We study a slotted version of the Aloha Medium Access (MAC) protocol in a Mobile Ad-hoc Network (MANET). Our model features transmitters randomly located in the Euclidean plane, according to a Poisson point process and a set of receivers representing the next-hop from every transmitter. We concentrate on the so-called outage scenario, where a successful transmission requires a Signal-to-Interference-and-Noise (SINR) larger than some threshold. We analyze the local delays in such a network, namely the number of times slots required for nodes to transmit a packet to their prescribed next-hop receivers. The analysis depends very much on the receiver scenario and on the variability of the fading. In most cases, each node has finite-mean geometric random delay and thus a positive next hop throughput. However, the spatial (or large population) averaging of these individual finite mean-delays leads to infinite values in several practical cases, including the Rayleigh fading and positive thermal noise case. In some cases it exhibits an interesting phase transition phenomenon where the spatial average is finite when certain model parameters (receiver distance, thermal noise, Aloha medium access probability) are below a threshold and infinite above. To the best of our knowledge, this phenomenon, which we propose to call the wireless contention phase transition, has not been discussed in the literature. We comment on the relationships between the above facts and the heavy tails found in the so-called "RESTART" algorithm. We argue that the spatial average of the mean local delays is infinite primarily because of the outage logic, where one transmits full packets at time slots when the receiver is covered at the required SINR and where one wastes all the other time slots. This results in the "RESTART" mechanism, which in turn explains why we have infinite spatial average. Adaptive coding offers another nice way of breaking the outage RESTART logic. We show examples where the average delays are finite in the adaptive coding case, whereas they are infinite in the outage case.
As already mentioned, the present paper assumes a full time-scale separation for the mobility on one side and for the MAC and physical layer on the other side. This assumption makes a major difference between what is done in this paper and what is done in DTNs, where one leverages node mobility to contribute to the transport of packets. There is a large number of publications on the throughput in DTNs and we will not review the literature on the topic which is huge. Let us nevertheless stress that there are some interesting connections between the line of though started in ( @cite_10 ), where it was first shown that mobility increases capacity and what is done in . We show in this section that mobility helps in a way which is quite different from that considered in @cite_10 : mobility may in certain cases break dependence and hence mitigate the RESTART phenomenon, it may hence decrease the mean local delay of the typical node (or equivalently increase its throughput), even if one does not use mobility to transport packets .
@cite_10
2115678412 1976081465 1483953177 2949226357
Gupta and Kumar (2000) introduced a random model to study throughput scaling in a wireless network with static nodes, and showed that the throughput per source-destination pair is Theta(1 radic(nlogn)). Grossglauser and Tse (2001) showed that when nodes are mobile it is possible to have a constant throughput scaling per source-destination pair. In most applications, delay is also a key metric of network performance. It is expected that high throughput is achieved at the cost of high delay and that one can be improved at the cost of the other. The focus of this paper is on studying this tradeoff for wireless networks in a general framework. Optimal throughput-delay scaling laws for static and mobile wireless networks are established. For static networks, it is shown that the optimal throughput-delay tradeoff is given by D(n)=Theta(nT(n)), where T(n) and D(n) are the throughput and delay scaling, respectively. For mobile networks, a simple proof of the throughput scaling of Theta(1) for the Grossglauser-Tse scheme is given and the associated delay scaling is shown to be Theta(nlogn). The optimal throughput-delay tradeoff for mobile networks is also established. To capture physical movement in the real world, a random-walk (RW) model for node mobility is assumed. It is shown that for throughput of Oscr(1 radic(nlogn)), which can also be achieved in static networks, the throughput-delay tradeoff is the same as in static networks, i.e., D(n)=Theta(nT(n)). Surprisingly, for almost any throughput of a higher order, the delay is shown to be Theta(nlogn), which is the delay for throughput of Theta(1). Our result, thus, suggests that the use of mobility to increase throughput, even slightly, in real-world networks would necessitate an abrupt and very large increase in delay. The goal of this paper is to increase our under- standing of the fundamental performance limits of mobile and Delay Tolerant Networks (DTNs), where end-to-end multihop paths may not exist and communication routes may only be available through time and mobility. We use analytical tools to derive generic theoretical upper bounds for the information propagation speed in large scale mobile and intermittently con- nected networks. In other words, we upper-bound the optimal performance, in terms of delay, that can be achieved using any routing algorithm. We then show how our analysis can be applied to specific mobility models to obtain specific analytical estimates. In particular, in 2-D networks, when nodes move at a maximum speed and their density is small (the network is sparse and asymptotically almost surely disconnected), we prove that the information propagation speed is upper bounded by in random waypoint-like models, while it is upper bounded by for other mobility models (random walk, Brownian motion). We also present simulations that confirm the validity of the bounds in these scenarios. Finally, we generalize our results to 1-D and 3-D networks. The goal of this paper is to increase our understanding of the fundamental performance limits of mobile and Delay Tolerant Networks (DTNs), where end-to-end multihop paths may not exist and communication routes may only be available through time and mobility. We use analytical tools to derive generic theoretical upper bounds for the information propagation speed in large scale mobile and intermittently connected networks. In other words, we upper-bound the optimal performance, in terms of delay, that can be achieved using any routing algorithm. We then show how our analysis can be applied to specific mobility models to obtain specific analytical estimates. In particular, in 2-D networks, when nodes move at a maximum speed v and their density is small (the network is sparse and asymptotically almost surely disconnected), we prove that the information propagation speed is upper bounded by (1 + O(v2))v in random waypoint-like models, while it is upper bounded by O(√vvv) for other mobility models (random walk, Brownian motion). We also present simulations that confirm the validity of the bounds in these scenarios. Finally, we generalize our results to 1-D and 3-D networks. The goal of this paper is to increase our understanding of the fundamental performance limits of mobile and Delay Tolerant Networks (DTNs), where end-to-end multi-hop paths may not exist and communication routes may only be available through time and mobility. We use analytical tools to derive generic theoretical upper bounds for the information propagation speed in large scale mobile and intermittently connected networks. In other words, we upper-bound the optimal performance, in terms of delay, that can be achieved using any routing algorithm. We then show how our analysis can be applied to specific mobility and graph models to obtain specific analytical estimates. In particular, in two-dimensional networks, when nodes move at a maximum speed @math and their density @math is small (the network is sparse and surely disconnected), we prove that the information propagation speed is upper bounded by ( @math in the random way-point model, while it is upper bounded by @math for other mobility models (random walk, Brownian motion). We also present simulations that confirm the validity of the bounds in these scenarios. Finally, we generalize our results to one-dimensional and three-dimensional networks.