41,754 research outputs found
Robust Lattice Alignment for K-user MIMO Interference Channels with Imperfect Channel Knowledge
In this paper, we consider a robust lattice alignment design for K-user
quasi-static MIMO interference channels with imperfect channel knowledge. With
random Gaussian inputs, the conventional interference alignment (IA) method has
the feasibility problem when the channel is quasi-static. On the other hand,
structured lattices can create structured interference as opposed to the random
interference caused by random Gaussian symbols. The structured interference
space can be exploited to transmit the desired signals over the gaps. However,
the existing alignment methods on the lattice codes for quasi-static channels
either require infinite SNR or symmetric interference channel coefficients.
Furthermore, perfect channel state information (CSI) is required for these
alignment methods, which is difficult to achieve in practice. In this paper, we
propose a robust lattice alignment method for quasi-static MIMO interference
channels with imperfect CSI at all SNR regimes, and a two-stage decoding
algorithm to decode the desired signal from the structured interference space.
We derive the achievable data rate based on the proposed robust lattice
alignment method, where the design of the precoders, decorrelators, scaling
coefficients and interference quantization coefficients is jointly formulated
as a mixed integer and continuous optimization problem. The effect of imperfect
CSI is also accommodated in the optimization formulation, and hence the derived
solution is robust to imperfect CSI. We also design a low complex iterative
optimization algorithm for our robust lattice alignment method by using the
existing iterative IA algorithm that was designed for the conventional IA
method. Numerical results verify the advantages of the proposed robust lattice
alignment method
Robust Sum MSE Optimization for Downlink Multiuser MIMO Systems with Arbitrary Power Constraint: Generalized Duality Approach
This paper considers linear minimum meansquare- error (MMSE) transceiver
design problems for downlink multiuser multiple-input multiple-output (MIMO)
systems where imperfect channel state information is available at the base
station (BS) and mobile stations (MSs). We examine robust sum mean-square-error
(MSE) minimization problems. The problems are examined for the generalized
scenario where the power constraint is per BS, per BS antenna, per user or per
symbol, and the noise vector of each MS is a zero-mean circularly symmetric
complex Gaussian random variable with arbitrary covariance matrix. For each of
these problems, we propose a novel duality based iterative solution. Each of
these problems is solved as follows. First, we establish a novel sum average
meansquare- error (AMSE) duality. Second, we formulate the power allocation
part of the problem in the downlink channel as a Geometric Program (GP). Third,
using the duality result and the solution of GP, we utilize alternating
optimization technique to solve the original downlink problem. To solve robust
sum MSE minimization constrained with per BS antenna and per BS power problems,
we have established novel downlink-uplink duality. On the other hand, to solve
robust sum MSE minimization constrained with per user and per symbol power
problems, we have established novel downlink-interference duality. For the
total BS power constrained robust sum MSE minimization problem, the current
duality is established by modifying the constraint function of the dual uplink
channel problem. And, for the robust sum MSE minimization with per BS antenna
and per user (symbol) power constraint problems, our duality are established by
formulating the noise covariance matrices of the uplink and interference
channels as fixed point functions, respectively.Comment: IEEE TSP Journa
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