Efficient communication in wireless networks is typically challenged by the possibility of interference among several transmitting nodes. Much important research has been invested in decreasing the number of collisions in order to obtain faster algorithms for communication in such networks. This paper proposes a novel approach for wireless communication, which embraces collisions rather than avoiding them, over an additive channel. It introduces a coding technique called Bounded-Contention Coding (BCC) that allows collisions to be successfully decoded by the receiving nodes into the original transmissions and whose complexity depends on a bound on the contention among the transmitters. BCC enables deterministic local broadcast in a network with n nodes and at most a transmitters with information of l bits each within O(a log n + al) bits of communication with full-duplex radios, and O((a log n + al)(log n)) bits, with high probability, with half-duplex radios. When combined with random linear network coding, BCC gives global broadcast within O((D + a + log n)(a log n + l)) bits, with high probability. This also holds in dynamic networks that can change arbitrarily over time by a worst-case adversary. When no bound on the contention is given, it is shown how to probabilistically estimate it and obtain global broadcast that is adaptive to the true contention in the network.
The capacity of multiuser networks has been a long-standing problem in information theory. Recently, have proposed a deterministic network model to approximate multiuser wireless networks. This model, known as the ADT network model, takes into account the broadcast nature of wireless medium and interference. We show that the ADT network model can be described within the algebraic network coding framework introduced by Koetter and Medard. We prove that the ADT network problem can be captured by a single matrix, and show that the min-cut of an ADT network is the rank of this matrix; thus, eliminating the need to optimize over exponential number of cuts between two nodes to compute the min-cut of an ADT network. We extend the capacity characterization for ADT networks to a more general set of connections, including single unicast multicast connection and non-multicast connections such as multiple multicast, disjoint multicast, and two-level multicast. We also provide sufficiency conditions for achievability in ADT networks for any general connection set. In addition, we show that random linear network coding, a randomized distributed algorithm for network code construction, achieves the capacity for the connections listed above. Furthermore, we extend the ADT networks to those with random erasures and cycles (thus, allowing bi-directional links). In addition, we propose an efficient linear code construction for the deterministic wireless multicast relay network model. 's proposed code construction is not guaranteed to be efficient and may potentially involve an infinite block length. Unlike several previous coding schemes, we do not attempt to find flows in the network. Instead, for a layered network, we maintain an invariant where it is required that at each stage of the code construction, certain sets of codewords are linearly independent. We examine the issue of separation and code design for network data transmission environments. We demonstrate that source-channel sep-aration holds for several canonical network channel models when the whole network operates over a common finite field. Our approach uses linear codes. This simple, unifying framework allows us to re-establish with economy the optimality of linear codes for single transmitter channels and for Slepian-Wolf source coding. It also enables us to establish the optimality of linear codes for multiple access channels and for erasure broadcast channels. Moreover, we show that source-channel separation holds for these networks. This robustness of separation we show to be strongly predicated on the fact that noise and inputs are independent. The linearity of source, channel, and network coding blurs the delineation between these codes, and thus we explore joint linear de-sign. Finally, we illustrate the fact that design for individual network modules may yield poor results when such modules are concatenated, demonstrating that end-to-end coding is necessary. Thus, we argue, it is the lack of decomposability into canonical network modules, rather than the lack of separation between source and channel coding, that presents major challenges for coding in networks. 