In a mobile ad hoc network (MANET), effective prediction of time-varying interferences can enable adaptive transmission designs and therefore improve the communication performance. This paper investigates interference prediction in MANETs with a finite number of nodes by proposing and using a general-order linear model for node mobility. The proposed mobility model can well approximate node dynamics of practical MANETs. In contrast to previous studies on interference statistics, we are able through this model to give a best estimate of the time-varying interference at any time rather than long-term average effects. Specifically, we propose a compound Gaussian point process functional as a general framework to obtain analytical results on the mean value and moment-generating function of the interference prediction. With a series form of this functional, we give the necessary and sufficient condition for when the prediction is essentially equivalent to that from a binomial point process (BPP) network in the limit as time goes to infinity. These conditions permit one to rigorously determine when the commonly used BPP approximations are valid. Finally, our simulation results corroborate the effectiveness and accuracy of the analytical results on interference prediction and also show the advantages of our method in dealing with complex mobilities.
In wireless networks, distance variations caused by node mobility generate fluctuations of the channel gains. Such fluctuations can be treated as another type of fading besides multi-path effects. In this paper, we characterize the interference statistics in mobile random networks by mapping the distance variations of mobile nodes to the channel gain fluctuations. Network performance is evaluated in terms of the outage probability. A nearest-interferer approximation is employed. This approximation provides a tight lower bound on the outage probability. Comparing to a static network, we show that the interference distribution does not change under high mobility and random walk models, but random waypoint mobility increases interference. In mobile networks, distance variations caused by node mobility generate fluctuations in the channel gains. Such fluctuations can be treated as another type of fading besides multipath effects. In this paper, the interference statistics in mobile random networks are characterized by incorporating the distance variations of mobile nodes to the channel gain fluctuations. The mean interference is calculated at the origin and at the border of a finite mobile network. The network performance is evaluated in terms of the outage probability. Compared to a static network, the interference in a single snapshot does not change under uniform mobility models. However, random waypoint mobility increases (decreases) the interference at the origin (at the border). Furthermore, due to the correlation of the node locations, the interference and outage are temporally and spatially correlated. We quantify the temporal correlation of the interference and outage in mobile Poisson networks in terms of the correlation coefficient and conditional outage probability, respectively. The results show that it is essential that routing, MAC, and retransmission schemes need to be smart (i.e., correlation-aware) to avoid bursts of transmission failures. In wireless networks, interference that is generated by undesired transmitters dominantly limits network performance. The correlation of node locations (in mobile or static networks) makes the interference temporally correlated. In this paper, we quantify the temporal correlation of the interference in mobile Poisson networks. More specifically, we obtain closed-form expressions for the interference correlation coefficient @math in Poisson line networks under various mobility models. When the mean speed of nodes @math increases, we show that @math is asymptotically proportional to @math . Moreover, multi-path fading and random MAC schemes reduce the temporal correlation of the interference. These results are extended to higher-dimensional networks. For communication between two neighboring nodes in wireless networks, the local delay is defined as the time it takes a node to successfully transmit a packet. Previous research focuses on the local delay in static or infinitely mobile Poisson networks with ALOHA. In this paper, we extend the local delay results to Poisson networks with finite mobility. The results obtained show that mobility helps reduce the local delay. Bounds of the local delay in mobile Poisson networks are derived for different mobility and transmission models. The phase transition that marks the jump of the local delay from finite to infinite is also characterized.