmit covariance matrix and the outage secrecy rate constraint. Hence, we propose a conservative approximation approach to reformulate this outage secrecy rate constraint by exploiting the Bernstein-type inequality to obtain a tractable solution for these two robust problems. Numerical results have been provided to validate the convergence and the performance of the proposed robust algorithms.

Robust interference control techniques for multiuser cognitive radios using worst-case performance optimization

Cognitive radio networks have the ability to efficiently utilize the scarce radio spectrum by allowing unlicensed users to access the licensed frequency bands in the absence of licensed users. Transmit beamformers can be designed by setting constraints on the interference temperature of the licensed users and signal to interference and noise ratios (SINRs) of the cognitive users. This design is however very sensitive to errors in channel state information (CSI). In this paper, we propose robust and non-robust beamforming techniques for multiuser cognitive radios. The proposed beamformer has the ability to maintain the SINRs of all unlicensed users above a target value for all possible errors in the CSI. The problem is formulated within a convex optimization framework with constraints on worst-case errors which can be solved using interior point methods. The performance of the robust beamformer is compared with non-robust beamformer in terms of BER of the unlicensed users and the probability density function of the achieved SINRs.

Robust interference control techniques for cognitive radios using worst-case performance optimization

Cognitive radio networks have the ability to efficiently utilize the radio spectrum by allowing unlicensed users to communicate in the licensed frequency bands. Transmit beamformers can be designed by setting constraints on the interference temperature of the licensed users and signal to interference and noise ratios (SINRs) of the cognitive users. This design is however very sensitive to errors in channel state information (CSI). In this paper, we propose robust beamforming techniques for cognitive radios using worst-case performance optimization. The proposed beamformer has the ability to control the interference leaked to licensed users below a target value for all possible errors in the CSI. The problem is formulated within a convex optimization framework with constraints on worst-case errors. The performance of the robust beamformer is compared with non-robust beamformer in terms of bit error rates of the licensed users and probability density function of the interference power.

Optimal subcarrier and bit allocation techniques for cognitive radio networks using integer linear programming

We propose an adaptive radio resource allocation algorithm for cognitive radio underlay networks. The proposed algorithm optimally allocates power and the available subcarriers in an OFDMA environment while ensuring interference leaked to the primary users is below a specific value. We formulate the radio resource allocation problem for cognitive radio network into integer linear programming framework. This algorithm yields an optimal solution for the proposed resource allocation problem. Simulation results have been provided to validate the performance of the algorithm.

A Novel Cooperative Relaying Strategy for Wireless Networks With Signal Quantization

We propose a cooperative signal-forwarding scheme for wireless sensor and mesh networks using semidefinite constraints. We consider a multiple source-destination scenario where a set of relays assists forwarding signals from sources to destinations. This paper assumes cooperation between relays so that the signals received from source nodes are passed between the relays. A semidefinite programming framework allows us to impose various quality-of-service (QoS) constraints for each source-destination pair. The proposed scheme outperforms a cooperative relaying strategy that is based on a minimum mean square error (MMSE). The design also considers quantization of signals that are passed between cooperating relays. The proposed scheme, even in the presence of signal-quantization noise, outperforms a noncooperative relaying scheme.