Institute for Systems Research
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Item Joint Optimal Power Control and Beamforming in Wireless Networks Using Antenna Arrays(1997) Rashid-Farrokhi, F.; Tassiulas, L.; Liu, K.J. Ray; ISR; CSHCNThe interference reduction capability of antenna arrays and the power control algorithms have been considered separately as means to increase the capacity in wireless communication networks. The MVDR (Minimum Variance Distortionless Responses) beamformer maximizes the Carrier to Interference Ratio (CIR) when it is employed in the receiver of a wireless link. In a system with omnidirectional antennas, power control algorithms are used to maximize CIR as well. In this paper we consider a system with beamforming capabilities in the receiver, and power control. an iterative algorithm is proposed to jointly update the transmission powers and the beamformer weights so that the coverage to the jointly optimal beamforming and transmission power vector. The algorithm is distributed and uses only local interference measurements. In an uplink transmission scenario it is shown how base assignment can be incorporated in addition to beamforming and power control such that a globally optimum solution is obtained. the network capacity increase and the saving in mobile power achieved by beamforming are evaluated through numerical study.Item Fast Blind Adaptive Algorithms for Equalization and Diversity Reception in Wireless Communications Using Antenna Arrays(1996) Li, Ye; Liu, K.J. Ray; ISRTo combat the multipath and time-variant fading of wireless communication channels, antenna arrays are usually used to improve the quality and increase the capacity of communication service. This paper investigates the fast blind adaptive algorithms for the equalization and diversity combining in wireless communication systems using antenna arrays. Two second- order statistics based algorithms, SOSA and MSOS, for equalization and diversity combining are proposed and their convergence in noiseless and noisy channels is analyzed. Since the proposed algorithms use only second-order statistics or correlation of the channel outputs, they converge faster than the higher-order statistics based algorithms, which is also confirmed by computer simulations examples.Item A Structured Low Rank Matrix Pencil for Spectral Estimation and System Identification(1996) Razavilar, J.; Li, Ye; Liu, K.J. Ray; ISRIn this paper we propose a new parameter estimation algorithm for damped sinusoidal signals. Parameter estimation for damped sinusoidal signals with additive white noise is a problem of significant interests in many signal processing applications, such as analysis of NMR data and system identification. The proposed algorithm estimates the signal parameters using a matrix pencil constructed from the measured data. To reduce the noise effect, rank deficient Hankel approximation of prediction matrix is used. We show that the performance of the estimation can be significantly improved by structured low rank approximation of the prediction matrix. Computer simulations also show that the noise threshold of our new matrix pencil algorithm is significantly low than those of the existing algorithms.Item Simultaneous Diversity Combining and Decoding for Fast Time- Varying Mobile Radio Channels(1996) Wang, H.; Liu, K.J. Ray; ISRIn slowly time-varying mobile radio channels, adaptive diversity combining can reduce multipath fading of desired signal and suppress interfering signals. However, for fast time-varying fading channels, there exist no effective techniques to achieve the same results. The continued use of decision directed adaptive array algorithms will cause error propagation. This paper presents a novel adaptive diversity combining technique with QRD- RLS based parallel weights tracking and a proposed M-D decoder. With moderate increase in complexity, this system significantly reduces error propagation in the decision directed array systems while maintaining the same tracking speed. Its effectiveness and much better performance then that of the conventional technique has been confirmed by computer simulation.Item Blind Adaptive Equalization of SIMO Channels Based on Second- Order Statistics(1996) Li, Ye; Liu, K.J. Ray; ISRThis article investigates blind adaptive equalization for single- input/multiple-output (SIMO) channels. A second-order statistics based algorithm (SOSA) and a modified second-order statistics based algorithm (MSOSA) for equalization of SIMO channels are presented. Computer simulation demonstrates that the new algorithms converge faster than fractionally spaced constant- modulus algorithm (FS-CMA). The proposed algorithms can be applied in wireless communication systems with antenna arrays to combat the multipath fading.Item Blind MIMO FIR Channel Identification Based on Second-Order Statistics with Multiple Signals Recovery(1996) Li, Ye; Liu, K.J. Ray; ISRTo separate and recover multiple signals in data communications, antenna arrays and acoustic sensor arrays, the impulse responses of multiple-input/multiple-output (MIMO) channels have to be identified explicitly or implicitly. This paper deals with the blind identification of MIMO FIR channels based on second-order statistics of the channel outputs. We first investigate the identifiability of MIMO FIR channels, and obtain a necessary and sufficient condition for MIMO FIR channels to be identifiable up to a unitary matrix using second-order statistics. Then, we extend the identification algorithms for single-input/multiple- output (SIMO) FIR channels, such as the algebraic algorithm and the subspace algorithm to the identification of MIMO FIR channels. We also present the application of the above blind identification algorithms to the separation of multiple signals in digital communication systems. Finally, we demonstrate the effectiveness of the algorithms presented in this paper by computer simulationsItem Adaptive Blind Multi-Channel Equalization for Multiple Signals Separation(1995) Li, Ye; Liu, K.J. Ray; ISRThis paper investigates adaptive blind equalization for multiple- input and multiple-output (MIMO) channels and its application to blind separation of multiple signals received by antenna arrays in communication systems. The performance analysis is presented for the CMA equalizer used in MIMO channels. Our analysis results indicate that a double infinite-length MIMO-CMA equalizer can recover one of the input signals, remove the intersymbol interference (ISI), and suppress the rest signals. In particular, for the MIMO FIR channels satisfying certain conditions, the MIMO-CMA FIR equalizer is able to remove the ISI and co-channel interference regardless of the initial setting of the blind equalizer. To recover all input signals simultaneously, a novel MIMO channel blind equalization algorithm is developed in this paper. The global convergence of the new algorithm for MIMO channels is proved. Hence, the new blind equalization algorithm for MIMO channels can be applied to separate and equalize the signals received by antenna arrays in communication systems. Finally, computer simulations are presented to confirm our analysis and illustrate the performance of the new algorithm.Item Two-Dimensional Spatial Smoothing for Multipath Coherent Signal Identification and Separation(1995) Wang, H.; Liu, K.J. Ray; ISRThe existing spatial smoothing (SS) technique, although it is effective in decorrelating coherent signals, is considered applicable only to uniformly spaced linear arrays which are very sensitive to the directions-of-arrival (DOAs) and can be used to estimate azimuth angles only. To significantly improve the robustness of DOA estimation and of beamforming and to estimate both azimuth and elevation angles in a 3D multipath mobile radio environment, we developed techniques for applying SS to arrays of nonlinear geometry. We found and proved the necessary and sufficient conditions on an array configuration for applying SS. This array must have an orientational invariance structure with an ambiguity free center array, and the number of subarrays must be larger than or equal to the size of the largest group of coherent signals. We also studied the cause of ambiguities in a multipath environment. We found the necessary and sufficient conditions for a three-sensor array manifold to be ambiguity free and identified several higher order ambiguity situations. If an array is also central symmetric, the forward/backward spatial smoothing can be used to improve the resolution. Finally, we expanded the application of our technique not only to MUSIC and adaptive beamforming algorithms but also to ESPRIT algorithms. All the predicted results are verified by simulations.Item A Fast Minimal-Symbol Subspace Approach to Blind Identification and Equalization(1995) Sampath, B.; Li, Ye; Liu, K.J. Ray; ISRA subspace-based blind channel identification algorithm using only the fact that the received signal can be oversampled is proposed. No direct use is made of the statistics of the input sequence or even of the fact that the symbols are from a finite set and therefore this algorithm can be used to identify even channels in which arbitrary symbols are sent. A modification of this algorithm which uses the extra information in the more common case when the symbols are from a finite set is also presented. This LS-Subspace algorithm operates directly on the data domain and therefore avoids the problems associated with other algorithms which use the statistical information contained in the received signal. In the noiseless case, it is possible for the proposed Basic Subspace algorithm to identify the channel exactly using the least number of symbols that can possibly be used. Thus, if the length of the impulse response of a channel is JT, T being the symbol interval, then it is possible to use this algorithm to identify the channel using an observation interval of just (J + 3)T. In the noisy case, simulations have shown that almost exact identification can be obtained by using a few more symbols than the theoretical minimum. This is orders of magnitude better than the other blind algorithms. Moreover, this algorithm is computationally very efficient and has no convergence problems.Item Static and Dynamic Convergence Behavior of Adaptive Blind Equalizers(1995) Li, Ye; Liu, K.J. Ray; ISRThis paper presents a theoretical analysis of the static and dynamic convergence behavior for a general class of adaptive blind equalizers. We first study the properties of prediction error functions of blind equalization algorithms, and then we use these properties to analyze the static and dynamic convergence behavior based on the independent assumption. We prove in this paper that with a small step-size, the ensemble average of equalizer coefficients will converge to the minimum of the cost function near the channel inverse. However, the convergence is not consistent. The correlation matrix of equalizer coefficients at equilibrium is determined by a Lyapunov equation. According to our analysis results, for a given channel and step-size, there is an optimal length for an equalizer to minimize the intersymbol interference. This result implies that a longer-length blind equalizer does not necessarily outperform a shorter one, as contrary to what conventionally conjectured. The theoretical analysis results are confirmed by computer simulations.