Variable observation window length blind equalization detector for underwater acoustic communication

Abstract

Aiming at the influence of underwater acoustic channel (UAC) profile difference on the performance of underwater acoustic communication, a variable observation window length blind equalization detector (VOWL-BED) is proposed in this paper. Compared with the existing methods, the observation window length (OWL) of the proposed VOWL-BED can be dynamically regulated on the basis of a given UAC profile and finally converge to the optimum OWL. We have verified the feasibility of the OWL regulation. The simulation results also demonstrate that the VOWL-BED can achieve better performance than the conventional invariable OWL blind equalization detector (IOWL-BED).

Ontology specific visual canvas generation to facilitate sense-making-an algorithmic approach

“…Among the layout-based methods, force-directed, radial, inverted-radial, and circular layouts are being criticized for space waste, rotated textual representations, and loss of hierarchical structures [15], [16]. Because additional complexity triggers by these techniques complicates ontological sense-making for stakeholders.…”

“…Excessive space waste, rotated text representation and loss of hierarchical structure, information flooding and density, and excessive consolidations will add to the user's cognitive burden. [15], [16] Euler Diagraming methods Information loss, learning curve, mathematical representations associated makes comprehension is going to be an additional overload to the end-user [9], [10] Int For domain experts, the likelihood of developing a cognitively sophisticated visual representation is higher. OntoSphere [25] Graph-based A 2D hierarchical network and a 3D spherical view are both shown well.…”

Adaptive modulation based on steady-state mean square error for underwater acoustic communication

“…The normalized least mean square algorithm can be used to update the tap coefficient vector of BED. To reduce the computational complexity, in the process of tap-length adjustment, the accumulated squared error (ASE) is adopted as the measure criterion [25] where the repetitive computation of division is avoided. Ideally, the out ASE will decrease with the increase of tap-length where where m ∈ {L, L − 1} , ζ is a forgetting factor used to weight the relative importance of preceding and recent samples, ζ ≤ 1 , the input sequence is equally divided into several segments, y mi (k) denotes the ith output of the BED for the mth segment input sequence, y R,mi (k) represents the real part of equalization out y mi (k) , y I,mi (k) is the imaginary part of y mi (k) and ASE m (k) denotes the ASE corresponding to the mth segment input sequence.…”

“…By comparing the sizes of ASE L (k) and ASE L−1 (k) , the adjustment of tap-length can be determined [25].…”

“…The threshold set of adjustments for modulation mode can be obtained according to (25). The SNR is divided into several intervals by the threshold set.…”