Gestion d'interférence pour les réseaux favorables à l'environnement

Recent breakthroughs: Performance vs. Complexity in Communications

We have also performed work which deals with understanding the tradeoff between performance, complexity and delay.  This is an important area because, in telecommunications, rate-reliability and encoding-decoding computational complexity (floating point operations - flops), are widely considered to be limiting and interrelated bottlenecks.  For this reason, any attempt to significantly reduce complexity may be at the expense of a substantial degradation in error-performance. Establishing this intertwined relationship constitutes an important research topic of substantial practical interest.

We have, up to this point in the project, derived fundamental limits on the complexity for ML-based sphere decoders achieving a vanishing performance-gap to brute force ML decoding, as well as on the sphere decoding complexity exponent for decoding full rate codes over the quasi-static MIMO channel.  Practical algorithms were designed.  We also provided the first ever transceiver that achieves a vanishing SNR-gap to exact lattice decoding (an NP hard problem) at a sub-exponential complexity.

We hope that our work can give concise insight on pertinent questions such as:

  • What is the complexity price to pay for near-optimal implementation of MIMO, multiuser and cooperative communications?
  • How does feedback reduce complexity?
  • What policies can regulate complexity at a limited performance loss?
  • How do complexity-constraints affect reliability in different MIMO settings?
  • How big of a MIMO system (how many transmit antennas or tones or relays or mac users) can your DSP chip sustain?
  • How should multiple users behave in the presence of complexity constraints?
  • What is the role of antenna selection in reducing complexity?
  • What are the cooperative protocols that perform best in the presence of computational constraints?


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Image-Net ANR Project.