Ubiquitous Cell-Free Massive MIMO Communications

Since the first cellular networks were trialled in the 1970s, we have witnessed an incredible wireless revolution. From 1G to 4G, the massive traffic growth has been managed by a combination of wider bandwidths, refined radio interfaces, and network densification, namely increasing the number of antennas per site. Due its cost-efficiency, the latter has contributed the most. Massive MIMO (multiple-input multiple-output) is a key 5G technology that uses massive antenna arrays to provide a very high beamforming gain and spatially multiplexing of users, and hence, increases the spectral and energy efficiency. It constitutes a centralized solution to densify a network, and its performance is limited by the inter-cell interference inherent in its cell-centric design. Conversely, ubiquitous cell-free Massive MIMO refers to a distributed Massive MIMO system implementing coherent user-centric transmission to overcome the inter-cell interference limitation in cellular networks and provide additional macro-diversity. These features, combined with the system scalability inherent in the Massive MIMO design, distinguishes ubiquitous cell-free Massive MIMO from prior coordinated distributed wireless systems. In this article, we investigate the enormous potential of this promising technology while addressing practical deployment issues to deal with the increased back/front-hauling overhead deriving from the signal co-processing.Comment: Published in EURASIP Journal on Wireless Communications and Networking on August 5, 201

Assessment of cockpit interface concepts for data link retrofit

The problem is examined of retrofitting older generation aircraft with data link capability. The approach taken analyzes requirements for the cockpit interface, based on review of prior research and opinions obtained from subject matter experts. With this background, essential functions and constraints for a retrofit installation are defined. After an assessment of the technology available to meet the functions and constraints, candidate design concepts are developed. The most promising design concept is described in detail. Finally, needs for further research and development are identified

A Survey of Medium Access Mechanisms for Providing QoS in Ad-Hoc Networks

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Wi-Fi QoS improvements for industrial automation

Digitalization caused a considerable increase in the use of industrial automation applications. Industrial automation applications use real-time traffic with strict requirements of connection of tens of devices, high-reliability, determinism, low-latency, and synchronization. The current solutions meeting these requirements are wired technologies. However, there is a need for wireless technologies for mobility,less complexity, and quick deployment. There are many studies on cellular technologies for industrial automation scenarios with strict reliability and latency requirements, but not many developments for wireless communications over unlicensed bands. Wireless Fidelity (Wi-Fi) is a commonly used and preferred technology in factory automation since it is supported by many applications and operates on a license free-band. However, there is still room for improving Wi-Fi systems performance for low-latency and high-reliable communication requirements in industrial automation use cases. There are various limitations in the current Wi-Fi system restraining the deployment for time-critical operations. For meeting the strict timing requirements of low delay and jitter in industrial automation applications, Quality of Service (QoS)in Wi-Fi needs to be improved. In this thesis, a new access category in Medium Access Control (MAC) layer for industrial automation applications is proposed.The performance improvement is analyzed with simulations, and a jitter definition for a Wi-Fi system is studied. Then, a fixed Modulation and Coding (MCS) link adaptation method and bounded delay is implemented for time-critical traffic in the simulation cases to observe performance changes. Finally, it is shown that the new access category with no backoff time can decrease the delay and jitter of time-critical applications. The improvements in Wi-Fi QoS are shown in comparison with the current standard, and additional enhancements about using a fixed modulation and coding scheme and implementation of a bounded delay are also analyzed in this thesi

An Intelligent Multi-stage Channel Acquisition Model for CR-WBANs: A Context Aware Approach

Cognitive Radio (CR) came as a solution to mitigate challenges that wireless body area networks (WBANs) suffer from. CR is an intelligence-based technology that senses, observes, and learns from its operating environment to access licensed bands in the spectrum when they are not being utilized by primary users. Deploying a CR technology in WBANs applications, enhances spectrum scalability, increases system robustness, and decreases latency. Accordingly, CR-WBANs help in building a more efficient and reliable ubiquitous healthcare system than conventional WBANs do. However, CR-WBANs are still evolving, and many challenges need to be investigated, in particular, is how to acquire a channel and prioritize data streams among multiple CR-users (i.e., multiple patients) based on the severity of their health status, in a manner to decrease network latency and increase network scalability. To address this challenge, this work proposes a novel intelligent channel acquisition model for multiple CR-WBANs within ubiquitous healthcare system, whereby contextual data, namely, channel properties, intra-node characteristics, and patients’ profile information, is integrated in channel acquisition decision process. The proposed work is a multi-stage fusion system that is composed of local and global decisions units. A fuzzy logic system is utilized to make decisions in the local unit, which are sensing the channel availability and assessing the severity of patients' health status. Moreover, a neural network is employed as a global sensing decision center, whereby local sensing decisions, channel properties, and intra-node characteristics are augmented in the decision process. Furthermore, a cluster-based heuristic algorithm is formulated, in the global decision unit, to prioritize data streams among CR-users based on the criticality of their health conditions (i.e., acute, urgent, and normal). Patients' local health assessments and avatars (e.g., age, medical history, etc.) are exploited in the prioritization process

Comparative Evaluation of UMTS, WLAN, BWA, MBWA, and UWB Systems

UMTS, WLAN, BWA and UWB systems are compared in this paper. The comparative analysis covers system capacity, QoS, and radiowave propagation

Distributed Implementation of eXtended Reality Technologies over 5G Networks

Mención Internacional en el título de doctorThe revolution of Extended Reality (XR) has already started and is rapidly expanding as technology advances. Announcements such as Meta’s Metaverse have boosted the general interest in XR technologies, producing novel use cases. With the advent of the fifth generation of cellular networks (5G), XR technologies are expected to improve significantly by offloading heavy computational processes from the XR Head Mounted Display (HMD) to an edge server. XR offloading can rapidly boost XR technologies by considerably reducing the burden on the XR hardware, while improving the overall user experience by enabling smoother graphics and more realistic interactions. Overall, the combination of XR and 5G has the potential to revolutionize the way we interact with technology and experience the world around us. However, XR offloading is a complex task that requires state-of-the-art tools and solutions, as well as an advanced wireless network that can meet the demanding throughput, latency, and reliability requirements of XR. The definition of these requirements strongly depends on the use case and particular XR offloading implementations. Therefore, it is crucial to perform a thorough Key Performance Indicators (KPIs) analysis to ensure a successful design of any XR offloading solution. Additionally, distributed XR implementations can be intrincated systems with multiple processes running on different devices or virtual instances. All these agents must be well-handled and synchronized to achieve XR real-time requirements and ensure the expected user experience, guaranteeing a low processing overhead. XR offloading requires a carefully designed architecture which complies with the required KPIs while efficiently synchronizing and handling multiple heterogeneous devices. Offloading XR has become an essential use case for 5G and beyond 5G technologies. However, testing distributed XR implementations requires access to advanced 5G deployments that are often unavailable to most XR application developers. Conversely, the development of 5G technologies requires constant feedback from potential applications and use cases. Unfortunately, most 5G providers, engineers, or researchers lack access to cutting-edge XR hardware or applications, which can hinder the fast implementation and improvement of 5G’s most advanced features. Both technology fields require ongoing input and continuous development from each other to fully realize their potential. As a result, XR and 5G researchers and developers must have access to the necessary tools and knowledge to ensure the rapid and satisfactory development of both technology fields. In this thesis, we focus on these challenges providing knowledge, tools and solutiond towards the implementation of advanced offloading technologies, opening the door to more immersive, comfortable and accessible XR technologies. Our contributions to the field of XR offloading include a detailed study and description of the necessary network throughput and latency KPIs for XR offloading, an architecture for low latency XR offloading and our full end to end XR offloading implementation ready for a commercial XR HMD. Besides, we also present a set of tools which can facilitate the joint development of 5G networks and XR offloading technologies: our 5G RAN real-time emulator and a multi-scenario XR IP traffic dataset. Firstly, in this thesis, we thoroughly examine and explain the KPIs that are required to achieve the expected Quality of Experience (QoE) and enhanced immersiveness in XR offloading solutions. Our analysis focuses on individual XR algorithms, rather than potential use cases. Additionally, we provide an initial description of feasible 5G deployments that could fulfill some of the proposed KPIs for different offloading scenarios. We also present our low latency muti-modal XR offloading architecture, which has already been tested on a commercial XR device and advanced 5G deployments, such as millimeter-wave (mmW) technologies. Besides, we describe our full endto- end complex XR offloading system which relies on our offloading architecture to provide low latency communication between a commercial XR device and a server running a Machine Learning (ML) algorithm. To the best of our knowledge, this is one of the first successful XR offloading implementations for complex ML algorithms in a commercial device. With the goal of providing XR developers and researchers access to complex 5G deployments and accelerating the development of future XR technologies, we present FikoRE, our 5G RAN real-time emulator. FikoRE has been specifically designed not only to model the network with sufficient accuracy but also to support the emulation of a massive number of users and actual IP throughput. As FikoRE can handle actual IP traffic above 1 Gbps, it can directly be used to test distributed XR solutions. As we describe in the thesis, its emulation capabilities make FikoRE a potential candidate to become a reference testbed for distributed XR developers and researchers. Finally, we used our XR offloading tools to generate an XR IP traffic dataset which can accelerate the development of 5G technologies by providing a straightforward manner for testing novel 5G solutions using realistic XR data. This dataset is generated for two relevant XR offloading scenarios: split rendering, in which the rendering step is moved to an edge server, and heavy ML algorithm offloading. Besides, we derive the corresponding IP traffic models from the captured data, which can be used to generate realistic XR IP traffic. We also present the validation experiments performed on the derived models and their results.This work has received funding from the European Union (EU) Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie ETN TeamUp5G, grant agreement No. 813391.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Narciso García Santos.- Secretario: Fernando Díaz de María.- Vocal: Aryan Kaushi