Adaptive Multicell 3D Beamforming in Multi-Antenna Cellular Networks

We consider a cellular network with multi-antenna base stations (BSs) and single-antenna users, multicell cooperation, imperfect channel state information, and directional antennas each with a vertically adjustable beam. We investigate the impact of the elevation angle of the BS antenna pattern, denoted as tilt, on the performance of the considered network when employing either a conventional single-cell transmission or a fully cooperative multicell transmission. Using the results of this investigation, we propose a novel hybrid multicell cooperation technique in which the intercell interference is controlled via either cooperative beamforming in the horizontal plane or coordinated beamfroming in the vertical plane of the wireless channel, denoted as adaptive multicell 3D beamforming. The main idea is to divide the coverage area into two disjoint vertical regions and adapt the multicell cooperation strategy at the BSs when serving each region. A fair scheduler is used to share the time-slots between the vertical regions. It is shown that the proposed technique can achieve performance comparable to that of a fully cooperative transmission but with a significantly lower complexity and signaling requirements. To make the performance analysis computationally efficient, analytical expressions for the user ergodic rates under different beamforming strategies are also derived.Comment: Accepted for publication in IEEE Transaction on Vehicular Technolog

Channel Reciprocity Attacks Using Intelligent Surfaces with Non-Diagonal Phase Shifts

While reconfigurable intelligent surface (RIS) technology has been shown to provide numerous benefits to wireless systems, in the hands of an adversary such technology can also be used to disrupt communication links. This paper describes and analyzes an RIS-based attack on multi-antenna wireless systems that operate in time-division duplex mode under the assumption of channel reciprocity. In particular, we show how an RIS with a non-diagonal (ND) phase shift matrix (referred to here as an ND-RIS) can be deployed to maliciously break the channel reciprocity and hence degrade the downlink network performance. Such an attack is entirely passive and difficult to detect and counteract. We provide a theoretical analysis of the degradation in the sum ergodic rate that results when an arbitrary malicious ND-RIS is deployed and design an approach based on the genetic algorithm for optimizing the ND structure under partial knowledge of the available channel state information. Our simulation results validate the analysis and demonstrate that an ND-RIS channel reciprocity attack can dramatically reduce the downlink throughput

A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks

Reconfigurable intelligent surfaces (RISs) have received considerable attention as a key enabler for envisioned 6G networks, for the purpose of improving the network capacity, coverage, efficiency, and security with low energy consumption and low hardware cost. However, integrating RISs into the existing infrastructure greatly increases the network management complexity, especially for controlling a significant number of RIS elements. To unleash the full potential of RISs, efficient optimization approaches are of great importance. This work provides a comprehensive survey on optimization techniques for RIS-aided wireless communications, including model-based, heuristic, and machine learning (ML) algorithms. In particular, we first summarize the problem formulations in the literature with diverse objectives and constraints, e.g., sum-rate maximization, power minimization, and imperfect channel state information constraints. Then, we introduce model-based algorithms that have been used in the literature, such as alternating optimization, the majorization-minimization method, and successive convex approximation. Next, heuristic optimization is discussed, which applies heuristic rules for obtaining low-complexity solutions. Moreover, we present state-of-the-art ML algorithms and applications towards RISs, i.e., supervised and unsupervised learning, reinforcement learning, federated learning, graph learning, transfer learning, and hierarchical learning-based approaches. Model-based, heuristic, and ML approaches are compared in terms of stability, robustness, optimality and so on, providing a systematic understanding of these techniques. Finally, we highlight RIS-aided applications towards 6G networks and identify future challenges.Comment: This paper has been accepted by IEEE Communications Surveys and Tutorial

CHANNEL MODELING FOR FIFTH GENERATION CELLULAR NETWORKS AND WIRELESS SENSOR NETWORKS

In view of exponential growth in data traffic demand, the wireless communications industry has aimed to increase the capacity of existing networks by 1000 times over the next 20 years. A combination of extreme cell densification, more bandwidth, and higher spectral efficiency is needed to support the data traffic requirements for fifth generation (5G) cellular communications. In this research, the potential improvements achieved by using three major 5G enabling technologies (i.e., small cells, millimeter-wave spectrum, and massive MIMO) in rural and urban environments are investigated. This work develops SPM and KA-based ray models to investigate the impact of geometrical parameters on terrain-based multiuser MIMO channel characteristic. Moreover, a new directional 3D channel model is developed for urban millimeter-wave (mmW) small cells. Path-loss, spatial correlation, coverage distance, and coherence length are studied in urban areas. Exploiting physical optics (PO) and geometric optics (GO) solutions, closed form expressions are derived for spatial correlation. Achievable spatial diversity is evaluated using horizontal and vertical linear arrays as well as planar 2D arrays. In another study, a versatile near-ground field prediction model is proposed to facilitate accurate wireless sensor network (WSN) simulations. Monte Carlo simulations are used to investigate the effects of antenna height, frequency of operation, polarization, and terrain dielectric and roughness properties on WSNs performance

Navigation coopérative de véhicules autonomes basée sur la communication V2X dans un réseau de 5ème génération

In today’s world, road transport is essential to our daily routines and business activities. However, the exponential growth in the number of vehicles has led to problems such as traffic congestion and road accidents. Vehicular communication presents an innovative solution, envisaging a future where vehicles communicate with each other, the road infrastructure, and even the road itself, sharing real-time data to optimize traffic flow and enhance safety. This thesis focuses on 5G and Beyond 5G (B5G) technologies, which promise to revolutionize Vehicle-to-Everything (V2X) communication. With the emergence of millimeter-wave (mmWave) communication, high-speed, low-latency data transmission is essential for vehicular networks. However, mmWave communication faces problems with signal attenuation and interference. Our research focuses on solving these problems using a deep learning-based approach. Three significant contributions are proposed. First, we introduce a classical optimization technique, the simulated annealing algorithm, to improve beam alignment in 5G vehicular networks. This reduces latency and improves data transmission between millimeter-wave base stations and vehicles. Our second contribution is a new approach involving a hybrid deep-learning model that predicts optimal beam angles. Combining a 1D CNN and a BiLSTM improves th accuracy of the prediction and reduces errors. This approach eliminates time-consuming computations and iterations critical to the success of B5G vehicular networks. The third contribution introduces a BiLSTM-based model to select the optimal beam pair angles at the mmWave base station (mmBS) and the moving vehicle side. This approach improves the reliability of data transmission while minimizing the error probabilities and overheads during beam search. This research contributes to advancing vehicular communications, offering innovative solutions for 5G and B5G networks. We aim to enhance the efficiency, reduce the latency, and improve the reliability of communications for connected vehicles. This thesis explores beam alignment through classical and deep learning techniques and presents solutions for the challenges of millimeter-wave vehicular networks. Our research provides the foundation for the next generation of vehicular communication and its vital role in making road transport safer and more efficient

Keilaavan millimetriaaltoradiolinkin suuntaaminen ja seuraaminen

In order to provide high-throughput mobile broadband in a dense urban information society, upcoming cellular networks will finally employ the under-utilized millimeter-wave (mmW) frequencies. The challenging mmW radio environment, however, necessitates massive cell densification with wireless backhauling using very directional links. This thesis investigates how these links between access points may be aligned efficiently, and how alignment reflects the network organization. The work provides a thorough presentation of different high-level aspects and background information required when designing a mmW small cell system. In terms of alignment functionality, both automatic link establishment and proactive tracking are considered. Additionally, the presentation includes an overview of beam steerable antennas, mmW propagation in urban environments, and network organization. The thesis further specifies requirements, proposes possible approaches and compares those with existing implementations. Most of existing mmW beam alignment solutions are intended for short-range indoor communications and do not address the issues in cellular systems. While existing functionality considers only a single link between two devices, efficient design should consider both the entire network and the underlying phenomena. The devices should further exploit the existing network infrastructure, location and orientation information, and the concepts of machine learning. Even though the world has recently seen advancements in the related fields, there is still much work to be done before commercial deployment is possible.Seuraavan sukupolven matkaviestinjärjestelmien erittäin nopeissa datayhteyksissä tullaan hyödyntämään millimetriaaltoteknologiaa. Näillä taajuuksilla radioympäristö on kuitenkin hyvin haastava, mikä edellyttää verkon solutiheyden moninkertaistamista, täysin langattomia tukiasemia ja erittäin suuntaavia antenneja. Tässä diplomityössä tutkitaan eri keinoja kuinka tukiasemien väliset linkit kohdistetaan tehokkaasti, ja miten se vaikuttaa verkon rakenteeseen ja hallintaan. Työ tarjoaa kattavan taustaselvityksen mm-aaltosoluverkon toteuttamiseen tarvittavista asioista. Keilanohjausta tarkastellaan sekä verkon automaattisen laajentamisen että kohteen aktiivisen seurauksen kannalta. Tämän lisäksi työssä tutkitaan keilattavia antenneja, mm-aaltojen etenemistä kaupunkiympäristöissä ja verkkorakennetta. Näiden lisäksi työssä rajataan edellytykset, esitetään mahdollisia ratkaisuja, ja vertaillaan näitä olemassa oleviin toteutuksiin. Nykyiset keilaustoteutukset ovat pääasiassa suunniteltu lyhyen kantaman sisäyhteyksille, eivätkä siten vastaa ongelman asettelua. Aikaisempi toiminnallisuus keskittyy yhteen ainoaan linkkiin vaikka tehokas toteutus huomioisi koko järjestelmän kohdistusongelman fysikaalista perustaa unohtamatta. Verkkolaitteiden tulisi hyödyntää olemassa olevaa radioverkkoa, sekä paikka- että suuntatietoja, ja koneoppimisen keinoja. Vaikka aiheeseen liittyvä teknologia on kehittynyt viime vuosina harppauksin, mm-aaltosoluverkot ovat kaikkea muuta kuin valmiita markkinoille

1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface

A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance

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