Decentralising Multicell Cooperative Processing: A Novel Robust Framework

EURASIP Journal on Wireless Communications and Networking, Jun 2009

Multicell cooperative processing (MCP) has the potential to boost spectral efficiency and improve fairness of cellular systems. However the typical centralised conception for MCP incurs significant infrastructural overheads which increase the system costs and hinder the practical implementation of MCP. In Frequency Division Duplexing systems each user feeds back its Channel State Information (CSI) only to one Base Station (BS). Therefore collaborating BSs need to be interconnected via low-latency backhaul links, and a Control Unit is necessary in order to gather user CSI, perform scheduling, and coordinate transmission. In this paper a new framework is proposed that allows MCP on the downlink while circumventing the aforementioned costly modifications on the existing infrastructure of cellular systems. Each MS feeds back its CSI to all collaborating BSs, and the needed operations of user scheduling and signal processing are performed in a distributed fashion by the involved BSs. Furthermore the proposed framework is shown to be robust against feedback errors when quantized CSI feedback and linear precoding are employed.

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Decentralising Multicell Cooperative Processing: A Novel Robust Framework

EURASIP Journal on Wireless Communications and Networking Hindawi Publishing Corporation Decentralising Multicell Cooperative Processing: A Novel Robust Framework Agisilaos Papadogiannis 1 Eric Hardouin 1 David Gesbert 0 0 Eurecom , 2229 route des Crˆetes, BP 193, 06904 Sophia-Antipolis , France 1 Orange Labs , 38-40 rue du G ́en ́eral Leclerc, 92794 Issy les Moulineaux , France Multicell cooperative processing (MCP) has the potential to boost spectral efficiency and improve fairness of cellular systems. However the typical centralised conception for MCP incurs significant infrastructural overheads which increase the system costs and hinder the practical implementation of MCP. In Frequency Division Duplexing systems each user feeds back its Channel State Information (CSI) only to one Base Station (BS). Therefore collaborating BSs need to be interconnected via low-latency backhaul links, and a Control Unit is necessary in order to gather user CSI, perform scheduling, and coordinate transmission. In this paper a new framework is proposed that allows MCP on the downlink while circumventing the aforementioned costly modifications on the existing infrastructure of cellular systems. Each MS feeds back its CSI to all collaborating BSs, and the needed operations of user scheduling and signal processing are performed in a distributed fashion by the involved BSs. Furthermore the proposed framework is shown to be robust against feedback errors when quantized CSI feedback and linear precoding are employed. 1. Introduction Cellular systems employing aggressive frequency reuse and especially full frequency reuse have recently attracted the attention due to the increasing demand for high quality and throughput wireless services (mobile Internet), together with the scarcity of radio spectrum. Although these systems lead to significant gains in spectrum usage, they incur important losses in cell throughput resulting from the increased amount of intercell interference (ICI). This mainly affects users located on the cell edge as they are more prone to ICI originating from neighbouring cells. Therefore ICI is a factor causing significant performance and fairness degradation in the network [ 1 ]. Furthermore ICI degrades performance of Multiple Input Multiple-Output (MIMO) systems; hence it impedes their deployment in a cellular context [ 2 ]. Multicell cooperative processing (MCP) has been recognized as an effective solution for ICI mitigation [ 1, 3, 4 ]. In MCP enabled systems BSs are grouped into cooperation clusters, each of which contains a subset of the network BSs. The BSs of each cluster exchange information and jointly process signals by forming virtual antenna arrays distributed in space. They can be seen as multiuser MIMO systems where the antennas are no longer collocated but remote. Notably, MCP has been shown to reduce ICI and boost performance; this especially suits the downlink as interference mitigation burdens the network infrastructure and not the receivers [ 3 ]. However, MCP comes at the cost of increased signaling and infrastructural overheads. On the downlink of cellular systems operating in Frequency Division Duplexing (FDD) mode, the overheads of MCP are related to the inherent need for Channel State Information (CSI) at the transmitter of multiuser MIMO systems and also to the distributed nature of collaborative BS processing [ 5 ]. The overheads related to MCP can be divided into two main categories. Signaling Overheads. (i) CSI estimation: users estimate a greater number of channel coefficients than a multiuser MIMO system, equal to the total number of cooperating antennas. (ii) CSI Feedback: feedback of the estimated high number of channel coefficients from users to BSs. (iii) Time synchronisation: collaborating BSs need to be tightly synchronised in time. Infrastructural Overheads. (i) Control Unit: the CU gathers CSI from the BSs, performs scheduling, and designs the transmission parameters according to the chosen transmission strategy. (ii) Low-latency backhaul links: collaborating BSs are connected with the CU via low-latency links in order to exchange CSI, scheduling decisions, and transmission parameters. Note that the signaling overheads are independent of the architectural conception for MCP, whereas the infrastructural overheads mentioned above are related to the existing conception for the architecture of MCP. A natural way for mitigating the aforementioned overheads is to limit the number of cooperating BSs per cluster. A simple technique that has been proposed is limited static clustering, where BS cooperation groups are of limited size and remain static; only neighbouring BSs collaborate [ 6, 7 ]. This has been shown to be a good trade-off between performance and overhead. However, even higher performance gains can be attained if the limited clusters are formed dynamically; in this case the cooperating BSs are not the neighbouring ones but rather the ones that (...truncated)


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Agisilaos Papadogiannis, Eric Hardouin. Decentralising Multicell Cooperative Processing: A Novel Robust Framework, EURASIP Journal on Wireless Communications and Networking, 2009, pp. 890685, Volume 2009, Issue 1, DOI: 10.1155/2009/890685