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 -