We define a cyclostationary signature as a feature which may be intentionally embedded in a digital communications signal, detected through cyclostationary analysis and used as a unique identifier. The purpose of this paper is to demonstrate how cyclostationary signatures can be exploited to overcome a number of the challenges associated with network coordination in emerging cognitive radio applications and spectrum sharing regimes. In particular we show their uses for signal detection, network identification and rendezvous and discuss these in the context of dynamic spectrum access. We present a theoretical discussion followed by application-oriented examples of the cyclostationary signatures used in practical cognitive radio and dynamic spectrum usage scenarios. We focus on orthogonal frequency division multiplexing (OFDM) based systems and present an analysis of a transceiver implementation employing these techniques developed on a cognitive radio test platform.

Orthogonal time frequency space (OTFS) modulation is a two-dimensional signaling technique that has recently emerged in the literature to tackle the time-varying (TV) wireless channels. OTFS deploys the Doppler-delay plane to multiplex the transmit data where the time variations of the TV channel are integrated over time and hence the equivalent channel relating the input and output of the system boils down to a time-invariant one. This signaling technique can be implemented on the top of a given multicarrier waveform with the addition of precoding and post-processing units to the modulator and demodulator. In this letter, we present discrete-time formulation of an OFDM-based OTFS system. We argue against deployment of window functions at the OTFS transmitter in realistic scenarios and thus limit any sort of windowing to the receiver side. We study the channel impact in discrete-time providing deeper insights into OTFS systems. Moreover, our derivations lead to simplified modulator and demodulator structures that are far simpler than those in the literature.

Head portraits are popular in traditional painting. Automating portrait painting is challenging as the human visual system is sensitive to the slightest irregularities in human faces. Applying generic painting techniques often deforms facial structures. On the other hand portrait painting techniques are mainly designed for the graphite style and/or are based on image analogies; an example painting as well as its original unpainted version are required. This limits their domain of applicability. We present a new technique for transferring the painting from a head portrait onto another. Unlike previous work our technique only requires the example painting and is not restricted to a specific style. We impose novel spatial constraints by locally transferring the color distributions of the example painting. This better captures the painting texture and maintains the integrity of facial structures. We generate a solution through Convolutional Neural Networks and we present an extension to video. Here motion is exploited in a way to reduce temporal inconsistencies and the shower-door effect. Our approach transfers the painting style while maintaining the input photograph identity. In addition it significantly reduces facial deformations over state of the art.

Due to its attractive properties, generalized frequency division multiplexing (GFDM) is recently being discussed as a candidate waveform for the fifth generation of wireless communication systems (5G). GFDM is introduced as a generalized form of the widely used orthogonal frequency division multiplexing (OFDM) modulation scheme and since it uses only one cyclic prefix (CP) for a group of symbols rather than a CP per symbol, it is more bandwidth efficient than OFDM. In this paper, we propose novel modem structures for GFDM by taking advantage of the particular structure in the modulation matrix. Our proposed transmitter is based on modulation matrix sparsification through application of fast Fourier transform (FFT) to reduce the implementation complexity. A unified demodulator structure for matched filter (MF), zero forcing (ZF), and minimum mean square error (MMSE) receivers is also derived. The proposed demodulation techniques harness the special block circulant property of the matrices involved in the demodulation stage to reduce the computational cost of the system implementation. We have derived the closed forms for the ZF and MMSE receiver filters. Additionally, our algorithms do not incur any performance loss as they maintain the optimal performance. The computational costs of our proposed techniques are analyzed in detail and are compared with the existing solutions that are known to have the lowest complexity. It is shown that through application of our structures a substantial amount of computational complexity reduction can be achieved.

The year 2009 marked the 10th anniversary of Mitola and Maguire Jr. introducing the concept of cognitive radio. This prompted an outpouring of research work related to CR, including the publication of more than 30 special issue scientific journals and more than 60 dedicated conferences and workshops. Although the theoretical research is blooming, with many interesting results presented, hardware and system development for CR is progressing at a slower pace. We provide synopses of the commonly used platforms and testbeds, examine what has been achieved in the last decade of experimentation and trials relating to CR, and draw several perhaps surprising conclusions. This analysis will enable the research community to focus on the key technologies to enable CR in the future.

Iris is a software architecture for building highly reconfigurable radio networks. It has formed the basis for a wide range of dynamic spectrum access and cognitive radio demonstration systems presented at a number of international conferences between 2007 and 2010. These systems have been developed using heterogeneous processing platforms including general-purpose processors, field-programmable gate arrays and the Cell Broadband Engine. Focusing on runtime reconfiguration, Iris offers support for all layers of the network stack and provides a platform for the development of not only reconfigurable point-to-point radio links but complete networks of cognitive radios. This article provides an overview of Iris, presenting the unique features of the architecture and illustrating how it can be used to develop a cognitive radio testbed.