Modeling and Analyzing Millimeter Wave Cellular Systems

By Jeffrey G. Andrews, Tianyang Bai, Mandar N. Kulkarni, Ahmed Alkhateeb, Abhishek K. Gupta, Robert W. Heath. IEEE Transactions on Communications, 2016

782 citations recorded by the source corpus.

We provide a comprehensive overview of mathematical models and analytical techniques for millimeter wave (mmWave) cellular systems. The two fundamental physical differences from conventional sub-6-GHz cellular systems are: 1) vulnerability to blocking and 2) the need for significant directionality at the transmitter and/or receiver, which is achieved through the use of large antenna arrays of small individual elements. We overview and compare models for both of these factors, and present a baseline analytical approach based on stochastic geometry that allows the computation of the statistical distributions of the downlink signal-to-interference-plus-noise ratio (SINR) and also the per link data rate, which depends on the SINR as well as the average load. There are many implications of the models and analysis: 1) mmWave systems are significantly more noise-limited than at sub-6 GHz for most parameter configurations; 2) initial access is much more difficult in mmWave; 3) self-backhauling is more viable than in sub-6-GHz systems, which makes ultra-dense deployments more viable, but this leads to increasingly interference-limited behavior; and 4) in sharp contrast to sub-6-GHz systems cellular operators can mutually benefit by sharing their spectrum licenses despite the uncontrolled interference that results from doing so. We conclude by outlining several important extensions of the baseline model, many of which are promising avenues for future research.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/2a7ca1866f1badac879949c610a642fb9cfa16fd

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In Electrical and Electronic Engineering

Citations in the corpus

Smart radio environments empowered by reconfigurable AI meta-surfaces: an idea whose time has come (2019, neutral): “, power consumption, hardware complexity, blocking of links) apply to using millimeter-wave communications [46, 47].”
Stochastic Geometric Coverage Analysis in mmWave Cellular Networks With Realistic Channel and Antenna Radiation Models (2018, neutral): “, Nakagami-m fading) [4]–[6] or a log-normal distribution [7].”
5G D2D Networks: Techniques, Challenges, and Future Prospects (2018, background): “Similarly, a comprehensive overview of mathematical models for mmWave technology is presented in [99].”
Millimeter Wave Communications for Future Mobile Networks (2017, background): “More recently, [17] gives a comprehensive overview of mathematical models and analytical frameworks for mmWave cellular networks.”
A Survey on Hybrid Beamforming Techniques in 5G: Architecture and System Model Perspectives (2018, background): “3(c)), provides a flexible compromise between hardware complexity and system performance [39].”
5G Key Technologies for Smart Railways (2020, background): “However, the cell size needs to be greatly reduced and new network architecture should be designed [10].”
Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both? (2019, methodology): “) apply to using millimeter-wave communications [67], [68].”
A Comprehensive Survey on Millimeter Wave Communications for Fifth-Generation Wireless Networks: Feasibility and Challenges (2020, methodology): “Reference [78] presents a comprehensive overview of mmWave cellular networks, mathematical models, and analytical frameworks based on stochastic geometry.”