251 research outputs found
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Line-of-Sight Detection for 5G Wireless Channels
With the rapid deployment of 5G wireless networks across the globe, precise positioning has become essential for many vertical industries reliant on 5G. The predominantly non-line-of-sight (NLOS) propagation instigated by the obstacles in the surrounding environment, especially in metro city areas, has made it particularly difficult to achieve high estimation accuracy for positioning algorithms that necessitate direct line-of-sight (LOS) transmission. In this scenario, correctly identifying the line-of-sight condition has become extremely crucial in precise positioning algorithms based on 5G. Even though numerous scientific studies have been conducted on LOS identification in the existing literature, most of these research works are based on either ultra-wideband or Wi-Fi networks. Therefore, this thesis focuses on this hitherto less investigated area of line-of-sight detection for 5G wireless channels.
This thesis examines the feasibility of LOS detection using three widely used channel models, the Tapped Delay Line (TDL), the Clustered Delay Line (CDL), and the Winner II channel models. The 5G-based simulation environment was constructed with standard parameters based on 3GPP specifications using MATLAB computational platform for the research. LOS and NLOS channels were defined to transmit random signal samples for each channel model where the received signal was subjected to Additive White Gaussian Noise (AWGN), imitating the authentic propagation environment. Variable channel conditions were simulated by randomly alternating the signal-to-noise ratio (SNR) of the received signal.
The research mainly focuses on machine learning (ML) based LOS classification. Additionally, the threshold-based hypothesis was also deployed for the same scenarios as a benchmark. The main objectives of the thesis were to find the statistical features or the combination of statistical features of the channel impulse response (CIR) of the received signal, which provide the best results and to identify the most effective machine learning method for LOS/NLOS classification. Furthermore, the results were verified through actual measurement samples obtained during the NewSense project.
The results indicate that the time-correlation feature of the channel impulse response used in isolation would be effective in LOS identification for 5G wireless channels. Additional derived features of the CIR do not significantly increase the classification accuracy. Positioning Reference Signals (PRS) were found to be more appropriate than Sounding Reference Signals (SRS) for LOS/NLOS classification. The study reinforced the significance of selecting the most suitable machine learning algorithm and kernel function as relevant for the task of obtaining the best results. The medium Gaussian support vector machines ML algorithm provided the overall highest precision in LOS classification for simulated data with up to 98% accuracy for the Winner II channel model with PRS. The machine learning algorithms proved to be considerably more effective than conventional threshold-based detection for both simulated and real measurement data. Additionally, the Winner II model with the richest features presented the best results compared with CDL and TDL channel models
Towards addressing training data scarcity challenge in emerging radio access networks: a survey and framework
The future of cellular networks is contingent on artificial intelligence (AI) based automation, particularly for radio access network (RAN) operation, optimization, and troubleshooting. To achieve such zero-touch automation, a myriad of AI-based solutions are being proposed in literature to leverage AI for modeling and optimizing network behavior to achieve the zero-touch automation goal. However, to work reliably, AI based automation, requires a deluge of training data. Consequently, the success of the proposed AI solutions is limited by a fundamental challenge faced by cellular network research community: scarcity of the training data. In this paper, we present an extensive review of classic and emerging techniques to address this challenge. We first identify the common data types in RAN and their known use-cases. We then present a taxonomized survey of techniques used in literature to address training data scarcity for various data types. This is followed by a framework to address the training data scarcity. The proposed framework builds on available information and combination of techniques including interpolation, domain-knowledge based, generative adversarial neural networks, transfer learning, autoencoders, fewshot learning, simulators and testbeds. Potential new techniques to enrich scarce data in cellular networks are also proposed, such as by matrix completion theory, and domain knowledge-based techniques leveraging different types of network geometries and network parameters. In addition, an overview of state-of-the art simulators and testbeds is also presented to make readers aware of current and emerging platforms to access real data in order to overcome the data scarcity challenge. The extensive survey of training data scarcity addressing techniques combined with proposed framework to select a suitable technique for given type of data, can assist researchers and network operators in choosing the appropriate methods to overcome the data scarcity challenge in leveraging AI to radio access network automation
Analysis and Design of Algorithms for the Improvement of Non-coherent Massive MIMO based on DMPSK for beyond 5G systems
Mención Internacional en el tÃtulo de doctorNowadays, it is nearly impossible to think of a service that does not rely on wireless communications.
By the end of 2022, mobile internet represented a 60% of the total global online traffic.
There is an increasing trend both in the number of subscribers and in the traffic handled by each
subscriber. Larger data rates, smaller extreme-to-extreme (E2E) delays and greater number of
devices are current interests for the development of mobile communications. Furthermore, it
is foreseen that these demands should also be fulfilled in scenarios with stringent conditions,
such as very fast varying wireless communications channels (either in time or frequency) or
scenarios with power constraints, mainly found when the equipment is battery powered.
Since most of the wireless communications techniques and standards rely on the fact that the
wireless channel is somehow characterized or estimated to be pre or post-compensated in transmission
(TX) or reception (RX), there is a clear problem when the channels vary rapidly or the
available power is constrained. To estimate the wireless channel and obtain the so-called channel
state information (CSI), some of the available resources (either in time, frequency or any
other dimension), are utilized by including known signals in the TX and RX typically known as
pilots, thus avoiding their use for data transmission. If the channels vary rapidly, they must be
estimated many times, which results in a very low data efficiency of the communications link.
Also, in case the power is limited or the wireless link distance is large, the resulting signal-tointerference-
plus-noise ratio (SINR) will be low, which is a parameter that is directly related to
the quality of the channel estimation and the performance of the data reception. This problem
is aggravated in massive multiple-input multiple-output (massive MIMO), which is a promising
technique for future wireless communications since it can increase the data rates, increase the
reliability and cope with a larger number of simultaneous devices. In massive MIMO, the base
station (BS) is typically equipped with a large number of antennas that are coordinated. In these
scenarios, the channels must be estimated for each antenna (or at least for each user), and thus,
the aforementioned problem of channel estimation aggravates. In this context, algorithms and
techniques for massive MIMO without CSI are of interest.
This thesis main topic is non-coherent massive multiple-input multiple-output (NC-mMIMO)
which relies on the use of differential M-ary phase shift keying (DMPSK) and the spatial
diversity of the antenna arrays to be able to detect the useful transmitted data without CSI knowledge. On the one hand, hybrid schemes that combine the coherent and non-coherent
schemes allowing to get the best of both worlds are proposed. These schemes are based on
distributing the resources between non-coherent (NC) and coherent data, utilizing the NC data
to estimate the channel without using pilots and use the estimated channel for the coherent
data. On the other hand, new constellations and user allocation strategies for the multi-user
scenario of NC-mMIMO are proposed. The new constellations are better than the ones in the
literature and obtained using artificial intelligence techniques, more concretely evolutionary
computation.This work has received funding from the European Union Horizon 2020 research and innovation
programme under the Marie Skłodowska-Curie ETN TeamUp5G, grant agreement No.
813391. The PhD student was the Early Stage Researcher (ESR) number 2 of the project.
This work has also received funding from the Spanish National Project IRENE-EARTH
(PID2020-115323RB-C33) (MINECO/AEI/FEDER, UE), which funded the work of some coauthors.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Luis Castedo Ribas.- Secretario: Matilde Pilar Sánchez Fernández.- Vocal: Eva Lagunas Targaron
Designing data-aided demand-driven user-centric architecture for 6G and beyond networks
Despite advancements in capacity-enhancing technologies like massive MIMO (multiple input, multiple output) and intelligent reflective surfaces, network densification remains crucial for significant capacity gains in future networks such as 6G. However, network densification increases interference and power consumption. Traditional cellular architectures struggle to minimize these without compromising service quality or capacity, which necessitates a shift to a user-centric radio access network (UC-RAN).
The UC-RAN approach offers additional degrees of freedom to ease the spectral-energy efficiency interlock while improving the service quality. However, its increased degrees of freedom make its optimal design and operation more challenging. This dissertation introduces four novel approaches for UC-RAN optimal design and operation. The objectives include mitigating interference, reducing power consumption, ensuring diverse user/vertical service quality, facilitating proactive network operation, risk-aware optimization, adopting an open radio access network, and enabling universal coverage.
First, we construct an analytical framework to assess the effects of incorporating Coordinated Multipoint (CoMP) technology into UC-RAN to reduce interference and power consumption. We use stochastic geometry tools to derive expressions for network-wide coverage, spectral efficiency, and energy efficiency as a function of UC-RAN Configuration and Optimization Parameters (COPs), including data base station densities and user-centric service zone sizes.
While the analytical framework provides insightful performance analysis that can guide overall system design, it cannot fully capture the dynamics of a UC-RAN system to enable optimal operation. Next, we present a Deep Reinforcement Learning (DRL) based method to dynamically orchestrate the UC-RAN service zone size to satisfy varying application demands of various service verticals during its operation. We define a novel multi-objective optimization problem that fairly optimizes otherwise conflicting key performance indicators (KPIs).
DRL's practical adaptation by the industry remains thwarted by the risk it poses to the safe operation of a live network. To address this challenge, we propose a digital twin-enabled approach to enrich the DRL-based optimization framework, ensuring risk-aware COP optimization. We use Open Radio Access Network standards-based simulations to show that the proposed risk-aware DRL framework can maximize system-level KPIs while maintaining safe operational requirements.
Lastly, we propose a hybrid model of aerial and terrestrial UC-RAN deployment to ensure universal coverage. We assess the impact of aerial base station parameters on system-level KPIs, providing a quantitative analysis of the advantages of a hybrid over a solely terrestrial UC-RAN. We develop a robust multi-objective function solvable via our DRL-based framework to balance and optimize these KPIs in a hybrid UC-RAN.
Our extensive analytical and system-level simulation results suggest that these contributions can foster the much-needed paradigm shift towards demand-driven, elastic, and user-centric architecture in emerging and future cellular networks
Security and Privacy for Modern Wireless Communication Systems
The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks
Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities
Smart cities demand resources for rich immersive sensing, ubiquitous
communications, powerful computing, large storage, and high intelligence
(SCCSI) to support various kinds of applications, such as public safety,
connected and autonomous driving, smart and connected health, and smart living.
At the same time, it is widely recognized that vehicles such as autonomous
cars, equipped with significantly powerful SCCSI capabilities, will become
ubiquitous in future smart cities. By observing the convergence of these two
trends, this article advocates the use of vehicles to build a cost-effective
service network, called the Vehicle as a Service (VaaS) paradigm, where
vehicles empowered with SCCSI capability form a web of mobile servers and
communicators to provide SCCSI services in smart cities. Towards this
direction, we first examine the potential use cases in smart cities and
possible upgrades required for the transition from traditional vehicular ad hoc
networks (VANETs) to VaaS. Then, we will introduce the system architecture of
the VaaS paradigm and discuss how it can provide SCCSI services in future smart
cities, respectively. At last, we identify the open problems of this paradigm
and future research directions, including architectural design, service
provisioning, incentive design, and security & privacy. We expect that this
paper paves the way towards developing a cost-effective and sustainable
approach for building smart cities.Comment: 32 pages, 11 figure
- …