Abstract
In this paper, we consider the problem of maximizing the worst user signal to interference noise ratio (SINR) for massive multiple input multiple output (MaMIMO). We reformulate the nonlinear optimization model as a joint chance-constrained geometric program. We derive then a deterministic equivalent for the obtained stochastic problem. Based on the optimality conditions, we propose a neurodynamic approach for the optimization problem. We show that the proposal dynamical neural network is convergent and stable in the sense of Lyapunov. Our numerical results indicate that our approach is robust and outperforms a state-of-art convex approximation.
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Tassouli, S., Lisser, A. (2023). Maximizing Signal to Interference Noise Ratio for Massive MIMO: A Stochastic Neurodynamic Approach. In: Younas, M., Awan, I., Grønli, TM. (eds) Mobile Web and Intelligent Information Systems. MobiWIS 2023. Lecture Notes in Computer Science, vol 13977. Springer, Cham. https://doi.org/10.1007/978-3-031-39764-6_15
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DOI: https://doi.org/10.1007/978-3-031-39764-6_15
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