New receiver for joint blind equalization and carrier phase recovery of QAM signals

Published versio

Blind adaptive equalization for QAM signals: New algorithms and FPGA implementation.

Adaptive equalizers remove signal distortion attributed to intersymbol interference in band-limited channels. The tap coefficients of adaptive equalizers are time-varying and can be adapted using several methods. When these do not include the transmission of a training sequence, it is referred to as blind equalization. The radius-adjusted approach is a method to achieve blind equalizer tap adaptation based on the equalizer output radius for quadrature amplitude modulation (QAM) signals. Static circular contours are defined around an estimated symbol in a QAM constellation, which create regions that correspond to fixed step sizes and weighting factors. The equalizer tap adjustment consists of a linearly weighted sum of adaptation criteria that is scaled by a variable step size. This approach is the basis of two new algorithms: the radius-adjusted modified multitmodulus algorithm (RMMA) and the radius-adjusted multimodulus decision-directed algorithm (RMDA). An extension of the radius-adjusted approach is the selective update method, which is a computationally-efficient method for equalization. The selective update method employs a stop-and-go strategy based on the equalizer output radius to selectively update the equalizer tap coefficients, thereby, reducing the number of computations in steady-state operation. (Abstract shortened by UMI.) Source: Masters Abstracts International, Volume: 45-01, page: 0401. Thesis (M.A.Sc.)--University of Windsor (Canada), 2006

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

Convergence analysis of blind equalization algorithms using constellation-matching

Two modified blind equalization algorithms are analyzed for performance. These algorithms add a constellation-matched error term to the cost functions of the generalized Sato and multimodulus algorithms. The dynamic convergence behavior and steady-state performance of these algorithms, and of a related version of the constant modulus algorithm, are characterized. The analysis establishes the improved performance of the proposed algorithms

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

CMF-DFE Based Adaptive Blind Equalization Using Particle Swarm Optimization

The channel matched filter (CMF) is the optimum receiver providing the maximum signal to noise ratio (SNR) for the frequency selective channels. The output intersymbol interference (ISI) profile of the CMF convolved by the channel can be blindly obtained by using the autocorrelation of the received signal. Therefore, the inverse of the autocorrelation function can be used to equalize the channel passed through its own CMF. The only missing part to complete the proposed blind operation is the CMF coefficients. Therefore, in this work, the best training algorithm investigation is subjected for blind estimation of the CMF coefficients. The proposed method allows using more effective training algorithms for blind equalizations. However, the expected high performance training is obtained when the swarm intelligence is used. Unlike the stochastic gradient algorithms, the particle swarm optimization (PSO) is known to have fast convergence because its performance is independent of the characteristics of the systems used. The obtained mean square error (MSE) and bit error rate (BER) performances are promising for high performance real-time systems as an alternative to non-blind equalization techniques

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

SGD Frequency-Domain Space-Frequency Semiblind Multiuser Receiver with an Adaptive Optimal Mixing Parameter

A novel stochastic gradient descent frequency-domain (FD) space-frequency (SF) semiblind multiuser receiver with an adaptive optimal mixing parameter is proposed to improve performance of FD semiblind multiuser receivers with a fixed mixing parameters and reduces computational complexity of suboptimal FD semiblind multiuser receivers in SFBC downlink MIMO MC-CDMA systems where various numbers of users exist. The receiver exploits an adaptive mixing parameter to mix information ratio between the training-based mode and the blind-based mode. Analytical results prove that the optimal mixing parameter value relies on power and number of active loaded users existing in the system. Computer simulation results show that when the mixing parameter is adapted closely to the optimal mixing parameter value, the performance of the receiver outperforms existing FD SF adaptive step-size (AS) LMS semiblind based with a fixed mixing parameter and conventional FD SF AS-LMS training-based multiuser receivers in the MSE, SER and signal to interference plus noise ratio in both static and dynamic environments