Machine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance

We investigate the performance of multi-user multiple-antenna downlink systems in which a BS serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is equipped with M RF chains and N antennas, where M < N. Upon receiving pilot sequences to obtain the channel state information, the BS determines the best subset of M antennas for serving the users. We propose a joint antenna selection and precoding design (JASPD) algorithm to maximize the system sum rate subject to a transmit power constraint and QoS requirements. The JASPD overcomes the non-convexity of the formulated problem via a doubly iterative algorithm, in which an inner loop successively optimizes the precoding vectors, followed by an outer loop that tries all valid antenna subsets. Although approaching the (near) global optimality, the JASPD suffers from a combinatorial complexity, which may limit its application in real-time network operations. To overcome this limitation, we propose a learning-based antenna selection and precoding design algorithm (L-ASPA), which employs a DNN to establish underlaying relations between the key system parameters and the selected antennas. The proposed L-ASPD is robust against the number of users and their locations, BS's transmit power, as well as the small-scale channel fading. With a well-trained learning model, it is shown that the L-ASPD significantly outperforms baseline schemes based on the block diagonalization and a learning-assisted solution for broadcasting systems and achieves higher effective sum rate than that of the JASPA under limited processing time. In addition, we observed that the proposed L-ASPD can reduce the computation complexity by 95% while retaining more than 95% of the optimal performance.Comment: accepted to the IEEE Transactions on Wireless Communication

Radio Resource Management for New Application Scenarios in 5G: Optimization and Deep Learning

The fifth-generation (5G) New Radio (NR) systems are expected to support a wide range of emerging applications with diverse Quality-of-Service (QoS) requirements. New application scenarios in 5G NR include enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low-latency communications (URLLC). New wireless architectures, such as full-dimension (FD) massive multiple-input multiple-output (MIMO) and mobile edge computing (MEC) system, and new coding scheme, such as short block-length channel coding, are envisioned as enablers of QoS requirements for 5G NR applications. Resource management in these new wireless architectures is crucial in guaranteeing the QoS requirements of 5G NR systems. The traditional optimization problems, such as subcarriers and user association, are usually non-convex or Non-deterministic Polynomial-time (NP)-hard. It is time-consuming and computing-expensive to find the optimal solution, especially in a large-scale network. To solve these problems, one approach is to design a low-complexity algorithm with near optimal performance. In some cases, the low complexity algorithms are hard to obtain, deep learning can be used as an accurate approximator that maps environment parameters, such as the channel state information and traffic state, to the optimal solutions. In this thesis, we design low-complexity optimization algorithms, and deep learning frameworks in different architectures of 5G NR to resolve optimization problems subject to QoS requirements. First, we propose a low-complexity algorithm for a joint cooperative beamforming and user association problem for eMBB in 5G NR to maximize the network capacity. Next, we propose a deep learning (DL) framework to optimize user association, resource allocation, and offloading probabilities for delay-tolerant services and URLLC in 5G NR. Finally, we address the issue of time-varying traffic and network conditions on resource management in 5G NR

Linear Transmit-Receive Strategies for Multi-user MIMO Wireless Communications

Die Notwendigkeit zur Unterdrueckung von Interferenzen auf der einen Seite und zur Ausnutzung der durch Mehrfachzugriffsverfahren erzielbaren Gewinne auf der anderen Seite rueckte die raeumlichen Mehrfachzugriffsverfahren (Space Division Multiple Access, SDMA) in den Fokus der Forschung. Ein Vertreter der raeumlichen Mehrfachzugriffsverfahren, die lineare Vorkodierung, fand aufgrund steigender Anzahl an Nutzern und Antennen in heutigen und zukuenftigen Mobilkommunikationssystemen besondere Beachtung, da diese Verfahren das Design von Algorithmen zur Vorcodierung vereinfachen. Aus diesem Grund leistet diese Dissertation einen Beitrag zur Entwicklung linearer Sende- und Empfangstechniken fuer MIMO-Technologie mit mehreren Nutzern. Zunaechst stellen wir ein Framework zur Approximation des Datendurchsatzes in Broadcast-MIMO-Kanaelen mit mehreren Nutzern vor. In diesem Framework nehmen wir das lineare Vorkodierverfahren regularisierte Blockdiagonalisierung (RBD) an. Durch den Vergleich von Dirty Paper Coding (DPC) und linearen Vorkodieralgorithmen (z.B. Zero Forcing (ZF) und Blockdiagonalisierung (BD)) ist es uns moeglich, untere und obere Schranken fuer den Unterschied bezueglich Datenraten und bezueglich Leistung zwischen beiden anzugeben. Im Weiteren entwickeln wir einen Algorithmus fuer koordiniertes Beamforming (Coordinated Beamforming, CBF), dessen Loesung sich in geschlossener Form angeben laesst. Dieser CBF-Algorithmus basiert auf der SeDJoCo-Transformation und loest bisher vorhandene Probleme im Bereich CBF. Im Anschluss schlagen wir einen iterativen CBF-Algorithmus namens FlexCoBF (flexible coordinated beamforming) fuer MIMO-Broadcast-Kanaele mit mehreren Nutzern vor. Im Vergleich mit bis dato existierenden iterativen CBF-Algorithmen kann als vielversprechendster Vorteil die freie Wahl der linearen Sende- und Empfangsstrategie herausgestellt werden. Das heisst, jede existierende Methode der linearen Vorkodierung kann als Sendestrategie genutzt werden, waehrend die Strategie zum Empfangsbeamforming frei aus MRC oder MMSE gewaehlt werden darf. Im Hinblick auf Szenarien, in denen Mobilfunkzellen in Clustern zusammengefasst sind, erweitern wir FlexCoBF noch weiter. Hier wurde das Konzept der koordinierten Mehrpunktverbindung (Coordinated Multipoint (CoMP) transmission) integriert. Zuletzt stellen wir drei Moeglichkeiten vor, Kanalzustandsinformationen (Channel State Information, CSI) unter verschiedenen Kanalumstaenden zu erlangen. Die Qualitaet der Kanalzustandsinformationen hat einen starken Einfluss auf die Guete des Uebertragungssystems. Die durch unsere neuen Algorithmen erzielten Verbesserungen haben wir mittels numerischer Simulationen von Summenraten und Bitfehlerraten belegt.In order to combat interference and exploit large multiplexing gains of the multi-antenna systems, a particular interest in spatial division multiple access (SDMA) techniques has emerged. Linear precoding techniques, as one of the SDMA strategies, have obtained more attention due to the fact that an increasing number of users and antennas involved into the existing and future mobile communication systems requires a simplification of the precoding design. Therefore, this thesis contributes to the design of linear transmit and receive strategies for multi-user MIMO broadcast channels in a single cell and clustered multiple cells. First, we present a throughput approximation framework for multi-user MIMO broadcast channels employing regularized block diagonalization (RBD) linear precoding. Comparing dirty paper coding (DPC) and linear precoding algorithms (e.g., zero forcing (ZF) and block diagonalization (BD)), we further quantify lower and upper bounds of the rate and power offset between them as a function of the system parameters such as the number of users and antennas. Next, we develop a novel closed-form coordinated beamforming (CBF) algorithm (i.e., SeDJoCo based closed-form CBF) to solve the existing open problem of CBF. Our new algorithm can support a MIMO system with an arbitrary number of users and transmit antennas. Moreover, the application of our new algorithm is not only for CBF, but also for blind source separation (BSS), since the same mathematical model has been used in BSS application.Then, we further propose a new iterative CBF algorithm (i.e., flexible coordinated beamforming (FlexCoBF)) for multi-user MIMO broadcast channels. Compared to the existing iterative CBF algorithms, the most promising advantage of our new algorithm is that it provides freedom in the choice of the linear transmit and receive beamforming strategies, i.e., any existing linear precoding method can be chosen as the transmit strategy and the receive beamforming strategy can be flexibly chosen from MRC or MMSE receivers. Considering clustered multiple cell scenarios, we extend the FlexCoBF algorithm further and introduce the concept of the coordinated multipoint (CoMP) transmission. Finally, we present three strategies for channel state information (CSI) acquisition regarding various channel conditions and channel estimation strategies. The CSI knowledge is required at the base station in order to implement SDMA techniques. The quality of the obtained CSI heavily affects the system performance. The performance enhancement achieved by our new strategies has been demonstrated by numerical simulation results in terms of the system sum rate and the bit error rate

Resource Allocation in Multi-user MIMO Networks: Interference Management and Cooperative Communications

Nowadays, wireless communications are becoming so tightly integrated in our daily lives, especially with the global spread of laptops, tablets and smartphones. This has paved the way to dramatically increasing wireless network dimensions in terms of subscribers and amount of flowing data. Therefore, the two important fundamental requirements for the future 5G wireless networks are abilities to support high data traffic and exceedingly low latency. A likely candidate to fulfill these requirements is multicell multi-user multi-input multiple-output (MU-MIMO); also termed as coordinated multi-point (CoMP) transmission and reception. To achieve the highest possible performance in MU-MIMO networks, a properly designed resource allocation algorithm is needed. Moreover, with the rapidly growing data traffic, interference has become a major limitation in wireless networks. Interference alignment (IA) has been shown to significantly manage the interference and improve the network performance. However, how practically use IA to mitigate interference in a downlink MU-MIMO network still remains an open problem. In this dissertation, we improve the performance of MU-MIMO networks in terms of spectral efficiency, by designing and developing new beamforming algorithms that can efficiently mitigate the interference and allocate the resources. Then we mathematically analyze the performance improvement of MUMIMO networks employing proposed techniques. Fundamental relationships between network parameters and the network performance is revealed, which provide guidance on the wireless networks design. Finally, system level simulations are conducted to investigate the performance of the proposed strategies

Optimizing Resource Allocation with Energy Efficiency and Backhaul Challenges

To meet the requirements of future wireless mobile communication which aims to increase the data rates, coverage and reliability while reducing energy consumption and latency, and also deal with the explosive mobile traffic growth which imposes high demands on backhaul for massive content delivery, developing green communication and reducing the backhaul requirements have become two significant trends. One of the promising techniques to provide green communication is wireless power transfer (WPT) which facilitates energy-efficient architectures, e.g. simultaneous wireless information and power transfer (SWIPT). Edge caching, on the other side, brings content closer to the users by storing popular content in caches installed at the network edge to reduce peak-time traffic, backhaul cost and latency. In this thesis, we focus on the resource allocation technology for emerging network architectures, i.e. the SWIPT-enabled multiple-antenna systems and cache-enabled cellular systems, to tackle the challenges of limited resources such as insufficient energy supply and backhaul capacity. We start with the joint design of beamforming and power transfer ratios for SWIPT in MISO broadcast channels and MIMO relay systems, respectively, aiming for maximizing the energy efficiency subject to both the Quality of Service (QoS) constraints and energy harvesting constraints. Then move to the content placement optimization for cache-enabled heterogeneous small cell networks so as to minimize the backhaul requirements. In particular, we enable multicast content delivery and cooperative content sharing utilizing maximum distance separable (MDS) codes to provide further caching gains. Both analysis and simulation results are provided throughout the thesis to demonstrate the benefits of the proposed algorithms over the state-of-the-art methods