7,917 research outputs found
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
Approaching Gaussian Relay Network Capacity in the High SNR Regime: End-to-End Lattice Codes
We present a natural and low-complexity technique for achieving the capacity
of the Gaussian relay network in the high SNR regime. Specifically, we propose
the use of end-to-end structured lattice codes with the amplify-and-forward
strategy, where the source uses a nested lattice code to encode the messages
and the destination decodes the messages by lattice decoding. All intermediate
relays simply amplify and forward the received signals over the network to the
destination. We show that the end-to-end lattice-coded amplify-and-forward
scheme approaches the capacity of the layered Gaussian relay network in the
high SNR regime. Next, we extend our scheme to non-layered Gaussian relay
networks under the amplify-and-forward scheme, which can be viewed as a
Gaussian intersymbol interference (ISI) channel. Compared with other schemes,
our approach is significantly simpler and requires only the end-to-end design
of the lattice precoding and decoding. It does not require any knowledge of the
network topology or the individual channel gains
Nested Lattice Codes for Gaussian Relay Networks with Interference
In this paper, a class of relay networks is considered. We assume that, at a
node, outgoing channels to its neighbors are orthogonal, while incoming signals
from neighbors can interfere with each other. We are interested in the
multicast capacity of these networks. As a subclass, we first focus on Gaussian
relay networks with interference and find an achievable rate using a lattice
coding scheme. It is shown that there is a constant gap between our achievable
rate and the information theoretic cut-set bound. This is similar to the recent
result by Avestimehr, Diggavi, and Tse, who showed such an approximate
characterization of the capacity of general Gaussian relay networks. However,
our achievability uses a structured code instead of a random one. Using the
same idea used in the Gaussian case, we also consider linear finite-field
symmetric networks with interference and characterize the capacity using a
linear coding scheme.Comment: 23 pages, 5 figures, submitted to IEEE Transactions on Information
Theor
The Multi-way Relay Channel
The multiuser communication channel, in which multiple users exchange
information with the help of a relay terminal, termed the multi-way relay
channel (mRC), is introduced. In this model, multiple interfering clusters of
users communicate simultaneously, where the users within the same cluster wish
to exchange messages among themselves. It is assumed that the users cannot
receive each other's signals directly, and hence the relay terminal in this
model is the enabler of communication. In particular, restricted encoders,
which ignore the received channel output and use only the corresponding
messages for generating the channel input, are considered. Achievable rate
regions and an outer bound are characterized for the Gaussian mRC, and their
comparison is presented in terms of exchange rates in a symmetric Gaussian
network scenario. It is shown that the compress-and-forward (CF) protocol
achieves exchange rates within a constant bit offset of the exchange capacity
independent of the power constraints of the terminals in the network. A finite
bit gap between the exchange rates achieved by the CF and the
amplify-and-forward (AF) protocols is also shown. The two special cases of the
mRC, the full data exchange model, in which every user wants to receive
messages of all other users, and the pairwise data exchange model which
consists of multiple two-way relay channels, are investigated in detail. In
particular for the pairwise data exchange model, in addition to the proposed
random coding based achievable schemes, a nested lattice coding based scheme is
also presented and is shown to achieve exchange rates within a constant bit gap
of the exchange capacity.Comment: Revised version of our submission to the Transactions on Information
Theor
Relaying Simultaneous Multicast Messages
The problem of multicasting multiple messages with the help of a relay, which
may also have an independent message of its own to multicast, is considered. As
a first step to address this general model, referred to as the compound
multiple access channel with a relay (cMACr), the capacity region of the
multiple access channel with a "cognitive" relay is characterized, including
the cases of partial and rate-limited cognition. Achievable rate regions for
the cMACr model are then presented based on decode-and-forward (DF) and
compress-and-forward (CF) relaying strategies. Moreover, an outer bound is
derived for the special case in which each transmitter has a direct link to one
of the receivers while the connection to the other receiver is enabled only
through the relay terminal. Numerical results for the Gaussian channel are also
provided.Comment: This paper was presented at the IEEE Information Theory Workshop,
Volos, Greece, June 200
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