Partial Joint Processing with Efficient Backhauling in Coordinated Multipoint Networks

Joint processing between base stations is a promising technique to improve the quality of service to users at the cell edge, but this technique poses tremendous requirements on the backhaul signaling capabilities, such as the distribution of channel state information and the precoding weights to the base stations involved in joint processing. Partial joint processing is a technique aimed to reduce feedback load, in one approach the users feed back the channel state information of the best links based on a channel gain threshold mechanism. However, it has been shown in the literature that the reduction in the feedback load is not reflected in an equivalent backhaul reduction, unless additional scheduling or precoding techniques are applied. The reason is that reduced feedback from users yields sparse channel state information at the Central Coordination Node. Under these conditions, existing linear precoding techniques fail to remove the interference and reduce backhaul, simultaneously, unless constraints are imposed on scheduling. In this paper, a partial joint processing scheme with efficient backhauling is proposed, based on a stochastic optimization algorithm called particle swarm optimization. The use of particle swarm optimization in the design of the precoder promises efficient backhauling with improved sum rate.