For an undirected graph G = (V; E), let G n denote the graph whose vertex set is V n in which two distinct vertices (u 1 ; u 2 ; : : : ; un ) and (v 1 ; v 2 ; : : : ; v n ) are adjacent iff for all i between 1 and n either u i = v i or u i v i 2 E. The Shannon capacity c(G) of G is the limit

, respectively. Although sev-eral DNA data embedding methods have been proposed over the last decade, it is still an open question to determine the maximum amount of information that can theoretically be embedded —that is, its Shannon capacity. This is the main question tackled in this paper. Index Terms — Data

An independent set with 108 vertices in the strong product of four 7cycles (C7 2 \Theta C7 2 \Theta C7 2 \Theta C7 ) is given. This improves the best known lower bound for the Shannon capacity of the graph C7 which is the zero-error capacity of the corresponding noisy channel. The search was done

We tackle the problem of estimating the Shannon capacity of cycles of odd length. We present some strategies which allow us to find tight bounds on the Shannon capacity of cycles of various odd lengths, and suggest that the difficulty of obtaining a general result may be related to different

We study and compare the Shannon capacity region and the stable throughput region for a random access system in which source nodes multicast their messages to multiple destination nodes. Under an erasure channel model which accounts for interference and allows for multipacket reception, we first

Markov Process. The increase in capacity that might be achieved through exploitation of timing information is evaluated for packet based binary and Gaussian single access channel as well as for Gaussian bursty two user multiple-access channel.

A number of results in the classical theory of point processes assert that the Poisson process is a “fixed point ” for the operations of (1) random splitting, (2) independent superposition and (3) random translation of points. That is, one may begin with a Poisson process (or a finite number of independent Poisson processes) and as a result of performing each of the above operations obtain a Poisson process (or a finite number of independent Poisson processes). Classical results in queueing theory also assert that the Poisson process is a fixed point for various queueing systems in the following sense: If the arrival process to such a queueing system is a Poisson process, then so is the equilibrium departure process. Examples of such queueing systems include the first-come-first-served (FCFS) exponential server queue (the M/M/l queue), a queue which dispenses i.i.d. services with

We here compare the limit performance asynchronous DS-CDMA systems based on different set of spreading sequences, namely chaos-based, ideal random, Gold codes and maximum-length codes. To do so we consider the Shannon capacity associated to each of those systems that is itself a random variable

Abstract—An equivalence result is established between the Shannon capacity and the stable capacity of communication networks. Given a discrete-time network with memoryless, timeinvariant, discrete-output channels, it is proved that the Shannon capacity equals the stable capacity. The results treat

the available resources (e.g. gates, wires, etc.) are used. I. INTRODUCTION The success of information theory in advancing reliable communication is in large part due to the surprising result of Shannon that noisy channels can have non-zero Shannon-capacity, i.e., using bounded transmit power, unboundedly low