Advanced MIMO Receivers

The use of multiple antennas at both ends of a wireless communication link theoretically allows for an increase in capacity which is linear with the minimum number of antenna elements at transmitter and receiver. This theoretical result has driven researchers in both industry and academia to focus their efforts in the study and deployment of the so-called Multiple-Input Multiple-Output (MIMO) systems in the recent past years. As a result, modern wireless standards such as the 3GPP Long Term Evolution (LTE) or the IEEE standard 802.16e (commonly referred to as WiMAX) already included support for MIMO techniques since their early definition. The improvement of multi-antenna techniques and receivers will be one of the keys to achieve the ambitious peak data rates of 1 Gigabit per second that emerging wireless standards, such as the 3GPP LTE-Advanced, are targeting.

 

In order to fully utilize the potential of the wireless MIMO channel, advanced signal processing at the receiver end is required. Since the optimum maximum likelihood detection of the information bits is usually too complex when both MIMO transmission and channel coding are combined, the research is focused on the study of iterative structures trying to approach the performance of the optimum receiver while keeping a manageable complexity. The iterative receiver structures can be separated into two big categories: the heuristic methods and the formal optimization methods.

 

In heuristic methods, such as the turbo-principle, information is iteratively passed between the different blocks of the receiver, such as channel estimator, detector and channel decoder. The kind of information that these blocks should exchange and how they should make use of it is not analytically justified. On the other hand, formal optimization methods, such as for instance Bayesian inference methods, provide a framework for the whole optimization of the receiver according to a design principle. Examples of these methods are the use of Factor Graphs or Variational Bayesian inference based on Kullback-Leibler divergence minimization.

 

This project aims at theoretically investigating these techniques while keeping in mind the practical constraints present in a real system, such as limited processing power at the receiver or limited system information. The potential benefits of iterative processing in practical systems such as 3GPP LTE or LTE-Advanced are evaluated, and the interaction and integration with higher layer features, such as H-ARQ or fast Link-Adaptation, are studied. Furthermore, optimization of the receiver in interference-limited scenarios, which are typical of cellular networks, is also considered.