891 research outputs found
A Dynamic Clustering and Resource Allocation Algorithm for Downlink CoMP Systems with Multiple Antenna UEs
Coordinated multi-point (CoMP) schemes have been widely studied in the recent
years to tackle the inter-cell interference. In practice, latency and
throughput constraints on the backhaul allow the organization of only small
clusters of base stations (BSs) where joint processing (JP) can be implemented.
In this work we focus on downlink CoMP-JP with multiple antenna user equipments
(UEs) and propose a novel dynamic clustering algorithm. The additional degrees
of freedom at the UE can be used to suppress the residual interference by using
an interference rejection combiner (IRC) and allow a multistream transmission.
In our proposal we first define a set of candidate clusters depending on
long-term channel conditions. Then, in each time block, we develop a resource
allocation scheme by jointly optimizing transmitter and receiver where: a)
within each candidate cluster a weighted sum rate is estimated and then b) a
set of clusters is scheduled in order to maximize the system weighted sum rate.
Numerical results show that much higher rates are achieved when UEs are
equipped with multiple antennas. Moreover, as this performance improvement is
mainly due to the IRC, the gain achieved by the proposed approach with respect
to the non-cooperative scheme decreases by increasing the number of UE
antennas.Comment: 27 pages, 8 figure
Fronthaul-Constrained Cloud Radio Access Networks: Insights and Challenges
As a promising paradigm for fifth generation (5G) wireless communication
systems, cloud radio access networks (C-RANs) have been shown to reduce both
capital and operating expenditures, as well as to provide high spectral
efficiency (SE) and energy efficiency (EE). The fronthaul in such networks,
defined as the transmission link between a baseband unit (BBU) and a remote
radio head (RRH), requires high capacity, but is often constrained. This
article comprehensively surveys recent advances in fronthaul-constrained
C-RANs, including system architectures and key techniques. In particular, key
techniques for alleviating the impact of constrained fronthaul on SE/EE and
quality of service for users, including compression and quantization,
large-scale coordinated processing and clustering, and resource allocation
optimization, are discussed. Open issues in terms of software-defined
networking, network function virtualization, and partial centralization are
also identified.Comment: 5 Figures, accepted by IEEE Wireless Communications. arXiv admin
note: text overlap with arXiv:1407.3855 by other author
Linear Precoding in Cooperative MIMO Cellular Networks with Limited Coordination Clusters
In a cooperative multiple-antenna downlink cellular network, maximization of
a concave function of user rates is considered. A new linear precoding
technique called soft interference nulling (SIN) is proposed, which performs at
least as well as zero-forcing (ZF) beamforming. All base stations share channel
state information, but each user's message is only routed to those that
participate in the user's coordination cluster. SIN precoding is particularly
useful when clusters of limited sizes overlap in the network, in which case
traditional techniques such as dirty paper coding or ZF do not directly apply.
The SIN precoder is computed by solving a sequence of convex optimization
problems. SIN under partial network coordination can outperform ZF under full
network coordination at moderate SNRs. Under overlapping coordination clusters,
SIN precoding achieves considerably higher throughput compared to myopic ZF,
especially when the clusters are large.Comment: 13 pages, 5 figure
Two-Stage Subspace Constrained Precoding in Massive MIMO Cellular Systems
We propose a subspace constrained precoding scheme that exploits the spatial
channel correlation structure in massive MIMO cellular systems to fully unleash
the tremendous gain provided by massive antenna array with reduced channel
state information (CSI) signaling overhead. The MIMO precoder at each base
station (BS) is partitioned into an inner precoder and a Transmit (Tx) subspace
control matrix. The inner precoder is adaptive to the local CSI at each BS for
spatial multiplexing gain. The Tx subspace control is adaptive to the channel
statistics for inter-cell interference mitigation and Quality of Service (QoS)
optimization. Specifically, the Tx subspace control is formulated as a QoS
optimization problem which involves an SINR chance constraint where the
probability of each user's SINR not satisfying a service requirement must not
exceed a given outage probability. Such chance constraint cannot be handled by
the existing methods due to the two stage precoding structure. To tackle this,
we propose a bi-convex approximation approach, which consists of three key
ingredients: random matrix theory, chance constrained optimization and
semidefinite relaxation. Then we propose an efficient algorithm to find the
optimal solution of the resulting bi-convex approximation problem. Simulations
show that the proposed design has significant gain over various baselines.Comment: 13 pages, accepted by IEEE Transactions on Wireless Communication
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