@conference{Sarigiannidis20145,

title = {HYRA: An efficient hybrid reporting method for XG-PON upstream resource allocation},

author = { P. Sarigiannidis and G. Papadimitriou and P. Nicopolitidis and E. Varvarigos and K. Yiannopoulos},

url = {https://www.researchgate.net/publication/301390310_HYRA_An_Efficient_Hybrid_Reporting_Method_for_XG-PON_Upstream_Resource_Allocation},

doi = {10.5220/0005048200050014},

year  = {2014},

date = {2014-01-01},

journal = {OPTICS 2014 - Proceedings of the 5th International Conference on Optical Communication Systems, Part of ICETE 2014 - 11th International Joint Conference on e-Business and Telecommunications},

pages = {5-14},

abstract = {The dynamic bandwidth allocation (DBA) process in the modern passive optical networks (PONs) is crucial since it greatly influences the whole network performance. Recently, the latest new generation PON (NGPON) standard, known as 10-gigabit-capable passive optical network (XG-PON), standardized by the international telecommunication union telecommunication standardization sector (ITU-T), emerges as one of the most efficient access networking framework to cope with the demanding needs of the fiber to the x (FTTX) paradigm, where x stands for home (FTTH), bulding (FTTB), or curve (FTTC). Motivated by the fact that the ITU-T specifications leave the bandwidth allocation process open for development by both industry and academia, we propose a novel DBA scheme for effectively delivering data in the upstream direction. Our idea is based on a subtle suggestion induced by the XG-PON specifications, each developed DBA method should combine both status reporting (SR) and traffic monitoring (TM) techniques. This means that a XGPON framework should be cognitive enough in order to be able either to request bandwidth reporting from the connected users or estimate users' bandwidth demands or both. In this article we cover this gap by proposing a robust learning from experience method by utilizing a powerful yet simple tool, the learning automata (LAs). By combining SR and TM methods, the proposed hybrid scheme, called hybrid reporting allocation (HYRA), is capable of taking efficient decisions on deciding when SR or TM method should be employed so as to maximize the efficacy of the bandwidth allocation process. Simulation results reveal the superiority of our scheme in terms of average packet delay offering up to 33% improvement.},

keywords = {Dynamic bandwidth allocation, Learning automata, Passive optical networks, XG-PON},

pubstate = {published},

tppubtype = {conference}

}

@conference{Sarigiannidis20147,

title = {IFAISTOS: A fair and flexible resource allocation policy for next-generation passive optical networks},

author = { P. Sarigiannidis and G. Papadimitriou and P. Nicopolitidis and E. Varvarigos and M. Louta and V. Kakali},

url = {https://www.researchgate.net/publication/282320629_IFAISTOS_A_fair_and_flexible_resource_allocation_policy_for_next-generation_passive_optical_networks},

doi = {10.1109/ICUMT.2014.7002071},

year  = {2014},

date = {2014-01-01},

journal = {International Congress on Ultra Modern Telecommunications and Control Systems and Workshops},

volume = {2015-January},

number = {January},

pages = {7-14},

abstract = {In modern, competitive, and dynamic access networks the underlying bandwidth distribution mechanism has to be capable of understanding user requirements, meeting stringent quality of service (QoS) demands, and satisfying a broad spectrum of user traffic dynamics. Undoubtedly, optical fiber is the dominant transmission medium enabling practical and cost-effective optical infrastructures in the last mile. Passive optical networks (PONs) represent one of the most promising player towards the fiber to the home (FTTH) vision allowing users to experience high quality, demanding multimedia services and applications. The 10-gigabit-capable passive optical network (XG-PON), one of the latest PON standard, incorporates a set of profound conditions a contemporary PON should ensure. Fairness provisioning constitutes one of the most critical features a PON should provide. However, ensuring fairness in an access network with numerous different users, requesting multiple traffic flows in any time, is not a straightforward task. In this work, we focus on the fairness issue by devising an adaptive, efficient, and fair dynamic bandwidth allocation (DBA) scheme called Insistent FAIr STrategy prOcesS (IFAISTOS). IFAISTOS investigates and maintains user traffic profiles. Overloaded users are carefully treated by gaining greater granting windows than other users, however bandwidth monopolization is prevented. Fairness is ensured for all users in terms of traffic load and average delay. A steering, adaptive mechanism records user traffic profiles by changing and defining bandwidth weights proportional to individual traffic needs. Extensive simulation results reveal the efficacy of the proposed DBA in terms of fairness and average packet delay. © 2014 IEEE.},

keywords = {Dynamic bandwidth allocation, Fairness, Learning automata, Passive optical networks, XG-PON},

pubstate = {published},

tppubtype = {conference}

}

@conference{Sarigiannidis2014f,

title = {An adaptive power management scheme for Ethernet Passive Optical Networks},

author = { P. Sarigiannidis and K. Anastasiou and E. Karapistoli and V. Kakali and M. Louta and P. Angelidis},

url = {https://www.researchgate.net/publication/286812658_An_adaptive_power_management_scheme_for_Ethernet_Passive_Optical_Networks?_sg=9Zz2iY14mUeTZMc-phRI6wlWdfdweiaLCUVbz0BGxvUSnU_GRkZWSEFJrAzLqjctJX0s3ulybPAE6OQ},

doi = {10.1109/ISCC.2014.6912460},

year  = {2014},

date = {2014-01-01},

journal = {Proceedings - International Symposium on Computers and Communications},

abstract = {Undoubtedly, energy consumption in communication networks poses a significant threat to the environmental stability. Access networks contribute to this consumption by being composed of numerous energy inefficient devices and network equipment. Passive Optical Networks (PONs), one of the most promising candidates in the field of access networking, should avoid this bottleneck in the backhaul power consumption by lowering the energy use of the optical devices. In this paper, we move towards that direction by introducing an energy efficient power management scheme that encompasses two major goals: a) to reduce the energy consumption by allowing the optical devices to enter the sleep mode longer, and b) to concurrently maintain the network performance. To this end, we focus on the energy consumed by the optical network units (ONUs). The intelligence of the ONUs is stimulated by enhancing the decision making in determining the duration of the sleep period with learning from experience mechanism. Learning automata (LAs) are charged to address this challenge. The evaluation of the proposed enhanced power management scheme reveals considerable improvements in terms of energy savings, while at the same time the network performance remains in high levels. © 2014 IEEE.},

keywords = {bandwidth allocation, energy efficiency, Learning automata, Passive optical networks},

pubstate = {published},

tppubtype = {conference}

}

@conference{Sarigiannidis2013470,

title = {Adaptive sensing policies for cognitive wireless networks using learning automata},

author = { P. Sarigiannidis and M. Louta and E. Balasa and T. Lagkas},

url = {https://www.researchgate.net/publication/267210747_Adaptive_Sensing_Policies_for_Cognitive_Wireless_Networks_using_Learning_Automata},

doi = {10.1109/ISCC.2013.6754991},

year  = {2013},

date = {2013-01-01},

journal = {Proceedings - International Symposium on Computers and Communications},

pages = {470-475},

abstract = {This paper introduces an adaptive spectrum sensing method for cognitive radio wireless networks. The proposed method enhances previously proposed random-based sensing policies, effectively selecting the licensed channels to be sensed by accurately estimating channels' availability, resulting, thus, to high system's resources utilization. The core mechanism of the adaptive method is an enhanced learning automaton, which efficiently interacts with the environment and provides accurate decisions on selecting the channel to be sensed on behalf of the secondary users. A thorough description of the introduced method is provided, while the performance of the enhanced sensing policies is verified through extensive simulation experiment. © 2013 IEEE.},

keywords = {cognitive radio, Learning automata, multi-channel MAC, wireless networks},

pubstate = {published},

tppubtype = {conference}

}

@conference{Sarigiannidis2011353,

title = {Using learning automata for adaptively adjusting the downlink-to-uplink ratio in IEEE 802.16e wireless networks},

author = { A. Sarigiannidis and P. Nicopolitidis and G. Papadimitriou and P. Sarigiannidis and M. Louta},

url = {https://www.researchgate.net/publication/221504931_Using_learning_automata_for_adaptively_adjusting_the_downlink-to-uplink_ratio_in_IEEE_80216e_wireless_networks},

doi = {10.1109/ISCC.2011.5983863},

year  = {2011},

date = {2011-01-01},

journal = {Proceedings - IEEE Symposium on Computers and Communications},

pages = {353-358},

abstract = {IEEE 802.16e allows for flexibly defining the relation of the downlink and uplink sub-frames' width from 3:1 to 1:1, respectively. However, the determination of the most suitable ratio is left open to the network designers and the research community. Existing scheduling and mapping schemes are inflexibly designed. In this paper, a novel adaptive mapping scheme is proposed aiming to dynamically adjust the downlink-to-uplink ratio, following adequately the modification of the load requests with respect to both downlink and uplink directions. A learning automaton is exploited in order to sense the performance of the downlink and uplink mapping processes and to determine the most appropriate length ratio of both sub-frames in order to maximize the network performance. The suggested ratio determination scheme is evaluated through realistic scenarios and it is compared with static schemes that maintain a fixed ratio. The results show that our proposed scheme introduces considerable improvement, increasing the network's service ratio and reducing the bandwidth waste. © 2011 IEEE.},

keywords = {IEEE 802.16, Learning automata, mapping, OFDMA, WiMAX},

pubstate = {published},

tppubtype = {conference}

}