3,690 research outputs found
Optimal Beamforming for Gaussian MIMO Wiretap Channels with Two Transmit Antennas
A Gaussian multiple-input multiple-output wiretap channel in which the
eavesdropper and legitimate receiver are equipped with arbitrary numbers of
antennas and the transmitter has two antennas is studied in this paper. Under
an average power constraint, the optimal input covariance to obtain the secrecy
capacity of this channel is unknown, in general. In this paper, the input
covariance matrix required to achieve the capacity is determined. It is shown
that the secrecy capacity of this channel can be achieved by linear precoding.
The optimal precoding and power allocation schemes that maximize the achievable
secrecy rate, and thus achieve the capacity, are developed subsequently. The
secrecy capacity is then compared with the achievable secrecy rate of
generalized singular value decomposition (GSVD)-based precoding, which is the
best previously proposed technique for this problem. Numerical results
demonstrate that substantial gain can be obtained in secrecy rate between the
proposed and GSVD-based precodings.Comment: Accepted for publication in IEEE Transactions on Wireless
Communication
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
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
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
A New SLNR-based Linear Precoding for Downlink Multi-User Multi-Stream MIMO Systems
Signal-to-leakage-and-noise ratio (SLNR) is a promising criterion for linear
precoder design in multi-user (MU) multiple-input multiple-output (MIMO)
systems. It decouples the precoder design problem and makes closed-form
solution available. In this letter, we present a new linear precoding scheme by
slightly relaxing the SLNR maximization for MU-MIMO systems with multiple data
streams per user. The precoding matrices are obtained by a general form of
simultaneous diagonalization of two Hermitian matrices. The new scheme reduces
the gap between the per-stream effective channel gains, an inherent limitation
in the original SLNR precoding scheme. Simulation results demonstrate that the
proposed precoding achieves considerable gains in error performance over the
original one for multi-stream transmission while maintaining almost the same
achievable sum-rate.Comment: 8 pages, 1 figur
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