In this paper, we propose a novel multichannel network with infrastructure support, which is called an MC-IS network, that has not been studied in the literature. To the best of our knowledge, we are the first to study such an MC-IS network. Our proposed MC-IS network has a number of advantages over three existing conventional networks: a single-channel wireless ad hoc network (called an SC-AH network), a multichannel wireless ad hoc network (called an MC-AH network), and a single-channel network with infrastructure support (called an SC-IS network). In particular, the network capacity of our proposed MC-IS network is @math times higher than that of an SC-AH network and an MC-AH network and the same as that of an SC-IS network, where @math is the number of nodes in the network. The average delay of our MC-IS network is @math times lower than that of an SC-AH network and an MC-AH network and @math times lower than the average delay of an SC-IS network, where @math and @math denote the number of channels dedicated for infrastructure communications and the number of interfaces mounted at each infrastructure node, respectively. Our analysis on an MC-IS network equipped with omnidirectional antennas has been extended to an MC-IS network equipped with directional antennas only, which are named as an MC-IS-DA network. We show that an MC-IS-DA network has an even lower delay of @math compared with an SC-IS network and our MC-IS network. For example, when @math and @math , an MC-IS-DA can further reduce the delay by 24 times lower than that of an MC-IS network and by 288 times lower than that of an SC-IS network.
In this paper, we analyze the capacity and the delay of an network. Although parts of the results on the analysis on the capacity and the delay contributed by ad hoc communications have appeared in @cite_40 , our analysis in this paper significantly differs from the previous work in the following aspects: We derive the capacity and the delay of an network contributed by infrastructure communications in this paper while @cite_40 only addresses the capacity and the delay contributed by ad hoc communications. We fully investigate the capacity and the delay of an network with consideration of both infrastructure communications and ad hoc communications. Specifically, we also analyze the average delay and the optimality of our results, all of which have not been addressed in @cite_40 . We also compare our results with other existing networks, such as an network, an network and an network and analyze the generality of our network in this paper. We extend our analysis with consideration of using directional antennas in an network. Discussions on the mobility are also presented in this paper (see Section for more details).
@cite_40
2145044978 2090609496 2171471095 2113134966
In ad hoc networks, performance objectives are often in contention with each other. Indeed, due to the transmission errors incurred over wireless channels, it is difficult to achieve a high rate of transmission in conjunction with reliable delivery of data and low latency. In order to obtain favorable throughput and delay performances, the system may choose to compromise on its reliability and have nodes forcibly dropping a small fraction of packets. The focus of this paper is on the characterization of tradeoffs between the achievable throughput, end-to-end delay and reliability in wireless networks with random access. We consider a multihop ad hoc network comprising several source-destination pairs communicating wirelessly via the slotted ALOHA channel access scheme. Employing ideas from statistical mechanics, we present an analytical framework for evaluating the throughput, end-to-end delay and reliability performances of the system. The main findings of this paper are (a) when the system is noise-limited, dropping a small fraction of packets in the network leads to a smaller end-to-end delay though the throughput suffers as well, and (b) when the system is interference-limited, however, there exist regimes where dropping a few packets in the network may actually reduce the end-to-end delay as well as increase the system throughput. We also present some empirical results which corroborate the results obtained analytically. Early simulation experience with wireless ad hoc networks suggests that their capacity can be surprisingly low, due to the requirement that nodes forward each others' packets. The achievable capacity depends on network size, traffic patterns, and detailed local radio interactions. This paper examines these factors alone and in combination, using simulation and analysis from first principles. Our results include both specific constants and general scaling relationships helpful in understanding the limitations of wireless ad hoc networks. We examine interactions of the 802.11 MAC and ad hoc forwarding and the effect on capacity for several simple configurations and traffic patterns. While 802.11 discovers reasonably good schedules, we nonetheless observe capacities markedly less than optimal for very simple chain and lattice networks with very regular traffic patterns. We validate some simulation results with experiments. We also show that the traffic pattern determines whether an ad hoc network's per node capacity will scale to large networks. In particular, we show that for total capacity to scale up with network size the average distance between source and destination nodes must remain small as the network grows. Non-local traffic-patterns in which this average distance grows with the network size result in a rapid decrease of per node capacity. Thus the question “Are large ad hoc networks feasible?” reduces to a question about the likely locality of communication in such networks. We consider the throughput delay tradeoffs for scheduling data transmissions in a mobile ad hoc network. To reduce delays in the network, each user sends redundant packets along multiple paths to the destination. Assuming the network has a cell partitioned structure and users move according to a simplified independent and identically distributed (i.i.d.) mobility model, we compute the exact network capacity and the exact end-to-end queueing delay when no redundancy is used. The capacity-achieving algorithm is a modified version of the Grossglauser-Tse two-hop relay algorithm and provides O(N) delay (where N is the number of users). We then show that redundancy cannot increase capacity, but can significantly improve delay. The following necessary tradeoff is established: delay rate spl ges O(N). Two protocols that use redundancy and operate near the boundary of this curve are developed, with delays of O( spl radic N) and O(log(N)), respectively. Networks with non-i.i.d. mobility are also considered and shown through simulation to closely match the performance of i.i.d. systems in the O( spl radic N) delay regime. In this paper, we consider the transport capacity of ad hoc networks with a random flat topology under the present support of an infinite capacity infrastructure network. Such a network architecture allows ad hoc nodes to communicate with each other by purely using the remaining ad hoc nodes as their relays. In addition, ad hoc nodes can also utilize the existing infrastructure fully or partially by reaching any access point (or gateway) of the infrastructure network in a single or multi-hop fashion. Using the same tools as in [1], we show that the per source node capacity of T(W log(N)) can be achieved in a random network scenario with the following assumptions: (i) The number of ad hoc nodes per access point is bounded above, (ii) each wireless node, including the access points, is able to transmit at W bits sec using a fixed transmission range, and (iii) N ad hoc nodes, excluding the access points, constitute a connected topology graph. This is a significant improvement over the capacity of random ad hoc networks with no infrastructure support which is found as T(W vN log(N)) in [1]. Although better capacity figures may be obtained by complex network coding or exploiting mobility in the network, infrastructure approach provides a simpler mechanism that has more practical aspects. We also show that even when less stringent requirements are imposed on topology connectivity, a per source node capacity figure that is arbitrarily close to T(1) cannot be obtained. Nevertheless, under these weak conditions, we can further improve per node throughput significantly.