Information theory in wireless systems
Information theory consitutes an important part of the mathematical foundations underlying modern wireless communications. Introduced by Claude Shannon in the 40s, information theory aims to meaningfully quantify the fundamental limits of information manipulation. In the setting of wireless communications, information theory offers insightful answers to the question of what is the most efficient way to communicate information between two or more nodes.
For example, in the case of the additive white Gaussian noise (AWGN) channel with a given signal-to-noise ratio (SNR), information theory was able to reveal the somewhat surprising fact that reliable communication can be achieved, not just for near-zero communication rates, but in fact for all rates up to the Shannon capacity which, for AWGN channel, took the simple form C=\log(1+SNR).
Modern wireless communications of course revolve around infinitely more complicated topologies, which often include multiple users, fluctuating channel strengths, as well as nodes that cooperate or compete. These are the settings of interest to us here at Eurecom, where we often apply information theoretic methods to analyze the fundamental limitations in such settings, as well as to invent novel transmission schemes that manage to come close to these information theoretic limits, with as much regard to practical considerations and with as little computational and implementational complexity as possible.
Specifically many of us here at Eurecom apply information theory to understand the maximum throughputs of networks with several interfering users, as well as settings relating to cellular topologies such as uplink communications (multiple access), downlink communications (broadcast), cooperative communications (relay channel), and multiple-input multiple-output (MIMO) settings.
As a tool, information theory has been indespensible to our ability to provide novel solutions that include:
- Multi-user diversity
- Modern algorithms for interference suppression
- Algorithms that increase network capacity and performance
- Multi-user cooperative and relaying schemes
- Transceivers that optimally tradeoff rate and reliability in non-ergodic communications
- Communications algorithms that efficiently traverse the tradeoff between quality-of-service and computational complexity
- Novel channel-feedback methods
as well as provide algorithmic solutions in the areas of:
- Resource optimization and cross-layer design
- Joint source-channel coding
- Clustering methods in Multicell MIMO
- Distributed Beamforming Cooperation in Multicell MIMO Channels
- Power Control
- Cognitive decision in spectrum sharing systems
- Reliability gains in satellite communications