scholarly journals An Adaptive Learning Based Network Selection Approach for 5G Dynamic Environments

Entropy ◽  
2018 ◽  
Vol 20 (4) ◽  
pp. 236
Author(s):  
Xiaohong Li ◽  
Ru Cao ◽  
Jianye Hao
Keyword(s):  
Adaptive Learning ◽  
Dynamic Environments ◽  
Network Selection ◽  
Selection Approach

A throughput-aware joint vehicle route and access network selection approach based on SMDP

2020 ◽  
Vol 17 (5) ◽  
pp. 243-265 ◽  
Author(s):  
Jiandong Xie ◽  
Sa Xiao ◽  
Ying-Chang Liang ◽  
Li Wang ◽  
Jun Fang
Keyword(s):  
Access Network ◽  
Network Selection ◽  
Vehicle Route ◽  
Selection Approach ◽  
Access Network Selection

Cloud Computing Based Cognitive Radio Networking

2013 ◽  
pp. 153-164 ◽  
Author(s):  
Sachin Shetty ◽  
Danda B. Rawat
Keyword(s):  
Cloud Computing ◽  
Cognitive Radio ◽  
Adaptive Learning ◽  
Dynamic Environments ◽  
Spectrum Management ◽  
Computational Capacity ◽  
Fast Processing ◽  
Limited Power ◽  
Adaptation And Learning ◽  
Computing Platforms

This chapter describes state-of-the art techniques to improve performance of spectrum sensing and spectrum management in Cognitive Radio Networks (CRN) by leveraging services available in cloud computing platforms. CRNs are capable of adaptive learning and reconfiguration to provide consistent communications in dynamic environments. However, ensuring adaptation and learning in CRN will require availability of large volume of data and fast processing. However, the performance and security of CRN is considerably constrained by its limited power, memory and computational capacity, it may not be able to achieve its full capability. Fortunately, the advent of cloud computing has the potential to mitigate these constraints due its vast storage and computational capacity.

Context awareness in network selection for dynamic environments

2007 ◽  
Vol 36 (1-3) ◽  
pp. 49-60 ◽  
Author(s):  
Daniel Díaz Sánchez ◽  
Andrés Marín López ◽  
Florina Almenárez Mendoza ◽  
Celeste Campo Vázquez ◽  
Carlos García-Rubio
Keyword(s):  
Context Awareness ◽  
Dynamic Environments ◽  
Network Selection ◽  
Selection For

scholarly journals Context Awareness in Network Selection for Dynamic Environments

2006 ◽  
pp. 216-227 ◽  
Author(s):  
Daniel Díaz ◽  
Andrés Marín ◽  
Florina Almenárez ◽  
Carlos García-Rubio ◽  
Celeste Campo
Keyword(s):  
Context Awareness ◽  
Dynamic Environments ◽  
Network Selection ◽  
Selection For

scholarly journals A Control Theoretic Model of Adaptive Learning in Dynamic Environments

2018 ◽  
Vol 30 (10) ◽  
pp. 1405-1421 ◽  
Author(s):  
Harrison Ritz ◽  
Matthew R. Nassar ◽  
Michael J. Frank ◽  
Amitai Shenhav
Keyword(s):  
Adaptive Learning ◽  
Pid Controller ◽  
Industrial Process ◽  
Dynamic Environments ◽  
Preliminary Evidence ◽  
Change Point Detection ◽  
Theoretic Model ◽  
Pid Algorithm ◽  
Delta Rule ◽  
Environmental Events

To behave adaptively in environments that are noisy and nonstationary, humans and other animals must monitor feedback from their environment and adjust their predictions and actions accordingly. An understudied approach for modeling these adaptive processes comes from the engineering field of control theory, which provides general principles for regulating dynamical systems, often without requiring a generative model. The proportional–integral–derivative (PID) controller is one of the most popular models of industrial process control. The proportional term is analogous to the “delta rule” in psychology, adjusting estimates in proportion to each error in prediction. The integral and derivative terms augment this update to simultaneously improve accuracy and stability. Here, we tested whether the PID algorithm can describe how people sequentially adjust their predictions in response to new information. Across three experiments, we found that the PID controller was an effective model of participants' decisions in noisy, changing environments. In Experiment 1, we reanalyzed a change-point detection experiment and showed that participants' behavior incorporated elements of PID updating. In Experiments 2–3, we developed a task with gradual transitions that we optimized to detect PID-like adjustments. In both experiments, the PID model offered better descriptions of behavioral adjustments than both the classical delta-rule model and its more sophisticated variant, the Kalman filter. We further examined how participants weighted different PID terms in response to salient environmental events, finding that these control terms were modulated by reward, surprise, and outcome entropy. These experiments provide preliminary evidence that adaptive learning in dynamic environments resembles PID control.

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