Cognitive relay nodes for airborne LTE emergency networks

This paper is proposing a novel concept of Cognitive Relay Node for intelligently improving the radio coverage of an airborne LTE emergency network, considering the scenarios outlined in the ABSOLUTE research project. The proposed network model was simulated comparing the different cases of deploying relay nodes to complement the coverage of an aerial LTE network. Simulation results of the proposed Cognitive Relay Nodes show significant performance improvement in terms of radio coverage quantified by the regional outage probability enhancement. Also, this paper is presenting the methodology and results of choosing the optimum aerial eNodeB altitude

Aerial-terrestrial communications: terrestrial cooperation and energy-efficient transmissions to aerial-base stations

Hybrid aerial-terrestrial communication networks based on low-altitude platforms are expected to meet optimally the urgent communication needs of emergency relief and recovery operations for tackling large-scale natural disasters. The energy-efficient operation of such networks is important given that the entire network infrastructure, including the battery-operated ground terminals, exhibits requirements to operate under power-constrained situations. In this paper, we discuss the design and evaluation of an adaptive cooperative scheme intended to extend the survivability of the battery-operated aerial-terrestrial communication links. We propose and evaluate a real-time adaptive cooperative transmission strategy for dynamic selection between direct and cooperative links based on the channel conditions for improved energy efficiency. We show that the cooperation between mobile terrestrial terminals on the ground could improve energy efficiency in the uplink, depending on the temporal behavior of the terrestrial and aerial uplink channels. The corresponding delay in having cooperative (relay-based) communications with relay selection is also addressed. The simulation analysis corroborates that the adaptive transmission technique improves overall energy efficiency of the network whilst maintaining low latency, enabling real-time applications

Comprehensive survey on quality of service provisioning approaches in cognitive radio networks : part one

Much interest in Cognitive Radio Networks (CRNs) has been raised recently by enabling unlicensed (secondary) users to utilize the unused portions of the licensed spectrum. CRN utilization of residual spectrum bands of Primary (licensed) Networks (PNs) must avoid harmful interference to the users of PNs and other overlapping CRNs. The coexisting of CRNs depends on four components: Spectrum Sensing, Spectrum Decision, Spectrum Sharing, and Spectrum Mobility. Various approaches have been proposed to improve Quality of Service (QoS) provisioning in CRNs within fluctuating spectrum availability. However, CRN implementation poses many technical challenges due to a sporadic usage of licensed spectrum bands, which will be increased after deploying CRNs. Unlike traditional surveys of CRNs, this paper addresses QoS provisioning approaches of CRN components and provides an up-to-date comprehensive survey of the recent improvement in these approaches. Major features of the open research challenges of each approach are investigated. Due to the extensive nature of the topic, this paper is the first part of the survey which investigates QoS approaches on spectrum sensing and decision components respectively. The remaining approaches of spectrum sharing and mobility components will be investigated in the next part

Study of cyclostationary feature detectors for cognitive radio

Master'sMASTER OF ENGINEERIN

Towards realisation of spectrum sharing of cognitive radio networks

Cognitive radio networks (CRN) have emerged as a promising solution to spectrum shortcoming, thanks to Professor Mitola who coined Cognitive Radios. To enable efficient communications, CRNs need to avoid interference to both Primary (licensee) Users (PUs), and among themselves (called self-coexistence). In this thesis, we focus on self-coexistence issues. Very briefly, the problems are categorised into intentional and unintentional interference. Firstly, unintentional interference includes: 1) CRNs administration; 2) Overcrowded CRNs Situation; 3) Missed spectrum detection; 4) Inter-cell Interference (ICI); and 5) Inability to model Secondary Users’ (SUs) activity. In intentional interference there is Primary User Emulation Attack (PUEA). To administer CRN operations (Prob. 1), in our first contribution, we proposed CogMnet, which aims to manage the spectrum sharing of centralised networks. CogMnet divides the country into locations. It then dedicates a real-time database for each location to record CRNs’ utilisations in real time, where each database includes three storage units: Networks locations storage unit; Real-time storage unit; and Historical storage unit. To tackle Prob. 2, our second contribution is CRNAC, a network admission control algorithm that aims to calculate the maximum number of CRNs allowed in any location. CRNAC has been tested and evaluated using MATLAB. To prevent research problems 3, 4, and to tackle research problem (5), our third contribution is RCNC, a new design for an infrastructure-based CRN core. The architecture of RCNC consists of two engines: Monitor and Coordinator Engine (MNCE) and Modified Cognitive Engine (MCE). Comprehensive simulation scenarios using ICS Designer (by ATDI) have validated some of RCNC’s components. In the last contribution, to deter PUEA (the intentional interference type), we developed a PUEA Deterrent (PUED) algorithm capable of detecting PUEAs commission details. PUED must be implemented by a PUEA Identifier Component in the MNCE in RCNC after every spectrum handing off. Therefore, PUED works like a CCTV system. According to criminology, robust CCTV systems have shown a significant prevention of clear visible theft, reducing crime rates by 80%. Therefore, we believe that our algorithm will do the same. Extensive simulations using a Vienna simulator showed the effectiveness of the PUED algorithm in terms of improving CRNs’ performance

New Waves of IoT Technologies Research – Transcending Intelligence and Senses at the Edge to Create Multi Experience Environments

The next wave of Internet of Things (IoT) and Industrial Internet of Things (IIoT) brings new technological developments that incorporate radical advances in Artificial Intelligence (AI), edge computing processing, new sensing capabilities, more security protection and autonomous functions accelerating progress towards the ability for IoT systems to self-develop, self-maintain and self-optimise. The emergence of hyper autonomous IoT applications with enhanced sensing, distributed intelligence, edge processing and connectivity, combined with human augmentation, has the potential to power the transformation and optimisation of industrial sectors and to change the innovation landscape. This chapter is reviewing the most recent advances in the next wave of the IoT by looking not only at the technology enabling the IoT but also at the platforms and smart data aspects that will bring intelligence, sustainability, dependability, autonomy, and will support human-centric solutions.acceptedVersio

Spectrum Pricing for Cognitive Radio

This thesis examines how the price paid by the end users via an auction model can be used in regulating and controlling the admission process given a dynamic spectrum access and a heterogeneous small cell network. The performance of the system is judged by the energy consumed, the system throughput and the delay. A first price auction model with a reserve price is designed to take into consideration the signal to noise ratio of the users by introducing a novel tax and subsidy scheme called the green payments. Furthermore, the use of multiple bidding process and an admittance threshold, known as the probability of being among the highest bidders, helps in further reducing the energy consumed and improves the system throughput. A utility function is also found useful in determining the satisfaction of the users and in formulating a theoretical model for the admission process. Bid learning performance using Linear Reinforcement learning, Q learning, and Bayesian learning is compared and the results show that Bayesian learning converges faster because it incorporates prior information. It is shown that incorporating a price based utility function into the punishment or the reward weighting factor can help the learning process to converge at the optimal bidding price. A game model is formulated to allow all users in the system to learn depending on their priority. This enables users to learn different parameters such as the best offered bid price and the appropriate time to participate in the auction process. Results show that provided all the users take part in the learning process, a Nash Equilibrium can be established. The energy and the delay associated with the auction process are also further reduced when all the users are learning the different parameters