International Journal of Wireless & Mobile Networks (IJWMN)

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IInntteerrnnaattiioonnaall JJoouurrnnaall ooff WWiirreelleessss && MMoobbiillee
NNeettwwoorrkkss ((IIJJWWMMNN))
IISSSSNN:: 00997755--33883344 [[OOnnlliinnee]];; 00997755--44667799 [[PPrriinntt]]
hhttttpp::////aaiirrccccssee..oorrgg//jjoouurrnnaall//iijjwwmmnn..hhttmmll

Optimization of 5G Virtual Cell Based Coordinated Multipoint Networks Using Deep
Machine Learning
Mohamed Elkourdi, Asim Mazin and Richard D. Gitlin
Department of Electrical Engineering, University of South Florida Tampa, USA
ABSTRACT
Providing seamless mobility and a uniform user experience, independent of location, is an
important challenge for 5G wireless networks. The combination of Coordinated Multipoint
(CoMP) networks and Virtual- Cells (VCs) are expected to play an important role in achieving
high throughput independent of the mobile’s location by mitigating inter-cell interference and
enhancing the cell-edge user throughput. Userspecific VCs will distinguish the physical cell from
a broader area where the user can roam without the need for handoff, and may communicate with
any Base Station (BS) in the VC area. However, this requires rapid decision making for the
formation of VCs. In this paper, a novel algorithm based on a form of Recurrent Neural
Networks (RNNs) called Gated Recurrent Units (GRUs) is used for predicting thetriggering
condition for forming VCs via enabling Coordinated Multipoint (CoMP) transmission.
Simulation results, show that based on the sequences of Received Signal Strength (RSS) values
of different mobile nodes used for training the RNN, the future RSS values from the closest three
BSs can be accurately predicted using GRU, which is then used for making proactive decisions
on enabling CoMP transmission and forming VCs.
KEYWORDS
Coordinated multipoint (CoMP), machine learning (ML), self-organizing networks (SON),
recurrent neural networks (RNN), gated recurrent unit (GRU).
For More Details : http://aircconline.com/ijwmn/V10N4/10418ijwmn01.pdf
Volume Link : http://airccse.org/journal/jwmn_current18.html

REFERENCES
[1] J. G. Andrews, S. Buzzi, W. Choi, S. V. Hanly, A. Lozano, A. C. K. Soong, and J. C. Zhang, “What
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[3] S. Deb, P. Monogioudis, J. Miernik and J. P. Seymour, "Algorithms for Enhanced Inter-Cell
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no. 1, pp. 137-150, Feb. 2014.doi: 10.1109/TNET.2013.2246820
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[12] K. Davaslioglu and E. Ayanoglu, “Interference-based cell selection in heterogeneous networks,” in
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[13] K. Cho, J. Chung, C. Gulcehre, and Y. Bengio, “Empirical evaluation of gated recurrent neural
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Emerging Wireless Technologies in the Internet of Things : A Comparative
Study
Shri Guru Govind Singh Institute of Technology, Nanded. MS. India
School of Computing, Engineering and Mathematics, Western Sydney University, Sydney,
Australia
ABSTRACT
The Internet of Things (IoT) incorporates multiple long-range, short-range, and personal area
wireless networks and technologies into the designs of IoT applications. This enables numerous
business opportunities in fields as diverse as e-health, smart cities, smart homes, among many
others. This research analyses some of the major evolving and enabling wireless technologies in
the IoT. Particularly, it focuses on ZigBee, 6LoWPAN, Bluetooth Low Energy, LoRa, and the
different versions of Wi-Fi including the recent IEEE 802.11ah protocol. The studies evaluate
the capabilities and behaviours of these technologies regarding various metrics including the data
range and rate, network size, RF Channels and Bandwidth, and power consumption. It is
concluded that there is a need to develop a multifaceted technology approach to enable
interoperable and secure communications in the IoT.
KEYWORDS
Internet of Things, Wireless Technologies, Low-power, M2M Communications.

REFERENCES
[1] B. Sanou, "The World in 2013: ICT facts and figures," International Telecommunications Union,
2013.
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[4] L. Li, H. Xiaoguang, C. Ke, and H. Ketai, "The applications of WiFi-based wireless sensor network in
internet of things and smart grid," in 2011 6th IEEE Conference on Industrial Electronics and
Applications (ICIEA), 2011, pp. 789-793.
[5] R. Want, B. N. Schilit, and S. Jenson, "Enabling the internet of things," Computer, pp. 28-35, 2015.
[6] D. Christin, A. Reinhardt, P. S. Mogre, and R. Steinmetz, "Wireless Sensor Networks and the Internet
of Things: Selected Challenges," presented at the The 8th GI/ITG
KuVSFachgesprächDrahtloseSensornetze,
2009.
[7] Z. Sheng, S. Yang, Y. Yu, A. Vasilakos, J. Mccann, and K. Leung, "A survey on the ietf protocol suite
for the internet of things: Standards, challenges, and opportunities," IEEE Wireless Communications, vol.
20, pp. 91-98, 2013.
[8] S. Lee, D. Yoon, and A. Ghosh, "Intelligent parking lot application using wireless sensor networks,"
in International Symposium on Collaborative Technologies and Systems, 2008, pp. 4857.
[9] W. Lemstra, V. Hayes, and J. Groenewegen, The innovation journey of Wi-Fi: The road to global
success: Cambridge University Press, 2010.
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60, 2013.
[11] E. Perahia and R. Stacey, Next Generation Wireless LANs: 802.11 n and 802.11 ac: Cambridge
university press, 2013.
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Survey," Computer Networks, vol. 38, pp. 393-422, 2002.
[13] L. Verma, M. Fakharzadeh, and S. Choi, "WiFi on Steroids: 802.11 ac and 802.11 ad," IEEE
Wireless Communications, vol. 20, pp. 30-35, 2013.
[14] T. Adame, A. Bel, B. Bellalta, J. Barcelo, and M. Oliver, "IEEE 802.11 AH: theWiFi approach for
M2M communications," IEEE Wireless Communications, vol. 21, pp. 144-152, 2014.
[15] IEEE. (2015, 30/05/2014). IEEE P802.11 Sub 1GHz Study Group. Available:
http://www.ieee802.org/11/Reports/tgah_update.html
[16] E. Khorov, A. Lyakhov, A. Krotov, and A. Guschin, "A survey on IEEE 802.11 ah: An enabling
networking technology for smart cities," Computer Communications, pp. 53-69, 2014.

[17] T. Adame, A. Bel, B. Bellalta, J. Barcelo, J. Gonzalez, and M. Oliver, "Capacity analysis of IEEE
802.11 ah WLANs for M2M communications," in Multiple Access Communcations, ed: Springer, 2013,
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802.11 ah and its restricted access window mechanism," in 2014 IEEE International Conference on
Communications Workshops (ICC), 2014, pp. 460-466.
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ah," IEEE Communications Surveys & Tutorials, vol. 17, pp. 1761-1775, 2015.
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Enterprise. Available: http://www.frontwave.eu/2014_12_01_archive.html
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for TV white space spectrum sharing," IEEE Communications Magazine, vol. 51, pp. 92100, 2013.
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Technologies, Applications, and Services," International Journal of Next-Generation Networks, vol. 6, pp.
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http://www.bluetooth.com/Pages/Bluetooth-Smart.aspx
[26] J. Decuir, "Bluetooth Smart Support for 6LoBTLE: Applications and connection questions," IEEE
Consumer Electronics Magazine, vol. 4, pp. 67-70, 2015.
[27] Bluetooth SIG. (2012, 11/04/2015). Bluetooth Core Version 4.0. Available:
https://www.bluetooth.org/Technical/Specifications/adopted.htm
[28] J. Hui and D. Culler. 6LoWPAN: Incorporating IEEE 802.15.4 into the IP architecture. Available:
http://www.ipso-alliance.org/wp-content/media/6lowpan.pdf
[29] M. B. Baria, A. P. Gharge, and N. D. Sheth, "A Review of Zigbee Smart Energy," in International
Journal of Engineering Development and Research, 2014.
[30] N. Baker, "ZigBee and Bluetooth: Strengths and weaknesses for industrial applications," Computing
and Control Engineering, vol. 16, pp. 20-25, 2005.
[31] A. J. Jara, L. Ladid, and A. F. Gómez-Skarmeta, "The Internet of Everything through IPv6: An
Analysis of Challenges, Solutions and Opportunities," JoWUA, vol. 4, pp. 97-118, 2013.
[32] L. Alliance, "LoRa alliance–wide area networks for IoT," ed, 2015.
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Technical Overview and Challenges," Journal of Networks, vol. 10, pp. 164-171, 2015. [34] F. Stroud.
(2015, 15/01/2015). 802.11ac. Available: http://www.webopedia.com/TERM/8/802_11ac.html

[35] P. Anitha and C. Chandrasekar, "Energy Aware Routing Protocol ForZigbee Networks," Journal of
Computer Applications (JCA), vol. 4, pp. 92-94, 2011.
[36] P. McDermott-Wells, "What is bluetooth?," IEEE Potentials, vol. 23, pp. 33-35, 2004.
[37] IEEE 802.11 Working Group, "IEEE Standard for Information Technology–Telecommunications
and information exchange between systems–Local and metropolitan area networks–Specific
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11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications Amendment
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[38] A. Dementyev, S. Hodges, S. Taylor, and J. Smith, "Power Consumption Analysis of Bluetooth Low
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[39] B. B. Olyaei, J. Pirskanen, O. Raeesi, A. Hazmi, and M. Valkama, "Performance comparison
between slotted IEEE 802.15. 4 and IEEE 802.1 lah in IoT based applications," in IEEE 9th International
Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Lyon,
France, 2013, pp. 332-337.

A Multi-Path Routing Determination Method for Improving the Energy
Efficiency in Selective Forwarding Attack Detection Based MWSNs
Won Jin Chung and Tae Ho Cho
Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of
Korea
ABSTRACT
A selective forwarding attack in mobile wireless sensor networks is an attack that selectively
drops or delivers event packets as the compromised node moves. In such an attack, it is difficult
to detect the compromised node compared with the selective forwarding attack occurring in the
wireless sensor network because all sensor nodes move. In order to detect selective forwarding
attacks in mobile wireless sensor networks, a fog computing-based system for a selective
forwarding detection scheme has been proposed. However, since the proposed detection scheme
uses a single path, the energy consumption of the sensor node for route discovery when the
sensor node moves is large. To solve this problem, this paper uses fuzzy logic to determine the
number of multi-paths needed to improve the energy efficiency of sensor networks.
Experimental results show that the energy efficiency of the sensor network is improved by
9.5737% compared with that of the existing scheme after 200 seconds when using the proposed
scheme.
KEYWORDS
Mobile wireless sensor networks, selective forwarding attack, network security, fuzzy logic,
AOMDV routing protocol

REFERENCES
[1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, "A survey on sensor networks,"
Communications Magazine, IEEE, vol. 40, pp. 102-114, 2002.
[2] K. Akkaya and M. Younis, “A survey on routing protocols for wireless sensor networks,” Ad Hoc
Netw., vol. 3, pp. 325–349, 2005.
[3] R.Javad, M.Moradi and A.S.Ismail, "Mobile wireless sensor networks overview," International
Journal of Computer Communications and Networks vol. 2, no. 1, pp. 17-22, 2012
[4] C. Zhu, et al. "A survey on communication and data management issues in mobile sensor networks",
Wireless Commun. Mobile Computing, vol. 14, no. 1, pp. 19-36, 2014
[5] I. Amundson and X. D. Koutsoukos, "A survey on localization for mobile wireless sensor networks."
Mobile entity localization and tracking in GPS-less environnments. Springer, Berlin, Heidelberg, 235-
254, 2009
[6] Y. Wang, G. Attebury, and B. Ramamurthy, “A survey of security issues in wireless sensor
networks,” IEEE Communications Surveys & Tutorials, vol. 8, pp. 2-23, 2007
[7] J. Sen, "A survey on wireless sensor network security," arXiv preprint arXiv:1011.1529, 2010.
[8] C. Perkins, E. Belding-Royer, and S. Das, "Ad hoc on-demand distance vector (AODV) routing,"
IETF RFC 3561, 2003
[9] N M. Marina and S. Das, "On-demand multipath distance vector routing in ad hoc networks", IEEE
International Conference on Network Protocols (ICNP), pp. 14–23, 2001
[10] Q. Yaseen, F. AlBalas, and Y. Jararweh, "A fog computing-based system for selective forwarding
detection in mobile wireless sensor networks". Foundations and Applications of Self* Systems, IEEE
International Workshops on. IEEE, 2016
[11] M. Radi, B. Dezfouli, K. A. Bakar and M. Lee, “Multipath routing in wireless sensor networks:
survey and research challenges,” Sensors, vol. 12, pp. 650-685, Jan. 2012
[12] R.U.Anitha and P. Kamalakkannan, "Enhanced cluster based routing protocol for mobile nodes in
wireless sensor network," Pattern Recognition, Informatics and Mobile Engineering (PRIME), 2013
International Conference on. IEEE, pp. 187-193, 2013

Quality of Service Routing in Mobile Ad Hoc Networks Using Location and
Energy Parameters
Shuchita Upadhayaya and Charu Gandhi
Department of Computer Science and Applications, Kurukshetra University, Kurukshetra, India
ABSTRACT
Mobile Ad hoc Networks are highly dynamic networks. Quality of Service (QoS) routing in such
networks is usually limited by the network breakage due to either node mobility or energy depletion of
the mobile nodes. Also, to fulfill certain quality parameters, presence of multiple node-disjoint paths
becomes essential. Such paths aid in the optimal traffic distribution and reliability in case of path
breakages. Thus, to cater such problem, we present a node-disjoint multipath protocol. The metric used to
select the paths takes into account the stability of the nodes and the corresponding links. The proposed
technique is also illustrated with an example.
KEYWORDS
QoS Rrouting, Mobile Ad hoc Networks, Energy-Aware Routing, Multipath Rrouting,
For More Details : http://airccse.org/journal/jwmn/1109s11.pdf
Volume Link : http://airccse.org/journal/j3current.html

REFERENCES
1. D. D. Perkins, H. D. Hughes & C. B. Owen, (2002) “Factors Affecting the Performance of Ad Hoc
Networks,” Proceedings of the IEEE International Conference on Communications (ICC), 2002, pp.2048-
2052.
2. Imrich Chlamtac, Marco Conti, Jennifer J.-N. Liu, (2003)"Mobile Ad hoc networking imperatives and
challenges" Ad Hoc Networks, Vol 1, pp.13-64.
3. C.E. Perkins & P. Bhagwat (1994)" Highly Dynamic Destination-Sequenced Distance Vector Routing
(DSDV) for Mobile Computers", ACM SIGCOMM Conference on Communications Architectures,
Protocols and Applications, Vol. 24, pp. 234-244.
4. M. Abolhasan, T.A. Wysocki, & E. Dutkiewicz, (2004) "A Review of Routing Protocols for Mobile Ad
hoc Networks", Ad hoc Networks, Vol. 2, pp. 1-22.
5. David B. Johnson, David A. Maltz, & Josh Broch, "DSR: The Dynamic Source Routing Protocol for
Multi-Hop Wireless Ad Hoc Networks", (2001), Ad Hoc Networking, AddisonWesley , pp. 139-172.
6. C. E. Perkins & E. M. Royer,(2003) "Ad hoc On-Demand Distance Vector Routing (AODV) ", IETF
RFC 3561
7. .Z. Hass & R. Pearlmann, "Zone routing Protocol"(1999),IETF Internet Draft
8. RFC2386.
9. Shigang Chen & Klara Nahrstedt, (1998) ".An Overview of Quality-of-Service Routing for the next
Generation High -Speed Networks: Problems and Solutions", IEEE Network Magazine, vol12, pp. 64 -79.
10. M. K. Marina & S. R. Das, (2001) "On-Demand MultiPath Distance Vector Routing in Ad hoc
Networks",Proceedings of the Ninth International Conference on Network Protocols (ICNP},IEEE
Computer Society Press, pp. 14-23.
11. Jiwon Park, Sangman Moht & Ilyong Chung (2008),” Multipath AODV Routing Protocol in Mobile
Ad Hoc Networks with SINR-Based Route Selection”, International Symposium on Wireless
Communication Systems, IEEE ,pp:682-688.
12. Lei Wang, Lianfang Zhang, Yantai Shu & Miao Dong (2000) ,” Multipath source routing in wireless
ad hoc networks”, Proceedings of Canadian Conference on Electrical and Computer Engineering, Vol 1,
pp. 479-483.
13. S. J. Lee and M. Gerla (2001) "Split Multipath Routing with Maximally Disjoint Paths in Ad hoc
Networks”,Proceedings of the IEEE International Conference on Communications(ICC), Vol 10, pp.
3201-3205.
14. X.Li,(2006), Ph.D thesis on "Multipath Routing and QoS Provisioning in Mobile Ad hoc Networks",
Queen Mary University of London.
15. Zhengyu W, Xiangjun D & Lin C,(2007), " A Grid-Based Energy Aware Node-Disjoint Multipath
Routing Algorithm for MANETs", Proceedings of International Conference on Natural Computation,
Vol. 5, pp. 244-248.

16. Do-Youn H, Eui-Hyeok K & Jae-Sung L, (2006),“ An Energy Aware Source Routing with Disjoint
Multipath Selection for Energy- Efficient Multihop Wireless Ad hoc Networks”, Proceedings of
International Federation for Information Processing, 2006, pp. 41-50.
17. M. Bheemalingaiah, M. M. Naidu, D. Sreenivasa Rao & G. Varaprasad (2009)," Energy Aware Node-
Disjoint Routing in Mobile Ad Hoc Networks", Journal of Theoretical and Applied Information
Technology, pp 416-431.
18. Liansheng T, Ling X, King T. K, M. Lei & Zukennan,(2006), "LAMOR: Lifetime-Aware Multipath
Optimized Routing Algorithm for Video Transmission over Ad hoc Networks", Proceedings of IEEE
Vehicular Technology Conference, Vol. 2, pp. 623-627.
19. W.Su,Sung-Ju Lee & M.Gerla (2000),” Mobility Prediction in Wireless Networks”.,MILCOM 2000,
Vol l, pp.491-495.
20. M. K. Marina & S. R. Das,(2001), "On-Demand MultiPath Distance Vector Routing in Ad hoc
Networks", In Proceedings of the Ninth International Conference on Network Protocols (ICNP}, IEEE
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networks", Proceedings of the IEEE international symposium on low power electronics and design,
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A Novel Robust and Low-Complexity Spacetime Codes for Industry 4.0
Systems
Mohamed S. Abouzeid
Department of Electronics and Electrical Communication, Faculty of Engineering, Tanta University,
Egypt
ABSTRACT
This paper proposes different robust and low-complexity space time codes which provide more reliability
for industrial automation. An innovative synchronized uplink system configuration for an Industrial
Environment is proposed. Mathematical framework for estimating the channel phase of each Slave Node
(SN) is developed. Furthermore, the channel is practically estimated based on an innovative method using
parallel sequence spread spectrum (PSSS) implemented in Universal Software Radio Peripheral (USRP).
The proposed space time codes are applied in the uplink of an industrial communication system where the
channel is modelled using Quasi Deterministic Radio Channel Generator (Quadriga) which follows a
geometry-based stochastic approach. Simulation results exposed that the proposed codes surpass
Alamouti code for Industrial Automation. The bit error rate (BER) performance demonstrates that the
achieved coding gain for the proposed codes is higher than Alamouti code leading to more robust
communication. Furthermore, a low complexity decoders based on minimum mean squared error
(MMSE) and zero forcing (ZF) algorithms are designed at the receiver side. The proposed codes give a
predominant execution against the state-of-the-art space time codes for Industry 4.0.
KEYWORDS
Industrial Communication, Space-Time Code, Quadriga, Minimum Mean squared Error decoder, Zero
Forcing Algorithm, Parallel Sequence Spread Spectrum, Software Defined Radio, Factory Automation.

REFERENCES
[1] M. Cheffena, "Industrial indoor multipath propagation — A physical-statistical approach," 2014 IEEE
25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC),
Washington DC, pp. 68-72, 2014.
[2] R. Croonenbroeck, A. Wulf, L. Underberg, W. Endemann and R. Kays, "Parallel Sequence Spread
Spectrum: Bit Error Performance under Industrial Channel Conditions," ICOF 2016; 19th International
Conference on OFDM and Frequency Domain Techniques, Essen, Germany, pp. 1-7, 2016.
[3] H. Igor, J. Bohuslava and J. Martin, "Proposal of communication standardization of industrial
networks in Industry 4.0," 2016 IEEE 20th Jubilee International Conference on Intelligent Engineering
Systems (INES), Budapest, pp. 119-124, 2016.
[4] R. Kraemer, M. Methfessel, R. Kays, L. Underberg and A. C. Wolf, "ParSec: A PSSS approach to
industrial radio with very low and very flexible cycle timing," 2016 24th European Signal Processing
Conference (EUSIPCO), Budapest, pp. 1222-1226, 2016.
[5] L. Underberg, A. Wulf, R. Croonenbroeck, W. Endemann and R. Kays, "Parallel Sequence Spread
Spectrum: Analytical and simulative approach for determination of bit error probability," 2016 IEEE 21st
International Conference on Emerging Technologies and Factory Automation (ETFA), Berlin, pp. 1-8,
2016.
[6] T. Olofsson, A. Ahlén and M. Gidlund, "Modeling of the Fading Statistics of Wireless Sensor
Network Channels in Industrial Environments," in IEEE Transactions on Signal Processing, vol. 64, no.
12, pp. 3021-3034, June15, 2016.
[7] S. Li, J. Zhang and X. Mu, "Noncoherent Massive Space-Time Block Codes for Uplink Network
Communications," in IEEE Transactions on Vehicular Technology, 2018.
[8] S. S. H. Bidaki, S. Talebi and M. Shahabinejad, "A Full-Rate Full-Diversity 2x2 Space-Time Block
Code with Linear Complexity for the Maximum Likelihood Receiver," in IEEE Communications Letters,
vol. 15, no. 8, pp. 842-844, August 2011.
[9] S. Kumagai, Y. Seki and F. Adachi, "Joint Tx/Rx Signal Processing for Distributed Antenna
MUMIMO Downlink," 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall), Montreal, QC,
pp. 1-5, 2016.
[10] . D. Nguyen, J. Joung and S. Sun, "Precoder design for distributed antenna systems (DAS) with
limited channel state information," 2015 IEEE International Conference on Communications (ICC),
London, pp. 1733-1738, 2015.
[11] H. Kim, S.-R. Lee, K.-J. Lee, and I. Lee, “Transmission schemes based on sum rate analysis in
distributed antenna systems,” IEEE Trans. Wireless Commun., vol. 11, no. 3, pp. 1201–1209, Mar. 2012.
[12] A. Liu and V. K.N. Lau, “Joint power and antenna selection optimization for energy efficiency in
large cloud radio access networks,” IEEE Trans. Signal Process., vol. 62, no. 5, pp. 1319–1328, Mar.
2014.
[13] J. Joung, Y. K. Chia, and S. Sun, “Energy-efficient, large-scale distributed-antenna system for
multiple users,” IEEE J. Sel. Topics Signal Process., vol. 8, pp. 954–965, Sep. 2014.

[14] E. Tanghe, W. Joseph, J. De Bruyne, L. Verloock and L. Martens, “The industrial indoor channel:
Statistical analysis of the power delay profile,” AEU-International Journal of Electronics and
Communications, pp. 806-812, 2010.
[15] A. F. Molisch, K. Balakrishnan , et al. "IEEE 802.15. 4a channel model-final report." IEEE P802
15.04, 2004.
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LACBER: New Location Aided Routing Protocol For GPS Scarce Manet
Dipankar Deb1
, Srijita Barman Roy2
, and Nabendu Chaki3
1
Netaji Subhas Mahavidyalaya, Department of Higher Education of Tripura, India
2
R.T. College, Department of Higher Education of Tripura, Agartala, India
3
Department of Computer Science & Engineering, University Calcutta, India
ABSTRACT
Completely GPS-free positioning systems for wireless, mobile, ad-hoc networks typically stress on
building a network-wide coordinate system. Such systems suffer from lack of mobility and high
computational overhead. On the other hand, specialized hardware in GPS-enabled nodes tends to increase
the solution cost. A number of GPS free position based routing algorithms have been studied by the
authors before proposing a new positioning framework in this paper. The proposed positioning
framework is characterized by using only a handful of GPS enabled nodes. Lower dependence on
specialized GPS hardware reduces the total cost of implementing the framework. A new location aided
routing protocol called Location Aided Cluster Based Energy-efficient Routing (LACBER) has been
proposed in the paper. Simulation results show that using the proposed positioning framework, LACBER
turns out to be efficient in lowering mean hop and hence in utilizing the limited energy of mobile nodes.
KEYWORDS
Routing Protocols, GPS-free positioning, mobility, Location Aided Routing
For More Details : http://airccse.org/journal/nsa/0809smn02.pdf
Volume Link : http://airccse.org/journal/j3current.html

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Improving MANET Routing Protocols Through the Use of Geographical
Information
Vasil Hnatyshin
Department of Computer Science, Rowan University, Glassboro, NJ, USA
ABSTRACT
This paper provides a summary of our research study of the location-aided routing protocols for mobile ad
hoc networks (MANET). This study focuses on the issue of using geographical location information to
reduce the control traffic overhead associated with the route discovery process of the ad-hoc on demand
distance vector (AODV) routing protocol. AODV performs route discovery by flooding the whole
network with the route request packets. This results in unnecessarily large number of control packets
traveling through the network. In this paper, we introduced a new Geographical AODV (GeoAODV)
protocol that relies on location information to reduce the flooding area to a portion of the network that is
likely contains a path to destination. Furthermore, we also compared GeoAODV performance with that of
the Location Aided Routing (LAR) protocol and examined four mechanisms for reducing the size of the
flooding area: LAR zone, LAR distance, GeoAODV static, and GeoAODV rotate. We employed OPNET
Modeler version 16.0 software to implement these mechanisms and to compare their performance through
simulation. Collected results suggest that location-aided routing can significantly reduce the control
traffic overhead during the route discovery process. The comparison study revealed that the LAR zone
protocol consistently generates fewer control packets than other location-aided mechanisms. However,
LAR zone relies on the assumption that location information and traveling velocities of all the nodes are
readily available throughout the network, which in many network environments is unrealistic. At the same
time, the GeoAODV protocols make no such assumption and dynamically distribute location information
during route discovery. Furthermore, the collected results showed that the performance of the GeoAODV
rotate protocol was only slightly worse than that of LAR zone. We believe that even though GeoAODV
rotate does not reduce the control traffic overhead by as much as LAR zone, it can become a preferred
mechanism for route discovery in MANET.
KEYWORDS
Mobile Ad-Hoc Networks; MANET Routing Protocols; Ad Hoc On Demand Distance Vector Routing;
Location-Aided Routing; Geographical AODV; OPNET Modeler
For More Details: http://airccse.org/journal/jwmn/0413wmn01.pdf

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Device-To-Device (D2D) Communication Under LTE-Advanced Networks
Magri Hicham1
, Noreddine Abghour2
and Mohammed Ouzzif1
1
RITM Research Lab,ESTC , Hassan II University ,Casablanca, Morocco
2
FSAC, Hassan II University,Casablanca, Morocco
ABSTRACT
Device-to-Device (D2D) communication is a new technology that offer many advantages for the
LTEadvanced network such us wireless peer-to-peer services and higher spectral efficiency. It is also
considered as one of promising techniques for the 5G wireless communications system and used in so
many different fields such as network traffic offloading, public safety, social services and applications
such as gaming and military applications . The goal of this paper is to present advances on the current
3GPP LTE-advanced system related to Device-to-Device (D2D). In this paper, we provide an overview of
the D2D types based on the communication spectrum of D2D transmission, namely Inband D2D
communication and Outband D2D communication. Then we present the advantages and disadvantages of
each D2D mode. Moreover, architecture and protocol enhancements for D2D communications under
LTE-A network are described.
KEYWORDS
D2D;LTE-advanced;Inband D2D;Outband D2D;3GPP;5G.
For More Details: http://aircconline.com/ijwmn/V8N1/8116ijwmn02.pdf

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Mobility Models for Delay Tolerant Network: A Survey
M Shahzamal1
, M F Pervez1
, M A U Zaman1
and M D Hossain2
1
Institute of Electronics, Bangladesh Atomic Energy Commission, Bangladesh
2
Institute of Computer Science, Bangladesh Atomic Energy Commission, Bangladesh
ABSTRACT
Delay Tolerant Network (DTN) is an emerging networking technology that is widely used in the
environment where end-to-end paths do not exist. DTN follows store-carry-forward mechanism to route
data. This mechanism exploits the mobility of nodes and hence the performances of DTN routing and
application protocols are highly dependent on the underlying mobility of nodes and its characteristics.
Therefore, suitable mobility models are required to be incorporated in the simulation tools to evaluate
DTN protocols across many scenarios. In DTN mobility modelling literature, a number of mobility
models have been developed based on synthetic theory and real world mobility traces. Furthermore, many
researchers have developed specific application oriented mobility models. All these models do not
provide accurate evaluation in the all scenarios. Therefore, model selection is an important issue in DTN
protocol simulation. In this study, we have summarized various widely used mobility models and made a
comparison of their performances. Finally, we have concluded with future research directions in mobility
modelling for DTN simulation.
KEYWORDS
Delay Tolerant Networking, Mobility Modelling, DTN Simulation
For More Details: http://airccse.org/journal/jwmn/6414ijwmn10.pdf

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Analyses and Performance of Techniques PAPR Reduction for STBC MIMO-
OFDM System in (4G) Wireless Communication
Leila Sahraoui, Djmail Messadeg, Nouredinne Doghmane
Department of Electronics Faculty sciences of Engineering University Baji Mokhtar, Annaba bp 12 el
hadjar, Algeria
ABSTRACT
An OFDM system is combined with multiple-input multiple-output (MIMO) in order to increase the
diversity gain and system capacity over the time variant frequency-selective channels. However, a major
drawback of MIMO-OFDM system is that the transmitted signals on different antennas might exhibit
high peak-to-average power ratio (PAPR).In this paper, we present a PAPR analysis reduction of space-
timeblock-coded (STBC) MIMO-OFDM system for 4G wireless networks. Several techniques have been
used to reduce the PAPR of the (STBC) MIMOOFDM system: clipping and filtering, partial transmit
sequence (PTS) and selected mapping (SLM). Simulation results show that clipping and filtering provides
a better PAPR reduction than the others methods and only SLM technique conserve the PAPR reduction
in reception part of signal.
KEYWORDS
MIMO-OFDM; peak-to-average power ratios; space-time coding system (STBC); clipping and filtering;
SLM; PTS.

REFERENCES
[1] Ms. V. B. Malode1, Dr. B. P. Patil2, “PAPR Reduction Using Modified Selective Mapping
Technique,” Vol.02, No.2, pp. 626-630 (2010).
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multicarrier transmission,” IEEE Wireless Commun., Vol. 12, pp. 56–65, April. 2005
[3] T. Jiang and Y. Wu, “An overview: Peak-to-average power ratio reduction techniques for OFDM
signals,” IEEE Trans. Broadcast., Vol. 54, pp. 257–268, June. 2008.
[4] M.-S. Beak, M.J. Kim, Y.-H.You, and H.-K. Song, “Semi-blind estimation and PAR reduction for
MIMO-OFDM system with multiple antennas,” IEEE Trans. Broadcast, Vol. 50, pp. 414-424, Dec. 2004.
[5] R. F. H. Fischer and C. Siegl, “Performance of peak-to-average power ratio reduction in singleand
multi-antenna OFDM via directed selected mapping,” IEEE Trans. Commun., Vol. 57, pp. 3205–3208,
Nov. 2009.
[6] Y. Lou J. Palicot, “A classification of methods for efficient power amplification of signals,” Annals of
Telecommunications, Vol. 63, No. 7-8, pp. 351–368 (2008).
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Communications Letters, Vol.2, No. 5, pp.131-133, May 1998.
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[11] Radhia Gharsallah, Ridha Bouallegue "Combined with space time coding multi user detection for
CDMA-OFDM/OQAM SYSTEM" International Journal of Wireless & Mobile Networks (IJWMN)
Vol.4, No. 4, August 2012
[12] R. W. B. Auml, R. F. H. Fischer, and J. B. Huber, “Reducing the peak-to- average power ratio of
multicarrier modulation by selected mapping,” Electron. Lett., Vol. 32, No. 22, pp. 2056-2057, Oct. 1996.
[13] X. Li and L. J. Cimini, Jr., “Effect of clipping and filtering on the performance of OFDM,” IEEE
Commun. Lett, Vol. 2, No. 5, pp. 131- 133, May 1998.
[14] Y. A. Khan, M. A. Matin, and S. I. Ferdous, “PAPR Reduction in MIMO-OFDM Systems using
Modified PTS and SLM without Side Information,” International Journal of Communication Networks
and Information Security, Vol. 2, No.3, pp240-247, December 2010.

[15] Yi-Sheng Su, Tsung-Cheng Wu, Chung-Hsuan Wang, and Min-Kuan Chang, “A Low-
complexityCross-Antenna Rotation and Inversion Scheme for PAPR Reduction of STBC MIMO-OFDM
Systems” 16th international workshop on computer aided modeling and design of communication links
and networks (CAMAD) IEEE 2011
[16] P. Mukunthan and P.Dananjayan, “Modified PTS with FECs for PAPR Reduction of OFDM
Signals,” International Journal of Computer Applications, Vol. 11, No.3, pp. 38-43, December 2010.
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Research IEEE 2011
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coded OFDM signal,” IEEE Transaction on broadcasting, Vol. 50, No.3, pp.335-341, Sept 2004.
[19] Pawan Sharma, Seema Verma "PAPR reduction of OFDM signals using selective mapping with
turbo codes" International Journal of Wireless & Mobile Networks (IJWMN) Vol. 3, No. 4, August 2011
[20] Yang Jie, Chen Lei, Liu Quan and Chan De, “A Modified selected mapping technique to reduce the
Peak to Average Power Ratio of OFDM signal,” IEEE transaction on consumer Electronics, Vol53, No.3,
pp. 846-851, August 2007.
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reduction”, Signal & Image Processing: An International Journal (SIPIJ) Vol.3, No.2, April 2012.

Frequency and Time Domain Packet Scheduling Based on Channel Prediction
with Imperfect CQI in LTE
Yongxin Wang1
, Kumbesan Sandrasegaran2
, Xinning Zhu3
, Jingjing Fei4
Xiaoying Kong5
and Cheng-
Chung Lin 6
1
FEIT, University of Technology, Sydney, Australia
2
3
Beijing University of Post and Telecommunications, Beijing, China
4
CSE, University of New South Wales, Sydney, Australia
5
6
ABSTRACT
Channel-dependent scheduling of transmission of data packets in a wireless system is based on
measurement and feedback of the channel quality. To alleviate the performance degradation due to
simultaneous multiple imperfect channel quality information (CQI), a simple and efficient packet
scheduling (PS) algorithm is developed in downlink LTE system for real time traffic. A frequency domain
channel predictor based on Kalman filter is first developed to restore the true CQI from erroneous channel
quality feedback. Then, a time domain grouping technique employing the joint of Proportional Fair (PF)
and Modified Largest Weighted Delay First (M-LWDF) algorithms is used. It was proved this proposal
achieves better performance in terms of system throughput and packet loss ratio by simulation results.
KEYWORDS
LTE, packet scheduling, channel estimation, Kalman filter, imperfect CQI

REFERENCES
[1] K. Kinoshita, "Current Status of “FDMA” 3G service and DoCoMo’s B3G Activities," in ICB3G –
2004, May 2004, pp. 13-21.
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2009.
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IEEE 9th Malaysia International Conference on, 2009, pp. 815-820.
[6] A. Pokhariyal, T. E. Kolding, and P. E. Mogensen, "Performance of Downlink Frequency Domain
Packet Scheduling for the UTRAN Long Term Evolution," in Personal, Indoor and Mobile Radio
Communications, 2006 IEEE 17th International Symposium on, 2006, pp. 1-5.
[7] J. Gross, J. Klaue, H. Karl, and A. Wolisz, "Subcarrier allocation for variable bit rate video streams in
wireless OFDM systems," in Vehicular Technology Conference, 2003. VTC 2003-Fall. 2003 IEEE
58th, 2003, pp. 2481-2485 Vol.4.
[8] N. Ruangchaijatupon and J. Yusheng, "Simple Proportional Fairness Scheduling for OFDMA
FrameBased Wireless Systems," in Wireless Communications and Networking Conference, 2008. WCNC
2008. IEEE, 2008, pp. 1593-1597.
[9] P. Jeongsik, H. Sungho, and C. Ho-Shin, "A Packet Scheduling Scheme to Support Real-Time Traffic
in OFDMA Systems," in Vehicular Technology Conference, 2007. VTC2007-Spring. IEEE 65th,
2007, pp. 2766-2770.
[10] X. Liu, L. Guangyi, Y. Wang, and P. Zhang, "Downlink Packet Scheduling for Real-Time Traffic in
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64th, 2006, pp. 1-5.
[11] A. Wang, L. Xiao, S. Zhou, X. Xu, and Y. Yao, "Dynamic resource management in the fourth
generation wireless systems," in Communication Technology Proceedings, 2003. ICCT 2003.
International Conference on, 2003, pp. 1095-1098 vol.2.
[12] A. Pokhariyal, K. I. Pedersen, G. Monghal, I. Z. Kovacs, C. Rosa, T. E. Kolding, and P. E.
Mogensen, "HARQ Aware Frequency Domain Packet Scheduler with Different Degrees of Fairness for
the UTRAN Long Term Evolution," in Vehicular Technology Conference, 2007. VTC2007-Spring. IEEE
65th, 2007, pp. 2761-2765.
[13] B. Kian Chung, S. Armour, and A. Doufexi, "Joint Time-Frequency Domain Proportional Fair
Scheduler with HARQ for 3GPP LTE Systems," in Vehicular Technology Conference, 2008. VTC 2008-
Fall. IEEE 68th, 2008, pp. 1-5.
[14] R. A. Akl, S. Valentin, G. Wunder, and S. Stanczak, "Compensating for CQI Aging By Channel
Prediction: The LTE Downlink."

[15] M. R. Souryal and R. L. Pickholtz, "Adaptive modulation with imperfect channel information in
OFDM," in Communications, 2001. ICC 2001. IEEE International Conference on, 2001, pp. 1861- 1865
vol.6.
[16] W. Yafeng and Y. Hongwen, "Retransmission priority scheduling algorithm for forward link packet
data service," in Communication Technology Proceedings, 2003. ICCT 2003. International Conference
on, 2003, pp. 926-930.
[17] I. C. Wong and B. Evans, "Optimal resource allocation in the OFDMA downlink with imperfect
channel knowledge," Communications, IEEE Transactions on, vol. 57, pp. 232-241, 2009.
[18] D. S. W. Hui and V. K. N. Lau, "Delay-sensitive cross-layer designs for OFDMA systems with
outdated CSIT," in Wireless Communications and Networking Conference, 2007. WCNC 2007. IEEE,
2007, pp. 264-269.
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Communications (ICC), 2010 IEEE International Conference on, 2010, pp. 1-5.
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latency-constrained networks," in European Wireless, 2012. EW. 18th European Wireless Conference,
2012, pp. 1-8.
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Technology Conference, 2006. VTC 2006-Spring. IEEE 63rd, 2006, pp. 1555-1559.
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2009. IEEE 69th, 2009, pp. 1-5.
[24] R. E. Kalman, "A new approach to linear filtering and prediction problems," Journal of basic
Engineering, vol. 82, pp. 35-45, 1960.
[25] A. G. Orozco-Lugo, F. A. Cruz Prez, and G. Hernandez Valdez, "Investigating the boundary effect
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Conference, 2001. VTC 2001 Fall. IEEE VTS 54th, 2001, pp. 2740-2744 vol.4.
[26] K. I. Pedersen, G. Monghal, I. Z. Kovacs, T. E. Kolding, A. Pokhariyal, F. Frederiksen, and P.
Mogensen, "Frequency Domain Scheduling for OFDMA with Limited and Noisy Channel Feedback," in
Vehicular Technology Conference, 2007. VTC-2007 Fall. 2007 IEEE 66th, 2007, pp. 1792-1796.
[27] M. Kohler, Using the Kalman filter to track human interactive motion: modelling and initialization of
the Kalman filter for translational motion: Citeseer, 1997.
[28] G. Monghal, K. I. Pedersen, I. Z. Kovacs, and P. E. Mogensen, "QoS Oriented Time and Frequency
Domain Packet Schedulers for The UTRAN Long Term Evolution," in Vehicular Technology
Conference, 2008. VTC Spring 2008. IEEE, 2008, pp. 2532-2536.

Optimization of the Recursive One-Sided Hypothesis Testing Technique for
Autonomous Threshold Estimation in Cognitive Radio
James Odinaka Okonkwo1
and Sylvester Ajah2
1
Department of Telecommunication Engineering, Federal University of Technology Minna, Niger State,
Nigeria
2
Computer Engineering Technology, AkanuIbiam Federal Polytechnic Unwana, Ebonyi State,
Nigeria
ABSTRACT
In this paper, an optimized Recursive One-Sided Hypothesis Testing (ROHT) threshold
estimation algorithm for energy detection based on Cognitive Radio (CR) application is
presented. The ROHT algorithm is well known to compute and correct threshold values based on
the choice of the parameter values; namely the coefficient of standard deviation (z-value) and the
stopping criteria (). A fixed computational process has been employed in most cases to estimate
these parameter values, thus rendering them non-adaptive under different sensing conditions.
Also, this fixed (manual tuning) process requires prior knowledge of some noise level to enable
pre-configuration of a predefined target false alarm rate. This renders the parameter estimation
process cumbrous and unworkable for real-time purposes, particularly for autonomous CR
applications. Furthermore, using wrong parameter values may lead to either too high or too low
false alarms or detection rates of the algorithm. Sequel to aforementioned mentioned constraints,
we propose a new mechanism for instantaneous parameter optimization of the ROHT algorithm
using Particle Swarm Optimization (PSO) algorithm. Our PSO-ROHT model design was
extensively tested under different conditions and results were compared to the non-optimized
ROHT. The results obtained show that the proposed design effectively adapts the parameter
values of the Recursive One-Sided Hypothesis Testing algorithm in accordance with the input
dataset under consideration. Also, that the proposed optimized model outperforms its non-
optimized counterpart following the estimated detection probability and false alarm probability
of both schemes, particularly in detecting Orthogonal Frequency-Division Multiplexing signals.
In detecting the Orthogonal Frequency-Division Multiplexing signals at signal-to-noise ratio of
3dB and above, the proposed model achieved a higher detection rate of 96.23% while
maintaining a low false alarm rate below 10%, which complies with the IEEE 802.22 standard
for Cognitive Radio application. Our PSO-ROHT algorithm is shown to be highly effective for
autonomous and full blind signal detection in CR, with strong potentials for application in other
areas requiring automatic threshold estimation.
KEYWORDS
Adaptive, Autonomous, Cognitive Radio, Energy detector, Optimization, Particle Swarm, PSO –
ROHT algorithm, ROHT, threshold
For More Details: http://aircconline.com/ijwmn/V10N6/10618ijwmn01.pdf
Volume Link: http://airccse.org/journal/jwmn_current18.html

REFERENCES
[1] M. Marcus, J. BUrtle, B. Franca, A. Lahjiouji and N. McNeil, "Report of the Unlicensed Devices and
Experimental Licenses Working Group," 2002.
[2] M. Rouse, "Techtarget.com," November 2008. [Online]. Available:
http://searchnetworking.techtarget.com/definition/cognitive-radio. [Accessed 19 June 2017].
[3] K. S. C. D. B. S. S. N. Carlos de M. Cordeiro, "IEEE 802.22: An Introduction to the First Wireless
Standard based on Cognitive Radios," JCM, vol. 1, no. 1, pp. 38-47, April 2006.
[4] N. T. Ng’ethe, "An Adaptive Threshold Energy Detection Technique with Noise Variance Estimation
for Cognitive Radio Sensor Networks (Doctoral dissertation, University of Cape Town)," 2015.
[5] E. N. O. A. M. A. O. U. a. M. S. A.J. Onumanyi, "A modified Otsu's algorithm for improving the
performance of the energy detector in cognitive radio," AEU- International Journal of Electronics and
Communication, vol. 79, pp. 53-63, 2017.
[6] S. Bames, P. v. Vuuren and B.T.Maharaj., "Spectrum occupancy investigation: Measurements in
South Africa," ELSEIVIER, vol. 46, no. 9, pp. 3098-3112, 2013.
[7] S. V. Vaseghi, Advanced Digital Signal Processing and Noise Reduction, WILEY, 2008.
[8] A. M. W. a. G. J. M. D. Datla, " A Spectrum Surveying Framework for Dynamic Spectrum Access
Networks," IEEE, vol. 58, no. 8, pp. 4158 - 4168, 2009.
[9] D. D. V. P. P. K. a. G. J. M. F. Weidling, "A framework for R.F. spectrum measurements and
analysis," in New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005. 2005 First
IEEE International Symposium on, Baltimore, MD, USA, USA, 2005.
[10] A. M. W. J. M. Dinesh Datla, "A Spectrum Surveying Framework for Dynamic Spectrum Access
Networks," IEEE Transactions on Vehicular Technology, vol. 58, no. 8, pp. 4158 - 4168, 2009.

Use of Genetic Algorithm In The Optimisation Of The Lte Deployment
Mohammed Jaloun1
, Zouhair Guennoun2
and Adnane Elasri3
1
Department of Electrical and Communication, EMI School, Rabat, Morocco
2
3
ABSTRACT
The purpose of this paper is to evaluate LTE deployment and to optimize RF parameters that include sub
channel power, antenna down tilt, azimuth and beam-width. An integer optimizing based on genetic
programming is developed by evaluating the signal-to-interference plus noise ratio. The simulation uses a
static model based on an OFDMA module designed for a Long Term Evolution (LTE) network from
3GPP [TR36.942]. The site location and initial antenna parameters are taken from real GSM network
already optimized for coverage. Our analysis shows that the LTE network performance could be
increased by more than 45% by adjusting both cells power and antenna parameters.
KEYWORDS
LTE, RF Optimization, Antenna, Genetic algorithm, Wireless
For More Details: http://airccse.org/journal/jwmn/0611wmn04.pdf
Volume Link: http://airccse.org/journal/jwmn_current11.html

REFERENCES
[1] 3GPP TR36.942 “Evolved Universal Terrestrial Radio Access Network (E-UTRA); Radio Frequency
(RF) system scenarios”, 02-2008.
[2] Engineering Optimization: An Introduction with Metaheuristic Applications. 173 By Xin-She Yang.
Copyright c 2010 John Wiley & Sons, Inc.
[3] A. Ligeti and J. Zander, “Minimal cost coverage planning for single frequency networks,”
Broadcasting, IEEE Transactions on, vol. 45, no. 1, pp. 78–87, March 1999.
[4] D. Yuan, “A decomposition method for pilot power optimization in WCDMA networks,” Department
of Science and Technology, Link¨oping University, Tech. Rep., 2003.
[5] P. V¨arbrand and D. Yuan, “A mathematical programming approach for pilot power optimization in
WCDMA networks,” in Australian Telecommunications, Networks and Applications Conference
ATNAC, 2003.
[6] C. Murphy, M. Dillon, and A. Diwan, “Performance gains using remote control antennas in CDMA
and 1xEV-DO networks,” in Vehicular Technology Conference, 2005. VTC-2005-Fall. 2005 IEEE 62nd,
vol. 1, September 2005, pp. 377–381.
[7] 3GPP TR System Simulation Evaluation for Link from eNode-B to RN.
[8] 3GPP TS36.912 “Feasibility study for Further Advancements for E-UTRA (LTE-Advanced)”.

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