Irene Macaluso

Learning and Reasoning in Cognitive Radio Networks

Cognitive radio networks challenge the traditional wireless networking paradigm by introducing concepts firmly stemmed into the Artificial Intelligence (AI) field, i.e., learning and reasoning. This fosters optimal resource usage and management allowing a plethora of potential applications such as secondary spectrum access, cognitive wireless backbones, cognitive machine-to-machine etc. The majority of overview works in the field of cognitive radio networks deal with the notions of observation and adaptations, which are not a distinguished cognitive radio networking aspect. Therefore, this paper provides insight into the mechanisms for obtaining and inferring knowledge that clearly set apart the cognitive radio networks from other wireless solutions.

Exclusive sharing & virtualization of the cellular network

The focus of this paper is to progress a discussion about the redefinition of what it means to be a cellular network operator. Alternative modes of ownership may allow for the creation of more flexible cellular networking environments in which competition for packages of resources, i.e. the licenced spectrum, the RAN and core network, ensures that the most efficient architectures win out. This paper introduces the concept of the Cellular Network Cloud and Utility Cellular Networking as a means to explore the possibilities for such revolution in the way in which cellular networks are owned and operated. As a step towards evaluating the technical feasibility of such a paradigm, a flexible auction model that enables us to auction spectrum as a continuous good, as opposed to a collection of discrete items, is presented.

Complexity of Spectrum Activity and Benefits of Reinforcement Learning for Dynamic Channel Selection

We explore the question of when learning improves the performance of opportunistic dynamic channel selection by characterizing the primary user (PU) activity using the concept of Lempel-Ziv complexity. We evaluate the effectiveness of a reinforcement learning algorithm by testing it with real spectrum occupancy data collected in the GSM, ISM, and DECT bands. Our results show that learning performance is highly correlated with the level of PU activity and the amount of structure in the use of spectrum. For low levels of PU activity and/or high complexity in its utilization of channels, reinforcement learning performs no better than simple random channel selection. We suggest that Lempel-Ziv complexity might be one of the features considered by a cognitive radio when deciding which channels to opportunistically explore.

Efficient sidelobe suppression for OFDM systems using advanced cancellation carriers

The concept of cancellation carriers (CCs) has been proposed in the literature for sidelobe suppression for orthogonal frequency division multiplexing (OFDM) systems. Subcarriers at the edges of the OFDM spectrum are used for sidelobe reduction while the remaining subcarriers are used for data transmission. Existing CCs techniques require performing complex optimization that should be applied for each OFDM symbol which is not suitable for real-time applications. In this paper, we propose a heuristic approach for CCs. The proposed algorithm involves few computations compared with all other techniques proposed in the literature. Simulation results show that sidelobe reduction performance can be close to the optimal. Moreover, the proposed algorithm is implemented in a software defined radio and implementation results prove that it can be introduced for real-time applications.

Spatial spectrum sharing-based carrier aggregation for heterogeneous networks

This paper considers spatial spectrum sharing-based carrier aggregation (SSS-CA) from a game theoretic perspective. In SSS-CA, a network operator can not only transmit on its own licensed spectrum but it can also access and aggregate the licensed spectrum of other operators on payment of a certain price. The difference between operators and the aggregators in each licensed spectrum makes this network heterogeneous. We first model the pairing problem between potential operators as a pairing game and then derive the condition for which both operators are incentivized to form an SSS-CA pair. We then introduce the power control game to derive the optimal transmit power of each spectrum aggregator. Finally, we consider the pricing optimization problem by forming a pricing adjustment game. We observe that these three problems are linked by the price function of the operators and hence can be jointly optimized by using a hierarchical game theoretic framework. We derive the Stackelberg equilibrium for the pricing and power joint optimization problem and present the numerical results to compare the performance improvement brought by our proposed joint optimization method.

OFDM Pulse-Shaped Waveforms for Dynamic Spectrum Access Networks

In dynamic spectrum access networks (DySPANs), users share access to available spectrum while minimizing the likelihood of harmful interference. In this demonstration we present a dynamic spectrum access network which employs a reconfigurable orthogonal frequency-division multiplexing (OFDM) based waveform. In order to avoid the creation of harmful interference, the out-of-band (OOB) emissions of the waveform are dynamically tailored to the properties of spectrum neighbours through the use of OFDM pulse shaping. The demonstration network is built upon the highly reconfigurable Iris 2.0 software radio platform and illustrates the capabilities of this platform as well as the utility of OFDM pulse shaping in the context of dynamic spectrum access networks.

Cloud-RAN platform for LSA in 5G networks — Tradeoff within the infrastructure

The focus of this paper is to provide a unique perspective on new network architectures supporting novel spectrum sharing mechanisms. A Cloud-based MD-MIMO RAN (Massive Distributed Multiple-Input Multiple-Output Radio Access Network) is proposed for exploitation of Licensed Shared Access (LSA) spectrum sharing concept. In order to deliver a particular service, the virtual network operator leases spectrum and antennas from the LSA spectrum pool and the Cloud-RAN platform following the pay-per-use model. The spectrum is turned into a consumable resource along with the infrastructure resources and there is a tradeoff in the system between using more antennas or more bandwidth. In this paper we propose a physical platform that can consume these resources dynamically.

Spectrum and Energy Efficient Block Edge Mask-Compliant Waveforms for Dynamic Environments

This paper suggests that the regulatory tool known as the Block Edge Mask (BEM), which is used to define the technical conditions governing a spectrum license, can be used as an instrument to jointly drive spectrum and energy efficiency in a network. The paper explores this concept and goes on to show how networks can generate waveforms which comply with the energy limits of the BEM and at the same time make best use of the spectral resources available to them. The focus of the paper is on orthogonal frequency division multiplexing (OFDM) waveforms. The spectrum- and energy-efficient BEM-compliant waveforms are generated using a process that optimally combines symbol shaping and subcarrier-power loading to create a waveform that minimizes the difference between the transmitted wave and BEM. Practical considerations relating to peak-to-average-power-ratio (PAPR) reduction and time and frequency synchronization issues are explored to ensure the BEM-compliant waveforms can be transmitted with minimal changes to the transceiver. It is envisaged that these techniques will be used by transmitters that change their frequency of operation and in doing so need to shape their transmitted waveforms to comply with the different BEMs governing the use of the different spectrum blocks they wish to access.

Carrier aggregation as a repeated game: Learning algorithms for efficient convergence to a Nash equilibrium

Carrier aggregation is a key feature of next generation wireless networks to deliver high-bandwidth links. This paper studies carrier aggregation for autonomous networks operating in shared spectrum. In our model, networks decide how many and which channels to aggregate in multiple frequency bands, hence extending the distributed channel allocation framework. Moreover, our model takes into the account physical layer issues, such as the out-of-channel interference in adjacent frequency channels and the cost associated with inter-band carrier aggregation. We propose learning algorithms that converge to Nash equilibria in a reasonable number of iterations under the assumption of incomplete and imperfect information.

Substitutability of Spectrum and Cloud-Based Antennas in Virtualized Wireless Networks

Some of the new trends emerging in future wireless networks enable a vastly increased fluidity in accessing a wide range of resources, thus supporting flexible network composition and dynamic allocation of resources to VNOs. In this work we study a new resource allocation opportunity that is enabled by the cloud radio access network architecture. In particular, we investigate the relationship between cloud-based antennas and spectrum as two important resources in virtualized wireless networks. We analyze the interplay between spectrum and antennas in the context of an auction-based allocation mechanism through which VNOs can bid for a combination of the two types of resources. Our analysis shows that the complementarity and partial substitutability of the two resources significantly impact the results of the allocation of those resources and uncovers the possibility of divergent interests between the spectrum and the infrastructure providers.