Adaptive multichannel sequential lattice prediction filtering method for range estimation in cognitive radios

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

“…The mixing coefficients, v m (n), are then fed back to type-1 combination processors so as to be used in the computation of equivalent desired signals, estimation errors, and equivalent reflection coefficients in Eqs. (17), (21), or (22) and (23) respectively. We call the complete algorithm as the R-CMLF algorithm, which includes the modified SPMLS algorithm in Table 2 as well as the combination algorithm presented in this subsection, and summarize it in Table 3.…”

“…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.…”

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

“…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.…”

“…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].…”