Opportunistic Spectrum Use Research Articles

Machine learning supervised antenna for software defined cognitive radios

ABSTRACT Software-defined radio (SDR) deals with the opportunistic detection and allocation of the radio frequency (RF) signals in the cognitive radio network (CRN). The opportunistic spectrum use between primary licenced and secondary unlicensed user depends upon the adequate detection of the spectrum hole. This paper presents a design of a smart compact wideband patch antenna for SDR in cognitive radios. The fractal slot antenna with electromagnetic bandgap structure is designed on 12 × 18 mm2 FR4 substrate as a primary perception module in the cognitive radio sensor network. The wide operating range of antenna 0.68–12.1 GHz is highly compatible with adaptive learning of SDR to deal with dynamic spectrum variation over a large bandwidth. The antenna exhibits high radiation efficiency with consideration of tangential losses of FR4 and sustainable gain 2.5 dBi over the complete impedance bandwidth. The proposed five-layered ANN-ML supervised SDR comprises multispectral resolution via coefficient estimation for primary and secondary user and elicits high throughput of proposed antenna during white space spectrum sensing. The data analytics for ANN-ML has modelled on Python 3.0 IDE and the results are validated to justify the candidature of the antenna for wideband sensing as per the Federal Communications Commission standards for CRN.

Multiplierless Filtered-OFDM Transmitter for Narrowband IoT Devices

In cyclic-prefix orthogonal frequency-division multiplexing (CP-OFDM)-based radio access, the coexistence of different technologies without precise time-frequency synchronization is limited due to high out-of-band (OOB) emissions. Therefore, the spectrum enhancement techniques play a key role in relaxing the synchronization and power control requirements. This allows a higher degree of opportunistic spectrum use with minimized interference. In addition, all the transmitting devices have to fulfill specific transmitted signal quality requirements, including the maximum OOB radiated signal power. With the orthogonal frequency-division multiplexing (OFDM)-based radio access, some additional signal processing for improved spectrum containment is commonly needed to achieve these requirements. The filtering and time-domain windowing are two fundamentally different approaches for spectrum enhancement. The filtered OFDM (F-OFDM) provides better spectrum localization than the time-windowing schemes [such as windowed overlap-add (WOLA)], with the cost of higher complexity. This article introduces low-complexity solutions for spectrally enhanced narrowband OFDM transmitters based on the use of lookup tables (LUTs). The proposed LUT approach, requiring only memory units and a low number of additions, allows to avoid all computationally expensive operations in online transmitter processing, as it builds the transmitted signal by summing the stored partial waveforms optimized offline. In certain cases, completely multiplication- and summation-free designs are possible. The transmitters of narrowband Internet of Things (NB-IoT) devices are natural applications for the proposed LUT approach, as they require additional digital baseband signal processing to reach the emission requirements. It is shown that the proposed LUT schemes can provide significant savings in real-time computations of NB-IoT devices, while fulfilling the 3GPP requirements.

Bit Stream Recognition and Analysis in Cognitive Radio

AbstractCognitive radios (CRs) observe their environment, learn from it and take their findings into account when dealing with signal reception or transmission. Until now, essentially spectral observations are under discussion in the CR context. Smart spectrum sensing may allow secondary or opportunistic use of spectrum and lead to dynamic spectrum access. In this paper we investigate a CR that also employs spectrum sensing but additionally may take results of modulation recognition as well as bit stream recognition into account when tuning its receiver. Bit stream analysis as an essential prerequisite to bit stream recognition is introduced as well. In data communications, bit streams play a prominent role in burst and frame structures, scrambling, synchronization, error correction coding or ciphering. Since modulation recognition found reasonable attention in recent publications, we here concentrate on bit stream recognition and analysis in a CR.