Large system analysis of multi-cell MIMO downlink: fairness scheduling and inter-cell cooperation

2012-01-27For the evolution of multiuser MIMO (MU-MIMO) technologies in the multi-cell environment, a lot of studies are considering the inter-cell cooperation schemes, which provide benefits of inter-cell interference mitigation and/or higher antenna array gain from a number of cooperating transmit antennas. In order to appreciate the full potentials of such schemes for the MU-MIMO system, the following aspects need to be considered: a multi-cell coverage with spatial user distribution and realistic pathloss model; multiuser scheduling, taking into account fairness issues; the type of downlink precoding and employed signal processing; the availability of channel state information at the transmitter; the type of inter-cell cooperation and corresponding system overhead. However, taking into account all these aspects in a single framework is very complicated and its analytical characterization is generally difficult. So far, the system performance has been evaluated through computationally very intensive Monte Carlo simulation. ❧ In this dissertation, an analytic tool is developed, based on the large random matrix theory. In the large system limit where both the number of antennas per base station (BS) and the number of users per cell grow to infinity with a constant ratio, the randomness in channels from BSs to users disappears and the MU-MIMO system becomes a deterministic network, which allows analytic and computationally efficient performance evaluation and optimization. Using this large system analysis, some relevant issues in the design of the MU-MIMO system in the multi-cell downlink are considered: 1) ergodic capacity of the MU-MIMO system with inter-cell cooperation subject to general fairness criteria; 2) the trade-off between the benefit of inter-cell cooperation and the cost of estimating cooperating cells' channels; and 3) the efficient performance achievement of the so-called ""massive MIMO"" system by the user location-based scheduling and transmission strategy