6,869 research outputs found
Weak Secrecy in the Multi-Way Untrusted Relay Channel with Compute-and-Forward
We investigate the problem of secure communications in a Gaussian multi-way
relay channel applying the compute-and-forward scheme using nested lattice
codes. All nodes employ half-duplex operation and can exchange confidential
messages only via an untrusted relay. The relay is assumed to be honest but
curious, i.e., an eavesdropper that conforms to the system rules and applies
the intended relaying scheme. We start with the general case of the
single-input multiple-output (SIMO) L-user multi-way relay channel and provide
an achievable secrecy rate region under a weak secrecy criterion. We show that
the securely achievable sum rate is equivalent to the difference between the
computation rate and the multiple access channel (MAC) capacity. Particularly,
we show that all nodes must encode their messages such that the common
computation rate tuple falls outside the MAC capacity region of the relay. We
provide results for the single-input single-output (SISO) and the
multiple-input single-input (MISO) L-user multi-way relay channel as well as
the two-way relay channel. We discuss these results and show the dependency
between channel realization and achievable secrecy rate. We further compare our
result to available results in the literature for different schemes and show
that the proposed scheme operates close to the compute-and-forward rate without
secrecy.Comment: submitted to JSAC Special Issue on Fundamental Approaches to Network
Coding in Wireless Communication System
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
Reduced-Dimension Linear Transform Coding of Correlated Signals in Networks
A model, called the linear transform network (LTN), is proposed to analyze
the compression and estimation of correlated signals transmitted over directed
acyclic graphs (DAGs). An LTN is a DAG network with multiple source and
receiver nodes. Source nodes transmit subspace projections of random correlated
signals by applying reduced-dimension linear transforms. The subspace
projections are linearly processed by multiple relays and routed to intended
receivers. Each receiver applies a linear estimator to approximate a subset of
the sources with minimum mean squared error (MSE) distortion. The model is
extended to include noisy networks with power constraints on transmitters. A
key task is to compute all local compression matrices and linear estimators in
the network to minimize end-to-end distortion. The non-convex problem is solved
iteratively within an optimization framework using constrained quadratic
programs (QPs). The proposed algorithm recovers as special cases the regular
and distributed Karhunen-Loeve transforms (KLTs). Cut-set lower bounds on the
distortion region of multi-source, multi-receiver networks are given for linear
coding based on convex relaxations. Cut-set lower bounds are also given for any
coding strategy based on information theory. The distortion region and
compression-estimation tradeoffs are illustrated for different communication
demands (e.g. multiple unicast), and graph structures.Comment: 33 pages, 7 figures, To appear in IEEE Transactions on Signal
Processin
Computation Over Gaussian Networks With Orthogonal Components
Function computation of arbitrarily correlated discrete sources over Gaussian
networks with orthogonal components is studied. Two classes of functions are
considered: the arithmetic sum function and the type function. The arithmetic
sum function in this paper is defined as a set of multiple weighted arithmetic
sums, which includes averaging of the sources and estimating each of the
sources as special cases. The type or frequency histogram function counts the
number of occurrences of each argument, which yields many important statistics
such as mean, variance, maximum, minimum, median, and so on. The proposed
computation coding first abstracts Gaussian networks into the corresponding
modulo sum multiple-access channels via nested lattice codes and linear network
coding and then computes the desired function by using linear Slepian-Wolf
source coding. For orthogonal Gaussian networks (with no broadcast and
multiple-access components), the computation capacity is characterized for a
class of networks. For Gaussian networks with multiple-access components (but
no broadcast), an approximate computation capacity is characterized for a class
of networks.Comment: 30 pages, 12 figures, submitted to IEEE Transactions on Information
Theor
Reliable Physical Layer Network Coding
When two or more users in a wireless network transmit simultaneously, their
electromagnetic signals are linearly superimposed on the channel. As a result,
a receiver that is interested in one of these signals sees the others as
unwanted interference. This property of the wireless medium is typically viewed
as a hindrance to reliable communication over a network. However, using a
recently developed coding strategy, interference can in fact be harnessed for
network coding. In a wired network, (linear) network coding refers to each
intermediate node taking its received packets, computing a linear combination
over a finite field, and forwarding the outcome towards the destinations. Then,
given an appropriate set of linear combinations, a destination can solve for
its desired packets. For certain topologies, this strategy can attain
significantly higher throughputs over routing-based strategies. Reliable
physical layer network coding takes this idea one step further: using
judiciously chosen linear error-correcting codes, intermediate nodes in a
wireless network can directly recover linear combinations of the packets from
the observed noisy superpositions of transmitted signals. Starting with some
simple examples, this survey explores the core ideas behind this new technique
and the possibilities it offers for communication over interference-limited
wireless networks.Comment: 19 pages, 14 figures, survey paper to appear in Proceedings of the
IEE
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
Algebraic Watchdog: Mitigating Misbehavior in Wireless Network Coding
We propose a secure scheme for wireless network coding, called the algebraic
watchdog. By enabling nodes to detect malicious behaviors probabilistically and
use overheard messages to police their downstream neighbors locally, the
algebraic watchdog delivers a secure global self-checking network. Unlike
traditional Byzantine detection protocols which are receiver-based, this
protocol gives the senders an active role in checking the node downstream. The
key idea is inspired by Marti et al.'s watchdog-pathrater, which attempts to
detect and mitigate the effects of routing misbehavior.
As an initial building block of a such system, we first focus on a two-hop
network. We present a graphical model to understand the inference process nodes
execute to police their downstream neighbors; as well as to compute, analyze,
and approximate the probabilities of misdetection and false detection. In
addition, we present an algebraic analysis of the performance using an
hypothesis testing framework that provides exact formulae for probabilities of
false detection and misdetection.
We then extend the algebraic watchdog to a more general network setting, and
propose a protocol in which we can establish trust in coded systems in a
distributed manner. We develop a graphical model to detect the presence of an
adversarial node downstream within a general multi-hop network. The structure
of the graphical model (a trellis) lends itself to well-known algorithms, such
as the Viterbi algorithm, which can compute the probabilities of misdetection
and false detection. We show analytically that as long as the min-cut is not
dominated by the Byzantine adversaries, upstream nodes can monitor downstream
neighbors and allow reliable communication with certain probability. Finally,
we present simulation results that support our analysis.Comment: 10 pages, 10 figures, Submitted to IEEE Journal on Selected Areas in
Communications (JSAC) "Advances in Military Networking and Communications
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