FPGA implementation of a cyclostationary detector for OFDM signals

Due to the ubiquity of Orthogonal Frequency Division Multiplexing (OFDM) based communications standards such as IEEE 802.11 a/g/n and 3GPP Long Term Evolution (LTE), a growing interest has developed in techniques for reliably detecting the presence of these signals in dynamic radio systems. A popular approach for detection is to exploit the cyclostationary nature of OFDM communications signals. In this paper, we focus on a frequency domain cyclostationary detection algorithm first introduced by Giannakis and Dandawate and study its performance in detecting IEEE 802.11a OFDM signals in the presence of practical radio impairments such as Carrier Frequency offset (CFO), Phase Noise, I/Q Imbalance, Multipath Fading and DC offset. We then present a hardware implementation of this algorithm developed using MathWorks HDL Coder and provide implementation results after targeting to a Xilinx 7 Series FPGA device

Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks

Cognitive radio has been widely considered as one of the prominent solutions to tackle the spectrum scarcity. While the majority of existing research has focused on single-band cognitive radio, multiband cognitive radio represents great promises towards implementing efficient cognitive networks compared to single-based networks. Multiband cognitive radio networks (MB-CRNs) are expected to significantly enhance the network's throughput and provide better channel maintenance by reducing handoff frequency. Nevertheless, the wideband front-end and the multiband spectrum access impose a number of challenges yet to overcome. This paper provides an in-depth analysis on the recent advancements in multiband spectrum sensing techniques, their limitations, and possible future directions to improve them. We study cooperative communications for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also investigate several limits and tradeoffs of various design parameters for MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE Journal, Special Issue on Future Radio Spectrum Access, March 201

SMARAD - Centre of Excellence in Smart Radios and Wireless Research - Activity Report 2011 - 2013

Centre of Excellence in Smart Radios and Wireless Research (SMARAD), originally established with the name Smart and Novel Radios Research Unit, is aiming at world-class research and education in Future radio and antenna systems, Cognitive radio, Millimetre wave and THz techniques, Sensors, and Materials and energy, using its expertise in RF, microwave and millimeter wave engineering, in integrated circuit design for multi-standard radios as well as in wireless communications. SMARAD has the Centre of Excellence in Research status from the Academy of Finland since 2002 (2002-2007 and 2008-2013). Currently SMARAD consists of five research groups from three departments, namely the Department of Radio Science and Engineering, Department of Micro and Nanosciences, and Department of Signal Processing and Acoustics, all within the Aalto University School of Electrical Engineering. The total number of employees within the research unit is about 100 including 8 professors, about 30 senior scientists and about 40 graduate students and several undergraduate students working on their Master thesis. The relevance of SMARAD to the Finnish society is very high considering the high national income from exports of telecommunications and electronics products. The unit conducts basic research but at the same time maintains close co-operation with industry. Novel ideas are applied in design of new communication circuits and platforms, transmission techniques and antenna structures. SMARAD has a well-established network of co-operating partners in industry, research institutes and academia worldwide. It coordinates a few EU projects. The funding sources of SMARAD are diverse including the Academy of Finland, EU, ESA, Tekes, and Finnish and foreign telecommunications and semiconductor industry. As a by-product of this research SMARAD provides highest-level education and supervision to graduate students in the areas of radio engineering, circuit design and communications through Aalto University and Finnish graduate schools. During years 2011 – 2013, 18 doctor degrees were awarded to the students of SMARAD. In the same period, the SMARAD researchers published 197 refereed journal articles and 360 conference papers

Enhanced Spectrum Sensing Techniques for Cognitive Radio Systems

Due to the rapid growth of new wireless communication services and applications, much attention has been directed to frequency spectrum resources. Considering the limited radio spectrum, supporting the demand for higher capacity and higher data rates is a challenging task that requires innovative technologies capable of providing new ways of exploiting the available radio spectrum. Cognitive radio (CR), which is among the core prominent technologies for the next generation of wireless communication systems, has received increasing attention and is considered a promising solution to the spectral crowding problem by introducing the notion of opportunistic spectrum usage. Spectrum sensing, which enables CRs to identify spectral holes, is a critical component in CR technology. Furthermore, improving the efficiency of the radio spectrum use through spectrum sensing and dynamic spectrum access (DSA) is one of the emerging trends. In this thesis, we focus on enhanced spectrum sensing techniques that provide performance gains with reduced computational complexity for realistic waveforms considering radio frequency (RF) impairments, such as noise uncertainty and power amplifier (PA) non-linearities. The first area of study is efficient energy detection (ED) methods for spectrum sensing under non-flat spectral characteristics, which deals with relatively simple methods for improving the detection performance. In realistic communication scenarios, the spectrum of the primary user (PU) is non-flat due to non-ideal frequency responses of the devices and frequency selective channel conditions. Weighting process with fast Fourier transform (FFT) and analysis filter bank (AFB) based multi-band sensing techniques are proposed for overcoming the challenge of non-flat characteristics. Furthermore, a sliding window based spectrum sensing approach is addressed to detect a re-appearing PU that is absent in one time and present in other time. Finally, the area under the receiver operating characteristics curve (AUC) is considered as a single-parameter performance metric and is derived for all the considered scenarios. The second area of study is reduced complexity energy and eigenvalue based spectrum sensing techniques utilizing frequency selectivity. More specifically, novel spectrum sensing techniques, which have relatively low computational complexity and are capable of providing accurate and robust performance in low signal-to-noise ratio (SNR) with noise uncertainty, as well as in the presence of frequency selectivity, are proposed. Closed-form expressions are derived for the corresponding probability of false alarm and probability of detection under frequency selectivity due the primary signal spectrum and/or the transmission channel. The offered results indicate that the proposed methods provide quite significant saving in complexity, e.g., 78% reduction in the studied example case, whereas their detection performance is improved both in the low SNR and under noise uncertainty. Finally, a new combined spectrum sensing and resource allocation approach for multicarrier radio systems is proposed. The main contribution of this study is the evaluation of the CR performance when using wideband spectrum sensing methods in combination with water-filling and power interference (PI) based resource allocation algorithms in realistic CR scenarios. Different waveforms, such as cyclic prefix based orthogonal frequency division multiplexing (CP-OFDM), enhanced orthogonal frequency division multiplexing (E-OFDM) and filter bank based multicarrier (FBMC), are considered with PA nonlinearity type RF impairments to see the effects of spectral leakage on the spectrum sensing and resource allocation performance. It is shown that AFB based spectrum sensing techniques and FBMC waveforms with excellent spectral containment properties have clearly better performance compared to the traditional FFT based spectrum sensing techniques with the CP-OFDM. Overall, the investigations in this thesis provide novel spectrum sensing techniques for overcoming the challenge of noise uncertainty with reduced computational complexity. The proposed methods are evaluated under realistic signal models