Skip to main content
Log in

HierHybNET: Cut-set upper bound of ad hoc networks with cost-effective infrastructure

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

This paper introduces an information-theoretic upper bound on the capacity scaling law for a hierarchical hybrid network (HierHybNET), consisting of both n wireless ad hoc nodes and m base stations (BSs) equipped with l multiple antennas per BS, where the communication takes place from wireless nodes to a remote central processor through BSs in a hierarchical way. We deal with a general scenario where m, l, and the backhaul link rate scale at arbitrary rates relative to n. Then, a generalized cut-set upper bound under the HierHybNET model is derived by cutting not only the wireless connections but also the wired connections. In addition, the corresponding infrastructure-limited regime is identified.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. We use the following notation: (1) \(f(x)=O(g(x))\) means that there exist constants C and c such that \(f(x)\le Cg(x)\) for all \(x>c\), (2) \(f(x)=o(g(x))\) means that \(\lim _{x\rightarrow \infty }\frac{f(x)}{g(x)}=0\), (3) \(f(x)=\Omega (g(x))\) if \(g(x)=O(f(x))\), (4) \(f(x)=w(g(x))\) if \(g(x)=o(f(x))\), and (5) \(f(x)=\varTheta (g(x))\) if \(f(x)=O(g(x))\) and \(g(x)=O(f(x))\) [22].

  2. To simplify notations, \(T_n(\alpha ,\beta ,\gamma ,\eta )\) will be written as \(T_n\) if dropping \(\alpha\), \(\beta\), \(\gamma\), and \(\eta\) does not cause any confusion.

  3. Here and in the sequel, the noise variance is assumed to be one to simplify the notation.

  4. To simplify notations, the terms including \(\epsilon\) are omitted if dropping them does not cause any confusion.

References

  1. Gupta, P., & Kumar, P. R. (2000). The capacity of wireless networks. IEEE Transactions on Information Theory, 46, 388–404.

    Article  MathSciNet  MATH  Google Scholar 

  2. Shin, W.-Y., Jeon, S.-W., Devroye, N., Vu, M. H., Chung, S.-Y., Lee, Y. H., et al. (2008). Improved capacity scaling in wireless networks with infrastructure. IEEE Transactions on Information Theory, 57, 5088–5102.

    Article  MathSciNet  Google Scholar 

  3. Özgür, A., Lévêque, O., & Tse, D. N. C. (2007). Hierarchical cooperation achieves optimal capacity scaling in ad hoc networks. IEEE Transactions on Information Theory, 53, 3549–3572.

    Article  MathSciNet  MATH  Google Scholar 

  4. Cover, T. M., & Thomas, J. A. (1991). Element of information theory. New York: Wiley.

    Book  MATH  Google Scholar 

  5. Meng, T., Wu, F., Yang, Z., Chen, G., & Vasilakos, A. V. (2015). Spatial reusability-aware routing in multi-hop wireless networks. IEEE Transactions on Computers, to appear.

  6. Li, P., Guo, S., Yu, S., & Vasilakos, A. V. (2012). CodePipe: An opportunistic feeding and routing protocol for reliable multicast with pipelined network coding. In Proceedings of IEEE INFOCOM, pp. 100–108.

  7. Li, P., Guo, S., Yu, S., & Vasilakos, A. V. (2014). Reliable multicast with pipelined network coding using opportunistic feeding and routing. IEEE Transactions on Parallel and Distributed Systems, 25, 3264–3273.

    Article  Google Scholar 

  8. Zhang, X. M., Zhang, Y., Yan, F., & Vasilakos, A. V. (2015). Interference-based topology control algorithm for delay-constrained mobile ad hoc networks. IEEE Transactions on Mobile Computing, 14, 742–754.

    Article  Google Scholar 

  9. Liu, J., Wan, J., Wang, Q., Deng, P., Zhou, K., & Qiao, Y. (2015). A survey on position-based routing for vehicular ad hoc networks. Telecommunication Systems, 59, 1–16.

    Article  Google Scholar 

  10. Liu, J., Wan, J., Wang, Q., Li, D., Qiao, Y., & Cai, H. (2015). A novel energy-saving one-sided synchronous two-way ranging algorithm for vehicular positioning. Mobile Networks and Applications, 20, 1–12.

    Article  Google Scholar 

  11. Jiang, T., Wang, H., & Vasilakos, A. V. (2012). QoE-driven channel allocation schemes for multimedia transmission of priority-based secondary users over cognitive radio networks. IEEE Journal on Selected Areas in Communications, 30, 1215–1224.

    Article  Google Scholar 

  12. Attar, A., Tang, H., Vasilakos, A. V., Yu, F. R., & Leung, V. C. M. (2012). A survey of security challenges in cognitive radio networks: Solutions and future research directions. Proceedings of the IEEE, 100, 3172–3186.

    Article  Google Scholar 

  13. Quan, W., Xu, C., Vasilakos, A. V., Guan, J., Zhang, H., & Grieco, L. A. (2014). TB2F: Tree-bitmap and bloom-filter for a scalable and efficient name lookup in content-centric networking. In Proceedings of IFIP networking conference, pp. 1–9.

  14. Vasilakos, A. V., Li, Z., Simon, G., & You, W. (2015). Information centric network: Research challenges and opportunities. Journal of Network and Computer Applications, 52, 1–10.

    Article  Google Scholar 

  15. Lopez-Perez, D., Chu, X., Vasilakos, A. V., & Claussen, H. (2013). On distributed and coordinated resource allocation for interference mitigation in self-organizing LTE networks. IEEE/ACM Transactions on Networking, 21, 1145–1158.

    Article  Google Scholar 

  16. Lopez-Perez, D., Chu, X., Vasilakos, A. V., & Claussen, H. (2014). Power minimization based resource allocation for interference mitigation in OFDMA femtocell networks. IEEE Journal on Selected Areas in Communications, 32, 333–344.

    Article  Google Scholar 

  17. Khan, M. A., Tembine, H., & Vasilakos, A. V. (2012). Game dynamics and cost of learning in heterogeneous 4G networks. IEEE Journal on Selected Areas in Communications, 30, 198–213.

    Article  Google Scholar 

  18. Duarte, P. B. F., Fadlullah, Z. M., Vasilakos, A. V., & Kato, N. (2012). On the partially overlapped channel assignment on wireless mesh network backbone: A game theoretic approach. IEEE Journal on Selected Areas in Communications, 30, 119–127.

    Article  Google Scholar 

  19. Vasilakos, A. V., Ricudis, C. Anagnostakis, K. G., Pedrycz, W., Pitsillides, A., & Gao, X. Z. (1998). Evolutionary-fuzzy prediction for strategic ID-QoS routing in broadband networks. In Proceedings of IEEE international conference on Fuzzy, pp. 1488–1493.

  20. Yang, M., Li, Y., Jin, D., Zeng, L., Wu, X., & Vasilakos, A. V. (2015). Software-defined and virtualized future mobile and wireless networks: A survey. Mobile Networks and Applications, 20, 4–18.

    Article  Google Scholar 

  21. Demestichas, P. P., Stavroulaki, V.-A. G., Papadopoulou, L.-M. I., Vasilakos, A. V., & Theologou, M. E. (2004). Service configuration and traffic distribution in composite radio environments. IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, 34, 69–81.

    Article  Google Scholar 

  22. Knuth, D. E. (1976). Big omicron and big omega and big theta. ACM Special Interest Group on Algorithms and Computation Theory (SIGACT) News, 8, 18–24.

  23. Franceschetti, M., Dousse, O., Tse, D. N. C., & Thiran, P. (2007). Closing the gap in the capacity of wireless networks via percolation theory. IEEE Transactions on Information Theory, 53, 1009–1018.

    Article  MathSciNet  MATH  Google Scholar 

  24. Shin, W.-Y., Chung, S.-Y., & Lee, Y. H. (2013). Parallel opportunistic routing in wireless networks. IEEE Transactions on Information Theory, 59, 6290–6300.

    Article  MathSciNet  Google Scholar 

  25. El Gamal, A., Mammen, J., Prabhakar, B., & Shah, D. (2006). Optimal throughput-delay scaling in wireless networks-Part I: The fluid model. IEEE Transactions on Information Theory, 52, 2568–2592.

    Article  MATH  Google Scholar 

  26. Niesen, U., Gupta, P., & Shah, D. (2010). The balanced unicast and multicast capacity regions of large wireless networks. IEEE Transactions on Information Theory, 56, 2249–2271.

    Article  MathSciNet  Google Scholar 

  27. Grossglauser, M., & Tse, D. N. C. (2002). Mobility increases the capacity of ad hoc wireless networks. IEEE/ACM Transactions on Networking, 10, 477–486.

    Article  Google Scholar 

  28. Cadambe, V. R., & Jafar, S. A. (2008). Interference alignment and degrees of freedom of the \(K\)-user interference channel. IEEE Transactions on Information Theory, 54, 3425–3441.

    Article  MathSciNet  MATH  Google Scholar 

  29. Niesen, U. (2011). Interference alignment in dense wireless networks. IEEE Transactions on Information Theory, 57, 2889–2901.

    Article  MathSciNet  Google Scholar 

  30. Yi, S., Pei, Y., Kalyanaraman, S., & Azimi-Sadjadi, B. (2007). How is the capacity of ad hoc networks improved with directional antennas? Wireless Networks, 13, 635–648.

    Article  Google Scholar 

  31. Li, P., Zhang, C., & Fang, Y. (2011). The capacity of wireless ad hoc networks using directional antennas. IEEE Transactions on Mobile Computing, 10, 1374–1387.

    Article  Google Scholar 

  32. Yoon, J., Shin, W.-Y., & Jeon, S.-W. (2014). Elastic routing in wireless networks with directional antennas. In Proceedings of IEEE international symposium of information theory (ISIT), pp. 1001–1005.

  33. Zemlianov, A., & de Veciana, G. (2005). Capacity of ad hoc wireless networks with infrastructure support. IEEE Journal of Selected Areas on Communications, 23, 657–667.

    Article  Google Scholar 

  34. Dousse, O., Thiran, P., & Hasler, M. (2002). Connectivity in ad-hoc and hybrid networks. In Proceedings of IEEE INFOCOM, pp. 1079–1088.

  35. Liu, B., Thiran, P., & Towsley, D. (2007). Capacity of a wireless ad hoc network with infrastructure. In Proceedings of ACM international symposium on mobile ad hoc networking and computing (MobiHoc), pp. 239–246.

  36. Kozat, U. C., & Tassiulas, L. (2003). Throughput capacity of random ad hoc networks with infrastructure support. In Proceedings of ACM international conference on mobile computing and networking (MobiCom), pp. 55–65.

  37. Çapar, Ç., Goeckel, D., Towsley, D., Gibbens, R., & Swami, A. (2011). Cut results for the capacity of hybrid networks. In Proceedings of annual confererence of international technology alliance (ACITA), pp. 1–2.

  38. Çapar, Ç., Goeckel, D., Towsley, D., Gibbens, R., & Swami, A. Capacity of hybrid networks. In Proceedings of annual conference of international technology alliance (ACITA), pp. 1–8.

  39. Jeong, C., & Shin, W.-Y. (2013). Large-scale ad hoc networks with rate-limited infrastructure: Information-theoretic operating regimes. In Proceedings of IEEE international symposium on information theory (ISIT), pp. 424–428.

  40. Marsch, P., & Fettweis, G. (2007). A framework for optimizing the uplink performance of distributed antenna systems under a constrained backhaul. In Proceedings of IEEE international conference on communications (ICC), pp. 975–979.

  41. Nazer, B., Sanderovich, A., Gastpar, M., & Shamai (Shitz), S. (2009). Structured superposition for backhaul constrained cellular uplink. In Proceedings of IEEE international symposium on information theory (ISIT), pp. 1530–1534.

  42. Sanderovich, A., Somekh, O., & Shamai (Shitz), S. (2007). Uplink macro diversity with limited backhaul capacity. In Proceedings of international symposium on information theory (ISIT), pp. 11–15.

  43. Shamai (Shitz), S., Simeone, O., Somekh, O., & Sanderovich, A. (2009). Uplink macro diversity of limited backhaul cellular network. IEEE Transactions on Information Theory, 55, 3457–3478.

    Article  MathSciNet  Google Scholar 

  44. Shamai (Shitz), S., Simeone, O., Somekh, O., & Sanderovich, A. (2008). Information-theoretic implications of constrained cooperation in simple cellular models. In Proceedings of IEEE annual international symposium on personal, indoor, and mobile radio communications (PIMRC), pp. 1–5.

  45. Gomez-Cuba, F., Rangan, S., & Erkip, E. (2014). Scaling laws for infrastructure single and multihop wireless networks in wideband regimes. In Proceedings of IEEE international symposium on information theory (ISIT), pp. 76–80.

  46. Viswanath, P., & Tse, D. N. C. (2003). Sum capacity of the vector Gaussian broadcast channel and uplink-downlk duality. IEEE Transactions on Information Theory, 49, 1912–1921.

    Article  MathSciNet  MATH  Google Scholar 

  47. Constantinescu, F., & Scharf, G. (1998). Generalized Gram-Hadamard inequality. Journal of Inequalities and Applications, 2, 381–386.

    MathSciNet  MATH  Google Scholar 

  48. Jovicic, A., Viswanath, P., & Kulkarni, S. R. (2004). Upper bounds to transport capacity of wireless networks. IEEE Transactions on Information Theory, 50, 2555–2565.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2014R1A1A2054577) and by the research fund of Dankook University(BK21 Plus) in 2014. This paper was presented in part at the 2014 IEEE International Symposium on Information Theory, Honolulu, HI, June/July 2014.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Won-Yong Shin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jeong, C., Shin, WY. HierHybNET: Cut-set upper bound of ad hoc networks with cost-effective infrastructure. Wireless Netw 22, 1133–1144 (2016). https://doi.org/10.1007/s11276-015-1017-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-015-1017-x

Keywords

Navigation