224 research outputs found
Improved Linear Precoding over Block Diagonalization in Multi-cell Cooperative Networks
In downlink multiuser multiple-input multiple-output (MIMO) systems, block
diagonalization (BD) is a practical linear precoding scheme which achieves the
same degrees of freedom (DoF) as the optimal linear/nonlinear precoding
schemes. However, its sum-rate performance is rather poor in the practical SNR
regime due to the transmit power boost problem. In this paper, we propose an
improved linear precoding scheme over BD with a so-called
"effective-SNR-enhancement" technique. The transmit covariance matrices are
obtained by firstly solving a power minimization problem subject to the minimum
rate constraint achieved by BD, and then properly scaling the solution to
satisfy the power constraints. It is proved that such approach equivalently
enhances the system SNR, and hence compensates the transmit power boost problem
associated with BD. The power minimization problem is in general non-convex. We
therefore propose an efficient algorithm that solves the problem heuristically.
Simulation results show significant sum rate gains over the optimal BD and the
existing minimum mean square error (MMSE) based precoding schemes.Comment: 21 pages, 4 figure
Cooperative Multi-Cell Block Diagonalization with Per-Base-Station Power Constraints
Block diagonalization (BD) is a practical linear precoding technique that
eliminates the inter-user interference in downlink multiuser multiple-input
multiple-output (MIMO) systems. In this paper, we apply BD to the downlink
transmission in a cooperative multi-cell MIMO system, where the signals from
different base stations (BSs) to all the mobile stations (MSs) are jointly
designed with the perfect knowledge of the downlink channels and transmit
messages. Specifically, we study the optimal BD precoder design to maximize the
weighted sum-rate of all the MSs subject to a set of per-BS power constraints.
This design problem is formulated in an auxiliary MIMO broadcast channel (BC)
with a set of transmit power constraints corresponding to those for individual
BSs in the multi-cell system. By applying convex optimization techniques, this
paper develops an efficient algorithm to solve this problem, and derives the
closed-form expression for the optimal BD precoding matrix. It is revealed that
the optimal BD precoding vectors for each MS in the per-BS power constraint
case are in general non-orthogonal, which differs from the conventional
orthogonal BD precoder design for the MIMO-BC under one single sum-power
constraint. Moreover, for the special case of single-antenna BSs and MSs, the
proposed solution reduces to the optimal zero-forcing beamforming (ZF-BF)
precoder design for the weighted sum-rate maximization in the multiple-input
single-output (MISO) BC with per-antenna power constraints. Suboptimal and
low-complexity BD/ZF-BF precoding schemes are also presented, and their
achievable rates are compared against those with the optimal schemes.Comment: accepted in JSAC, special issue on cooperative communications on
cellular networks, June 201
Group Sparse Precoding for Cloud-RAN with Multiple User Antennas
Cloud radio access network (C-RAN) has become a promising network
architecture to support the massive data traffic in the next generation
cellular networks. In a C-RAN, a massive number of low-cost remote antenna
ports (RAPs) are connected to a single baseband unit (BBU) pool via high-speed
low-latency fronthaul links, which enables efficient resource allocation and
interference management. As the RAPs are geographically distributed, the group
sparse beamforming schemes attracts extensive studies, where a subset of RAPs
is assigned to be active and a high spectral efficiency can be achieved.
However, most studies assumes that each user is equipped with a single antenna.
How to design the group sparse precoder for the multiple antenna users remains
little understood, as it requires the joint optimization of the mutual coupling
transmit and receive beamformers. This paper formulates an optimal joint RAP
selection and precoding design problem in a C-RAN with multiple antennas at
each user. Specifically, we assume a fixed transmit power constraint for each
RAP, and investigate the optimal tradeoff between the sum rate and the number
of active RAPs. Motivated by the compressive sensing theory, this paper
formulates the group sparse precoding problem by inducing the -norm as
a penalty and then uses the reweighted heuristic to find a solution.
By adopting the idea of block diagonalization precoding, the problem can be
formulated as a convex optimization, and an efficient algorithm is proposed
based on its Lagrangian dual. Simulation results verify that our proposed
algorithm can achieve almost the same sum rate as that obtained from exhaustive
search
Soft Pilot Reuse and Multi-Cell Block Diagonalization Precoding for Massive MIMO Systems
The users at cell edge of a massive multiple-input multiple-output (MIMO)
system suffer from severe pilot contamination, which leads to poor quality of
service (QoS). In order to enhance the QoS for these edge users, soft pilot
reuse (SPR) combined with multi-cell block diagonalization (MBD) precoding are
proposed. Specifically, the users are divided into two groups according to
their large-scale fading coefficients, referred to as the center users, who
only suffer from modest pilot contamination and the edge users, who suffer from
severe pilot contamination. Based on this distinction, the SPR scheme is
proposed for improving the QoS for the edge users, whereby a cell-center pilot
group is reused for all cell-center users in all cells, while a cell-edge pilot
group is applied for the edge users in the adjacent cells. By extending the
classical block diagonalization precoding to a multi-cell scenario, the MBD
precoding scheme projects the downlink transmit signal onto the null space of
the subspace spanned by the inter-cell channels of the edge users in adjacent
cells. Thus, the inter-cell interference contaminating the edge users' signals
in the adjacent cells can be efficiently mitigated and hence the QoS of these
edge users can be further enhanced. Our theoretical analysis and simulation
results demonstrate that both the uplink and downlink rates of the edge users
are significantly improved, albeit at the cost of the slightly decreased rate
of center users.Comment: 13 pages, 12 figures, accepted for publication in IEEE Transactions
on Vehicular Technology, 201
Multi-user Linear Precoding for Multi-polarized Massive MIMO System under Imperfect CSIT
The space limitation and the channel acquisition prevent Massive MIMO from
being easily deployed in a practical setup. Motivated by current deployments of
LTE-Advanced, the use of multi-polarized antennas can be an efficient solution
to address the space constraint. Furthermore, the dual-structured precoding, in
which a preprocessing based on the spatial correlation and a subsequent linear
precoding based on the short-term channel state information at the transmitter
(CSIT) are concatenated, can reduce the feedback overhead efficiently. By
grouping and preprocessing spatially correlated mobile stations (MSs), the
dimension of the precoding signal space is reduced and the corresponding
short-term CSIT dimension is reduced. In this paper, to reduce the feedback
overhead further, we propose a dual-structured multi-user linear precoding, in
which the subgrouping method based on co-polarization is additionally applied
to the spatially grouped MSs in the preprocessing stage. Furthermore, under
imperfect CSIT, the proposed scheme is asymptotically analyzed based on random
matrix theory. By investigating the behavior of the asymptotic performance, we
also propose a new dual-structured precoding in which the precoding mode is
switched between two dual-structured precoding strategies with 1) the
preprocessing based only on the spatial correlation and 2) the preprocessing
based on both the spatial correlation and polarization. Finally, we extend it
to 3D dual-structured precoding.Comment: accepted to IEEE Transactions on Wireless Communication
Decomposition by Successive Convex Approximation: A Unifying Approach for Linear Transceiver Design in Heterogeneous Networks
We study the downlink linear precoder design problem in a multi-cell dense
heterogeneous network (HetNet). The problem is formulated as a general
sum-utility maximization (SUM) problem, which includes as special cases many
practical precoder design problems such as multi-cell coordinated linear
precoding, full and partial per-cell coordinated multi-point transmission,
zero-forcing precoding and joint BS clustering and beamforming/precoding. The
SUM problem is difficult due to its non-convexity and the tight coupling of the
users' precoders. In this paper we propose a novel convex approximation
technique to approximate the original problem by a series of convex
subproblems, each of which decomposes across all the cells. The convexity of
the subproblems allows for efficient computation, while their decomposability
leads to distributed implementation. {Our approach hinges upon the
identification of certain key convexity properties of the sum-utility
objective, which allows us to transform the problem into a form that can be
solved using a popular algorithmic framework called BSUM (Block Successive
Upper-Bound Minimization).} Simulation experiments show that the proposed
framework is effective for solving interference management problems in large
HetNet.Comment: Accepted by IEEE Transactions on Wireless Communicatio
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