We consider the capacity problem for wireless networks. Networks are modeled as random unit-disk graphs, and the capacity problem is formulated as one of finding the maximum value of a multicommodity flow. In this paper, we develop a proof technique based on which we are able to obtain a tight characterization of the solution to the linear program associated with the multiflow problem, to within constants independent of network size. We also use this proof method to analyze network capacity for a variety of transmitter receiver architectures, for which we obtain some conclusive results. These results contain as a special case (and strengthen) those of Gupta and Kumar for random networks, for which a new derivation is provided using only elementary counting and discrete probability tools.
As mentioned before, @cite_19 sparked significant interest in these problems. Follow up results from the same group were reported in @cite_10 @cite_29 . Some information theoretic bounds for large-area networks were obtained in @cite_30 . When nodes are allowed to move, assuming transmission delays proportional to the mixing time of the network, the total network throughput is @math , and therefore the network can carry a non-vanishing rate per node @cite_2 . Using a linear programming formulation, non-asymptotic versions of the results in @cite_19 are given in @cite_22 ; an extended version of that work can be found in @cite_15 . An alternative method for deriving transport capacity was presented in @cite_1 . The capacity of large Gaussian relay networks was found in @cite_3 . Preliminary versions of our work based on network flows have appeared in @cite_9 @cite_32 ; and network flow techniques have been proposed to study network capacity problems (cf., e.g., @cite_34 , [Ch. 14.10] cover-thomas:it-book ), and network coding problems @cite_0 . From the network coding literature, of particular relevance to this work is the work on multiple unicast sessions @cite_26 .
@cite_30 @cite_26 @cite_22 @cite_29 @cite_9 @cite_1 @cite_32 @cite_3 @cite_0 @cite_19 @cite_2 @cite_15 @cite_34 @cite_10
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This paper considers network communications under a hard timeliness constraint , where a source node streams perishable information to a destination node over a directed acyclic graph subject to a hard delay constraint. Transmission along any edge incurs unit delay, and it is required that every information bit generated at the source at the beginning of time @math to be received and recovered by the destination at the end of time @math , where @math is the maximum allowed end-to-end delay. We study the corresponding delay-constrained unicast capacity problem. This paper presents the first example showing that network coding (NC) can achieve strictly higher delay-constrained throughput than routing even for the single unicast setting and the NC gain can be arbitrarily close to 2 in some instances. This is in sharp contrast to the delay-unconstrained ( @math ) single-unicast case where the classic min-cut max-flow theorem implies that coding cannot improve throughput over routing. Motivated by the above findings, a series of investigation on the delay-constrained capacity problem is also made, including: 1) an equivalent multiple-unicast representation based on a time-expanded graph approach; 2) a new delay-constrained capacity upper bound and its connections to the existing routing-based results [ 2011]; 3) an example showing that the penalty of using random linear NC can be unbounded; and 4) a counter example of the tree-packing Edmonds’ theorem in the new delay-constrained setting. Built upon the time-expanded graph approach, we also discuss how our results can be readily extended to cyclic networks. Overall, our results suggest that delay-constrained communication is fundamentally different from the well-understood delay-unconstrained one and call for investigation participation. The capacity of a particular large Gaussian relay network is determined in the limit as the number of relays tends to infinity. Upper bounds are derived from cut-set arguments, and lower bounds follow from an argument involving uncoded transmission. It is shown that in cases of interest, upper and lower bounds coincide in the limit as the number of relays tends to infinity. Hence, this paper provides a new example where a simple cut-set upper bound is achievable, and one more example where uncoded transmission achieves optimal performance. The findings are illustrated by geometric interpretations. The techniques developed in this paper are then applied to a sensor network situation. This is a network joint source-channel coding problem, and it is well known that the source-channel separation theorem does not extend to this case. The present paper extends this insight by providing an example where separating source from channel coding does not only lead to suboptimal performance-it leads to an exponential penalty in performance scaling behavior (as a function of the number of nodes). Finally, the techniques developed in this paper are extended to include certain models of ad hoc wireless networks, where a capacity scaling law can be established: When all nodes act purely as relays for a single source-destination pair, capacity grows with the logarithm of the number of nodes. Gupta and Kumar (see IEEE Transactions an Information Theory, vol.46, no.2, p.388-404, 2000) determined the capacity of wireless networks under certain assumptions, among them point-to-point coding, which excludes for example multi-access and broadcast codes. We consider essentially the same physical model of a wireless network under a different traffic pattern, namely the relay traffic pattern, but we allow for arbitrarily complex network coding. In our model, there is only one active source destination pair, while all other nodes assist this transmission. We show code constructions leading to achievable rates and derive upper bounds from the max-flow min-cut theorem. It is shown that lower and upper bounds meet asymptotically as the number of nodes in the network goes to infinity, thus proving that the capacity of the wireless network with n nodes under the relay traffic pattern behaves like log n bits per second. This demonstrates also that network coding is essential: under the point-to-point coding assumption considered by , the achievable rate is constant, independent of the number of nodes. Moreover, the result of this paper has implications' and extensions to fading channels and to sensor networks. We introduce a new class of problems called network information flow which is inspired by computer network applications. Consider a point-to-point communication network on which a number of information sources are to be multicast to certain sets of destinations. We assume that the information sources are mutually independent. The problem is to characterize the admissible coding rate region. This model subsumes all previously studied models along the same line. We study the problem with one information source, and we have obtained a simple characterization of the admissible coding rate region. Our result can be regarded as the max-flow min-cut theorem for network information flow. Contrary to one's intuition, our work reveals that it is in general not optimal to regard the information to be multicast as a "fluid" which can simply be routed or replicated. Rather, by employing coding at the nodes, which we refer to as network coding, bandwidth can in general be saved. This finding may have significant impact on future design of switching systems.