Reliability Centric Performance Analysis of O-LSDC-Assisted Massive MIMO with Artificial Intelligence for 6G Networks

DOI

10.2991/978-94-6239-723-1_38How to use a DOI?

Keywords

Massive MIMO; Sixth-Generation (6G) Wireless Systems; Reliability Analysis; Ultra-Reliable Low-Latency Communication (URLLC)

Abstract

Massive multiple-input multiple-output (MIMO) is one of the key technologies behind sixth-generation (6G) wireless systems, but its performance is limited by channel estimation errors that are particularly pronounced in high mobility and sub-THz operating regimes. Towards improving channel state information accuracy, AI-assisted channel optimization strategies such as the Optimized Linear Scalable Dispersion Code (O-LSDC) have been recently proposed. Although past works have focused on the spectral efficiency gains of O-LSDC, the reliability implications of O-LSDC are still not well understood. We first investigate the effect of reliability from the perspective of performance evaluation, and present a performance evaluation of the O-LSDC-assisted massive MIMO systems with reliable transport level under realistic 6G operation conditions. We analyze massive MIMO and systems augmented with O-LSDC in terms of BER, SNR, outage probability and power succession, using an analytical simulation-based framework. Our results show that O-LSDC yields limited gains in spectral efficiency (generally 10–15% at high SNR) but significant gains in terms of reliability, yielding a 2–3 dB reduction in SNR for a target BER of 10⁻⁷ and an order-of-magnitude reduction in outage probability. These improvements represent a 30–50% saving in transmit power, invaluable for ultra-reliable low-latency communication (URLLC) and energy-scarce 6G use cases and applications. Results demonstrate that O-LSDC should not be considered as the maximum throughput but rather a reliability-enabling technology which is ideal for sub-THz bands, high-mobility cases and mission-critical services. In addition to the results, this work also brings insights into the system level implications of AI-assisted channel denoising towards meeting the stringent 6G reliability requirements.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Sagar S. Sutar
AU  - Chetan More
PY  - 2026
DA  - 2026/07/14
TI  - Reliability Centric Performance Analysis of O-LSDC-Assisted Massive MIMO with Artificial Intelligence for 6G Networks
BT  - Proceedings of the International Conference on Responsible, Risk-aware, and Regulated AI (RRRAI 2026)
PB  - Atlantis Press
SP  - 425
EP  - 439
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-723-1_38
DO  - 10.2991/978-94-6239-723-1_38
ID  - Sutar2026
ER  -