Doppler Shift Estimation and Jamming Detection for Cellular Networks

Transcription

1 Australian Journal o Basic and Applied Sciences, 4(12): , 2010 ISSN oppler Shit Estimation and Jamming etection or Cellular Networks Javad Ashar Jahanshahi, Seyed Ali Ghorashi Cognitive Telecommunications Research Group, epartment o Electrical Engineering, Faculty o Electrical and Computer Engineering, Shahid Beheshti University G.C., Evin , Tehran, IRAN. Abstract: The estimation o mobile terminal speed at base station in cellular networks helps BTS in many aspects including channel estimation, adaptive reception, anti-jamming and handover operations. In this paper, we introduce a new algorithm to estimate the channel oppler shit seen by BTS, using the measured received signals at base station. We have improved an LCR based oppler shit estimation algorithm by using only inherent inormation which are available in common receivers without any excessive hardware. An improved scheme or jamming detection based on the proposed oppler shit estimation is proposed, as well. The perormance o the proposed algorithm in a TETRA network is modeled and the simulation has shown acceptable results in a wide range o velocities and jammers. Key words: TETRA (Terrestrial Trunked Radio), oppler shit, jamming detection INTROUCTION The speed o a mobile terminal in wireless communication networks is an important piece o inormation that can improve system perormance in many ways. Knowing the speed o the mobile terminal enables the receiver to perorm more eicient channel estimation. Similarly, in adaptive transmission, it helps the transmitter to adjust a suitable modulation/coding scheme according to the channel condition. In addition, based on the speed inormation o the mobile terminals, handover time can be determined more accurately. Speed inormation can also be used in anti-jamming techniques, when the receiver tries to dierentiate between signal attenuations caused by jamming and channel eects (Sampath, A. and J. Holtzman, 1993). eanwhile, speed estimation by additional sensors like gyroscopes or accelerometers, and systems like GPS (Global Positioning System), increases the complexity and overall costs o the user terminals, and urthermore, reduces the handset battery lie time. Thereore, several techniques have been proposed in literature or mobile terminal speed estimation based on channel oppler shit measurement, and some o them have been implemented in existing mobile communication systems. Covariance estimation schemes estimate oppler requency shit by computing covariance value between training received samples (Baddour. K.E. and N.C. Beaulieu, 2005; Anim-Appiah, K.., 1999; Holtzmann, J.. and A. Sampath, 1995; Tepedelenlio glu, C., 2001; Austin,.. and G.L. St uber, 1994). Other schemes or oppler requency shit estimation have used spectral analysis and variance (ottier,.,. Castelain, 1999), estimation o channel envelope and angle (Tepedelenliogl, C., G.B. Giannak, 2001), statistical inormation o channel phase variations (Hua Jingyu, 2004), Eigen based spectral estimation (Austin,.., 1994), spectrum estimation method based on channel power spectrum density (Jingyu, H. Han, 2004), multi vector test by using maximum likelihood approaches (Krasny, L., 2001), wavelet analysis by tracking changes in the temporal scale (Narashimhan, R. and.c. Cox, 1999) and channel auto-correlation (Yi Sha, Na Yao, Xiaojing Xu, 2008). In (Cho,.G.,. Hong, 2004), authors proposed an LCR based algorithm that estimates terminal s speed over each speed estimation window, and consequent windows do not overlap each other. They also used a single threshold or signal power comparisons. However, the proposed algorithm in (Cho,.G.,. Hong, 2004) cannot ollow the mobile terminal s variation in low SNR conditions. In this paper the oppler shit estimation algorithm is improved by utilizing a speed estimation window that slides over bursts with overlaps and by introducing two dierent low and high thresholds or power level comparisons. These thresholds are Corresponding Author: Javad Ashar Jahanshahi, Cognitive Telecommunications Research Group, epartment o Electrical Engineering, Faculty o Electrical and Computer Engineering, Shahid Beheshti University G.C., Evin , Tehran, IRAN. j.ashar@ mail.sbu.ac.ir, a_ghorashi@sbu.ac.ir 6590

2 updated or each speed estimation window s movement or better tracking the oppler shit variations even in low SNR conditions. This algorithm uses only inherent cellular system inormation, which means there is no need or any hardware modiication o the user terminal, as well as cellular network signaling structure. The proposed algorithm is modeled in a TETRA network and simulation results show an acceptable accuracy in oppler shit estimation in Rician channels. The novel oppler shit estimation algorithm is used in order to improve the jamming detection method which is proposed by (Kurhila,.,. Torvinen, 2006). This paper is organized as ollows. In section 2, we present the proposed algorithm or oppler shit estimation in details. Section 3 gives a brie overview o the proposed jamming detection algorithm in (Kurhila,.,. Torvinen, 2006). In section 4, simulation results and comparisons o the shit oppler estimation algorithm and jamming detection method are presented. Finally, in section 5, we provide our concluding remarks. 2. Proposed Algorithm: Fig. 1 shows the structure o received complex samples over one speed estimation window (i.e. 0.5 or 1 second). This window slides over samples o received signal. Each window divided into groups o samples where: WL N [ ] where [.] is the rounding down operator, WL is the number o samples within a speed estimation window and is the segmentation actor. The segmentation actor will be updated or each received burst. (1) Fig. 1: The structure o speed estimation window. The lowchart o the proposed oppler shit estimation algorithm is illustrated in Fig.2. At the irst stage, the power o the received signal is calculated. This power is measured within a ixed size speed estimation WL window. Then, the power meter computes group powers S ( i), i {1, 2, 3,..., } i* 1 S () i. s( z) ( n) z ( i 1)* 1 2 (2) where S () i is the power o samples over i the group. In the third stage, RS meter computes the root mean square o group powers during speed estimation window as: WL RS 1. S ( i ) WL i 1 2 (3) 6591

3 Fig. 2: Signal Flow o the Proposed Algorithm. The calculated RS is then used to determine low and high thresholds or level crossing calculations. T Then, high and low level crossing thresholds TH and L are calculated. These thresholds should be ractions o the RS value calculated in previous stage: T T H L yrs. xrs. o x y 1 In the ourth stage, level crossing counter counts level crossing requency times group powers S () i cross thresholds TH and in positive slope. T L L R (4), which indicates how many In Rician ading channel with 2-dimensional isotropic scattering, the oppler shit is given by (Theodore Rappaport, 2001): k ( k 1). LR. e. 2 ( k 1). I0(2. k( k 1)) TH TL RS 2 where denotes the oppler shit, k denotes the Rician ading actor, I0 represents the modiied zero order Bessel unction, and e is Euler s number. The oppler shit o each received burst is given by its angle (6) 6592

4 Aust. J. Basic & Appl. Sci., 4(12): , 2010 o arrival n, the carrier requency c, the propagation speed c (which is the speed o light), and the mobile terminal speed v. It can be calculated as: c v..cos( ) C n (7) For the maximum value o the oppler shit, the mobile terminal speed is given by v max. C c For example, when signaling is done with c 396Hz (8) in a typical value or a TETRA system, 100 km/h terminal speed results in maximum oppler shit o. The estimated speed is max 37Hz reported in the ith stage. In the last stage, segmentation actor or the next incoming burst is updated as: s [ ] scaling actor * max [.] s Where is again a rounding down operator, is sampling requency, is maximum oppler requency and scaling actor which is dependent on the channel type. In rapidly changing channels, since the amplitude o the signal varies more rapidly during a burst, the limits o scaling actor cannot be set as high as what it is in static channels. A rapidly changing channel may appear in some situations, i.e. where the speed o the user terminal is high. The algorithm interrupts until it receives new bursts. Algorithm started again (go to stage two) by using this new value o when a new burst arrives. 3. Jamming etection: In this section, the proposed jamming detection method which is based on the (Kurhila,.,. Torvinen, 2006) is described. The peak and mean values o the correlation between RF received samples and training sequences (generated by the receiver) are measured in the synchronization block by match ilter. The oppler shit estimator block estimates the eective user velocity which will be used or weighting the mean values. Based on the estimated oppler shit and synchronization values, the state o modulation is decided in modulation detection block. The relations and procedure between these mentioned blocks are shown in Fig. 4. max (9) Fig. 3: The overall procedure o jamming detection algorithm The rest o the jammer detection algorithm is the same as (Kurhila,.,. Torvinen, 2006) and the overall o the jammer detection procedure is shown in igure 4 and in Appendix. The dierence between our improved algorithm and Kurhila et.al (Kurhila,.,. Torvinen, 2006) method is in the oppler shit estimation process. Using our proposed oppler shit estimation makes the modulation detection process more accurate. 6593

5 Fig. 4: odulation detection algorithm 4. Simulation Results and Analysis: In order to evaluate the perormance o the proposed algorithm or estimating the user speed by BTS, simulations are perormed according to conditions reported in Table 1. In the simulation, we used the simulated S generated data in a TETRA system and passed them through a Rician ading channel. The initial value or is set 40. Table 1: Simulation Parameters Parameters Values Carrier Frequency 396 Hz odulation ode / 4 QPSK Access ethod TA with 4 timeslots per carrier Channel odel Rician Fading Channel Speed o obile km/h Length o Speed Estimation Window 500 msec (34 Bursts), 1 Burst= msec Rician Factor (k) 1 Sampling Frequency 8 khz Simulation length 1000 Bursts For the simulation results, is deined to indicate the normalized relative estimation error: ˆ max max max (9) where, ˆmax is estimated aximum oppler shit. Fig. 5 illustrates the perormance o the proposed algorithm in three dierent maximum oppler shits versus SNR. It shows that how the perormance o oppler shit estimation algorithm improves when SNR increases. In Fig. 6, the accuracy o the proposed algorithm in tracking the speed o users, is shown. Ater receiving 34 burst (34 burst = 0.5 sec), the proposed algorithm starts and initial estimation o user s speed is perormed. Then, a speed is estimated or each coming burst. It can be seen that the proposed algorithm outperorms the reerence algorithm (Cho,.G.,. Hong, 2004) in ollowing the mobile terminal s variation in low SNR conditions. Fig. 7 shows the perormance o two jamming detection methods by (Kurhila,.,. Torvinen, 2006) and our improved jamming detection algorithm in a ixed Signal to Jammer (SJR=8 db) over the TETRA network. Note that, in these simulation results, a simple type o jammer detection algorithm o Kurhila et.al (Kurhila,.,. Torvinen, 2006) is considered and just the used oppler shit estimation method is dierent. In this simulation it is expected that 52% o inormation should be corrupted. This eature is shown by the constant red line. 6594

6 Fig. 5: Normalized relative error in various oppler shits. Fig. 6: Comparisons between Proposed ethod and Hong ethod Fig. 7: Percentage o corrupted bursts due to jammer, the constant curve is expected corruption percentage and the other curves are the algorithms estimation. SJR=8 db, Lower Limit=9, Upper Limit=12, oppler Shit=20 Hz. RSSI=-60dBm. We can see rom the curves that the perormance o the improved jamming detection method is better than Kurhila et.al method (Kurhila,.,. Torvinen, 2006) especially in low SNRs. As shown in Fig. 7, the proposed method results is closer to the expected corruption percentage 52% than Kurhila et.al algorithm (Kurhila,.,. Torvinen, 2006) result, and it converges to the constant curve more rapidly. 6595

7 5. Conclusion: In this paper, a level crossing based algorithm or oppler shit estimation is improved and used to enhance the perormance o Jammer detection. It is shown that the perormance o the improved algorithm in moderate SNRs (i.e., SNR=5 db) or TETRA users is acceptable. The application o the proposed algorithm is modeled and simulation results have shown a good perormance in a wide range o velocities and jammers. ACKNOWLEGENT The authors would like to thank epelmaan Pardaz Ltd. or partially supporting o this research. Appendix The Flowchart o the jamming detection algorithm s details is shown in Fig. 8 which is proposed in the (Kurhila,.,. Torvinen, 2006). Appendix: Fig. 8: The jamming etection Algorithm in details (Kurhila,.,. Torvinen, 2006) REFERENCES Austin,.., Gordon. L. Stuber, Eigen-based oppler estimation or dierentially coherent CP, IEEE Trans on Vehicular Technology, pp: Anim-Appiah, K.., On Generalized Covariance-Based Velocity Estimation, IEEE Transactions on Vehicular Technology, 48(5): Austin,.. and G.L. St uber, Velocity Adaptive Hando Algorithms or icrocellular Systems, IEEE Transactions on Vehicular Technology, 43(3): Baddour. K.E. and N.C. Beaulieu, Robust oppler Spread Estimation in Nonisotropic Fading Channels, IEEE Transactions on Wireless Communications, 4(6): Cho,.G.,. Hong, Velocity Estimation Apparatus and ethod Using Level Crossing Rate, US patent A1. Hua Jingyu, You Xiaohu, Sheng Bin, et al A scheme or the oppler shit estimation despite the power control in mobile communication systems, IEEE Vehicular Technology Conerence. Spring, pp:

8 Holtzmann, J.. and A. Sampath, Adaptive Averaging ethodology or Handos in Cellular Systems, IEEE Transactions on Vehicular Technology, 44(1): Jingyu, H. Han,. Qingmin, Y. Xiaohu, A Scheme or the SNR estimation and its application in oppler shit estimation o obile Communication Systems, Proc. o IEEE, spring, 65(1): Krasny, L., oppler spread estimation in mobile radio systems, IEEE Communication Letters, pp: Kurhila,.,. Torvinen, Base Station, United State Patent. ottier,.,. Castelain, A oppler estimation or UTS-F based on channel power statistics, IEEE Vehicular Technology Conerence, pp: Narashimhan, R. and.c. Cox, Speed Estimation on Wireless Systems Using Wavelets, IEEE Trans. On Communications, 47(9). Sampath, A. and J. Holtzman, Estimation o maximum oppler requency or hando decisions, Proc. IEEE VTC, pp: Tepedelenlio glu, C., A. Abdi, G.B. Giannakis and. Kaveh, Estimation o oppler spread and signal strength in mobile communications with applications to hando and adaptive transmission, John Wiley & Sons, Ltd. Tepedelenliogl, C., G.B. Giannak, Estimation o oppler Spread and Signal Strength in obile Communications with Application to hando and adaptive transmission, Wireless Communications and obile Computing, 1(2): Theodore Rappaport, Wireless Communications: Principles and Practice, 2nd Edition, Prentice Hall. Yi Sha, Na Yao, Xiaojing Xu, Improvement and Perormance Analysis o A Scheme or the aximum oppler Frequency Estimation, IEEE-WiCome Conerence. 6597