Kibet Langat

Large Scale Fading Pre-coder for Massive MIMO Without Cell Cooperation

In massive MIMO technology the channel is estimated using uplink training by sending an orthogonal pilot sequence from users to the base station. These sequences are re-used in the cell and also outside the cell. This gives rise to a channel estimation error referred to as pilot contamination. Large scale fading precoding which is based on the cooperation between cells has been proposed to mitigate pilot contamination. However this approach is known to limit in data transmission rate. In this paper, we propose a novel uplink training scheme to mitigate pilot contamination using a large scale fading precoding without the need of cooperation between cells. This achieves a higher transmission rate over existing method. The simulation results show that the proposed scheme improves 5% outage rate 10 times, over the existing method.

Low complexity MMSE channel prediction for block fading channels in LTE downlink

The third generation partnership project (3GPP) long term evolution (LTE) uses high performance strategies such as adaptive link adaptation, multiuser resource scheduling and adaptive MIMO precoding to enhance effective utilization of the availably radio resources. These processes require the transmitter to have an accurate knowledge of the channel state information (CSI). This is typically provided via feedback from the receiver. Due to processing and feedback delays, the CSI used at the transmitter is outdated leading to performance degradation causing a decrease in the overall system capacity. Channel prediction can be used to alleviate this problem. The minimum mean square error (MMSE) has been proven to have high performance in channel estimation and prediction. However this superior performance is accompanied by a high computational complexity. In this paper, we present a low complexity approximate MMSE (AMMSE) algorithm for channel prediction in block fading channels. Simulation results indicate that our proposed algorithm offers superior performance compared to the recursive least squares and normalized least mean square techniques and comparable performance to the full complexity MMSE algorithm.