Algorithm to Enhance Radio Wave Propagation Strength

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26/07/18 Computer Science Reference this

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A New Algorithm to Enhance Radio Wave Propagation Strength in Dead Spots for Cellular Mobile WiFi Downloads Using Cloud Networks

Signal loss is a major problem for cellular wireless devices, resulting in dropped calls and failure in downloading data. Our research uses a combination of different interaction models to provide an easy interface to replace traditional control methods for maintaining signal levels. The lossy WiFi wave propagation around and within buildings is studied utilizing college buildings at the University of Bridgeport (UB) campus in Bridgeport CT. These buildings serve as good experimental settings because they exemplify typical signal dead spots, locations where little to no WiFi signal is available. In this paper, we investigate path loss propagation inside and outside buildings and we identify and categorize these problems. We then apply our path loss propagation algorithmic models to show that signal strength is significantly improved when compared to existing algorithms. Finally, we show the efficiency of our model and explain the specifics of our algorithm.

Cellular Mobile Communication keeps growing so fast on the market worldwide so that they become our everyday companions. Over the last twenty years, globally, Mobile Communication users have raised a specifically rich multimedia service which forces telecommunication vendors as well as the operators to set significant efforts in order to fulfill client’s needs. The use of Wi-Fi for internet is widely increasing especially in mobile devices where Wi-Fi enabled, which gives results in expanding hotspots, and user acceptance also grows. Cisco Visual Networking Index (VNI) presented its research about global mobile data traffic, and VNI research indicated that this traffic will increase 18-fold from 2011 to 2016, and will reach 10.8 exabytes per month. Recent technologists and mobile industries never viewed the roles for Wi-Fi in the new phones networks. The changes in the mobile and the offloading data traffic to Wi-Fi can and it plays the significant role to avoid clogged networks are realized by mobile operators [12]. From all these we conclude that the key component of the information security is the data transfer and it’s daily importance in our life. Wireless Local Area Networks (WLAN) gained high acceleration, the reason of the necessity to pre-evaluate signals that are transmitted under Line-of-Sight (LOS) and /or none (NLOS) radio wave propagation in the indoor environments. These transmissions have main problem which is the difficulty to predict indoor radio wave propagations because of the invisibility between the transmitter and the receiver [15].

Related work

Yuko MIURA, et. al. [1] proposed a propagation model which accurately predicts outdoor-to-indoor propagation loss; this model depends on the angle dependency of the losses with the paths that penetrate the indoor area. Radio waves transmitted from the base station first propagate outdoors to the building’s external wall. Next, the radio waves penetrate the structure’s external wall. Last, the penetration waves propagate inside the building for the receiver. Outdoor-to-indoor propagation loss is estimated by predicting the propagation losses of those three parts. The losses of those three propagation processes might be calculated individually, and the path loss between base station and mobile station is usually expressed since the amount of these losses in dB [1]. Greg Durgin et. al. [2] developed measurement-based path loss for propagation prediction; these measurements aided the development of outdoor-to-indoor communication systems for wireless internet access, wireless cable distribution, and wireless local loops. Iskandar et. al. [3] evaluated the propagation loss as a function of elevation and azimuth angels, and observed the link budget in the estimation to the required transmitted power at several transmission rates of IMT-2000. Gerd Wölfle et. al. [4] proposed a new concept called dominant model in which focuses on the dominant paths between transmitter and receiver for the planning of wireless networks. [4] Prepared a comparison between cellular or WLAN in urban considering indoors either direct ray or ray tracing propagation and urban city centers in multi-floor buildings. Oliver Stäbler et. al. [5] presented a deterministic approach for the evaluation of 3GPP Long Term Evolution (LTE) networks in urban and indoor, beside evaluated the signal levels in the expected MIMO capacity. N. Faruk et. al. [6] conducted measurements at 203.25 MHz and 583.25 MHz frequencies along ten routes in Ilorin City, in order to fit the measured data with lognormal propagation loss, [6] used least square regression method, and investigated the behavior of the TV signals in the same environment in building penetration loss across the routes. Thomas Schwengler, et. al. [7] presented propagation at 5.725 GHz – 5.825 GHz within the U.S Unlicensed National Information Infrastructure (U-NII) band. Measured propagation path loss in a residential area at 5.8 GHz. Separated the data sets into line of sight (LOS) and non-line of sight (NLOS), as much as obtained noteworthy results since propagation models were designed for cellular and PCs use at lower frequency and narrow-band channels. Sheryl L. Howard et. al. [8] presented the use of error-control coding (ECC) which used in wireless sensor networks (WSNs) in order to determine the energy efficiency of ECC in WSNs. As much as derived an expression for critical distance dCR, where the decoder’s energy consumption per bit equals the transmit energy savings per bit, also showed that in crowded environments and office buildings dCR dropped significantly to 3m or greater at 10 GHz without considering the interference. Alyosha Molnar, et. al. [9] presented 900 MHz, ultra-low power RF transceiver for wireless WSNs, and demonstrated them to communicate over 16 meters through walls at a bit rate of 20 kbps. Jun Wang et. al. [10] used an adaptive back-off strategy to achieve fairly uniform cluster head distribution across the network.


  1. Yuko MIURA, Yasuhiro ODA, and Tokio TAGA, Outdoor-To-Indoor Propagation Modeling with The Identification of Path Passing Through Wall Openings, Wireless Laboratories, NTT DoCoMo, Inc. 3-5 Hikari-no-oka, Yokosuka-shi, Kanagawa, 239-8536, Japan, 0-7803-7589-0/02/$17.00 ©2002 IEEE.
  2. Greg Durgin, Theodore S. Rappaport, Hao Xu, Measurements and Models for Radio Path Loss and Penetration Loss In and Around Homes and Trees at 5.85 GHz, IEEE Transactions on Communications, Vol. 46, No. 11, November 1998.
  3. Iskandar and Shigeru Shimamoto, Prediction of Propagation Path Loss for Stratospheric Platforms Mobile Communications in Urban Site LOS/NLOS Environment, pp. 5643-5648, 1-4244-0355-3/06/$20.00 (c) 2006 IEEE.
  4. Gerd Wölfle, René Wahl, Pascal Wildbolz, and Philipp Wertz, Dominant Path Prediction Model for Indoor and Urban Scenarios, AWE Communications GmbH, Otto-Lilienthal-Str. 36, 71034 Boeblingen, Germany,
  5. Oliver Stäbler, Reiner Hoppe, Gerd Wölfle, Thomas Hager, Timm Herrmann, Consideration of MIMO in the Planning of LTE Networks in Urban and Indoor Scenarios, AWE Communications GmbH Otto-Lilienthal-Straße 36, 71034 Böblingen, Germany.
  6. N. Faruk, A. A. Ayeni, Y. A. Adediran, Characterization Of Propagation Path Loss at VHF/UHF Bands for Ilorin City, Nigeria, Nigerian Journal of Technology (NIJOTECH) Vol. 32. No. 2. July 2013, pp. 253-265Copyright© Faculty of Engineering, University of Nigeria, Nsukka, ISSN 1115-8443.
  7. Thomas Schwengler, and Mike Gilbert, Propagation Models at 5.8 GHz –Path Loss & Building Penetration, U S WEST Advanced Technologies, Boulder, CO 80303. Tel. & e-mail respectively: 303-541-6052, [email protected] and 303-541-6257, [email protected].
  8. Sheryl L. Howard, Christian Schlegel and Kris Iniewski, Error Control Coding in Low-Power Wireless Sensor Networks: When is ECC Energy-Efficient, Dept. of Electrical & Computer Engineering University of Alberta Edmonton, AB Canada T6G 2V4 Email: sheryl,schlegel,[email protected].
  9. Alyosha Molnar, Benson Lu, Steven Lanzisera, Ben W. Cook and Kristofer S. J. Pister, An Ultra-low Power 900 MHz RF Transceiver for Wireless Sensor Networks, IEEE 2004 CUSTOM INTEGRATED CIRCUITS CONFERENCE, 0-7803-8495-4/04/$20.00 02004 IEEE.
  10. Jun Wang, Yong-Tao Cao, Jun-Yuan Xie, CCF and Shi-Fu Chen, Energy Efficient Backoff Hierarchical Clustering Algorithms for Multi-Hop Wireless Sensor Networks, JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 26(2): 283{291 Mar. 2011. DOI 10.1007/s11390011-1131-x, 2011 Springer Science +Business Media, LLC & Science Press, China. Mar. 2011, Vol.26, No.2.

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