International Conference on Communication and Signal Processing, April 6-8, 2016, India Direction of Arrival Estimation in Smart Antenna for Marine Communication Deepthy M Vijayan, Sreedevi K Menon Abstract A communication link between fishing vessels or with the sea shore using Wi-Fi is explored in this paper. For effective communication a high gain antenna is mounted at the sea shore which acts as base station (BS). Although narrow band directional antenna used in the Base Station (BS) ensures the connectivity for a long distance, the coverage will be poor without any steering mechanism, as the antenna beam is fixed in a particular direction. The proposed smart antenna with adaptive beamforming and multiple access technique can ensure the coverage without losing connectivity at long distance. The main objective is to find the Direction of Arrival (DoA) of the received signal from the fishing vessel moving at a constant velocity for adaptive steering of the antenna beam. The DoA parameter gives the phase and amplitude of the signal transmitted from the fishing vessel. The same phase and amplitude help to calculate the beam forming vectors in the smart antenna and it adaptively steers the beam towards fishing vessel. This paper describes how DoA estimation can apply to a beam forming Wi-Fi antenna array which is used for marine communication, and give a mathematical model of DoA estimation. Keywords-Directional Antennas, Smart antenna, multiple access technique, Direction Of Arrival (DoA), adaptive beamforming I. INTRODUCTION Present marine communication systems are based on satellite communication [1], which is really expensive and uses licensed spectrum. Therefore, this expensive communication system is not affordable for fishermen community. One of the solutions is to use high directional Wi-Fi antennas [2] at the sea shore as base station. The narrow beam [3] directional Antenna gives a highly directional beam for large distances. These directional antennas used at the base station (BS) for marine communication, ensures the connectivity for large distances, but loses coverage when a vessel moves out of the beam range. One of the solutions is mechanical steering of base station antenna, but it requires large power utilization [4] and creates an unstable platform. Deepthy M Vijayan is M. Tech student at Wireless Network and Applications, Amrita Center for Wireless Network and Applications, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam (E-mail: deepthymvijayan@gmail.com ) Sreedevi K Menon is Assistant Professor at Department of Electronics and Communication, Amrita Vishwa Vidyapeetham Amritapuri, Kollam (E-mail: sreedevikmenon@am.amrita.edu ). Another solution is using wide beam directional antennas [5], but the problem is the reduction in gain as the beam width increases [5]. Due to this reason, there is a need to adaptively reorient the beam of the BS for a stable coverage and connectivity for long distance. The proposed Smart Antenna [6] design and DoA estimation [7] algorithm aims at electrically reorient the beam with time multiplexed beam forming techniques [8] on Smart Antennas. There is a need to find the Direction of arrival (DOA) [16] of the received signal from the fishing vessel moving at a constant velocity for such electrical orientation of the BS. The DoA parameter gives the phase and amplitude of the signal transmitted from the fishing vessel, which enables adaptive steering of the BS beam towards fishing vessels by calculating the beam forming vectors[9] in the smart antenna. An adaptive antenna can increase the coverage without affecting the gain at long range. It collects information from the received data at the base station and estimate the direction of arrival using some algorithms with the help of a Digital Signal Processor (DSP). Then it gives a beam forming circuit, thus it adaptively steers the beam according to the DoA of receiving signals. The diversity will be better. The beam forming can improve the downlink performance by directionally sending the information to the fishing vessel. Beam forming reduces the interference and increases the SNR [10]. The Shannon's principle [11] says the channel capacity directly proportional to the SNR and bandwidth. Therefore, the smart antenna improves the channel capacity also. A smart antenna reduces the co-channel interference [12] that allows more users to use the same frequency spectrum at the same time bringing about a large increase in capacity. The smart antenna system estimates the direction of arrival of the signal, using some algorithm like subspace based algorithm [13]. There are analog and digital beamformers [14] are using for beamforming network. The analog beamformer uses phase shifter to shift the phase of the antenna output. Digital beamformer find the direction of arrival of the desired user and calculates the beamforming vector. Analog beamformer uses large number of phase shifters and digital beamformer uses large number of ADC and down converters for each antenna elements output. Therefore, these systems are expensive. In the present work, main aim is to develop an algorithm for marine communication suitable for the hybrid beam forming [15] architecture, which involves both digital and analog beam forming. 978-1-5090-0396-9/16/$31.00 2016 IEEE 1535
Section II gives the System archietecture. In Section III gives Received signal model, Section IV discuss DoA estimation using MUSIC algorithm, Section V gives Simulation and performance analysis, Section VI discuss improved MUSIC algorithm for coherent signal, Section VII gives Hybrid algorithm for DoA estimation and analysis, Section VIII discuss Conclusion and Future work by the digital beamformer involved in the final beamforming. The linear complexity and the system cost is less than the digital beamformer which does not has subarray concept. The Fig. 2 shows the architecture for the hybrid beamformer using 4 element antenna array. In this structure, two elements are grouped into one subarray. This structure uses less number of digital components than a digital beamformer. II. SYSTEM ARCHITECTURE A. Overall System Architecture The smart antenna is nothing but an antenna array with smart signal processing algorithms. The only change in the communication system is a beamforming network. Here the architecture describes about a Time Division Beamforming network which added with antenna arrays. Before transmission of the signal it is modulated with suitable modulation technique and given to a beamformer. The beamformer calculate weights according to direction of arrival from the received signal and then multiplexed with the time slot in TDMA network and transmit to the channel. The total processing time includes time for DoA estimation, beamformer, TDMA processing time and other circuit processing time. Fig. 2. Hybrid Beamformer III. RECEIVED SIGNAL MODEL The received signal output from antenna array is modeled here. The number of subarrays, M=2 the received signal at m th Subarray is given by, Fig. 1. Overall Architecture for Smart Antenna System The adaptive steering of the beam is enabled by DoA estimation. The DoA estimation is given by the received signal model from the vessel. Assumes there are several vessels and at time t1, vessels from one sector want to communicate with the base station. When the base station receives the signal, it calculates the DoA parameter which consists of phase and amplitude of the signal. The same phase and amplitude used to calculate the weight for beamforming then transmit to the channel with in the time t1.the time t1 should be greater than the total processing [17] time of the network. B. Hybrid Beamformer The hybrid beamformer has both digital and analog beamforming part, in which antenna elements are grouped into analogue subarray [18] which has unique characteristics. First beamforming is done by analog beamformer and then (1) Where m=0 to M-1 and is the radiation pattern, the relative movement of vessel creates the effect Doppler shift [17] in frequency, f D with the base station antenna. When designing the beamforming antenna the mutual coupling [18] between elements and other antenna imperfections are ignored. The subarrays have same characteristics therefore,.the antenna consists of isotropic elements with omnidirectional radiation pattern and =1. Then, (2) (3), is the position of the first element in the subarray. Substituting equation 2 and 3 into equation 1and applying above conditions, 1536
The noise factor n(t) is added with equation 4 and the phase shift is given by, (4) (5) The first component will become the array factor. The main beam of the antenna is directed towards the angle (Ɵ0, ф 0). The normalized radiation pattern is given by, This is equal to, (6) Fig. 3. Flow Chart for MUSIC Algorithm (7) Where =2, =1 and d=λ/2. Let the weight applied to the m th subarray is and the final beamformed sum is given by, T is the sampling period. IV. DOA ESTIMATION USING MUSIC ALGORITHM In this section MUSIC algorithm is analyzed for the DoA Estimation problem. A long range Wi-Fi smart antenna for base station on the sea shore with frequency 2.5GHz and information from fishing vessel are received at the base station. Our problem is to find the Direction of Arrival (DOA) of the received signal from the vessel which is moving at a constant velocity 40 miles/hr., Carrier frequency, f=2.5 GHz Wavelength = (9) Doppler shift, f D = velocity of boat/wavelength (10) Consider a 4 x 1 antenna array Element spacing, = /2 Let the signal is arriving from the angle 45 degrees. (8) V. SIMULATION AND PERFORMANCE ANALYSIS The simulation shows how a signal which is arrived at 45 degree is given by the MUSIC algorithm. The conditions are a single narrow band signal with incident angle is 45 and without multipath propagation. The noise is assumed to be ideal Gaussian white noise, the SNR is 20dB and the number of snapshot is 200. The 4x1 antenna array with element spacing half of the input signal wavelength is considered. A. Basic Simulation of DoA Estimation Algorithm Fig. 4. Basic simulation of DOA estimation 1537
B. The relationship between DOA estimation and the number of array elements Fig. 7. The relationship between DOA estimation and SNR Fig. 5. The relationship between DOA estimation and the number of array elements C. The relationship between DOA estimation and the number of snapshots The accuracy of DoA estimation depends on the number of snapshots. As the number of snapshot increases the processor takes more time to do the computation. E.The MUSIC algorithm for coherent signals When the signals are coherent, let the incident angle is 20, 60 respectively, and those two signals are not correlated, the noise is ideal Gaussian white noise, the SNR is 20dB, the element spacing is half of the input signal wavelength, array element number is 4, and the number of snapshots is 200. Fig. 8. MUSIC algorithm for coherent signals Fig. 6. The relationship between DOA estimation and the number of snapshots D. The relationship between DOA estimation and SNR With the other conditions remaining unchanged, with the increase in the number of SNR, the beam width of DOA estimation spectrum becomes narrow, the direction of the signal becomes clearer, and the accuracy of MUSIC algorithm is also increased. When number signals increases, here the number of vessel increases the DOA estimation using the MUSIC algorithm fails. VI. IMPROVED MUSIC ALGORITHM FOR COHERENT SIGNAL To improve the performance when multiple sources come spatial smoothening is needed. A transformation matrix is multiplied with the complex conjugate of the signal matrix and added the correlated output with signal correlated matrix. It helps to separate the noise subspace from signal subspace and finds the DoA estimation from spectral power.. 1538
Fig. 9. The Improved MUSIC algorithm for coherent signals. Fig. 11. Flow chart for hybrid algorithm For multiple sources the hybrid model gives accuracy even for a 4 to 5 degree sources separation. Therefore this will be a good solution for the DoA estimation problem. Fig. 10. Comparison MUSIC with Improved MUSIC for coherent signals. VII. HYBRID ALGORITHM FOR DOA ESTIMATION AND ANALYSIS Hybrid beamforming architecture uses the subarray concept and therefore it can reduce the algorithm complexity by a factor equal to the number of subarray elements. Here, the adaptive beamforming technic uses the improved MUSIC algorithm. Fig. 12. DOA Estimation Received Signal from 45 degree The resolution decreases for less separation of sources. The total processing time estimated for DoA algorithm is 0.26 sec. The analysis is shown in Fig.13. 1539
Fig. 13. DOA Estimation multiple sources: Resolution Analysis VIII. CONCLUSION AND FUTURE WORK The DoA estimation plays an important role in adaptive beamforming technic. Here, DoA estimation is used for a smart antenna for marine communication. The smart antenna can increase the signal to noise ratio and therefore the capacity. Therefore, a smart antenna can improve the communication performance. The DoA estimation problem for the given scenario is solved and the simulation results are given in the work. The solution is arrived by comparing the digital and hybrid beamformer. From the simulations, it could be seen that when the number of array elements and snapshots increases the resolution for DoA increases. When the signal is coherent, the effectiveness of classical MUSIC algorithm fails and improved MUSIC algorithm increases the resolution and accuracy. An algorithm is proposed for the hybrid beam former which reduces the cost and algorithm complexity. In future this work is extended for the analysis of different beamforming algorithm using a low cost hardware such as smart antenna using FPGA. [6] A.M.Elmurtada., Department of Electrical & Electronic, Faculty of Engg., Adaptive Smart Antennas in 3G Networks and Beyond, IEEE students conference on Research and Development,2012. [7] I. S. Reed, J. Mallett and L. Brennan, Rapid convergence rate in adaptive arrays, IEEE Transactions on Aerospace and Electronic Systems, vol. 10, No. 6, pp. 853 863, Nov. 1974. [8] Chuck Powell Technical Analysis : Beamforming Vs. MIMO antennas White paper: RF systems, 2014. [9] J. C. Liberti and T. S. Rappaport, Smart Antennas for Wireless Communications: IS-95 and Third Generation CDMA Applications. Upper Saddle River, New Jersey: Prentice-Hall, Inc., 1997. [10] Ianxin Dai and Ming Chen, Downlink capacity evaluation of SA- MIMO system under single user scenario, IET International Conference, 2013. [11] Theodore Rappaport, Wireless Communications Principle and Practice, 2010. [12] Fan Huang, Improvement of Cell Edge Performance by Coupling Allocation with Beamforming, Proceedings of the IEEE ICCS, 2014. [13] A. Gershman. Class slides. Advanced Topics in DSP. McMaster University. Personal Communication, 2010. [14] Val Dyadyuk, Adaptive Antenna Arrays for Ad-Hoc Millimeter- Wave Wireless Communications-Advanced Trends in Wireless Communications, 2011. [15] Xiaojing Huang, Member, IEEE, A Hybrid Adaptive Antenna Array, IEEE Transaction on wireless communications, VOL. 9, NO. 5, MAY 2010. [16] International Journal of Innovative Science and Modern Engineering http://www.ijisme.org/. [17] Doppler shift Estimation and correction in wireless communication, http://www.nutaq.com/blog/doppler-shift-estimation-and-correctionwireless-communications [18] Antenna Mutual Coupling, http://www.antennatheory.com/definitions/mutualcoupling.php ACKNOWLEDGEMENT The project is partially funded by a grant from the Information Technology Research Agency (ITRA)- Department of Electronics and information Technology (Deity), Government of India. REFERENCES [1] Ki-BeomKim, Experimental Study of Propagation Characteristic for Maritime Wireless Communication, Proceedings of ISAP, Nagoya, Japan, 2012. [2] Seyed Dawood Sajjadi Torshizi, An Investigation of Vegetation Effect on the Performance of IEEE 802.11n Technology at 5.18 GH, IET International Conference, 2012. [3] Paulo Cardieri, Theodore S. Rappaport, Fellow, IEEE, Application of Narrow-Beam Antennas and Fractional Loading Factor in Cellular Communication Systems, IEEE transactions on vehicular technology, vol. 50, no. 2, March 2001. [4] Gareth Rees, W G Rees, Physical Principle of Remote Sensing, 2013. [5] Free Net Antennas, http://www.freenet-antennas.com, 2016. 1540