We consider multicasting using random linear network coding over a multihop wireless network in the bandwidth limited regime. We address the associated medium access problem and propose a scheduling technique that activates hyperarcs rather than links, as in classical scheduling approaches. We encapsulate the constraints on valid network configurations in a conflict graph model and formulate a joint optimization problem taking into account both the network coding subgraph and the schedule. Next, using Lagrangian relaxation, we decompose the overall problem into two subproblems, a multiple-shortest-paths problem and a maximum weighted stable set (MWSS) problem. We show that if we use a greedy heuristic for the MWSS part of the problem, the overall algorithm is completely distributed. We provide extensive simulation results for both the centralized optimal and the decentralized algorithms. The optimal algorithm improves performance by up to a factor of two over widely used techniques such as orthogonal or two-hop-constrained scheduling. The decentralized algorithm is shown to buy its distributed operation with some throughput losses. Experimental results on randomly generated networks suggest that these losses are not large. Finally, we study the power consumption of our scheme and quantify the tradeoff between power and bandwidth efficiency.

This paper addresses the problem of inter-session network coding to maximize throughput for multiple communication sessions in wireless networks. We introduce virtual multicast connections which can extract packets from original sessions and code them together. Random linear network codes can be used for these virtual multicasts. The problem can be stated as a flow-based convex optimization problem with side constraints. The proposed formulation provides a rate region which is at least as large as the region without inter-session network coding. We show the benefits of our technique for several scenarios by means of simulation.

We consider the problem of setting up a multicast connection of minimum cost using network coding. It is well-known that this can be posed in the form of a convex program. Our contribution is an asynchronous algorithm for solving the optimization problem, in analogy to the well-known distributed asynchronous Bellman-Ford algorithm for routing. Furthermore, we provide extensive simulation results showing fast convergence despite the lack of any central clock in the network and robustness with respect to link- or node failures.

We consider a wireless multi-hop network and design an algorithm for jointly optimal scheduling of packet transmissions and network coding. We consider network coding across different users, however with the restriction that packets have to be decoded after one hop. We compute the stability region of this scheme and propose an online algorithm that stabilizes every arrival rate vector within the stability region. The online algorithm requires computation of stable sets in an appropriately defined conflict graph. We show by means of simulations that this inherently hard problem is tractable for some instances and that network coding extends the stability region over routing and leads, on average, to a smaller backlog.

Whereas the theory and application of optimal network coding are well studied for the single-session multicast scenario, there is no known optimal network coding strategy for a more general connection problem where there are more than one session and receivers may demand different sets of information. Though there have been a number of recent studies that demonstrate various utilities of network coding in the multi- session scenario, they rely on very restricted classes of codes in terms of the coding operations allowed and/or the location of decoding. In this paper, we propose a novel inter-session network coding strategy for a general connection problem. Our coding strategy allows fairly general random linear coding over a large finite field, in which decoding is done at receivers and the mixture of information at interior nodes is controlled by evolutionary mechanisms. We demonstrate how our coding strategy may surpass existing end-to-end pairwise XOR coding schemes in terms of effectiveness and practicality.

We consider the problem of applying network coding in wireless networks with random medium access. To optimize the network coding subgraph and the transmission attempt probabilities jointly is a tractable problem only for rather small networks. Therefore, we suggest a suboptimal, yet practical and decentralized algorithm to combine network coding with random access. We illustrate the performance gains of our approach with simulations.

In this paper we consider the application of network coding to a multiple unicast setup. We present two suboptimal, yet practical code construction techniques. One consists of a linear program and the other of an integer program with fewer variables and constraints. We discuss the performance of the proposed techniques as well as their complexity

We consider the problem of source number estimation in the presence of unknown spatially nonuniform noise. A sequential hypothesis test is formulated based on Gerschgorin's theorem. As no assumption is made on the distribution of the noise or the signals, the bootstrap is used to estimate the distribution of the proposed test statistics. Performance of the new detector is assessed through simulations and its advantage is illustrated in different scenarios against a Gerschgorin-based information criterion.