Rahim Tafazolli

Holographic Beamforming is a promising concept to reduce the power consumption of Multiple Input Multiple Output (MIMO) antenna arrays. In a holographic approach, the impedance of antenna patches is varied through the inclusion of tuning elements, such as varactor diodes, which allow electronic control of the phase and amplitude of each antenna. In this work, we provide the electromagnetic framework for the design of a Holographic MIMO Surface (HMIMOS). We analyze its performance and compare its power consumption to passive Reconfigurable Intelligent Surfaces (RIS) and MIMO Active Phased Arrays (APA) at 5G Frequency Range (FR) 2. The results show that the power consumption of HMIMOS is lower than of MIMO APAs, but significantly higher than of RISs. However, a combination of active and passive elements on a RIS can offer many benefits in terms of environmental awareness and intelligence for Integrated Sensing and Communication (ISAC) in Beyond 5G (B5G) networks.

Substrate integrated waveguides (SIW) technology is employed to design a uniform long slot leaky-wave antenna (LWA) in millimeter-wave (mmWave) band. The structure is then loaded by a 3D printed sinusoidal periodic pattern of Photopolymer VeroClear dielectric. This makes a periodic LWA which means that it is possible to regulate a desired higher order space harmonic to form the beam and to tilt it to the direction of interest. To showcase the practicality of method, the dielectric pattern is designed in such a way that the beam is tilted to the backward-quadrant at θm = −15 • at f = 35 GHz. The structure is fabricated and the S-parameters are measured which shows a good agreement with the simulated results. Index Terms—leaky wave antenna (LWA), 3D printing, sub-strate integrated waveguide (SIW), periodic structure.

Integrating Low Earth Orbit (LEO) satellites with terrestrial network infrastructures to support ubiquitous Inter-net service coverage has recently received increasing research momentum. One fundamental challenge is the frequent topology change caused by the constellation behaviour of LEO satellites. In the context of Software Defined Networking (SDN), the controller function that is originally required to control the conventional data plane fulfilled by terrestrial SDN switches will need to expand its responsibility to cover their counterparts in the space, namely LEO satellites that are used for data forwarding. As such, seamless integration of the fixed control plane on the ground and the mobile data plane fulfilled by constellation LEO satellites will become a distinct challenge. For the very first time in the literature, we propose in this paper the Virtual Data-Plane Addressing (VDPA) scheme by leveraging IP addresses to represent virtual switches at the fixed space locations which are periodically instantiated by the nested LEO satellites traversing them in a predictable manner. With such a scheme the changing data-plane network topology incurred by LEO satellite constellations can be made completely agnostic to the control plane on the ground, thus enabling a native approach to supporting seamless communication between the two planes. Our simulation results prove the superiority of the proposed VDPA based flow rule manipulation mechanism in terms of control plane performance.

The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation usingML in O-RAN.We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support forML techniques. The survey then explores challenges in network automation usingML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects whereML techniques can benefit.

Data discovery for sensor data in an M2M network uses probabilistic models, such as Gaussian Mixing Models (GMMs) to represent attributes of the sensor data. The parameters of the probabilistic models can be provided to a discovery server (DS) that respond to queries concerning the sensor data. Since the parameters are compressed compared to the attributes of the sensor data itself, this can simplify the distribution of discovery data. A hierarchical arrangement of discovery servers can also be used with multiple levels of discovery servers where higher level discovery servers using more generic probabilistic models.