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Prediction of wireless communication systems in the context of modeling 2-3-4
1.
International Journal of
Electronics and Communication Engineering & Technology (IJECET), ISSN INTERNATIONAL JOURNAL OF ELECTRONICS AND 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 1, January- February (2013), © IAEME COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) ISSN 0976 – 6464(Print) ISSN 0976 – 6472(Online) Volume 4, Issue 1, January- February (2013), pp. 11-17 IJECET © IAEME: www.iaeme.com/ijecet.asp Journal Impact Factor (2012): 3.5930 (Calculated by GISI) ©IAEME www.jifactor.com PREDICTION OF WIRELESS COMMUNICATION SYSTEMS IN THE CONTEXT OF MODELING T.Regua, Dr.G.Kalivarathanb a Research Scholar, CMJ University, Meghalaya, Shillong. b Principal/ PSN Institute of Technology and Science, Tirunelveli, Tamilnadu, Supervisor, CMJ University, Shillong. Email:sakthi_eswar@yahoo.com ABSTRACT This paper is focused with the use of numerous antenna elements in wireless communication over frequency non-selective radio channels. Both experimental results and theoretical analysis are discussed with definite details. New transmit strategies are derived and balanced to existing transmit strategies, such as beamforming and space time block coding (STBC). It is seen that the best transmission algorithm is principally dependent on the channel characteristics, such as the number of transmit and receive antennas and the continuation of a line of sight component. Rayleigh desertion multiple input multiple output (MIMO) channels are studied using an eigenvalue analysis and faithful expressions for the bit error charge and outage capacities for beamforming and STBC is found. In general MIMO fading channels are correlated and there exists a mutual coupling between antenna elements. These investigations are supported by indoor MIMO measurements. It is seen that the mutual coupling can, in some scenarios, increase the outage capacity. An adaptive antenna testbed is used to obtain measurement results for the SIMO channel. The results are analyzed and design oriented guidelines are obtained for how a beamformer executed in hardware shall be constructed. The property of nonlinear transmit amplifiers in array antennas are also analyzed, and it is seen that an array condenses the effective intermodulation distortion (IMD) transmitted by the array antenna by a spatial filtering of the IMD. A novel frequency allocation algorithm is proposed that reduces IMD even additional. The use of a low cost antenna with switchable directional properties, the switched freeloading antenna, is studied in a MIMO context and compared to array techniques. It is found that it has comparable performance, at a fraction of the cost for an array antenna. Keywords: antenna array, calibration, mutual coupling, MIMO system, flat fading, nonlinear amplifier, switched parasitic antenna, analog beamformer, adaptive antenna testbed. 11
2.
International Journal of
Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 1, January- February (2013), © IAEME 1.0 INTRODUCTION Wireless systems are now popular worldwide to help people and machines to communicate with each other irrespectively of their location. So far, using a cellular system is by far the most common wireless method to access data or to perform voice dialing. But in a near future, we will be surrounded by a numerous of options to set up an unwired connection over the radio interface. One of the slogans for the fourth generation wireless communications system (4G) is “always best connected”, meaning that your wireless equipment should connect to the network or system that at the moment is the “best” for you. Various connections ranges from satellites that provides low bit rates but global coverage and cellular systems with continental coverage to high bit rate local area networks and personal area networks with a maximum range of a few to a hundred meters. If these systems should co-exist, then we would obtain a crowded frequency spectrum, since there are many different actors that want their share of the limited frequency resource. To use a signaling strategy that is spectrally efficient is thus of utmost importance. The current trend to achieve high spectral efficiency is by utilizing adaptivity in the ever changing radio environment and sources of interference. Adaptivity on the physical layer can be used in all possible dimensions: Time, frequency, power and space. Adaptivity can also be used on higher signaling layers to boost performance even further; an example is multi-user scheduling. This thesis is devoted to the physical layer of wireless communication systems and will focus mainly on the adaptive utilization of the space dimension. Space utilization is possible through the use of multiple antenna elements arranged in an array, for the transmission and/or reception of the signals. Or, in some cases a single antenna element that has several polarizations or modes is used to obtain polarization or angle diversity. Due to the use of multiple antennas, the antenna gain is increased and this leads to an increased range and coverage. This is useful in remote areas with low population. A large area can thus be served with less base stations. Alternatively, the transmit power of the mobile units can be reduced due to the increased gain, or sensitivity, of the receiving base station antenna array. By using the spatial dimension provided by multiple antenna elements, it is possible to suppress interfering signals in a way that is not possible with a single antenna. Hence, the system can be tuned to be less susceptible to interference and the distance between base stations using the same time/frequency channel can be reduced, which is beneficial in densely populated areas. This leads to a system capacity improvement. A receiver array antenna can be used to localize the transmitter, just as we can use our both ears to localize the source of a sound in a room without using our eyes. This has application in positioning services and emergency call localization. The maximum likelihood (ML) detector for linear space- time codes used over a flat fading MIMO channel with spatially and temporally colored Gaussian noise is now derived. The derivation gives insight to the problems associated with spatial multiplexing transmission and the resulting detector is used in other sections of this thesis. Furthermore, the pairwise error probability (PEP) for the linear space time code is also studied. The PEP characterizes the performance of a system with coding over a finite number of blocks, M, and captures the diversity advantage of a code. 2.0 MIMO SYSTEM PERFORMANCE MIMO systems in flat fading channels are in this chapter analyzed and compared, with and without partial Channel State Information (CSI) at the transmitter, under the assumption of different fading statistics. It will be assumed that the signals from different receive and/or transmit antennas are correlated. Mutual coupling is also introduced between the antenna elements. Measurement results from a MIMO testbed are presented and it is verified that the Rayleigh fading assumption is valid in some cases. Accurate and tractable channel modeling is critical to realize the full potential of antenna arrays. Two subgroups of channel models for MIMO systems can be identified. One is based on the physical properties of the channel, and is a parametric model where the parameters are; the number of scatterers, angle of arrival of the signal from the scattered, time delay, and power decay profiles. These parameters are often modelled as random variables from a given distribution. Such models can 12
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International Journal of
Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 1, January- February (2013), © IAEME adapt to the multipath environment and the array geometry and its flexibility makes them attractive for computer simulations. For analytical derivations, however, a reductionistic approach is taken, where random matrix theory is used to define the channel with fewer parameters than in the physical model, although at the expense of lower accuracy in capacity prediction. To generate signal correlation matrices CR and CT, a model that resembles the physical scattering in the channel can be used. Many different approaches to model signal correlation, or directly the channel matrix for MIMO systems have been presented. 3.0 STOCHASTIC CHANNEL MODELS The propagation scenario in a wireless communication system is very complex and the signal transmitted from an antenna will reach the receiving antenna after many path reflections. If the scattering is rich enough, then a stochastic method is suitable to model the channel. The well known stochastic models for the SISO channel are in this section extended to the MIMO channel. The elements of the H matrix are assumed to be random variables taken from a probability distribution function (PDF). The elements are in general correlated but sometimes the correlation is neglected in an initial analysis to make it tractable. The correlation depends on the scattering scenario, the antenna element radiation patterns, their configuration and separation distance. It is modelled using a general correlation model, where the covariance matrix of the channel matrix elements is defined. However, in the definition of the Rayleigh fading MIMO channel, a special structure will be imposed on the correlation matrix. This structure makes further analysis of the Rayleigh fading MIMO case possible, since the joint pdf of the eigenvalues to HH* for this case is known. 4.0 SIGNAL CORRELATION An important property of the MIMO channel that essentially determines the channel capacity is the correlation between the channel coefficients. When multi antenna systems are analyzed, it is commonly assumed that the fading between pairs of transmit and receive antennas are independent and identically distributed random variables with a Rayleigh distribution 5.0 MEASURED MIMO CHANNELS To collect real MIMO channel data, measurements were performed in an indoor environment at the Signals and Systems Group, Uppsala University. The aim of the measurements was to verify the assumptions made in the theoretical analysis regarding fading distribution, signal correlation and the flat fading assumption. The measurements were performed in an indoor office environment using a 4 × 4 MIMO system at the frequency 1.8 GHz. A Vector Network Analyzer was used to measure the channel coefficients for the 16 channels using a switching method. Each complete “MIMO snapshot” of all the 16 channels were measured in less than 3 seconds, which is fast enough in indoor environments to ensure that the channel remains stationary during each measurement. The switching method was also used on an 8×8 MIMO system. Between each measurement, the receiving array was moved one eight of a wavelength in the broadside direction. The antenna elements were microstrip patch antennas placed in a linear array with an inter-element spacing (δ) of half a wavelength (δ = λc/2). The patch antennas had a half power beam width of 80◦ and a half power bandwidth of 170 MHz 13
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International Journal of
Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 1, January- February (2013), © IAEME and the measurement SNR was set to 20 dB in all measurements. Two scenarios were investigated, one line of sight (LOS) and one non-LOS (NLOS) setup. In the LOS scenario, the two arrays were placed facing each other in an 8×6 meter laboratory room containing various instruments, tables and cabinets and ND = 146 measurements were conducted. In the NLOS scenario, ND = 220 measurements were conducted and the receive array was placed outside the laboratory room, centered in a long corridor with the array broadside parallel with the corridor. The transmit array was kept in the adjacent laboratory. To make comparisons with the theoretical models, each element of the measured H matrix was normalized as 6.0 COHERENCE BANDWIDTH The initial measurements aimed to verify the flat Rayleigh fading assumption. The measured power spectrum in the NLOS case from one transmit antenna to the four receive antennas. The coherence bandwidth (at correlation coefficient 0.9) is estimated to Bc = 2.8 MHz so the flat fading assumption is valid if the signalling bandwidth is less than Bc. If a system with higher bitrate is required, then transmission over many subchannels can be used, where the bandwidth of each subchannel is less than Bc Fig.1. Power spectrum for NLOS channel. Each curve represents the received power in one out of four half-a-wavelength spaced antennas from one transmit antenna. 14
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International Journal of
Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 1, January- February (2013), © IAEME Figure 2 shows the normalized channel amplitudes for all 16 channels in the NLOS case and it is immediately apparent that the channels fading patterns are different. The correlation between these subchannels is further explored below. In Figure 3, the estimated probability density functions of the normalized amplitudes in the LOS and NLOS cases are shown. The curves are fitted to a Nakagami-m distribution using a moment based method [100]. The Nakagami-m distribution .The mf -parameter was estimated to mf = 1.17 in the NLOS case and mf = 6.41 in the LOS case. The measured data was also used FIG.2. Channel amplitudes in a 4 × 4 MIMO NLOS channel as a function of measurement. An important property of the MIMO channel that essentially determines the channel capacity is the correlation between the channel coefficients. When multi antenna systems are analyzed, it is commonly assumed that the fading between pairs of transmit and receive antennas are independent and identically distributed random variables with a Rayleigh distribution. On the other hand, a small antenna element spacing is often necessary to fit multiple antennas on a portable device. Small element spacing also introduces mutual coupling between the antenna elements which affects the achievable capacity of the system, although not necessarily in a negative way the distances from the different transmitting array antennas via the scattered to the receiver array are approximately equal, so the correlation between two paths from different transmit elements to one receive elements is expected to be high as compared to the large 15
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International Journal of
Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 1, January- February (2013), © IAEME FIG.3. CUMULATIVE DISTRIBUTION OF PHASE IN NLOS CHANNEL. 7.0 CONCLUSIONS The aim of this work was to investigate the impact of using MCPA’s at the base station for the downlink in a wireless system. The derivations was simplified by assuming a switched beam configuration, often implemented using a beamforming network, such as the Butler matrix. This technique was shown to have the interesting property that the IMD follows the same radiation patterns as the original desired signals patterns. This allowed for the concept of a beam-frequency scheme and a frequency channel allocation algorithm for reducing the harmful IMD in that particular cell was developed. A base station with more antenna elements gave a reduction of intermodulation distortion which could be of the order of several dB. REFERENCES [1] P.K. Bondyopadhyay, “The first application of array antenna,” in Proceedings of IEEE International Conference on Phased Array Systems and Technology., Dana Point , USA, 2000, pp. 29–32. [2] S. Andersson, B. Carlqvist, B. Hagerman, and R. Lagerholm, “Enhancing cellular network capacity with adaptive antennas,” Ericsson Review, vol. 76, pp. 138–141, 1999. [3] H. Dam, M. Berg, S. Andersson, R. Bormann, M. Frerich, and T. Henβ, “Performance evaluation of adaptive antenna base stations in a commercial GSM network,” in Proceedings of Vehicular Technology Conference (VTC), Piscataway, USA, 1999, pp. 47–51. 16
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Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 1, January- February (2013), © IAEME [4] K. Sheikh, D. Gesbert, D. Gore, and A.J. Paulraj, “Smart antennas for broadband wireless access networks,” IEEE Signal Processing Magazine, vol. 37, no. 11, pp. 100–105, Nov. 1999. [5] R.T. Derryberry, S.D. Gray, D.M. Ionescu, G.Mandyam, and B. Raghothaman, “Transmit diversity in 3G CDMA systems,” IEEE Communications Magazine, vol. 40, no. 4, pp. 68–75, April 2002. [6] S. Andersson and B. Hagerman, “Adaptive antennas in wireless systems basic background and field-trial results,” in Proceedings of Radiovetenskaplig Konferens, Karlskrona, Sweden, 1999, pp. 249–253. [7] S. Andersson, U.Forss´en, J.Karlsson, T.Witzschel, P. Fischer, and A. Krug, “Ericsson/Mannesmann GSM field trials with adaptive antennas,” in Proceedings of Vehicular Technology Conference (VTC), Phoenix,USA, May 1997, pp. 1587–1591. [8] J. Strandell, M. Wennstro¨m, A. Rydberg, T. O¨ berg, O. Gladh, L. Rexberg, E. Sandberg, B.V. Andersson, and M. Appelgren, “Experimental evaluation of an adaptive antenna for a TDMA telephony system,” in IEEE Personal Indoor and Mobile Radio Communications Conference (PIMRC), Helsinki, Finland, Sep. 1997, pp. 79–84. [9] J. Strandell, M. Wennstro¨m, T. O¨ berg, and A. Rydberg, “Design and evaluation of a fully adaptive antenna for telecommunication systems,” in Proceedings Antenn97 conference, Gothenburg, Sweden, 1997, pp. 357–366. [10] S.M. Simmonds and M. Beach, “Downlink calibration requirements for the TSUNAMI(II) adaptive antenna testbed,” in Proceedings of the Ninth International Symposium on Personal, Indoor and Mobile Radio Communications, Boston, USA, 8-11 September 1998. [11] P.E. Mogensen, K.I. Pedersen, P. Leth-Espensen, B. Fleury, F. Fredriksen, and K.Olesen, “Preliminary results from an adaptive antenna array testbed for GSM/UMTS,” in Proceedings of Vehicular Technology Conference (VTC), Phoenix,USA, May 1997, pp. 1592–1596. [12] G. Tsoulos, M. Beach, and J. McGeehan, “Space division multiple acess (SDMA) field trials. Part 2: Calibration and linearity issues,” IEE Proceedings - Radar, Sonar and Navigation, vol. 145, no. 1, pp. 79–84, Feb. 1998. [13] S.M. Alamouti, “A simple transmit diversity technique for wireless communications,” IEEE Journal on selected areas in communications, vol. 16, no. 8, pp. 1451–1458, Oct. 1998. [14] T. O¨ berg, Modulation, Detection and Coding. Chichester: John Wiley and Sons, 2001. [15] J.G. Proakis, Digital Communications. Singapore: McGraw-Hill, 1989. [16] Dr. V. Murali Krishna, Karimella Vikram and Prof. Narasimha, “Broadband Wireless Communication” International journal of Electronics and Communication Engineering &Technology (IJECET), Volume3, Issue2, 2012, pp. 217 - 226, Published by IAEME. [17] Gangadhar P Maddani, Sameena N Mahagavin and Shivasharanappa N Mulgi, “Rectangular Microstrip Array Antennas For Wide Triple Band Operation” International journal of Electronics and Communication Engineering &Technology (IJECET), Volume1, Issue1, 2010, pp. 53 - 61, Published by IAEME. [18] Gangadhar P Maddani, Sameena N Mahagavin and Shivasharanappa N Mulgi, “Design And Development Of Microstrip Array Antenna For Wide Dual Band Operation” International journal of Electronics and Communication Engineering &Technology (IJECET), Volume1, Issue1, 2010, pp. 107 - 116, Published by IAEME. 17
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