Mobile Phone Antenna with Reduced Radiation into Inner Ear

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1 Int. J. Communications, Netwok and System Sciences, 2014, 7, Published Online Novembe 2014 in SciRes. Mobile Phone Antenna with Reduced Radiation into Inne Ea Jamal S. Rahhal Electical Engineeing Depatment, The Univesity of Jodan, Amman, Jodan Received 19 Septembe 2014; evised 25 Octobe 2014; accepted 7 Novembe 2014 Copyight 2014 by autho and Scientific Reseach Publishing Inc. This wok is licensed unde the Ceative Commons Attibution Intenational License (CC BY). Abstact Health hazads ae of geat concen to cellula phone uses. One impotant measue of the effect of electomagnetic adiation into human body is the specific absoption ate (SAR). If the human body is exposed to electomagnetic adiation, the amount of powe absobed by its tissues pe mass volume should be limited and not to exceed a maximum SAR value. The cellula phone adiated though its antenna in all diections a cetain amount of electomagnetic enegy. This enegy is concentated in the nea field egion, whee the use s head is located duing the call. The closest ogan that is vey sensitive to tempeatue change is the inne ea (it is just unde the cellula phone antenna) whee it contains a contolled viscosity liquid. In this pape we devise a twoantenna design to geneate a low adiation in the diection of use s head while using the cellula phone. By ceating a null in the adiation patten in the diection of use s head we minimize the isk of hazads on the use. We optimize the null steeing such that the device maintains a good connection to its base station and keeps the SAR level unde the allowed maximum value using Lagange method. To implement the analytical solution in eal time simulated annealing (SA) algoithm is used. Results showed that we could stee the adiation patten to optimize the adiated powe in the diection of base station unde the limited SAR level constaint. Simulated annealing algoithm is adopted to find the nea optimal delay value to stee the antenna adiation patten since it finds the global optimal point. It shows that a eal time pocessing on the mobile unit can be pefomed to solve fo the best null diection while the device is active. Keywods Electomagnetic Radiation, SAR, Patch Antenna, EM, Health Hazads, Lagange Multiplie and Simulated Annealing 1. Intoduction The design of antennas fo wieless pesonal communication systems is the subject of much eseach that is mo- How to cite this pape: Rahhal, J.S. (2014) Mobile Phone Antenna with Reduced Radiation into Inne Ea. Int. J. Communications, Netwok and System Sciences, 7,

2 tivated by size, efficiency and health issues. In addition to maximizing the antenna adiated/accepted powe of the handsets, the effects on the antenna pefomance fom suounding objects such as the human body must be consideed. On the othe hand the effect of adiation on the human body must be also consideed. The closest human sensitive pat to the handset in calling position is the human bain and ea in most mobile models o at least close enough to cause hamful effects. The tissues of these ogans ae mostly neves plus liquid and hence, they cay electical signals that might be affected by electomagnetic adiation fom the wieless device [1]-[9]. The RF enegy is scatteed and attenuated as it popagates though the tissues of the head, and maximum enegy absoption is expected in the moe absoptive high wate-content tissues nea the suface of the head. Inne ea (that contains high wate-content) is just unde the mobile phone and it will be subject to the stongest adiation fom the mobile unit as shown in Figue 1. The electomagnetic (EM) penetation into human head causes pemanent damage to tissues that ae exposed to high density EM enegy. This could cause some ogans to malfunction o at least a distubance in thei functionality. The amount of exposed enegy that can be handled by human tissues is measued by the specific absoption ate (SAR) that is given by [10]-[15]: 2 σ E SAR = W/kg (1) 2ρ whee E is the electic field intensity, σ is the tissue conductivity and ρ is the tissue density. The dependency of σ on fequency is the esult of inteaction between the EM waves and the tissue mateial, such that the existence of ions will incease the conductivity and will change the pemittivity of the tissue. The complex natue of the pemittivity eflected into changing the conductivity of the tissue. The effect of adiation in the inne ea has two folds: the effect on the neual tissues (heaing) and the effect on the filling liquid (balance). The adiation devices must be compliant to the SAR standad IEEE C95.1. The IEEE exposue citeia ae based on a detemination that potentially hamful biological effects can occu at an SAR level of 4 W/kg as aveaged ove the whole-body. Appopiate safety factos wee then added to aive at limits fo both whole-body exposue (0.4 W/kg fo contolled o occupational exposue and 0.08 W/kg fo uncontolled o geneal population exposue, espectively) and fo patial-body (localized SAR), this might occu in the head of the use of a hand-held cellula telephone [9]. The natue of the tissues in the inne ea makes its elative dielectic pemittivity in the ode of ( j17.98 at 900 MHz and j13.98 at 1.8 GHz) and its conductivity (0.97 at 900 MHz and 1.4 at 1.8 GHz). The poblem with the inne ea aises fom the fact that its inne liquid heat dissipation is not suited to dissipate heat geneated fom high adiation nea the ea. This will maximize the isk of losing balance and/o changing the physical chaacteistics of the inne ea and hence, heaing impaiment might occu [10] [12]. This pape pesents an antenna design to minimize adiation in the inne ea diection and at the same time poduce an acceptable adiation patten that can be used to communicate with base stations. 2. Antenna System Desciption The poposed antenna system in this pape consists of gound plane and two H shaped PCB tacks on the othe side that is using a coaxial feede as shown in Figue 2. The adiation patten fo each antenna in the nea field and the fa field ae shown in Figue 3 and Figue 4. The combination of the two antenna elements makes it possible to stee the adiation patten towad the base station and ceates a null towad the human head. This will Inne Ea EM Waves Head Phone Figue 1. Mobile phone adiation into human head showing the inne ea location. 475

3 Figue 2. One element antenna geomety. Z Theta Y Z Theta Phi X Figue 3. Nea field adiation patten fo one antenna element; (showing half space). Y Phi X X Theta Z X Phi Theta Z Phi Y Y Figue 4. Fa field adiation patten fo one antenna element; (showing half space). educe the SAR in the human head and maintains the communication with the base station. The two antenna elements poposed hee will have an H shape each as shown in Figue 5. The nea field adiation patten fo each element as seen fom Figue 3 is close to Omni diectional. Theefoe, the electic field can be expessed in mathematical fom as [16]-[18]: whee (,, ) ( ) E xyz,, = Eo (2) E xyz is the electic field adiated fom the antenna in the nea field and E o is constant in all diections. To stee the adiation patten, a vaiable delay element is intoduced in the feeding cicuit of one of the elements. Assuming that it is equied to stee the adiation patten main beam in θ diection and stee the null is 476

4 Figue 5. The two elements antenna geomety. β diection (that is usually in the nomal diection to the font plane of the cellula phone) then the delay τ is found by: Tδ τ = (3) 2π whee δ is the equivalent phase shift in adians and T is the caie peiod. To find the optimal δ that maximises the adiation patten in the base station diection and at the same time minimize the adiation patten in the human head diection to maintain acceptable SAR value, we solve the following equations: And E h ( ) ( 1 e j β δ o ) The electic field to the base station diection is given by: Then to find the optimal δ we maximize: = E + (4) j( β δ) 2 σ E 2 o 1+ e SAR = (5) 2ρ E ( ) ( 1 e j θ δ o ) = E + (6) P 1+ e = K SAR 1+ e 2 j( θ δ) 2 j( β δ) whee P is the eceived powe fom base station and K is a constant. This equation is epesented gaphically as shown in Figue 6 and it can be maximized analytically. Fo example if θ = 45 and β = 20 in the y-z plane. Hee the optimal δ has moe than one optimal δ 85, 42,3,47,90 as shown in Figue 7. The adiation patten in the nea field fo the two value { } elements at δ = 3 is shown in Figue 8. The above example shows that thee ae seveal solutions fo Equation (7). This is due to the existence of nulls in the dominato of the equation. As both θ and β get close to each othe the optimization becomes less efficient and the maximum value fo P SAR becomes less fo example fo θ = 45 and β = 40 the δ 70, 25, 20,65 as shown in Figue 9. optimal δ has moe than one optimal value { } (7) 477

5 P SAR β θ Figue 6. The geometical epesentation of the pefomance equation. Figue 7. P SAR vs the phase shift δ fo θ = 45 and β = 20. Figue 8. Nea field adiation patten fo two antenna elements. 478

6 Figue 9. β = 40. P SAR vs the phase shift δ fo θ = 45 and Diect maximization of Equation (7) yields to solution whee the SAR in the dominato of the equation is vey small and hence any value fo the electic field in the base station diection will maximize the equation. This happens when β δ nπ and any value of the E will maximize P SAR. To solve this poblem, we use a constaint optimization technique, such that we impose the constaint not to exceed a cetain value of SAR and maximize the eceived powe in the diection of the base station. Lagange multiplie method can be used to find the optimal value of δ, such that we maximize the adiated enegy in the base station diection unde the constaint not to exceed a maximum value fo the SAR. The cost function can be witten as: 2 2 j( θ δ) σ j( β δ) C = max 1 e + + λ 1+ e SAR 2ρ whee λ is the Lagange multiplie. Solving fo λ we find that: And fom the constaint: Fom Equation (10) we find that: C σ = 2sin ( θ δ) 2λ sin ( β δ) = 0 δ 2ρ ( ) ( ) 2ρsin θ δ λ = σsin β δ (8) (9) (10) ρsar cos( β δ) + 1 = (11) σ λσ = + sin sin = 2ρ ( ) g ( ) 1 δ θ β δ δ λ The function g ( λ ) is assumed hee since analytical esult is had to get. A good appoximation yields to: λσ θ + β 2ρ δ λσ ρ (12) (13) 479

7 Solving fo δ as a function of λ and substituting in Equation (11). Then finding the value of λ fom Equation (11) that satisfies the constaint and substituting it in Equation (10) to get the optimal value of δ. And: g 1 1 ρsar λ = g β cos 1 σ 1 1 ( opt ) ( opt ) ρsar 2ρsin θ δ β cos 1 = σ σsin β δ Note that the analytical solution is had to get since Equations (10) and (11) ae not linea. We need a numeical technique to find δ such that, the solution can be found fast and the mobile device can detemine the optimal delay in eal time. We popose to use an iteative technique based on simulated annealing (SA) method to opt solve fo the optimal delay [19]-[22]. This technique will esult in a sub-optimal value fo δ but it should opt convege to a solution in eal time. Equation (10) has moe than one solution depending on the values of θ and β, some of them ae local optimal values. We need to find the global optimal value, and theefoe, simulated annealing algoithm is selected since it conveges to the global optimal point (o nea optimal). Stating fom the cost function defined as: An appoximation of this cost function is given by: opt ( opt ) opt (14) (15) F = ˆ δ h δ, βθ,, ρσ,,sar (16) σ 1 ρsar cos 1 2ρsin ( θ δopt ) 2ρ σ F = σsin ( β δopt ) 1 ρsar β θ cos 1 σ The devised system need to know the base station diection as well as the SAR diection ( βθ, ), these angels should be known each optimization update. The SAR angle is easy to obtain since it is always nomal to the speake of the phone as shown in Figue 10. θ is usually unknown and need to be estimated on eal time. To estimate the aival angle many techniques may be used. Hee we may use simple method to measue θ, such that, by using the eceived signal stength indicato (RSSI) of the device when on eceiving mode and sweeping the delay between the elements to get maximum RSSI. The simulated annealing algoithm does not equie deivative infomation; it needs to be supplied with a cost function fo each tial solution it geneates. The algoithm simulates a small andom displacement of an atom that esults in a change in enegy. If the change in enegy is negative, the enegy state of the new configuation is lowe and the new configuation is accepted. If the change in enegy is positive, the new configuation has a highe enegy state; howeve, it may still be accepted accoding to the Boltzmann pobability facto given by: (17) whee E kt e b k b is the Boltzmann constant, T is the cuent tempeatue and P = (18) E is the change in enegy (cost θ θ β Head Phone Figue 10. Usual expected diections fo θ and β. 480

8 function). The solution is stated at a high tempeatue, whee it has a high cost. Random petubations ae then made to the solution. If the cost is lowe, the new solution is made the cuent solution; if it is highe, it may still be accepted accoding the pobability given by the Boltzmann facto. The Boltzmann pobability is compaed to a andom numbe dawn fom a unifom distibution between 0 and 1; if the andom numbe is smalle than the Boltzmann pobability, the solution is accepted. This allows the algoithm to escape local minima. As the tempeatue is gadually loweed, the pobability that a wose solution is accepted becomes smalle. Although the algoithm is not guaanteed to find the best optimum, it will often find nea optimum and it is also a simple algoithm to implement. The simulated annealing algoithm is given as in the following pseudo code: s s 0 ; e E(s) // Initial solution, enegy. s best s; e best e // Initial best solution. k 0 // Enegy evaluation count. while k < k max and e > e max // Loop: T tempeatue(k/k max ) // Tempeatue calculation. s new neighbou(s) // Pick some neighbou. e new E(s new ) // Compute its enegy. P ee,, T > andom() then // Check if should we use it. if ( ) e new if new best etun s best s s new ; e e // Change state. new < e then // A new best? s best s new ; e best e new k k + 1 // Save new neighbou to best found. // loop. // Retun the best solution found. In the following we use MatLab to calculate the optimal delay fo the pevious examples using the simulated annealing algoithm. 3. Numeical Calculation and Results To demonstate the pefomance of the devised system and to find the optimal delay value using simulated annealing algoithm we use MatLab softwae to find the optimal delay fo the examples discussed in the pevious section: In the fist example whee θ = 45 and β = 20. Figue 11 shows a numeical calculation of the cost function given in Equation (17). Hee the optimal δ has moe than one solution at the zeo cossing points one of them is the global minimum cost solution. Figue 11. The cost function vs δ fo θ = 45 and β =

9 Using simulated annealing we solve the same example as shown in Figue 12. Hee the global optimal δ is found to be at afte 10 iteations. In the second example fo θ = 45 and β = 40. Figue 13 shows a numeical calculation of the cost function given in Equation (17). A gain, the optimal δ has moe than one solution at the zeo cossing points one of them is the global minimum cost solution. Using simulated annealing we solve the same example as shown in Figue 14. Hee the global optimal δ is found to be at afte 6 iteations. The simulated annealing algoithm in both examples aives in few iteations at the global optimal solution. Next we discuss the esults obtained fo the whole devised system. 4. Discussion of Results The poposed system uses two H shaped patch antennas with delay element to stee the adiation patten of the esultant aay in a way that ensues the safety of the use and at the same time maintain the connectivity with Figue 12. The cost function and δ vs iteation numbe fo θ = 45 and β = 20. Figue 13. The cost function vs δ fo θ = 45 and β =

10 Figue 14. The cost function and δ vs iteation numbe fo θ = 45 and β = 40. the cellula netwok. Maximizing the adiated powe towad the base station while keeping the SAR level unde the allowable maximum value is used as the optimization citeia. This poblem is solved using Lagange multiplie method and yields a numeically challenging solution; theefoe, simulated annealing algoithm is used to find a sub-optimal solution that woks fast in eal time fo the mobile unit. Numeical calculations showed good and efficient solutions fo the optimal delay value when using the simulated annealing algoithm. The design is simple and can be easily implemented on mobile units; it does not need lage pocessing powe fom the mobile unit. It can be implemented in eal time with minimum cost. Local field is also affects the use and needs to be investigated in futue wok. Refeences [1] Hanus, X., Luong, M. and Lethimonnie, F. (2005) Electomagnetics Fields and SAR Computations in a Human Head with a Multi-Pot Diven RF Coil at 11.7 Tesla. Poceedings of the Intenational Society fo Magnetic Resonance in Medicine, 13, 876. [2] CENELEC, EN (2006) Human Exposue to Radio Fequency Fields fom Hand-Held and Body-Mounted Wieless Communication Devices Human Models, Instumentation, and Pocedues Pat 1: Pocedue to Detemine the Specific Absoption Rate (SAR) fo Hand-Held Devices Used in Close Poximity to the Ea (Fequency Range of 300 MHz to 3 GHz). [3] Gabiel, C. (2000) Dielectic Popeties of Tissues. In: Klauenbeg, B.J. and Miklavcic, D., Eds., RFR Dosimety and Its Relationship to the Biological Effects of EMFs, NATO Science Seies 3, 82, [4] Gabiel, C. and Peyman, A. (2006) Dielectic Measuement: Eo Analysis and Assessment of Uncetainty. Physics in Medicine and Biology, 51, [5] IEC (2005) Human Exposue to Radio Fequency Fields fom Hand-Held and Body-Mounted Wieless Communication Devices Human Models, Instumentation, and Pocedues Pat 1: Pocedue to Detemine the Specific Absoption Rate (SAR) fo Hand-Held Devices Used in Close Poximity to the Ea (Fequency Range of 300 MHz to 3 GHz). [6] IEEE Std 1528T (2003) IEEE Recommended Pactice fo Detemining the Peak Spatial Aveage Specific Absoption Rate (SAR) in the Human Head fom Wieless Communications Devices: Measuement Techniques. [7] Kuste, N. and Balzano, Q. (1992) Enegy Absoption Mechanism by Biological Bodies in the Nea Field of Dipole Antennas above 300 MHz. IEEE Tansactions on Vehicula Technology, 41, [8] Kuste, N., Balzano, Q. and Lin, J. (1997) Mobile Communication Safety. Chapman and Hall, New Yok, [9] IEEE Std C (2005) IEEE Standad fo Safety Levels with Respect to Human Exposue to Radio Fequency Electomagnetic Fields, 3 khz to 300 GHz. 483

11 [10] Schmid, T., Egge, O. and Kuste, N. (1996) Automated E-Field Scanning System fo Dosimetic Assessments. IEEE Tansactions on Micowave Theoy and Techniques, 44, [11] Gandhi, O., Lazzi, G. and Fuse, C. (1996) EM Absoption in the Human Head Neck fo Mobile Telephones at 835 and 1900 MHz. IEEE Tansactions on Micowave Theoy and Techniques, 44, [12] Dimbylow, P. and Mann, S. (1994) SAR Calculations in an Anatomically Realistic Model of the Head fo Mobile Communication Tansceives at 900 MHz and 1.8 GHz. Physics in Medicine and Biology, 39, [13] Bentsen, S. and Honsleth, S. (1994) Retaded Time Absobing Bounday Conditions. IEEE Tansactions on Antenna and Popagation, 42, [14] Agawal, A., Desai, N.R., Makke, K., Vaghese, A., Mouadi, R., Sabanegh, E. and Shama, R. (2009) Effects of Radiofequency Electomagnetic Waves (RF-EMW) fom Cellula Phones on Human Ejaculated Semen: An in Vito Pilot Study. Fetility and Steility, 92, [15] BS EN : (2006) Human Exposue to Radio Fequency Fields fom Hand-Held and Body-Mounted Wieless Communication Devices Human Models, Instumentation, and Pocedues Pat 1: Pocedue to Detemine the Specific Absoption Rate (SAR) fo Hand-Held Devices Used in Close Poximity to the Ea (Fequency Range of 300 MHz to 3 GHz). [16] Chistodoulous, C. and Hescovicl, N. (2000) Smat Antennas in Wieless Communications: Base-Station Divesity and Handset Beamfoming. IEEE Antennas and Popagation Magazine, 42, [17] Rahhal, J. and Al-Nadi, D.A. (2007) A Geneal Configuation Antenna Aay fo Multi-Use Systems with Genetic and Ant Coloni Optimization. Jounal of Electomagnetics, 27, [18] Mouhamadou, M., Vaudon, P. and Rammal, M. (2006) Smat Antenna Aay Pattens Synthesis: Null Steeing and Multi-Use Beamfoming by Phase Contol. Pogess in Electomagnetics Reseach, 60, [19] Ingbe, L. (1993) Simulated Annealing: Pactice vesus Theoy. Mathematical and Compute Modelling, 18, [20] Johnson, D.S., Aagon, C.R., McGeoch, L.A. and Schevon, C. (1989) Optimization by Simulated Annealing: An Expeimental Evaluation; Pat I, Gaph Patitioning. Opeations Reseach, 37, [21] van Laahoven, P.J.M. and Aats, E.H.L. (1987) Simulated Annealing: Theoy and Applications. Mathematics and Its Applications, 37, [22] Coleman, C.M., Rothwell, E.J. and Ross, J.E. (2004) Investigation of Simulated Annealing, Ant-Colony Optimization, and Genetic Algoithms fo Self-Stuctuing Antennas. IEEE Tansactions on Antennas and Popagation, 52,

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