Hybrid solutions to instantaneous MIMO blind separation and decoding: narrowband, QAM and square cases

Future wireless communication systems are desired to support high data rates and high quality transmission when considering the growing multimedia applications. Increasing the channel throughput leads to the multiple input and multiple output and blind equalization techniques in recent years. Thereby blind MIMO equalization has attracted a great interest.Both system performance and computational complexities play important roles in real time communications. Reducing the computational load and providing accurate performances are the main challenges in present systems. In this thesis, a hybrid method which can provide an affordable complexity with good performance for Blind Equalization in large constellation MIMO systems is proposed first. Saving computational cost happens both in the signal sep- aration part and in signal detection part. First, based on Quadrature amplitude modulation signal characteristics, an efficient and simple nonlinear function for the Independent Compo- nent Analysis is introduced. Second, using the idea of the sphere decoding, we choose the soft information of channels in a sphere, and overcome the so- called curse of dimensionality of the Expectation Maximization (EM) algorithm and enhance the final results simultaneously. Mathematically, we demonstrate in the digital communication cases, the EM algorithm shows Newton -like convergence.Despite the widespread use of forward -error coding (FEC), most multiple input multiple output (MIMO) blind channel estimation techniques ignore its presence, and instead make the sim- plifying assumption that the transmitted symbols are uncoded. However, FEC induces code structure in the transmitted sequence that can be exploited to improve blind MIMO channel estimates. In final part of this work, we exploit the iterative channel estimation and decoding performance for blind MIMO equalization. Experiments show the improvements achievable by exploiting the existence of coding structures and that it can access the performance of a BCJR equalizer with perfect channel information in a reasonable SNR range. All results are confirmed experimentally for the example of blind equalization in block fading MIMO systems

Improved multiple input multiple output blind equalization algorithms for medical implant communication

Medical implant sensor that is used to monitor the human physiology signals is helpful to improve the quality of life and prevent severe result from the chronic diseases. In order to achieve this, the wireless implant communication link that delivers the monitored signal to a multiple antennas external device is an essential portion. However, the existing conventional narrow band Medical Implant Communications System (MICS) has low data rate because of the bandlimited channel is allocated. To improve the data rate in the radio frequency communication, ultra-wide band technology has been proposed. However, the ultra-wide band technology is relatively new and requires living human to be the test subject in order to validate the technology performance. In this condition, the test on the new technology can rise ethical challenge. As a solution, we improve the data rate in the conventional narrow band MICS. The improvement of data rate on the narrow band implies the information bandwidth is larger than the allocated channel bandwidth, and therefore the high frequency components of the information can loss. In this case, the signal suffers the intersymbol-interference (ISI). Instead of that, the multiple antennas external device can receive the signal from other transmitting implant sensor which has the same operating frequency. As a result, the signal is further hampered by co-channel interference (CCI). To recover the signal from the ISI and CCI, multiple-input multiple output (MIMO) blind equalization that has source separation ability can be exploited. Cross-Correlation Constant Modulus Algorithm (CC-CMA) is the conventional MIMO blind equalization algorithm that can suppress ISI and CCI and able to perform source separation. However, CC-CMA has only been analyzed and simulated in the modulation of Phase Shift Keying (PSK). The performance of CC-CMA in multi-modulus modulation scheme such as 4-Pulse-amplitude modulation (PAM) and 16-Quadrature amplitude modulation (QAM), which has higher data rate than PSK, has not been analyzed. Therefore, our work is to analysis and optimize CC-CMA on the multi-modulus modulation scheme. From our analysis, we found that the cost function of CC-CMA is biased cost function. Instead of that, from our simulation, CC-CMA introduces an unexpected shrinking effect whereby the amplitudes of the equalizer outputs have been reduced, especially in multi-modulus modulation scheme. This shrinking effect is not severe in PSK because the decision of a PSK symbol is based on phase, but not amplitude. Unfortunately, this is severe in multi-modulus modulation scheme. To overcome this shrinking effect in multi-modulus modulation scheme, we propose Cross-Independent Constant Modulus Algorithm (CI-CMA). Based on the convergence analysis, we identify the new optimum dispersion value and mixing parameter in CI-CMA. From the simulation results, we confirm that CI-CMA is able to perform equalization and source separation in the multi-modulus modulation scheme. In order to improve the steady state performance of CI-CMA, we perform the steady state mean square error (MSE) analysis of CI-CMA using the energy preservation theorem that was developed by Mai and Sayed in 2001, and our result is more accurate than the previous work. From our analysis, only the reduction in adaptation step size can reduce the steady state MSE, but it is well known that the MSE is indeed a tradeoff with the speed of convergence. Therefore without sacrificing convergence speed, our last effort is to propose hybrid algorithms. The hybrid algorithms are done by combining a new adaptive constant modulus algorithm (ACMA), a decision directed algorithm and a cross-correlation function. From the simulation results, we found that the hybrid algorithms can show low steady state error and thereby improve the reliability of the communication link. The main achievement of this thesis is the discovery of new dispersion value through the convergence analysis

Image restoration and reconstruction using projections onto epigraph set of convex cost fuchtions

Cataloged from PDF version of article.This thesis focuses on image restoration and reconstruction problems. These inverse problems are solved using a convex optimization algorithm based on orthogonal Projections onto the Epigraph Set of a Convex Cost functions (PESC). In order to solve the convex minimization problem, the dimension of the problem is lifted by one and then using the epigraph concept the feasibility sets corresponding to the cost function are defined. Since the cost function is a convex function in R N , the corresponding epigraph set is also a convex set in R N+1. The convex optimization algorithm starts with an arbitrary initial estimate in R N+1 and at each step of the iterative algorithm, an orthogonal projection is performed onto one of the constraint sets associated with the cost function in a sequential manner. The PESC algorithm provides globally optimal solutions for different functions such as total variation, `1-norm, `2-norm, and entropic cost functions. Denoising, deconvolution and compressive sensing are among the applications of PESC algorithm. The Projection onto Epigraph Set of Total Variation function (PES-TV) is used in 2-D applications and for 1-D applications Projection onto Epigraph Set of `1-norm cost function (PES-`1) is utilized. In PES-`1 algorithm, first the observation signal is decomposed using wavelet or pyramidal decomposition. Both wavelet denoising and denoising methods using the concept of sparsity are based on soft-thresholding. In sparsity-based denoising methods, it is assumed that the original signal is sparse in some transform domain such as Fourier, DCT, and/or wavelet domain and transform domain coefficients of the noisy signal are soft-thresholded to reduce noise. Here, the relationship between the standard soft-thresholding based denoising methods and sparsity-based wavelet denoising methods is described. A deterministic soft-threshold estimation method using the epigraph set of `1-norm cost function is presented. It is demonstrated that the size of the `1-ball can be determined using linear algebra. The size of the `1-ball in turn determines the soft-threshold. The PESC, PES-TV and PES-`1 algorithms, are described in detail in this thesis. Extensive simulation results are presented. PESC based inverse restoration and reconstruction algorithm is compared to the state of the art methods in the literature.Tofighi, MohammadM.S