Int. J. Electron. Commun. (AEÜ) 63 (2009) 1012–1025

www.elsevier.de/aeue

Integrated voice and data transmission employing adaptive modulation inwireless networks

Rajarshi Mahapatra∗, Anindya Sundar Dhar, Debasish DattaDepartment of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur 721 302, India

Received 7 September 2007; accepted 22 August 2008

Abstract

In this paper, we propose an integrated voice and data transmission technique using adaptive modulation, wherein theeffective data transmission rate (throughput) is improved by allowing the bit-error rate (BER) for voice to increase beyondthe BER needed for data. In a given integrated frame with data and voice bits, voice bits are allowed to use higher-ordermodulation as compared to data bits due to the higher tolerance of voice to BER. This leads to a larger symbol durationfor voice which is subsequently shrunk into fewer bits, provided the resulting increase in voice BER does not exceed itsmaximum acceptable limit. This shrinking operation releases some bits from the voice quota, which are eventually utilizedfor data transmission in addition to the predefined data bits in the same frame. In this work, we first examine the feasibility ofthe proposed scheme for a time-invariant AWGN channel. Subsequently, we use our methodology in an adaptive manner forfading channels. We also examine the performance of the proposed scheme for a Global System for Mobile communication(GSM) system. Using the proposed scheme, our analysis indicates that one can dynamically enhance the overall throughputsignificantly in a broadband wireless network.� 2008 Elsevier GmbH. All rights reserved.

Keywords: Adaptive modulation; Integrated voice and data systems

1. Introduction

Continued increase in demand for all types of wirelessservices (voice, data, and multimedia) has been fueling theneed for a higher capacity and a lower bit-error rate (BER)in wireless networks. However, the wireless networks lagbehind the wireline communication networks in terms oftransmission quality. To fulfill the evolving demands in wire-less networks with transmission challenges (e.g., limitedbandwidth, fading, power efficiency for mobile units, etc.)[1,2], several techniques have been proposed from time totime. By utilizing the available information on the time-varying nature of wireless channels, a variety of adaptive

∗Corresponding author.E-mail address: raja@ece.iitkgp.ernet.in (R. Mahapatra).

1434-8411/$ - see front matter � 2008 Elsevier GmbH. All rights reserved.doi:10.1016/j.aeue.2008.08.002

transmission techniques have been developed to improve thespectral efficiency of wireless systems. These techniques as-sume that the channel quality is known at both transmitterand receiver ends. According to the channel quality, theseadaptive schemes change appropriate transmission parame-ters, such as transmitted power, symbol rate, constellationsize, coding rate, or any combination of these parameters[3–13].In general, voice transmission has low data rate re-

quirements with real-time delay constraints, while datatransmission demands higher rates with less stringent delayrequirements, but video transmission needs a higher datarate along with real-time delay constraints. This suggeststhat low-fixed-rate transmission combined with power adap-tation, where the transmitter adjusts its power to maintaina constant carrier-to-noise ratio (CNR) at the receiver, is

R. Mahapatra et al. / Int. J. Electron. Commun. (AEÜ) 63 (2009) 1012–1025 1013

well suited to voice, while bursty variable-rate transmis-sion, which maximizes average spectral efficiency, is bestsuited to data communication. On the other hand, high datarate transmission combined with power adaptation is wellsuited for video. In addition, voice, data, and video typicallyhave different BER requirements, which must be incorpo-rated into their respective transmission schemes. Accordingto European Telecommunications Standards Institute (ETSI)specification, General Packet Radio Service (GPRS) andEnhanced Data Rates for Global System for Mobile com-munication (GSM) Evolution (EDGE) support the simulta-neous voice and data communication (SVDC) for class-Aand class-B users [14]. In a GPRS network, the mobile userenjoys SVDC by the dynamic allocation of the TDMA slots.In GSM/GPRS, a maximum of four TDMA slots are allo-cated in uplink and downlink in order to increase the datarate. Based on different multislot classes, these four TDMAslots in both ways are dynamically allocated among the mo-bile users. These multislot classes are product dependent,and determine the maximum achievable data rates in uplinkand downlink directions.Furthermore, to support a high bit rate, future wireless

communication systems can provide SVDC along with adap-tive modulation and coding [15–19]. One such techniquewas proposed in [20], where a significant improvement inperformance was obtained by making use of speech mod-eling either at the receiver or at the transmitter. When it isused at the receiver, such modeling aids the data decodingprocess. In [21], Alouini et al. proposed an SVDC schemeto take advantage of the time-varying nature of fading todynamically allocate the transmitted power between the in-phase and quadrature channels. In this paper, they used fixed-rate binary phase shift keying (BPSK) modulation on thequadrature channel for voice, and variable-rate M-ary am-plitude modulation (M-AM) on the inphase channel for data.In order to improve the spectral efficiency, an adaptive tech-nique employing a variable rate uniform M-ary quadratureamplitude modulation (M-QAM) was proposed in [22] forsimultaneous voice and a single-class data transmission. Inthis work, Hwang et al. applied different switching thresh-olds according to the required BER. Specifically, it startsto transmit voice with BPSK when the CNR allows BPSKtransmission with the target BER for voice. When the CNRis improved to support M-QAM with the target BER fordata, it begins to transmit both data and voice with the sameBER target. In [23], Hossain et al. proposed a technique us-ing adaptive hierarchical modulation for simultaneous voiceand multi-class data transmission over fading channels. Theyalso extended the work proposed in [21] to make the systemspectrally efficient. It may be noted that in order to supportadaptive modulation, a wireless system needs the feedbackinformation from the receiver about the channel condition,which is estimated by channel estimation techniques. How-ever, delay in estimation, channel estimation error, and errorin feedback information might impair the adaptive scheme,which we have not considered in our work [24–26].

In this paper, we propose an integrated (i.e., simultaneous)voice and data transmission technique using an adaptivemodulation scheme, wherein one can employ a dynamic ca-pacity allocation for voice and data bits to improve overallspectral efficiency (and hence network throughput). In theproposed scheme the effective transmission rate for data bitsis enhanced by allowing the BER for voice to increase be-yond the BER needed for data. In a given integrated framehaving data and voice bits, voice bits are allowed to usehigher-order modulation as compared to data bits due to thehigher tolerance of voice to BER. This leads to a larger sym-bol duration for voice which is subsequently compressed(shrunk) into fewer bit intervals, provided the resultingincrease in voice BER does not exceed its maximum ac-ceptable limit. This shrinking operation releases some bitintervals from the voice quota, which are eventually utilizedfor the transmission of data bits in addition to the preallo-cated data bits in the same frame. With this technique, thetransmitter is able to send more data bits in a given timeframe, while maintaining the same number of voice bits(albeit with an acceptable increase in voice BER due to thehigher-order modulation and symbol compression). How-ever, this operation is carried out if and only if the BER ofthe transmission system is suitable for data transmission.The rest of this paper is organized as follows. Section 2

presents the proposed scheme to improve the datatransmission rate in a BER-aware integrated voice and datacommunication technique. The practical implementation ofthe proposed scheme is also described in this section. InSection 3, we present the numerical results on the perfor-mance of the proposed scheme for a time-invariant AWGNchannel. For a fading channel, we use our methodology inan adaptive manner and find the performance of the pro-posed scheme in Section 4. In Section 5, the performanceof the proposed scheme is examined in a GSM-TDMA sys-tem. Finally, Section 6 presents summary of our work onthis topic.

2. Capacity allocation for voice and data usingBER-aware adaptive modulation

In this section, we describe an integrated voice and datatransmission scheme for improving the data throughput. Inthe proposed scheme, we consider a time frame that con-tains a certain number of bits, each with a bit interval ofT, wherein some bit intervals are used for voice commu-nication and the rest for data transmission. For successfultransmission, voice and data have different upper limits ofBER and, voice can be successfully transmitted with higherBER as compared to data. Therefore, in a given integratedframe with data and voice bits, voice bits can be allowed touse higher-order modulation as compared to data bits andthe symbol intervals for the chosen higher-order modula-tion can also be shrunk unless the voice BER does not ex-ceed its maximum acceptable limit (BERmax(voice)). Thus

1014 R. Mahapatra et al. / Int. J. Electron. Commun. (AEÜ) 63 (2009) 1012–1025

10 15 20 25 30 35 4010−8

10−7

10−6

10−5

10−4

10−3

10−2

10−1

100

CNR (dB)

BE

R

abcdef

Fig. 1. BER performance of 64-QAM modulation with different values of symbol duration (Ts). a: Ts = 6T , b: Ts = 5T , c: Ts = 4T , d:Ts = 3T , e: Ts = 2T , f: Ts = T .

our aim is to employ higher-order modulation for voice andshrink the voice-symbol interval to some extent by usingadaptive modulation and allow the voice BER to increase,which does not exceed BERmax(voice). Having carried outthis task, one can eventually save some bit intervals from thevoice field and allocate these additional bits for data trans-mission. Therefore, we propose to use a higher constellationsize for voice and then shrink the symbol duration for voiceby a few bit intervals, thereby gaining in spectral efficiencyand data throughput by using the additional bits of the voicefield for data transmission in a given integrated frame.To explain the proposed scheme in detail, we consider

a communication link with a BER�BERmax(data), whereBERmax(data) is the maximum acceptable BER for success-ful data transmission (we assume that BERmax(voice) is al-ways greater than BERmax(data)). Hence, the voice and datacan be communicated over this channel between two endnodes, since the voice can tolerate a higher BER than thedata. Thus, at a particular BER�BERmax(data), system canemploy in general a suitable M-ary QAM and select thesame constellation size for voice and data transmission, i.e.,Mv = Md, where Mv and Md are the constellation sizes forvoice and data, respectively. As mentioned earlier, by ex-ploiting the nature of higher tolerance to BER, voice bits cannow choose the higher constellation size using adaptive mod-ulation while ensuring that the voice BER does not exceedBERmax(voice). This would make ?he constellation size ofvoice larger than the data constellation size, i.e., Mv > Md.

With this consideration, voice bits can be transmit-ted using the constellation size Mv of symbol intervalTs (=kvT, kv = log2 Mv). Next the shrinking of symbol

interval of voice constellation can take place by reducing thesymbol interval from Ts to Ts−sh = Ts − T, Ts − 2T, . . . etc..This operation becomes feasible if the impact of increas-ing the constellation size followed by shrinking operationdoes not push the voice BER above the acceptable limitBERmax(voice). Following this operation, the transmittedvoice symbols take lesser transmission time (as comparedto the unshrunk symbol) to transmit the same information,which in turn releases some bit intervals from the voicesegment of the frame, which are eventually used to transmitsome additional data bits.It is important to note that the proposed scheme will work

if the receiver BER is below its maximum acceptable limitBERmax(data), which may be typically 10−7 (say) [27,28].Having ensured this with an appropriate Md (for a givenCNR), one can choose a value of Mv�Md and shrink thevoice-symbol intervals from Ts to Ts−sh < Ts. It is thereforeworthwhile to examine how the receiver BERwould increase(for voice bits) if the symbol duration is shrunk from Ts toTs−sh for a given Mv. Fig. 1 shows the BER performance of64-QAM modulation with different symbol durations. It isfound that as the symbol duration of 64-QAM shrinks from6T to T, BER increases steadily for a given CNR (see theAppendix for details). Using these results, while shrinkingthe symbol duration, one therefore needs to ensure that thedegraded BER does not exceed BERmax(voice). It may benoted that in this work, we considered the system withoutchannel coding.The implementation of the proposed scheme is described

in the following in further details. In the first step, wecalculate the suitable constellation sizes for voice (Mv)

R. Mahapatra et al. / Int. J. Electron. Commun. (AEÜ) 63 (2009) 1012–1025 1015

and data (Md) for specified values of BERmax(voice) andBERmax(data) at the prevailing receiver CNR using Eq. (8)(see the Appendix). In Eq. (8), we consider Mi = Mv orMd, where i represents v and d for voice and data respec-tively, BER represents BERmax(voice) or BERmax(data) ofan M-QAM system in an AWGN channel, and � is thereceived CNR. It may be noted that the value of Mi maynot be equal to a feasible constellation size. To make it afeasible number, we rounddown Mi to M̂i as

M̂i = 2�log2 Mi � and ki = �log2 M̂i� (1)

where ki (=kv or kd) is the number of bits per symbol (voiceor data) of the corresponding constellation size.To shrink the symbol duration, we first calculate the BER

for the modified values, M̂v and M̂d, which might decreasebelow Mv and Md due to a rounddown operation. If M̂d�

the lowest permissible constellation size Mdo (say) and thecorresponding BER for data�BERmax(data), the shrinkingoperation is undertaken for voice transmission. Otherwise,the shrinking operation as well as the data transmission be-comes infeasible and voice transmission takes place withthese appropriate constellation size and symbol duration. Itmay be noted that the shrinking operation for voice sym-bols with a given M̂v as the constellation size can be carriedout, provided that the corresponding BER for voice is be-low BERmax(voice). However, the BER value correspond-ing to M̂v may be very close to the BERmax(voice), andhence an attempt to shrink the corresponding symbol inter-val Ts (=kvT ) to Ts−sh (=kv−shT , kv−sh < kv) might pushthe voice BER beyond BERmax(voice). In view of this, hav-ing estimated M̂v in the first step along with a search fora possible Ts−sh, it would often be more useful to explorenext with the lower possible value for constellation sizeM ′

v = M̂v/2 (k′v = log2 M

′v, k

′v�kv) and then attempt the

Table 1. Transmission strategy of integrated voice and data frame

ReceivedCNR (dB)

M̂v kv M̂d kd Transmission status Feasibility of shrinking

5 0 0 0 0 No transmission Not applicable

10 4 2 0 0 Voice only BERmax(data)<BER�BERmax(voice) Shrinking not allowed12 4 2 0 014 8 3 0 0

15 8 3 4 2 Voice and data BER�BERmax(data) Allowed17 16 4 4 220 16 4 8 322 32 5 16 325 64 6 32 428 128 7 64 530 256 8 64 532 256 8 128 635 256 8 256 740 256 8 256 8

shrinking operation on symbol duration k′vT with k′

v reducedto k′

v−sh < k′v. With a lower value of constellation size, one

is expected to gain some margin in BER, which can be sub-sequently utilized for shrinking the symbol duration in stepsof T The shrinking of symbol interval T ′

s is continued untilthe BER remains below BERmax(voice), and thus eventuallywe get the minimum symbol interval T ′

s−sh for a particularconstellation size M ′

v.However, it may be noted that M ′

v should not be reducedbelow 4 (corresponding to a symbol duration=2T ), becausethe next lower value of M ′

v (=2, corresponding to a symbolduration=T ) will not permit any shrinking operation. Thus,to make the symbol shrinking feasible, we would in generalset up a lower limit Mvo for M ′

v, which would be in general�4 (corresponding to QPSK for the m-ary PSK family).However, for data transmission, since we do not propose tocarry out any shrinking, the minimum value for constellationsize can go down to 2 (i.e., Mvo=2, corresponding to BPSKfor the m-ary PSK family) if necessary.With k′

v−sh(min), representing the minimum value ofT ′s−sh at a given constellation size M ′

v, we define the shrink-ing factor (SF) for voice transmission as

SF = k′v−sh(min)

log2(M ′v)

(2)

For each constellation size M ′v from M̂v to 4 (M̂v,

M̂v/2, M̂v/4 . . ., with 4 for QPSK), we find out the min-imum value for the shrunk symbol interval k′

v−sh(min)and the corresponding SF. Thereafter, we finally select aspecific combination of M ′

v and the corresponding k′v−sh

(hereafter designated as M ′v(final), k

′v−sh(final)), that offers

the minimum value for SF (i.e., maximum shrinking).If the integrated (voice and data) frame consists

of N (=Nv + Nd) bits with Nv bits for voice and

1016 R. Mahapatra et al. / Int. J. Electron. Commun. (AEÜ) 63 (2009) 1012–1025

Nd bits for data, the percentage increase in data throughputthrough this scheme can be expressed as

�� = (1 − SF)Nv

Nv + Nd× 100% (3)

Using these expressions for SF and ��, we evaluate theperformance of the proposed scheme in the following.

3. Performance in a time-invariant AWGNchannel

In this section, the feasibility of the proposed technique isfirst examined for a time-invariant AWGN channel (i.e., for

Table 2. Selection of a reduced symbol interval on possible voice constellation size for a CNR of 17dB

ReceivedCNR (dB)

M̂v M ′v k′

v Search fork′v−sh(min)

BER aftershrinking

SF

17 16 16 4 4 5.7e−4 1.003 2.2e−3 0.752 9.4e−3 0.501 4.2e−2 0.25

8 3 3 1.5e−6 1.002 6.6e−5 0.671 3.2e−3 0.33

4 2 2 7.2e−13 1.001 2.7e−7 0.50

Table 3. Selection of a reduced symbol interval on possible voice constellation size for a CNR of 25dB

ReceivedCNR (dB)

M̂v M ′v k′

v Search fork′v−sh(min)

BER aftershrinking

SF

25 64 64 6 6 3.0e−5 1.005 1.1e−4 0.834 4.4e−4 0.673 1.7e−3 0.502 7.3e−3 0.331 3.3e−2 0.17

32 5 5 1.0e−8 1.004 2.4e−7 0.803 6.0e−6 0.602 1.5e−4 0.401 4.4e−3 0.20

16 4 4 6.8e−16 1.003 2.1e−12 0.752 7.0e−9 0.501 2.6e−5 0.25

8 3 3 1.0e−31 1.002 8.5e−22 0.671 7.7e−12 0.33

4 2 2 4.8e−71 1.001 1.4e−36 0.50

a static case without fading) using numerical results. Sub-sequently, in the next section, we extend the investigationfor a fading channel with a provision for adaptive modula-tion. We consider the typical values for BERmax(voice) andBERmax(data) as 10−3 and 10−7, respectively, and the min-imum realizable constellation size as 4. We also assume thatthe integrated frame contains an equal number of bits forvoice and data (i.e., Nv = Nd = N/2), as a specific case.Table 1 shows the constellation size of voice and data trans-mission for different values of received CNR. As evidentfrom Table 1, based on BER calculation, only the voice com-munication is supported when CNR�10dB. However, thesystem enables data transmission along with voice, whenCNR�15dB.

R. Mahapatra et al. / Int. J. Electron. Commun. (AEÜ) 63 (2009) 1012–1025 1017

Table 4. Results of the proposed scheme for different values of CNR (M ′v and k′

v−sh in bold face indicate the values of M ′v(final) and

k′v−sh(final) offering minimum values for SF)

ReceivedCNR (dB)

M̂v M̂d M ′v k′

v k′v−sh BER after

shrinkingSF

15 8 4 8 3 3 9.9e−5 1.004 2 1 3.4e−5 0.50

17 16 4 16 4 4 5.7e−4 1.008 3 2 6.6e−5 0.674 2 1 2.7e−7 0.50

20 16 8 16 4 2 5.8e−4 0.508 3 1 6.7e−5 0.334 2 1 7.6e−13 0.50

22 32 16 32 5 3 7.9e−4 0.6016 4 2 2.5e−5 0.508 3 1 8.4e−7 0.33

25 64 32 64 6 4 4.4e−4 0.6732 5 2 1.5e−4 0.4016 4 1 2.6e−5 0.25

28 128 64 128 7 4 9.1e−4 0.5764 6 2 4.5e−4 0.3332 5 1 1.5e−4 0.20

30 256 64 256 8 6 6.9e−4 0.75128 7 3 3.8e−4 0.4264 6 2 1.9e−5 0.3332 5 1 3.5e−6 0.20

32 256 128 256 8 4 5.3e−4 0.50128 7 2 2.7e−4 0.2864 6 1 1.1e−4 0.16

35 256 256 256 8 2 5.3e−4 0.25128 7 1 2.8e−4 0.14

40 256 256 256 8 1 2.9e−5 0.12

The recursive process of the proposed scheme for choos-ing the final values for M ′

v(final) and k′v−sh(final) is de-

scribed in Table 2 for a typical case. As evident fromTable 2, at a given CNR of 17dB, the voice bits can support16-QAM modulation for successful communication with aBER of 5.7 × 10−4. Hence, the shrinking operation is ex-plored from 16-QAM modulation down to 4-QAM and foreach constellation size, we shrink the symbol interval downto a single bit interval. At each step, the BER and SF areevaluated. After going through the entire exercise, we selectthe final constellation size M ′

v(final) and reduced symbolinterval k′

v−sh(final) corresponding to the lowest SF. InTable 2, we observe that the lowest SF is 0.25, but the corre-sponding BER> 10−3, which is not suitable for voice com-munication. Therefore, we leave this one and choose the nextmodulation level, which gives the lowest SF= 0.33. Again,the corresponding BER is not suitable for voice. Thereafter,we opt for the next case of SF = 0.50. The correspondingBER is suitable for voice. Thus, finally, using BER criteria

we select the reduced symbol interval k′v−sh(final) and the

constellation size M ′v(final) corresponding to the SF= 0.50.

It is not always needed to go to the lowest available constel-lation size for the minimum value of SF, which can gain ahigher constellation size. Table 3 presents an example of therecursive process for choosing the final values of M ′

v(final)and k′

v−sh(final) at a CNR of 25dB. In this case, althoughone can go for M ′

v=4, the corresponding value of the lowestSF would be 0.50, which is higher than the lowest SF withM ′

v = 16. Thus, down the line, while decreasing M ′v, one

need not reduce further the value of M ′v once k′

v−sh has re-duced to 1. With this consideration, we estimate the valuesof M ′

v(final) and k′v−sh(final) for a value of CNR. Table 4

presents the final results of a shrinking operation on voiceconstellation for a range of CNR values with relevant param-eters. The values of M ′

v and k′v−sh at each CNR are shown

in bold face corresponding to M ′v(final) and k′

v−sh(final)and the corresponding minimum achievable value of SF,when system BER is suitable for voice communication. The

1018 R. Mahapatra et al. / Int. J. Electron. Commun. (AEÜ) 63 (2009) 1012–1025

Fig. 2. Pseudo-code for implementing the proposed scheme.

entire scheme is presented in the form of a pseudo codein Fig. 2.Table 5 shows the final transmission parameters of the

voice and data bits along with the corresponding increase inthe data throughput (��) and SF at different values of re-ceived CNR. At each CNR, voice bits are transmitted usingM ′

v(final) and k′v−sh(final), if the corresponding data BER

permits shrinking. [As evident from Table 5, the systemcan achieve gain in data throughput above 15dB of CNR,when it becomes possible to shrink the voice-symbol inter-val with BER�10−7 for data transmission.] The BERs forvoice communication after shrinking are also shown in thistable, which are calculated using k′

v(final) and k′v(final) =

log2(M′v(final)) with the help of Eq. (12).

Fig. 3 shows the plot of �� vs. CNR, wherein we ob-serve that the proposed data transmission scheme achieves

increasing benefit with an increase in CNR. As evident fromFig. 3, the percentage increase in data throughput appearsas a staircase function of the received CNR. This is due tothe fact that the same constellation size (an integer) has tobe selected for a range of CNR values to maintain a partic-ular BER. Therefore, the value of �� remains fixed withina specific range of CNR values and increases with CNR insteps. However, the scope of improvement reduces at higherCNR, and �� gradually attains a saturation.So far, we have considered the case of an integrated frame

with r = Nd/Nv = (N − Nv)/Nv = 1. In Fig. 4, we presentthe plot of �� vs. SF for different values of r. At a lowervalue of r, the number of free intervals after shrinking is lessthan that at higher value of r for a particular value of SF.Therefore, as expected, �� vs. SF plots move upward withhigher values of r.

R. Mahapatra et al. / Int. J. Electron. Commun. (AEÜ) 63 (2009) 1012–1025 1019

Table 5. Final transmission parameter of the proposed scheme along with the data throughput and shrinking factor

Received CNR (dB) k′v (final) k′

v−sh (final) Voice BER Data BER SF ��%

5 0 0 No transmission No transmission Not applicable Not applicable

10 2 Not applicable 7.8e−4 No transmission 0 012 2 3.4e−5 0 014 3 4.4e−4 0 0

15 2 1 3.4e−5 9.3e−9 0.50 2517 2 1 2.7e−7 7.2e−13 0.50 2520 2 1 7.6e−13 2.5e−11 0.33 33.322 3 1 8.4e−7 6.7e−9 0.33 33.325 4 1 2.6e−5 1.04e−8 0.25 37.528 5 1 1.5e−4 1.2e−8 0.20 4030 5 1 3.5e−6 1.5e−12 0.20 4032 6 1 1.1e−4 2.4e−10 0.16 41.635 7 1 2.8e−4 2.4e−10 0.14 42.840 8 1 2.9e−5 4.8e−28 0.12 43.7

0 5 10 15 20 25 30 35 400

5

10

15

20

25

30

35

40

45

50

Received CNR (dB)

Δ η

No Dat aTransmission

DataTransmission

Fig. 3. Plot of percentage increase in data throughput �� vs. average CNR in AWGN channel.

4. Performance in a fading channel

In the previous section, the percentage increase in datathroughput (��) is computed in a time-invariant AWGNchannel, where it appears as a staircase function of receivedCNR. However, the scenario becomes different in the caseof a fading channel. Due to the time-varying nature of the re-ceived CNR, the proposed scheme is employed in a dynamicmanner with an adaptive constellation size and shrinkingratio, governed by the instantaneous value of the receivedCNR. Thus, the performance needs to be evaluated in terms

of the value of ��, averaged over all possible values of CNRfor a given average CNR (�̄) in a fading channel. To deter-mine the average value of �� for an average received CNR�̄, we first split the given range of � of a fading channel intoa set of discrete intervals, each corresponding to a specificM̂v. Specifically, we divide the given range of � into K fad-ing regions, and define the value of �� for � values falling inthe ith region as ��i (�). The regions of � and the associatedvalues of constellation size M ′

v(final) and k′v−sh(final) are

assumed to have been pre-computed and stored in a lookuptable. The average value for �� for a given value of average

1020 R. Mahapatra et al. / Int. J. Electron. Commun. (AEÜ) 63 (2009) 1012–1025

0.10.150.20.250.30.350.40.450.50

10

20

30

40

50

60

SF

Δ η

r = 2

r = 0.1

Fig. 4. Plots of percentage increase in data throughput (��) vs.shrinking factor (SF) for different values of r = Nd/Nv.

0 5 10 15 20 25 30 35 400

5

10

15

20

25

30

35

40

45

50

Average Received CNR (dB)

Δ η a

v

Max 256−QAMMax 64−QAMMax 16−QAM

Fig. 5. Plots of ��av vs. average received CNR in Rayleigh fadingchannel for various maximum constellation sizes.

received CNR �̄ can therefore be expressed as

��av =K−1∑i

∫ �i+1

�i��i (�)p(�)d� (4)

where p(�) is the probability density function of the receivedCNR for the fading channel.Fig. 5 shows the plot of ��av vs. �̄ of the proposed

scheme in a Rayleigh fading channel wherein the maxi-mum constellation size is limited to 256-QAM. As evidentfrom Fig. 5, the value of ��av increases smoothly with �̄(instead of staircase variation in case of a time-invariantAWGN channel, as shown in Fig. 3) and tends to attain asaturation at higher values of �̄. The plots in Fig. 5 showthe decrease in data throughput at higher CNR values if the

0 5 10 15 20 25 30 35 400

5

10

15

20

25

30

35

40

45

50

Average Received CNR (dB)

Δ η a

v

Max 256−QAMMax 256−QAM−Coste. Restricted

Fig. 6. Plots of ��av vs. average CNR in Rayleigh fading chan-nel for all constellation and square-constellation cases (maximum256-QAM).

0 5 10 15 20 25 30 35 400

5

10

15

20

25

30

35

40

45

50

Average Received CNR (dB)

Δ η a

v

RayleighRician factor = 1Nakagami m = 5

Fig. 7. Plot of ��av vs. average CNR in different fading channels.

maximum constellation size is restricted further to 64-QAMand 16-QAM.At times, a practical system may not support all feasible

constellation sizes. Fig. 6 shows the comparison betweenthe two cases, with and without constellation size restrictionin a Rayleigh fading channel. In the first case, without anyrestriction, all feasible constellations up to 256-QAM havebeen considered, while in that case of restricted constella-tions, only square-constellations have been considered. Asexpected, the plots show that ��av is reduced with restric-tion is constellation size. Fig. 7 shows the plots of ��av vs. �̄for an ideal case (i.e., without any restriction or upper limiton constellation size) in the presence of Rayleigh fading,Rician fading with Rician factor 1, and Nakagami-m fadingwith m = 5 [1].

R. Mahapatra et al. / Int. J. Electron. Commun. (AEÜ) 63 (2009) 1012–1025 1021

It is worthwhile to mention here that in the techniques forsimultaneous voice data transmission proposed in [20–23],the authors used spectrally efficient modulation for datatransmission than voice transmission, whereas in our pro-posed technique, we use higher-order modulation for voiceas compared to the modulation used for data transmission.

5. Implementation in GSM-based systems

In this section, we examine the proposed scheme forGSM-TDMA frames. In a GSM system, one TDMA slotis assigned to each user for either voice or data communi-cation. Each TDMA slot consists of 156.25 bits, of which114 bits are used for communication, wherein all the bitshave the same time interval. These 114 bits are divided intotwo blocks of 57 bits [1]. Presently, these bits are used for

Table 6. Application of the proposed scheme in GSM-TDMA slots

Received CNR (dB) Nv N ′v−sh �Nd Transmission status Feasibility of shrinking

5 0 0 0 No Transmission Not applicable

10 60 Not applicable 0 Voice only Not allowed12 60 014 60 0

15 60 30 30 Voice and data Allowed17 60 30 3020 60 20 4022 60 20 4025 60 15 4528 60 12 4830 60 12 4832 60 10 5035 60 10 5040 60 10 50

Table 7. Final transmitted bits of a GSM-TDMA slot for integrated voice and data transmission

Received CNR (dB) Nd �Nd Nd−sh Transmission status Feasibility of shrinking

5 54 0 0 No transmission Not applicable

10 54 0 0 Voice only Not allowed14 54 0 0

15 54 30 84 Voice and data Allowed17 54 30 8420 54 40 9422 54 40 9425 54 45 9928 54 48 10230 54 48 10232 54 50 10435 54 50 10440 54 50 104

either voice or data communication. To implement ourscheme, we assume that each TDMA slot supports inte-grated voice and data communication, and out of 114 bits,60 bits are used for voice (i.e., Nv = 60) and the rest i.e., 54bits (Nd = 54) are used for data transmission (i.e., the valueof r close to 1). The number of voice bits needs to be cho-sen with discretion. This is due to the fact that the numberof bits used for voice communication should be an integralmultiple of the number of bits per symbol for each modula-tion. In the present work, we have taken the case of Mv of4, 8, 16, 32, and 64 with kv = 2, 3, 4, 5, and 6, respectively.Table 6 shows the number of bit intervals used for voice

communication due to a shrinking operation. Initially, 60bits are allocated for voice communication over 60-bit inter-vals. The system uses these bits completely for voice whenCNR< 15dB. But with CNR�15dB, the system undertakesthe data transmission, and the symbol intervals for the voice

1022 R. Mahapatra et al. / Int. J. Electron. Commun. (AEÜ) 63 (2009) 1012–1025

Table 8. Analysis of the proposed scheme on the bits of a GSM-TDMA slot

Received CNR (dB) N N ′v−sh Nd−sh k′

v (final) k′v−sh (final) SF ��

15 114 30 84 2 1 0.50 26.3117 114 30 84 2 1 0.50 26.3120 114 20 94 3 1 0.33 35.0822 114 20 94 3 1 0.33 35.0825 114 15 99 4 1 0.25 39.4728 114 12 102 5 1 0.20 42.1030 114 12 102 5 1 0.20 42.1032 114 10 104 6 1 0.16 43.8635 114 10 104 6 1 0.16 43.8640 114 10 104 6 1 0.16 43.86

0 5 10 15 20 25 30 35 400

5

10

15

20

25

30

35

40

45

50

Received CNR (dB)

Δ η

No DataTransmission

DataTransmission

Fig. 8. Plot of �� vs. CNR in AWGN channel.

are allowed to shrink. Depending on different shrinking fordifferent modulations (constellation size), the same numberof voice bits (Nv) are sent with variable bit intervals (N ′

v−sh),which varies from 30 down to 10 corresponding to the CNRranging from 15 to 40dB. Therefore, the system can sendthe voice using fewer bit intervals, and can thus increase thenumber of unused bit intervals for data transmission. Theseunused bit intervals (�Nd = (1 − SF)Nv) are added to thepreviously allocated data bits (Nd).Hence, the number of bits available for data transmission

increases to Nd−sh = Nd + (1 − SF)Nv. The number of in-creased bits for data transmission is given in Tables 7 and8 along with the relevant parameters. Fig. 8 shows the vari-ation of �� in an AWGN channel with varying CNR. Us-ing the value of �� from Fig. 8, the plot of ��av vs. �̄ hasbeen obtained for a Rayleigh fading channel and is shownin Fig. 9.

6. Conclusion

In this paper, we have examined an integrated voice anddata transmission technique, where the symbol duration ofvoice is shrunk with the knowledge of voice BER usingadaptive modulation. In doing so, the system is able to sendthe same voice information with compressed symbol dura-tion, and consequently offers some additional bits for datatransmission. These additional bits for data transmissionalong with the predefined data bits increase the effectivetransmission rate. Hence, using the proposed scheme onecan enhance dynamically the overall throughput in a broad-band wireless network, provided the receiver CNR remainswithin the desirable range. In this technique, power remainsinvariant for voice and data. However, the shrinking of thesymbol period will increase the complexity of the system atthe expense of the enhanced data rate.

R. Mahapatra et al. / Int. J. Electron. Commun. (AEÜ) 63 (2009) 1012–1025 1023

0 5 10 15 20 25 30 35 400

5

10

15

20

25

30

35

40

45

50

Average Received CNR (dB)

Δ η a

v

Fig. 9. Plot of ��av vs. average received CNR in Rayleigh fadingchannel.

The feasibility study of the proposed scheme in GSM-TDMA slot indicates that the scheme can be applied toa GSM/GPRS system, by which the system will supportsimultaneous voice and data communication for more num-ber of users in the present scenario.

Appendix

BER for M-QAM receiver with compressed symbolinterval.The BER of square M-QAM with Gray-bit mapping in a

time-invariant AWGN as a function of received CNR � andconstellation size M = 2k can be approximately expressedas [29]

BER = 2

k

(1 − 1√

M

)erfc

(√1.5

�

M − 1

)(5)

where CNR � is given by [30]

� = Eb

�and Eb = A2T

2(6)

with Eb being the energy per bit interval, A the amplitudeof the received signal, T the bit interval, and � is the double-sided noise power spectral density. In (5), the approximationis tightest at high CNRs. This expression is, however noteasily differentiable or invertible in its rate k, and so wenext consider another approximation for BER, tight to within1dB for k�2 and BER�10−3, given by [11]

BER ≈ 0.2 exp

[−1.6�

M − 1

](7)

From (7), the constellation size M can be derived at a par-ticular value of � as

M ≈ 1 − 1.6�

log(BER/0.2)(8)

Next, we consider that the signal is modulated with aconstellation size M with a symbol interval Ts = kT , wherek = log2 M . If we compress the symbol interval, the bitinterval is also shrunk and modified with Tsh. Therefore,the compressed symbol-interval Ts−sh is given as kshT =kT sh. As mentioned earlier, bit energy is related to the bitinterval; therefore, due to shrinking, the bit energy is alsomodified and can be expressed as Eb−sh = A2Tsh/2, whichsubsequently modified the SNR value as �sh = Eb−sh/�. Ifwe take the ratio between two CNR values before and aftershrinking, we get

�

�sh= Eb

Eb−sh= T

Tsh(9)

Using (9), the modified CNR (�sh) due to shrinking can beexpressed in terms of the CNR (�) before shrinking as

�sh = �TshT

= �kshk

(10)

Finally, the BER of the M-QAM system with a compressedsymbol interval as a function of modified CNR �sh isapproximately expressed as

BER = 2

k

(1 − 1√

M

)erfc

(√1.5

�shM − 1

)(11)

Substituting the value of �sh from (10) into (11), we get

BER(k, ksh) = 2

k

(1 − 1√

M

)erfc

(√1.5 × � × kshk(M − 1)

)(12)

References

[1] Rappaport TS. Wireless communication: principle andpractice. 2nd ed., Pearson Education; 2003.

[2] Jakes WC. Microwave mobile communication. 2nd ed., NewYork: IEEE Press; 1994.

[3] Cavers JK. Variable-rate transmission for Rayleigh fadingchannels. IEEE Trans Commun 1972; 15–22.

[4] Hayes JF. Adaptive feedback communications. IEEE TransCommun Technol 1968; 29–34.

[5] Vucetic B. An adaptive coding scheme for time-varyingchannels. IEEE Trans Commun 1991;COM-39:653–63.

[6] Webb WT, Steele R. Variable rate QAM for mobile radio.IEEE Trans Commun 1995; 2223–30.

[7] Lee WCY. Estimate of channel capacity in Rayleigh fadingenvironment. IEEE Trans Veh Technol 1990;39:187–90.

[8] Kamio Y, Sampei S, Sasaoka S, Morinaga N. Performanceof modulation-level-controlled adaptive-modulation underlimited transmission delay time for land mobile communi-cations. In: Proceedings of the IEEE veh technol conf(VTC95), Chicago, Illinois, 1995, p. 221–5.

[9] Goldsmith AJ, Varaiya P. Capacity of fading channelwith channel side information. IEEE Trans Inform Theory1997;43(3):1986–97.

[10] Ue T, Sampei S, Morinaga N. Symbol rate and modulationlevel controlled adaptive modulation/TDMA/TDD forpersonal communication systems. IEICE Trans Commun1998;E78-B:1117–24.

1024 R. Mahapatra et al. / Int. J. Electron. Commun. (AEÜ) 63 (2009) 1012–1025

[11] Chung S, Goldsmith AJ. Degree of freedom in adaptivemodulation: a unified view. IEEE Trans Commun2001;49(9):1561–71.

[12] Matsuoka H, Sampei S, Morinaga N, Kamio Y. Adaptivemodulation system with variable coding rate concatenatedcode for high quality multi-media communication systems.IEICE Trans Commun 1996;E79-B:328–34.

[13] Catreux S, Erceg V, Gesbert D, Heath Jr RW. Adaptivemodulation and MIMO coding for broadband wireless datanetworks. IEEE Commun Mag 2002; 108–15.

[14] Pecen M, Howell A. Simultaneous voice and data operationfor GPRS/EDGE: class A dual transfer mode. IEEE PersonalCommun 2001; 14–29.

[15] Bremer G, Ko KD. Simultaneous voice and data on thegeneral switched telephone network using framed QADM.IEEE Commun Mag 1996;34:58–63.

[16] Zhang K, Pahlavan K. An integrated voice/data system formobile indoor radio networks. IEEE Trans Veh Technol1990;VT-39:75–82.

[17] Nanda S. Analysis of packet reservation multiple access:Voice data integration for wireless networks. In: Proceedingsof the IEEE global commun conference (GLOBECOM’90),San Diego, CA, December 1990. p. 1984–8.

[18] Wilson N, Ganesh R, Joseph K, Raychaudhuri D. PacketCDMA versus dynamic TDMA for multiple access in anintegrated voice/data PCN. IEEE J Select Areas Commun1993;SAC-11:870–84.

[19] Famolari D, Anjum F. Improving simultaneous voiceand data performance in Bluetooth systems. In: Globaltelecommunication conference, 2002. p. 1810–4.

[20] Seshadri N, Kim J. Coding and modulation for simultaneousvoice and data transmission. In: Global telecommunicationconference, 1995. p.17–21.

[21] Alouini MS, Tang X, Goldsmith AJ. An adaptive modulationscheme for simultaneous voice and data transmissionover fading channels. IEEE J Select Areas Commun1999;17(5):837–50.

[22] Hwang CS, Kim Y. An adaptive modulation method forintegrated voice/data traffic over Nakagami fading channels.In: IEEE veh technol conference, 2001. p. 1649–52.

[23] Hossain MdJ, Vinhaladevuni FK, Alouini MS, BhargavaVK, Goldsmith AJ. Adaptive hierarchical modulation forsimultaneous voice and multi-class data transmission overfading channels. In: 4th IEEE workshop on signal processing.2003. p. 105–9.

[24] Tang X, Alouini MS, Goldsmith A. Effect of channelestimation error on M-QAM BER performance in Rayleighfading. IEEE Trans Commun 1999;47:1856–64.

[25] Paris JF, Aguayo-Torres MC, Entrambasaguas JT. Impact ofchannel estimation error on adaptive modulation performancein flat fading. IEEE Trans Commun 2004;52(5):716–20.

[26] Oien GE, Holm H, Hole KJ. Impact of channel prediction onadaptive coded modulation performance in Rayleigh fading.IEEE Trans Veh Technol 2004;53(3):758–69.

[27] Wang J, Elicin MA, Liu JCL. Multimedia support for wirelessW-CDMA with dynamic spreading. Wireless Networks, vol.8. Dordrecht: Kluwer Academic Publishers; 2002. p. 355–70.

[28] Ling H, Xiaodong D, Zhimin Z, Wei D. Research on theadaptive QoS paradigm of wireless broadband applicationsin NGN. In: Proceedings of ICCT 2003, 2003.

[29] Proakis JG. Digital communication. 4th ed., McGraw-HillInternational Edition; 2001.

[30] Taub H, Schilling DL. Principles of communication systems.2nd ed., New Delhi: Tata McGraw-Hill; 1991.

Rajarshi Mahapatra is a Re-search Scholar at the Departmentof Electronics and ElectricalCommunication Engineering ofIndian Institute of Technology,Kharagpur. He obtained B. Tech.and M. Tech. degrees in Opticsand Optoelectronics from theDepartment of Applied Physics,Calcutta University, Kolkata, in1998 and 2000, respectively. Hiscurrent research interests include

adaptive modulation, space-time coding, and emerging techniquesin wireless networks.

Anindya Sundar Dhar receivedhis Bachelor degree in Elec-tronics and TelecommunicationEngineering from Bengal Engi-neering College, Sibpur, India,in 1987. In 1989, he received hisM.Tech. degree in Integrated Cir-cuits and Systems Engineeringfrom Indian Institute of Tech-nology, Kharagpur. He receivedhis Ph.D. degree from the sameInstitute in 1994, where he is

presently serving as an Assistant Professor at the Department ofElectronics and Electrical Communication Engineering. His re-search interests include VLSI for communication and DSP archi-tectures for real-time signal processing.

Debasish Datta received hisB.Tech. degree in 1973 from theInstitute of Radiophysics andElectronics, Calcutta University,and M.Tech. and Ph.D. degreesfrom IIT Kharagpur in 1976and 1986, respectively. He hasbeen engaged in teaching andresearch at IIT Kharagpur in theDepartment of Electronics andElectrical Communication Engi-neering for the last 28 years and

currently he serves therein as the Head of the Department. Dur-ing the period 1999–2002, he also served as the Chairman ofG.S. Sanyal School of Telecommunications at IIT Kharagpur.In the early phase of his career, he worked for the Transmis-sion R & D Division in Indian Telephone Industries, Bangalore,during 1976–1978, and in the Production Management Di-vision of Audio and Intercom Systems of Philips India Ltd,Calcutta, during 1980–1981. During his stay at IIT Kharagpur, he

R. Mahapatra et al. / Int. J. Electron. Commun. (AEÜ) 63 (2009) 1012–1025 1025

was awarded an Indo-US Fellowship by the Department of Sci-ence and Technology, Government of India, and the United StatesAgency for International development, to carry out research atStanford University for year during 1992–1993 in the

area of Coherent Optical Communications. Thereafter, he visitedUniversity of California at Davis during 1997–1999 and ChonbukNational University, South Korea, during 2003–2004 to carry outcollaborative research in the area of optical networking.