A survey on clustering algorithms for wireless sensor networks
Computer Communications
Downlink multicell processing with limited-backhaul capacity
EURASIP Journal on Advances in Signal Processing - Multiuser MIMO Transmission with Limited Feedback, Cooperation, and Coordination
Networked MIMO with clustered linear precoding
IEEE Transactions on Wireless Communications
Interference-aware scheduling in the multiuser MIMO-OFDM downlink
IEEE Communications Magazine
A WiMAX-based implementation of network MIMO for indoor wireless systems
EURASIP Journal on Advances in Signal Processing - Multiuser MIMO Transmission with Limited Feedback, Cooperation, and Coordination
Proceedings of the twenty-second annual ACM symposium on Parallelism in algorithms and architectures
Distributed uplink signal processing of cooperating base stations based on IQ sample exchange
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Coordinated Multi-Point in Mobile Communications: From Theory to Practice
Coordinated Multi-Point in Mobile Communications: From Theory to Practice
Backhaul network pre-clustering in cooperative cellular mobile access networks
WOWMOM '11 Proceedings of the 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks
PON in adolescence: from TDMA to WDM-PON
IEEE Communications Magazine
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Coordinated Multi-Point (CoMP) transmission and reception is a promising solution for managing interference and increasing performance in future wireless cellular systems. Due to its strict requirements in terms of capacity, latency, and synchronization among cooperating Base Stations (BSs), its successful deployment depends on the capability of the mobile backhaul network infrastructure. We deal with the feasibility of CoMP transmission/reception, in particular of Joint Transmission (JT). For this, we first evaluate which cluster sizes are reasonable from the wireless point-of-view to achieve the desired performance gains. Thereafter, we analyze how different backhaul topologies (e.g., mesh and tree structures) and backhaul network technologies (e.g., layer-2 switching and single-copy multicast capabilities) can support these desired clusters. We study for different traffic scenarios and backhaul connectivity levels, which part of the desired BS clusters are actually feasible according to the backhaul characteristics. We found out that a significant mismatch exists between the desired and feasible clusters. Neglecting this mismatch causes overheads in real JT implementations, which complicates or even prevents their deployment. Based on our findings, we propose a clustering system architecture that not only includes wireless information, as done in the state of the art, but also combines wireless and backhaul network feasibility information in a smart way. This avoids unnecessary signaling and User Equipment (UE) data exchange among BSs which are not eligible to take part in the cooperative cluster. Evaluations show that our scheme reduces the signaling and UE data exchange overhead by up to 85% compared to conventional clustering approaches, which do not take into account the backhaul network's status.