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On the power of uniform power: capacity of wireless networks with bounded resources

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Abstract

The throughput capacity of arbitrary wireless networks under the physical Signal to Interference Plus Noise Ratio (SINR) model has received much attention in recent years. In this paper, we investigate the question of how much the worst-case performance of uniform and non-uniform power assignments differ under constraints such as a bound on the area where nodes are distributed or restrictions on the maximum power available. We determine the maximum factor by which a non-uniform power assignment can outperform the uniform case in the SINR model. More precisely, we prove that in one-dimensional settings the capacity of a non-uniform assignment exceeds a uniform assignment by at most a factor of \(O(\log L_{\max })\) when the length of the network is \(L_{\max }\). In two-dimensional settings, the uniform assignment is at most a factor of \(O(\log P_{\max })\) worse than the non-uniform assignment if the maximum power is \(P_{\max }\). We provide algorithms that reach this capacity in both cases. These bounds are tight in the sense that previous work gave examples of networks where the lack of power control causes a performance loss in the order of these factors. To complement our theoretical results and to evaluate our algorithms with concrete input networks, we carry out simulations on random wireless networks. The results demonstrate that the link sets generated by the algorithms contain around 20–35 % of all links. As a consequence, engineers and researchers may prefer the uniform model due to its simplicity if this degree of performance deterioration is acceptable.

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Notes

  1. The SINR diagram of a set of transmitters divides the plane into \(n+1\) regions or reception zones, one region for each transmitter that indicates the set of locations in which it can be heard successfully, and one more region that indicates the set of locations in which no sender can be heard. This concept is perhaps analogous to the role played by Voronoi diagrams in computational geometry.

  2. Observe that the players are assumed to have unlimited computational power, since the problem of selecting the largest subset of nodes transmitting with fixed power levels has been shown to be NP-hard [5].

  3. Zander [17] ignores the influence of noise. See [22] for an approach that handles noise as well.

  4. This algorithm can be generalized to higher dimensions at the expense of a higher constant in its approximation guarantee. The only adjustments affect the lines 8–12 and 13–17, where cones instead of sectors are considered. We omit the explicit treatment of higher dimensional cases to increase the clarity of the arguments.

References

  1. Gupta, P., & Kumar, P. R. (2000). The capacity of wireless networks. IEEE Transactions on Information Theory, 46(2), 388–404.

    Article  MathSciNet  MATH  Google Scholar 

  2. Moscibroda., T. & Wattenhofer, R. (2006). The complexity of connectivity in wireless networks. In Proceedings of 25th Conference of the IEEE Computer and Communications Societies (INFOCOM) (pp 1–13).

  3. Moscibroda, T., Wattenhofer, R., & Weber, Y. (2006). Protocol design beyond graph-based models. In Proceedings of 5th ACM SIGCOMM Workshop on Hot Topics in Networks (HOTNETS).

  4. Avin, C., Emek, Y., Kantor, E., Lotker, Z., Peleg, D., & Roditty, L. (2009). Sinr diagrams: towards algorithmically usable sinr models of wireless networks. In PODC (pp. 200–209).

  5. Goussevskaia, O., Oswald, Y. A., & Wattenhofer, R. (2007) Complexity in geometric sinr. In MobiHoc’07: Proceedings of the 8th ACM international symposium on Mobile ad hoc networking and computing (pp. 100–109). New York, NY: ACM.

  6. Moscibroda, T., Oswald, Y.A., & Wattenhofer, R. (2007). How optimal are wireless scheduling protocols? In Proceedings of 26th IEEE Conference on Computer Communications (INFOCOM) (pp. 1433–1441).

  7. Lotker, Z., Parter, M., Peleg, D., & Pignolet, Y. A. (2011). Distributed power control in the SINR model. In INFOCOM 2011. 30th IEEE International Conference on Computer Communications (pp. 2525–2533).

  8. Rappaport, T. (2002). Wireless communications. New Jersey: Prentice Hall.

    Google Scholar 

  9. Tsai, Y.-C., & Su, S.-L. (2015). An SINR-based routing and MAC design for QoS in wireless ad hoc networks. IEEE Transactions on Information Theory, 21(4), 1141–1154.

    Google Scholar 

  10. El-Fadeel, G. A., El-Sawy, A., & Adib, M. J. (2012). C4. vertical handoff in heterogeneous wireless networks with predictive sinr using gm (1, 1). In: 2012 29th National Radio Science Conference (NRSC), (pp. 175–184).

  11. Moscibroda, T. ( 2007). The worst-case capacity of wireless sensor networks. In Proceedings of 6th Conference on Information Processing in Sensor Networks (IPSN) (pp. 1–10).

  12. Grönkvist, J. (2005). Interference-Based Scheduling in Spatial Reuse TDMA. Ph.D. dissertation, Royal Institute of Technology, Stockholm, Sweden.

  13. Lebhar, E., & Lotker, Z. (2009). Unit disk graph and physical interference model: putting pieces together. In Proceedings of 23rd IEEE International Parallel and Distributed Processing Symposium (IPDPS) (pp. 1–8).

  14. Goussevskaia, O., Halldorsson, M., Wattenhofer, R., & Welzl, E. (2009). Capacity of arbitrary wireless networks. In 28th Annual IEEE Conference on Computer Communications (INFOCOM).

  15. Halldórsson, M. M., & Wattenhofer, R. (2009). Wireless communication is in APX. In S. Albers, A. Marchetti-Spaccamela, Y. Matias, S. E. Nikoletseas, & W. Thomas (Eds.) ICALP (1), ser. Lecture Notes in Computer Science (Vol. 5555, pp. 525–536). Berlin: Springer.

  16. Avin, C., Lotker, Z., Pasquale, F., & Pignolet, Y.-A. (2009). A note on uniform power connectivity in the sinr model. In Algorithmic Aspects of Wireless Sensor Networks (pp. 116–127).

  17. Zander, J. (1992). Performance of optimum transmitter power control in cellular radio systems. IEEE Transactions on Information Theory, 41, 57–62.

    Google Scholar 

  18. Fanghänel, A., Kesselheim, T., Räcke, H., & Vöcking, B. (2009). Oblivious interference scheduling. In Proceedings of 28th Symposium on Principles of Distributed Computing (PODC) (pp. 220–229).

  19. Fanghänel, A., Kesselheim, T., & Vöcking, B. (2009). Improved algorithms for latency minimization in wireless networks. In Proceedings of 36th International Colloquium on Automata, Languages and Programming (ICALP) (pp. 447–458).

  20. Halldórsson, M. M. (2009). Wireless scheduling with power control. In A. Fiat, & P. Sanders, (Eds.) ESA, ser. Lecture Notes in Computer Science (Vol. 5757, pp. 361–372). Berlin: Springer

  21. Collotta, M., & Pau, G. (2015). Bluetooth for internet of things: A fuzzy approach to improve power management in smart homes. IEEE Transactions on Information Theory, 44, 137–152.

    Google Scholar 

  22. Pillai, S., Suel, T., & Cha, S. (2005). The Perron–Frobenius theorem. IEEE Transactions on Information Theory, 22(2), 62–75.

    Google Scholar 

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Acknowledgments

Zvi Lotker: This Research was supported in part by Fondation des Sciences Mathmatiques de Paris and by the Ministry of Science Technology and by Space, Israel, French-Israeli Project MAIMONIDE 31768XL, and by the French-Israeli Laboratory FILOFOCS. Yvonne-Anne Pignolet: Part of this research was done when the author was at ETH Zurich, Switzerland.

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Avin, C., Lotker, Z. & Pignolet, YA. On the power of uniform power: capacity of wireless networks with bounded resources. Wireless Netw 23, 2319–2333 (2017). https://doi.org/10.1007/s11276-016-1282-3

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