The optimal strategy for dynamic joint source-channel coding with feedback was recently shown to be a simple mapping between the source symbols and channel inputs, where the mapping only depends on the decoder's posterior belief about the source. In this work, we derive the optimal joint source-channel coding strategies for two specific channels - binary erasure channels and Z-channels. It is found that the mappings required for the optimal strategies and the way they are used vary significantly with the channel cost of transmission.

In linear function computation, multiple source nodes communicate across a relay network to a single destination whose goal is to recover linear functions of the original source data. When the relay network is a linear deterministic network, a duality relation is established between function computation and broadcast with common messages. Using this relation, a compact sufficient condition is found describing those cases where the cut-set bound is tight. These insights are used to develop results for the case where the relay network contains Gaussian multiple-access channels. The proposed scheme decouples the physical and network layers. Using lattice codes for both source quantization and computation in the physical layer, the original Gaussian sources are converted into discrete sources and the Gaussian network into a linear deterministic network. Network codes for computing functions of discrete sources across the deterministic network are then found by applying the duality relation. The distortion for computing the sum of an arbitrary number of independent Gaussian sources over the Gaussian network is proven to be within a constant factor of the optimal performance. Furthermore, the constant factor results are extended to include asymmetric functions for the case of two sources.

The generalized deterministic models recently proposed by Niesen and Maddah-Ali [1] successfully capture real-interference alignment as observed in Gaussian models. Simpler deterministic models, like ADT models [2], cannot demonstrate this phenomenon because they are limited in the set of channel gains they can model. This paper reinterprets the Niesen and Maddah-Ali models through the lens of carry-free operations. We further explore these carry-free models by considering i.i.d. unknown fading networks. In the unknown fading context, a carry-free model can be further simplified to a max-superposition model, where signals are superposed by a nonlinear max operation. Unlike in relay-networks with known fading and linear superposition, we find that decode-and-forward can perform arbitrarily better than compress-and-forward in max-superposition relay networks with unknown fading.

Can we design a communication network just like a huge linear time-invariant filter? To answer this question, we generalize the celebrated mincut-maxflow theorem to linear time-invariant networks where edges are labeled with transfer functions instead of integer capacity constraints. We prove that when the transfer functions are linear time-invariant, the fundamental design limit, mincut, is achievable by a linear time-invariant scheme regardless of the topology of the network. Whereas prior works are based on layered networks, our proof has a novel way of converting an arbitrary relay network to an equivalent acyclic single-hop relay network, which we call Network Linearization. This theorem also reveals a strong connection between network coding and linear system theory.

Recently, we considered a vector version of Witsenhausen's counterexample and used a new lower bound to show that in that limit of infinite vector length, certain quantization-based strategies are provably within a constant factor of the optimal cost for all possible problem parameters. In this paper, finite vector lengths are considered with the vector length being viewed as an additional problem parameter. By applying the ldquosphere-packingrdquo philosophy, a lower bound to the optimal cost for this finite-length problem is derived that uses appropriate shadows of the infinite-length bounds. We also introduce lattice-based quantization strategies for any finite length. Using the new finite-length lower bound, we show that the lattice-based strategies achieve within a constant factor of the optimal cost uniformly over all possible problem parameters, including the vector length. For Witsenhausen's original problem - which corresponds to the scalar case - lattice-based strategies attain within a factor of 8 of the optimal cost. Based on observations in the scalar case and the infinite-dimensional case, we also conjecture what the optimal strategies could be for any finite vector length.