1,348 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
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
Cooperative Transmission for a Vector Gaussian Parallel Relay Network
In this paper, we consider a parallel relay network where two relays
cooperatively help a source transmit to a destination. We assume the source and
the destination nodes are equipped with multiple antennas. Three basic schemes
and their achievable rates are studied: Decode-and-Forward (DF),
Amplify-and-Forward (AF), and Compress-and-Forward (CF). For the DF scheme, the
source transmits two private signals, one for each relay, where dirty paper
coding (DPC) is used between the two private streams, and a common signal for
both relays. The relays make efficient use of the common information to
introduce a proper amount of correlation in the transmission to the
destination. We show that the DF scheme achieves the capacity under certain
conditions. We also show that the CF scheme is asymptotically optimal in the
high relay power limit, regardless of channel ranks. It turns out that the AF
scheme also achieves the asymptotic optimality but only when the
relays-to-destination channel is full rank. The relative advantages of the
three schemes are discussed with numerical results.Comment: 35 pages, 10 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
On the Capacity of the Binary-Symmetric Parallel-Relay Network
We investigate the binary-symmetric parallel-relay network where there is one
source, one destination, and multiple relays in parallel. We show that
forwarding relays, where the relays merely transmit their received signals,
achieve the capacity in two ways: with coded transmission at the source and a
finite number of relays, or uncoded transmission at the source and a
sufficiently large number of relays. On the other hand, decoding relays, where
the relays decode the source message, re-encode, and forward it to the
destination, achieve the capacity when the number of relays is small. In
addition, we show that any coding scheme that requires decoding at any relay is
suboptimal in large parallel-relay networks, where forwarding relays achieve
strictly higher rates.Comment: Author's final version (to appear in Transactions on Emerging
Telecommunications Technologies
The Balanced Unicast and Multicast Capacity Regions of Large Wireless Networks
We consider the question of determining the scaling of the -dimensional
balanced unicast and the -dimensional balanced multicast capacity
regions of a wireless network with nodes placed uniformly at random in a
square region of area and communicating over Gaussian fading channels. We
identify this scaling of both the balanced unicast and multicast capacity
regions in terms of , out of total possible, cuts. These cuts
only depend on the geometry of the locations of the source nodes and their
destination nodes and the traffic demands between them, and thus can be readily
evaluated. Our results are constructive and provide optimal (in the scaling
sense) communication schemes.Comment: 37 pages, 7 figures, to appear in IEEE Transactions on Information
Theor
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