Medium access control design for distributed opportunistic radio networks

Existing wireless networks are characterized by a fixed spectrum assignment policy. However, the scarcity of available spectrum and its inefficient usage demands for a new communication paradigm to exploit the existing spectrum opportunistically. Future Cognitive Radio (CR) devices should be able to sense unoccupied spectrum and will allow the deployment of real opportunistic networks. Still, traditional Physical (PHY) and Medium Access Control (MAC) protocols are not suitable for this new type of networks because they are optimized to operate over fixed assigned frequency bands. Therefore, novel PHY-MAC cross-layer protocols should be developed to cope with the specific features of opportunistic networks. This thesis is mainly focused on the design and evaluation of MAC protocols for Decentralized Cognitive Radio Networks (DCRNs). It starts with a characterization of the spectrum sensing framework based on the Energy-Based Sensing (EBS) technique considering multiple scenarios. Then, guided by the sensing results obtained by the aforementioned technique, we present two novel decentralized CR MAC schemes: the first one designed to operate in single-channel scenarios and the second one to be used in multichannel scenarios. Analytical models for the network goodput, packet service time and individual transmission probability are derived and used to compute the performance of both protocols. Simulation results assess the accuracy of the analytical models as well as the benefits of the proposed CR MAC schemes

Population adaptation for genetic algorithm-based cognitive radios

Abstract — Genetic algorithms are best suited for optimization problems involving large search spaces. The problem space encountered when optimizing the transmission parameters of an agile or cognitive radio for a given wireless environment and set of performance objectives can become prohibitively large due to the high number of parameters and their many possible values. Recent research has demonstrated that genetic algorithms are a viable implementation technique for cognitive radio engines. However, the time required for the genetic algorithms to come to a solution substantionally increases as the system complexity grows. In this paper, we present a population adaptation technique for genetic algorithms that takes advantage of the information from previous cognition cycles in order to reduce the time required to reach an optimal decision. Our simulation results demonstrate that the amount of information from the previous cognition cycle can be determined from the environmental variation factor (EVF), which represents the amount of change in the environment parameters since the previous cognition cycle. I

Advanced PHY/MAC Design for Infrastructure-less Wireless Networks

Wireless networks play a key role in providing information exchange among distributed mobile devices. Nowadays, Infrastructure-Less Wireless Networks (ILWNs), which include ad hoc and sensor networks, are gaining increasing popularity as they do not need a fixed infrastructure. Simultaneously, multiple research initiatives have led to different findings at the physical (PHY) layer of the wireless communication systems, which can effectively be adopted in ILWNs. However, the distributed nature of ILWNs demand for different network control policies that should have into account the most recent findings to increase the network performance. This thesis investigates the adoption of Multi-Packet Reception (MPR) techniques at the PHY layer of distributed wireless networks, which is itself a challenging task due to the lack of a central coordinator and the spatial distribution of the nodes. The work starts with the derivation of an MPR system performance model that allows to determine optimal points of operation for different radio conditions. The model developed and validated in this thesis is then used to study the performance of ILWNs in high density of transmitters and when the spectrum can be sensed a priori (i.e. before each transmission). Based on the theoretical analysis developed in the thesis, we show that depending on the propagation conditions the spectrum sensing can reduce the network throughput to a level where its use should be avoided. At the final stage, we propose a crosslayered architecture that improves the capacity of an ILWN. Different Medium Access Control (MAC) schemes for ILWNs adopting MPR communications are proposed and their performance is theoretically characterized. We propose a cross-layer optimization methodology that considers the features of the MPR communication scheme together with the MAC performance. The proposed cross-layer optimization methodology improves the throughput of ILWNs, which is validated through theoretical analysis and multiple simulation results

Novel QoS-aware proactive spectrum access techniques for cognitive radio using machine learning

Traditional cognitive radio (CR) spectrum access techniques have been primitive and inefficient due to being blind to the occupancy conditions of the spectrum bands to be sensed. In addition, current spectrum access techniques are also unable to detect network changes or even consider the requirements of unlicensed users, leading to a poorer quality of service (QoS) and excessive latency. As user-specific approaches will play a key role in future wireless communication networks, the conventional CR spectrum access should also be updated in order to be more effective and agile. In this paper, a comprehensive and novel solution is proposed to decrease the sensing latency and to make the CR networks (CRNs) aware of unlicensed user requirements. As such, a proactive process with a novel QoS-based optimization phase is proposed, consisting of two different decision strategies. Initially, future traffic loads of the different radio access technologies (RATs), occupying different bands of the spectrum, are predicted using the artificial neural networks (ANNs). Based on these predictions, two strategies are proposed. In the first one, which solely focuses on latency, a virtual wideband (WB) sensing approach is developed, where predicted relative traffic loads in WB are exploited to enable narrowband (NB) sensing. The second one, based on Q -learning, focuses not only on minimizing the sensing latency but also on satisfying other user requirements. The results reveal that the first strategy manages to significantly reduce the sensing latency of the random selection process by 59.6%, while the Q -learning assisted second strategy enhanced the full-satisfaction by up to 95.7%

Novel Approaches for the Performance Enhancement of Cognitive Radio Networks

This research is dedicated to the study of the challenges faced by Cognitive Radio (CR) networks, which include self-coexistence of the networks in the spectral environment, security and performance threats from malicious entities, and fairness in spectrum contention and utilization. We propose novel channel acquisition schemes that allow decentralized CR networks to have multiple channel access with minimal spectrum contentions. The multiple channel acquisition schemes facilitate fast spectrum access especially in cases where networks cannot communicate with each other. These schemes enable CR networks to self-organize and adapt to the dynamically changing spectral environment. We also present a self-coexistence mechanism that allows CR networks to coexist via the implementation of a risk-motivated channel selection based deference structure (DS). By forming DS coalitions, CR networks are able to have better access to preferred channels and can defer transmission to one another, thereby mitigating spectrum conflicts. CR networks are also known to be susceptible to Sybil threats from smart malicious radios with either monopolistic or disruptive intentions. We formulate novel threat and defense mechanisms to combat Sybil threats and minimize their impact on the performance of CR networks. A dynamic reputation system is proposed that considerably minimizes the effectiveness of intelligent Sybil attacks and improves the accuracy of spectrum-based decision-making processes. Finally, we present a distributed and cheat-proof spectrum contention protocol as an enhancement of the adaptive On-Demand Spectrum Contention (ODSC) protocol. The Modified On-Demand Spectrum Contention (MODSC) protocol enhances fairness and efficiency of spectrum access. We also show that there is substantial improvement in spectrum utilization with the incorporation of channel reuse into the MODSC protocol

Pilot Design for Non-contiguous Spectrum Usage in OFDM-based Cognitive Radio Networks

Publication in the conference proceedings of EUSIPCO, Bucharest, Romania, 201

Towards Scalable Design of Future Wireless Networks

Wireless operators face an ever-growing challenge to meet the throughput and processing requirements of billions of devices that are getting connected. In current wireless networks, such as LTE and WiFi, these requirements are addressed by provisioning more resources: spectrum, transmitters, and baseband processors. However, this simple add-on approach to scale system performance is expensive and often results in resource underutilization. What are, then, the ways to efficiently scale the throughput and operational efficiency of these wireless networks? To answer this question, this thesis explores several potential designs: utilizing unlicensed spectrum to augment the bandwidth of a licensed network; coordinating transmitters to increase system throughput; and finally, centralizing wireless processing to reduce computing costs. First, we propose a solution that allows LTE, a licensed wireless standard, to co-exist with WiFi in the unlicensed spectrum. The proposed solution bridges the incompatibility between the fixed access of LTE, and the random access of WiFi, through channel reservation. It achieves a fair LTE-WiFi co-existence despite the transmission gaps and unequal frame durations. Second, we consider a system where different MIMO transmitters coordinate to transmit data of multiple users. We present an adaptive design of the channel feedback protocol that mitigates interference resulting from the imperfect channel information. Finally, we consider a Cloud-RAN architecture where a datacenter or a cloud resource processes wireless frames. We introduce a tree-based design for real-time transport of baseband samples and provide its end-to-end schedulability and capacity analysis. We also present a processing framework that combines real-time scheduling with fine-grained parallelism. The framework reduces processing times by migrating parallelizable tasks to idle compute resources, and thus, decreases the processing deadline-misses at no additional cost. We implement and evaluate the above solutions using software-radio platforms and off-the-shelf radios, and confirm their applicability in real-world settings.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133358/1/gkchai_1.pd

Mobilidade em redes de rádio cognitivo

Dissertação apresentada para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologi