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The Information-Theoretic Constant-Gap Optimality of Treating Interference as Noise in Interference Networks

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Part of the Signals and Communication Technology book series (SCT)

Abstract

Treating interference as noise is one of the simplest methods for the management of interference in wireless networks. Despite its simplicity, treating interference as noise (TIN) was shown to be information-theoretically optimal for certain Gaussian interference channels (IC) with very-weak (noisy) interference. In this chapter, we consider cellular networks, such as networks that consists of a point-to-point channel interfering with a multiple access channel (MAC). The sum-capacity of such networks is studied with main focus on the constant-gap optimality of TIN rather than on its exact optimality. It turns out that TIN in its naive variant, where all transmitters are active and receivers use TIN for decoding, is not the best choice for certain networks. In fact, a scheme that combines both time division multiple access and TIN (TDMA-TIN) strictly outperforms the naive TIN scheme. Furthermore, it is shown that in some regimes, TDMA-TIN achieves the sum-capacity within a constant gap for Gaussian networks. Additionally, it is shown that, even for very-weak interference, there are some regimes where a combination of interference alignment with power control and treating interference as noise at the receiver side outperforms TDMA-TIN. As a consequence, on the one hand treating interference as noise in a cellular uplink is approximately optimal in certain regimes. On the other hand, those regimes cannot be simply described by the strength of interference, requiring a careful design of wireless networks.

Keywords

Interference Alignment Multiple Access Channel (MAC) Time Division Multiple Access (TDMA) Channel Interference (IC) Generalized Degrees Of Freedom (GDoF) 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported by the German Research Foundation (DFG) under Grant SE 1697/7-1 and SE 1697/10-1.

References

  1. 1.
    Cover T, Thomas J (2006) Elements of information theory, 2nd edn. Wiley, New YorkGoogle Scholar
  2. 2.
    Jafar SA, Interference alignment: a new look at signal dimensions in a communication network. Found Trends Commun Inf Theory 7(1):1–136. http://dx.doi.org/10.1561/010000047 Google Scholar
  3. 3.
    Carleial AB (1978) Interference channels. IEEE Trans Inf Theory IT-24:60–70Google Scholar
  4. 4.
    Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423Google Scholar
  5. 5.
    Etkin RH, Tse DNC, Wang H (2008) Gaussian interference channel capacity to within one bit. IEEE Trans Inf Theory 54(12):5534–5562Google Scholar
  6. 6.
    Kramer G (2004) Outer bounds on the capacity of Gaussian interference channel. IEEE Trans Inf Theory 50(3):581–586Google Scholar
  7. 7.
    Annapureddy VS, Veeravalli VV (2009) Gaussian interference networks: sum capacity in the low interference regime and new outer bounds on the capacity region. IEEE Trans Inf Theory 55(9):3032–3050Google Scholar
  8. 8.
    Shang X, Kramer G, Chen B (2009) A new outer bound and the noisy-interference sum-rate capacity for Gaussian interference channels. IEEE Trans Inf Theory 689–699Google Scholar
  9. 9.
    Motahari AS, Khandani AK (2009) Capacity bounds for the Gaussian interference channel. IEEE Trans Inf Theory 55(2):620–643Google Scholar
  10. 10.
    Etkin RH (2009) New sum-rate upper bound for the two-user Gaussian interference channel. In: Proceedings of the IEEE ISIT, Seoul, Jun-Jul 2009, pp. 2582–2586Google Scholar
  11. 11.
    Chaaban A, Sezgin A (2011) An extended etkin-type outer bound on the capacity of the Gaussian interference channel. In: Proceedings of the 16th Asilomar conference on SSC, Pacific Grove 2011Google Scholar
  12. 12.
    Shang X, Kramer G, Chen B (2008) Throughput optimization in multi-user interference channels. In: Proceedings of IEEE military communications conference (MILCOM), San Diego, CA, Nov 2008Google Scholar
  13. 13.
    Huang C, Cadambe VR, Jafar SA (2012) Interference alignment and the generalized degrees of freedom of the \(X\) channel. IEEE Trans Inf Theory 58(8):5130–5150Google Scholar
  14. 14.
    Gherekhloo S, Di C, Chaaban A, Sezgin A (2014) (Sub- )optimality of treating interference as noise in the cellular uplink with weak interference. IEEE Trans Inf Theory, in revision. arXiv:1401.8265
  15. 15.
    Geng C, Sun C, Jafar SA, On the optimality of treating interference as noise: general message sets. arXiv:1401.2592
  16. 16.
    Gherekhloo S, Chaaban A, Sezgin A (2014) Extended generalized DoF optimality regime of treating interference as noise in the X channel. In: 11th international symposium on wireless communications systems (ISWCS), Aug 2014, pp. 971–975Google Scholar
  17. 17.
    Chaaban A, Sezgin A (2012) Sub-optimality of treating interference as noise in the cellular uplink. In: Proceedings of the 16th international ITG workshop on smart antennas WSA, Dresden, Germany, Mar 2012Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute for Digital Communication SystemsRuhr-University BochumBochumGermany
  2. 2.Department of Electrical EngineeringKing Abdullah University of Science and TechnologyThuwalSaudi Arabia

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