Performance Analysis of User Clustering Algorithms against User Density and Maximum Number of Relays for D2D Advertisement Dissemination

최대 전송횟수 제한 및 사용자 밀집도 변화에 따른 사용자 클러스터링 알고리즘 별 D2D 광고 확산 성능 분석

  • Received : 2016.02.29
  • Accepted : 2016.04.01
  • Published : 2016.04.30


In this paper, in order to resolve the problem of reduction for D2D (device to device) advertisement dissemination efficiency of conventional dissemination algorithms, we here propose several clustering algorithms (modified single linkage algorithm (MSL), K-means algorithm, and expectation maximization algorithm with Gaussian mixture model (EM)) based advertisement dissemination algorithms to improve advertisement dissemination efficiency in D2D communication networks. Target areas are clustered in several target groups by the proposed clustering algorithms. Then, D2D advertisements are consecutively distributed by using a routing algorithm based on the geographical distribution of the target areas and a relay selection algorithm based on the distance between D2D sender and D2D receiver. Via intensive MATLAB simulations, we analyze the performance excellency of the proposed algorithms with respect to maximum number of relay transmissions and D2D user density ratio in a target area and a non-target area.


User clustering Algorithm;D2D Advertisement Dissemination;User Density;Social Commerce Service


  1. J. G. Andrews, et al., "What will 5G be?." IEEE Journal on Sel. Areas in Communications, vol. 32, no. 6, pp. 1065-1082, June 2014.
  2. L. Lei, et al., "Operator controlled device-to-device communications in LTE-advanced networks", IEEE Wireless Communications, vol. 19, no. 3, pp. 96-104, June 2012.
  3. N. NaderiAlizadeh, et al., "ITLinQ: a new approach for spectrum sharing in device-to-device communication systems," IEEE Journal on Sel. Areas in Communications, vol. 32, no. 6, pp. 1139-1151, Sep. 2014.
  4. J. Kim, et al., "VADA: Wi-Fi Direct Based Voluntary Advertisement Dissemination Algorithm for Social Commerce Services," IEEE VTC 2015 Spring, pp. 1-6, May 2015.
  5. J. Kim, et al., "Geographical Proximity Based Target-Group Formation Algorithm for Efficient D2D Advertisement Dissemination," IEEE PerCom 2015, pp. 275-278, Mar. 2015.
  6. C. D. Manning, et al, Introduction to Information Retrieval, Cambridge, Cambridge University Press, 2008.
  7. S. Han, et al., "Performance Analysis of Hierarchical/Non-Hierarchical Clustering Algorithm for D2D Advertisement Dissemination," KICS 2015 Fall Conference, pp 36-37, Nov. 2015.
  8. C. M. Bishop, Pattern Recognition and Machine Learning, New York, Springer, 2006.
  9. SMALL ENTERPRISE AND MARKET SERVICE. SEMAS Marketing Area Analysis System [Internet]. Available:


Supported by : National Research Foundation of Korea (NRF)