AI-Enhanced Pilot-Assisted Channel Estimation and Signal Detection for 6G with Zero CSI

Bibin Babu; Muhammad Yunis Daha; Kiran Khurshid; Muhammad Usman Hadi

AI-Enhanced Pilot-Assisted Channel Estimation and Signal Detection for 6G with Zero CSI

Bibin Babu
, Muhammad Yunis Daha
, Kiran Khurshid
, Muhammad Usman Hadi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper proposes a feed-forward neural network-based artificial intelligence (AI)-enhanced system for pilot-assisted channel estimation and signal detection in sixth-generation MIMO wireless communication, operating without channel state information (CSI). Least square estimation is employed to determine channel characteristics using pilot symbols. These characteristics are then utilized by the AI model to estimate the channel, which, combined with received data symbols, enables signal detection via AI model. The proposed model has shown some promising results for the future research in MIMO technology. Overall, this paper stands as a comprehensive guide for the future developments in channel estimation and signal detection at zero CSI.

Original language	English
Title of host publication	2025 2nd International Conference on Microwave, Antennas & Circuits (ICMAC)
Number of pages	4
ISBN (Electronic)	979-8-3315-1842-4
Publication status	Published online - 20 May 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 8 Decent Work and Economic Growth
SDG 9 Industry, Innovation, and Infrastructure
SDG 10 Reduced Inequalities
SDG 11 Sustainable Cities and Communities
SDG 12 Responsible Consumption and Production

Keywords

channel estimation
signal detection
Zero CSI
B5G
MIMO

Cite this

@inproceedings{6979da12469d46249e79b066c95147b8,

title = "AI-Enhanced Pilot-Assisted Channel Estimation and Signal Detection for 6G with Zero CSI",

abstract = "This paper proposes a feed-forward neural network-based artificial intelligence (AI)-enhanced system for pilot-assisted channel estimation and signal detection in sixth-generation MIMO wireless communication, operating without channel state information (CSI). Least square estimation is employed to determine channel characteristics using pilot symbols. These characteristics are then utilized by the AI model to estimate the channel, which, combined with received data symbols, enables signal detection via AI model. The proposed model has shown some promising results for the future research in MIMO technology. Overall, this paper stands as a comprehensive guide for the future developments in channel estimation and signal detection at zero CSI.",

keywords = "channel estimation, signal detection, Zero CSI, B5G, MIMO",

author = "Bibin Babu and Daha, \{Muhammad Yunis\} and Kiran Khurshid and Hadi, \{Muhammad Usman\}",

year = "2025",

month = may,

day = "20",

language = "English",

isbn = "979-8-3315-1843-1",

booktitle = "2025 2nd International Conference on Microwave, Antennas \& Circuits (ICMAC)",

}

TY - GEN

T1 - AI-Enhanced Pilot-Assisted Channel Estimation and Signal Detection for 6G with Zero CSI

AU - Babu, Bibin

AU - Daha, Muhammad Yunis

AU - Khurshid, Kiran

AU - Hadi, Muhammad Usman

PY - 2025/5/20

Y1 - 2025/5/20

N2 - This paper proposes a feed-forward neural network-based artificial intelligence (AI)-enhanced system for pilot-assisted channel estimation and signal detection in sixth-generation MIMO wireless communication, operating without channel state information (CSI). Least square estimation is employed to determine channel characteristics using pilot symbols. These characteristics are then utilized by the AI model to estimate the channel, which, combined with received data symbols, enables signal detection via AI model. The proposed model has shown some promising results for the future research in MIMO technology. Overall, this paper stands as a comprehensive guide for the future developments in channel estimation and signal detection at zero CSI.

AB - This paper proposes a feed-forward neural network-based artificial intelligence (AI)-enhanced system for pilot-assisted channel estimation and signal detection in sixth-generation MIMO wireless communication, operating without channel state information (CSI). Least square estimation is employed to determine channel characteristics using pilot symbols. These characteristics are then utilized by the AI model to estimate the channel, which, combined with received data symbols, enables signal detection via AI model. The proposed model has shown some promising results for the future research in MIMO technology. Overall, this paper stands as a comprehensive guide for the future developments in channel estimation and signal detection at zero CSI.

KW - channel estimation

KW - signal detection

KW - Zero CSI

KW - B5G

KW - MIMO

UR - https://pure.ulster.ac.uk/en/publications/6979da12-469d-4624-9e79-b066c95147b8

M3 - Conference contribution

SN - 979-8-3315-1843-1

BT - 2025 2nd International Conference on Microwave, Antennas & Circuits (ICMAC)

ER -

AI-Enhanced Pilot-Assisted Channel Estimation and Signal Detection for 6G with Zero CSI

Abstract

UN SDGs

Keywords

Other files and links

Fingerprint

Edge intelligence for 5G networks and beyond

Cite this

AI-Enhanced Pilot-Assisted Channel Estimation and Signal Detection for 6G with Zero CSI

Abstract

UN SDGs

Keywords

Other files and links

Fingerprint

Student theses

Edge intelligence for 5G networks and beyond

Cite this