Signal Processing and Learning for Next Generation Multiple Access in 6G

Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission. Emerging applications and scenarios in the sixth generation (6G) wireless systems will require massive connectivity and transmission of a deluge of data, which calls for more flexibility in the design concept that goes beyond orthogonality. Furthermore, recent advances in signal processing and learning have attracted considerable attention, as they provide promising approaches to various complex and previously intractable problems of signal processing in many fields. This article provides an overview of research efforts to date in the field of signal processing and learning for next-generation multiple access, with an emphasis on massive random access and non-orthogonal multiple access. The promising interplay with new technologies and the challenges in learning-based NGMA are discussed

Power Allocation in Uplink NOMA-Aided Massive MIMO Systems

In the development of the fifth-generation (5G) as well as the vision for the future generations of wireless communications networks, massive multiple-input multiple-output (MIMO) technology has played an increasingly important role as a key enabler to meet the growing demand for very high data throughput. By equipping base stations (BSs) with hundreds to thousands antennas, the massive MIMO technology is capable of simultaneously serving multiple users in the same time-frequency resources with simple linear signal processing in both the downlink (DL) and uplink (UL) transmissions. Thanks to the asymptotically orthogonal property of users' wireless channels, the simple linear signal processing can effectively mitigate inter-user interference and noise while boosting the desired signal's gain, and hence achieves high data throughput. In order to realize this orthogonal property in a practical system, one critical requirement in the massive MIMO technology is to have the instantaneous channel state information (CSI), which is acquired via channel estimation with pilot signaling. Unfortunately, the connection capability of a conventional massive MIMO system is strictly limited by the time resource spent for channel estimation. Attempting to serve more users beyond the limit may result in a phenomenon known as pilot contamination, which causes correlated interference, lowers signal gain and hence, severely degrades the system's performance. A natural question is ``Is it at all possible to serve more users beyond the limit of a conventional massive MIMO system?''. The main contribution of this thesis is to provide a promising solution by integrating the concept of nonorthogonal multiple access (NOMA) into a massive MIMO system. The key concept of NOMA is based on assigning each unit of orthogonal radio resources, such as frequency carriers, time slots or spreading codes, to more than one user and utilize a non-linear signal processing technique like successive interference cancellation (SIC) or dirty paper coding (DPC) to mitigate inter-user interference. In a massive MIMO system, pilot sequences are also orthogonal resources, which can be allocated with the NOMA approach. By sharing a pilot sequence to more than one user and utilizing the SIC technique, a massive MIMO system can serve more users with a fixed amount of time spent for channel estimation. However, as a consequence of pilot reuse, correlated interference becomes the main challenge that limits the spectral efficiency (SE) of a massive MIMO-NOMA system. To address this issue, this thesis focuses on how to mitigate correlated interference when combining NOMA into a massive MIMO system in order to accommodate a higher number of wireless users. In the first part, we consider the problem of SIC in a single-cell massive MIMO system in order to serve twice the number of users with the aid of time-offset pilots. With the proposed time-offset pilots, users are divided into two groups and the uplink pilots from one group are transmitted simultaneously with the uplink data of the other group, which allows the system to accommodate more users for a given number of pilots. Successive interference cancellation is developed to ease the effect of pilot contamination and enhance data detection. In the second part, the work is extended to a cell-free network, where there is no cell boundary and a user can be served by multiple base stations. The chapter focuses on the NOMA approach for sharing pilot sequences among users. Unlike the conventional cell-free massive MIMO-NOMA systems in which the UL signals from different access points are equally combined over the backhaul network, we first develop an optimal backhaul combining (OBC) method to maximize the UL signal-to-interference-plus-noise ratio (SINR). It is shown that, by using OBC, the correlated interference can be effectively mitigated if the number of users assigned to each pilot sequence is less than or equal to the number of base stations. As a result, the cell-free massive MIMO-NOMA system with OBC can enjoy unlimited performance when the number of antennas at each BS tends to infinity. Finally, we investigate the impact of imperfect SIC to a NOMA cell-free massive MIMO system. Unlike the majority of existing research works on performance evaluation of NOMA, which assume perfect channel state information and perfect data detection for SIC, we take into account the effect of practical (hence imperfect) SIC. We show that the received signal at the backhaul network of a cell-free massive MIMO-NOMA system can be effectively treated as a signal received over an additive white Gaussian noised (AWGN) channel. As a result, a discrete joint distribution between the interfering signal and its detected version can be analytically found, from which an adaptive SIC scheme is proposed to improve performance of interference cancellation

COGNITIVE MULTI-USER FREE SPACE OPTICAL COMMUNICATION

Increasing deployment of terrestrial, aerial, and space-based assets designed with more demanding services and applications is dramatically escalating the need for high capacity, high data-rate, adaptive, and flexible communication networks. Cognitive, multi-user Free Space Optical Communication (FSOC) networks provide a solution to address these challenges. Such FSOC networks can potentially merge automation and intelligence, as well as offer the benefits of optical communication with enhanced bandwidth and data-rate over long communication networks. Extensive research has investigated various designs, techniques, and methods to enable desired FSOC systems. This dissertation reports the investigation and analysis of novel, state-of-the-art methodologies and algorithms for supporting cognitive, multi-user FSOC. This work details an investigation of the ability of diverse Optical-Multiple Access Control (O-MAC) techniques for performing multi-point communication. Independent Component Analysis (ICA) and Non-Orthogonal Multiple Access (NOMA) techniques were experimentally validated, both singularly and in a combined approach, in a high-speed FSOC link. These methods proved to successfully support multi-user FSOC when users share allocation resources (e.g., time, bandwidth, and space, among others). Additionally, transmission and channel parameters that can affect signal reconstruction performance were identified. To introduce cognition and flexibility into the network, the research reported herein details the use of several Machine Learning (ML) algorithms for estimating crucial parameters at the Physical Layer (PHY) of FSOC networks (e.g., number of transmitting users, modulation format, and quality of transmission [QoT]) for automatically supporting and decoding multiple users. In particular, a novel methodology based on a weighted clustering analysis for automatic and blind user discovery is presented in this work. Extensive experimental analysis was conducted under multiple communication scenarios to identify system performance and limitations. Experimental results demonstrated the ability of the proposed techniques to successfully estimate parameters of interest with high accuracy. Finally, this dissertation presents the design and testing of a modular, multiple node, high-speed, real-time Optical Wireless Communication (OWC) testbed, which provides a hardware and software platform for testing proposed methods and for further research development. This dissertation successfully proves the feasibility of cognitive, multi-user FSOC through the developed and presented methodologies, as well as extensive experimental analyses. The main strength of the research outcomes of this work consists of exploiting software solutions (e.g., O-MAC, signal processing, and ML techniques) to intelligently support multiple users into a single optical channel (i.e., same allocation resources). Accordingly, Size, Weight and Power (SWaP) requirement can be reduced while achieving an increased network capacity