  A prevalent theory circulating among the non-scientific community is that the
intensive deployment of base stations over the territory significantly
increases the level of electromagnetic field (EMF) exposure and affects
population health. To alleviate this concern, in this work, we propose a
network architecture that introduces tethered unmanned aerial vehicles (TUAVs)
carrying green antennas to minimize the EMF exposure while guaranteeing a high
data rate for users. In particular, each TUAV can attach itself to one of the
possible ground stations at the top of some buildings. The location of the
TUAVs, transmit power of user equipment and association policy are optimized to
minimize the EMF exposure. Unfortunately, the problem turns out to be
mixed-integer non-linear programming (MINLP), which is non-deterministic
polynomial-time (NP) hard. We propose an efficient low-complexity algorithm
composed of three submodules. Firstly, we propose an algorithm based on the
greedy principle to determine the optimal association matrix between the users
and base stations. Then, we offer two approaches, a modified K-mean and shrink
and realign (SR) process, to associate each TUAV with a ground station.
Finally, we put forward two algorithms based on the golden search and SR
process to adjust the TUAV's position within the hovering area over the
building. After that, we consider the dual problem that maximizes the sum rate
while keeping the exposure below a predefined value, such as the level enforced
by the regulation. Next, we perform extensive simulations to show the
effectiveness of the proposed TUAVs to reduce the exposure compared to various
architectures. Eventually, we show that TUAVs with green antennas can
effectively mitigate the EMF exposure by more than 20% compared to fixed green
small cells while achieving a higher data rate.
