Vision-Assisted User Clustering for Robust mmWave-NOMA Systems

When operated in the mmWave band, user channels get highly correlated which can be exploited in mmWave-NOMA systems to cluster a set of "correlated" users together. Identifying the set of users to cluster greatly affects the viability of NOMA systems. Typically, only channel state information (CSI) is used to make these clustering decisions. When any problem arises in accessing up-to-date and accurate CSI, user clustering will not properly function due to its hard-dependency on CSI, and obviously, this will negatively affect the robustness of the NOMA systems. To improve the robustness of the NOMA systems, we propose to utilize emerging trends such as location-aware and camera-equipped base stations (CBSs) which do not require any extra radio frequency resource consumption. Specifically, we explore three different dimensions of feedback that a CBS can benefit from to solve the user clustering problem, namely CSI-based feedback and non-CSI-based feedback, comprised of user equipment (UE) location and the CBS camera feed. We first investigate how the vision assistance of a CBS can be used in conjunction with other dimensions of feedback to make clustering decisions in various scenarios. Later, we provide a simple user case study to illustrate how to implement vision-assisted user clustering in mmWave-NOMA systems to improve robustness, in which a deep learning (DL) beam selection algorithm is trained on the images captured by the CBS to perform NOMA clustering. We demonstrate that user clustering without CSI can achieve comparable performance to accurate CSI-based solutions, and user clustering can continue to function without much performance loss even in the scenarios where CSI is severely outdated or not available at all.Comment: Accepted in the Proceedings of IEEE Future Networks World Forum (IEEE FNWF 2020

Federated learning empowered ultra-dense next-generation wireless networks

The evolution of wireless networks, from first-generation (1G) to fifth generation (5G), has facilitated real-time services and intelligent applications powered by artificial intelligence (AI) and machine learning (ML). Nevertheless, prospective applications like autonomous driving and haptic communications necessitate the exploration of beyond fifth-generation (B5G) and sixth-generation (6G) networks, leveraging millimeter-wave (mmWave) and terahertz (THz) technologies. However, these high-frequency bands experience significant atmospheric attenuation, resulting in high signal propagation loss, which necessitates a fundamental reconfiguration of network architectures and paves the way for the emergence of ultra-dense networks (UDNs). Equipped with massive multiple-input multiple-output (mMIMO) and beamforming technologies, UDNs mitigate propagation losses by utilising narrow line-of-sight (LoS) beams to direct radio waves toward specific receiving points, thereby enhancing signal quality. Despite these advancements, UDNs face critical challenges, which include worsened mobility issues in dynamic UDNs due to the susceptibility of LoS links to blockages, data privacy concerns at the network edge when implementing centralised ML training, and power consumption challenges stemming from the deployment of dense small base stations (SBSs) and the integration of cutting edge techniques like edge learning. In this context, this thesis begins by investigating the prevailing issue of beam blockage in UDNs and introduces novel frameworks to address this emerging challenge. The main theme of the first three contributions is to tackle beam blockages and frequent handovers (HOs) through innovative sensing-aided wireless communications. This approach seeks to enhance the situational awareness of UDNs regarding their surroundings by using a variety of sensors commonly found in urban areas, such as vision and radar sensors. While all these contributions share the common goal of proposing sensing-aided proactive HO (PHO) frameworks that intelligently predict blockage events in advance and performs PHO, each of them presents distinctive framework features, contributing significantly to the improvement of UDN operations. To provide further details, the first contribution adhered to conventional centralised model training, while the other contributions employed federated learning (FL), a decentralised collaborative training approach primarily designed to safeguard data privacy. The utilisation of FL technology offers several advantages, including enhanced data privacy, scalability, and adaptability. Simulation results from all these frameworks have demonstrated the remarkable performance of the proposed latency-aware frameworks in improving UDNs’ reliability, maintaining user connectivity, and delivering high levels of quality of experience (QoE) and throughput when compared to existing reactive HO procedures lacking proactive blockage prediction. The fourth contribution is centred on optimising energy management in UDNs and introduces FedraTrees, a lightweight algorithm that integrates decision tree (DT)-based models into the FL setup. FedraTrees challenges the conventional belief that FL is exclusively suited for Neural Network (NN) models by enabling the incorporation of DT models within the FL context. While FedraTrees offers versatility across various applications, this thesis specifically applies it to energy forecasting tasks with the aim of achieving the energy efficiency requirement of UDNs. Simulation results demonstrate that FedraTrees performs remarkably in predicting short-term energy patterns and surpasses the state-of-the-art long short-term memory (LSTM)-based federated averaging (FedAvg) algorithm in terms of reducing computational and communication resources demands

Intelligent Multi-Modal Sensing-Communication Integration: Synesthesia of Machines

In the era of sixth-generation (6G) wireless communications, integrated sensing and communications (ISAC) is recognized as a promising solution to upgrade the physical system by endowing wireless communications with sensing capability. Existing ISAC is mainly oriented to static scenarios with radio-frequency (RF) sensors being the primary participants, thus lacking a comprehensive environment feature characterization and facing a severe performance bottleneck in dynamic environments. To date, extensive surveys on ISAC have been conducted but are limited to summarizing RF-based radar sensing. Currently, some research efforts have been devoted to exploring multi-modal sensing-communication integration but still lack a comprehensive review. Therefore, we generalize the concept of ISAC inspired by human synesthesia to establish a unified framework of intelligent multi-modal sensing-communication integration and provide a comprehensive review under such a framework in this paper. The so-termed Synesthesia of Machines (SoM) gives the clearest cognition of such intelligent integration and details its paradigm for the first time. We commence by justifying the necessity of the new paradigm. Subsequently, we offer a definition of SoM and zoom into the detailed paradigm, which is summarized as three operation modes. To facilitate SoM research, we overview the prerequisite of SoM research, i.e., mixed multi-modal (MMM) datasets. Then, we introduce the mapping relationships between multi-modal sensing and communications. Afterward, we cover the technological review on SoM-enhance-based and SoM-concert-based applications. To corroborate the superiority of SoM, we also present simulation results related to dual-function waveform and predictive beamforming design. Finally, we propose some potential directions to inspire future research efforts.Comment: This paper has been accepted by IEEE Communications Surveys & Tutorial

LiDAR aided simulation pipeline for wireless communication in vehicular traffic scenarios

Abstract. Integrated Sensing and Communication (ISAC) is a modern technology under development for Sixth Generation (6G) systems. This thesis focuses on creating a simulation pipeline for dynamic vehicular traffic scenarios and a novel approach to reducing wireless communication overhead with a Light Detection and Ranging (LiDAR) based system. The simulation pipeline can be used to generate data sets for numerous problems. Additionally, the developed error model for vehicle detection algorithms can be used to identify LiDAR performance with respect to different parameters like LiDAR height, range, and laser point density. LiDAR behavior on traffic environment is provided as part of the results in this study. A periodic beam index map is developed by capturing antenna azimuth and elevation angles, which denote maximum Reference Signal Receive Power (RSRP) for a simulated receiver grid on the road and classifying areas using Support Vector Machine (SVM) algorithm to reduce the number of Synchronization Signal Blocks (SSBs) that are needed to be sent in Vehicle to Infrastructure (V2I) communication. This approach effectively reduces the wireless communication overhead in V2I communication

A Prospective Look: Key Enabling Technologies, Applications and Open Research Topics in 6G Networks

The fifth generation (5G) mobile networks are envisaged to enable a plethora of breakthrough advancements in wireless technologies, providing support of a diverse set of services over a single platform. While the deployment of 5G systems is scaling up globally, it is time to look ahead for beyond 5G systems. This is driven by the emerging societal trends, calling for fully automated systems and intelligent services supported by extended reality and haptics communications. To accommodate the stringent requirements of their prospective applications, which are data-driven and defined by extremely low-latency, ultra-reliable, fast and seamless wireless connectivity, research initiatives are currently focusing on a progressive roadmap towards the sixth generation (6G) networks. In this article, we shed light on some of the major enabling technologies for 6G, which are expected to revolutionize the fundamental architectures of cellular networks and provide multiple homogeneous artificial intelligence-empowered services, including distributed communications, control, computing, sensing, and energy, from its core to its end nodes. Particularly, this paper aims to answer several 6G framework related questions: What are the driving forces for the development of 6G? How will the enabling technologies of 6G differ from those in 5G? What kind of applications and interactions will they support which would not be supported by 5G? We address these questions by presenting a profound study of the 6G vision and outlining five of its disruptive technologies, i.e., terahertz communications, programmable metasurfaces, drone-based communications, backscatter communications and tactile internet, as well as their potential applications. Then, by leveraging the state-of-the-art literature surveyed for each technology, we discuss their requirements, key challenges, and open research problems

A prospective look: key enabling technologies, applications and open research topics in 6G networks

The fifth generation (5G) mobile networks are envisaged to enable a plethora of breakthrough advancements in wireless technologies, providing support of a diverse set of services over a single platform. While the deployment of 5G systems is scaling up globally, it is time to look ahead for beyond 5G systems. This is mainly driven by the emerging societal trends, calling for fully automated systems and intelligent services supported by extended reality and haptics communications. To accommodate the stringent requirements of their prospective applications, which are data-driven and defined by extremely low-latency, ultra-reliable, fast and seamless wireless connectivity, research initiatives are currently focusing on a progressive roadmap towards the sixth generation (6G) networks, which are expected to bring transformative changes to this premise. In this article, we shed light on some of the major enabling technologies for 6G, which are expected to revolutionize the fundamental architectures of cellular networks and provide multiple homogeneous artificial intelligence-empowered services, including distributed communications, control, computing, sensing, and energy, from its core to its end nodes. In particular, the present paper aims to answer several 6G framework related questions: What are the driving forces for the development of 6G? How will the enabling technologies of 6G differ from those in 5G? What kind of applications and interactions will they support which would not be supported by 5G? We address these questions by presenting a comprehensive study of the 6G vision and outlining seven of its disruptive technologies, i.e., mmWave communications, terahertz communications, optical wireless communications, programmable metasurfaces, drone-based communications, backscatter communications and tactile internet, as well as their potential applications. Then, by leveraging the state-of-the-art literature surveyed for each technology, we discuss the associated requirements, key challenges, and open research problems. These discussions are thereafter used to open up the horizon for future research directions

Device-Agnostic Millimeter Wave Beam Selection using Machine Learning

Most research in the area of machine learning-based user beam selection considers a structure where the model proposes appropriate user beams. However, this design requires a specific model for each user-device beam codebook, where a model learned for a device with a particular codebook can not be reused for another device with a different codebook. Moreover, this design requires training and test samples for each antenna placement configuration/codebook. This paper proposes a device-agnostic beam selection framework that leverages context information to propose appropriate user beams using a generic model and a post processing unit. The generic neural network predicts the potential angles of arrival, and the post processing unit maps these directions to beams based on the specific device's codebook. The proposed beam selection framework works well for user devices with antenna configuration/codebook unseen in the training dataset. Also, the proposed generic network has the option to be trained with a dataset mixed of samples with different antenna configurations/codebooks, which significantly eases the burden of effective model training.Comment: 30 pages, 19 figures. This article was submitted to IEEE Trans. Wirel. Commun. on Nov 14 202