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736 IEICE TRANS. COMMUN., VOL.E98–B, NO.4 APRIL 2015 PAPER Novel Synchronization and BER Improvement Method for Public Safety Mobile Communication Systems Employing Heterogeneous Cognitive Radio Masafumi MORIYAMA ,†† a) and Takeo FUJII , Members SUMMARY In this paper, a novel synchronization method is pro- posed for a heterogeneous cognitive radio that combines public safety mo- bile communication systems (PMCSs) with commercial mobile wireless communication systems (CMWCSs). The proposed method enables self- synchronization of the PMCSs as well as co-synchronization of PMCSs and CMWCSs. In this paper, the self-synchronization indicates that each sys- tem obtains own timing synchronization. The co-synchronization indicates that a system recognizes data transmitted from other systems correctly. In our research, we especially focus on PMCS self-synchronization because it is one of the most dicult parts of our proposed cognitive radio that im- proves PMCS’s communication quality. The proposed method is utilized for systems employing dierentially encoded π/4 shift QPSK modulation. The synchronization can be achieved by correlating envelopes calculated from a PMCS’s received signals with subsidiary information (SI) sent via a CMWCS. In this paper, the performance of the proposed synchronization method is evaluated by computer simulation. Moreover, because this SI can also be used to improve the bit error rate (BER) of PMCSs, BER im- provement and ecient SI sending methods are derived, after which their performance is evaluated. key words: public safety, cognitive radio, synchronization, π/4 shift QPSK 1. Introduction Public safety mobile communication systems (PMCSs) are widely used for public safety applications involving fire- fighters, the police, local government, etc. The Association of Radio Industries and Businesses (ARIB) has standardized several PMCSs, e.g., Narrow Band Digital Telecommunica- tion System (RCR STD-39) and Digital Mobile Telecom- munication System For Local Government (RCR STD-T79) in Japan [1]. A PMCS employs long-range communication systems using very high frequency (VHF) for carrier waves. By using long-range systems, a PMCS covers its required service areas eectively, including underpopulated areas. However, in urban areas, the communication quality of PMCSs is sometimes inferior to that of commercial mo- bile wireless communication systems (CMWCSs), e.g., cel- lular communication systems, employing short-range sys- tems. CMWCSs support urban areas with reliable service owing to high-density base station allocation, whereas PM- Manuscript received June 16, 2014. Manuscript revised November 14, 2014. The authors are with the Advanced Wireless Communica- tion Research Center, The University of Electro-Communications, Chofu-shi, 182-8585 Japan. †† The author is with Police Info-Communications Research Center, National Police Academy, Fuchu-shi, 183-8558 Japan. a) E-mail: [email protected] DOI: 10.1587/transcom.E98.B.736 CSs cannot provide these areas with reliable service because of low-density base station allocation. In PMCSs employing long-range systems, when a service area covered by a cer- tain base station suers from shadowing [2] and becomes a low signal to noise power radio (SNR) area, sparse adjacent base stations can hardly cover this area. In this area, com- munication of PMCSs is sometimes cut o, while CMWCSs overcome this interruption by allocating many base stations that can mutually cover neighbor areas in urban areas. To enhance the reliability and service coverage of PMCSs in urban areas, we proposed a heterogeneous cognitive radio combining PMCSs with CMWCSs [3]. (For simplicity, we hereinafter refer to heterogeneous cognitive radio as cogni- tive radio.) Figure 1 shows the system model of the cogni- tive radio. When the communication quality of the PMCS is degraded by shadowing, the cognitive radio acquires ad- ditional information from the CMWCS to improve the com- munication quality of the PMCS. We refer to this informa- tion as subsidiary information (SI). In order for the cognitive radio shown in Fig. 1 to work appropriately, synchronization is required. First, the PMCS and the CMWCS need to acquire self-synchronization from their received signals. Then, co-synchronization of the PMCS and the CMWCS must be achieved. In this paper, the self-synchronization indicates that each system obtains own timing synchronization. The co-synchronization in- dicates that a system recognizes the data transmitted from other systems correctly. The co-synchronization is usu- ally obtained by synchronizing absolute time acquired from systems transmitting accurate time such as Global Posi- tioning System (GPS) signals or by exchanging side in- formation to synchronize systems. In cognitive networks, Fig. 1 Schematic of the proposed cognitive radio system. Copyright c 2015 The Institute of Electronics, Information and Communication Engineers

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Page 1: PAPER Novel Synchronization and BER Improvement … ·  · 2017-12-02Novel Synchronization and BER Improvement Method for Public Safety Mobile Communication Systems Employing Heterogeneous

736IEICE TRANS. COMMUN., VOL.E98–B, NO.4 APRIL 2015

PAPER

Novel Synchronization and BER Improvement Method for PublicSafety Mobile Communication Systems Employing HeterogeneousCognitive Radio

Masafumi MORIYAMA†,††a) and Takeo FUJII†, Members

SUMMARY In this paper, a novel synchronization method is pro-posed for a heterogeneous cognitive radio that combines public safety mo-bile communication systems (PMCSs) with commercial mobile wirelesscommunication systems (CMWCSs). The proposed method enables self-synchronization of the PMCSs as well as co-synchronization of PMCSs andCMWCSs. In this paper, the self-synchronization indicates that each sys-tem obtains own timing synchronization. The co-synchronization indicatesthat a system recognizes data transmitted from other systems correctly. Inour research, we especially focus on PMCS self-synchronization becauseit is one of the most difficult parts of our proposed cognitive radio that im-proves PMCS’s communication quality. The proposed method is utilizedfor systems employing differentially encoded π/4 shift QPSK modulation.The synchronization can be achieved by correlating envelopes calculatedfrom a PMCS’s received signals with subsidiary information (SI) sent viaa CMWCS. In this paper, the performance of the proposed synchronizationmethod is evaluated by computer simulation. Moreover, because this SIcan also be used to improve the bit error rate (BER) of PMCSs, BER im-provement and efficient SI sending methods are derived, after which theirperformance is evaluated.key words: public safety, cognitive radio, synchronization, π/4 shift QPSK

1. Introduction

Public safety mobile communication systems (PMCSs) arewidely used for public safety applications involving fire-fighters, the police, local government, etc. The Associationof Radio Industries and Businesses (ARIB) has standardizedseveral PMCSs, e.g., Narrow Band Digital Telecommunica-tion System (RCR STD-39) and Digital Mobile Telecom-munication System For Local Government (RCR STD-T79)in Japan [1]. A PMCS employs long-range communicationsystems using very high frequency (VHF) for carrier waves.By using long-range systems, a PMCS covers its requiredservice areas effectively, including underpopulated areas.

However, in urban areas, the communication qualityof PMCSs is sometimes inferior to that of commercial mo-bile wireless communication systems (CMWCSs), e.g., cel-lular communication systems, employing short-range sys-tems. CMWCSs support urban areas with reliable serviceowing to high-density base station allocation, whereas PM-

Manuscript received June 16, 2014.Manuscript revised November 14, 2014.†The authors are with the Advanced Wireless Communica-

tion Research Center, The University of Electro-Communications,Chofu-shi, 182-8585 Japan.††The author is with Police Info-Communications Research

Center, National Police Academy, Fuchu-shi, 183-8558 Japan.a) E-mail: [email protected]

DOI: 10.1587/transcom.E98.B.736

CSs cannot provide these areas with reliable service becauseof low-density base station allocation. In PMCSs employinglong-range systems, when a service area covered by a cer-tain base station suffers from shadowing [2] and becomes alow signal to noise power radio (SNR) area, sparse adjacentbase stations can hardly cover this area. In this area, com-munication of PMCSs is sometimes cut off, while CMWCSsovercome this interruption by allocating many base stationsthat can mutually cover neighbor areas in urban areas. Toenhance the reliability and service coverage of PMCSs inurban areas, we proposed a heterogeneous cognitive radiocombining PMCSs with CMWCSs [3]. (For simplicity, wehereinafter refer to heterogeneous cognitive radio as cogni-tive radio.) Figure 1 shows the system model of the cogni-tive radio. When the communication quality of the PMCSis degraded by shadowing, the cognitive radio acquires ad-ditional information from the CMWCS to improve the com-munication quality of the PMCS. We refer to this informa-tion as subsidiary information (SI).

In order for the cognitive radio shown in Fig. 1 to workappropriately, synchronization is required. First, the PMCSand the CMWCS need to acquire self-synchronization fromtheir received signals. Then, co-synchronization of thePMCS and the CMWCS must be achieved. In this paper,the self-synchronization indicates that each system obtainsown timing synchronization. The co-synchronization in-dicates that a system recognizes the data transmitted fromother systems correctly. The co-synchronization is usu-ally obtained by synchronizing absolute time acquired fromsystems transmitting accurate time such as Global Posi-tioning System (GPS) signals or by exchanging side in-formation to synchronize systems. In cognitive networks,

Fig. 1 Schematic of the proposed cognitive radio system.

Copyright c© 2015 The Institute of Electronics, Information and Communication Engineers

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MORIYAMA and FUJII: NOVEL SYNCHRONIZATION AND BER IMPROVEMENT METHOD FOR PUBLIC SAFETY MOBILE COMMUNICATION SYSTEMS737

although many researchers have researched several co-synchronization methods such as Timing-sync Protocol forSensor Network (TPSN) [4] and Flooding Time Synchro-nization Protocol (FTSP) [5], self-synchronization methodshave hardly been researched.

In this paper, we mainly focus on self-synchronizationfor the PMCS. This is because the PMCS is often employedin extremely low signal-to-noise power ratio (SNR) envi-ronments and therefore cannot frequently obtain the self-synchronization. In low SNR environments, the PMCSrequires not only conventional synchronization techniquessuch as the Maximum Amplitude Method (MAM) [6] andcorrelation of synchronous words (SW) [7] but also someassistance for the self-synchronization of the proposed cog-nitive radio. In this connection, we add another explana-tion as to why we focus on the self-synchronization for thePMCS. In our research, to prevent significant informationfrom leaking out from the CMWCSs and their networksthat convey the SI, we forbid the CMWCs to deliver all theinformation of the PMCS [3]. Hence, the cognitive radioneeds to acquire both PMCS’s and CMWCS’s signals to ob-tain meaningful data. The cognitive radio therefore mustobtain self-synchronization of the PMCS to extract signalseven if the PMCS is in low SNR environments. To real-ize self-synchronization of the PMCS, we studied a self-synchronization method employing GPS signals in our pastresearch [8]. However, the method was not able to solvethe self-synchronization problem completely. In enclosedspace, such as an underground mall, where GPS signals arenot accessible, a GPS-based method will not work.

We therefore propose a novel synchronization methodthat can acquire a PMCS’s self- and co-synchronizationeven if the cognitive radio is in a place where GPS sig-nals cannot reach the cognitive radio. The proposed methodcan acquire self- and co-synchronization by correlating en-velopes calculated from a PMCS’s received signals withthe SI when the PMCS employ differentially encoded π/4shift QPSK, which is often used as the PMCS modulationmethod.

The proposed method has two advantages. One is thatthe method achieves timing self-synchronization withoutdegradation caused by an accidental frequency synchroniza-tion error. The other is that the employed SI can be utilizednot only for synchronization but also for BER improvementof the PMCS. Hence, we deal with BER improvement aswell as synchronization in this paper. Because the SI canbe used for both functions, CMWCS’s radio resource con-sumption is reduced.

This paper is organized as follows. In Sect. 2, the struc-ture of the proposed cognitive radio system is explained.In Sect. 3, we present the synchronization methods employ-ing the SI. The BER improvement and efficient SI sendingmethod are explained in Sect. 4. Section 5 presents com-puter simulation results on self-synchronization probabil-ities, jitter characteristics, and BER improvement perfor-mance. The conclusions are presented in Sect. 6.

Fig. 2 Cognitive radio combined with the PMCS and the CMWCS.

2. Structure of the Cognitive Radio System

Figure 2 shows the construction of the proposed cognitiveradio combining a PMCS with a CMWCS [3]. The cogni-tive radio enhances BER performance by adding the SI tothe received signals of the PMCS. In the PMCS transmitter,the information data are divided into two parts and codedby separate Forward Error Correction (FEC) encoders intoseparate codewords (see modules (A) and (C) in Fig. 2). Inthis paper, we adopt convolutional code as FEC. The con-volutional codes are usually used in PMCSs that are narrowband voice communication systems [3], while turbo code [9]and low-density parity check (LDPC) code [10] are oftenemployed for broadband communication systems. One ofthe codewords is modulated and transmitted by an antenna(Fig. 2, module (B)). A part of the remaining codewords isperiodically removed to reduce the amount of informationarriving at the CMWCS. The removal rate can be changeddepending on the grade of deterioration of the PMCS’s com-munication quality. These signals constitute the SI, which istransmitted by the CMWCS (Fig. 2, modules (D) and (E)).

In the cognitive radio, signals transmitted by the PMCSare demodulated (Fig. 2, module (F)) and then combinedwith the SI from the CMWCS (Fig. 2, module (G)); we referto these combined signals as integrated received codewords.These integrated received codewords are decoded and re-stored to receive data (Fig. 2, module (H)). Because the SI isadded, BER performance is improved, owing to the increasein the free distance [14] of the received codewords comparedwith that of the conventional codewords of the PMCS.

Here, we assume that the receivers of the CMWCS in-cluded in the cognitive radio are extant products such asmachine-to-machine (M2M) modules. In this paper, theM2M modules indicate small devices that can be set onother devices and can communicate other systems by us-ing wireless systems without operating personnel [11], [12].By employing M2M modules, we expect to realize the pro-posed cognitive radio easily and inexpensively. In the caseof the extant module, the cognitive radio acquires correcthard-decision values as the SI after a cyclic redundancycheck (CRC) and an automatic repeat-request (ARQ) areperformed in the M2M. Although in the present study wesuppose that the hard-decision values are always correct, infuture work we will consider situations where the cognitiveradio does not acquire correct values.

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738IEICE TRANS. COMMUN., VOL.E98–B, NO.4 APRIL 2015

3. Proposed Synchronization Method

To realize the proposed cognitive radio, an effective self-synchronization method is required for the PMCS in low-SNR environments. In the present section, we proposea novel synchronization method utilizing the SI. The pro-posed method is capable of obtaining not only the self-synchronization but also the co-synchronization between thePMCS and the CMWCS.

3.1 Variety of Self-Synchronization

In this paper, we deal solely with timing synchronization,although self-synchronization comprises carrier frequencysynchronization, carrier phase synchronization, and timingsynchronization [13]. We do not deal with carrier frequencysynchronization and compensation of frequency offset inthis paper, because the frequency offset cannot be compen-sated for easily even if the SI is employed for synchroniza-tion. We will research this problem in future work. Hence,in this paper, we assume that the phase rotation caused bythe frequency offset is less than ±π/4 per symbol. In thiscase, the BER degradation caused by the phase rotation(within ±π/4) can be compensated by additional SI, as ex-plained in Sect. 4, whenever the timing synchronization isrecovered. The reason why the carrier phase synchroniza-tion is not considered is most PMCSs employ differentialencoding. Although we deal solely with timing synchro-nization, note that the timing synchronization is not contam-inated by the frequency offset in the proposed method. Forsimplicity, we hereinafter refer to timing synchronization assynchronization.

3.2 Problem with a Simple Method

For application of the SI to the synchronization of thePMCS, we consider the synchronization method shown inFig. 3 to be the simplest. In the transmitter of the PMCS,the SI is defined as modulated signals, in-phase (I), andquadrature-phase (Q) signals (see signals (A) in Fig. 3),which are removed periodically to reduce CWMCS resource

Fig. 3 Cognitive radio system employing a simple synchronizationmethod.

consumption and avoid all PMCS information (Fig. 3, mod-ule (B)).

In the cognitive radio, the synchronization is recoveredby correlating the filtered received signals of the PMCS withthe SI from the CWMCS (Fig. 3, module (C)). Moreover,it appears that this SI is also used for BER improvement.However, this method suffers from two disadvantages. Oneis that the correlation falls as the phase rotations of the re-ceived signals increase (the frequency offset increases) inthe down-converter (Fig. 3, module (D)). The other disad-vantage is that, in regard to BER improvement, the cogni-tive radio does not demodulate all the received SI (Fig. 3,module (E)) when the PMCS employs differential encod-ing (Fig. 3, module (F)) and the SI is removed periodically(Fig. 3, module (B)). This is because in differential decod-ing, the removed SI cannot be demodulated without a neigh-boring symbol.

3.3 Details of Proposed Synchronization Method

In this section, we propose a novel synchronization methodthat can recover the synchronization by correlating en-velopes calculated from the received PMCS signals with theSI. To achieve this correlation, the SI conveys the magnitudeof the phase rotations caused by the modulation. Figure 4shows the cognitive radio system employing the proposedsynchronization method.

The proposed method has two advantageous features.One is that the method can be used even if a frequency er-ror occurs. This is because the synchronization is recoveredfrom the correlation of the PMCS’s envelopes, which arenot affected by the frequency error. The other advantage isthat the received SI can be demodulated without a neigh-boring symbol, because the SI consists of phase rotations,which represent information when differential encoding isemployed.

The current section is organized as follows. InSect. 3.3.1, we explain the characteristics of differentiallyencoded ±π/4 shift QPSK. Using the characteristics, syn-chronization can be obtained by correlating SI with receivedsignals. A detailed explanation is presented in Sect. 3.3.2.

Fig. 4 Cognitive radio system employing the proposed synchronizationmethod.

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MORIYAMA and FUJII: NOVEL SYNCHRONIZATION AND BER IMPROVEMENT METHOD FOR PUBLIC SAFETY MOBILE COMMUNICATION SYSTEMS739

Fig. 5 Constellation of π/4 shift QPSK (roll-off factor α = 0.5).

Fig. 6 Envelope of π/4 shift QPSK (roll-off factor α = 0.5).

3.3.1 Characteristics of Differentially Encoded π/4 ShiftQPSK

First, characteristics of the envelope of differentially en-coded π/4 shift QPSK are clarified for derivation of the pro-posed method. Figures 5 and 6 show a constellation and anenvelope of π/4 shift QPSK employing the raised-cosine fil-ter (roll-off factor α = 0.5) to limit bandwidths, respectively.Figure 5 shows that if a phase rotates by ±3π/4, the ampli-tude of the envelope at the midpoint of the transition fromone symbol to the adjacent symbol is smaller than the aver-age amplitude. If the phase rotates by ±π/4, the amplitudeis increased.

Here, the amplitude at the midpoint is derived usingmathematical expressions. Because variations in the en-velopes are attributed to band limits, we first show the im-pulse response of the raised-cosine filter as follows [15].

Fig. 7 In-phase and quadrature-phase signals of π/4 shift QPSK whenbandwidth is limited by filtering raised-cosine (α = 0.5).

h(t) =sin(πt/T )πt/T

· cos(απt/T )1 − (2αt/T )2

, (1)

where t and T represent time and symbol duration, respec-tively. α is the roll-off factor. Figure 7 shows an example ofthe amplitude of the in-phase (I) and quadrature-phase (Q)signals of ±π/4 shift QPSK when the bandwidth is limitedby filtering h(t). m indicates the symbol number.

In Fig. 7, we focus on the midpoint between m and m+1. The amplitudes of I and Q at the midpoint between m andm + 1, Im and Qm, respectively, are given as follows:

Im =√

P∞∑

n=−∞cos

(n−1∑

k=−∞Δθk + θ0

)· h{ T2 − (n − m)T }

Qm =√

P∞∑

n=−∞sin

(n−1∑

k=−∞Δθk + θ0

)· h{ T2 − (n − m)T },

(2)

where P and n represent the power of the envelope at thesymbol decision points and the index of the symbol, respec-tively. θ0 and Δθk are the initial phase and the phase rotationfrom the kth symbol to the k+1th symbol, respectively. Theamplitude of the envelope at the midpoint is given by

Am =

√I2m + Q2

m (3)

To simplify Eqs. (2) and (3), when h(t) is restricted to therange −T/2 to T/2, the approximate amplitude A′m of Eq. (3)is as follows:

A′m =2√

P cos(απ/2)π(1 − α2)

×√{cos(θm)+cos(θm+Δθm)}2+{sin(θm)+sin(θm+Δθm)}2

=2√

2P cos(απ/2)π(1 − α2)

√cos(Δθm) + 1, (4)

where θm =m−1∑

k=−∞Δθk + θ0 denotes the phase of the mth

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740IEICE TRANS. COMMUN., VOL.E98–B, NO.4 APRIL 2015

Fig. 8 Histogram of the midpoint amplitude of π/4 shift QPSK (roll offfactor α = 0.5).

symbol. From Eq. (4), when Δθm is ±π/4, cos(Δθm) be-comes positive, and A′m become large (when α = 0.5 andand P = 1, A′m = 1.11) On the other hand, when Δθm is±3π/4, cos(Δθm) becomes negative, and A′m becomes small(A′m = 0.459).

Although A′m is not the accurate amplitude Am, A′mtracks the amplitude Am strongly in the midpoint of the π/4shift QPSK symbol transition. The pulse responses of thetwo immediate symbols located around the focused mid-points, h(−T/2) and h(T/2), affect Am the most; that is,|h(T/2 + n)| decreases as the symbol number |n| increases(n is an integer).

To arrive at a more accurate amplitude A′m, we utilizecomputer calculations. Figure 8 shows a histogram whenA′m is calculated 100,000 times by selecting Δθm, ±π/4 or±3π/4, at random. In the calculations, P = 1, α = 0.5, andthe side lobe of the impulse response h(t) is restricted to theduration for 21 symbols. From Fig. 8, we confirm that ifa phase rotates by ±3π/4, the amplitude of the midpoint issmaller than 0.8. If a phase rotates by ±π/4, the amplitudeis larger than 0.8.

As reference information, the pattern of the largestAm in Δθm = ±3π/4 is that the rotation remains un-changed for the phases, e.g., · · · , Δθm−2 = −3π/4, Δθm−1 =

−3π/4, Δθm = −3π/4, Δθm+1 = −3π/4, Δθm+2 = −3π/4, · · · .By contrast, the pattern of the smallest Am in Δθm = ±π/4is Δθm−2 = ∓3π/4, Δθm−1 = ∓π/4, Δθm = ±π/4, Δθm+1 =

∓π/4, Δθm+2 = ∓3π/4, when we focus on only five symbols.Moreover, the influence of the roll-off factor α is note-

worthy. As the roll-off factor α decreases, the midpointsof the envelopes are not sharply divided into two groups,as shown in Fig. 8. The threshold between ±3π/4 and±π/4 becomes unclear. Because the amplitude of the sidelobe of h(t) increases, Am is affected by the side lobes ofmore symbols. However, the aforementioned characteristic,Am, becomes large if Δθm = ±3π/4 (Am becomes small ifΔθm = ±π/4) is stationary.

3.3.2 Correlation between SI and Received Signals

We derive the proposed synchronization method from thecharacteristics of the midpoint of differentially encoded π/4shift QPSK. In our proposed method, the synchronizationis obtained by correlating the SI with envelopes calculated

Fig. 9 Proposed timing recovery method; correlation between the zero-mean envelope and the SI sequence.

from PMCS’s received signals.First, we explain how to send the SI. As described ear-

lier, the SI is defined as the magnitude of the phase rotation(Fig. 4, module (A)). To correlate the SI directly with theenvelopes calculated from the PMCS and reduce CMWCSresource consumption, the SI is expressed with 1 bit. If themagnitude of a phase rotation is ±3π/4, the SI is defined as−1. In the same way, a phase rotation of ±π/4 is convertedinto +1. Then, the converted SI is removed periodically andtransmitted by the CMWCS (Fig. 4, module (B)).

Next, we explain how to calculate the envelopes of thePMCS’s received signals. The envelopes are calculated fromthe filtered received signals I and Q by taking the squareroot of the sum of the squares (Fig. 4, module (C)). Thenthe envelopes are shifted such that their mean becomes zerowithout changing the waveform (Fig. 4, module (D)). Unlessthese envelopes are shifted, a high correlation value cannotprobably be obtained after correlating. This is because theoffset due to the non-zero mean degrades the correlation.

Finally, we explain the correlation between the SI andthe zero-mean envelopes. By arranging SI in the symbolinterval, this SI sequence can be defined as the forecastedenvelopes of the PMCS’s signal. This is because the signof the forecasted envelope corresponds with that of the realzero-mean envelope at the midpoint in most cases. From thecharacteristics of differentially encoded π/4 shift QPSK, ifthe phase rotates by±3π/4(±π/4), the amplitude of the zero-mean envelope at the midpoint is usually a negative (posi-tive) value. Because the SI has been defined as ±3π/4→ −1and ±π/4 → +1, the sign of the forecasted envelope (SI se-quence) and the zero-mean envelope at the midpoint are thesame in most cases.

Figure 9 shows the correlations between the zero-meanenvelopes and the SI sequences defined as the forecasted en-velopes, while the SI sequences are shifted laterally (Fig. 9,module (E)). When the signs of SI and the zero-mean enve-lope amplitude are the same at specific points, the multiplied

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MORIYAMA and FUJII: NOVEL SYNCHRONIZATION AND BER IMPROVEMENT METHOD FOR PUBLIC SAFETY MOBILE COMMUNICATION SYSTEMS741

value becomes positive. Because correlation is calculatedby adding all the multiplied values, the correlation value ishigher as the number of the positive multiplied values in-crease. By shifting the SI sequence laterally and search-ing for the point where the correlation value is the highest,the cognitive radio can achieve timing recovery. The pointbefore half symbol duration from the maximum correlationvalue becomes the self-synchronization point. In addition,the proposed method can also obtain the co-synchronizationbetween the PMCS and the CWMCS.

4. Proposed BER Improvement Method

The SI of the proposed method can also be used for BERimprovement because the SI is produced from phase rota-tions caused by modulation. The concrete information usedfor BER improvement is −SQ, as shown in Fig. 5. In thispaper, this SI used for BER improvement is denoted by SIA.The BER improvement method is explained in Sect. 4.1.

Moreover, we introduce SIB, specially designed forBER improvement, in Sect. 4.2. We describe the BER im-provement method using both of SIA and SIB in Sect. 4.3.

4.1 BER Improvement Method Using SIA

In this subsection, the BER improvement method is ex-plained. Figure 10 shows the method. The integrated re-ceived codewords shown in Fig. 2 are produced by combin-ing the received signals of the PMCS with the SIA equiv-alent of −SQ shown in Fig. 5. By decoding the integratedreceived codewords using the Viterbi algorithm, which isemployed for most of the PMCSs, the BER of the PMCS isimproved owing to the free distance increase [14] as men-tioned in Sect. 2. When the original coding rate R is 1/2in the PMCS, the integrated received codewords can be de-coded as the punctured code [14] of coding rate 1/3 by in-serting zero into place where the SI is removed in the trans-mitter.

4.2 SIB Solely Used for BER Improvement

In terms of BER improvement, the optimal SI is not SIA. Wetherefore introduce SIB solely used for BER improvement.SIB is produced from the encoder (module (C) of Fig. 2),whose generator polynomial [14] of convolutional code ischosen so that the free distance [14] of the integrated re-ceived codewords can be maximized [3]. The BER can de-

Fig. 10 Decoding of the integrated received codewords.

crease with increasing free distance in the Viterbi decoding.Hence, further BER improvement can be obtained by em-ploying SIB rather than SIA.

We summarize features of SIA and SIB. SIA is usedfor both synchronization and BER improvement. However,its capability for BER improvement is inferior to that ofSIB. The employment of SIB improves the BER effectivelyby maximizing the free distance of the integrated receivedcodewords, but it is not used for synchronization.

Table 1 shows the free distance of the integrated re-ceived codewords. In Table 1, we assume that SIA and SIB

have no error and infinite reliability [3]. The parameters ofthe convolutional code of the PMCS are listed as follows:constraint length K = 6, coding rate R = 1/2, generatorpolynomial g1 = 47, and g2 = 75 (octal). SIA and SIB

are compared under the same removal rate. In Table 1, forexample, a removal rate of 2/3 indicates that two bits are re-moved for every three SI bits in module (D) of Fig. 2. In thecase of SIB, the generator polynomial is changed depend-ing on the removal rate so that the free distance can be thelargest.

4.3 Adaptive Coding

In order to transmit the SI effectively for conservingCMWCS resources, we consider a method that controls thequantity of both SIA and SIB. Because both SIA and SIB areproduced from the encoders shown in Fig. 2, we refer to themethod sending both SIA and SIB as adaptive coding.

Figure 11 shows the cognitive radio that deals withboth SIA and SIB. The integrated received codewords ofFig. 11 are shown in Fig. 12. The generator polynomial ofSIB is switched when the removal rate of SIA is changed.In the case of adaptive coding, the free distance of the inte-grated received codewords becomes the intermediate value

Table 1 Free distance of the integrated received codewords when theremoval rate is the same.

Removal Free distance {generator polynomial of SI (octal)}Rate SIA SIB

1/3 15{75} 19{41}1/2 11{75} 15{57}2/3 8{75} 10{61}1/1 8

Fig. 11 Proposed system adopting adaptive coding.

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Fig. 12 Decoding of the integrated received codewords in the case ofadaptive coding.

between SIA and SIB alone.The concrete procedure for adaptive coding is as fol-

lows. First, the cognitive radio performs the evaluation un-der the condition that the synchronization is stable. Whenthe synchronization is not stable, the quantity of the send-ing SIA will be increased by reducing the removal rate tostabilize the synchronization. After stabilizing the synchro-nization, the sending SIB will be increased until the BERreaches the specified value, for example, BER < 10−4, thatis required in most speech communication systems. In caseof difficulty in synchronization, the quantity of SIA is in-creased. When SIB increases, the BER is degraded even ifthe synchronization is stable.

5. Computer Simulations

We evaluate the proposed method using computer simu-lation. First, we show the synchronization performancewhen the quantity of SIA is changed. The performance isalso compared with a conventional synchronization method.Next, we evaluate the BER improvement when the quantityof SIA and SIB is changed.

5.1 Simulation Conditions

In the computer simulation, we employ the RCR STD-39 [1]as the PMCS. Table 2 shows the parameters of RCR STD-39. In the simulation, the SIA and SIB received by the cog-nitive radio have no-error, as mentioned in Sect. 2. We sim-ulate the performance in AWGN (Additive White GaussianNoise), 1 path Rayleigh fading, and IEEE802.22 (WRAN)multipath model Profile C [18]. We consider that the Pro-file C can be representative of PMCS’s real multipath fad-ing when long-delay multipath waves occur. The MaximumDoppler frequency f d is 10 Hz and 40 Hz in the simulation.The fading parameters are shown in Table 3.

5.2 Synchronization Performance

Figures 13 and 14 show the synchronization probabilitieswhen the removal rate of SIA is changed. Here, the synchro-nization probability is referred to as the probability that thesimulated timing falls within ±0.5 symbols of the optimalsynchronization point. In the case of the Profile C, we de-fine the mean excess delay as the optimal synchronization

Table 2 Parameters of RCR STD-39.

Access method TDM(4multiplexing)/TDMA(Down-link/Up-link)

Modulation Differentially encoded π/4 shift QPSKSymbol duration T 62.5 μs

Slot length 10 ms (160 symbols)Frame length 40 ms

Transmission rate 16 kbaud (32 kbps)Forward error correction Convolutional code

Coding rate 1/2Constraint length 6

Generator polynomial g1 = 53, g2 = 75 (octal)Interleave Interleave in a slot

2 frames interleaveSynchronous word (SW) 20 bit

Table 3 Fading parameters.

(1) AWGNModel (2) 1 path Rayleigh fading

(3) IEEE802.22 (WRAN) multipath model Profile Caa(mean excess delay:21 μs, RMS delay spread:8.4 μs)

Maximum (1) f d = 10 Hz, f d · T = 62.5 × 10−5

Doppler aaa(Speed:28 km/h Carrier frequency:400 MHz)frequency (2) f d = 40 Hz, f d · T = 250 × 10−5

aaa(Speed:108 km/h Carrier frequency:400 MHz)

Fig. 13 Synchronization probabilities (AWGN and 1path Rayleigh fad-ing).

point [19], [20] in this paper. The synchronization timing issearched from the range of one frame. The quantity of theSI removal (removal rate) is changed from 1/10 to 2/3. Forcomparison, Figs. 13 and 14 include the performance of theconventional synchronization method [7] that correlates thereceived signal of the PMSC with the 20 bits synchronouswords (SW) shown in Table 2.

As shown in Figs. 13 and 14, we can confirm that thesynchronization probability increases with decreasing re-moval rate. In the 1 path Rayleigh fading, a higher SNRis required to reach high synchronization probabilities com-pared with the AWGN environment. This is because the re-ceived signals of the PMCS sometimes almost disappear inslow and non-selective fading environments. In the Profile

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Fig. 14 Synchronization probabilities (IEEE 802.22 multipath modelProfile C).

Fig. 15 Synchronization probabilities by combining the proposed andthe conventional method (AWGN and 1path Rayleigh fading).

C, although the signals rarely disappear owing to frequencyselective fading (the time diversity effect), inter-Symbol-interference (ISI) makes the synchronization probabilitiesdeteriorate. To mitigate the signal disappearance and ISI,Space diversity techniques [16] may be required.

Aside from space diversity techniques, we consider an-other synchronization improvement method combining theproposed method with the conventional method. Figures 15and 16 show the performance of this combined method thatachieves synchronization by adding the correlation valuesbetween the received signals and the SW (the conventionalmethod [7]) to the correlation values between the envelopesand SIA (the proposed method). By combining, a furtherimprovement in the synchronization probability is achievedfor low SNRs. Figure 17 shows the root mean sequence(RMS) jitter [17] characteristics of this combined methodin the case of f d=40 Hz. In the case of AWGN and 1 pathRayleigh fading, the jitter decreases as the removal rate de-creases. In the case of the Profile C, the jitter can hardly

Fig. 16 Synchronization probabilities by combining the proposed andthe conventional method (IEEE 802.22 multipath model Profile C).

Fig. 17 RMS jitter characteristics ( f d=40 Hz).

decrease with increasing SNR. It appears that the jitter islarge. However, since we define the mean excess delay asthe optimal synchronization point, we cannot correctly as-sess the jitter shown in Fig. 17 shows suitable values. Inthe frequency selective fading channels, the instant optimalsynchronization point usually differs from the mean excessdelay. The influence of the jitter is evaluated in Sect. 5.4.

5.3 Influences of Roll-Off Factor

Here, we consider the influences of the roll-off factor. Thesynchronization probabilities are shown in Fig. 18 when theroll-off factor α is changed to 0.2, 0.5, and 0.8. The otherparameters are the same as in Tables 2 and 3. The removalrate is 1/3 and f d=40 Hz. Figure 18 shows that the roll-off factor does not significantly affect the synchronizationprobabilities.

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Fig. 18 Influences of the roll-off factor ( f d=40 Hz).

Fig. 19 The BER improvement when changing removal rate of SIA (Re-moval Rate 2/3).

Fig. 20 The BER improvement when changing removal rate of SIA (Re-moval Rate 1/3).

5.4 BER Performance

Figures 19 and 20 show the BER performance using the SIA

Fig. 21 The BER performance when using SIA, SIB, and SIA + SIB

( f d=10 Hz).

shown in Fig. 4 and Fig. 10, when the synchronization fallswithin ±0.5 symbols of the optimal synchronization pointmentioned in Sect. 5.2, and there is no frequency error inthe down-converter of the PMCS. In the case of AWGN and1 path Rayleigh fading, although the simulated BER per-formance includes the effect of the jitter shown in Fig. 17,the BER performance is hardly degraded. The BER perfor-mance is improved as the removal rate decreases; the quan-tity of SIA increases. In the case of the Profile C, the BERperformance of the proposed method (with jitter) can alsobe improved by adding SI. However, the performance is in-ferior to that of the optimal synchronization point (withoutjitter), which is the mean excess delay of delay profile. Fromthis result, we think that there is large jitter when employingthe proposed method in frequency selective fading environ-ments as shown in Fig. 17. However, since the proposedmethod has high synchronization probability, we considerthat the detritions caused by the jitter can be compensatedby increasing amount of SI. For reference, the reason whyBER decreases with increasing f d is attributed to enhance-ment of interleave effect. The benefit of the interleave is toprovide time diversity (when used along with FEC.) [21].

Figure 21 shows the performance of the BER improve-ment method employing SIA, SIB, and SIA+SIB shown inFigs. 11 and 12, when we assume that the synchronizationhas already been obtained, f d=10 Hz, and the jitter is set tozero. In this figure, the total removal rate is fixed at 1/3; i.e.,the total quantity of SI is the same. In the case of SIA+SIB,the removal rates of SIA and SIB are 2/3. In terms of theBER improvement, we confirm that SIB is superior to SIA ifthe system can synchronize properly.

6. Conclusion

A heterogeneous cognitive radio that combines PMCS withCMWCS was considered in this paper. We proposed syn-chronization and BER improvement methods that employ

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the SI transmitted by the CMWCS. Synchronization isobtained by correlating the SI with the envelopes of thePMCS’s signals in differentially encoded π/4shift QPSK.The BER is improved by decoding the integrated receivedcodewords constructed by the SI and the received signalsof the PMCS. Moreover, to realize both synchronizationand BER improvement simultaneously using minimum SI,adaptive coding was adopted. In future work, we will addthe proposed synchronization method to a forward/reverseprotection scheme and evaluate its synchronization perfor-mance.

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Masafumi Moriyama received the B.E. de-gree from Kitami Institute of Technology, Hok-kaido, Japan, in 1999. He joined National PoliceAgency in 2000 and National Institute of Infor-mation and Communication Technology (NICT)as a guest researcher from 2004 to 2006. From2006, he has been a researcher at the Police Info-Communications Research Center in NationalPolice Academy, Tokyo, Japan. He is engagedin research on wireless communication systemsfor public safety. He is currently a Ph.D. course

student of The University of Electro-Communications, Tokyo, Japan.

Takeo Fujii was born in Tokyo, Japan, in1974. He received the B.E., M.E., and Ph.D. de-grees in electrical engineering from Keio Uni-versity, Yokohama, Japan, in 1997, 1999, and2002, respectively. From 2000 to 2002, he wasa research associate in the Department of Infor-mation and Computer Science, Keio University.From 2002 to 2006, he was an Assistant Profes-sor in the Department Electrical and ElectronicEngineering, Tokyo University of Agricultureand Technology. Since 2006, he has been an As-

sociate Professor in the Advanced Wireless Communication Research Cen-ter, The University of Electro-Communications. His current research inter-ests are in cognitive radio and ad hoc wireless networks. He received theBest Paper Award in IEEE VTC 1999-Fall, 2001 Active Research Award inRadio Communication Systems from IEICE Technical Committee of RCS,2001 Ericsson Young Scientist Award, Young Researchers’ Award from theIEICE in 2004, and The Young Researcher Study Encouragement Awardfrom IEICE Technical Committee on AN in 2009. He is a member ofIEEE.