scholarly journals Artificial Intelligence-Based Discontinuous Reception for Energy Saving in 5G Networks

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 778 ◽  
Author(s):  
Mudasar Latif Memon ◽  
Mukesh Kumar Maheshwari ◽  
Navrati Saxena ◽  
Abhishek Roy ◽  
Dong Ryeol Shin
Keyword(s):  
Artificial Intelligence ◽  
Energy Efficiency ◽  
High Data ◽  
Multiple Beam ◽  
Data Rates ◽  
Packet Arrival ◽  
Long Sleep ◽  
High Data Rates ◽  
Discontinuous Reception ◽  
Sleep Cycles

5G is expected to deal with high data rates for different types of wireless traffic. To enable high data rates, 5G employs beam searching operation to align the best beam pairs. Beam searching operation along with high order modulation techniques in 5G, exhausts the battery power of user equipment (UE). LTE network uses discontinuous reception (DRX) with fixed sleep cycles to save UE energy. LTE-DRX in current form cannot work in 5G network, as it does not consider multiple beam communication and the length of sleep cycle is fixed. On the other hand, artificial intelligence (AI) has a tendency to learn and predict the packet arrival-time values from real wireless traffic traces. In this paper, we present AI based DRX (AI-DRX) mechanism for energy efficiency in 5G enabled devices. We propose AI-DRX algorithm for multiple beam communications, to enable dynamic short and long sleep cycles in DRX. AI-DRX saves the energy of UE while considering delay requirements of different services. We train a recurrent neural network (RNN) on two real wireless traces with minimum root mean square error (RMSE) of 5 ms for trace 1 and 6 ms for trace 2. Then, we utilize the trained RNN model in AI-DRX algorithm to make dynamic short or long sleep cycles. As compared to LTE-DRX, AI-DRX achieves 69 % higher energy efficiency on trace 1 and 55 % more energy efficiency on trace 2, respectively. The AI-DRX attains 70 % improvement in energy efficiency for trace 2 compared with Poisson packet arrival model for λ = 1 / 20 .

Detektion von Bauteilfehlern aus Maschinendaten/Detection of workpiece flaws out of machine tool data – possibilities and limits

2019 ◽  
Vol 109 (11-12) ◽  
pp. 828-832
Author(s):  
M. Weigold ◽  
A. Fertig ◽  
C. Bauerdick
Keyword(s):  
Quality Assurance ◽  
Machine Tool ◽  
Machine Tools ◽  
Machining Time ◽  
Parallel Detection ◽  
High Data ◽  
Novel Approach ◽  
Data Rates ◽  
High Data Rates

Durch zunehmende Vernetzung und Digitalisierung von Werkzeugmaschinen und Automatisierungskomponenten ergibt sich die Möglichkeit, Signale mit hohen Datenraten und großer Vielfalt aufzuzeichnen. Der vorliegende Beitrag beschreibt erste Untersuchungen zur Realisierbarkeit einer prozessparallelen Detektion von Bauteilfehlern auf Basis interner Werkzeugmaschinendaten. Dabei werden Potenziale und Grenzen für diesen neuartigen Ansatz zur hauptzeitparallelen Qualitätssicherung aufgezeigt.   The increasing networking and digitization of machine tools and automation components provides the opportunity to record signals with high data rates and great diversity. This paper describes first investigations on the feasibility of a process-parallel detection of component defects on the basis of internal machine tool data. Potentials and limits for this novel approach to quality assurance parallel to machining time are presented.

scholarly journals IPv4 Reassembly Errors at High Data Rates

2007 ◽  
Author(s):  
J. Heffner ◽  
M. Mathis ◽  
B. Chandler
Keyword(s):  
High Data ◽  
Data Rates ◽  
High Data Rates
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