"Graph Entropy, Network Coding and Guessing games"

We introduce the (private) entropy of a directed graph (in a new network coding sense) as well as a number of related concepts. We show that the entropy of a directed graph is identical to its guessing number and can be bounded from below with the number of vertices minus the size of the graph’s shortest index code. We show that the Network Coding solvability of each specific multiple unicast network is completely determined by the entropy (as well as by the shortest index code) of the directed graph that occur by identifying each source node with each corresponding target node. Shannon’s information inequalities can be used to calculate up- per bounds on a graph’s entropy as well as calculating the size of the minimal index code. Recently, a number of new families of so-called non-shannon-type information inequalities have been discovered. It has been shown that there exist communication networks with a ca- pacity strictly ess than required for solvability, but where this fact cannot be derived using Shannon’s classical information inequalities. Based on this result we show that there exist graphs with an entropy that cannot be calculated using only Shannon’s classical information inequalities, and show that better estimate can be obtained by use of certain non-shannon-type information inequalities

Zero-error capacity of binary channels with memory

We begin a systematic study of the problem of the zero--error capacity of noisy binary channels with memory and solve some of the non--trivial cases.Comment: 10 pages. This paper is the revised version of our previous paper having the same title, published on ArXiV on February 3, 2014. We complete Theorem 2 of the previous version by showing here that our previous construction is asymptotically optimal. This proves that the isometric triangles yield different capacities. The new manuscript differs from the old one by the addition of one more pag

Non-linear index coding outperforming the linear optimum

The following source coding problem was introduced by Birk and Kol: a sender holds a word $x\in\{0,1\}^n$, and wishes to broadcast a codeword to $n$ receivers, $R_1,...,R_n$. The receiver $R_i$ is interested in $x_i$, and has prior \emph{side information} comprising some subset of the $n$ bits. This corresponds to a directed graph $G$ on $n$ vertices, where $i j$ is an edge iff $R_i$ knows the bit $x_j$. An \emph{index code} for $G$ is an encoding scheme which enables each $R_i$ to always reconstruct $x_i$, given his side information. The minimal word length of an index code was studied by Bar-Yossef, Birk, Jayram and Kol (FOCS 2006). They introduced a graph parameter, \minrk_2(G), which completely characterizes the length of an optimal \emph{linear} index code for $G$. The authors of BBJK showed that in various cases linear codes attain the optimal word length, and conjectured that linear index coding is in fact \emph{always} optimal. In this work, we disprove the main conjecture of BBJK in the following strong sense: for any $\epsilon > 0$ and sufficiently large $n$, there is an $n$-vertex graph $G$ so that every linear index code for $G$ requires codewords of length at least $n^{1-\epsilon}$, and yet a non-linear index code for $G$ has a word length of $n^\epsilon$. This is achieved by an explicit construction, which extends Alon's variant of the celebrated Ramsey construction of Frankl and Wilson. In addition, we study optimal index codes in various, less restricted, natural models, and prove several related properties of the graph parameter \minrk(G).Comment: 16 pages; Preliminary version appeared in FOCS 200

Interlocked permutations

The zero-error capacity of channels with a countably infinite input alphabet formally generalises Shannon's classical problem about the capacity of discrete memoryless channels. We solve the problem for three particular channels. Our results are purely combinatorial and in line with previous work of the third author about permutation capacity.Comment: 8 page

Complexity and capacity bounds for quantum channels

We generalise some well-known graph parameters to operator systems by considering their underlying quantum channels. In particular, we introduce the quantum complexity as the dimension of the smallest co-domain Hilbert space a quantum channel requires to realise a given operator system as its non-commutative confusability graph. We describe quantum complexity as a generalised minimum semidefinite rank and, in the case of a graph operator system, as a quantum intersection number. The quantum complexity and a closely related quantum version of orthogonal rank turn out to be upper bounds for the Shannon zero-error capacity of a quantum channel, and we construct examples for which these bounds beat the best previously known general upper bound for the capacity of quantum channels, given by the quantum Lov\'asz theta number

The Zero-Undetected-Error Capacity Approaches the Sperner Capacity

Ahlswede, Cai, and Zhang proved that, in the noise-free limit, the zero-undetected-error capacity is lower bounded by the Sperner capacity of the channel graph, and they conjectured equality. Here we derive an upper bound that proves the conjecture.Comment: 8 Pages; added a section on the definition of Sperner capacity; accepted for publication in the IEEE Transactions on Information Theor