Signal strength and spectral efficiency in heterogeneous ultradense networks

Abstract

To meet the rapid data growth requirements of mobile users, heterogeneous ultradense networks improve the spatial reuse efficiency of spectrum resources through the ultradense deployment of small base stations (SBSs) in a given area in the forthcoming fifth‐generation mobile communication networks. It has become one of research hotspots that solve this problem in the recent years. Using the stochastic geometry, the cumulative distribution function and the probability density function of distance from user to its kth closest SBS are derived in this paper. Furthermore, the means of received signal strength from the users' kth closest base station and the mean of signal‐to‐interference‐plus‐noise ratio are derived. The expression of the relationship between spectral efficiency and the density of the SBSs is presented. Finally, The results show that the signal‐to‐interference‐plus‐noise ratio is inversely proportional to the density of SBSs in the 1‐tier ultradense networks. In the 2‐tier heterogeneous ultradense networks, the spectral efficiency of the base station increases first and then decreases with the increasing of the density of SBSs. Moreover, allocating more spectrum resource to SBSs will increase the area spectral efficiency when the number of SBS is far more than the number of macro base stations.

Spatiotemporal correlation and joint coverage probability in ultra‐dense mobile networks

“…With traditional handoff, E[g(l 1 )] and E[g 2 (l 1 )] are the same as those given in ( 9) and (10), respectively. In addition, E[g(l 2 )], E[g 2 (l 2 )], and E[g(l 1 )g(l 2 )] are given in (11), (12), and (13). (6), and f l (l) is given in (4), the temporal correlation coefficient of the signal received by the typical user with handoff skipping is derived in (14).…”

“…Similarly, substituting (10), (11), (12) and (6), and f l (l) is given in (4), we derive the temporal correlation coefficient of the signal received by the typical user with handoff skipping in (7). ▪…”

“…UDN improves spectrum reuse efficiency by deploying the low‐power stations densely (typically 1000 stations per square kilometer), 10 which can significantly increase the capacity of a network. Stochastic geometry is always the default tool for modeling UDN to analyze network performances 11 . The work 12 proposes the closed‐form expressions for the outage probability of a typical user.…”

“…Stochastic geometry is always the default tool for modeling UDN to analyze network performances. 11 The work 12 proposes the closed-form expressions for the outage probability of a typical user. The expressions of network performance metrics are analyzed in ultra-dense heterogeneous networks.…”