Random Matrix Methods for Wireless Communications: Applications to wireless communications

By Romain Couillet, Merouane Debbah. 2011

388 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/547cff71c8dfc32f17fe344d6c1ab6a0cfdd710d

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In Computer Networks and Communications

In Electrical and Electronic Engineering

In Statistics and Probability

Citations in the corpus

The Dynamics of Learning: A Random Matrix Approach (2018, background): “For certain simple distributions of M, one may define a so-called deterministic equivalent (Hachem et al., 2007; Couillet & Debbah, 2011) Q̄M for QM, which is a deterministic matrix such that for all A ∈ Rn×n and all a,b ∈ Rn of bounded (spectral and Euclidean, respectively) norms, 1n tr (AQM) − 1n…”
Tensor Programs III: Neural Matrix Laws (2020, background): “[8] Romain Couillet and Merouane Debbah.”
Twelve Scientific Challenges for 6G: Rethinking the Foundations of Communications Theory (2022, background): “Over the last couple of years, random matrix theory (RMT) has found its applications in the field of wireless communications, and it has been regarded as a powerful tool that can be leveraged in order to model and optimize wireless systems, as well as quantify their performance [116].”
Energy Efficiency in Wireless Networks via Fractional Programming Theory (2015, background): “However, in this case, additional mathematical tools like random matrix theory (RMT) [165, 47] and stochastic geometry (SG) [11, 175] are required to statistically characterize the network interference levels.”
Living on the edge: The role of proactive caching in 5G wireless networks (2014, background): “Ultimately, solving these complex networks requires a multi-disciplinary approach by weaving notions from machine learning [13], social networking, graph theory and random matrix theory [14].”
Quantized Precoding for Massive MU-MIMO (2016, background): “tion ( x) = R x 1 p1 2ˇ e is the cumulativet2=2 dt distribution function of a Gaussian random variable. Let also ˆ be deﬁned as follows: ˆ= G2ˆ (1 inG2)ˆ+ 1 : (31) Following the same approach as in [52]–[54], one can show that, for the three linear-quantized precoders (WF, ZF, and MRT) introduced in”
Approximation of Rectangular Beta-Laguerre Ensembles and Large Deviations (2013, background): “In fact, data matrices with complex-valued entries arise frequently, for example, in signal processing applications (e.g., Couillet and Debbah, 2011) and statistics (e.g., James (1964) and Picinbono (1996)).”
Energy-Efficient Design of MIMO Heterogeneous Networks With Wireless Backhaul (2015, methodology): “Stochastic geometry is a powerful tool to analyze the interference in large HetNets with a random topology [30], whereas random matrix theory enables a deterministic abstraction of the physical layer, for a fixed network topolo gy [31].”