Parallel transmission, as defined in high-speed Ethernet standards, enables to use less expensive optoelectronics and offers backwards compatibility with legacy Optical Transport Network (OTN) infrastructure. However, optimal parallel transmission does not scale to large networks, as it requires computationally expensive multipath routing algorithms to minimize differential delay, and thus the required buffer size, optimize traffic splitting ratio, and ensure frame synchronization. In this paper, we propose a novel framework for high-speed Ethernet, which we refer to as network coded parallel transmission, capable of effective buffer management and frame synchronization without the need for complex multipath algorithms in the OTN layer. We show that using network coding can reduce the delay caused by packet reordering at the receiver, thus requiring a smaller overall buffer size, while improving the network throughput. We design the framework in full compliance with high-speed Ethernet standards specified in IEEE802.3ba and present solutions for network encoding, data structure of coded parallel transmission, buffer management and decoding at the receiver side. The proposed network coded parallel transmission framework is simple to implement and represents a potential major breakthrough in the system design of future high-speed Ethernet.
Since the early work from over a decade ago, the network coding has gained significant attention. @cite_4 showed that a multicast connection can achieve maximum flow capacity between source and any receiver using network coding, which is otherwise not achievable with traditional store-and-forward networking. Other than single source multicast network coding problem, multi-source multicast problem using network coding has also been studied. Here, multiple disjoint subgraphs are created of the given network, and network coding is applied simultaneously to multiple sessions. @cite_2 proposed a linear programming based optimization model to find an optimal solution to construct the subgraphs. In @cite_1 , the efficiency of various coding schemes was studied for multicast connections and the simplest linear network coding was shown to be sufficient to reach the optimum, i.e., the max-flow between source and every receiver. In @cite_6 , it has been shown algebraically that network coding can be reduced to operations on matrices, which allows for the use of random linear network coding.
@cite_1 @cite_4 @cite_6 @cite_2
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Network coding shows that the data rate can be increased if information is allowed to be encoded in the network nodes. The recent work of S.-Y.R. (see IEEE Trans. Inform. Theory, vol.49, no.2, p.371-81, 2003) and R. Koetter and M. Medard (see Proc. INFOCOM, 2002) shows that linear network coding is sufficient for a single source multicast network. The restrictiveness of the use of linear code is still an unknown in the general multisource multicast network. We characterize the achievable information rate region for a single source node multi-source multicast network with two sinks. We further show that linear coding is sufficient for achieving the maximum network capacity. One central issue in practically deploying network coding is the adaptive and economic allocation of network resource. We cast this as an optimization, where the net-utility-the difference between a utility derived from the attainable multicast throughput and the total cost of resource provisioning-is maximized. By employing the MAX of flows characterization of the admissible rate region for multicasting, this paper gives a novel reformulation of the optimization problem, which has a separable structure. The Lagrangian relaxation method is applied to decompose the problem into subproblems involving one destination each. Our specific formulation of the primal problem results in two key properties. First, the resulting subproblem after decomposition amounts to the problem of finding a shortest path from the source to each destination. Second, assuming the net-utility function is strictly concave, our proposed method enables a near-optimal primal variable to be uniquely recovered from a near-optimal dual variable. A numerical robustness analysis of the primal recovery method is also conducted. For ill-conditioned problems that arise, for instance, when the cost functions are linear, we propose to use the proximal method, which solves a sequence of well-conditioned problems obtained from the original problem by adding quadratic regularization terms. Furthermore, the simulation results confirm the numerical robustness of the proposed algorithms. Finally, the proximal method and the dual subgradient method can be naturally extended to provide an effective solution for applications with multiple multicast sessions 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 consider the issue of network capacity. Recent work by Li and Yeung examined the network capacity of multicast networks and related capacity to cutsets. Capacity is achieved by coding over a network. We present a new framework for studying networks and their capacity. Our framework, based on algebraic methods, is surprisingly simple and effective. For networks which are restricted to using linear codes (we make the meaning of linear codes precise, since the codes are not bit-wise linear), we find necessary and sufficient conditions for any given set of connections to be achievable over a given network. For multicast connections, linear codes are not a restrictive assumption, since all achievable connections can be achieved using linear codes. Moreover, coding can be used to maintain connections after permanent failures, such as the removal of an edge from the network. We show necessary and sufficient conditions for a set of connections to be robust to a set of permanent failures. For multicast connections, we show the rather surprising result that, if a multicast connection is achievable under different failure scenarios, a single static code can ensure robustness of the connection under all of those failure scenarios.