Cost-Efficient Millimeter Wave Base Station Deployment in Manhattan-Type Geometry

Abstract

Urban millimeter wave (mmWave) communications are limited by link outage due to frequent blockages by obstacles. One approach to this problem is to increase the density of base stations (BSs) to achieve macro diversity gains. Dense BS deployment, however, incurs the increased BS installation cost as well as power consumption. In this work, we propose a framework for connectivity-constrained minimum cost mmWave BS deployment in Manhattan-type geometry (MTG). A closed-form expression of network connectivity is characterized as a function of various factors such as obstacle sizes, BS transmit power, and the densities of obstacles and BSs. Optimization that attains the minimum cost is made possible by incorporating a tight lower bound of the analyzed connectivity expression. A low-complexity algorithm is devised to effectively find an optimal tradeoff between the BS density and transmit power that results in the minimum BS deployment cost while guaranteeing network connectivity. Numerical simulations corroborate our analysis and quantify the best tradeoff of the BS density and transmit power. The proposed BS deployment strategies are evaluated in different network cost configurations, providing useful insights in mmWave network planning and dimensioning.INDEX TERMS Millimeter wave network, connectivity, base station deployment cost, Manhattan-type geometry, lattice process.

Link Quality-Guaranteed Minimum-Cost Millimeter-Wave Base Station Deployment

“…An efficient strategy for BS deployment is minimizing the number of deployed BSs subject to per-UE quality constraints. In this category, BS density optimization [17]- [22] and sitespecific BS deployment [23]- [26] methods have been previously studied. The BS density optimization methods [17]- [22] rely on stochastic geometry to find minimum BS density subject to cell coverage constraints.…”

“…In this category, BS density optimization [17]- [22] and sitespecific BS deployment [23]- [26] methods have been previously studied. The BS density optimization methods [17]- [22] rely on stochastic geometry to find minimum BS density subject to cell coverage constraints. While these prior works [17]- [22] provide theoretical insights into the distribution of BSs, they rather fit traditional macro-cellular environments.…”

“…The BS density optimization methods [17]- [22] rely on stochastic geometry to find minimum BS density subject to cell coverage constraints. While these prior works [17]- [22] provide theoretical insights into the distribution of BSs, they rather fit traditional macro-cellular environments. In the context of small-cell, the site-specific BS deployment methods [23]- [26] have been studied, which find the minimum number of BSs installed on predetermined candidate locations.…”

“…Instead of (19), we take in the two inequalities in (23) and (22) as the UE outage constraint to formulate the BS deployment optimization problem below.…”

“…Incorporating the BS association constraints in ( 5)-( 7) and UE outage constraints (22), (23) into the minimum-cost BS deployment criterion gives…”