In this paper, we propose successive optimization non-linear precoding strategies for physical-layer security in wireless networks. We also investigate different precoding techniques for multi-user MIMO systems under various conditions of channel state information (CSI) between the access point and the users and the eavesdroppers. A non-linear precoding technique based on Successive Optimization Tomlinson-Harashima Precoding (SO-THP) and Simplified Generalized Matrix Inversion (S-GMI) technique is proposed along with a strategy for injecting artificial noise prior to transmission. Simulation results show that the proposed SO-THP+S-GMI precoding technique outperforms existing non-linear and linear precoding algorithms in terms of BER and secrecy rate performances.
In recent years, precoding techniques have been widely applied to multi-user MIMO systems. Precoding techniques rely on pre-processing the transmitted data with the help of channel state information (CSI). Different kinds of linear precoding techniques such as Zero-forcing (ZF) precoding, Minimum Mean Square Error (MMSE) precoding and Block Diagonalization (BD) precoding are introduced in @cite_15 - @cite_10 , respectively. Some non-linear precoding techniques like Tomlinson-Harashima precoding (THP) @cite_16 , Vector Perturbation (VP) precoding @cite_6 have also been reported. The precoding techniques can also contribute to the performance of relay systems @cite_9 These precoding techniques have been proven effective in improving the sum-rate and the bit-error rate (BER) performances. However, precoding strategies require CSI at the transmit side, whereas in reality CSI is available to the users of interest but is rarely available to the eavesdroppers. In most situations, a designer only has access to imperfect CSI for the users of interest. Dealing with imperfect CSI and the absence of CSI of the eavesdropper is a major challenge. Moreover, a technique that is particularly effective for improving the secrecy rate of physical-layer security is the introduction of artificial noise at the transmitter @cite_1 .
@cite_9 @cite_1 @cite_6 @cite_15 @cite_16 @cite_10
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In this paper we introduce a novel linear precoding technique. The approach used for the design of the precoding matrix is general and the resulting algorithm can address several optimization criteria with an arbitrary number of antennas at the user terminals. We have achieved this by designing the precoding matrices in two steps. In the first step we minimize the overlap of the row spaces spanned by the effective channel matrices of different users using a new cost function. In the next step, we optimize the system performance with respect to specific optimization criteria assuming a set of parallel single- user MIMO channels. By combining the closed form solution with Tomlinson-Harashima precoding we reach the maximum sum-rate capacity when the total number of antennas at the user terminals is less or equal to the number of antennas at the base station. By iterating the closed form solution with appropriate power loading we are able to extract the full diversity in the system and reach the maximum sum-rate capacity in case of high multi-user interference. Joint processing over a group of multi-user MIMO channels in different frequency and time slots yields maximum diversity regardless of the level of multi-user interference. In this work, we propose a linear base station (BS) precoding strategy based on switched relaying (SR) processing for downlink multiuser multiple-input multiple-output (MIMO) relay systems. The BS and the MIMO relay station (RS) are both equipped with a codebook of interleaving matrices. For a given channel state information (CSI) the selection function at the BS chooses the optimum interleaving matrix from the codebook based on the maximum sum received signal-to-interference-plus-noise ratio (SINR) or sum rate to design the linear BS precoder. Prior to the payload transmission the BS sends the index corresponding to the selected interleaving matrix to the RS, where the optimum interleaving matrix is selected as the optimum relay processing matrix. The entries of the codebook are randomly generated unitary matrices. Simulation results show that the performance of the proposed techniques is significantly better than prior art in the case of perfect CSI.1 Millimeter wave (mmWave) MIMO will likely use hybrid analog and digital precoding, which uses a small number of RF chains to reduce the energy consumption associated with mixed signal components like analog-to-digital components not to mention baseband processing complexity. However, most hybrid precoding techniques consider a fully connected architecture requiring a large number of phase shifters, which is also energy-intensive. In this paper, we focus on the more energy-efficient hybrid precoding with subconnected architecture, and propose a successive interference cancelation (SIC)-based hybrid precoding with near-optimal performance and low complexity. Inspired by the idea of SIC for multiuser signal detection, we first propose to decompose the total achievable rate optimization problem with nonconvex constraints into a series of simple subrate optimization problems, each of which only considers one subantenna array. Then, we prove that maximizing the achievable subrate of each subantenna array is equivalent to simply seeking a precoding vector sufficiently close (in terms of Euclidean distance) to the unconstrained optimal solution. Finally, we propose a low-complexity algorithm to realize SIC-based hybrid precoding, which can avoid the need for the singular value decomposition (SVD) and matrix inversion. Complexity evaluation shows that the complexity of SIC-based hybrid precoding is only about 10 as complex as that of the recently proposed spatially sparse precoding in typical mmWave MIMO systems. Simulation results verify that SIC-based hybrid precoding is near-optimal and enjoys higher energy efficiency than the spatially sparse precoding and the fully digital precoding. In this paper, we propose a linear precoder for the downlink of a multi-user MIMO system with multiple users that potentially act as eavesdroppers. The proposed precoder is based on regularized channel inversion (RCI) with a regularization parameter α and power allocation vector chosen in such a way that the achievable secrecy sum-rate is maximized. We consider the worst-case scenario for the multi-user MIMO system, where the transmitter assumes users cooperate to eavesdrop on other users. We derive the achievable secrecy sum-rate and obtain the closed-form expression for the optimal regularization parameter αLS of the precoder using large-system analysis. We show that the RCI precoder with αLS outperforms several other linear precoding schemes, and it achieves a secrecy sum-rate that has same scaling factor as the sum-rate achieved by the optimum RCI precoder without secrecy requirements. We propose a power allocation algorithm to maximize the secrecy sum-rate for fixed α. We then extend our algorithm to maximize the secrecy sum-rate by jointly optimizing α and the power allocation vector. The jointly optimized precoder outperforms RCI with αLS and equal power allocation by up to 20 percent at practical values of the signal-to-noise ratio and for 4 users and 4 transmit antennas.