Sentence examples for expansion vector from inspiring English sources

By performing independent samples of the expansion space concurrently on different processors, the results may be combined after a variational calculation to form an improved expansion vector.

We use high-resolution angular quantization of a single S=1 specular component, such that the CSI uncertainty of this single expansion vector is only due to the channel estimation error.

Expressing (tilde{H}_{0} (t)equivvec{mathcal{R}}(t cdot boldsymbol {sigma }) in the Pauli basis, where the expansion vector (vec{mathcal{R}}(t)) is some convolution of both control and noise fields, we obtain the computational expression (mathbf {a}_{1} tau) = int_{0}^{tau}dtvec{mathcal {R}}(t)).

In this paper, we have formulated the capacity expansion with the combined road pricing problem as a bi-level program, where the upper level optimizes the link capacity expansion vector and maximizes the social welfare, while the lower level determines the demand and the flow satisfying the Wardrop principles.

Under line of sight (LOS) conditions, the selected expansion vectors of the sparse approximation are robust w.r.t.

In such scenarios, the angular CSI feedback corresponding to the expansion vectors (mathbf {d}_{u}^{s}) is relatively stable, whereas the phase of the expansion coefficient vectors (mathbf {c}_{u}^{s}) varies significantly over time.

By repeatedly generating expansion vectors using a Monte Carlo technique for configuration generation, a sequential improvement in the variational energy can be achieved.

If we apply the CSI feedback of Section 3, however, the transmitter only has knowledge of the channel decomposition expansion vectors (mathbf {d}_{u}^{s}) and the norm of their corresponding expansion coefficients (left |mathbf {c}_{u}^{s}right |).

From the angular CSI feedback provided by the users, the base station has at time instant n delayed knowledge of the channel expansion vectors (mathbf {d}_{u}^{s}[n-m]) and the norm of the corresponding expansion coefficient vectors (left |mathbf {c}_{u}^{s}[n-m]right |), where m represent the delay of the CSI feedback path.

Given the sparse approximation, the CSI feedback is set equal to the corresponding codebook indices of the optimal azimuth and elevation angles (left (phi _{u}^{s}[n],theta _{u}^{s}[n]right)) of the expansion vectors (mathbf {d}_{u}^{s}[n]).

The selected expansion vectors of such a sparse approximation, parametrized by azimuth and elevation angles, are relatively robust with respect to channel estimation errors as well as channel variations over time.