TY - GEN

T1 - Performance of Large Intelligent Surface-enabled Cooperative Networks over Nakagami-m Channels

AU - Makin, Madi

AU - Nauryzbayev, Galymzhan

AU - Arzykulov, Sultangali

AU - Hashmi, Mohammad S.

N1 - KAUST Repository Item: Exported on 2023-01-27
Acknowledgements: VI. ACKNOWLEDGEMENT This work was supported by the Nazarbayev University Faculty Development Competitive Research Program under Grant no. 240919FD3935.

PY - 2021/9

Y1 - 2021/9

N2 - In this paper, we analyze the system performance of a large intelligent surface (LIS) enabled wireless system over Nakagami-m channels. We derive closed-form expressions of the outage probability, ergodic capacity, and average bit error rate using different approximation methods, namely, central limit theorem (CLT), Gamma and generalized-K (K_{\mathrm{G}}). The effects of a number of passive LIS pixels (N) and fading parameters on the system performance are examined. Results show that the Gamma and K_{\mathrm{G}} approximations are precise given different values of N, while the CLT approximation's accuracy depends on the number of LIS elements implemented. Finally, analytical findings are validated by thorough Monte Carlo simulations.

AB - In this paper, we analyze the system performance of a large intelligent surface (LIS) enabled wireless system over Nakagami-m channels. We derive closed-form expressions of the outage probability, ergodic capacity, and average bit error rate using different approximation methods, namely, central limit theorem (CLT), Gamma and generalized-K (K_{\mathrm{G}}). The effects of a number of passive LIS pixels (N) and fading parameters on the system performance are examined. Results show that the Gamma and K_{\mathrm{G}} approximations are precise given different values of N, while the CLT approximation's accuracy depends on the number of LIS elements implemented. Finally, analytical findings are validated by thorough Monte Carlo simulations.

UR - http://hdl.handle.net/10754/675322

UR - https://ieeexplore.ieee.org/document/9625422/

UR - http://www.scopus.com/inward/record.url?scp=85123015600&partnerID=8YFLogxK

U2 - 10.1109/VTC2021-Fall52928.2021.9625422

DO - 10.1109/VTC2021-Fall52928.2021.9625422

M3 - Conference contribution

SN - 9781665413688

BT - 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)

PB - IEEE

ER -