Multiuser Detection
On the achievable throughput of a multiantenna Gaussian broadcast channel
IEEE Transactions on Information Theory
On beamforming with finite rate feedback in multiple-antenna systems
IEEE Transactions on Information Theory
Sum power iterative water-filling for multi-antenna Gaussian broadcast channels
IEEE Transactions on Information Theory
Subchannel Allocation in Multiuser Multiple-Input–Multiple-Output Systems
IEEE Transactions on Information Theory
What is the value of limited feedback for MIMO channels?
IEEE Communications Magazine
| Hi-index | 0.00 |
For downlink transmission in a multiuser Multiple-Input Multiple-Output (MIMO) communication system, quantized Channel State Information (CSI) is fed back to the base station in an uplink channel of finite rate. The quantized CSI is obtained via Channel Vector Quantization (CVQ) of the so-called composite channel vector, i.e., the product of the channel matrix and an estimation of the receive filter, which cannot be computed exactly at the stage of quantization because of its dependency on the finally chosen precoder. Here, the state-of-the-art approach estimates the receive filter and quantize the composite channel vector such that its Euclidean distance to the estimated composite channel vector is minimized. In this paper, we propose an alternative CVQ method which determines the estimated receive filter vector and the quantized composite channel vector such that the resulting Signal-to-Interference-and-Noise Ratio (SINR), or an approximation thereof, is maximized. Since the SINR is related to the individual user rates, and therefore related to the sum rate of the system, the presented solution aims at maximizing the system sum rate. Simulation results of a multiuser MIMO system with linear zero-forcing precoding show that the proposed schemes achieve significant performance improvements compared to the state-of-the-art method, especially in the low signal-to-noise ratio region.