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.

摘要

软件定义无线电 (SDR) 处理认知无线电网络 (CRN) 中射频 (RF) 信号的机会检测和分配。主要许可用户和次要未许可用户之间的机会性频谱使用取决于对频谱空洞的充分检测。本文介绍了一种用于认知无线电中 SDR 的智能紧凑型宽带贴片天线的设计。具有电磁带隙结构的分形缝隙天线设计在12×18 mm ²FR4 基板作为认知无线电传感器网络中的主要感知模块。天线 0.68-12.1 GHz 的宽工作范围与 SDR 的自适应学习高度兼容，以处理大带宽上的动态频谱变化。考虑到 FR4 的切向损耗和在整个阻抗带宽上的可持续增益 2.5 dBi，该天线表现出高辐射效率。所提出的五层 ANN-ML 监督 SDR 包括通过对主要和次要用户的系数估计的多光谱分辨率，并在白色空间频谱感测期间引起所提出天线的高吞吐量。ANN-ML 的数据分析以 Python 3.0 IDE 为模型，并根据联邦通信委员会的 CRN 标准对结果进行验证，以证明天线用于宽带传感的候选资格。