Open AccessSpectrum shaping using NC‐OFDM for cognitive radio applications
Author(s) -
Sanker Penilop Parukutty,
Jayaprakash Narayanan Bhaghath Perumbilavil,
Kaliappan Mourougayane
Publication year - 2020
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2018.5945
Subject(s) - orthogonal frequency division multiplexing , cognitive radio , computer science , software defined radio , transmitter , transceiver , field programmable gate array , computer hardware , matlab , interference (communication) , embedded system , electronic engineering , channel (broadcasting) , telecommunications , wireless , engineering , operating system
Cognitive radio (CR) is an innovative technology that supports dynamic spectrum access to utilise the spectrum more efficiently in an opportunistic manner without interfering with the licensed users. Multi‐carrier modulation techniques such as orthogonal frequency division multiplexing (OFDM) is identified as one of the suitable candidates for the design of CR systems. To provide high data rates while avoiding interference with licensed users, a variant of OFDM called non‐contiguous OFDM (NC‐OFDM) is used in CR. NC‐OFDM is useful for dynamic shaping of spectrum based on the channel impairments, interference and jamming threats. In this work, the customised NC‐OFDM transceiver link is simulated and compared with the customised OFDM link in the MATLAB environment. It is implemented in Zynq‐7000 System‐on‐Chip (SoC) 7Z020 with radio‐frequency front‐end board, AD‐FMCOMMS3‐EBZ Software Defined Radio platform. Implementation of this link is carried out on Spartan‐3A FPGA and compared the hardware resource utilisation with Zynq platform. It is observed that the Zynq platform utilises less hardware resources for the implementation. The transmitter spectrum, spectrum after de‐activating unwanted sub‐carriers, the spectrum shaping using raised cosine filtering, spectrum sensing using the correlation of known pattern and FFT‐based spectrum sensing are also simulated in the MATLAB.