Title
Uplink Coverage and Capacity Analysis of mMTC in Ultra-Dense Networks
Abstract
In this paper, we investigate the uplink coverage and ergodic capacity of massive Machine-Type Communication (mMTC) considering an Ultra-Dense Network (UDN) environment. In MTC, devices equipped with sensing, computation, and communication capabilities connect to the Internet providing what is known as Internet-of-Things (IoT). A dense network would provide an all-in-one solution where scalable connectivity, high capacity, and uniform deep coverage are byproducts. To account for short link distances, the path loss is modeled as stretched exponential path loss (SEPL). Moreover, the fading is modeled as a general <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(\alpha -\mu)$</tex-math></inline-formula> channel, where tractable and insightful results are derived for the Rayleigh fading special case. We consider the direct MTC access mode where mMTC nodes connect directly to the small cell. The analytical results disclose the impact of the system parameters and propagation environment parameters on the network performance. In particular, our results reveal that significant coverage enhancements and high uplink capacity are achievable at moderate cell densities, low transmission power, and moderate bandwidth. Moreover, the uplink network performance is independent of the maximum transmission power in the considered dense network scenario, allowing for longer battery lifetime of future IoT devices. The accuracy of the derived analytical results is assessed via extensive simulations.
Year
DOI
Venue
2020
10.1109/TVT.2019.2954233
IEEE Transactions on Vehicular Technology
Keywords
Field
DocType
5G,mMTC,power control,truncated channel inversion,UDN,uplink coverage,ergodic capacity,stochastic geometry
Computer science,Computer network,Ultra dense,Telecommunications link
Journal
Volume
Issue
ISSN
69
1
0018-9545
Citations 
PageRank 
References 
5
0.40
0
Authors
3
Name
Order
Citations
PageRank
Mahmoud I. Kamel124711.53
Walaa Hamouda258562.64
Amr Youssef323829.69