6,192 research outputs found
On the Achievable Rates of Multihop Virtual Full-Duplex Relay Channels
We study a multihop "virtual" full-duplex relay channel as a special case of
a general multiple multicast relay network. For such channel,
quantize-map-and-forward (QMF) (or noisy network coding (NNC)) achieves the
cut-set upper bound within a constant gap where the gap grows {\em linearly}
with the number of relay stages . However, this gap may not be negligible
for the systems with multihop transmissions (i.e., a wireless backhaul
operating at higher frequencies). We have recently attained an improved result
to the capacity scaling where the gap grows {\em logarithmically} as ,
by using an optimal quantization at relays and by exploiting relays' messages
(decoded in the previous time slot) as side-information. In this paper, we
further improve the performance of this network by presenting a mixed scheme
where each relay can perform either decode-and-forward (DF) or QMF with
possibly rate-splitting. We derive the achievable rate and show that the
proposed scheme outperforms the QMF-optimized scheme. Furthermore, we
demonstrate that this performance improvement increases with .Comment: To be presented at ISIT 201
Sign-Compute-Resolve for Tree Splitting Random Access
We present a framework for random access that is based on three elements:
physical-layer network coding (PLNC), signature codes and tree splitting. In
presence of a collision, physical-layer network coding enables the receiver to
decode, i.e. compute, the sum of the packets that were transmitted by the
individual users. For each user, the packet consists of the user's signature,
as well as the data that the user wants to communicate. As long as no more than
K users collide, their identities can be recovered from the sum of their
signatures. This framework for creating and transmitting packets can be used as
a fundamental building block in random access algorithms, since it helps to
deal efficiently with the uncertainty of the set of contending terminals. In
this paper we show how to apply the framework in conjunction with a
tree-splitting algorithm, which is required to deal with the case that more
than K users collide. We demonstrate that our approach achieves throughput that
tends to 1 rapidly as K increases. We also present results on net data-rate of
the system, showing the impact of the overheads of the constituent elements of
the proposed protocol. We compare the performance of our scheme with an upper
bound that is obtained under the assumption that the active users are a priori
known. Also, we consider an upper bound on the net data-rate for any PLNC based
strategy in which one linear equation per slot is decoded. We show that already
at modest packet lengths, the net data-rate of our scheme becomes close to the
second upper bound, i.e. the overhead of the contention resolution algorithm
and the signature codes vanishes.Comment: This is an extended version of arXiv:1409.6902. Accepted for
publication in the IEEE Transactions on Information Theor
A Unified Approach for Network Information Theory
In this paper, we take a unified approach for network information theory and
prove a coding theorem, which can recover most of the achievability results in
network information theory that are based on random coding. The final
single-letter expression has a very simple form, which was made possible by
many novel elements such as a unified framework that represents various network
problems in a simple and unified way, a unified coding strategy that consists
of a few basic ingredients but can emulate many known coding techniques if
needed, and new proof techniques beyond the use of standard covering and
packing lemmas. For example, in our framework, sources, channels, states and
side information are treated in a unified way and various constraints such as
cost and distortion constraints are unified as a single joint-typicality
constraint.
Our theorem can be useful in proving many new achievability results easily
and in some cases gives simpler rate expressions than those obtained using
conventional approaches. Furthermore, our unified coding can strictly
outperform existing schemes. For example, we obtain a generalized
decode-compress-amplify-and-forward bound as a simple corollary of our main
theorem and show it strictly outperforms previously known coding schemes. Using
our unified framework, we formally define and characterize three types of
network duality based on channel input-output reversal and network flow
reversal combined with packing-covering duality.Comment: 52 pages, 7 figures, submitted to IEEE Transactions on Information
theory, a shorter version will appear in Proc. IEEE ISIT 201
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