For computing the transmit beamforming weight in real time, we propose a recursive inverse matrix computation (RIMC) scheme based on a recursive least square (RLS) algorithm [12], in which the inverse of the auto-correlation matrix of the CSI for beamforming weight computation is calculated over successive time intervals.
It is composed of four main components: similarity matrix computation, relational clustering, adaptive training scheme, and decision level fusion.
This approach, called ensemble of hidden Markov models (eHMM), has four main components: similarity matrix computation, relational clustering, adaptive training scheme, and decision level fusion.
In the proposed RIMC scheme, an inverse matrix computation is carried out recursively by using an RLS algorithm, and the computation circuits of {R m (k)−1, m = 0,…, N s − 1} can be communalized.
An efficient computation scheme is introduced for the admittance matrix as well as a multiexcitation procedure for the acoustic plane-waves.
On the other hand, in the update of the atoms of the dictionary matrix, we use a simple steepest ascent algorithm since the introduction of the computation scheme in[53] needs high computation costs.
The new user selection (scheduling) scheme proposed in this article is based on a determinant property and an iterative matrix computation, which is different from [3, 4, 9 12].
PCA computation consists of four stages [21, 37, 38]: mean computation, covariance matrix computation, eigenvalue matrix, thus eigenvector computation, and PCs matrix computation.
Similar to most recent schemes, we incorporate zero-forcing beamforming (ZFBF) as the precoding strategy, since it effectively removes the mutual interference among concurrent transmissions by using a low-complexity precoding matrix computation.
Eigenvalue matrix computation can be illustrated using the two Eqs.
