Year: 2024
Type: article
Abstract: Variational Bayesian learning (VBL)-based sparse channel state information (CSI) estimation is conceived for multiple input multiple output (MIMO) orthogonal time frequency space (OTFS) and for orthogonal time sequence multiplexing (OTSM)-based systems relying on low-resolution analog-to-digital convertors (ADCs). First, the CSI estimation model is developed for MIMO-OTFS systems considering quantized outputs. Then a novel VBL technique is developed for exploiting the inherent DD domain sparsity. Subsequently, an end-to-end system model is derived for MIMO-OTSM systems, once again, using only finite-resolution ADCs. Similar to OTFS systems, it is demonstrated that the channel is sparse in the delay-sequency (DS)-domain. Thus the sparse CSI estimation problem of the MIMO-OTSM system can also be solved using the VBL technique developed for its OTFS counterpart. A bespoke minimum mean square error (MMSE) receiver is developed for data detection, which unlike the conventional MMSE receiver also accounts for the quantization error. Finally, finite-resolution ADCs emerge as a solution, offering reduced costs and energy consumption amid the growing challenge posed by energy-intensive high-resolution ADCs in Next-Generation (NG) systems. The efficacy of the proposed techniques is validated by simulation results, surpassing the state-of-the-art and signalling a transition towards more sustainable communication technologies.
Authors: Anand Mehrotra, Suraj Srivastava, N. Shanmughanadha Reddy, Aditya Jagannatham, Lajos Hanzo
Language: English
Topic: PAPR reduction in OFDM
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: G108985073, UKRI-India Future Networks Initiative, Platform Driving The Ultimate Connectivity, Pervasive Wireless Intelligence Beyond the Generations (PerCom), G2544329817, Reliable and Robust Quantum Computing, TITAN Extension, Pervasive Wireless Intelligence Beyond the Generations, Ubiquitous Quantum Communications, G5793758419, G6495392424
OpenAlex ID: W4404563423