The problem of communication among nodes in an is considered, where radio power decay and interference are limiting factors. It has been shown previously that, with simple multi-hopping, the achievable total communication rate in such a network is at most @math . In this work, we study the benefit of node cooperation in conjunction with multi-hopping on the network capacity. We propose a multi-phase communication scheme, combining distributed MIMO transmission with multi-hop forwarding among clusters of nodes. We derive the network throughput of this communication scheme and determine the optimal cluster size. This provides a constructive lower bound on the network capacity. We first show that in a rate of @math can be achieved with transmission power scaling of @math , where @math is the signal path-loss exponent. We further extend this result to , where we show a rate of @math can be achieved with transmission power scaling of @math in a random network with unit node density. In particular, as @math approaches 2, only constant transmission power is required. Finally, we study a random network with density @math and show that a rate of @math is achieved and the required power scales as @math .
: The seminal work @cite_5 provides both upper-bounds and constructive communication schemes to show that the point-to-point capacity of a dense wireless network is @math . In @cite_7 , @cite_4 , @cite_11 , and @cite_3 , the authors derive information theoretic upper-bounds on the network capacity. These works study the capacity scaling of extended networks and obtain upper-bounds on the capacity by using cut-set bounds. In particular, Xie prove that for environments with path-loss exponent @math and constant per node power, the expected transport capacity grows at most linearly in the number of nodes, so that for an extended network with uniformly distributed source-destination pairs, the network capacity is @math @cite_11 .
@cite_4 @cite_7 @cite_3 @cite_5 @cite_11
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In analyzing the point-to-point wireless channel, insights about two qualitatively different operating regimes-bandwidth and power-limited-have proven indispensable in the design of good communication schemes. In this paper, we propose a new scaling law formulation for wireless networks that allows us to develop a theory that is analogous to the point-to-point case. We identify fundamental operating regimes of wireless networks and derive architectural guidelines for the design of optimal schemes. Our analysis shows that in a given wireless network with arbitrary size, area, power, bandwidth, etc., there are three parameters of importance: the short-distance signal-to-noise ratio (SNR), the long-distance SNR, and the power path loss exponent of the environment. Depending on these parameters, we identify four qualitatively different regimes. One of these regimes is especially interesting since it is fundamentally a consequence of the heterogeneous nature of links in a network and does not occur in the point-to-point case; the network capacity is both power and bandwidth limited. This regime has thus far remained hidden due to the limitations of the existing formulation. Existing schemes, either multihop transmission or hierarchical cooperation, fail to achieve capacity in this regime; we propose a new hybrid scheme that achieves capacity. We derive upper bounds on the transport capacity of wireless networks. The bounds obtained are solely dependent on the geographic locations and power constraints of the nodes. As a result of this derivation, we are able to conclude the optimality, in the sense of scaling of transport capacity with the number of nodes, of a multihop communication strategy for a class of network topologies. In this paper, we study the asymptotic throughput capacity of a static multi-channel multi-interface infrastructure wireless mesh network (InfWMN) wherein each infrastructure node has m interfaces and c channels of unequal bandwidth are available. First, an upper bound on the InfWMN per-user capacity is established. Then, the feasible lower bound is derived by construction. We prove that both lower and upper bounds are tight. We limit our analysis for more practical case of @math . However, for the asymptotic upper bound, our analysis can be used for the general case in which there is no constraint on m and c. Our study shows that in such a network with Nc randomly distributed mesh clients, Nr regularly placed mesh routers, and Ng gateways, the asymptotic per-client throughput capacity has different bounds, which depend on the ratio between the total available bandwidth for the network and the sum of m first greatest data rates of c available channels, i.e., @math . The results of this paper are more general compared to the existing published researches. In addition, in the case that @math , our results reduce to the previously reported studies. This implies that our study is comprehensive compared to the formerly published researches. In this paper, we present a methodology to analytically compute the throughput capacity, or the maximum end-to-end throughput of a given source and destination pair in a multi-hop wireless network. The end-to-end throughput capacity is computed by considering the interference due to neighboring nodes, as well as various modes of hidden node interference. Knowing the throughput capacity is important because it facilitates the design of routing policy, admission control for realtime traffic, as well as load control for wireless networks. We model location-dependent neighboring interference and we use a contention graph to represent these interference relationships. Based on the contention graph, we formulate the individual link capacity as a set of fixed point equations. The end-to-end throughput capacity can then be determined once these link capacities are obtained. To illustrate the utility of our proposed methodology, we present two important applications: (a) route optimization to determine the path with the maximum end-to-end throughput capacity and, (b) optimal offered load control for a given path so that the maximum end-to-end capacity can be achieved. Extensive simulations are carried out to verify and validate the proposed analytical methodology.