On Managing Interference in a One-Dimensional Space Over Time-Invariant Channels Conference Paper uri icon


  • 2017 IEEE. Real interference alignment is efficient in breaking-up a one-dimensional space over time-invariant channels into fractional dimensions. As such, multiple symbols can be simultaneously transmitted with fractional degrees-of-freedom (DoF). Of particular interest is when the one dimensional space is partitioned into two fractional dimensions. In such scenario, the interfering signals are confined to one sub-space and the intended signal is confined to the other sub-space. Existing real interference alignment schemes yield poor achievable rate at finite signal-to-noise ratio (SNR), which is of interest from a practical point of view. In this paper, we propose a radically novel nonlinear interference alignment technique, which we refer to as Interference Dissolution (ID). ID allows to break-up a one dimensional space into two fractional dimensions while achieving near-capacity performance for the entire SNR range. This is achieved by aligning signals by signals, as opposed to aligning signals by the channel. We introduce ID by considering a timeinvariant, point-to-point multiple-input single-output (MISO) channel. This channel has a one-dimensional space and offers one DoF. We show that, by breaking-up the one dimensional space into two sub-spaces, ID achieves a rate of two symbols per channel use while providing 1/2 DoF for each symbol. We also propose a decoder and prove its optimality. We compare numerically the performance of ID in terms of the achievable rate performance to that of existing schemes and demonstrate ID's superiority.

name of conference

  • 2017 IEEE International Conference on Communications (ICC)

published proceedings

  • 2017 IEEE International Conference on Communications (ICC)

author list (cited authors)

  • Chraiti, M., Ghraveb, A., & Assi, C.

citation count

  • 4

complete list of authors

  • Chraiti, Mohaned||Ghraveb, Ali||Assi, Chadi

publication date

  • May 2017