The capacity of multiuser networks has been a long-standing problem in information theory. Recently, Avestimehr et al. 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 as well as the multiuser interference inherent in the wireless medium. For the types of connections we consider, we show that the results of Avestimehr et al. under the ADT model can be reinterpreted within the algebraic network coding framework introduced by Koetter and Médard. Using this framework, we propose an efficient distributed linear code construction for the deterministic wireless multicast relay network model. 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.

This work considers the multiple-access multicast error-correction scenario over a packetized network withzmalicious edge adversaries. The network has min-cutmand packets of lengthl, and each sink demands all information from the set of sourcesS. The capacity region is characterized for both a “side-channel” model (where sources and sinks share some random bits that are secret from the adversary) and an “omniscient” adversarial model (where no limitations on the adversary's knowledge are assumed). In the “side-channel” adversarial model, the use of a secret channel allows higher rates to be achieved compared to the “omniscient” adversarial model, and a polynomial-complexity capacity-achieving code is provided. For the “omniscient” adversarial model, two capacity-achieving constructions are given: the first is based on random subspace code design and has complexity exponential inlm, while the second uses a novel multiple-field-extension technique and has O(lm|S|) complexity, which is polynomial in the network size. Our code constructions are “end-to-end” in that all nodes except the sources and sinks are oblivious to the adversaries and may simply implement predesigned linear network codes (random or otherwise). Also, the sources act independently without knowledge of the data from other sources.

This paper introduces an efficient polynomial-time code construction algorithm for cyclic networks, which achieves the optimal multicast rate. Until this work, no explicit capacity-achieving polynomial-time code construction forcyclicnetworks has been known. This new construction algorithm has the additional advantage that as sinks are added or removed from the network, it can modify the existing code in an efficient localized manner, which is beneficial also for acyclic networks. For decoding this code, a polynomial-time sequential decoder for convolutional network codes is also proposed.

We consider the problem of correcting errors and erasures with network coding. Unlike existing works which consider performance limits for worst-case locations of given numbers of errors and erasures, we consider the performance of given (not necessarily optimal) coding and forwarding strategies for given (not necessarily worst-case) models of error and erasure locations. Our approach characterizes decoding success in terms of the rank of certain matrices corresponding to useful and erroneous information received at the sink nodes. We use this approach to analyze random coding and forwarding strategies on a family of simple networks with random error and erasure locations, and show that the relative performance of the strategies depends on the erasure and error probabilities.

In this paper we derive capacity regions for network error correction with both known and unknown topologies (coherent and non-coherent network coding) under a multiple-source multicast transmission scenario. For the multiple-source non-multicast scenario, given any achievable network code for the error-free case, we construct a code with a reduced rate region for the case with errors.

In this work we introduce a construction and analysis of network codes for two sources. The region of achievable rates for this problem is still unknown. The scheme we suggest is based on modifying the multicommodity flow solution and thus improving the achievable rate region, w.r.t the uncoded case. The similarity to the flow problem allows our method to be implemented distributively. We show how the construction algorithm can be combined with distributed backpressure routing algorithms for wireless ad-hoc networks. For both the nondistributed case and the distributed case, the computational complexity of our algorithm for network coding is comparable to that of the parallel multicommodity flow problem. We provide non trivial upper and lower bounds on the performance of our scheme, using random coding techniques.

In this work we address the problem of network codes for cyclic networks. We show that network codes can be constructed for cyclic networks as long as at least one edge in each cycle has a delay, but it is not required that every edge would have a delay. We then present the algorithm for constructing an optimal multicast network code, developed in our previous work, and analyze its computational complexity, showing that it is polynomial in the graph size. We discuss the properties of the resulting codes, and show the ability to modify the code in a localized manner when sinks are added or removed. This property is also applicable to acyclic networks. Finally, we propose the sequential decoding algorithm we developed in an earlier work for decoding the resulting codes. For this we analyze its decoding delay, for both acyclic and cyclic networks

Convolutional network codes are considered for cyclic graphs. In CNC each node receives several streams and generates output streams whose current symbols depend on the current input symbols and previous input symbols in the node memory. A multicast CNC can be constructed using an algorithm, in order to minimize the memory and overhead, coefficients of lower polynomial degree are drawn to consideration. For CNC the overhead is the initial delay before the sinks start receiving symbols. CNC with the sequential decoder achieves good performance for some networks.

In this work, we consider the decoding problem for unknown Gaussian linear channels. Important examples of linear channels are the intersymbol interference (ISI) channel and the diversity channel with multiple transmit and receive antennas employing space-time codes (STC). An important class of decoders is based on the generalized likelihood ratio test (GLRT). Our work deals primarily with a decoding algorithm that uniformly improves the error probability of the GLRT decoder for these unknown linear channels. The improvement is attained by increasing the minimal distance associated with the decoder. This improvement is uniform, i.e., for all the possible channel parameters, the error probability is either smaller by a factor (that is exponential in the improved distance), or for some, may remain the same. We also present an algorithm that improves the average (over the channel parameters) error probability of the GLRT decoder. We provide simulation results for both decoders.