Cognitive Communications in White Space: Opportunistic Scheduling, Spectrum Shaping and Delay Analysis

abstract: A unique feature, yet a challenge, in cognitive radio (CR) networks is the user hierarchy: secondary users (SU) wishing for data transmission must defer in the presence of active primary users (PUs), whose priority to channel access is strictly higher.Under a common thread of characterizing and improving Quality of Service (QoS) for the SUs, this dissertation is progressively organized under two main thrusts: the first thrust focuses on SU's throughput by exploiting the underlying properties of the PU spectrum to perform effective scheduling algorithms; and the second thrust aims at another important QoS performance of the SUs, namely delay, subject to the impact of PUs' activities, and proposes enhancement and control mechanisms. More specifically, in the first thrust, opportunistic spectrum scheduling for SU is first considered by jointly exploiting the memory in PU's occupancy and channel fading. In particular, the underexplored scenario where PU occupancy presents a {long} temporal memory is taken into consideration. By casting the problem as a partially observable Markov decision process, a set of {multi-tier} tradeoffs are quantified and illustrated. Next, a spectrum shaping framework is proposed by leveraging network coding as a {spectrum shaper} on the PU's traffic. Such shaping effect brings in predictability of the primary spectrum, which is utilized by the SUs to carry out adaptive channel sensing by prioritizing channel access order, and hence significantly improve their throughput. On the other hand, such predictability can make wireless channels more susceptible to jamming attacks. As a result, caution must be taken in designing wireless systems to balance the throughput and the jamming-resistant capability. The second thrust turns attention to an equally important performance metric, i.e., delay performance. Specifically, queueing delay analysis is conducted for SUs employing random access over the PU channels. Fluid approximation is taken and Poisson driven stochastic differential equations are applied to characterize the moments of the SUs' steady-state queueing delay. Then, dynamic packet generation control mechanisms are developed to meet the given delay requirements for SUs.Dissertation/ThesisPh.D. Electrical Engineering 201

A Reinforcement Learning based Cognitive Approach for Quality of Experience Management in the Future Internet

This thesis aims at providing an innovative contribution to the definition of the Future Internet Core Platform, in the frame of the "La Sapienza" University research activities on the EU FP7 FI-WARE project. The thesis introduces and designs an innovative "Cognitive Application Interface" in charge of deriving key parameters driving the Network Control elements to meet personalised Application Quality of Experience Requirements. The thesis proposes the innovative concept of a dynamic association between Applications and Classes of Service. A Reinforcement Learning based approach is followed. A solution based on a standard Q-learning algorithm is proposed. Simulation results obtained using the OPNET simulation tool are described. Preliminary work on an alternative solution based on a Foe Q-Learning algorithm is also illustrated. The proposed framework is very flexible, allows QoE personalization, requires low processing capabilities and entails a very limited signalling overhead

A Reinforcement Learning based Cognitive Approach for Quality of Experience Management in the Future Internet

This thesis aims at providing an innovative contribution to the definition of the Future Internet Core Platform, in the frame of the "La Sapienza" University research activities on the EU FP7 FI-WARE project. The thesis introduces and designs an innovative "Cognitive Application Interface" in charge of deriving key parameters driving the Network Control elements to meet personalised Application Quality of Experience Requirements. The thesis proposes the innovative concept of a dynamic association between Applications and Classes of Service. A Reinforcement Learning based approach is followed. A solution based on a standard Q-learning algorithm is proposed. Simulation results obtained using the OPNET simulation tool are described. Preliminary work on an alternative solution based on a Foe Q-Learning algorithm is also illustrated. The proposed framework is very flexible, allows QoE personalization, requires low processing capabilities and entails a very limited signalling overhead

Active Learning in Cognitive Radio Networks

In this thesis, numerous Machine Learning (ML) applications for Cognitive Radios Networks (CRNs) are developed and presented which facilitate the e cient spectral coexistence of a legacy system, the Primary Users (PUs), and a CRN, the Secondary Users (SUs). One way to better exploit the capacity of the legacy system frequency band is to consider a coexistence scenario using underlay Cognitive Radio (CR) techniques, where SUs may transmit in the frequency band of the PU system as long as the induced to the PU interference is under a certain limit and thus does not harmfully a ect the legacy system operability