arXiv subjects

Praful Mankar

Publications and source records attributed to Praful Mankar.

2 recordsLinked to original sources

arXiv2026

GLRT for Reconfigurable Intelligent Surface aided Spectrum Sensing

Spectrum sensing (SS) is crucial for realising cognitive radio networks, where the secondary user (SU) needs to detect the presence of a primary user (PU) in order to utilise the spectrum. However, the ability of detection is influenced by unknown propagation environment factors such as multipath fading, correlated noise, transmission power of PU, etc. This paper investigates reconfigurable intelligent surfaces (RIS)-aided SS under correlated noise conditions using a generalised likelihood ratio test (GLRT) and energy detector (ED) frameworks. We first derive maximum likelihood estimates of the unknown channel state and transmit power, and employ these estimates to construct the GLRT-based test statistic using the signal received with an optimally configured RIS. The RIS phase shift matrix is optimally determined to maximise the gain of the estimated channel. Besides, the detection and false alarm probabilities of ED with optimally configured RIS are also derived. The numerical receiver output characteristics (ROC) demonstrate that the proposed GLRT achieves superior detection probability compared to ED, particularly under correlated noise and limited number of observations.

eess.SP↗

arXiv2025

Detection of Number of Subcarriers of OFDM Systems using Eigen-Spectral Analysis

Orthogonal Frequency-Division Multiplexing (OFDM) is widely used in modern wireless communication systems due to its robustness against time-dispersive channels. In this work, we consider a non-cooperative scenario where the receiver does not have prior knowledge of the OFDM parameters such as the number of subcarriers and the aim is to estimate them using the received data. Such a setup has applications in cognitive radio networks. For this blind OFDM parameter estimation problem, we provide a novel method based on eigen-spectral analysis of the covariance matrix corresponding to the received data. In particular, we show that the covariance matrix exhibits a distinctive rank property under correct segmentation of the received symbols, reflecting a characteristic behavior in its eigenvalue spectrum that facilitates accurate estimation of the number of subcarriers. The proposed method is more general than existing approaches in the literature, as it can detect an arbitrary number of subcarriers and its performance remains independent of the modulation scheme. The numerical results show that the proposed method accurately detects the number of subcarriers with high probability even at low SNR.

cs.IT↗