arXiv subjects

Hamid Saeedi-Sourck

Publications and source records attributed to Hamid Saeedi-Sourck.

3 recordsLinked to original sources

arXiv2023

Low-PAPR Joint Channel Estimation and Data Detection in ZP-OTFS System

Orthogonal Time Frequency Space (OTFS) systems face significant challenges in channel estimation due to high pilot overhead and peak-to-average power ratio (PAPR). To address these issues, we propose a two-step channel estimation method for Zero-Pad OTFS (ZP-OTFS), a modified OTFS system characterized by multiple zero rows along the delay axis. This method strategically inserts pilot sequences into the zero bins of the ZP-OTFS system, effectively mitigating overhead and PAPR. Comprehensive simulation results validate the effectiveness of our proposed method, demonstrating its superior performance over traditional embedded pilot estimation in high Signal-to-Noise Ratio (SNR) scenarios. Specifically, our method achieves a lower normalized mean square error (NMSE) and better bit error rates (BER) at high SNRs.

cs.IT↗

arXiv2022

Superimposed Channel Estimation in OTFS Modulation Using Compressive Sensing

Orthogonal time frequency space (OTFS) technique is a two-dimensional modulation method that multiplexes information symbols in the delay-Doppler (DD) domain. OTFS combats high Doppler shift existing in high speed wireless communication. However, conventional channel estimation in OTFS suffers from high pilot overhead because guard symbols occupy a significant part of the DD domain grids. In this paper, a superimposed channel estimation is proposed which can completely estimate channel parameters without considering pilot overhead and performance degradation. As the channel state information (CSI) in the DD domain is sparse, a sparse recovery algorithm orthogonal matching pursuit (OMP) is used. Besides, our proposed method does not suffer from high peak to average power ratio (PAPR). To detect information symbols, a message passing (MP) detector, which exploits the sparsity of DD channel representation, is employed.

cs.IT↗

arXiv2020

Spectral Domain Spline Graph Filter Bank

In this paper, we present a structure for two-channel spline graph filter bank with spectral sampling (SGFBSS) on arbitrary undirected graphs. Our proposed structure has many desirable properties; namely, perfect reconstruction, critical sampling in spectral domain, flexibility in choice of shape and cut-off frequency of the filters, and low complexity implementation of the synthesis section, thanks to our closed-form derivation of the synthesis filter and its sparse structure. These properties play a pivotal role in multi-scale transforms of graph signals. Additionally, this framework can use both normalized and non-normalized Laplacian of any undirected graph. We evaluate the performance of our proposed SGFBSS structure in nonlinear approximation and denoising applications through simulations. We also compare our method with the existing graph filter bank structures and show its superior performance.

eess.SP↗