Validation of Parallel Distributed Adaptive Signal Processing (PDASP) Framework through Processing-Inefficient Low-Cost Platforms

Article Multidisciplinary Sciences

Variable Matrix-Type Step-Size Affine Projection Sign Algorithm for System Identification in the Presence of Impulsive Noise

Jaewook Shin et al.

Summary: This paper presents a novel variable matrix-type step-size affine projection sign algorithm (VMSS-APSA) that improves filter performance, particularly in the presence of impulsive noise, through mathematical derivation and optimization.

SYMMETRY-BASEL (2022)

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Article Engineering, Electrical & Electronic

Diffusion Bayesian Subband Adaptive Filters for Distributed Estimation Over Sensor Networks

Fuyi Huang et al.

Summary: This paper proposed two diffusion Bayesian subband adaptive filter (DBSAF) algorithms from a Bayesian learning perspective to achieve better learning performance in sensor networks with highly correlated input signals and time-varying parameters. Analyzing the performance of the proposed algorithms through numerical simulations and deriving a lower bound on the time-varying step-size for optimal steady-state performance in nonstationary scenarios.

IEEE TRANSACTIONS ON COMMUNICATIONS (2021)

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Article Engineering, Electrical & Electronic

Diffusion Bias-Compensation RLS Estimation Over Noisy Node-Specific Networks

Chenxue Zheng et al.

Summary: The study focuses on the node-specific parameter estimation problem and proposes a bias-compensation recursive-least-square algorithm based on a diffusion mode of cooperation. The stability and mean-square deviation of the algorithm are analyzed for evaluating the network's steady-state performance, and simulation results demonstrate the efficiency of the proposed method.

CIRCUITS SYSTEMS AND SIGNAL PROCESSING (2021)

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Article Computer Science, Information Systems

A cooperative resource allocation model for IoT applications in mobile edge computing

Xianwei Li et al.

Summary: With the advancement in IoT technology, Mobile Edge Computing (MEC) emerges as a new method to address the limited computation capacity of IoT devices. The proposed system cooperatively allocates computation and communication resources to minimize the overhead of IoT devices, as well as reduce latency and energy consumption. Results show that the system provides significant improvements compared to traditional models.

COMPUTER COMMUNICATIONS (2021)

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Article Mathematics