2019
[6]
W. Boukley Hassan, A. Doufexi, G. Oikonomou, M. Beach, "EVM Prediction for Massive MIMO", in Proc. IEEE PIMRC, 2019 (accepted, to appear)
@inproceedings{Boukley-2019-PIMRC, title = {EVM Prediction for Massive MIMO}, author = {Boukley Hassan, Wael and Angela Doufexi and George Oikonomou and Mark Beach}, year = {2019}, month = sep, booktitle = {Proc. IEEE PIMRC}, note = {accepted, to appear}, abstract = {Signal to interference plus noise ratio (SINR) is a widely common performance metric used in the majority of massive multiple-input, multiple-output (Ma-MIMO) research. This metric requires prior knowledge of the user channel vectors and the interference caused by inaccurate channel state information (CSI). However, the interference caused by inaccurate CSI can't be calculated for realworld scenarios. On the other hand, a comprehensive performance indicator can be achieved by the Error Vector Magnitude (EVM) metric in real-world scenarios. This considers all impairments upon the transmitted symbol as seen at the receiver. However, measuring the EVM values for a subset of users requires each user to retransmit data symbols. This paper presents an estimation method with high accuracy by associating EVM to SINR values for Ma-MIMO with zero-forcing (ZF) and Minimum Mean Square Error (MMSE). Also introduced is a novel EVM prediction method for subset of users taken from the original set of simultaneous users in a single cell Ma-MIMO. This method jointly relies on the channel correlation between users and the EVM performance to predict the EVM values for a subset of the available users without the need to retransmit data symbols. This method considers the user channel vector and the interference caused by inaccurate CSI, which make it suitable for Ma-MIMO algorithms, such as user grouping and power control. Real-world experimental data-sets with real-time results are carried out to validate the EVM prediction method using software-defined radio Ma-MIMO testbed.}, }
Signal to interference plus noise ratio (SINR) is a widely common performance metric used in the majority of massive multiple-input, multiple-output (Ma-MIMO) research. This metric requires prior knowledge of the user channel vectors and the interference caused by inaccurate channel state information (CSI). However, the interference caused by inaccurate CSI can't be calculated for realworld scenarios. On the other hand, a comprehensive performance indicator can be achieved by the Error Vector Magnitude (EVM) metric in real-world scenarios. This considers all impairments upon the transmitted symbol as seen at the receiver. However, measuring the EVM values for a subset of users requires each user to retransmit data symbols. This paper presents an estimation method with high accuracy by associating EVM to SINR values for Ma-MIMO with zero-forcing (ZF) and Minimum Mean Square Error (MMSE). Also introduced is a novel EVM prediction method for subset of users taken from the original set of simultaneous users in a single cell Ma-MIMO. This method jointly relies on the channel correlation between users and the EVM performance to predict the EVM values for a subset of the available users without the need to retransmit data symbols. This method considers the user channel vector and the interference caused by inaccurate CSI, which make it suitable for Ma-MIMO algorithms, such as user grouping and power control. Real-world experimental data-sets with real-time results are carried out to validate the EVM prediction method using software-defined radio Ma-MIMO testbed.
[5]
R. Singh, S. Armour, A. Khan, M. Sooriyabandara, G. Oikonomou, "The Advantage of Computation Offloading in Multi-Access Edge Computing", in Proc. IEEE FMEC, 2019 (accepted, to appear)
@inproceedings{Singh-2019-FMEC, title = {The Advantage of Computation Offloading in Multi-Access Edge Computing}, author = {Raghubir Singh and Simon Armour and Aftab Khan and Mahesh Sooriyabandara and George Oikonomou}, year = {2019}, month = jun, booktitle = {Proc. IEEE FMEC}, note = {accepted, to appear}, oa-url = {https://research-information.bristol.ac.uk/en/publications/the-advantage-of-computation-offloading-in-multiaccess-edge-computing(c528b331-9ae0-436f-961e-9976ed62bba9).html }, abstract = {Computation offloading plays a critical role inreducing task completion time for mobile devices. The advantagesof computation offloading to cloud resources in Mobile CloudComputing have been widely considered. In this paper, we haveinvestigated different scenarios for offloading to less distantMulti-Access Edge Computing (MEC) servers for multiple userswith a range of mobile devices and computational tasks. Wepresent detailed simulation data for how offloading can bebeneficial in a MEC network with varying quantitative mobileuser demand, heterogeneity in mobile device on-board and MECprocessor speeds, computational task complexity, communicationspeeds, link access delays and mobile device user numbers.Unlike previous work where simulations considered only limitedcommunication speeds for offloading, we have extended the rangeof link speeds and included two types of communication delay.We find that more computationally complex applications areoffloaded preferentially (especially with the higher server:mobiledevice processor speed ratios) while low link speeds and anydelays caused by network delays or excessive user numbersdegrade any advantages in reduced task completion times offeredby offloading. Additionally, significant savings in energy usage bymobile devices are guaranteed except at very low link speeds.}, }
Computation offloading plays a critical role inreducing task completion time for mobile devices. The advantagesof computation offloading to cloud resources in Mobile CloudComputing have been widely considered. In this paper, we haveinvestigated different scenarios for offloading to less distantMulti-Access Edge Computing (MEC) servers for multiple userswith a range of mobile devices and computational tasks. Wepresent detailed simulation data for how offloading can bebeneficial in a MEC network with varying quantitative mobileuser demand, heterogeneity in mobile device on-board and MECprocessor speeds, computational task complexity, communicationspeeds, link access delays and mobile device user numbers.Unlike previous work where simulations considered only limitedcommunication speeds for offloading, we have extended the rangeof link speeds and included two types of communication delay.We find that more computationally complex applications areoffloaded preferentially (especially with the higher server:mobiledevice processor speed ratios) while low link speeds and anydelays caused by network delays or excessive user numbersdegrade any advantages in reduced task completion times offeredby offloading. Additionally, significant savings in energy usage bymobile devices are guaranteed except at very low link speeds.
[4]
L. Li, G. Oikonomou, M. Beach, R. Nejabati, D. Simeonidou, "An SDN Agent-enabled Rate Adaptation Framework for WLAN", in Proc. IEEE ICC, 2019 (accepted, to appear)
@inproceedings{Li-2019-ICC, title = {An SDN Agent-enabled Rate Adaptation Framework for WLAN}, keywords = "Rate/Link adaptation, Software-Defined Networking (SDN), SDN agent, IEEE 802.11, Software-Defined Radio, Communications Systems and Networks Group", author = {Li Li and George Oikonomou and Mark Beach and Reza Nejabati and Dimitra Simeonidou}, booktitle = {Proc. IEEE ICC}, publisher = {IEEE}, year = {2019}, month = may, note = {accepted, to appear}, oa-url = {https://research-information.bristol.ac.uk/en/publications/an-sdn-agentenabled-rate-adaptation-framework-for-wlan(8744fc84-b6b4-4a72-b968-9ac13c267217).html}, abstract = {Rate or link adaptation is the determination of the optimal modulation and coding scheme (MCS) that will maximize the performance under the current wireless channel conditions. A Software-Defined Networking (SDN) agent is a software element bridging an SDN controller and any legacy wireless network elements by providing the abstraction of these elements. In this paper, we present the work of an SDN approach for designing and implementing a Rate/Link Adaptation (RA) framework for wireless local area networks (WLAN). The framework provides support for real-time RA applications and flexibility to satisfy various degrees of Quality of Service (QoS) or Quality of Experience (QoE) requirements. We implement the proposed framework as an extension to the Wireless Open-Access Research Platform (WARP), an FPGA based Software-Defined Radio (SDR) platform, with evaluation results indicating the feasibility of using SDN-RA under the stringent time constraints posed by the WLAN. To demonstrate the effectiveness of decoupling rate decision functions from the underlying wireless interface card and to highlight its applicability for a diverse set of scenarios, we present a use case deployed over the framework focusing on rate adaptation for individual traffic, and display optimization in different aspects, such as the reduction transmission errors.}, }
Rate or link adaptation is the determination of the optimal modulation and coding scheme (MCS) that will maximize the performance under the current wireless channel conditions. A Software-Defined Networking (SDN) agent is a software element bridging an SDN controller and any legacy wireless network elements by providing the abstraction of these elements. In this paper, we present the work of an SDN approach for designing and implementing a Rate/Link Adaptation (RA) framework for wireless local area networks (WLAN). The framework provides support for real-time RA applications and flexibility to satisfy various degrees of Quality of Service (QoS) or Quality of Experience (QoE) requirements. We implement the proposed framework as an extension to the Wireless Open-Access Research Platform (WARP), an FPGA based Software-Defined Radio (SDR) platform, with evaluation results indicating the feasibility of using SDN-RA under the stringent time constraints posed by the WLAN. To demonstrate the effectiveness of decoupling rate decision functions from the underlying wireless interface card and to highlight its applicability for a diverse set of scenarios, we present a use case deployed over the framework focusing on rate adaptation for individual traffic, and display optimization in different aspects, such as the reduction transmission errors.
[3]
M. Baddeley, U. Raza, M. Sooriyabandara, G. Oikonomou, R. Nejabati, D. Simeonidou, "Atomic-SDN: A Synchronous Flooding Framework for SDN Control of Low-Power Wireless", in Proc. ACM EWSN, 2019
@inproceedings{Baddeley-2019-EWSN, title = {Atomic-SDN: A Synchronous Flooding Framework for SDN Control of Low-Power Wireless}, author = {Michael Baddeley and Usman Raza and Mahesh Sooriyabandara and George Oikonomou and Reza Nejabati and Dimitra Simeonidou}, booktitle = {Proc. ACM EWSN}, publisher = {Association for Computing Machinery (ACM)}, year = {2019}, month = feb, oa-url = {https://research-information.bristol.ac.uk/en/publications/atomicsdn(35df9370-3ded-45dc-acc2-26bd36aad29b).html}, gsid = {15963983663240748841}, abstract = {We present Atomic-SDN, a highly flexible framework capable of dynamically scheduling synchronous flooding phases to accommodate multiple traffic patterns resulting from application-level requirements. Specifically, Atomic-SDN accommodates the complex and varying traffic generated in a Software Defined Networking (SDN) control solutions for low-power wireless networks, where the high-overhead and centralized nature of SDN causes considerable problems due to the constrained nature of the network. By utilizing the high-reliability and low-latency properties of synchronous flooding, our results show that Atomic-SDN is capable of providing minimal bounded latency guarantees for network-wide SDN operations. This reduces the time to perform SDN operations on all nodes by orders-of-magnitude, and allows core SDN concepts to be pushed to the very edge of IoT networks.}, }
We present Atomic-SDN, a highly flexible framework capable of dynamically scheduling synchronous flooding phases to accommodate multiple traffic patterns resulting from application-level requirements. Specifically, Atomic-SDN accommodates the complex and varying traffic generated in a Software Defined Networking (SDN) control solutions for low-power wireless networks, where the high-overhead and centralized nature of SDN causes considerable problems due to the constrained nature of the network. By utilizing the high-reliability and low-latency properties of synchronous flooding, our results show that Atomic-SDN is capable of providing minimal bounded latency guarantees for network-wide SDN operations. This reduces the time to perform SDN operations on all nodes by orders-of-magnitude, and allows core SDN concepts to be pushed to the very edge of IoT networks.
[2]
A. Elsts, J. Pope, X. Fafoutis, R. Piechocki, G. Oikonomou, "Instant: A TSCH Schedule for Data Collection from Mobile Nodes", in Proc. ACM EWSN, 2019
@inproceedings{Elsts-2019-ewsn, title = {Instant: A TSCH Schedule for Data Collection from Mobile Nodes}, author = {Atis Elsts and James Pope and Xenofon Fafoutis and Robert Piechocki and George Oikonomou}, year = {2019}, month = feb, booktitle = {Proc. ACM EWSN}, publisher = {Association for Computing Machinery (ACM)}, oa-url = {https://research-information.bristol.ac.uk/en/publications/instant(97ed3512-b98b-4bd7-9d31-5c0584bace9b).html}, gsid = {2350264299336058644}, abstract = {Low-power wearable devices are becoming increasingly important for fitness and healthcare applications. However, existing protocols based on the IEEE 802.15.4 low-power wireless standard are not optimized for data collection from mobile devices. This paper presents Instant: a schedule for the IEEE 802.15.4 TSCH protocol tailored for this application. We evaluate the data collection speed, energy consumption, and fairness of Instant, and show that Instant achieves several times higher data collection speed from mobile nodes compared with the state-of-the-art Orchestra schedule.}, }
Low-power wearable devices are becoming increasingly important for fitness and healthcare applications. However, existing protocols based on the IEEE 802.15.4 low-power wireless standard are not optimized for data collection from mobile devices. This paper presents Instant: a schedule for the IEEE 802.15.4 TSCH protocol tailored for this application. We evaluate the data collection speed, energy consumption, and fairness of Instant, and show that Instant achieves several times higher data collection speed from mobile nodes compared with the state-of-the-art Orchestra schedule.
[1]
M. Baddeley, A. Stanoev, U. Raza, G. Oikonomou, R. Nejabati, D. Simeonidou, M. Sooriyabandara, "Atomic-SDN: A Synchronous Flooding Framework for SDN Control of Low-Power Wireless", IEEE Access, IEEE, 2019 (in press)
@article{Baddeley-2019-access, title = {Atomic-SDN: A Synchronous Flooding Framework for SDN Control of Low-Power Wireless}, author = {Michael Baddeley and Aleksandar Stanoev and Usman Raza and George Oikonomou and Reza Nejabati and Dimitra Simeonidou and Mahesh Sooriyabandara}, journal = {IEEE Access}, publisher = {Association for Computing Machinery (ACM)}, year = {2019}, publisher = {IEEE}, gsid = {17470899592040512837}, doi = {10.1109/ACCESS.2019.2920100}, oa-url = {http://dx.doi.org/10.1109/ACCESS.2019.2920100}, note = {in press}, abstract = {The adoption of Software Defined Networking (SDN) within traditional networks has provided operators the ability to manage diverse resources and easily reconfigure networks as requirements change. Recent research has extended this concept to IEEE 802.15.4 low-power wireless networks, which form a key component of the Internet of Things (IoT). However, the multiple traffic patterns necessary for SDN control makes it difficult to apply this approach to these highly challenging environments. This paper presents Atomic-SDN, a highly reliable and low-latency solution for SDN in low-power wireless. Atomic-SDN introduces a novel Synchronous Flooding (SF) architecture capable of dynamically configuring SF protocols to satisfy complex SDN control requirements, and draws from the authors' previous experiences in the IEEE EWSN Dependability Competition: where SF solutions have consistently outperformed other entries. Using this approach, Atomic-SDN presents considerable performance gains over other SDN implementations for low-power IoT networks. We evaluate Atomic-SDN through simulation and experimentation, and show how utilizing SF techniques provides latency and reliability guarantees to SDN control operations as the local mesh scales. We compare Atomic-SDN against other SDN implementations based on the IEEE 802.15.4 network stack, and establish that Atomic-SDN improves SDN control by orders-of-magnitude across latency, reliability, and energy-efficiency metrics.}, }
The adoption of Software Defined Networking (SDN) within traditional networks has provided operators the ability to manage diverse resources and easily reconfigure networks as requirements change. Recent research has extended this concept to IEEE 802.15.4 low-power wireless networks, which form a key component of the Internet of Things (IoT). However, the multiple traffic patterns necessary for SDN control makes it difficult to apply this approach to these highly challenging environments. This paper presents Atomic-SDN, a highly reliable and low-latency solution for SDN in low-power wireless. Atomic-SDN introduces a novel Synchronous Flooding (SF) architecture capable of dynamically configuring SF protocols to satisfy complex SDN control requirements, and draws from the authors' previous experiences in the IEEE EWSN Dependability Competition: where SF solutions have consistently outperformed other entries. Using this approach, Atomic-SDN presents considerable performance gains over other SDN implementations for low-power IoT networks. We evaluate Atomic-SDN through simulation and experimentation, and show how utilizing SF techniques provides latency and reliability guarantees to SDN control operations as the local mesh scales. We compare Atomic-SDN against other SDN implementations based on the IEEE 802.15.4 network stack, and establish that Atomic-SDN improves SDN control by orders-of-magnitude across latency, reliability, and energy-efficiency metrics.
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