Coordinated Multi-Point (CoMP) Adaptive Estimation and Prediction Schemes using Superimposed and Decomposed Channel Tracking

Abstract—Performance of future wireless technologies will depend heavily on cooperation between different transmission/reception nodes in the access network. CoMP (Coordinated Multi-Point) transmission increases the cell edge user performance by reducing the inter-cell interference. UEs (User Equipments) simultaneously receive data from multiple base stations (eNBs) grouped into a joint transmission cluster. Clustering choices need to be optimized by joint use of CoMP adaptive channel estimation and prediction schemes for energy efficiency and capacity improvements. In this paper, various multi-point (multi-eNB) channel estimation/prediction schemes are proposed and analyzed to improve the joint transmission set clustering accuracy. Multi-point CIRs (Channel Impulse Responses) can be tracked either by superimposed or decomposed methods. The latter scheme tracks each multipath component of every CoMP measurement set member and yields more accurate estimates, however leads to significantly higher computation complexity as opposed to the superimposed tracking which tracks the overall CIR. Therefore, UEs need to dynamically switch between the two schemes depending on the serving cluster size and recently observed CoMP characteristics. It is shown that increasing the channel estimation/prediction filter size yields significantly more capacity and energy efficiency improvements for UEs served by larger clusters. It is also demonstrated that the serving eNB can maximize the performance gains by setting the channel prediction range equal to observed system delay between the multi-point CSI reports and data transmission. I