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Adaptive algorithms based on kernel structures have been a topic of significant research over the past few years. The main advantage is that they form a family of universal approximators, offering an elegant solution to problems with nonlinearities. Nevertheless, these methods deal with kernel expansions, creating a growing structure also known as dictionary, whose size depends on the number of new...
In this work, we propose an adaptive buffer-aided space-time coding scheme for cooperative wireless networks. A maximum likelihood receiver and adjustable code vectors are considered subject to a power constraint with an amplify-and-forward cooperation strategy. Each relay is equipped with a buffer and is capable of storing the received symbols before forwarding them to the destination. We also present...
This paper describes a novel technique for promoting sparsity in the modified filtered-x algorithms required for active noise control. The proposed algorithms are based on recent techniques incorporating approximations to the l0-norm in the cost functions that are used to derive adaptive filtering algorithms. In particular, zero-attracting and reweighted zero-attracting filtered-x adaptive algorithms...
In this paper, a complex adaptive re-weighting algorithm based on the homotopy technique is developed and used for beamforming. A multi-candidate scheme is also proposed and incorporated into the adaptive re-weighting homotopy algorithm to choose the regularization factor and improve the signal-to-interference plus noise (SINR) performance. The proposed algorithm is used to minimize the degradation...
In this paper, we propose a novel adaptive reduced-rank strategy for very large multiuser multi-input multi-output (MIMO) systems. The proposed reduced-rank scheme is based on the concept of joint iterative optimization (JIO) of filters according to the minimization of the bit error rate (BER) cost function. The proposed optimization technique adjusts the weights of a projection matrix and a reduced-rank...
We introduce a new linearly constrained minimum variance (LCMV) beamformer that combines the set-membership (SM) technique with the conjugate gradient (CG) method, and develop a low-complexity adaptive filtering algorithm for beamforming. The proposed algorithm utilizes a CG-based vector and a variable forgetting factor to perform the data-selective updates that are controlled by a time-varying bound...
This work presents generalized low-rank signal decompositions with the aid of switching techniques and adaptive algorithms, which do not require eigen-decompositions, for space-time adaptive processing. A generalized scheme is proposed to compute low-rank signal decompositions by imposing suitable constraints on the filtering and by performing iterations between the computed subspace and the low-rank...
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