2,534 research outputs found
Study of Gaussian Relay Channels with Correlated Noises
In this paper, we consider full-duplex and half-duplex Gaussian relay
channels where the noises at the relay and destination are arbitrarily
correlated. We first derive the capacity upper bound and the achievable rates
with three existing schemes: Decode-and-Forward (DF), Compress-and-Forward
(CF), and Amplify-and-Forward (AF). We present two capacity results under
specific noise correlation coefficients, one being achieved by DF and the other
being achieved by direct link transmission (or a special case of CF). The
channel for the former capacity result is equivalent to the traditional
Gaussian degraded relay channel and the latter corresponds to the Gaussian
reversely-degraded relay channel. For CF and AF schemes, we show that their
achievable rates are strictly decreasing functions over the negative
correlation coefficient. Through numerical comparisons under different channel
settings, we observe that although DF completely disregards the noise
correlation while the other two can potentially exploit such extra information,
none of the three relay schemes always outperforms the others over different
correlation coefficients. Moreover, the exploitation of noise correlation by CF
and AF accrues more benefit when the source-relay link is weak. This paper also
considers the optimal power allocation problem under the correlated-noise
channel setting. With individual power constraints at the relay and the source,
it is shown that the relay should use all its available power to maximize the
achievable rates under any correlation coefficient. With a total power
constraint across the source and the relay, the achievable rates are proved to
be concave functions over the power allocation factor for AF and CF under
full-duplex mode, where the closed-form power allocation strategy is derived.Comment: 24 pages, 7 figures, submitted to IEEE Transactions on Communication
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
- …