Coverage and Rate Analysis for Millimeter-Wave Cellular Networks

By Tianyang Bai, Robert W. Heath. IEEE Transactions on Wireless Communications, 2014

1,387 citations recorded by the source corpus.

Millimeter wave (mmWave) holds promise as a carrier frequency for fifth generation cellular networks. Because mmWave signals are sensitive to blockage, prior models for cellular networks operated in the ultra high frequency (UHF) band do not apply to analyze mmWave cellular networks directly. Leveraging concepts from stochastic geometry, this paper proposes a general framework to evaluate the coverage and rate performance in mmWave cellular networks. Using a distance-dependent line-of-site (LOS) probability function, the locations of the LOS and non-LOS base stations are modeled as two independent non-homogeneous Poisson point processes, to which different path loss laws are applied. Based on the proposed framework, expressions for the signal-to-noise-and-interference ratio (SINR) and rate coverage probability are derived. The mmWave coverage and rate performance are examined as a function of the antenna geometry and base station density. The case of dense networks is further analyzed by applying a simplified system model, in which the LOS region of a user is approximated as a fixed LOS ball. The results show that dense mmWave networks can achieve comparable coverage and much higher data rates than conventional UHF cellular systems, despite the presence of blockages. The results suggest that the cell size to achieve the optimal SINR scales with the average size of the area that is LOS to a user.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/73ff6d01bb56a75d1c87f456848d84c1c6ee75ae

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Citations in the corpus

Propagation Path Loss Models for 5G Urban Micro- and Macro-Cellular Scenarios (2015, methodology): “s the realistic sensitivity of the receivers in real-world measurement systems [10]. We note that a complete path loss study should also include a LOS/NLOS probability model such as described in [21]–[25], but this is beyond the scope of this work. Note that the parameters in the paper are not ﬁnali”
Tractable Resource Management With Uplink Decoupled Millimeter-Wave Overlay in Ultra-Dense Cellular Networks (2015, methodology): “The value of RL with the 2D blockage model [12], [13], [29], [30] is directly computed by utilizing building perimeters, areas, and coverage from actual geographical data.”
Tractable Model for Rate in Self-Backhauled Millimeter Wave Cellular Networks (2014, background): “Coverage was shown [24] to exhibit a non-monotonic trend with BS density.”
Millimeter Wave Channel Modeling and Cellular Capacity Evaluation (2013, background): “The form for the LOS probability (8b) can be derived on the basis of random shape theory arguments [51]; see also [52] for a discussion of outage modeling and its effect on capacity.”
User Association in 5G Networks: A Survey and an Outlook (2015, background): “In [46, 173], the users are assumed to be associat ed with the specific BS offering the minimum path-loss, where stochastic geometry based mmWave modeling incorporating blockage effects was considered.”
Downlink Cellular Network Analysis With Multi-Slope Path Loss Models (2014, result): “Section IV specializes in the interference-limited case, derives key differential properties of the network performance and contrasts them with those found under the standard path loss function.”
An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems (2015, methodology): “Using such models, it is possible to evaluate coverage and capacity in mmWave cellular networks analytically using stochastic geometry [18].”
Stochastic Geometric Coverage Analysis in mmWave Cellular Networks With Realistic Channel and Antenna Radiation Models (2018, methodology): “are commonly used in stochastic geometric mmWave SINR coverage analysis [5]–[7].”
Millimeter Wave Cellular Networks: A MAC Layer Perspective (2015, methodology): “All this may entail a substantial modiﬁcation/extension of the methodology of cellular network analysis [25], [42]–[46] in general and mmWave cellular networks in particular [29], [47], as the main assumptions made in those frameworks of Voronoi serving regions does not hold.”
A Planar Switchable 3-D-Coverage Phased Array Antenna and Its User Effects for 28-GHz Mobile Terminal Applications (2017, neutral): “As a key technology in the fifth-generation (5G) communication systems, millimeter-wave (mm-wave) and centimeter-wave (cm-wave) technology promises gigabit-per-second data rate [1]–[3].”
Ultra-Dense Networks: A Survey (2016, background): “Due to the short wavelengths, a high antenna gain can be obtained by the use of antenna arrays at the transmitter and the receiver [116].”
A Primer on Cellular Network Analysis Using Stochastic Geometry (2016, methodology): “The stochastic geometry tools introduced in this tutorial can also be used to analyze wireless systems operating at mmWave frequencies (above 30 GHz) [55], [56].”
Frequency Selective Hybrid Precoding for Limited Feedback Millimeter Wave Systems (2015, background): “Further, system level evaluation of mmWave network performance indicate that mmWave cellular systems can achieve a similar spectral efficiency to that obtained at lower-frequency while providing orders of magnitudes more data rate thanks to the larger bandwidth [11]–[14].”
Joint Communication and Control for Wireless Autonomous Vehicular Platoon Systems (2018, background): “) +1 k exp kd i 1;i P t ˙2 Lnon-platoon i kd i 1;i P t Lplatoon i kd i 1;i P t ; (44) where = (!) 1 , (a) is based on the approximation of tail probability of a Gamma function [49], (b) follows Binomial theorem and the assumption that mis an integer, and the changes in (c) follow the deﬁniti”