Joint Transmission-Sensing Framework Using Changepoint Detection

2021 ◽  
Author(s):  
Akpaki Steaven V. Chede ◽  
Michel Dossou ◽  
Jerome Louveaux
Keyword(s):  
Changepoint Detection ◽  
Joint Transmission

Distributed Pre-Coder Design for Multi-Cell Joint Transmission with Interference Controlling

2010 ◽  
Vol E93-B (11) ◽  
pp. 3176-3179
Author(s):  
Huan SUN ◽  
Shengjie ZHAO ◽  
Mingli YOU
Keyword(s):  
Joint Transmission

scholarly journals Resource Allocation in Virtualized CoMP-NOMA HetNets: Multi-Connectivity for Joint Transmission

2021 ◽  
pp. 1-1
Author(s):  
Sepehr Rezvani ◽  
Nader Mokari ◽  
Mohammad R. Javan ◽  
Eduard A. Jorswieck
Keyword(s):  
Resource Allocation ◽  
Joint Transmission

scholarly journals On the Performance of UAV-Aided Content Caching in Small-Cell Networks with Joint Transmission

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1040
Author(s):  
Menghan Wei ◽  
Youjia Chen ◽  
Ming Ding
Keyword(s):  
Transmission Rate ◽  
High Mobility ◽  
Limited Resource ◽  
Small Cell ◽  
Base Stations ◽  
Cell Content ◽  
Small Cell Networks ◽  
Content Caching ◽  
Cell Networks ◽  
Joint Transmission

Unmanned aerial vehicles (UAVs), featured by the high-mobility and high-quality propagation environment, have shown great potential in wireless communication applications. In this paper, a novel UAV-aided small-cell content caching network is proposed and analyzed, where joint transmission (JT) is considered in the dense small-cell networks and mobile UAVs are employed to shorten the serving distance. The system performance is evaluated in terms of the average cache hit probability and the ergodic transmission rate. From the analytical results, we find that (i) the proposed UAV-aided small-cell network shows superior caching performance and, even with a small density of UAVs the system’s cache hit probability, can be improved significantly; (ii) the content’s optimal caching probability to maximize the cache hit probability is proportional to the (K+1)-th root of its request probability, where K is the number of small-cell base stations that serve each user by JT; (iii) caching the most popular content in UAVs may lead to a low transmission rate due to the limited resource offered by the low-density UAVs. Simulation results are presented to validate the theoretical results and the performance gain achieved by the optimal caching strategy.

scholarly journals Unsupervised Offline Changepoint Detection Ensembles

2021 ◽  
Vol 11 (9) ◽  
pp. 4280
Author(s):  
Iurii Katser ◽  
Viacheslav Kozitsin ◽  
Victor Lobachev ◽  
Ivan Maksimov
Keyword(s):  
Numerical Experiment ◽  
Failure Time ◽  
Cost Functions ◽  
Technical Diagnostics ◽  
Classification Problems ◽  
Tennessee Eastman Process ◽  
Changepoint Detection ◽  
Aggregation Functions ◽  
The Individual ◽  
Ensemble Algorithms

Offline changepoint detection (CPD) algorithms are used for signal segmentation in an optimal way. Generally, these algorithms are based on the assumption that signal’s changed statistical properties are known, and the appropriate models (metrics, cost functions) for changepoint detection are used. Otherwise, the process of proper model selection can become laborious and time-consuming with uncertain results. Although an ensemble approach is well known for increasing the robustness of the individual algorithms and dealing with mentioned challenges, it is weakly formalized and much less highlighted for CPD problems than for outlier detection or classification problems. This paper proposes an unsupervised CPD ensemble (CPDE) procedure with the pseudocode of the particular proposed ensemble algorithms and the link to their Python realization. The approach’s novelty is in aggregating several cost functions before the changepoint search procedure running during the offline analysis. The numerical experiment showed that the proposed CPDE outperforms non-ensemble CPD procedures. Additionally, we focused on analyzing common CPD algorithms, scaling, and aggregation functions, comparing them during the numerical experiment. The results were obtained on the two anomaly benchmarks that contain industrial faults and failures—Tennessee Eastman Process (TEP) and Skoltech Anomaly Benchmark (SKAB). One of the possible applications of our research is the estimation of the failure time for fault identification and isolation problems of the technical diagnostics.

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