Islam Fayez Abd Alyafawi

Network Store: Exploring Slicing in Future 5G Networks

In this paper, we provide a revolutionary vision of 5G networks, in which SDN technologies are used for the programmability of the wireless network, and where a NFV-ready network store is provided to Mobile Network Operators (MNO), Enterprises, and Over-The-Top (OTT) third parties. The proposed network serves as a digital distribution platform of programmable Virtualized Network Functions (VNFs) that enables 5G application use-cases. Currently existing application stores, such as Apples App Store for iOS applications, Googles Play Store for Android, or Ubuntus Software Center, deliver applications to user specific software platforms. Our vision is to provide a digital marketplace, gathering 5G enabling Network Applications and Network Functions, written to run on top of commodity cloud infrastructures, connected to remote radio heads (RRH). The 5G Network Store will be the same to the network provider as the application store is currently to a software platform.

Critical issues of centralized and cloudified LTE-FDD Radio Access Networks

Cloudification of the Centralized-Radio Access Network (C-RAN) in which signal processing runs on general purpose processors inside virtual machines has lately received significant attention. Due to short deadlines in the LTE frequency division duplex access method, processing time fluctuations introduced by the virtualization process have a deep impact on C-RAN performance. This paper evaluates bottlenecks of the OpenAirInterface (OAI is an open-source software-based implementation of LTE) cloud performance, provides feasibility studies on C-RAN execution, and introduces recommendations for cloud architecture that significantly reduces the encountered execution problems. In typical cloud environments, the OAI processing time deadlines cannot be guaranteed. Our proposed cloud architecture shows good characteristics for OAI cloud execution. As an example, in our setup more than 99.5% processed LTE subframes reach reasonable processing deadlines close to performance of a dedicated machine of a single core CPU.

Opportunistic routing for multi-flow video dissemination over Flying Ad-Hoc Networks

A reliable and robust routing service for Flying Ad-Hoc Networks (FANETs) must be able to adapt to topology changes. User experience on watching live video sequences must also be satisfactory even in scenarios with buffer overflow and high packet loss ratio. In this paper, we introduce a Cross-layer Link quality and Geographical-aware beaconless opportunistic routing protocol (XLinGO). It enhances the transmission of simultaneous multiple video flows over FANETs by creating and keeping reliable persistent multi-hop routes. XLinGO considers a set of cross-layer and human-related information for routing decisions, as performance metrics and Quality of Experience (QoE). Performance evaluation shows that XLinGO achieves multimedia dissemination with QoE support and robustness in a multi-hop, multi-flow, and mobile network environments.

Experimental comparison of bluetooth and wifi signal propagation for indoor localisation

Systems for indoor positioning using radio technologies are largely studied due to their convenience and the market opportunities they offer. The positioning algorithms typically derive geographic coordinates from observed radio signals and hence good understanding of the indoor radio channel is required. In this paper we investigate several factors that affect signal propagation indoors for both Bluetooth and WiFi. Our goal is to investigate which factors can be disregarded and which should be considered in the development of a positioning algorithm. Our results show that technical factors such as device characteristics have smaller impact on the signal than multipath propagation. Moreover, we show that propagation conditions differ in each direction. We also noticed that WiFi and Bluetooth, despite operating in the same radio band, do not at all times exhibit the same behaviour.

Real-time passive capturing of the GSM radio

This paper addresses the problem of service development based on GSM handset signaling. The aim is to achieve this goal without the participation of the users, which requires the use of a passive GSM receiver on the uplink. Since no tool for GSM uplink capturing was available, we developed a new method that can synchronize to multiple mobile devices by simply overhearing traffic between them and the network. Our work includes the implementation of modules for signal recovery, message reconstruction and parsing. The method has been validated against a benchmark solution on GSM downlink and independently evaluated on uplink channels. Initial evaluations show up to 99% success rate in message decoding, which is a very promising result. Moreover, we conducted measurements that reveal insights on the impact of signal power on the capturing performance and investigate possible reactive measures.

SDR-based passive indoor localization system for GSM

This study deals with indoor positioning using GSM radio, which has the distinct advantage of wide coverage over other wireless technologies. In particular, we focus on passive localization systems that are able to achieve high localization accuracy without any prior knowledge of the indoor environment or the tracking device radio settings. In order to overcome these challenges, newly proposed localization algorithms based on the exploitation of the received signal strength (RSS) are proposed. We explore the effects of non-line-of-sight communication links, opening and closing of doors, and human mobility on RSS measurements and localization accuracy. We have implemented the proposed algorithms on top of software defined radio systems and carried out detailed empirical indoor experiments. The performance results show that the proposed solutions are accurate with average localization errors between 2.4 and 3.2 meters.

Towards a Cloud-Native Radio Access Network

Commoditization and virtualization of wireless networks are changing the economics of mobile networks to help network providers, e.g. Mobile Network Operator (MNO), Mobile Virtual Network Operator (MVNO), move from proprietary and bespoke hardware and software platforms towards an open, cost-effective, and flexible cellular ecosystem. In addition, rich and innovative local services can be efficiently materialized through cloudification by leveraging the existing infrastructure. In this work, we present a Radio Access Network as a Service (RANaaS), in which a Cloudified Centralized Radio Access Network (C-RAN) is delivered as a service. RANaaS describes the service life-cycle of an on-demand, elastic, and pay as you go RAN instantiated on top of the cloud infrastructure. Due to short deadlines in many examples of RAN, the fluctuations of processing time, introduced by the virtualization framework, have a deep impact on the C-RAN performance. While in typical cloud environments, the deadlines of processing time cannot be guaranteed, the cloudification of C-RAN, in which signal processing runs on general purpose processors inside Virtual Machines (VMs), is a challenging subject. We describe an example of real-time cloudified LTE network deployment using the OpenAirInterface (OAI) LTE implementation and OpenStack running on commodity hardware. We also show the flexibility and performance of the platform developed. Finally, we draw general conclusions on the RANaaS provisioning problem in future 5G networks.

High-Performance Wideband SDR Channelizers

The essential process to analyze signals from multicarrier communication systems is to isolate independent communication channels using a channelizer. To implement a channelizer in software-defined radio systems, the Polyphase Filterbank (PFB) is commonly used. For real-time applications, the PFB has to process the digitized signal faster or equal to its sampling rate. Depending on the underlying hardware, PFB can run on a CPU, a Graphical Processing Unit (GPU), or even a Field-Programmable Gate Arrays (FPGA). CPUs and GPUs are more reconfigurable and scalable platforms than FPGAs. In this paper, we optimize an existing implementation of a CPU-based channelizer and implement a novel GPU-based channelizer. Our proposed solutions deliver an overall improvement of 30 % for the CPU optimization on Intel Core i7-4790 @ 3.60 GHz, and a 3.2-fold improvement for the GPU implementation on AMD R9 290, when compared to the original CPU-based implementation.

Robust indoor localization of narrowband signals

Many location-based services target users in indoor environments. Similar to the case of dense urban areas where many obstacles exist, indoor localization techniques suffer from outlying measurements caused by severe multipath propagation and non-line-of-sight (NLOS) reception. Obstructions in the signal path caused by static or mobile objects downgrade localization accuracy. We use robust multipath mitigation techniques to detect and filter out outlying measurements in indoor environments. We validate our approach using a power-based localization system with GSM. We conducted experiments without any prior knowledge of the tracked devices radio settings or the indoor radio environment. We obtained localization errors in the range of 3m even if the sensors had NLOS links to the target device.

Towards self-learning radio-based localization systems

Location-awareness indoors will be an inseparable feature of mobile services/applications in future wireless networks. Its current ubiquitous availability is still obstructed by technological challenges and privacy issues. We propose an innovative approach towards the concept of indoor positioning with main goal to develop a system that is self-learning and able to adapt to various radio propagation environments. The approach combines estimation of propagation conditions, subsequent appropriate channel modelling and optimisation feedback to the used positioning algorithm. Main advantages of the proposal are decreased system set-up effort, automatic re-calibration and increased precision.