Performance assessment & synergic operation of algorithmic solutions enabling opportunistic networks– D4.2

Deliverable D4.2 del projecte europeu OneFITPeer ReviewedPostprint (updated version

Unified radio and network control across heterogeneous hardware platforms

Experimentation is an important step in the investigation of techniques for handling spectrum scarcity or the development of new waveforms in future wireless networks. However, it is impractical and not cost effective to construct custom platforms for each future network scenario to be investigated. This problem is addressed by defining Unified Programming Interfaces that allow common access to several platforms for experimentation-based prototyping, research, and development purposes. The design of these interfaces is driven by a diverse set of scenarios that capture the functionality relevant to future network implementations while trying to keep them as generic as possible. Herein, the definition of this set of scenarios is presented as well as the architecture for supporting experimentation-based wireless research over multiple hardware platforms. The proposed architecture for experimentation incorporates both local and global unified interfaces to control any aspect of a wireless system while being completely agnostic to the actual technology incorporated. Control is feasible from the low-level features of individual radios to the entire network stack, including hierarchical control combinations. A testbed to enable the use of the above architecture is utilized that uses a backbone network in order to be able to extract measurements and observe the overall behaviour of the system under test without imposing further communication overhead to the actual experiment. Based on the aforementioned architecture, a system is proposed that is able to support the advancement of intelligent techniques for future networks through experimentation while decoupling promising algorithms and techniques from the capabilities of a specific hardware platform

Cognitive networking for next generation of cellular communication systems

This thesis presents a comprehensive study of cognitive networking for cellular networks with contributions that enable them to be more dynamic, agile, and efficient. To achieve this, machine learning (ML) algorithms, a subset of artificial intelligence, are employed to bring such cognition to cellular networks. More specifically, three major branches of ML, namely supervised, unsupervised, and reinforcement learning (RL), are utilised for various purposes: unsupervised learning is used for data clustering, while supervised learning is employed for predictions on future behaviours of networks/users. RL, on the other hand, is utilised for optimisation purposes due to its inherent characteristics of adaptability and requiring minimal knowledge of the environment. Energy optimisation, capacity enhancement, and spectrum access are identified as primary design challenges for cellular networks given that they are envisioned to play crucial roles for 5G and beyond due to the increased demand in the number of connected devices as well as data rates. Each design challenge and its corresponding proposed solution are discussed thoroughly in separate chapters. Regarding energy optimisation, a user-side energy consumption is investigated by considering Internet of things (IoT) networks. An RL based intelligent model, which jointly optimises the wireless connection type and data processing entity, is proposed. In particular, a Q-learning algorithm is developed, through which the energy consumption of an IoT device is minimised while keeping the requirement of the applications--in terms of response time and security--satisfied. The proposed methodology manages to result in 0% normalised joint cost--where all the considered metrics are combined--while the benchmarks performed 54.84% on average. Next, the energy consumption of radio access networks (RANs) is targeted, and a traffic-aware cell switching algorithm is designed to reduce the energy consumption of a RAN without compromising on the user quality-of-service (QoS). The proposed technique employs a SARSA algorithm with value function approximation, since the conventional RL methods struggle with solving problems with huge state spaces. The results reveal that up to 52% gain on the total energy consumption is achieved with the proposed technique, and the gain is observed to reduce when the scenario becomes more realistic. On the other hand, capacity enhancement is studied from two different perspectives, namely mobility management and unmanned aerial vehicle (UAV) assistance. Towards that end, a predictive handover (HO) mechanism is designed for mobility management in cellular networks by identifying two major issues of Markov chains based HO predictions. First, revisits--which are defined as a situation whereby a user visits the same cell more than once within the same day--are diagnosed as causing similar transition probabilities, which in turn increases the likelihood of making incorrect predictions. This problem is addressed with a structural change; i.e., rather than storing 2-D transition matrix, it is proposed to store 3-D one that also includes HO orders. The obtained results show that 3-D transition matrix is capable of reducing the HO signalling cost by up to 25.37%, which is observed to drop with increasing randomness level in the data set. Second, making a HO prediction with insufficient criteria is identified as another issue with the conventional Markov chains based predictors. Thus, a prediction confidence level is derived, such that there should be a lower bound to perform HO predictions, which are not always advantageous owing to the HO signalling cost incurred from incorrect predictions. The outcomes of the simulations confirm that the derived confidence level mechanism helps in improving the prediction accuracy by up to 8.23%. Furthermore, still considering capacity enhancement, a UAV assisted cellular networking is considered, and an unsupervised learning-based UAV positioning algorithm is presented. A comprehensive analysis is conducted on the impacts of the overlapping footprints of multiple UAVs, which are controlled by their altitudes. The developed k-means clustering based UAV positioning approach is shown to reduce the number of users in outage by up to 80.47% when compared to the benchmark symmetric deployment. Lastly, a QoS-aware dynamic spectrum access approach is developed in order to tackle challenges related to spectrum access, wherein all the aforementioned types of ML methods are employed. More specifically, by leveraging future traffic load predictions of radio access technologies (RATs) and Q-learning algorithm, a novel proactive spectrum sensing technique is introduced. As such, two different sensing strategies are developed; the first one focuses solely on sensing latency reduction, while the second one jointly optimises sensing latency and user requirements. In particular, the proposed Q-learning algorithm takes the future load predictions of the RATs and the requirements of secondary users--in terms of mobility and bandwidth--as inputs and directs the users to the spectrum of the optimum RAT to perform sensing. The strategy to be employed can be selected based on the needs of the applications, such that if the latency is the only concern, the first strategy should be selected due to the fact that the second strategy is computationally more demanding. However, by employing the second strategy, sensing latency is reduced while satisfying other user requirements. The simulation results demonstrate that, compared to random sensing, the first strategy decays the sensing latency by 85.25%, while the second strategy enhances the full-satisfaction rate, where both mobility and bandwidth requirements of the user are simultaneously satisfied, by 95.7%. Therefore, as it can be observed, three key design challenges of the next generation of cellular networks are identified and addressed via the concept of cognitive networking, providing a utilitarian tool for mobile network operators to plug into their systems. The proposed solutions can be generalised to various network scenarios owing to the sophisticated ML implementations, which renders the solutions both practical and sustainable

From Sensing to Predictions and Database Technique: A Review of TV White Space Information Acquisition in Cognitive Radio Networks

Strategies to acquire white space information is the single most significant functionality in cognitive radio networks (CRNs) and as such, it has gone some evolution to enhance information accuracy. The evolution trends are spectrum sensing, prediction algorithm and recently, geo-location database technique. Previously, spectrum sensing was the main technique for detecting the presence/absence of a primary user (PU) signal in a given radio frequency (RF) spectrum. However, this expectation could not materialized as a result of numerous technical challenges ranging from hardware imperfections to RF signal impairments. To convey the evolutionary trends in the development of white space information, we present a survey of the contemporary advancements in PU detection with emphasis on the practical deployment of CRNs i.e. Television white space (TVWS) networks. It is found that geo-location database is the most reliable technique to acquire TVWS information although, it is financially driven. Finally, using financially driven database model, this study compared the data-rate and spectral efficiency of FCC and Ofcom TV channelization. It was discovered that Ofcom TV channelization outperforms FCC TV channelization as a result of having higher spectrum bandwidth. We proposed the adoption of an all-inclusive TVWS information acquisition model as the future research direction for TVWS information acquisition techniques

Protocols, performance assessment and consolidation on interfaces for standardization – D3.3

The following document presents a detailed description of the protocol for the “ Control Channels for the Cooperation of the Cognitive Management System ” (C4MS) which provides the necessary means to enable proper management of Opportunistic Networks. Additionally, the document defines the methodology that was applied for the purpose of signalling evaluation. The protocol overview presented in section 2 of the main document, provides the C4MS principles. The section includes, among others, the description of the protocol identifiers, procedures, protocol state machines and message format as well as the security asp ects. Section 3 provides a high-level description of the data structures defined within the scope of OneFIT project. The data structures are classified into five categories, i.e.: Profiles, Context, Decisions,Knowledge and Policies. The high level description is complemented by some detailed data structures in the Appendix to D3.3 Section 3[10]. Section 4 provides details on the evaluation methodology applied for the purpose of C4MS performance assessment. The section presents the evaluation plan along with a description of metrics that are to be exploited in the scope of WP3. Section 5 and Section 6 are composed of the signalling evaluation results. Section 5 focuses on the estimation of the signalling load imposed by ON management in different ON phases. Additionally some results for the initialization phase (not explicitly mentioned in the previous phases of the project)and security related aspects are also depicted. Section 6 on the other hand is focused on the evaluation of the signalling traffic generated by different ON related algorithms. Conclusions to the document are drawn in section 7. Detailed description of the C4MS procedures, implementation options based on IEEE 802.21, DIAMTER and 3GPP are depicted in the appendix to the D3.3[10] . Additionally, the appendix incorporates the detailed definition of the information data structures and final set of Message Sequence Charts (MSCs) provided for the OneFIT project.Peer ReviewedPreprin

Cognitive radio adaptive rendezvous protocols to establish network services for a disaster response

Disasters are catastrophic events that cause great damage or loss of life. In disasters, communication services might be disrupted due to damage to the existing network infrastructure. Temporary systems are required for victims and first responders, but installing them requires information about the radio environment and available spectrum. A cognitive radio (CR) can be used to provide a flexible and rapidly deployable temporary system due to its sensing, learning and decision-making capabilities. This thesis initially examines the potential of CR technology for disaster response networks (DRN) and shows that they are ideally suited to fulfill the requirements of a DRN. A software defined radio based prototype for multiple base transceiver stations based cellular network is proposed and developed. It is demonstrated that system can support a large number of simultaneous calls with sufficient call quality, but only when the background interference is low. It is concluded that to provide call quality with acceptable latency and packet losses, the spectrum should be used dynamically for backhaul connectivity. The deployment challenges for such a system in a disaster include the discovery of the available spectrum, existing networks, and neighbours. Furthermore, to set up a network and to establish network services, initially CR nodes are required to establish a rendezvous. However, this can be challenging due to unknown spectrum information, primary radio (PR) activity, nodes, and topology. The existing rendezvous strategies do not fulfill the DRN requirements and their time to rendezvous (TTR) is long. Therefore, we propose an extended modular clock algorithm (EMCA) which is a multiuser blind rendezvous protocol, considers the DRN requirements and has short TTR. For unknown nodes and topologies, a general framework for self-organizing multihop cooperative fully blind rendezvous protocol is also proposed, which works in different phases, can terminate when sufficient nodes are discovered, and is capable of disseminating the information of nodes which enter or leave a network. A synchronization mechanism is presented for periodic update of rendezvous information. An information exchange mechanism is also proposed which expedites the rendezvous process. In both single and multihop networks, EMCA provides up to 80% improvement in terms of TTR over the existing blind rendezvous strategies while considering the PR activity. A simple Random strategy, while being poorer than EMCA, is also shown to outperform existing strategies on average. To achieve adaptability in the presence of unknown PR activity, different CR operating policies are proposed which avoid the channels detected with PR activity to reduce the harmful interference, provide free channels to reduce the TTR, and can work with any rendezvous strategy. These policies are evaluated over different PR activities and shown to reduce the TTR and harmful interference significantly over the basic Listen before Talk approach. A proactive policy, which prefers to return to channels with recent lower PR activity, is shown to be best, and to improve the performance of all studied rendezvous strategies

Mobile ad hoc networks in transportation data collection and dissemination

The field of transportation is rapidly changing with new opportunities for systems solutions and emerging technologies. The global economic impact of congestion and accidents are significant. Improved means are needed to solve them. Combined with the increasing numbers of vehicles on the road, the net economic impact is measured in the many billions of dollars. Promising methodologies explored in this thesis include the use of the Internet of Things (IoT) and Mobile Ad Hoc Networks (MANET). Interconnecting vehicles using Dedicated Short Range Communication technology (DSRC) brings many benefits. Integrating DSRC into roadway vehicles offers the promise of reducing the problems of congestion and accidents; however, it comes with risks such as loss of connectivity due to power outages as well as controlling and managing loading in such networks. Energy consumption of vehicle communication equipment is a crucial factor in high availability sensor networks. Sending critical emergency messaged through linked vehicles requires that there always be energy and communication reserves. Two algorithms are described. The first controls energy consumption to guarantee an energy reserve for sending alert signals. The second exploits Long Term Evolution (LTE) to guarantee a reliable communication path

New concepts for traffic, resource and mobility management in software-defined mobile networks

The evolution of mobile telecommunication networks is accompanied by new demands for the performance, portability, elasticity, and energy efficiency of network functions. Network Function Virtualization (NFV), Software Defined Networking (SDN), and cloud service technologies are claimed to be able to provide most of the capabilities. However, great leap forward will only be achieved if resource, traffic, and mobility management methods of mobile network services can efficiently utilize these technologies. This paper conceptualizes the future requirements of mobile networks and proposes new concepts and solutions in the form of Software-Defined Mobile Networks (SDMN) leveraging SDN, NFV and cloud technologies. We evaluate the proposed solutions through testbed implementations and simulations. The results reveal that our proposed SDMN enhancements supports heterogeneity in wireless networks with performance improvements through programmable interfaces and centralized control

Facilitating Flexible Link Layer Protocols for Future Wireless Communication Systems

This dissertation addresses the problem of designing link layer protocols which are flexible enough to accommodate the demands offuture wireless communication systems (FWCS).We show that entire link layer protocols with diverse requirements and responsibilities can be composed out of reconfigurable and reusable components.We demonstrate this by designing and implementinga novel concept termed Flexible Link Layer (FLL) architecture.Through extensive simulations and practical experiments, we evaluate a prototype of the suggested architecture in both fixed-spectrumand dynamic spectrum access (DSA) networks. FWCS are expected to overcome diverse challenges including the continual growthin traffic volume and number of connected devices.Furthermore, they are envisioned to support a widerange of new application requirements and operating conditions.Technology trends, including smart homes, communicating machines, and vehicularnetworks, will not only grow on a scale that once was unimaginable, they will also become the predominant communication paradigm, eventually surpassing today's human-produced network traffic. In order for this to become reality, today's systems have to evolve in many ways.They have to exploit allocated resources in a more efficient and energy-conscious manner.In addition to that, new methods for spectrum access and resource sharingneed to be deployed.Having the diversification of applications and network conditions in mind, flexibility at all layers of a communication system is of paramount importance in order to meet the desired goals. However, traditional communication systems are often designed with specific and distinct applications in mind. Therefore, system designers can tailor communication systems according to fixedrequirements and operating conditions, often resulting in highly optimized but inflexible systems.Among the core problems of such design is the mix of data transfer and management aspects.Such a combination of concerns clearly hinders the reuse and extension of existing protocols. To overcome this problem, the key idea explored in this dissertation is a component-based design to facilitate the development of more flexible and versatile link layer protocols.Specifically, the FLL architecture, suggested in this dissertation, employs a generic, reconfigurable data transfer protocol around which one or more complementary protocols, called link layer applications, are responsible for management-related aspects of the layer. To demonstrate the feasibility of the proposed approach, we have designed andimplemented a prototype of the FLL architecture on the basis ofa reconfigurable software defined radio (SDR) testbed.Employing the SDR prototype as well as computer simulations, thisdissertation describes various experiments used to examine a range of link layerprotocols for both fixed-spectrum and DSA networks. This dissertation firstly outlines the challenges faced by FWCSand describes DSA as a possible technology component for their construction.It then specifies the requirements for future DSA systemsthat provide the basis for our further considerations.We then review the background on link layer protocols, surveyrelated work on the construction of flexible protocol frameworks,and compare a range of actual link layer protocols and algorithms.Based on the results of this analysis, we design, implement, and evaluatethe FLL architecture and a selection of actual link layer protocols. We believe the findings of this dissertation add substantively to the existing literature on link layer protocol design and are valuable for theoreticians and experimentalists alike

Cognitive MAC protocols for mobile Ad-Hoc networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The term of Cognitive Radio (CR) used to indicate that spectrum radio could be accessed dynamically and opportunistically by unlicensed users. In CR Networks, Interference between nodes, hidden terminal problem, and spectrum sensing errors are big issues to be widely discussed in the research field nowadays. To improve the performance of such kind of networks, this thesis proposes Cognitive Medium Access Control (MAC) protocols for Mobile Ad-Hoc Networks (MANETs). From the concept of CR, this thesis has been able to develop a cognitive MAC framework in which a cognitive process consisting of cognitive elements is considered, which can make efficient decisions to optimise the CR network. In this context, three different scenarios to maximize the secondary user's throughput have been proposed. We found that the throughput improvement depends on the transition probabilities. However, considering the past information state of the spectrum can dramatically increases the secondary user's throughput by up to 40%. Moreover, by increasing the number of channels, the throughput of the network can be improved about 25%. Furthermore, to study the impact of Physical (PHY) Layer errors on cognitive MAC layer in MANETs, in this thesis, a Sensing Error-Aware MAC protocols for MANETs has been proposed. The developed model has been able to improve the MAC layer performance under the challenge of sensing errors. In this context, the proposed model examined two sensing error probabilities: the false alarm probability and the missed detection probability. The simulation results have shown that both probabilities could be adapted to maintain the false alarm probability at certain values to achieve good results. Finally, in this thesis, a cooperative sensing scheme with interference mitigation for Cognitive Wireless Mesh Networks (CogMesh) has been proposed. Moreover, a prioritybased traffic scenario to analyze the problem of packet delay and a novel technique for dynamic channel allocation in CogMesh is presented. Considering each channel in the system as a sub-server, the average delay of the users' packets is reduced and the cooperative sensing scenario dramatically increases the network throughput 50% more as the number of arrival rate is increased