Multi-user MIMO beamforming:implementation, verification in L1 capacity, and performance testing

Abstract. A certain piece of technology takes a lot of effort, research, and testing to reach the productisation phase. Radio features are implemented in layer 1 (L1) before moving to the hardware implementation phase, where their functioning is tested and verified. The target of the thesis is to implement and verify beamforming based multi-user multiple-input multiple-output (MU-MIMO) in L1 capacity and performance testing (PET) environment. The L1 testing environment mainly focuses on 4G and 5G stand-alone (SA) cases, while the focus of this thesis work is only on 5G SA technology, which features beamforming and MU-MIMO. Beamforming and MU-MIMO have been tested in an end-to-end system but not specifically in L1. The L1 testing provides a deeper analysis of beamforming and MU-MIMO in L1 and aids in problem identification at an early productisation phase, saving both time and money. L1 PET has multiple components that work together for L1 data transmission in both uplink (UL) and downlink (DL) directions and handle the verification of the transmitted data. The main components that play a key role in the implementation of multi-user MIMO beamforming concern frame design setup, message setup for UL and DL using correct channels and interfaces, transmission of the generated data in UL and DL, and message capturing at L1 end (whether correct messages are transmitted or not). For verification purposes, methods such as analysing plots from L1 log results based on comparison with radio specifications are used to determine whether the generated test output is correct or not. Finally, performance metrics, such as error vector magnitude (EVM), UE per transmission time interval (TTI), number of layers per UE, channel quality indicator (CQI), physical resource block (PRB) count, and throughput, are evaluated to assess the capacity and performance correctness of the implemented test setup

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

Mitigating Smart Jammers in Multi-User MIMO

Wireless systems must be resilient to jamming attacks. Existing mitigation methods based on multi-antenna processing require knowledge of the jammer's transmit characteristics that may be difficult to acquire, especially for smart jammers that evade mitigation by transmitting only at specific instants. We propose a novel method to mitigate smart jamming attacks on the massive multi-user multiple-input multiple-output (MU-MIMO) uplink which does not require the jammer to be active at any specific instant. By formulating an optimization problem that unifies jammer estimation and mitigation, channel estimation, and data detection, we exploit that a jammer cannot change its subspace within a coherence interval. Theoretical results for our problem formulation show that its solution is guaranteed to recover the users' data symbols under certain conditions. We develop two efficient iterative algorithms for approximately solving the proposed problem formulation: MAED, a parameter-free algorithm which uses forward-backward splitting with a box symbol prior, and SO-MAED, which replaces the prior of MAED with soft-output symbol estimates that exploit the discrete transmit constellation and which uses deep unfolding to optimize algorithm parameters. We use simulations to demonstrate that the proposed algorithms effectively mitigate a wide range of smart jammers without a priori knowledge about the attack type.Comment: arXiv admin note: text overlap with arXiv:2201.0877

An overview of 5G technologies

Since the development of 4G cellular networks is considered to have ended in 2011, the attention of the research community is now focused on innovations in wireless communications technology with the introduction of the fifth-generation (5G) technology. One cycle for each generation of cellular development is generally thought to be about 10 years; so the 5G networks are promising to be deployed around 2020. This chapter will provide an overview and major research directions for the 5G that have been or are being deployed, presenting new challenges as well as recent research results related to the 5G technologies. Through this chapter, readers will have a full picture of the technologies being deployed toward the 5G networks and vendors of hardware devices with various prototypes of the 5G wireless communications systems

An overview of 5G technologies

Since the development of 4G cellular networks is considered to have ended in 2011, the attention of the research community is now focused on innovations in wireless communications technology with the introduction of the fifth-generation (5G) technology. One cycle for each generation of cellular development is generally thought to be about 10 years; so the 5G networks are promising to be deployed around 2020. This chapter will provide an overview and major research directions for the 5G that have been or are being deployed, presenting new challenges as well as recent research results related to the 5G technologies. Through this chapter, readers will have a full picture of the technologies being deployed toward the 5G networks and vendors of hardware devices with various prototypes of the 5G wireless communications systems

Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking

The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out

Security Concerns on Machine Learning Solutions for 6G Networks in mmWave Beam Prediction

6G – sixth generation – is the latest cellular technology currently under development for wireless communication systems. In recent years, machine learning (ML) algorithms have been applied widely in various fields, such as healthcare, transportation, energy, autonomous cars, and many more. Those algorithms have also been used in communication technologies to improve the system performance in terms of frequency spectrum usage, latency, and security. With the rapid developments of ML techniques, especially deep learning (DL), it is critical to consider the security concern when applying the algorithms. While ML algorithms offer significant advantages for 6G networks, security concerns on artificial intelligence (AI) models are typically ignored by the scientific community so far. However, security is also a vital part of AI algorithms because attackers can poison the AI model itself. This paper proposes a mitigation method for adversarial attacks against proposed 6G ML models for the millimeter-wave (mmWave) beam prediction using adversarial training. The main idea behind generating adversarial attacks against ML models is to produce faulty results by manipulating trained DL models for 6G applications for mmWave beam prediction. We also present a proposed adversarial learning mitigation method’s performance for 6G security in mmWave beam prediction application a fast gradient sign method attack. The results show that the defended model under attack’s mean square errors (i.e., the prediction accuracy) are very close to the undefended model without attack

Identification and Mitigation of Information Leakage Caused by Side Channel Vulnerabilities in Network Stack

Keeping users sensitive information secure and private in todays network is challenging. Networks are large, complicated distributed systems and are subject to a wide variety of attacks, such as eavesdropping, identity spoofing, hijacking, etc. What is worse, encrypting data is often not enough in light of advanced threats such as side channel attacks, which enable malicious attackers to infer sensitive data from insignificant network information unexpectedly. For this purpose, we pro- pose series of techniques to prevent such information leakage at different layers in network stacks, and raise awareness of its severity. More specifically, 1) we propose a practical physical (PHY) layer security framework FOG, for effective packet header obfuscation using MIMO, to keep eavesdroppers from receiving any meaningful packet information; 2) we identify and fix a subtle yet serious pure off-path side channel vulnerability (CVE-2016-5696) introduced in both TCP specification and its implementation in Linux kernel, which prevents malicious attackers from exploiting it to indicate arbitrary connections state, reset the connection or even further hijack the connection; 3) we propose a principled TCP side channel vulnerability discovery solution based on model checking and program analysis, and automatically identify 12 new side channel vulnerabilities (and 3 old ones) from TCP implementation in Linux and FreeBSD kernel code. The ultimate goal is to help guide the future design and implementation of network stacks.Keeping users’ sensitive information secure and private in today’s network is challenging. Network nowadays are subject to a wide variety of attacks, such as eavesdropping, identity spoofing, denial of service, etc. What is worse, encrypting sensitive data is often not enough in light of advanced threats such as side channel attacks, which enable malicious attackers to infer sensitive data from “insignificant” network information unexpectedly. For this purpose, we propose series of techniques to prevent such information leakage at different layers in network stack, and raise awareness of its severity. In our first work, we propose a practical physical (PHY) layer security framework FOG, for effective packet header obfuscation using MIMO, to prevent eavesdroppers from receiving any packet headers to profile users. Secondly, we identify and fix a subtle yet serious pure off-path side channel vulnerability (CVE-2016-5696) introduced in both TCP specification and its implementation in Linux kernel. This vulnerability allows malicious attackers to indicate arbitrary TCP connection’s state, reset the connection or even further hijack the connection. Motivated by the fact that most previous TCP side channel vulnerabilities are manually identified, in our last work, we propose a principled TCP side channel vulnerability discovery solution based on model checking and program analysis. It automatically identifies 12 new side channel vulnerabilities (and 3 old ones) from TCP implementation in Linux and FreeBSD kernel code. The ultimate goal of my research is to help guide the future design and implementation of network stacks