Analysis of Trafﬁc Agent Scheme for Coverage

Improvement in Wireless Local Area Networks

Hai-Feng Yuan, Yang Yang, Wen-Bing Yao and Yong-Hua Song

School of Engineering and Design

Brunel University, Uxbridge, London, UB8 3PH, United Kingdom

Abstract. Wireless Local Area Network (WLAN) can provide high data-rate

wireless multimedia applications to end users in a limited geographical area and

has been widely deployed in recent years. For indoor WLAN systems, how to

efﬁciently improve service coverage is a challenging problem. In this paper, we

propose a coverage improvement scheme that can identify suitable Mobile Sta-

tions(MS) in good service zones and use them as Trafﬁc Agents (TA) to relay

trafﬁc for those out-of-coverage MS’s. The service coverage area of WLAN sys-

tem is therefore expanded. Mathematical analysis, veriﬁed by computer simula-

tions, shows that the scheme can effectively reduce blocking probability when the

system is lightly loaded.

1 Introduction

In recent years, the proliferation of mobile devices like laptops and Personal Digital

Assistant (PDA) has resulted in the rapid evolution of Wireless Local Area Networks

(WLAN). WLAN can provide high-bandwidth wireless data communications in a lim-

ited geographical area. WLAN is becoming commonly used in ofﬁces, residential apart-

ments, hospitals, and other indoor environments. For indoor WLAN systems, signal dis-

persion is highly disturbed. The propagation of radio signals heavily depends on ofﬁce

dimensions, obstructions, partitioning materials and even the moving objects. There-

fore, how to effectively guarantee the radio signal coverage for complicated indoor

wireless areas is a very challenging problem.

Fig. 1 shows a WLAN deployment example for ofﬁce area. The Access Point (AP),

usually installed in the ceiling of central area, provides wireless data service for all Mo-

bile Stations (MS) located in its covered area. According to the received signal strength

from the AP, the whole ofﬁce area can be further divided into ﬁve service zones, num-

bered from 0 to 4. Speciﬁcally, zone-0 represents the area out-of-coverage so that can-

not support any data services. While zone-1 to zone-4 can support different access data

rates, i.e. 1Mbps, 2Mbps, 5.5Mbps, 11Mbps, as speciﬁed in the IEEE 802.11b stan-

dard [1].

The coverage situation of radio signals is almost ﬁxed when the system is deployed.

On the other hand, the required bandwidth from a user or MS is usually application

dependent, not relevant to its location. The coverage problem occurs when a zone-0

MS has a service request. In [5] and [6], the coverage extension schemes using different

Yuan H., Yang Y., Yao W. and Song Y. (2005).

Analysis of Trafﬁc Agent Scheme for Coverage Improvement in Wireless Local Area Networks.

In Proceedings of the 4th International Workshop on Wireless Information Systems, pages 3-12

DOI: 10.5220/0002556900030012

 SciTePress

AP

Zone 0

Zone 1

Zone 2

Zone 3

Zone 4

MS1

11Mbps

5.5Mbps

2Mbps

1Mbps

MS0

Fig.1. A WLAN Deployment Example.

antenna diversity technologies were proposed and studied. To implement these schemes

in real systems, extra hardware devices and more signal-processing power are required.

Other researchers tried to solve the coverage problem by ﬁnding the optimal installment

positions for all APs [7–10]. This kind of solutions is, however, highly environment

dependant.

The concept of mixed-mode MS, which can switch between the “infrastructure

mode” and the “ad-hoc mode” dynamically, was presented in [2] to improve system

efﬁciency and utilization. Based on this concept, a scheme for relieving congested traf-

ﬁc in hot spots is proposed in [3]. Inspired by these work, we propose in this paper the

Trafﬁc Agent (TA) scheme to improve the coverage area of WLAN systems. The TA

scheme uses idle high-zone MS’s to relay trafﬁc from zone-0 MS’s to the AP.

The rest of this paper is organized as follows. In Section 2, the TA scheme is pro-

posed and the complete MS working ﬂow is given. Mathematical analysis of throughput

and blocking performance are derived in Section 3 and Section 4, respectively. In Sec-

tion 5, analytical results, veriﬁed by computer simulations, are compared between the

original system and the system using the TA scheme.

2 The Trafﬁc Agent Scheme

On receiving a service request, the MS in zone-0 will switch to “ad-hoc” mode and try

to ﬁnd an idle MS in high service zones to relay trafﬁc. Take MS0 and MS1 in Fig. 1 as

an example. Suppose MS1 is idle and within the coverage of MS0. Instead of blocking

its service request, MS0 can use MS1 as an agent to relay its trafﬁc to the AP.

A “Coverage Improvement Algorithm” will be performed to ﬁnd TAs, when a zone-

0 MS, say “MS-B”, has a service request. We present in Table 1 and Table 2 the algo-

rithms for the Service-Request MS (i.e. MS-B) and the Trafﬁc Agent MS, respectively.

When the Service-Request algorithm is triggered, MS-B will ﬁrst switch to the “ad-hoc

mode” and mark the initial frequency channel as No.1 channel. MS-B will then adver-

tise Request-For-Agent (RFA) messages to all the neighboring MS’s within its radio

coverage in all available channels. The RFA message contains MS-B’s identiﬁcation

and all idle neighboring MS’s can receive the RFA message. As the response, they will

send back positive acknowledgments (ACK) and become candidate TA MS’s (as shown

in Table 2). If two or more ACKs are received, MS-B will select the candidate MS

with the largest zone number (strongest wireless connection with the AP) as its TA

Next, MS-B will establish connection and exchange data with the selected TA in the

“ad-hoc mode”. The TA will subsequently establish connection and exchange data with

the AP in the “infrastructure mode”. By this two-hop wireless connection, the requested

services from the out-of-coverage zone are accommodated.

Table 1. Service-Request MS Algorithm

if (Receive a service request) then

Switch to “ad-hoc mode”;

Set Channel = 1;

loop

if (Channel N o. > Max Channel ) then

Block service request;

else

Advertise Request-For-Agent message;

if (receive positive response) then

Select an agent & connect;

Transmit data from trafﬁc agent;

end if;

Channel++;

endif

endloop

endif

3 Throughput Analysis

Consider a Basic Service Set (BSS) with one AP and a ﬁnite number of MS’s randomly

distributed in ﬁve service zones. Under the Distributed Co-ordination Function (DCF)

We assume in this study the ad-hoc connection between MS-B and its TA has sufﬁcient band-

width.

Table 2. Trafﬁc Agent MS Algorithm

if (MS is idle) then

if (Receive trafﬁc agent request) then

Advertise acknowledge (ACK) message;

if (receive commission) then

Date transmission by TA in “ad-hoc mode”;

end if

else

Data transmission in “infrastructure mode”;

end if

scheme and the ideal channel assumption (i.e. no packet loss, hidden terminal, or cap-

ture effect [11]), the throughput performance for the systems without and with the TA

scheme are analyzed in the following two sections, respectively.

3.1 Throughput without TA Scheme

Let n

(0 ≤ i ≤ 4) be the number of zone-i MS’s and let n be the total number of

MS’s. The percentage of zone-i MS’s is therefore given by P

= n

/n. Let τ be the

probability that a MS has packets to transmit at a speciﬁc time slot. The probability P

that at least one transmission occurs at a speciﬁc time slot is derived as

= 1 − (1 − τ )

n−n

. (1)

The success probability P

of a transmission period is therefore

(n − n

)τ(1 − τ)

(n−n

−1)

. (2)

Based on the approach given in [12] and [13], system throughput S is derived as

S =

i=0

(1 − P

)σ + P

(

+ SIF S + DIF S + ACK) + P

(1 − P

)(

+ DIF S)

(3)

where P is average payload length in a packet. Symbol σ denotes the slot size and

is the channel transmission bit rate in zone-i. SIF S, DIF S and ACK denote

respectively the Short Inter-Frame Spacing, the DCF Inter-Frame Spacing, and the ACK

message transmission time [1].

3.2 Throughput with TA Scheme

Let α

i,j

be the random variable denoting the number of zone-j MS’s that are within the

coverage area of a typical zone-i MS. Given α

i,j

≥ 1, the conditional expected number

i,j

of the neighboring MS’s is given by

i,j

= E[α

i,j

| α

i,j

≥ 1] =

i,j

1 − P {α

i,j

= 0}

. (4)

Under the TA scheme, some idle zone-i (1 ≤ i ≤ 4) MS’s are used to relay trafﬁc

for the active zone-0 MS’s, if any. Let η

(1 ≤ i ≤ 4) be the active probability of a zone-

i MS, i.e. the probability that a zone-i MS has packets to transmit or relay at a speciﬁc

time slot. Recall an MS has probability τ to generate new packets for transmission, so

we get (η

− τ) to be probability that a zone-i MS is serving as a TA. For the special

case i = 0, we have η

= τ. Given α

i,j

· η

≥ 1, the conditional expected number

i,j

of the active neighboring MS’s is derived as

i,j

= E[α

i,j

· η

|α

i,j

· η

> 1] =

i,j

· η

1 − (1 − η

)

i,j

. (5)

The probability (η

− τ) that a Zone-4 MS can be used as a TA is given by

− τ = (1 − η

)



4,0



[1 + (β

0,4

− 1)(1 − η

)]



1 −

1 + (β

0,4

− 1)(1 − η

)



4,0

−1

· P

{α

4,0

· η

≥ 1}

(1 − η

) ·

4,0

· η

1 + (β

0,4

− 1)(1 − η

)



1 −

1 + (β

0,4

− 1)(1 − η

)



4,0

−1

(6)

An idle zone-3 MS can serve as a TA only when all the zone-4 MS’s are busy. Therefore,

we obtain

− τ =

(1 − η

) ·

3,0

· η

0,4

1 + (β

0,3

− 1)(1 − η

)



1 −

1 + (β

0,3

− 1)(1 − η

)



3,0

−1

(7)

Similar, we get

− τ =

(1 − η

) ·

2,0

· η

0,4

· η

0,3

1 + (β

0,2

− 1)(1 − η

)



1 −

1 + (β

0,2

− 1)(1 − η

)



2,0

−1

(8)

and

− τ =

(1 − η

) ·

1,0

· η

0,4

· η

0,3

· η

0,2

1 + (β

0,1

− 1)(1 − η

)

1 −

1 + (β

0,1

− 1)(1 − η

)

1,0

−1

(9)

The probability P

′

that at least one transmission occurs at a speciﬁc time slot is

given by

′

= 1 −

i=1

(1 − η

)

. (10)

The success probability P

s,i

of a transmission or relay period for a zone-i MS is given

s,i

(1 − η

)

−1

j=1,j6=i

(1 − η

)

′

, 1 ≤ i ≤ 4 . (11)

The total success probability P

′

is the summation of P

s,i

, i.e.

′

i=1

(1 − η

)

−1

j=1,j6=i

(1 − η

)

′

. (12)

Finally, system throughput under the TA scheme is derived to be

′

i=1

′

(1 − P

′

)σ + P

′

(

+ SIF S + DIF S + ACK) + P

′

(1 − P

′

)(

+ DIF S)

(13)

4 Blocking Probability

When the TA scheme is not used, all zone-0 MS’s cannot get access to the AP so

that their service requests will be blocked. The corresponding blocking probability is

b,0

= 1. For the MS’s in other zones, they have the same blocking probability

b,i

= 1 − (1 − τ )

n−n

−1

, 1 ≤ i ≤ 4 . (14)

The overall blocking probability P

is simply the weighted summation of P

b,i

, i.e.

i=0

· P

b,i

= P



1 − (1 − τ)

n−n

−1



· (1 − P

) .

(15)

When the TA scheme is used, the average total number of service requests generated

by all-zone MS’s is kept unchanged, i.e.

j=0

· τ. The percentage P

′

of the zone-0

requests that cannot identify any TAs is derived as

′

· η

−

i=1

· (η

− τ) · (1 − η

)

−1

j=1,j6=i

(1 − η

)

j=0

· τ

. (16)

So the corresponding blocking probability is P

′

b,0

= 1. The percentage P

′

(1 ≤ i ≤ 4)

of the new and relay transmissions from the zone-i MS’s is

′

· η

j=0

· τ

, 1 ≤ i ≤ 4 . (17)

The corresponding blocking probability P

′

b,i

for the MS’s in zone-1 to zone-4 is given

′

b,i

= 1 − (1 − η

)

−1

j=1,j6=i

(1 − η

)

, 1 ≤ i ≤ 4 . (18)

The overall blocking probability for the systems using the TA scheme is therefore

′

i=0

′

· P

′

b,i

= P

′

i=1





1 − (1 − η

)

−1

j=1,j6=i

(1 − η

)





· P

′

(19)

5 Analytical and Simulation Results

The system parameters for deriving the numerical and simulation results are summa-

rized in Table 3. In addition, we assume the random variables α

i,j

(0 ≤ i, j ≤ 4) have

the same uniform distribution in the range [0, 4]. So, we obtain

i,j

= 2 and β

i,j

= 2.5.

Table 3. System Parameters

(1, 2, 5.5, 11) × 10

bps, i = 1, 2, 3, 4.

n 40

P 1024 bytes

0.2, 0.2, 0.2, 0.2, 0.2, i = 0, 1, 2, 3, 4.

SIF S 10µs

DIF S 50µs

ACK 19.2µs

σ 20µs

Fig. 2 shows the system throughput as a function of the probability τ that a new

service request is generated by an MS in each time slot. The analytical results shown in

solid lines are perfectly veriﬁed by the simulation results in markers. As seen, although

the TA scheme increases the active probability of in-coverage MS’s from τ to η

(1 ≤

i ≤ 4) and decreases the success probability of a busy period from P

in (2) to P

′

(12), it can still offer the same maximum throughput performance as the system without

using the TA scheme. Speciﬁcally, when the system is lightly loaded, say τ ≤ 0.005, the

use of TA scheme can slightly improve the system throughput because a small amount

of zone-0 trafﬁc is relayed to the AP through some two-hop connections. When the

probability τ becomes large, most MS’s are busy and cannot serve as TA. In addition,

due to more frequent packet collisions, the success probability of a busy period becomes

smaller and the throughput curve under the TA scheme is lower.

0 0.01 0.02 0.03 0.04 0.05 0.06

0.5

1.5

2.5

x 10

New Request Generation Probability

System Throughput (kbps)

With TA scheme

Without TA scheme

∆ ∆ ∆ Simulation Results

Analytical Results

Fig.2. System throughput.

Fig. 3 shows the overall blocking probability as a function of τ . As expected, the

TA scheme can offer much better blocking performance when the system is lightly

loaded. In this case, the TA scheme can accommodate most zone-0 service requests

by identifying suitable TAs to relay their trafﬁc to the AP. When τ is large, few in-

coverage MS’s are suitable for serving the zone-0 MS’s as TAs. If any, they will further

increase the active probability of in-coverage MS’s and incur more collisions in packet

transmission. The resulting overall blocking probability, calculated by (19), is therefore

larger than that of the system without using the TA scheme.

6 Conclusions

For indoor WLAN systems, how to efﬁciently improve service coverage is a challenging

problem. The Trafﬁc Agent scheme proposed in this paper can identify suitable MS’s in

good service zones as agents to relay trafﬁc for those out-of-coverage MS’s. Analytical

results, veriﬁed by simulation results, show that the TA scheme can reduce the system

blocking probability by establishing the two-hop trafﬁc connections between the out-

of-coverage MS’s and the AP when the system is lightly loaded. The service coverage

of indoor WLAN systems is therefore enhanced.

0 0.01 0.02 0.03 0.04 0.05 0.06

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

New Request Generation Probability

Blocking Probability

Without TA scheme

With TA scheme

Analytical Results

∆ ∆ ∆ Simulation Results

Fig.3. Overall blocking probability.

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