Forthcoming wireless systems will have to cope with a heterogeneous and dense traffic. High demands in terms of bandwidth use efficiency and increased flexibility are therefore required for the related air-interface technology. In this context, Filter-Bank MultiCarrier with Offset QAM (FBMC/OQAM) waveform has been recognized as an enabling technology providing strong assets with a well confined spectrum and enhanced spectral efficiency with respect to Cyclic Prefix Orthogonal Frequency Division Multiplexing (CP-OFDM). Nonetheless, FBMC-OQAM presents a few challenges to overcome: the intrinsic interference must be taken into consideration in the channel estimation processes and multiple antenna schemes design. Those challenges are induced by the reduction of the orthogonality property to the real field. In this paper, OFDM precoding/decoding is proposed as an alternative to the OQAM signaling for filter-bank based waveforms. The OFDM precoding makes the waveforms near-complex orthogonal which enables a straightforward adaptation to channel estimation techniques and MIMO scheme design. Two waveforms based on this precoding, namely Fast Fourier Transform FBMC (FFT-FBMC) and Block Filtered OFDM (BF-OFDM), have been proposed. A common framework is proposed to study the two waveforms. The paper also investigates changing the time structure of the transmitted signal by introducing the rate factor parameter. It is shown that intrinsic interference can be greatly reduced at the price of a slight side-lobe increase.

A novel joint polarization demultiplexing and IQ imbalance compensation scheme for coherent optical communications that uses global update is proposed and analyzed through numerical simulations. We describe the system model and derive its related equations. Next, we formulate our blind $M$ -QAM arbitrary approach based on EASI algorithm and using the second-order statistics of the observed signals. A comparison of the proposed joint method with the traditional constant modulus algorithm (CMA) for polarization demultiplexing followed by blind signal estimation (BASS) for IQ imbalance compensation is also reported. Evaluated metrics (EVM, MSE, BER) demonstrate its effectiveness compared with CMA cascaded with BASS algorithm.

This letter proposes a new methodology based on the recently proposed Julia language for efficient software-defined radio (SDR) prototyping. SDRs are immensely popular as they allow to have a flexible approach for sounding, monitoring, or processing radio signals through the use of generic analog components and lot of digital signal processing. As, in this paradigm, most of the processing is done at software levels (i.e., on a CPU), an efficient software methodology has to be envisioned. Right now, most of the existing methods focus on low-level languages (C or C++) for good runtime performance (at the cost of easy prototyping) or high-level languages (such as Python) for flexibility (at the price of runtime performance). In this article, we propose a new methodology based on Julia language that addresses this two-language problem and paves the way for efficient prototyping without giving up runtime performance. To prove the benefits of the proposed approach, a performance benchmark with several optimization levels compares the Julia approach with C++ and Python.

This paper proposes to explore the Radio Frequency Fingerprint (RFF) identification with a virtual database generator. RFF is a unique signature created in the emitter transmission chain by the hardware impairments. These impairments may be used as a secure identifier as they cannot be easily replicated for spoofing purposes. In recent years, the RFF identification relies mainly on Deep Learning (DL), and large databases are consequently needed to improve identification in different environmental conditions. In this paper, we introduce the so-called RiFyFiVDG, referring to Radio Frequency Fingerprint Virtual Database Generator, and explore individually the impairment impact on the classification accuracy to highlight the most relevant impairment. Different transmission scenarios are then explored, such as the impact of the data type (being a preamble or a payload) and the data size. Design rules of real databases are finally drawn for the different scenarios. We found out that the power amplifier imperfections play the biggest role in RFF accuracy and that average accuracy levels of 94% can be reached when combining the various hardware impairments at the transmitter.