Comparison of spectrum occupancy measurements using software defined radio RTL-SDR with a conventional spectrum analyzer approach

In the present day Cognitive Radio has become a realistic option for solution of the spectrum scarcity problem in wireless communication. Recently, the TV band has attracted attention due to the considerable potential for exploitation of available TV white space which is not utilized based on time and location. In this paper, we investigate spectrum occupancy of the UHF TV band in the frequency range from 470 to 862MHz by using two different devices, the low cost device RTL-SDR and high cost spectrum analyzer. The spectrum occupancy measurements provide evidence of the utility of using the inexpensive RTL SDR and illustrate its effectiveness for detection of the percentage of spectrum utilization compared with results from the conventional high cost Agilent spectrum analyzer, both systems employing various antennas

Spectrum occupancy measurements and lessons learned in the context of cognitive radio

Various measurement campaigns have shown that numerous spectrum bands are vacant even though licenses have been issued by the regulatory agencies. Dynamic spectrum access (DSA) based on Cognitive Radio (CR) has been regarded as a prospective solution to improve spectrum utilization for wireless communications. Empirical measurement of the radio environment to promote understanding of the current spectrum usage of the different wireless services is the first step towards deployment of future CR networks. In this paper we present our spectrum measurement setup and discuss lessons learned during our measurement activities. The main contribution of the paper is to introduce global spectrum occupancy measurements and address the major drawbacks of previous spectrum occupancy studies by providing a unifying methodological framework for future spectrum measurement campaigns

Experimental detection using cyclostationary feature detectors for cognitive radios

© 2014 IEEE. Signal detection is widely used in many applications. Some examples include Cognitive Radio (CR) and military intelligence. Without guaranteed signal detection, a CR cannot reliably perform its role. Spectrum sensing is currently one of the most challenging problems in cognitive radio design because of various factors such as multi-path fading and signal to noise ratio (SNR). In this paper, we particularly focus on the detection method based on cyclostationary feature detectors (CFD) estimation. The advantage of CFD is its relative robustness against noise uncertainty compared with energy detection methods. The experimental result present in this paper show that the cyclostationary feature-based detection can be robust compared to energy-based technique for low SNR levels

Cooperative wideband spectrum sensing with multi-bit hard decision in cognitive radio

Cognitive radio offers an increasingly attractive solution to overcome the underutilization problem. A sensor network based cooperative wideband spectrum sensing is proposed in this paper. The purpose of the sensor network is to determine the frequencies of the sources and reduced the total sensing time using a multi-resolution sensing technique. The final result is computed by data fusion of multi-bit decisions made by each cooperating secondary user. Simulation results show improved performance in energy efficiency

Quality measurements of an UWB reduced-size CPW-fed aperture antenna

The paper presents a characterization of a compact co-planar waveguide (CPW)-fed slot loaded low return loss planar printed antenna designed for wireless communication and ultra-wideband (UWB) applications. Following a review of the antenna design, which was implemented and simulated using Agilent's Advanced Design System (ADS), the paper presents laboratory measurements of relative gain and impulse response transformed from the frequency domain. An antenna quality metric based on time-domain S21 is discussed and related to antenna quality metrics such as the System Fidelity Factor (SFF)

On Multilingual Training of Neural Dependency Parsers

We show that a recently proposed neural dependency parser can be improved by joint training on multiple languages from the same family. The parser is implemented as a deep neural network whose only input is orthographic representations of words. In order to successfully parse, the network has to discover how linguistically relevant concepts can be inferred from word spellings. We analyze the representations of characters and words that are learned by the network to establish which properties of languages were accounted for. In particular we show that the parser has approximately learned to associate Latin characters with their Cyrillic counterparts and that it can group Polish and Russian words that have a similar grammatical function. Finally, we evaluate the parser on selected languages from the Universal Dependencies dataset and show that it is competitive with other recently proposed state-of-the art methods, while having a simple structure.Comment: preprint accepted into the TSD201