BioMed Research International

Volume 2015 (2015), Article ID 943415, 9 pages

http://dx.doi.org/10.1155/2015/943415

## Joint Minimization of Uplink and Downlink Whole-Body Exposure Dose in Indoor Wireless Networks

^{1}Information Technology Department, Ghent University/iMinds, Gaston Crommenlaan 8, 9050 Ghent, Belgium^{2}Orange Labs Networks and Carriers, 38-40 rue Général Leclerc, 92794 Issy Les Moulineaux, France

Received 4 September 2014; Accepted 12 November 2014

Academic Editor: Francisco Falcone

Copyright © 2015 D. Plets et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

The total whole-body exposure dose in indoor wireless networks is minimized. For the first time, indoor wireless networks are designed and simulated for a minimal exposure dose, where both uplink and downlink are considered. The impact of the minimization is numerically assessed for four scenarios: two WiFi configurations with different throughputs, a Universal Mobile Telecommunications System (UMTS) configuration for phone call traffic, and a Long-Term Evolution (LTE) configuration with a high data rate. Also, the influence of the uplink usage on the total absorbed dose is characterized. Downlink dose reductions of at least 75% are observed when adding more base stations with a lower transmit power. Total dose reductions decrease with increasing uplink usage for WiFi due to the lack of uplink power control but are maintained for LTE and UMTS. Uplink doses become dominant over downlink doses for usages of only a few seconds for WiFi. For UMTS and LTE, an almost continuous uplink usage is required to have a significant effect on the total dose, thanks to the power control mechanism.

#### 1. Introduction

The vast expansion of the use of wireless networks in everyday life has led to a greater awareness of exposure of the general public to RF (radio-frequency) electromagnetic fields used for wireless telecommunication. International organizations such as IEEE [1] and ICNIRP (International Commission on Non-Ionizing Radiation Protection) [2] have issued safety guidelines to limit the maximal electric-field strength due to wireless communications. Also, on a national level, authorities have implemented laws and norms to limit the exposure to electromagnetic fields. A lot of research has been done on the characterization of RF exposure (e.g., [3–7]), and measurements have indicated that exposure in indoor environments cannot be neglected [8].

Exposure studies mostly consider either the fields generated due to traffic from base station to user device (downlink) or exposure due to the electromagnetic waves induced in the body by the user device (uplink). Further, software tools for predicting the received signal quality [9–15] very often focus on Quality of Services parameters and do not account for exposure values. In [16], the authors presented the WiCa Heuristic Indoor Propagation Prediction (WHIPP) tool, a set of heuristic planning algorithms, experimentally validated for network planning in indoor environments [16]. The path loss prediction algorithm takes into account the effect of the environment on the wireless propagation channel and bases its calculations on the determination of the dominant path between transmitter and receiver, that is, the path along which the signal encounters the least obstruction. The WHIPP tool is designed for optimal network planning with a minimal number of access points (AP) [16]. In [17], this tool was extended for automatic network planning with limited or minimized downlink electric-field strength in indoor wireless networks, without impairing coverage. In [18], it was further extended with prediction algorithms to simulate and visualize electric-field strengths due to DL traffic and localized Specific Absorption Rate values in 10 g tissue () due to UL traffic.

In this paper, instead of separating between UL (due to the mobile device’s transmitted signal) and DL (due to the electric-fields E originating from the base stations or APs) traffic, exposure is spatially calculated as a whole-body dose due to both UL and DL [19]. Different optimization scenarios will be simulated using the WHIPP tool, and the impact on the whole-body dose will be numerically assessed. Additionally, the impact of the actual usage time on the dose reduction will be investigated. To the authors’ knowledge, no indoor wireless network design solutions are yet available for the minimization of the total whole-body doses, where UL and DL contributions are both considered. Further, the impact of such redesign on the total exposure dose has not been quantified before, neither at specific locations in the building, nor globally over the entire building. Four scenarios (using WiFi, Universal Mobile Telecommunications System (UMTS), or Long-Term Evolution (LTE)) will be defined to investigate the influence of the number of indoor base stations, power control, and uplink transmission duration on the exposure.

In Section 2, the WHIPP tool used for minimization and assessment of exposure doses is discussed, the minimization metric (whole-body dose) is mathematically formulated, and the simulation scenarios are presented. In Section 3, the results for the different scenarios are presented and the impact of optimizing the network topology on the total dose is assessed, as well as the influence of the uplink usage on the total dose. Finally, conclusions and future work are presented in Section 4.

#### 2. Materials and Methods

##### 2.1. WHIPP Prediction Tool

The WHIPP algorithm is a heuristic planning algorithm, developed and validated for the prediction of path loss in indoor environments [16]. It takes into account the effect of the environment on the wireless propagation channel and has been developed for the prediction of the path loss on a grid over an entire building floor or at specific locations. The spatial granularity of the prediction is determined by the density of the grid points on the building floor. The algorithm bases its calculations on the determination of the dominant path between transmitter and receiver, that is, the path along which the signal encounters the lowest obstruction. This approach is justified by the fact that more than 95% of the energy received is contained in only 2 or 3 paths [11]. The dominant path is determined with a multidimensional optimization algorithm that searches the lowest total path loss, consisting of a distance loss (taking into account the length of the propagation path), a cumulated wall loss (taking into account the walls penetrated along the propagation path), and an interaction loss (taking into account the propagation direction changes of the path, e.g., around corners). The performance of the model has been validated with a large set of measurements in various buildings [16]. In contrast to many existing tools, no tuning of the tool’s parameters is performed for the validation. Excellent correspondence between measurements and predictions is obtained, even for other buildings and floors [16]. The WHIPP tool contains a user interface that was developed in collaboration with usability experts. This allows visualizing not only path loss, throughput, or electric-field values, but, based on the formulations presented in the next section, also power densities, (localized or whole-body) absorption values, and doses.

##### 2.2. Minimization Metric: Whole-Body Exposure Dose

The aim of the paper is to minimize the median whole-body absorbed dose (J/kg) [19, 20] over a given building floor where indoor base stations are installed. The total whole-body dose at a certain location in a building is calculated as the sum of the whole-body dose (J/kg) due to downlink and the whole-body dose (J/kg) due to the user device’s uplink:

In the following sections, it will be explained how the downlink and uplink whole-body exposure doses are calculated.

###### 2.2.1. Downlink Whole-Body Absorbed Dose

To calculate (J/kg), the whole-body SAR (W/kg) due to downlink is multiplied by (s); the time duration of the exposure accounts for the downlink exposure due to all base stations and is calculated as follows: where () is the received power density due to base station (WiFi AP, UMTS/LTE femtocell) and (W/kg per ) is the reference whole-body SAR (for 1 of received power density) due to using a certain Radio Access Technology (RAT). Power densities from RATs using different frequencies will contribute to according to the reference whole-body SAR for the RAT at that frequency. Therefore, (3) sums over the power densities from each of the base stations . The power density () is related to the electric-field strength as follows: where (V/m) is the electric-field strength due to base station , observed at the considered location and with an assumed duty cycle of 100%. is the free-space impedance, equal to 377 Ω. For WiFi, the actual duty cycle DC [-] of the traffic generated by [21] must also be accounted for, since it represents the relative transmission time of a signal. In WiFi, signals are not transmitted continuously and therefore the predicted power densities at 100% operation need to be multiplied by the duty cycle. For UMTS and LTE, the duty cycle is 100% for downlink. When accounting for the duty cycle, (4) can be rewritten as follows:

###### 2.2.2. Uplink Whole-Body Absorbed Dose

To calculate (J/kg), the whole-body SAR (W/kg) due to uplink is multiplied by (s), the time duration of the usage. is a value between 0 and : is the SAR due to the UL traffic from the mobile device towards base station it is connected to, using a certain RAT. It is calculated as follows: where (W) is the mobile device’s power transmitted towards the base station it is connected to, DC [-] is again the WiFi duty cycle of the UL traffic, and (W/kg per W) is the reference whole-body SAR (for 1 W of transmitted power) due to the mobile device operating at RAT. For UMTS and LTE, the duty cycle is 100% for uplink.

In future research, also whole-body absorption due to the uplink transmission of* other* users will be accounted for.

###### 2.2.3. Input Parameters

The equations formulated above now allow calculating absorbed doses. However, some of the parameters are required as input or need to be calculated by the WHIPP tool.(i)In (5), (V/m) (electric-field strength due to base station ) can be calculated by the WHIPP tool as described in [17, 18, 22], where a far-field conversion formula between path loss and electric-field strength is presented:
with PL (dB) as the path loss between the transmitter and a receiver at a certain location, (dB*μ*V/m) as the received field strength for an ERP (Effective Radiated Power) of 1 kW, and (MHz) as the frequency. Using (8) and the identity
and knowing that for dipoles ERP (dBm) = EIRP (dBm) − 2.15, we obtain the following formula for the electric-field strength (dBV/m) at a certain location, as a function of the (dBm) of the base station, the path loss, and the base station’s frequency (MHz):
or, with expressed in (V/m),
PL (dB) is here predicted by the WHIPP tool [16].(ii)The* duty cycle* (in (5) and (7)) depends on the type and amount of traffic over the air [21]. In the following sections, simplified duty cycle value assumptions will be made, depending on the considered network topology.(iii)In (7), (dBm) (mobile device’s power transmitted towards the base station BS it is connected to) for phone call connections with a UMTS femtocell will be calculated with the WHIPP tool as described in [18]:
where (dBm) is the sensitivity of the UMTS femtocell base station for maintaining a UMTS phone call, determined and validated at −110 dBm [18]. PL (dB) is again the path loss between transmitter and receiver locations, as calculated by the WHIPP tool [16]. Thanks to power control, values will be lower for good connections with the base stations (low PL). The lower and upper limits for values are −57 and 23 dBm, respectively (see also [23]). For LTE, (dBm) will be modeled as described in [24, 25]:
with as the required received power at the femtocell base station (FBS) over a bandwidth of one resource block, as a path-loss compensation factor, as the number of resource blocks being used, and as a network-controlled factor, reflecting power increase or decrease commands. will be assumed equal to 1 and equal to zero. For the 20-MHz channel used in this paper, is equal to 100. For the considered indoor environment, is set equal to −96 dBm [25]. The lower and upper limits for values are −40 and 23 dBm, respectively [24]. For WiFi, no power control is used and a fixed value of 20 dBm for is assumed.(iv)The* reference SAR values* for 1 W/m^{2} observed power density from (3) and for 1 W transmitted power from (7) are listed in Table 1 for the three considered RATs. The downlink whole-body reference SAR values for UMTS and WiFi are obtained from [20]. As human model, the Duke model of the Virtual Family was used [26]. It is generated from a set of magnetic resonance images of whole-body scans from a 34-year-old male (height of 1.74 m, weight of 72 kg, and body mass index of 23.1 kg/m). Since the LTE downlink frequency band (2.6 GHz) is close to the WiFi band (2.4 GHz), it is fair to assume the same value for LTE as for WiFi. The uplink whole-body reference SAR values for UMTS are also obtained from [20] (cell phone placed to the right side of the head of the human model). The whole-body reference SAR value values for WiFi and LTE for data usage are obtained through Finite-Difference Time-Domain (FDTD) simulations, where the mobile device is held in front of the body. The resolution of the human model was chosen to be 2 mm × 2 mm × 2 mm, resulting in a total of about 110 million voxels [26]. The same mobile phone is assumed as in [18].