M Tech Dissertations

Permanent URI for this collectionhttp://drsr.daiict.ac.in/handle/123456789/3

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Subject: search.filters.subject.MIMO systems

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    State space based channel modelling
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2010) Nema, Swati; Chakka, Vijaykumar
    xAbstract: An efficient channel estimation of single-input single output (SISO)/multiple-input multiple-output (MIMO) wireless channel is required for analysis, prediction, fault detection, channel equalization and optimization. This thesis presents a modified recursive MIMO Output-Error State Space Model Identification (MOESP) technique by using fast Givens transformations for LQ update and Lanczos algorithm with modified partial orthogonalization for singular value decomposition (SVD) update. It is based on one of the Subspace System Identification (SSI) technique known as MOESP method. Fast Givens transformation requires 4m multiplications which are less as compared to Givens rotations, as it requires 6m multiplications where m is size of the input vector. Lanczos algorithm with modified partial orthogonalization is applied thereby minimizing the number of vectors to be reorthogonalized for finding the basis for Krylov subspace in the SVD update. Complexity involved in modified partial orthogonalization is O(k:p)3. In existing recursive technique, more number of vectors needs to be orthogonalized by selective orthogonalization method according to the simulation results. Hence, proposed technique offers less complexity and is used for model identification of slow SISO/MIMO wireless channels.
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    Channel estimation and tracking OFDM and MIMO systems
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2009) Singh, Smriti; Vijaykumar, Chakka
    In this thesis, To estimate and track the slow Time varying channels in OFDM and MIMO systems. In first part: we have used a two-dimensional recursive least square adaptive channel estimation technique is used. In orthogonal frequency division multiplexing (OFDM) system, time- and frequency-domain two-dimensional minimum mean square error (2D-MMSE) channel estimation is optimum. However, accurate channel statistics, which are often time varying and unavailable in practice, are required to realize it.2DRLS adaptive channel estimation does not require accurate channel statistics, and at the same time can make full use of time and frequencydomain correlations of the frequency response of time-varying wireless channels. With properly chosen parameters, 2D-RLS adaptive channel estimation can converge into the steady state in only several OFDM symbols time. Although the 2D RLS algorithm creates adaptive letters with a fast convergence speed, this algorithm diverges when the inverse correlation matrix of input loses the properties of positive definiteness or Hermitian symmetry. The diverging of the 2D RLS algorithm same as standard RLS limits the application of this algorithm. We proposed a QR decomposition-based 2DRLS (inverse QR-2DRLS) algorithm, which can resolve this instability. Instead of propagating inverse of correlation matrix of the input signal, it propogates square root of inverse correlation matrix of the input signal. Therefore, this algorithm guarantees the property of positive definiteness and is more numerically stable than the standard RLS algorithm. The parallel implementation of the inverse QR-2DRLS algorithm permits a direct computation of the least squares weight coefficients matrix MATLAB simulations demonstrate that performance of QR-2D-RLS adaptive channel estimation is same as of 2D-RLS adaptive channel estimation and is very effective and suitable for a broad range of channel conditions. In the second part of the thesis: Since, In MIMO systems, accurate channel estimation is necessary to fully exploit the benefits of spatial diversity offered by such systems. And for time-varying channels, these channel estimates should also be updated accordigly to track the variation of channel. we have used One such method of channel estimation using adaptive SVD updates for channel estimation and tracking of slow-time varying channels in MIMO system. The channel estimates are then further used for symbol detection using V-BLAST/ZF detection algorithm which ensures interference reduction and give better BER vs. SNR performance than SVD based MIMO system.
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    Symbol detection in MIMO systems
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2008) Dhaka, Kalpana; Chakka, Vijaykumar
    Multiple input multiple output symbol detection methods are observed under frequency flat fading AWGN channel condition. The modulation method employed is Quadrature amplitude modulation (QAM). The various symbol detection techniques are compared to observe their behavior under AWGN channel condition condition. Maximum likelihood (ML) symbol detection method gives the best performance but because of its high complexity it can’t be used. Sphere decoder reduces the complexity to some extent providing similar performance as ML estimate. The other methods used are Zero forcing and Minimum mean square stimation. These two methods when used successively for interference cancellation improves performance to large extent along with reduction in the cost. The technique employed for successive detectionis devised by bell laboratory hence it is called as V-BLAST method. Maximum a posteriori when used along with V-BLAST MMSE algorithm further improves the performance. These methods even works well under Rayleigh channel condition. Finally, the simulation results for performance of V-BLAST under both the channel condition are observed for all the symbol detection techniques. To increase the diversity for improved performance space time trellis code are employed. Their performance is observed for QPSK modulation scheme.