858 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
Filter and nested-lattice code design for fading MIMO channels with side-information
Linear-assignment Gel'fand-Pinsker coding (LA-GPC) is a coding technique for
channels with interference known only at the transmitter, where the known
interference is treated as side-information (SI). As a special case of LA-GPC,
dirty paper coding has been shown to be able to achieve the optimal
interference-free rate for interference channels with perfect channel state
information at the transmitter (CSIT). In the cases where only the channel
distribution information at the transmitter (CDIT) is available, LA-GPC also
has good (sometimes optimal) performance in a variety of fast and slow fading
SI channels. In this paper, we design the filters in nested-lattice based
coding to make it achieve the same rate performance as LA-GPC in multiple-input
multiple-output (MIMO) channels. Compared with the random Gaussian codebooks
used in previous works, our resultant coding schemes have an algebraic
structure and can be implemented in practical systems. A simulation in a
slow-fading channel is also provided, and near interference-free error
performance is obtained. The proposed coding schemes can serve as the
fundamental building blocks to achieve the promised rate performance of MIMO
Gaussian broadcast channels with CDIT or perfect CSITComment: submitted to IEEE Transactions on Communications, Feb, 200
Compute-and-Forward: Harnessing Interference through Structured Codes
Interference is usually viewed as an obstacle to communication in wireless
networks. This paper proposes a new strategy, compute-and-forward, that
exploits interference to obtain significantly higher rates between users in a
network. The key idea is that relays should decode linear functions of
transmitted messages according to their observed channel coefficients rather
than ignoring the interference as noise. After decoding these linear equations,
the relays simply send them towards the destinations, which given enough
equations, can recover their desired messages. The underlying codes are based
on nested lattices whose algebraic structure ensures that integer combinations
of codewords can be decoded reliably. Encoders map messages from a finite field
to a lattice and decoders recover equations of lattice points which are then
mapped back to equations over the finite field. This scheme is applicable even
if the transmitters lack channel state information.Comment: IEEE Trans. Info Theory, to appear. 23 pages, 13 figure
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