5,012 research outputs found
Using Network Coding to Achieve the Capacity of Deterministic Relay Networks with Relay Messages
In this paper, we derive the capacity of the deterministic relay networks
with relay messages. We consider a network which consists of five nodes, four
of which can only communicate via the fifth one. However, the fifth node is not
merely a relay as it may exchange private messages with the other network
nodes. First, we develop an upper bound on the capacity region based on the
notion of a single sided genie. In the course of the achievability proof, we
also derive the deterministic capacity of a 4-user relay network (without
private messages at the relay). The capacity achieving schemes use a
combination of two network coding techniques: the Simple Ordering Scheme (SOS)
and Detour Schemes (DS). In the SOS, we order the transmitted bits at each user
such that the bi-directional messages will be received at the same channel
level at the relay, while the basic idea behind the DS is that some parts of
the message follow an indirect path to their respective destinations. This
paper, therefore, serves to show that user cooperation and network coding can
enhance throughput, even when the users are not directly connected to each
other.Comment: 12 pages, 5 figures, submitted to IEEE JSAC Network codin
Wireless Network Information Flow: A Deterministic Approach
In a wireless network with a single source and a single destination and an
arbitrary number of relay nodes, what is the maximum rate of information flow
achievable? We make progress on this long standing problem through a two-step
approach. First we propose a deterministic channel model which captures the key
wireless properties of signal strength, broadcast and superposition. We obtain
an exact characterization of the capacity of a network with nodes connected by
such deterministic channels. This result is a natural generalization of the
celebrated max-flow min-cut theorem for wired networks. Second, we use the
insights obtained from the deterministic analysis to design a new
quantize-map-and-forward scheme for Gaussian networks. In this scheme, each
relay quantizes the received signal at the noise level and maps it to a random
Gaussian codeword for forwarding, and the final destination decodes the
source's message based on the received signal. We show that, in contrast to
existing schemes, this scheme can achieve the cut-set upper bound to within a
gap which is independent of the channel parameters. In the case of the relay
channel with a single relay as well as the two-relay Gaussian diamond network,
the gap is 1 bit/s/Hz. Moreover, the scheme is universal in the sense that the
relays need no knowledge of the values of the channel parameters to
(approximately) achieve the rate supportable by the network. We also present
extensions of the results to multicast networks, half-duplex networks and
ergodic networks.Comment: To appear in IEEE transactions on Information Theory, Vol 57, No 4,
April 201
On the Capacity Region of the Deterministic Y-Channel with Common and Private Messages
In multi user Gaussian relay networks, it is desirable to transmit private
information to each user as well as common information to all of them. However,
the capacity region of such networks with both kinds of information is not easy
to characterize. The prior art used simple linear deterministic models in order
to approximate the capacities of these Gaussian networks. This paper discusses
the capacity region of the deterministic Y-channel with private and common
messages. In this channel, each user aims at delivering two private messages to
the other two users in addition to a common message directed towards both of
them. As there is no direct link between the users, all messages must pass
through an intermediate relay. We present outer-bounds on the rate region using
genie aided and cut-set bounds. Then, we develop a greedy scheme to define an
achievable region and show that at a certain number of levels at the relay, our
achievable region coincides with the upper bound. Finally, we argue that these
bounds for this setup are not sufficient to characterize the capacity region.Comment: 4 figures, 7 page
Computation Alignment: Capacity Approximation without Noise Accumulation
Consider several source nodes communicating across a wireless network to a
destination node with the help of several layers of relay nodes. Recent work by
Avestimehr et al. has approximated the capacity of this network up to an
additive gap. The communication scheme achieving this capacity approximation is
based on compress-and-forward, resulting in noise accumulation as the messages
traverse the network. As a consequence, the approximation gap increases
linearly with the network depth.
This paper develops a computation alignment strategy that can approach the
capacity of a class of layered, time-varying wireless relay networks up to an
approximation gap that is independent of the network depth. This strategy is
based on the compute-and-forward framework, which enables relays to decode
deterministic functions of the transmitted messages. Alone, compute-and-forward
is insufficient to approach the capacity as it incurs a penalty for
approximating the wireless channel with complex-valued coefficients by a
channel with integer coefficients. Here, this penalty is circumvented by
carefully matching channel realizations across time slots to create
integer-valued effective channels that are well-suited to compute-and-forward.
Unlike prior constant gap results, the approximation gap obtained in this paper
also depends closely on the fading statistics, which are assumed to be i.i.d.
Rayleigh.Comment: 36 pages, to appear in IEEE Transactions on Information Theor
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