Interference Alignment And Physical-Layer Security For Multiuser Wireless Networks

With the rapid growth of user scale and the expansion of services in wireless networks over the past few years, multi-user interference and wireless transmission security are becoming serious problems. Owing to their capabilities of performing interference management and anti-eavesdropping transmission effectively, respectively, interference alignment(IA) and physical-layer security(PLS) have attracted increasing attention from both academic and industrial communities, and has been one of the most widely explored topics in wireless communications. The key idea of IA is that the transmit signals are designed in such a way that the interference signals at each receiver are aligned into a low-dimensional subspace as far as possible while the useful signal can occupy the subspace which is not affected by the interference, thus maximizing the degrees of freedom(DoF). The PLS, from the perspective of information transmission, can provide reliable communication for the legitimate user while making the eavesdroppers retrieve almost nothing from their observations. This thesis investigates the IA and PLS for multiuser wireless networks. Specifically, the main contributions of this thesis are summarized as follows:1. The exsiting researches on IA usually consider multiple-input multiple-output(MIMO) systems where multiple antenna elements as well as multiple expensive RF links are required. Hence, how to jointly design the antenna selection scheme and IA is the key to ensure the quality of interference management while at the same time reducing the hardware cost. For the downlink of multi-cell MIMO cellular networks, we first give a processing flow which utilizes the base station(BS) controller to jointly perform the BS antenna selection and IA, and analyze the optimal BS antenna selection scheme for the iterative IA algorithm. And on this basis, by simulating and analyzing the convergence property of the iterative IA algorithm, a BS antenna selection scheme with low computational complexity is proposed, which is based on the greedy search strategy and partial iterations mechanism. Complexity analysis and simulation results show that the proposed scheme can obtain the sum rate performance close to that of the optimal BS antenna selection scheme while requiring less computational complexity. Moreover, by adjusting the number of partial iterations, the proposed scheme can achieve an effective tradeoff between sum rate performance and computational complexity.2. Most of the existing IA algorithms focus on the alignment of interference into a low-dimensional subspace without considering the power of the useful signal. To further improve the sum rate performance of IA in multiuser MIMO interference networks, we incorporate the optimization methods on the Grassmann manifold into the design of IA, and propose a new IA algorithm without the assumption of channel reciprocity. The proposed algorithm combines the extreme eigenvalues method and the modified steepest descent method on the Grassmann manifold to minimize the distances not only between the interference subspace and the subspace spanned by interference, but also between the desired signal subspace and the subspace spanned by useful signal. Utilizing the subspace optimization above, both the interference and useful signal are aligned to their respective subspaces, which implying that the proposed algorithm can insure the power of the useful signal while aligning the interference. Numerical results show that the proposed algorithm can significantly improve the sum rate performance. The performance of the proposed algorithm is superior to that of the alternating minimization IA and the joint signal IA algorithms. Moreover, by properly choosing the attenuation factor of step size, the proposed algorithm can achieve an effective tradeoff between sum rate performance and convergence speed, which gives the IA scheme more design flexibility.3. In the iterative IA, the transmitters and receivers are assumed to know the local channel state information(CSI) as well as interference covariance matrices. However, a complicated training procedure is required to obtain the prior information. From the view of practical engineering application, it is necessary to investigate the low-complexity distributed implementation method of the iterative IA algorithm. For the multiuser MIMO interference networks, a new distributed iterative IA algorithm with low-complexity is developed by using a subspace tracking approach based on the complex fast data projection method(FDPM). The proposed algorithm can iteratively design the precoding and interference suppression matrices through the training procedure of interference subspace tracking. Complexity analysis and simulation results show that the proposed algorithm can obtain the sum rate performance close to that of the original iterative IA algorithm while requiring lower computational complexity. On this basis, the proposed algorithm is further extended to the MIMO cognitive networks(CN) with multiple secondary user pairs. By utilizing the layered precoding design, a new distributed iterative IA algorithm for MIMO CN is developed, which can eliminate the interference from secondary users to the primary user as well as the interference among secondary users.4. In wireless powered communication networks, energy receivers(ERs) have potential to eavesdrop on the unauthorized messages of information receivers(IRs) when they are harvesting energy. Therefore, it is of great significance to investigate how to provide security of information transfer by PLS techniques while guaranteeing the quality of energy transfer. Considering the multicast scenario in the presence of multiple IRs and ERs, we first give a transmission scheme based on the rank-two beamformed Alamouti space-time coding. Then, under both, signal-interference-noise ratio(SINR) and harvested energy constraints, a novel secure multicast design to minimize the transmit power of access point(AP) is proposed, which utilizes the rank-two beamforming. Subsequently, the rank-two semidefinite relaxation(SDR) technique is used to solve the nonconvex optimization problem of the proposed design. The sufficient conditions are also derived under which the rank-two SDR is tight, namely the optimal solution to the optimization problem can be obtained. Moreover, we also present the detailed procedure to obtain the optimal solution, and give the corresponding rank-two Gaussian randomization procedure to obtain a suboptimal solution when SDR is not tight. Simulation results show that the proposed design can effectively reduce transmit power of AP while guaranteeing the security of information transfer and the quality of energy transfer.