Abstract

Despite coverage enhancement in rural areas is one of the main requirements in next generations of wireless networks (i.e., 5G and 6G), the low expected profit prevents telecommunication providers from investing in such sparsely populated areas. Hence, it is required to design and deploy cost efficient alternatives for extending the cellular infrastructure to these regions. A concrete mathematical model that characterizes and clearly captures the aforementioned problem might be a key-enabler for studying the efficiency of any potential solution. Unfortunately, the commonly used mathematical tools that model large scale wireless networks are not designed to capture the unfairness, in terms of cellular coverage, suffered by exurban and rural areas. In big cities, in fact, cellular deployment is essentially capacity driven and thus cellular base station densities are maximum in the town centers and decline when getting far from them. In this paper, a new stochastic geometry-based model is implemented in order to show the coverage spatial variation among urban, suburban, and exurban settlements. Indeed, by implementing inhomogeneous Poisson point processes (PPPs) it is possible to study the performance metrics in a realistic scenario where terrestrial base stations (TBSs) are clustered around the urban center while outer aerial base stations (ABSs) are uniformly distributed outside an urban exclusion zone. Based on this, our simulation results can quantify the improvement, in terms of coverage probability, that even a surprisingly low density of ABSs can bring to peripheral regions depending on the extension of the exclusion zone, enabling us to draw insightful considerations.

Highlights

  • Even though wireless connectivity is rapidly spreading all around the world, the number of uncovered users living in rural and low income areas is still large because telecommunication operators are not motivated to invest in low return on investment (ROI) rate zones [1]

  • The coverage probability is defined as the complementary cumulative distribution function (CCDF) of the SINR evaluated at a designated threshold τ, that is

  • On the other hand, assuming re = 8 km, deploying drones with a density of 0.15 aerial base stations (ABSs)/km2 allows to even the coverage probability with negligible deterioration for urban users, and by going up to just 0.2 ABSs/km2 rural zones become much better covered than urban ones

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Summary

INTRODUCTION

Even though wireless connectivity is rapidly spreading all around the world, the number of uncovered users living in rural and low income areas is still large because telecommunication operators are not motivated to invest in low return on investment (ROI) rate zones [1]. A. RELATED WORK This subsection provides a concise summary of the related papers belonging to two general fields of study: (i) coverage enhancement in rural areas and (ii) stochastic geometry-based analysis of UAV networks. Authors in [11] firstly developed a simplified model to evaluate the required subscription fees for users living in rural and low income environments in order to amortize the costs of a 5G architecture, eventually proving that the UAV-based solution would be effective. In [13], TV band white space (TVWS) with 5G infrastructure is suggested as a supplement for rural communications, since it may be cost-effective from a service provider’s perspective Another important field of study regards the improvement of coverage and capacity for rural vehicular users, which has been evaluated in case of deployment of high-altitude platforms (HAPs) in [14]. While all the aforementioned works referred to untethered UAVs (U-UAVs), in [26] an innovative stochastic geometry approach is proposed to derive the probability distribution of the minimum inclination angle of the wires supplying power and data to tethered UAVs (T-UAVs)

CONTRIBUTIONS The contributions of this paper can be summarized as follows:
CHANNEL MODELING
ASSOCIATION POLICY
INTERFERENCE AND SIGNAL TO INTERFERENCE PLUS
COVERAGE PROBABILITY
DISTANCE TO THE NEAREST TBS
ASSOCIATION PROBABILITIES
RESULTS AND DISCUSSION
CONCLUSION AND FUTURE WORK
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