Adaptive multichannel sequential lattice prediction filtering method for ARMA spectrum estimation in subbands

Abstract

A multichannel characterization for autoregressive moving average (ARMA) spectrum estimation in subbands is considered in this article. The fullband ARMA spectrum estimation can be realized in two-channels as a special form of this characterization. A complete orthogonalization of input multichannel data is accomplished using a modified form of sequential processing multichannel lattice stages. Matrix operations are avoided, only scalar operations are used, and a multichannel ARMA prediction filter with a highly modular and suitable structure for VLSI implementations is achieved. Lattice reflection coefficients for autoregressive (AR) and moving average (MA) parts are simultaneously computed. These coefficients are then converted to process parameters using a newly developed Levinson-Durbin type multichannel conversion algorithm. Hence, a novel method for spectrum estimation in subbands as well as in fullband is developed. The computational complexity is given in terms of model order parameters, and comparisons with the complexities of nonparametric methods are provided. In addition, the performance is visually and statistically compared against those of the nonparametric methods under both stationary and nonstationary conditions.

Adaptive reconfigurable V-BLAST type equalizer for cognitive MIMO-OFDM radios

“…If the expression on the righthand side of Equation (21) is substituted for Rx k y k (n)w k (n) in Equation (19) and also (19) is replaced with w Hopt k (n), then the minimum cost can be stated as follows: (22) which demonstrates that the cost function is minimized by choosing w opt k (n) to be equal to the eigenvector of the matrix Rx k y k and that the corresponding eigenvalue λ is the minimum eigenvalue of the matrix Rx k y k and is represented with λ min . Consequently, the optimal coefficient vectors for the kth equalizer and the kth ADIR filter, p opt k (n) and w opt k (n), are given by Equations (16) and (21), respectively.…”

“…Since the findings in these papers show that the unit energy constrained channel shortener equalization resulted in better performance, we have used unit energy constraint for the MIMO channel shortening optimization problem under consideration in this paper. Accordingly, the contributions of the paper can be stated as follows: (1) the proposed equalizer has a front-end MIMO-DFE as opposed to the MIMO feed forward equalizer (MIMO-FFE) in [22], (2) a modified version of sequential processing multichannel lattice stages (SPMLSs) [23] is utilized in the design of front-end MIMO-DFE and a complete modified Gram-Schmidt orthogonalization of multichannel input data, which avoids matrix inversions, enables scalar only operations and contributes to the flexibility, reconfigurability, and reprogrammability of the receiver, is attained, (3) the proposed equalizer can be viewed as a V-BLAST receiver for frequency selective channels, (4) spectrum sensing or range estimation can be accomplished at no cost by simply reconfiguring the front-end MIMO-DFE as multichannel spectral analysis or positioning filter as shown in [19,20], respectively, and (5) a detailed computational complexity and performance analysis is presented. The first contribution is important from the perspective of interference removal and, by means of that, error performance, whereas the second one is considered the key since matrix inversion is a major bottleneck in the design of embedded receiver architectures that increases computational complexity [24].…”

“…Adaptive positioning, determining the coordinates of a cognitive radio in space, is a step towards realization of accurate location awareness in cognitive radios. The author has recently proposed a spectrum estimation method in [19] and a range estimation method in [20] that are suitable for spectrum sensing and positioning functions of cognitive radios, respectively. In this paper, we focus on the reception mode of operation of cognitive MIMO-OFDM radios and propose a new minimum MSE channel shortening equalizer design, which consists of adaptive fron-tend MIMO-DFE and multiple Viterbi detection sections.…”

Sequential convex combinations of multiple adaptive lattice filters in cognitive radio channel identification

“…The author has recently proposed a receiver (equalizer) architecture for use in cognitive MIMO-OFDM radios that performs joint channel estimation and data detection, addresses the receiver complexity problems, and contributes to the flexibility, reconfigurability, and reprogrammability of receiver [19]. It was also shown in [20][21][22] that this receiver architecture can be configured for spectrum sensing as well as adaptive positioning function of cognitive radio virtually at no cost.…”

“…In order to capture the statistical variations in the CR environment, the combination of multiple lattice filters with different exponential weighting factors has been considered so as to bring together the convergence properties of fast filters that have small exponential weighting factors and steady-state MSD properties of slow filters that have large exponential weighting factors. Accordingly, the author envisions multiple adaptive lattice filters as channels of sequential processing multichannel lattice stages (SPMLSs) [19][20][21][22]50] and proposes to sequentially combine these multiple lattice filters in a CR channel identification task. In view of the aforementioned issues concerning convex vs. affine combinations, the focus is on convex combinations of multiple adaptive lattice filters.…”