Joint detection in the uplink of a cellular network involving several non-colocated base stations is a promising means to turn inter-cell interference into useful signal energy and hence dramatically increase spectral efficiency of reuse one networks. To maximize the benefits of the new degrees of freedom that come with base station cooperation, cluster centric schedulers need to be aware of the interference situation in all participating cells. Since the required exchange of information on the backhaul infrastructure can be subject to significant delays, scheduling decisions are potentially based on outdated channel state information and will thus lead to suboptimal system performance. In this contribution we extend our framework for studying the impact of these signaling delays on the system performance by allowing bigger cooperation clusters. By comparing new algorithms and introducing the possibility of channel prediction, we provide further insight into the system behavior.
Today, it is well understood that interference poses the main capacity limitation and thus challenge for future cellular networks. A promising concept that addresses interference is multi-cell cooperative signal processing, often referred to as Network MIMO. While in recent publications, it is often assumed that the required exchange of information among the base stations can be done with unlimited capacity, current network infrastructures do not necessarily support very high data rates and backhaul has been identified as a major cost driver. For the case of unlimited backhaul availability, it has been shown that large gains can be achieved through intelligent resource assignment (scheduling). In this paper, we introduce a low-complexity algorithm for uplink scheduling in cooperative cellular networks under the assumption of a capacity constrained backhaul with the target of maximizing the tradeoff between backhaul and sum rate.
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