Recovery under Side Constraints

This paper addresses sparse signal reconstruction under various types of structural side constraints with applications in multi-antenna systems. Side constraints may result from prior information on the measurement system and the sparse signal structure. They may involve the structure of the sensing matrix, the structure of the non-zero support values, the temporal structure of the sparse representationvector, and the nonlinear measurement structure. First, we demonstrate how a priori information in form of structural side constraints influence recovery guarantees (null space properties) using L1-minimization. Furthermore, for constant modulus signals, signals with row-, block- and rank-sparsity, as well as non-circular signals, we illustrate how structural prior information can be used to devise efficient algorithms with improved recovery performance and reduced computational complexity. Finally, we address the measurement system design for linear and nonlinear measurements of sparse signals. Moreover, we discuss the linear mixing matrix design based on coherence minimization. Then we extend our focus to nonlinear measurement systems where we design parallel optimization algorithms to efficiently compute stationary points in the sparse phase retrieval problem with and without dictionary learning

Energy Efficient ADC Bit Allocation and Hybrid Combining for Millimeter Wave MIMO Systems

Low resolution analog-to-digital converters (ADCs) can be employed to improve the energy efficiency (EE) of a wireless receiver since the power consumption of each ADC is exponentially related to its sampling resolution and the hardware complexity. In this paper, we aim to jointly optimize the sampling resolution, i.e., the number of ADC bits, and analog/digital hybrid combiner matrices which provides highly energy efficient solutions for millimeter wave multiple-input multiple output systems. A novel decomposition of the hybrid combiner to three parts is introduced: the analog combiner matrix, the bit resolution matrix and the baseband combiner matrix. The unknown matrices are computed as the solution to a matrix factorization problem where the optimal, fully digital combiner is approximated by the product of these matrices. An efficient solution based on the alternating direction method of multipliers is proposed to solve this problem. The simulation results show that the proposed solution achieves high EE performance when compared with existing benchmark techniques that use fixed ADC resolutions

Efficient and Secure Resource Allocation in Mobile Edge Computing Enabled Wireless Networks

To support emerging applications such as autonomous vehicles and smart homes and to build an intelligent society, the next-generation internet of things (IoT) is calling for up to 50 billion devices connected world wide. Massive devices connection, explosive data circulation, and colossal data processing demand are driving both the industry and academia to explore new solutions. Uploading this vast amount of data to the cloud center for processing will significantly increase the load on backbone networks and cause relatively long latency to time-sensitive applications. A practical solution is to deploy the computing resource closer to end-users to process the distributed data. Hence, Mobile Edge Computing (MEC) emerged as a promising solution to providing high-speed data processing service with low latency. However, the implementation of MEC networks is handicapped by various challenges. For one thing, to serve massive IoT devices, dense deployment of edge servers will consume much more energy. For another, uploading sensitive user data through a wireless link intro-duces potential risks, especially for those size-limited IoT devices that cannot implement complicated encryption techniques. This dissertation investigates problems related to Energy Efficiency (EE) and Physical Layer Security (PLS) in MEC-enabled IoT networks and how Non-Orthogonal Multiple Access (NOMA), prediction-based server coordination, and Intelligent Reflecting Surface (IRS) can be used to mitigate them. Employing a new spectrum access method can help achieve greater speed with less power consumption, therefore increasing system EE. We first investigated NOMA-assisted MEC networks and verified that the EE performance could be significantly improved. Idle servers can consume unnecessary power. Proactive server coordination can help relieve the tension of increased energy consumption in MEC systems. Our next step was to employ advanced machine learning algorithms to predict data workload at the server end and adaptively adjust the system configuration over time, thus reducing the accumulated system cost. We then introduced the PLS to our system and investigated the long-term secure EE performance of the MEC-enabled IoT network with NOMA assistance. It has shown that NOMA can improve both EE and PLS for the network. Finally, we switch from the single antenna scenario to a multiple-input single-output (MISO) system to exploit space diversity and beam forming techniques in mmWave communication. IRS can be used simultaneously to help relieve the pathloss and reconfigure multi-path links. In the final part, we first investigated the secure EE performance of IRS-assisted MISO networks and introduced a friendly jammer to block the eavesdroppers and improve the PLS rate. We then combined the IRS with the NOMA in the MEC network and showed that the IRS can further enhance the system EE

RIS-Aided Cell-Free Massive MIMO Systems for 6G: Fundamentals, System Design, and Applications

An introduction of intelligent interconnectivity for people and things has posed higher demands and more challenges for sixth-generation (6G) networks, such as high spectral efficiency and energy efficiency, ultra-low latency, and ultra-high reliability. Cell-free (CF) massive multiple-input multiple-output (mMIMO) and reconfigurable intelligent surface (RIS), also called intelligent reflecting surface (IRS), are two promising technologies for coping with these unprecedented demands. Given their distinct capabilities, integrating the two technologies to further enhance wireless network performances has received great research and development attention. In this paper, we provide a comprehensive survey of research on RIS-aided CF mMIMO wireless communication systems. We first introduce system models focusing on system architecture and application scenarios, channel models, and communication protocols. Subsequently, we summarize the relevant studies on system operation and resource allocation, providing in-depth analyses and discussions. Following this, we present practical challenges faced by RIS-aided CF mMIMO systems, particularly those introduced by RIS, such as hardware impairments and electromagnetic interference. We summarize corresponding analyses and solutions to further facilitate the implementation of RIS-aided CF mMIMO systems. Furthermore, we explore an interplay between RIS-aided CF mMIMO and other emerging 6G technologies, such as next-generation multiple-access (NGMA), simultaneous wireless information and power transfer (SWIPT), and millimeter wave (mmWave). Finally, we outline several research directions for future RIS-aided CF mMIMO systems.Comment: 30 pages, 15 figure