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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)