Downlink Cellular Network Analysis With Multi-Slope Path Loss Models

By Xinchen Zhang, Jeffrey G. Andrews. IEEE Transactions on Communications, 2014

324 citations recorded by the source corpus.

Existing cellular network analyses, and even simulations, typically use the standard path loss model where received power decays like ||x||-α over a distance ||x}}. This standard path loss model is quite idealized, and in most scenarios the path loss exponent α is itself a function of ||x||, typically an increasing one. Enforcing a single path loss exponent can lead to orders of magnitude differences in average received and interference powers versus the true values. In this paper, we study multi-slope path loss models, where different distance ranges are subject to different path loss exponents. We focus on the dual-slope path loss function, which is a piece-wise power law and continuous and accurately approximates many practical scenarios. We derive the distributions of SIR, SNR, and finally SINR before finding the potential throughput scaling, which provides insight on the observed cell-splitting rate gain. The exact mathematical results show that the SIR monotonically decreases with network density, while the converse is true for SNR, and thus the network coverage probability in terms of SINR is maximized at some finite density. With ultra-densification (network density goes to infinity), there exists a phase transition in the near-field path loss exponent α0: if α0 >1 unbounded potential throughput can be achieved asymptotically; if α0 <;1, ultra-densification leads in the extreme case to zero throughput.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/01bf6966aa116e505631689786917b2f15b4d55a

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Citations in the corpus

Millimeter Wave Communications for Future Mobile Networks (2017, background): “There are also reﬁned models, e.g., for cellular networks and n can in some scenarios be smaller than 2 [31].”
5G: A Tutorial Overview of Standards, Trials, Challenges, Deployment, and Practice (2017, background): “In fact, there is a belief [25] that, dependent on the short range pathloss scenario, an unlimited increase in the number of small cells may be counter-productive due to other-cell interference (OCI).”
Cognitive and Energy Harvesting-Based D2D Communication in Cellular Networks: Stochastic Geometry Modeling and Analysis (2014, methodology): “3The channel model can be extended by incorporating multi-slope path-loss models as in [21].”
Modeling and Analyzing Millimeter Wave Cellular Systems (2016, background): “This behavior is similar to that for Sub-6GHz systems under dual-slope pathloss models [76], wherein a “close-in” path loss exponent is used for distances less than a deterministic corner distance Rc, and then changes to larger path loss exponent outside Rc.”
Tractable Resource Management With Uplink Decoupled Millimeter-Wave Overlay in Ultra-Dense Cellular Networks (2015, methodology): “Analyzing the effect of mmW reflections is expected to be viable via utilizing multiple-slope path loss models [23], [25], deferred to future work.”
A Primer on Cellular Network Analysis Using Stochastic Geometry (2016, background): “increases as a function of the link distance [34], [35].”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, unknown): “signal at mobile users can be largely dominated by the LoS interfering links. This makes the singleslope path loss model used in regular HetNets not suitable for propagation modeling in dense HetNets [284]. In order to model, optimize, and analyze dense HetNets, several performance metrics have been”