mobile infostations: a paradigm for wireless data

MOBILE INFOSTATIONS: A PARADIGM FOR WIRELESS DATA COMMUNICATIONS Dave Cavalcanti, Djamel Sadok, Judith Kelner Center of Computer Science Federal University of Pernambuco PO. Box: 7851, Recife-PE {datc, jamel, jk}@cin.ufpe.br Brazil

ABSTRACT This paper presents a new wireless system for data communications based on the “many time many where” paradigm and on the waterfilling concept, i.e., to transmit only when source and destination are close together. We associeted the ideas of Infostations with limited coverage at high data rates and mobility to propose a hybrid system with fixed and mobile Infostations. It will increase coverage and capacity, reduce delay and make information accessible simultaneously from as many points, as possible. We evaluate the improvements in capacity, coverage and in the amount of information transmitted using a one-dimensional model.

KEY WORDS Wireless communications, Mobility, Infostations, Capacity

1. INTRODUCTION Over the recent years we have been faced with a tremendous growth in wireless voice communications, while wireless data services have not experienced equal success. The main goal of cellular systems design has always been to provide ubiquitous coverage at some minimal quality. But the idea of having wireless connectivity "anytime, anywhere" imposes barriers to wireless data services such as low speed, high power consumption, and high cost per transmitted bit [1]. Recently, new ideas are emerging contrasting the "anytime, anywhere" paradigm, such as Infostations and ad hoc networks, both based on the waterfilling concept, i.e., to transmit only when source and destination are close together. Results obtained in these topics have provided good estimates that permit such systems to become the basis for low cost, wireless data transmission. A new system has been proposed at WINLAB [2] consisting of isolated pockets of high bandwidth wireless connectivity called Infostations. The core idea of such a discontinuous coverage system is that as a user passes by

an Infostation, his/her terminal is quickly filled with information at a high data rate. As pointed out in [2] the Infostations could be placed along roadways, in building entrances, at airports and in other predictable and accessible locations and can be seen as independent access ports to the Internet. The idea of ad hoc wireless networks is also contrasting the ubiquitous coverage paradigm of the cellular systems. But the capacity of ad hoc networks is constrained by the mutual interference of concurrent transmissions between nodes. On the other hand, new results in [3], which introduce mobility into a fixed network model, show that the average throughput per source-destination pair can be kept constant even as the number of nodes per unit area increases. This dramatically improvement is obtained through the exploitation of the time-variation of the user's channel due to mobility. The new idea in [3] is to split the packets of each source node to as many nodes as possible. Therefore, strategies of this type incur additional delay, because packets have to be buffered until the channel becomes sufficiently strong. Similarly, to taking advantage of users mobility to improve the capacity of an ad-hoc network, we expect that mobility can play an equally important role in order to extend the coverage, increase capacity and reduce the delay of an Infostations system. We will add mobility into the Infostation architecture to increase the system's capacity, to reduce delay and to make information accessible simultaneously from as many points, as possible. We propose a hybrid system where some Infostations are mobile and we evaluate some of the improvements in capacity, coverage and throughput that can be achieved. This paper is organized as follows. First we discuss the Infostation paradigm. We then describe the proposed system a state the new challenges that arise when we add mobility to the system. We describe next the analytical model used and present the results of capacity, coverage and throughput gains obtained with the mobile Infostation. To sum up we draw some conclusions and state future work possibilities.

2. THE INFOSTATION CONCEPT The architecture of an Infostations system can be seen in Figure 1. The pockets of high bit rate connectivity can be seen as independent access ports to the Internet, or organized into groups having a common server (cluster controller) that is a gateway to the Internet. Associated with these locations are characteristics of user mobility patterns resulting in different scenarios [4]. The drivethrough scenario corresponds to high-speed users that pass through the coverage area in seconds, as in a roadway; the walk-through scenario is characterized by slow moving users, such as in a sidewalk or a mall; finally we have the sit-through scenario where stationary users, such as those in an airport or a classroom disputing access.

Cluster

Internet Internet

Controller Ri

Cluster Controller

Cluster Controller

Rb

previous works [1, 2, 4, 5, 6 and 7]. Some of these problems are: radio design, capacity, coverage, system layout, multiple access, and delay in file delivery.

3. A MOBILE INFOSTATION SYSTEM Related research using the idea of mobile basestations has been done in the cellular systems context. A channel allocation scheme in a system with mobile basestations has been proposed in [8] to improve the cellular system’s capacity. A broadband mobile cellular system called Carrucel has been proposed in [9], with basestations arranged along a railway, moving in the same direction and approximately at the same speed as the traffic flows. A dynamic ad hoc network in which some nodes are moving basestations was proposed in [10], as a solution for networking in the battlefield. In this paper we propose to add moving basestations in to an Infostations system to increase capacity, to reduce delay and to spread information to as many points as possible. We propose to achieve this by using Mobile Infostations (MI) as intermediate relays nodes between source and destination nodes. The MIs will be used as access point for mobile users, as well. See Figure 2.

LAN

Rr

Gateway

Internet

Figure 1: Infostations system architecture This system has a hierarchical architecture where each controller has information on all requests that were made in that cluster and the number of users being served at every Infostation in that cluster [4]. The cluster controller can also coordinate the delivery of information to users. In some cases, as in the drive-through scenario, Infostations communicate with each other through a radio path in order to delivery packets to the next Infostation in the mobile path. The access between the Infostation and cluster controller could be wired or dial-up, depending on traffic and latency [6]. The controllers could be connected to the Internet through a fixed backbone.

Figure 2: Example of a Mobile Infostations System

It is also important to note that the hierarchical architecture with cluster controllers does not require any coordination between Infostations in order to reuse channels, as in a cellular system.

The main difference from a conventional Infostations system is the existence of MIs that can communicate with mobile users, with the Fixed Infostations (FI) and, eventually, with other MIs. There are a variety of possible scenarios where MIs can be used including the three scenarios already defined for the Infostation architecture. Initially, we can think about the drive-through scenario. Antennas can be placed on the top of buses that have regular routes along such highways, transforming them in MIs. The buses will also serve as access points for static or mobile users out of the fixed Infostations coverage radius. If we think about a user or various users inside a bus (MI) we visualize the sit-through scenario where the coverage is continuous, but users have to contend for access. The walk-through can also be possible, if we have medium to low speed users, as in a sidewalk connecting to MIs that pass close to them regularly.

Several problems are related to an Infostations network, ranging from the physical to application layers, and they have been studied but not yet completely solved in

There are many issues related to an Infostations network as we described in the previous section. As we propose to change the network including MIs, we have a range of

Let Ri be the rate between a cluster controller and the Internet, Rb the rate of each link between the cluster controller and the Infostations and Rr the radio rate in the downlink, see Figure 1. As described in [4], there are different combinations between these three parameters. However, the idea of short connections at high data rates restricts the possibilities to the case where Rr >> Ri, Rb.

new points to reexamine. In the radio design, e.g., the interference among MIs and between MIs and FIs has to be addressed. There are also new problems in multiple users access, as in a combination of the drive and sitthrough scenarios, where mobile users and MI co-located users, such as those travelling along a bus, will try to access the MI channel. Other key issues including coordination between Infostations and topology management have also to be studied.

obtained in [6], including MIs in a periodic highway model. We show that the optimal power control based in a reduced coverage is also optimum for maximizing capacity in a Mobile Infostations system. We also show that coverage depends on the time and on the relative speed between MI and user. Finally, we estimate the gains in terms of coverage, capacity and throughput achieved when we associate the concepts of mobility and Infostations.

We propose here a connectivity model, which describes how communications take place between the network components. The communication infrastructure of the fixed Infostations system was maintained, with a fixed backbone connecting FIs to gateways (cluster controllers) and connecting gateways to the Internet. FIs continue to communicate with users through a wireless channel at high data rates. But now, FIs have also to be connected to MIs through wireless links. Finally, there are wireless connections between MIs, as well as, between MIs and users. Figure 3 shows the connectivity model.

In our model we consider fixed and mobile Infostations connected through a radio link. But we do not address the connection issues between them; we consider only connections between users and Infostations. The fixed ones are distributed periodically along an infinite line and the MIs move along this line with constant speed. We consider a single user that moves with constant speed in the same line. Further, we have fixed users uniformly distributed along the distance. Figure 4 shows the model that consists of FI, with heights of h units, periodically spaced by a distance 2r units; and a MI, with height of hm units, travelling at a speed vi. We assume r >> h, hm. At any instant, the MI’s position is xi. We assume a single mobile user (MU) that travels at a constant speed v and we call xu the position of this user. Additionally, we have fixed users that we call static users (SU) at position xs and we assume the xi, xu and xs are uniformly distributed into the interval [0, r].

The data rates between the components are also showed in Figure 3. Ri, Rb and Rr have the same meaning as explained above. Rf is the data rate between fixed and mobile Infostations and Rm is the data rate between MI´s. Internet Ri G atew ay Rb FI R

FI

...

FI

-r R

r

U ser

MI

R

m

xu

0

xi

f

MI Rr

U ser

hm vi

v d

h

U ser

Figure 3: Connectivity model. Here, we continue assuming that Rr >> Ri, Rb and we consider that MIs take short time connections with FIs and the maximum amount of information have to be transferred during this time. To achieve this, Rf has to be a high data transmission rate, up to several megabits per second. The Rm data rate has to be sufficiently high, to meet delay constraints to the applications, because this transmission rate is closely related with the file delivery problem.

4. CAPACITY AND COVERAGE IN AN ONE-DIMENSIONAL MODEL The objective here is to maximize the data rate of a mobile Infostation in a one-dimensional system given a certain average power constraint. We extend the results

... r

xs Figure 4: One-dimensional model. We define a new variable called xr, the distance between MU and MI, i.e., xr = xi - xu. The results obtained in [6] for an Infostation system can be easily adapted to our model of mobile Infostations, and are presented next. Considering a line-of-sight case with free space propagation and a path loss exponent n = 2, we calculated the Signal-to-Noise Ratio (SNR) as a function of the relative distance xr SNR ( xr ) =

αhm2 x + hm2 2 r

,

(1)

where α is the value of SNR bellow the antenna (xr = 0). We assume that the MI uses information about the relative position xr, to adjust its transmitted power, P(xr) that is of the form P( x r ) = P ⋅ g ( x r ) ,

(2)

where P is the average power, which is fixed and we call g(xr) the power gain that has a unit average.

At a given distance xr between the MU and the MI, the Shannon capacity of a AWGN channel is given by 1 C ( x r ) = log 2 (1 + g ( x r ) SNR ( x r )) 2 .

(3) g (xr)

1 0 .5 0

(5)

The distance xr can be expressed as a function of the time and the relative speed between MU and MI vr= vi – v. Writing xr = vrt, we have the following expression for the optimum power allocation as a function of the time,

100 200 300 400 500 600 700 800 900 1000 xr

Figure 5: Optimal power control. 4 .5 sa m e d i re cti o n co n tra ry d i re cti o n

4 3 .5 3 2 .5 g (t)

,

 (v r t c ) 2 − (v r t ) 2 , t ≤ min(t c , T )  g (t ) =  αh m2 0, otherwise 

40 dB

2

1 .5 1 0 .5 0

0

10

20

30 40 t (se c)

50

60

70

Figure 6: Optimal power control.

4.2 MU -- (MI and FI) connection ,

(6)

where tc is a constant that can be expressed by tc= xc/vr, which can be interpreted as the maximum time of a connection MU to MI and T = r/vr.

4.1 MU -- MI connection In this part we evaluate coverage and capacity of a MI with a mobile user connected to it, which depends on both speed and moving direction. The optimal power gain as a function of the relative distance between user and Infostation is presented in Figure 5 with parameters hm = 5, R = r =1000 and α=30, 40 dB. The power gain is maximum when the user is beneath the MI and decreases as the distance between them increases and becomes null at a threshold distance xc. In Figure 6 we have plotted the power allocation as a function of the time, for MU and MI moving in the same direction and in contrary directions with speeds vi = 60 KM/h, v = 80 Km/h and parameters hm = 5, r =1000 and α =30dB. In both cases we assume that at the initial instant MU and MI are together at xi = xu = 0. Figure 6 shows that the coverage time can be very different depending on the relative speed. As we expected, when MU and MI move in different directions the coverage time is drastically reduced.

In this case, we consider a mobile user and a MI travelling at the same direction, and a FI at position r. Again, we assume that at the initial instant the MU and MI are together at xi = xu = 0. Here, the same equations obtained above can be used for the FI, which is represented by a MI with null speed and located at position r. The parameters used were h=10, hm=5, r =2000, vi = 60 Km/h, v = 80 Km/h and α =30dB. In Figure 7, we can see again the capacity gain obtained adding a MI to the system. The MU stays connected to the MI until the threshold distance separates them and it takes a longer time than the connection of the MU to the FI. 7 MU - MI M U - FI

6 5 4 C(t)

0

1 ) ⋅ dx r = 1 r

2 1 .5

(4)

where xc is a constant that can be interpreted as a threshold distance for communication and r is the maximum value for xr, which we consider here equal to the distance between fixed Infostations, see Figure 4. We can calculate xc using the following condition r

30 dB

3 2 .5

0

 x c2 − x r2 , x c ≤ min( x c , r )  g ( x r ) =  αhm2 0, otherwise 

∫ g(x

4 3 .5

Then, we have to calculate the power gain g(xr) that maximizes the average capacity of the MI. This is an optimization problem. A similar problem is solved in [6] and we can adapt the result to our system, such that the optimum power gain can be expressed by

r

4 .5

3 2 1 0

0

10

20

30

40 t (se c)

50

60

70

Figure 7: Capacity as a function of the time.

We can also observe that the coverage area of the MI does not overlap with the FI coverage area what represents a gain in coverage for the system.

and R(xu ,xi) bps the bit rate at the terminal, the SNR constraint can be written as

PR ( xu , xi ) ≥γ. R( xu , xi )

4.3 SU - MI connection In this scenario we consider an user in a fixed position xs, which is uniformly distributed between 0 and r. Figure 8 shows the instantaneous capacity for a MI traveling at vi = 60 Km/h connected to a static user (SU) at position xs= 0 and for the same MI connected to a mobile user (MU) whose speed is 80Km/h. As we can see, the only difference in the two cases is the connection time, which is longer when both antenna and user are moving at constant speeds and in same direction.

Hence, the bit rate is constrained by R( x u , x i ) ≤

KPT 2 γN 0 (( x i , xu ) 2 + d min )

v

C(t)

4

2

(8)

hm vi

MI - MU M I - SU

5

n

Where K is a constant characterizing antenna gains, carrier frequency and system losses not related to propagation.

7 6

(7)

d

dmin 0 xu= xi=0 ⇒ d = dmin

3 2 1 0

Figure 9: Single Mobile Infostation model 0

10

20

30

40 t (se c)

50

60

70

Figure 8: Instantaneous capacity for Mobile and Static users.

We use a single rate within the coverage area. In this case, at a maximum distance xc between user and MI this rate will be Rmax(xc) bps. Therefore, we can choose the maximum coverage distance xc meters such that we maximize the number of bits transmitted, I = max

5.THROUGHPUT IN A SINGLE MOBILE INFOSTATION Depending on user’s speed and on the distance to other Infostations, it may take long before there is another opportunity for connection. Hence, we want to maximize the amount of information transferred while the terminal is in contact with the current MI. In this section, we evaluate the amount of information that can be transferred in a connection of a mobile user to a single MI. The design problem here is to find the optimum transmission rate and coverage distance that maximize the amount of information transferred. The model used is shown in Figure 9. It consists of a MI and a user moving in a straight line along the x-axis at constant speeds vi and v m/s, respectively. The user approaches the MI reaching a minimum distance dmin at the origin before moving away. The positions of the user and the MI are denoted by xu and xi, respectively. We assume that the Infostation operates at a transmit power PT and that the minimal signal-to-noise ratio required at the mobile receiver is γ. We further assume an AWGN channel with noise power density N0 and propagation exponent n. If PR(xu ,xi) is the power received

xc

xc Rmax ( xc ) vi − v bits.

(9)

Where xc/(vi - v) is the time spent within coverage. By substituting equation (8) into equation (9), differentiating with respect to xc, and setting the derivative to zero, we find the optimal coverage distance: xc =

d min n −1

(10)

The amount of information transferred is a decreasing function of the propagation exponent n and the speeds vi and v,  (n − 1) n −1  KPT I =  n n −1  n  (v i − v)γN 0 d min .

(11)

For the case of free space propagation (n=2), I becomes I=

KPT 2(v i − v)γN 0 d min .

(12)

In order to compare the information transferred using a single rate against a continuously changing rate, we plot in Figure 10 the maximum rate possible as a function of xr (distance between user and MI) and the single rate used. In the Figure, n= 2 and the distance plotted are

normalized by dmin. Thus the value of xr corresponds to dmin= 5 meters, which is the value adopted for the MI antennas height in the previous sections. The rate in the vertical axis is also normalized by Rmax(0, 0). The area of the rectangle in Figure 10 is the number of bits transferred at the single rate corresponding to the height of the rectangle. Equation (10) indicates that the rectangle shown, with the boundary xr = 5, is the largest rectangle that can fit under the curve. The area under the curve is the number of bits that would be received from a MI transmitting exactly Rmax(xu, xi) bps at each point using a variable rate scheme. We can clearly observe the improvement obtained with a variable rate in the amount of information transferred. To a practical implementation, we can consider that the MI could have zones of different allowable rates, what is equivalent to approximate the area under the curve in Figure 10 with a series of rectangles. 1 co n ti n o u s ra te si n g l e ra te

0 .9

No rm a l i ze d ra te

0 .8 0 .7 0 .6 0 .5 0 .4 0 .3 0 .2 0 .1 0

0

2

4

6

8

10 xr

12

14

16

18

20

Figure 10: Continuous and single rates.

6. CONCLUSION In this paper we have presented a new mobile Infostation system in other to increase coverage, capacity and reduce delay of a system with just fixed Infostations. The proposed system is not suitable for real time applications. The mobile Infostations system is a low cost network for messaging services. We have used the fact that mobility increases capacity of an ad hoc network and we have extended some results presented in [6], by adding the MIs into the system’s onedimensional model. The results support the informationtheoretic argument of the Infostation paradigm that capacity is maximized with a reduced coverage. Not only for fixed but also for mobile Infostations there is a cutoff distance beyond which no communication takes place. We have represented the optimal power control and capacity as functions of the time. Results show the gain in coverage achieved: a MI can attend users (mobile or fixed), outside the fixed system’s coverage. It is important to note, in this case, that the connection time and capacity

depend on the relative speed between user and antenna. Therefore multiple access schemes have to consider this parameter to support different classes of users, as in a combination of various scenarios as presented in section 3. Finally, we have calculated the maximum amount of information that can be transferred in a single MI. Results showed that a scheme with variable rates increase the system performance. As future works, we intend to extend the studies to a twodimensional model. We will also address the problem of how information will be transmitted to the end user, considering the connections between fixed and mobile Infostations.

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