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Signal processing and channel capacity enhancement using MIMO Technology
 

Signal processing and channel capacity enhancement using MIMO Technology

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    Signal processing and channel capacity enhancement using MIMO Technology Signal processing and channel capacity enhancement using MIMO Technology Document Transcript

    • Signal processing and channel capacity enhancement using MIMO Technology Akhilesh Kumar, Abhay Mukhetjee, Kamta Nath Mishra, Anil Kumar Chaudhary Department of electronics design &technology National Institute of Electronics and Infonnation Technology MMM EC Gorakhpur India 273010 Abstract- This paper will describe the signal processing and channel enhancement using MIMO technology In order to have flexibility in transmission system, wireless systems are always considered better as compared to wired channel. Having known the drawbacks of the Single Input Single Output system, and having computed the advantages of Multiple Input Multiple Out, several techniques have been developed to implement space multiplexed codes. In wireless MIMO the transmitting end as well as the receiving end is equipped with multiple antenna elements, as such MIMO can be viewed as an extension of the very popular 'smart antennas'. In MIMO though the transmit antennas and receive antennas are jointly combined in such a way that the quality (Bit Error Rate) or the rate (Bit/Sec) of the communication is improved. At the system level, careful design of MIMO signal processing and coding algorithms can help increase dramatically capacity and coverage. KEYWORDS: Wireless, MIMO, Channel, Transmitter, Receiver I. INTRODUCTION The paper will present a continuous higher data rate for wireless system under limited power, bandwidth and complexity. Another domain can be exploited to significantly increase channel capacity: the use of mUltiple transmit and receive antennas. MIMO channel capacity depends heavily on the statistical properties and antenna element correlations of the channel [1]. Antenna correlation varies drastically as a function of the scattering environment, the distance between transmitter and receiver, the antenna configurations, and the Doppler spread [3]." The channel gain matrix is very small, leading to limited capacity gains. We focus on MIMO channel capacity in the Shannon theoretic sense. The Shannon capacity of a single-user time-invariant channel is defined as the maximum mutual infonnation between the channel input and output. This maximum mutual infonnation is shown by Shannon's capacity theorem to be the maximum data rate that can be transmitted over the channel with arbitrarily small error probability. When the channel is time-varying channel capacity has multiple definitions, depending on what is known about the channel state or its distribution at the transmitter and/or receiver and whether capacity is measured based on averaging the rate over all channel states Idistributions or maintaining a constant fixed or minimum rate. Specifically, when the instantaneous channel gains, called the channel state infonnation (CSI), are known perfectly at both transmitter and receiver, the transmitter can adapt its transmission strategy relative to the instantaneous channel state. In this case, the Shannon (ergodic) capacity [10] [11] is the maximum mutual infonnation averaged over all channel states. This ergodic capacity is typically achieved using an adaptive transmission policy where the power and data rate vary relative to the channel state variations. For single-user MIMO channels with perfect transmitter and receiver CSI the ergodic and outage capacity calculations are straightforward since the capacity is known for every channel state. In multiuser channels, capacity becomes a -dimensional region defining the set of all rate vectors (RJ"Rk) simultaneously achievable by all K users. The multiple capacity defmitions for time-varying channels under different transmitter and receiver CSI and cm assumptions extend to the capacity region of the multiple-access channel (MAC) and broadcast channel (BC) in the obvious way. However, these MIMO multiuser capacity regions, even for time-invariant channels, are difficult to find. Few capacity results exist for time varying multiuser MIMO channels, especially under the realistic assumption that the transmitter(s) and/or receiver(s) have CDI only. Many practical MIMO techniques have been developed to capitalize on the theoretical capacity gains predicted by Shannon theory. A major focus of such work is space-time coding: recent work in this area is summarized in. Other techniques for MIMO systems include space-time modulation, adaptive modulation and coding, space-time equalization, space-time signal processing, space-time CDMA and space-time OFDM. II. MIMO CHANNEL MODELING It is common to model a wireless channel as a sum of two components, a LOS component and a NLOS component A. LOS Component Model The Rician factor is the ratio between the power of the LOS component and the mean power of the NLOS component [6]. For MIMO systems, however, the higher the Rician factor K, the more dominant NLOS becomes. Since NLOS is a time­ invariant, it allows high antenna correlation, low spatial degree of freedom, hence, a lower MIMO capacity for the same SNR. In fixed wireless network (macro cell) MIMO improve the quality of service in areas that are far away from the base station, or are physically limited to using low antennas. In an indoor environment, many simulations and measurements have shown that typically the multipath scattering is rich enough that the LOS component rarely dominates. This plays in favor of in­ building MIMO deployments (e.g., WLAN).
    • B. Correlation Modelfor NLOS Component: In the absence of a LOS component, the channel matrix modeled with Gaussian random variables (i.e., Rayleigh fading). The antenna elements can be correlated, often due to insufficient antenna spacing and existence of few dominant scatters. Antenna correlation is considered the leading cause of rank deficiency in the channel matrix, to obtain the highest diversity. In the Rician channel case, the channel matrix can be represented as a sum of the line-of-sight (LOS) and non-line­ of-sight (NLOS) components H = HLOS + HNLOS Where, HLOS � E{H} and HNLOS � H - HLOS. According to this model HNLOS = (R T) 112 H,v(RR) y, • • . (1) Where, RR is the M xM correlation matrix of the receive antennas, RT is the N x N correlation matrix of the transmit antennas, and Hw is a complex N xM matrix whose elements are zero-mean independent and identically distributed (i.i.d)complex Gaussian random variables For a MIMO system the channel matrix is written as [hii .. hinT ] h�Rl . hn�nT Where, hi) = a + jf3 III. SPACE DIVERSITY Diversity techniques can be used to improve system performance in fading channels. Instead of transmitting and receiving the desired signal through one channel, we obtain L copies of the desired signal through M different channels [7]. The idea is that while some copies may undergo deep fades, others may not. We might still be able to obtain enough energy to make the correct decision on the transmitted symbol. Another approach to achieve diversity is to use M antennas to receive M copies of the transmitted signal shown in fig.2. The antennae should be spaced far enough apart so that different received copies of the signal undergo independent fading. Transmitt er Fig.2. Space diversity Different from frequency diversity and temporal diversity, no additional work is required on the transmission end, and no additional bandwidth or transmission time is required. However, physical constraints may limit its applications. Sometimes, several transmission antennae are also employed to send out several copies of the transmitted signal. Spatial diversity can be employed to combat both frequency selective fading and time selective fading. IV. SPATIAL MULTIPLEXING Spatial mUltiplexing is shown in a, which improves the average capacity behavior by sending as many independent signals as we have antennas for a specific error rate. In the uplink scenario, a base station employs multiple receive antennas and beam forming to separate transmissions from the different mobiles. Layered space-time architectures exploit the spatial multiplexing gain by sending independently encoded data streams in diagonal layers(D-BLAST) as originally proposed or in horizontal layers, which is the so-called vertical layered space-time(V-BLAST) scheme. A.D-BLAST Based on this fundamental idea, a class of layered space­ time architecture was proposed and labeled BLAST. Using BLAST the scattering characteristics of the propagation environment is used to enhance the transmission accuracy by treating the multiplicity of the propagation environment is used to enhance the transmission accuracy by treating the multiplicity of scattering paths as separate parallel sub channels. .Antenna index .:.:.....:::..:--..:::-;,:. ���;��4 3 2 :�i�: :-�:::...�.. �2 2 4 3 2 �.:s,,,:-�: • • •..::...:..:�,..: 3 3 2 4 3 2 :�t:��� 4 4 3 2 4 3 2 Time �- .... -� .::. ., " " -. .'- Demodulated block Fig.3. D-BLAST: Diagonal Layering. (Numbers in blocks represent the layer that can transmit its symbols at that antenna and symbol period. Filled blocks represent space time wastage.) B.V-BLAST The Vertical BLAST or V-BLAST architecture [3] is a simplified version of D-BLAS, hat tries to reduce its computational complexity. We use space time block code in V­ BLAST and it can be decoded by ML. The original scheme D-BLAST was a wireless set up that used a multi element antenna array at both the transmitter and receiver, as well as diagonally layered coding sequence. The
    • coding sequence was to be dispersed across diagonals in space­ time. This will shown in figA. AI AI A2 A2 A3 A3 A4 A3 AI AI A2 A2 A4 A3 A4 A4 ..................... ............ . .......... ... ........ ... ........ .......... ......... demodulated demodulated demodulated demodulated Fig.4. V-Blast Architecture demodulated demodulated demodulated demodulated V. INTRODUCTION TO VARIOUS SYSTEMS Presently four different types (Input and output refers to number of antennas) of systems can be categorized as far as diversity is concerned A. Single Input Single Output (SISO)-No diversity This system has single antenna both side. Due to single transmitter and receiver antenna, it is less complex than multiple input and multiple output (MIMO). SISO is the simplest antenna technology. In some environments, SISO systems are vulnerable to problems caused by multipath effects. In a digital communications system, it can cause a reduction in data speed and an increase in the number of errors. Capacity [10] of a SISO is given by: S1S0 Tx C=log2(1+plhI2b/s/Hz ...(2) ------- Fig.5.A SISO System S1S0 Rx Where h is the normalized complex gain of a fixed wireless channel and p is the SNR the plot between the C and SNR will be 4 .5 .---,----,--�--r---,----,--�--r-_____, 4 2 1 52'---....1. 4 --6'---....1.8--1'-0 ----1.1 2--1'-4----1.--'------'20 SNR(d8) Fig 5.1 Graph Between C And SNR(SISO) B. Multiple Inputs Multiple Outputs (MIMO)-Transmit- receive diversity Multiple antennas can be used either at the transmitter or receiver or at both. These various configurations are referred to as multiple input single output MISO, Single Input Multiple Output SIMO, or Multiple Input Multiple Output MIMO. TABLE I COMPARISON OF DIFFERENT ANTENNA SYSTEMS Transmit Receive Data Compared to Type Antenna Antenna Rate Single Antenna Technologies SIMO Single Multiple Same Greater range MISO Multiple Single Same Same range but More reliable MIMO Multiple Multiple Greater Greater range The SIMO and MISO architectures are a form of receive and transmit diversity schemes respectively. On the other hand, MIMO architectures can be used for combined transmit and receive diversity, as well as for the parallel transmission of data or spatial multiplexing. When used for spatial mUltiplexing MIMO technology promises high bit rates in a narrow band­ width and as such it is of high significance to spectrum users [9]. VI.MATHEMATICAL MODEL OF MIMO Consider a wireless communication system with Nt transmit (Tx) and Nr receive (Rx) antennas. The idea is to transmit different streams of data on the different transmit antennas,
    • but at the same carrier frequency. The stream on the p-th transmit antenna, as function of the time t, will be denoted by sp(t). When a transmission occurs, the transmitted signal from the p-th Tx antenna might find different paths to arrive at the q-th Rx antenna, namely, a direct path and indirect paths through a number of reflections[4]. For such a system, all the multi-path components between the p-th Tx and q-th Rx antenna can be summed up to one term, say hqp(t). Since the signals from all transmit antennas are sent at the same frequency, the q-th receive antenna will not only receive signals from the p-th, but from all Nt transmitters. This can be denoted by the following equation (the additive noise at the receiver is omitted for clarity). Xq(t) = L��l hqp(t)sp(t), .......(4) To capture all Nt received signals into one equation, the matrix notation can be used: x(t) = H(t)s(t), .................(5) Figure 6: Schematic Representation OfA MIMO Communication System where, set) is an Ncdimensional column vector with sp(t) being its p-th element, x(t) is Nr-dimensional with xq(t) on its q-th position and the matrix H(t) is Nr x Nt with hqp(t) as its (q,p)-th element, with p = 1, ..., Nt and q = 1, ..., Nr. A schematic representation of a MIMO communication scheme can be found in Figure 6. Mathematically, a MIMO transmission can be seen as a set of equations (the recordings on each Rx antenna) with a number of unknowns (the transmitted signals). If every equation represents a unique combination of the unknown variables and the number of equations is equal to the number of unknowns, then their exists a unique solution to the problem. If the number of equations is larger than the number of unknowns, a solution can be found by performing a projection using the least squares method , also known as the Zero Forcing (ZF) method. For the symmetric case, the ZF solution results in the unique solution. VII.CAPACITY ENHANCEMENT USING MIMO MIMO technologies overcome the deficiencies of these traditional methods through the use of spatial diversity. Data in a MIMO system is transmitted over T transmit antennas through what is referred to as a "MIMO channel" to R receive antennas supported by the receiver terminal. For a memory less Ix1 (SISO) system the capacity is given by: C=lOg2(1+plhI 2)b/s/Hz ...(6) Where h is the normalizes complex gain of a fixed wireless channel or that of a particular realization of a random channel. As we deploy more Rx antennas the statistics of capacity will improve and with M Rx antennas, we have a SIMO system with capacity given by C=lOg2(1+PLf'!,1Ihd 2) bls Hz ...(7) Where hi is the gain of Rx antenna. Now, we consider the use of diversity at both transmitter and receiver giving rise to a MIMO system. For N Tx and M Rx antennas, we have the now famous capacity equation [3], [4], [5] C=log2[det(/M+( p/N)HH*)] ...(8) Where (* ) means transpose-conjugate and H is the MxN channel matrix For the i.i.d. Rayleigh fading case we have the impressive linear capacity growth discussed above. For a wider range of channel models including, for example, correlated fading and specular components, we must ask whether this behavior still holds. Below we report a variety of work on the effects of feedback and different channel models. It is important to note that (8) can be rewritten as [4]. ...(9) Where A.i (i=1......m) are the nonzero eignvalues of W,m= min(M,N), and W={HH*, M � N H*H, N<M ... (10) This formulation can be easily obtained from the direct use of Eigen value properties. Alternatively, we can decompose the MIMO channel into m equivalent parallel SISO channels by performing a singular value decomposition (SVD) of [4], [8].
    • 8180,M180,MIMO Capacity -- siso ,,.. , .. " "··"MI80, NT=2,=NR=1 -'-"MIMO, NT=NR=2 ............... . .. <:.. / "6 � I � I £.5 /... .� I '. '" g.4 () . / ,/ " . .. . . .......... ..... . ......', .. ......', ....... � ... 10 20 40 50 60 8NR 70 00 90 100 Fig.7.Comparison between SISO,MISO and MIMO MIMO Capacity 1 6r;::::::r::::::::::r::==:r::::::::;r----,--.-----,-.-----,.-"'�,.. -- 8180 : • ,.. ,.; '" 14 "'''''''MIMO,NT=NR=2 .• . ........ :. >'"' . ... �<� - ' - " MIMO,NT=NR=3 � -- ' : 12 ---MIMO,NT=NR=4 //J,: �:;;;;.,.... i� ·'. <" · //: ..,..,,':' . N'10 ....... .... ...-:... ... . . ......................... .. � / : ./ ." ..,, , (fj . / . ... " ,I" I � s ! /I/!/ . ,.'''' .. " , ,, .. , , , , , . ' " : / I' "" . ,, ,. g, I I ", ;. , . � 6· !/ ,/ ' 1/ ,'. f:< t . O��-�-����-�-��-�� o 10 20 30 40 50 60 70 80 90 100 8NR Fig .8 Capacity Enhancement in MIMO VIII. CONCLUSIONS AND FUTURE TRENDS This paper reviews the major features of MIMO links for use in future wireless networks. Information theory reveals the great capacity gains which can be realized from MIMO. Whether we achieve this fully or at least partially in practice depends on a sensible design of transmit and receive signal processing algorithms. It is clear that the success of MIMO algorithm integration into commercial standards such as 3G, WLAN, and beyond will rely on a fine compromise between rate maximization (BLAST type) and diversity (space-time coding) solutions, also including the ability to adapt to the time changing nature of the wireless channel using some form of (at least partial) feedback. IX.REFERENCES [1] A. Goldsmith et aI., "Capacity Limits of MIMO Channels," IEEE JSAC, vol. 21, June 2003, pp. 684-702. [2]H. Weingarten, Y. Steinberg, and S. Shamai, "The Capacity Region of the Gaussian MIMO Broadcast Channel," Proc. Conf. Info. Sciences and Systems (CISS), Princeton, NJ, Mar. 2004. [3] G. 1. Foschini and M. J. Gans, "On limits of wireless communications in a fading environment when using multiple antennas," Wireless Pers. Commun., vol. 6, pp. 311-335, Mar. 1998. [4] E. Telatar, "Capacity of multiantenna Gaussian channels," AT&T Bell Laboratories, Tech. Memo., June 1995 [5] I. E. Telatar, "Capacity of multi antenna Gaussian channels," Eur. Trans. Commun., vol. 10, no. 6, pp. 585-595, 1999. [6] Techniques for 3G and beyond. John Wiley and Sons, January 2003 [7] Wireless Communication by Theodore S.Rappaport. [8]Royal institute of technology, stockholm, lecture notes, url: [9] D. Gesbert, M. Shafi, D. S. Shiu, P. Smith, and A. Naguib, "From theoryto practice: An overview of MIMO space-time coded wireless systems," IEEE 1. Select. Areas Commun.Special Issue on MIMO Systems, pt.I,vol. 21, pp. 281-302, Apr. 2003. [10] Wikipedia.