Channel Capacity Issues For Mobile Teams.ppt

1、1,Channel Capacity Issues For Mobile Teams,Ameesh Pandya and Greg Pottie, UCLA Electrical Engineering Department,2,Introduction,Channel capacity models for ideal and broadband jamming environments (individual links) Connectivity Issue: Bound on the number of nodes to have 99% of the connectivity in

2、a fixed area for randomly (Poisson, for example) distributed nodes. Probability of the multi-hop connection in a random mobile network Capacity of a mobile network considering delay. Distributed network control problem Forced change in the position of the node (UAV) to perform the requested activity

3、 (from the ground station) while maintaining highest possible QoS.,3,Channel Capacity Model,Two Channel Models: Air to Air Channel. Ground to Ground Channel. Assumptions: Isotropic antenna. Spread spectrum modulation. For Low probability of intercept (LPI), Pr/WsN0 = 0.1, where Pr is the received po

4、wer and Ws is the bandwidth of spread spectrum signal. Broadband Jammer (valid assumption).,4,Air to Air Channel (No Jammer),Channel capacity for this case is 2 Mbps is the control traffic data rate. For 1W distance achieved at 2Mbps is 75.5 km and for 2W, 106.78 km. High values are result of ideal

5、channel with 100MHz bandwidth.,5,Ground to Ground Channel (No Jammer),Channel capacity for this case is For 1W distance achieved at 2Mbps is 98m and for 2W, 118.22m. Here =3.7. K = KF where F is the fading margin and K is the propagation constant. is the path loss coeffecient.,6,Air to Air Channel i

6、n presence of Broadband Jammer,Capacity:is the average jamming power at distance r from the receiver, f is the spread factor.For CDMA, with jamming and Nu simultaneous users, channel capacity is given by (assuming identical signal power):For 10W jamming power distance of 50.3 Km is achieved at 10W.,

7、7,Ground to Ground Channel in presence of Broadband Jammer,is the average jamming power at distance r from the receiver, K1 is the propagation constant for jammer.For CDMA with jamming and Nu simultaneous users, capacity is given by (assuming identical signal power):The simulation is carried for = 4

8、.5.,8,Connectivity Issue,Problem Definition: Consider a closed surface of area A. Say, a square. Randomly place n nodes in that area with some distribution other than uniform. How large must n be to have 99% connectivity? Solution for uniformly distributed nodes could be found by continuum percolati

9、on. Motivation: Deals with the issue of sensor coverage. Provides guidance on minimum node density to achieve communications connectivity.,9,Approach,Nodes generated according to Poisson distribution with intensity n (# of nodes).Region is a square with unit area. Plotting number of nodes required f

10、or 99% connectivity as a function of radio range.,10,Simulation Results,The graphs show the average of 100 iterations. Above is the plot of number of nodes connected to form a largest cluster when the radio range is 20% of the area. For this case we require approximately 30 nodes for the 99% connect

11、ivity.,11,Simulation Results,First plot displays number of nodes required for 99% connectivity with transmission range (avg for 100 runs). The second figure shows the best fitting polynomial plot for the behavior of first figure. The simulation result of number nodes required for the 99% connectivit

12、y, N, as a function of radio range, R is: N O(e-R).,12,Probability of the multi-hop Connection,(x,y) coordinates of the mobile locations N(0,s2) pdf of the link distance r is: Pr2-hop connection=P2 2Asymptotically m hop connection probability: 2Upper bound on average number of hops between node pair

13、s:,13,Lower Bound on P2,The lower bound on P2 is function of R2/s2 = g2 i.e. P2 f(g). Skipping the derivation. Numerical Integration is employed to solve the complicated integration. Hence the approximation error.,14,Capacity of a Mobile Network,3 deals with the capacity of the wireless network for

14、a fixed channel model. The throughput per session for fixed wireless network model can at best be O(1/n). 4 discusses the increase in throughput for mobile ad-hoc network but assumes loose delay constraint i.e. delay is tolerable . Hence, the end result is infinite delay. Motivation: The actual capa

15、city of wireless ad-hoc network (with no delay constraint) will give the real picture of the QoS provision. The delay in many applications is not admissible and hence the capacity is less than the fixed wireless network.,15,Capacity of a Mobile Network,The major difference in the capacity for the fi

16、xed wireless network and mobile Ad-hoc network is the energy used for updating the routing table for the later case. One way of updating routing table is flooding. Capacity may tend to zero if the energy usage for updating routing table goes very high. Hence, the throughput for the case of Ad-hoc wi

17、reless network with no loose delay constraint is of O(1/n) signaling for updating routing table.,16,Distributed Network Control Problem,Problem DefinitionConsider the network connected in a multi-hop fashion as shown in the figure above. Suppose a request comes from ground station to node N1 to move

18、 from its current location to Y for some specific function. This can cause a change in the QoS assurance for the network. So, objective is to minimize the Euclidean distance between N1 and Y subject to QoS constraints.,N1,Y,17,Distributed Network Control Problem,Formulating as an Optimization proble

19、m. Let the current position of Node Ni (node in consideration) be defined as Qi(t) = Xi(t), Yi(t). Let the requested position of Node Ni be Qj = Xj, Yj. Objective Function: min. |Qi(t) Qj| The cost function is minimized subject to QoS constraints.,18,Distributed Control Algorithm,Prerequisites for Q

20、oS Constraints 1: Power Control Power received from transmitter j, at receiver i is given by GijFijPj. The nonnegative number Gij is the path gain in absence of fading from the jth transmitter to the ith receiver. Fij is the Rayleigh fading between each transmitter j and receiver i. Signal to Interf

21、erence ratio for user i : Outage Probability: Oi = Pr (SIRi SIRth) where SIRth is the given threshold. The outage probability can be expressed as Outage probability over a path S:,19,Prerequisites for QoS Constraints (Contd.),Constellation Size M used by a hop can be closely approximated for M-QAM m

22、odulation: In the above expression for M, K is given by The data rate of the ith hop Link Capacity: Cj packets/sec., J links. K classes of traffic, for each QoS of class k, the bandwidth required is bk Hz. Delay guarantee in a service level agreement (SLA) is dk,UB sec. Minimum probability of delive

23、ring the packet across the unreliable network required in SLA : pk,LB. # of packets dynamically admitted in the kth class of traffic : nk. Probability that link will be maintained during transmission: pj.,20,Problem Formulation,21,Constraint Sets Description,CS 1) Data rates demanded by existing use

24、rs. CS 2) Outage probability limitations demanded by users using single hop. CS 3) Outage probability limitations for users using a multi-hop path. CS 4) Set of all feasible powers Pi. CS 5) Link Capacity Constraint. CS 6) Delay Guarantee constraint. CS 7) Delivery probability constraint. CS 8) Guar

25、anteed data rate to each class of traffic. CS 9) Service Level Agreement (SLA) constraint that give a class of traffic the sole right to traverse a link j*. CS 10) Specifies end to end delay guarantee and a delay requirement for a particular traffic class k* on a link j*. CS 11) Upper bound constrai

26、nt on pj. CS 12) Positivity Constraints.,22,Hurdles and Solution,All the above mentioned constraints assures the QoS. Too many constraints to solve an optimization problem. Prioritizing (i.e. weighted) QoS. Deriving Cost function for QoS and maximizing it. Since all the constraints are monomials, Ge

27、ometric programming might help. Transforming into Convex optimization problem as in 1.,23,Future Work,Designing “robust” distribution control algorithm for the nodes to acknowledge the special requested activities. Deriving the capacity of mobile wireless network. Critical probability for the networ

28、k connectivity. Designing MAC layer clustering algorithm which adapts with minimum delay to the change in the network configuration. Robust and fault-tolerant.,24,References,1 Mung Chiang, et. al., “Resource Allocation for QoS Pro-visioning in Wireless Ad Hoc Network”, GLOBECOM 2001. 2 Leonard E. Mi

29、ller, “Probability of a Two-Hop Connection in a Random Mobile Network”, Conference on Information Sciences and Systems, The John Hopkins University, 2001. 3 Piyush Gupta and P. R. Kumar, “The Capacity of Wireless Networks”, IEEE Transactions on Information Theory, 46(2):388-404, March 2000. 4 Matthias Grossglauser and David Tse, “Mobility Increases the Capacity of Ad-hoc Wireless Networks”, IEEE INFOCOM, 2001.,