Monitoring Heterogeneous Networks Radiations Through a Dedicated Sar Application

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Monitoring Heterogeneous Networks Radiations Through A Dedicated SAR Application

Groza Claudiu*and Ionel Petrut†

* Computer Science Department, Politehnica University Timisoara and Lasting Software, Timisoara, Romania

† Communication Department, Politehnica University Timisoara and Lasting Software, Timisoara, Romania

Email: [anonimizat]; [anonimizat]

Abstract

Recently, the use of wireless communication devices that are used in close proximity to the human body has been increasing rapidly. The risk of electromagnetic radiation was often considered and the Specific Absorption Rate [1] is one of important characteristics used to evaluate the EM energy absorbed by human body. The Specific Absorption Rate (SAR) represents a limiting factor in the high-field Magnetic Resonance. This paper presents a theoretical approach in analyzing the electromagnetic field penetration and monitoring of network radiation through a dedicated application.

Keywords: Electromagnetic Radiation; Networks; Specific Absorption Rate; Wireless Communications.

Introduction in Heterogeneous Networks

While it is certain that there is a huge demand for wireless broadband data, we have to state that this growth is generated by the rapid growth of smartphones and new bandwidth-heavy applications. As macro-cellular-based mobile networks cannot deal with this demand, in order to increase capacity and coverage and reduce the costs, the network operators will have to be progressively deployed. The small cells will have to be deployed in order to augment the capacity of the traditional macrocellular network and to offload some of traffic from it. The deployed small cells have the ability to provide exponential capacity growth for data traffic. This system is called a heterogeneous network and is known to be the future of cellular communications.

A heterogeneous network is the network connecting computers and other electronic devices with different operating systems and protocols [1]. Heterogeneous networks, comprising macrocells complemented by large numbers of small cells, are therefore becoming increasingly important. As a key to the successful deployment of the macrocells we mention the development of an understanding of the characteristics of such heterogeneous networks.

Heterogeneous networks differ from traditional homogeneous macrocellular networks in some significant respects. Unlike the macrocells, which are situated by cell planning in order to provide complete coverage, small cells are typically located according to the expected density of traffic, in so-called HotSpots or HotZones [2]. This gives rise to different and potentially stronger interference conditions which need to be managed between the macrocells and small cells, as well as between the small cells themselves in dense small cell deployments.

To get the most out of small cell deployments, it is important to understand how to optimize the association of user equipment to cells and to balance the load between the macrocells and small cells in a way that maximizes the total system capacity [3].

In cellular networks, when a mobile phone moves from a cell to another cell and performs cell selection and reselection, it has to measure the signal strength and the quality of the signal of the neighbor cells. In Long Term Evolution Networks, a User Equipment (UE) measures three important parameters regarding signal: Received Signal Strength Indicator (RSSI), Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ) [4].

RSSI is one of the simplest approaches that was used for estimation of distances between nodes, based on the strength of the signal that is received by another node [5]. In this context, a sender node sends a signal with a determined strength that decreases as the signal propagates. The bigger the distance to the receiver node, the less the signal strength when it arrives at that node.

The main advantage of this method is its low cost, since most receivers are capable of estimating the received signal strength.

However, this method is very susceptible to noise and interference, which results in high inaccuracy in distance estimation. Although this approach has been demonstrated to perform poorly, it is the most used solution for distance estimation

RSRP measures the Reference Signal (RS) power, excluding all off the noise and the interference power [6]. RSRP is based on coverage, making it ideally independent of the network load.

RSRP can be defined as the average power of Reference Signal resource elements, but it is clear that RSRP doesn`t depend directly on the number of antennas used during data transmission. In order to improve the accuracy of RSRP`s estimates, the User Equipment can also measure the Reference Signal that is transmitted from an eventual second antenna port.

RSRQ measures the total received signal and the noise power normalized to 1PRB bandwidth [7]. The ratio of the RSRQs depends directly on the ratio of the RSRPs, thus making the RSRQ redundant as the trigger for intra-frequency. In contrast, the RSRQ ratio can be a useful trigger for inter-frequency if it can be used to direct Users Equipment to a less cramed layer.

In analog and digital communications, Signal-to-Noise Ratio (SNR), is a measure of signal strength relative to background noise. The ratio is usually measured in decibels (dB) using a signal-to-noise ratio formula. Communications engineers always tried to maximize SNR. Traditionally, this can be done by using the narrowest possible receiving-system bandwidth consistent with the data speed desired.

SNR can be increased by providing the source with a higher level of signal output power if necessary. In some high-level systems, internal noise is minimized by lowering the temperature of the receiving circuitry. In wireless systems, it is always important to optimize the performance of the transmitting and receiving antennas.

Specific Absorption Rate concept

The advanced wireless systems used have begun to create seamless interconnectivity between the human species through excellence in communications. These advances bring various positive advantages to society, but also impact human beings in numerous ways. One form of intervention with the human body is the propagation of Radio Frequency waves through biological tissues within the human body.

Propagation of electric fields and electromagnetic waves into the human body and into biological tissues has previously been studied extensively for localized radiation sources such as mobile phones and microwave ovens.

Absorption of RF energy by tissues and the human body is typically used to study both short term and long term impacts caused to the human body, but have previously not been studied in detail for other emerging wireless systems.

The Radio Frequency concerns regarding the electromagnetic waves absorption in the human body is becoming more and more of a problem.

The extensive use of wireless networks, mobile phones and other devices are the primary sources of electromagnetic radiation and wave absorption into the human body.

Therefore, the most important parameter used to in order assess human exposure to Radio Frequency electromagnetic fields emitted from mobile phones is given in terms of absorption rate.

Defining the concept, the Specific Absorption Rate (SAR) represents the Radio Frequency (RF) power absorbed per unit of mass of a certain object, and is being measured in Watts per body kilogram (W/kg) [8].

Specific Absorption Rate can be defined as the average energy deposition in a region of a certain mass over an extended period of time due to the application of an excitation pulse [9].

The Specific Absorption Rate describes the potential heating of tissue on account of the application of the Radio Frequency energy that is necessary in order to produce the Magnetic Resonance (MR) signal [10].

The Specific Absorption Rate can easily be calculated using the following formula [6]:

SAR = σ E2 / 2

where:

σ is the conductivity of the material

E is the Electric Field

ρ is the mass density.

Nonhomogeneous Radio Frequency fields will lead to local exposure to radiation, where a part of the absorbed radiation is applied to a body region, leading to a concept called local Specific Absorption Rate. Hot spots may occur on the tissues and in order to avoid or to minimize the effects, the frequency and the power of the RF irradiation should be kept at the minimum. Radiation over the whole human body leads to a concept called global Specific Absorption Rate.

The precision and the reliability of a SAR value depends directly on three important parameters: tissue density, tissue conductivity and electric field. The most significant parameter is the induced electric field, a complex function of physical and biological variables including the microwave frequency, the radiation source size and the composition of the affected tissue. It is practically impossible to measure the absorbed energy in the human body, as the absorption rate fluctuates from person to person with the age factor and other specific characteristics.

The Specific Absorption Rate increases with the electromagnetic field strength, the Radio Frequency power, the transmitter type and the size of the human body.

When doubling the electromagnetic field strength from 1.5 Tesla to 3 Tesla, it will lead to a quadruple value of the SAR level. In high and ultrahigh electromagnetic fields, some of the multiple echo’s and multiple-slice pulse sequences will be able to create a higher level of SAR than it was initially recommended.

The SAR measures exposure to fields between 100 kHz and 10 GHz. SAR is commonly used to measure power absorbed from mobile phones, microwaves and also power absorbed during Magnetic Resonance Imaging (MRI) scans.

The value will depend heavily on the geometry of the part of the body that is exposed to the RF energy and on the exact location and geometry of the RF source [11]. Thus tests must be made with each specific source, such as a mobile phone model, and at the intended position of use.

The SAR value is measured at the location that has the highest absorption rate in the entire body. Usually there is no threatening increase in the body temperature that could be measured. In the high magnetic fields is possible for the temperature to increase, but with at most 1°C.

Anatomical studies have been researched for many decades in an attempt to understand the occurrence of high risk occurrences due to RF radiation on the human body. Most of the previous work have utilized Magnetic Resonance Imaging (MRI) and computed tomography (CT) scan techniques to understand the positioning and properties of human tissues.

The rapid expansion of wireless networks and cell phones has pushed the researchers onto the necessity of studying mobile phones and wireless networks for radiation performance in order to properly address some of the safety concerns [12].

Nowadays, automatic positioning systems, actual phones and a head-like object filled with the appropriate tissue and equivalent liquid is employed to measure the SAR for mobile phones. Many efforts using numerical methods are aimed to define human head models, and phone models to allow the comparison between numerical and experimental procedures for SAR evaluation.

It is anticipated that the number of measurements required in order to properly evaluate the Specific Absorption Rate for such types of usage described above will increase and the SAR measurements will become more and more time consuming [13].

SAR Watch – Tracking Radiation Exposure

The application presented, the SAR WATCH – Tracking Radiation Exposure application [6], adopts a conventional architecture of four logical levels. The choice made is motivated by the fact that the development process for this type of design is well known and does not present too much complexity for its implementation [6].

The main disadvantage encountered when implementing the app is the lack of performance due to crossing several levels just to obtain a set of data. Because the application grouped the existing information in the form of day-based collections, it was possible to implement a caching mechanism. Thus, database access requests have been reduced, and more than 75% of the required information is already loaded into the app`s memory. The level of data persistence is largely comprised of the SQLite database and the auxiliary control structures.

The access level to the app`s database contains query, processing, and input structures for records in the database. This level also implements the main caching mechanism that was described earlier. The base level of the application is built into two parts. The right part is the key element in the application's operation because it contains the logic of making the measurements. The left side has the role of processing the information for the higher level. The level of data presentation has in its components the main elements in the user interface and the structures allowing data to be displayed in a user-friendly form.

The application aims to measure radiation according to the sources of exposure. The classification mode is given by the level of control (amelioration) that the user can exercise over the sources. Also, account must be taken of the physical capabilities of the phone, which are sometimes limited by manufacturer's constructive decisions.

Depending on the nature of the radiation, two categories will be introduced: the radiation produced by the mobile phone and the ambient radiation produced by the wireless communications equipment.

Radiation specific to the mobile phone includes the radiation whose source is strictly limited to the mobile terminal's antenna. These radiations are the most harmful to the human body and represent the bulk of the cumulative total to which a human is exposed.

Depending on the frequency of changing the location of your mobile phone, there is a background-based mechanism that measures ambient exposure. Of course, this activity can also be launched manually, at the explicit request of the user.

The measurement period is in the order of minutes. Thus, the algorithm adapts dynamically to the distance between the last two measurements and schedules the next measurement taking into account the user's predisposition to move.

Using a refining approach to the composition of this category, we can go further determining the hypostasis in which the radiation was produced. The paper proposes measuring the values ​​that come from the use of the mobile phone for the basic purpose, the initiation of a voice call, but also packet data transmission values.

Initiating and maintaining a voice call involves a complex exchange of information between the mobile phone and the operator's equipment. In this case, we focus our attention on the mobile phone. Data packet transmission should not be neglected as in the context of service offered by the operator, the radio activity of a phone is given by the mobile data.

Account must be taken of the multitude of factors that directly or indirectly influence the value of the SAR parameter:

phone features

user-relative phone position

the distance between the phone and the operator antenna

the data transmission standard

Radiation specific to wireless transmission equipment includes the radiation, which by their nature cannot be controlled by the user. They will be referred to as ambient exposure because the user is indirectly affected. These radiations have a much lower impact, but they should not be neglected. The specific reason why they are monitored is that the exposure is for a long time, and cumulative over time may have harmful effects on human health. Environmental exposure analysis is limited by the hardware components we identify inside a phone.

The quick navigation menu of the app is available by dragging from left to right on the screen surface or by simply tapping the title. Returning to the previous activity is done by moving it backwards or by touching the “Back” button. The menu consists of two categories, each defining how data is presented. The first category proposes data interpretation in graphical form, and the second will attempt to map the measurements.

The percentage of exposure by sources is illustrated by a graphic. The graphic has two constituent parts, and the whole graphic represents the total exposure of the user to radiation. One side will highlight the exposure due to the use of the mobile phone, and the other side will highlight the exposure due to the analyzed environment. Also, there is a color shade in the center of the graphic which represents the threat level.

As presented earlier, one of the key elements of user interaction is deep navigation and refinement of the information presented. The trigger point of the navigation is reflected by touching the internal portion of the chart or tapping descriptive text at the bottom of the screen.

Regarding the environmental exposure, the composition of the screen consists of the two elements of the environmental exposure analysis: operator cells and Wi-Fi networks. Each is represented in the form of an individual graphic. The illustrated intervals represent a time group of data. Thus, the user can have an overview and can make comparisons based on the history of the data. At the bottom right of each graph, details of the last measurement that has been made are noted. Thus, the user can decide on the timeliness of the update and can act by triggering an explicit measurement.

The logic for exposure due to the phone follows the guidelines presented earlier. The two areas of interest represented on the screen are:

exposure due to making a voice call

exposure due to the use of the mobile data service

Highlighting or tinting of the areas where a measurement was made is done by fastening of a circle that is colored according to the level of exposure, which will originate in the coordinates attached to this measurement. The granularity of a map view is given by the spacing between the last measurements and the current user position.

Following the intensive testing of the application for different mobile terminals, we found the following:

inconsistency in mobile cellular return

missing programmed alarms

differences in accuracy in setting the current location

Under the Android operating system, the implementation of the methods that directly call the data of hardware resources (modem, sensors, etc.) remains in the assignment sheet of the mobile terminal manufacturer, not the operating system vendor.

Effective testing of the app on Android phones has revealed malfunctioning or even null implementation (which directly returns null) to some critical methods in the application's logic. Therefore we are trying to find some complementary solutions.

future directions in developing the sar watch application

The perspectives of the paper outline the processing of data extracted through the application. Therefore, the application proposes the meeting and intersection of the data collected on the unique user in a global database. This global database will be interrogated for the following purposes:

building the “heat map” for querying environmental exposures for a particular physical location

defining user groups

Correlation of data gathered for a geographic region in order to determine certain factors like diseases, access to information, etc.

Today we are monitoring only the RSSI parameter in order to determine the radiation level, but in the future we will focus on analyzing the feasibility of introducing additional parameters like RSRP and SNR, in order to improve the accuracy of the analyses, by comparing the level of radiation with the Quality of Services (QoS) received by the user of the application and constantly trying to find the balance between the radiation exposures and the Quality of Experience.

Another important point to be developed is the user documentation with advice on avoiding or reducing exposure to radiation. This module should be interlaced in the presentation of the data to the user and must contain prompt solutions.

The application is currently available in the online application store [14]. In order to adopt as much users as possible, a marketing plan should be developed that proposes splitting the application into parts with basic functionality offered free user and in extensible parts, which are user-selected features in the application configuration.

CONCLUSION

Unfortunately, the high levels of Specific Absorption Rate represent a major concern in parallel transmission of spatially-tailored multidimensional excitation pulses, especially the potential for a relatively high ratio of local SAR to average SAR.

The safety standards impose limits on maximum SAR and the ratio of local to global SAR is often considerably greater than the regulatory ratio required in order to maintain safety compliance for the human body.

By making a quantitative comparison between the current functionalities of the application and the general specifications of the app, we notice that the original purpose originally was achieved. Therefore, both the radiation exposure analysis module and the ambient radiation analysis module have been fully implemented.

Data interpretation is a continuous point of work as it has deficiencies closely linked to the values ​​resulting from studies on exposure to electromagnetic radiation. The finest point, but also the most expensive and time consuming, is designing the user interface. The usability degree of the app determines the percentage of installation of the application.

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“When does your electromagnetic exposure exceed the recommended safety limits?” online resource found at: https://www.home-biology.com/electromagnetic-field-radiation-meters/safe-exposure-limits

SAR Watch – Tracking Radiation Exposure, Application, available on GooglePlay: https://play.google.com/store/apps/details?id=ro.upt.sarwatch#details-reviews