Deploying sub-6GHz network together with millimeter wave (mmWave) is a promising solution to simultaneously achieve sufficient coverage and high data rate. In the heterogeneous networks (HetNets), the traditional coupled access, i.e., the users are constrained to be associated with the same base station in both downlink and uplink, is no longer optimal, and the concept of downlink and uplink decoupling has recently been proposed. In this paper, we propose an analytical framework to investigate the traditional sub-6GHz HetNets integrating with mmWave small cells (SCells) with decoupled access, where both the uplink power control and mmWave interference are taken into account. Using the tools from stochastic geometry, the performance metrics of signal-to-interference-plus-noise ratio coverage probability, userperceived rate coverage probability, and area sum rate are derived. The impact of the densification of different SCells on the network performance is also analyzed to give insights on the network design.Simulation results validate the accuracy of our analysis, and reveal that mmWave interference can not be neglected when the mmWave SCells are extremely dense and that different kinds of SCells have various effects on the network performance and thus need to be organized properly.
Index TermsHeterogeneous networks, millimeter wave, downlink and uplink decoupling, stochastic geometry.
Deploying Sub-6GHz networks together with millimeter wave (mmWave) is a promising solution to achieve high data rates in traffic hotspots while guaranteeing sufficient coverage, where mmWave small cells are densely deployed to provide high quality of service. In this paper, we propose an analytical framework to investigate the integrated Sub-6GHz-mmWave cellular networks, in which the Sub-6GHz base stations (BSs) are modeled as a Poisson point process, and the mmWave BSs are clustered following a Poisson cluster process in traffic hotspots. We conduct stochastic geometry-based analysis and derive the performance metrics including the association probability, signal-to-interferenceplus-noise ratio coverage probability and average achievable rate, which are validated to be accurate by Monte Carlo simulations. We analyze the impact of various deployment parameters on the network performance to give insights on the network design. In particular, it is shown that deploying mmWave small cells in traffic hotspots will outperform both traditional Sub-6GHz heterogeneous network and isolated mmWave system in terms of the coverage probability. It can also be shown that extremely high and extremely small association weight for mmWave BSs will deteriorate the performance for cell edge users and cell interior users, respectively. Moreover, there exists an optimal pre-decided dispersion parameter of mmWave BSs that contributes to the maximum coverage probability.Heterogeneous cellular networks, Sub-6GHz, millimeter wave, Poisson point process, Poisson cluster process.
This paper develops a general and tractable framework for the finite-sized downlink terahertz (THz) network. Specifically, the molecular absorption loss, receiver locations, directional antennas, and dynamic blockage are taken into account. Using the tools from stochastic geometry, the exact expressions of the blind probability, signal-to-interference-plus-noise ratio (SINR) coverage probability, and area spectral efficiency (ASE) for the reference receivers and random receivers are derived. The upper bounds of the SINR coverage probability are also obtained by using the generalized dominant interferers approach. Numerical results validate the accuracy of our theoretical analysis and suggest that two or more dominant interferers are required to provide sufficiently tight approximations for the SINR coverage probability. We also show that densifying the finite terahertz networks over a certain density threshold will degrade the coverage probability while the ASE keeps increasing. Moreover, deploying more obstructions appropriately in ultra-dense THz networks will benefit both the coverage probability and ASE.
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