Pub Date : 2017-11-20DOI: 10.1109/WCNCW.2018.8368990
Mustafa Emara, Miltiades Filippou, D. Sabella
The need for efficient use of network resources is continuously increasing with the grow of traffic demand, however, current mobile systems have been planned and deployed so far with the mere aim of enhancing radio coverage and capacity. Unfortunately, this approach is not sustainable anymore, as 5G communication systems will have to cope with huge amounts of traffic, heterogeneous in terms of latency among other Quality-of-Service (QoS) requirements. Moreover, the advent of Multi-access Edge Computing (MEC) brings up the need to more efficiently plan and dimension network deployment by means of jointly exploiting the available radio and processing resources. From this standpoint, advanced cell association of users can play a key role for 5G systems. Focusing on a Heterogeneous Network (HetNet), this paper proposes a comparison between state-of-the-art (i.e., radio-only) and MEC-aware cell association rules, taking the scenario of task offloading in the Uplink (UL) as an example. Numerical evaluations show that the proposed cell association rule provides nearly 60% latency reduction, as compared to its standard, radio-exclusive counterpart.
{"title":"MEC-aware cell association for 5G heterogeneous networks","authors":"Mustafa Emara, Miltiades Filippou, D. Sabella","doi":"10.1109/WCNCW.2018.8368990","DOIUrl":"https://doi.org/10.1109/WCNCW.2018.8368990","url":null,"abstract":"The need for efficient use of network resources is continuously increasing with the grow of traffic demand, however, current mobile systems have been planned and deployed so far with the mere aim of enhancing radio coverage and capacity. Unfortunately, this approach is not sustainable anymore, as 5G communication systems will have to cope with huge amounts of traffic, heterogeneous in terms of latency among other Quality-of-Service (QoS) requirements. Moreover, the advent of Multi-access Edge Computing (MEC) brings up the need to more efficiently plan and dimension network deployment by means of jointly exploiting the available radio and processing resources. From this standpoint, advanced cell association of users can play a key role for 5G systems. Focusing on a Heterogeneous Network (HetNet), this paper proposes a comparison between state-of-the-art (i.e., radio-only) and MEC-aware cell association rules, taking the scenario of task offloading in the Uplink (UL) as an example. Numerical evaluations show that the proposed cell association rule provides nearly 60% latency reduction, as compared to its standard, radio-exclusive counterpart.","PeriodicalId":122391,"journal":{"name":"2018 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130756811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-14DOI: 10.1109/WCNCW.2018.8369025
Kilian Roth, J. Nossek
Leveraging the available millimeter wave spectrum will be important for 5G. In this work, we investigate the performance of digital beamforming with low resolution Analog-to-Digital-Converters based on link level simulations including channel estimation, Multiple Input Multiple Output (MIMO) equalization and channel decoding. We consider the recently agreed 3GPP NR type 1 Orthogonal Frequency Division Multiplexing (OFDM) reference signals. The comparison shows sequential Dichotomous Coordinate Descent (DCD) outperforms Minimum Mean Square Error (MMSE)-based MIMO equalization both in terms of detection performance and complexity. We also show that the DCD based algorithm is more robust to channel estimation errors. In contrast to the common believe we also show that the complexity of MMSE equalization for a massive MIMO system is not dominated by the matrix inversion but by the computation of the Gram matrix.
{"title":"Robust massive MIMO equilization for mmWave systems with low resolution ADCs","authors":"Kilian Roth, J. Nossek","doi":"10.1109/WCNCW.2018.8369025","DOIUrl":"https://doi.org/10.1109/WCNCW.2018.8369025","url":null,"abstract":"Leveraging the available millimeter wave spectrum will be important for 5G. In this work, we investigate the performance of digital beamforming with low resolution Analog-to-Digital-Converters based on link level simulations including channel estimation, Multiple Input Multiple Output (MIMO) equalization and channel decoding. We consider the recently agreed 3GPP NR type 1 Orthogonal Frequency Division Multiplexing (OFDM) reference signals. The comparison shows sequential Dichotomous Coordinate Descent (DCD) outperforms Minimum Mean Square Error (MMSE)-based MIMO equalization both in terms of detection performance and complexity. We also show that the DCD based algorithm is more robust to channel estimation errors. In contrast to the common believe we also show that the complexity of MMSE equalization for a massive MIMO system is not dominated by the matrix inversion but by the computation of the Gram matrix.","PeriodicalId":122391,"journal":{"name":"2018 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117170429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-30DOI: 10.1109/WCNCW.2018.8369028
Sahar Imtiaz, H. Ghauch, Muhammad Mahboob Ur Rahman, G. Koudouridis, J. Gross
Next generation cellular networks are expected to improve aggregate multi-user sum rates by a thousand-fold, implying the deployment of cloud radio access networks (CRANs) that consist of a dense set of radio heads. Such a densification of the network inevitably results in high interference coordination complexity and is associated with significant channel state information (CSI) acquisition overhead. The main hypothesis behind this study is that both the coordinated resource allocation complexity and the signaling overhead can be significantly reduced by exploiting explicit knowledge about a terminal's position to make resource allocation predictions. More specifically, we present a design of a learning-based resource allocation scheme for 5G systems that uses Random Forests as multi-class classifier to predict the modulation and coding scheme of a terminal at any given position served by the CRAN. Through performance evaluations it is shown that the signaling overhead is significantly reduced while the learning-based resource allocation scheme can achieve a comparable spectral efficiency to CSI-based schemes. We demonstrate the robustness of the proposed scheme for a varying accuracy of users' positions, showing that even for quite large variations the learning-based approach can still exhibit good performance.
{"title":"Random forests resource allocation for 5G systems: Performance and robustness study","authors":"Sahar Imtiaz, H. Ghauch, Muhammad Mahboob Ur Rahman, G. Koudouridis, J. Gross","doi":"10.1109/WCNCW.2018.8369028","DOIUrl":"https://doi.org/10.1109/WCNCW.2018.8369028","url":null,"abstract":"Next generation cellular networks are expected to improve aggregate multi-user sum rates by a thousand-fold, implying the deployment of cloud radio access networks (CRANs) that consist of a dense set of radio heads. Such a densification of the network inevitably results in high interference coordination complexity and is associated with significant channel state information (CSI) acquisition overhead. The main hypothesis behind this study is that both the coordinated resource allocation complexity and the signaling overhead can be significantly reduced by exploiting explicit knowledge about a terminal's position to make resource allocation predictions. More specifically, we present a design of a learning-based resource allocation scheme for 5G systems that uses Random Forests as multi-class classifier to predict the modulation and coding scheme of a terminal at any given position served by the CRAN. Through performance evaluations it is shown that the signaling overhead is significantly reduced while the learning-based resource allocation scheme can achieve a comparable spectral efficiency to CSI-based schemes. We demonstrate the robustness of the proposed scheme for a varying accuracy of users' positions, showing that even for quite large variations the learning-based approach can still exhibit good performance.","PeriodicalId":122391,"journal":{"name":"2018 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117253925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/WCNCW.2018.8369023
P. Kuo, Alain A. M. Mourad, Chenguang Lu, M. Berg, S. Duquennoy, Ying-Yu Chen, Yi-Huai Hsu, Aitor Zabala, Riccardo Ferrari, Sergio González, Chi-Yu Li, Hsu-Tung Chien
Put together, the edge and fog form a large diverse pool of computing and networking resources from different owners that can be leveraged towards low latency applications as well as for alleviating high traffic volume in future networks including 5G and beyond. This paper sets out a framework for the integration of edge and fog computing and networking leveraging on ongoing specifications by ETSI MEC ISG and the OpenFog Consortium. It also presents the technological gaps that need to be addressed before such an integrated solution can be developed. These noticeably include challenges relating to the volatility of resources, heterogeneity of underlying technologies, virtualization of devices, and security issues. The framework presented is a Launchpad for a complete solution under development by the 5G-CORAL consortium.
{"title":"An integrated edge and Fog system for future communication networks","authors":"P. Kuo, Alain A. M. Mourad, Chenguang Lu, M. Berg, S. Duquennoy, Ying-Yu Chen, Yi-Huai Hsu, Aitor Zabala, Riccardo Ferrari, Sergio González, Chi-Yu Li, Hsu-Tung Chien","doi":"10.1109/WCNCW.2018.8369023","DOIUrl":"https://doi.org/10.1109/WCNCW.2018.8369023","url":null,"abstract":"Put together, the edge and fog form a large diverse pool of computing and networking resources from different owners that can be leveraged towards low latency applications as well as for alleviating high traffic volume in future networks including 5G and beyond. This paper sets out a framework for the integration of edge and fog computing and networking leveraging on ongoing specifications by ETSI MEC ISG and the OpenFog Consortium. It also presents the technological gaps that need to be addressed before such an integrated solution can be developed. These noticeably include challenges relating to the volatility of resources, heterogeneity of underlying technologies, virtualization of devices, and security issues. The framework presented is a Launchpad for a complete solution under development by the 5G-CORAL consortium.","PeriodicalId":122391,"journal":{"name":"2018 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122315537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/WCNCW.2018.8369007
Yu-Jia Chen, Wei-Yuan Cheng, Li-Chun Wang
This paper proposes a learning-assisted beam search scheme for indoor millimeter wave (mmWave) networks with multi-base stations. Recently, directional antennas are often used to achieve the high data rates and compensate the high freespace loss in the mmWave frequency range. However, establishing reliable communication links with narrow beamwidth is a challenging task in indoor moving environments since the sector search space scales with device mobility and base station density. To tackle such an issue, we develop a multi-state Q-learning approach that incorporates the base station selection into the beam selection process. By exploiting the radio environment data from ray tracing simulation, the proposed learning approach can enable fast and reliable beam selection for different indoor environments and mobility patterns. Simulation results show that the proposed scheme outperforms the beam search schemes based on the existing exhaustive search approach and the original Q-learning approach in terms of beam search latency, link outage times, and aggregated throughput.
{"title":"Learning-assisted beam search for indoor mmWave networks","authors":"Yu-Jia Chen, Wei-Yuan Cheng, Li-Chun Wang","doi":"10.1109/WCNCW.2018.8369007","DOIUrl":"https://doi.org/10.1109/WCNCW.2018.8369007","url":null,"abstract":"This paper proposes a learning-assisted beam search scheme for indoor millimeter wave (mmWave) networks with multi-base stations. Recently, directional antennas are often used to achieve the high data rates and compensate the high freespace loss in the mmWave frequency range. However, establishing reliable communication links with narrow beamwidth is a challenging task in indoor moving environments since the sector search space scales with device mobility and base station density. To tackle such an issue, we develop a multi-state Q-learning approach that incorporates the base station selection into the beam selection process. By exploiting the radio environment data from ray tracing simulation, the proposed learning approach can enable fast and reliable beam selection for different indoor environments and mobility patterns. Simulation results show that the proposed scheme outperforms the beam search schemes based on the existing exhaustive search approach and the original Q-learning approach in terms of beam search latency, link outage times, and aggregated throughput.","PeriodicalId":122391,"journal":{"name":"2018 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)","volume":"291 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117306884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/WCNCW.2018.8369006
Luca Commardi, Osamah Ibrahiem Abdullaziz, Kiril Antevski, Shahzoob Bilal Chundrigar, Robert Gdowski, P. Kuo, Alain A. M. Mourad, Li-Hsing Yen, Aitor Zabala
Pushing contents, applications, and network functions closer to end users is necessary to cope with the huge data volume and low latency required in future 5G networks. Edge and fog frameworks have emerged recently to address this challenge. Whilst the edge framework was more infrastructure-focused and more mobile operator-oriented, the fog was more pervasive and included any node (stationary or mobile), including terminal devices. This article analyzes the opportunities and challenges to integrate, federate, and jointly orchestrate the edge and fog resources into a unified framework.
{"title":"Opportunities and challenges of joint edge and Fog orchestration","authors":"Luca Commardi, Osamah Ibrahiem Abdullaziz, Kiril Antevski, Shahzoob Bilal Chundrigar, Robert Gdowski, P. Kuo, Alain A. M. Mourad, Li-Hsing Yen, Aitor Zabala","doi":"10.1109/WCNCW.2018.8369006","DOIUrl":"https://doi.org/10.1109/WCNCW.2018.8369006","url":null,"abstract":"Pushing contents, applications, and network functions closer to end users is necessary to cope with the huge data volume and low latency required in future 5G networks. Edge and fog frameworks have emerged recently to address this challenge. Whilst the edge framework was more infrastructure-focused and more mobile operator-oriented, the fog was more pervasive and included any node (stationary or mobile), including terminal devices. This article analyzes the opportunities and challenges to integrate, federate, and jointly orchestrate the edge and fog resources into a unified framework.","PeriodicalId":122391,"journal":{"name":"2018 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133584988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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