Securing Downlink Massive MIMO-NOMA Networks with Artificial Noise

In this paper, we focus on securing the confidential information of massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks by exploiting artificial noise (AN). An uplink training scheme is first proposed with minimum mean squared error estimation at the base station. Based on the estimated channel state information, the base station precodes the confidential information and injects the AN. Following this, the ergodic secrecy rate is derived for downlink transmission. An asymptotic secrecy performance analysis is also carried out for a large number of transmit antennas and high transmit power at the base station, respectively, to highlight the effects of key parameters on the secrecy performance of the considered system. Based on the derived ergodic secrecy rate, we propose the joint power allocation of the uplink training phase and downlink transmission phase to maximize the sum secrecy rates of the system. Besides, from the perspective of security, another optimization algorithm is proposed to maximize the energy efficiency. The results show that the combination of massive MIMO technique and AN greatly benefits NOMA networks in term of the secrecy performance. In addition, the effects of the uplink training phase and clustering process on the secrecy performance are revealed. Besides, the proposed optimization algorithms are compared with other baseline algorithms through simulations, and their superiority is validated. Finally, it is shown that the proposed system outperforms the conventional massive MIMO orthogonal multiple access in terms of the secrecy performance

Two-step multiuser equalization for hybrid mmWave massive MIMO GFDM systems

Although millimeter-wave (mmWave) and massive multiple input multiple output (mMIMO) can be considered as promising technologies for future mobile communications (beyond 5G or 6G), some hardware limitations limit their applicability. The hybrid analog-digital architecture has been introduced as a possible solution to avoid such issues. In this paper, we propose a two-step hybrid multi-user (MU) equalizer combined with low complexity hybrid precoder for wideband mmWave mMIMO systems, as well as a semi-analytical approach to evaluate its performance. The new digital non-orthogonal multi carrier modulation scheme generalized frequency division multiplexing (GFDM) is considered owing to its efficient performance in terms of achieving higher spectral efficiency, better control of out-of-band (OOB) emissions, and lower peak to average power ratio (PAPR) when compared with the orthogonal frequency division multiplexing (OFDM) access technique. First, a low complexity analog precoder is applied on the transmitter side. Then, at the base station (BS), the analog coefficients of the hybrid equalizer are obtained by minimizing the mean square error (MSE) between the hybrid approach and the full digital counterpart. For the digital part, zero-forcing (ZF) is used to cancel the MU interference not mitigated by the analog part. The performance results show that the performance gap of the proposed hybrid scheme to the full digital counterpart reduces as the number of radio frequency (RF) chains increases. Moreover, the theoretical curves almost overlap with the simulated ones, which show that the semi-analytical approach is quite accurate.publishe

Optimizing multiuser MIMO for access point cooperation in dense wireless networks

As the usage of wireless devices continues to grow rapidly in popularity, wireless networks that were once designed to support a few laptops must now host a much wider range of equipments, including smart phones, tablets, and wearable devices, that often run bandwidth-hungry applications. Improvements in wireless local access network (WLAN) technology are expected to help accommodate the huge traffic demands. In particular, advanced multicell Multiple-Input Multiple-Output (MIMO) techniques, involving the cooperation of APs and multiuser MIMO processing techniques, can be used to satisfy the increasing demands from users in high-density environments. The objective of this thesis is to address the fundamental problems for multiuser MIMO with AP cooperation in dense wireless network settings. First, for a very common multiuser MIMO linear precoding technique, block diagonalization, a novel pairing-and-binary-tree based user selection algorithm is proposed. Second, without the zero-forcing constraint on the multiuser MIMO transmission, a general weighted sum rate maximization problem is formulated for coordinated APs. A scalable algorithm that performs a combined optimization procedure is proposed to determine the user selection and MIMO weights. Third, we study the fair and high-throughput scheduling problem by formally specifying an optimization problem. Two algorithms are proposed to solve the problem using either alternating optimization or a two-stage procedure. Fourth, with the coexistence of both stationary and mobile users, different scheduling strategies are suggested for different user types. The provided theoretical analysis and simulation results in this thesis lay out the foundation for the realization of the clustered WLAN networks with AP cooperation.Ph.D

Interference management in 5G cellular networks

University of Technology Sydney. Faculty of Engineering and Information Technology.This dissertation is concerned with the nonconvex optimization problems of interference management under the consideration of new disruptive technologies in the fifth-generation cellular networks. These problems are the key to the successful roll-out of these new technologies but have remained unsolved due to their mathematical challenge. Therefore, this dissertation provides novel minorants/majorants of the nonconvex functions which are then used for the successive convex approximation framework. The first considered technology is heterogeneous networks (HetNet) in which base stations (BSs) of various sizes and types are densely deployed in the same area. Although HetNet provides a significant improvement in spectral efficiency and offloading, designing an optimal power transmission and association control policy is challenging, especially when both quality-of-service (QoS) and backhaul capacity are considered. Maximizing the total network throughput or the fairness among users in HetNet are challenging mixed integer nonconvex optimization problems. Iterative algorithms based on alternating descent and successive convex programming are proposed to address such problems. Next, we consider a full-duplex multi-user multiple-input multiple-output (FD MU-MIMO) multicell network in which base stations simultaneously serve both downlink (DL) users and uplink (UL) users on the same frequency band via multiple antennas to potentially double the spectral efficiency. Since the use of FD radios introduces additional self-interference (SI) and cross interference of UL between DL transmissions, the minimum cell throughput maximization and the sum network throughput maximization with QoS guarantee are nonconvex challenging problems. To solve such challenging optimization problems, we develop path-following algorithms based on successive convex quadratic programming framework. As a byproduct, the proposed algorithms can be extended to the optimal precoding matrix design in a half-duplex MU-MIMO multicell network with the Han-Kobayashi transmission strategy. Finally, the last research work stems from the need of prolonging user equipments’ battery life in power-limited networks. Toward this end, we consider the optimal design of precoding matrices in the emerging energy-harvesting-enabled (EH-enabled) MU-MIMO networks in which BSs can transfer information and energy to UEs on the same channel using either power splitting (PS) or time switching (TS) mechanisms. The total network throughput maximization problem under QoS constraints and EH constraints with either PS or TS in FD networks is computationally difficult due to nonconcave objective function and nonconvex constraints. We propose new inner approximations of these problems based on which a successive convex programming framework is applied to address them