783 research outputs found
TACT: A Transfer Actor-Critic Learning Framework for Energy Saving in Cellular Radio Access Networks
Recent works have validated the possibility of improving energy efficiency in
radio access networks (RANs), achieved by dynamically turning on/off some base
stations (BSs). In this paper, we extend the research over BS switching
operations, which should match up with traffic load variations. Instead of
depending on the dynamic traffic loads which are still quite challenging to
precisely forecast, we firstly formulate the traffic variations as a Markov
decision process. Afterwards, in order to foresightedly minimize the energy
consumption of RANs, we design a reinforcement learning framework based BS
switching operation scheme. Furthermore, to avoid the underlying curse of
dimensionality in reinforcement learning, a transfer actor-critic algorithm
(TACT), which utilizes the transferred learning expertise in historical periods
or neighboring regions, is proposed and provably converges. In the end, we
evaluate our proposed scheme by extensive simulations under various practical
configurations and show that the proposed TACT algorithm contributes to a
performance jumpstart and demonstrates the feasibility of significant energy
efficiency improvement at the expense of tolerable delay performance.Comment: 11 figures, 30 pages, accepted in IEEE Transactions on Wireless
Communications 2014. IEEE Trans. Wireless Commun., Feb. 201
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
Energy-Efficient Softwarized Networks: A Survey
With the dynamic demands and stringent requirements of various applications,
networks need to be high-performance, scalable, and adaptive to changes.
Researchers and industries view network softwarization as the best enabler for
the evolution of networking to tackle current and prospective challenges.
Network softwarization must provide programmability and flexibility to network
infrastructures and allow agile management, along with higher control for
operators. While satisfying the demands and requirements of network services,
energy cannot be overlooked, considering the effects on the sustainability of
the environment and business. This paper discusses energy efficiency in modern
and future networks with three network softwarization technologies: SDN, NFV,
and NS, introduced in an energy-oriented context. With that framework in mind,
we review the literature based on network scenarios, control/MANO layers, and
energy-efficiency strategies. Following that, we compare the references
regarding approach, evaluation method, criterion, and metric attributes to
demonstrate the state-of-the-art. Last, we analyze the classified literature,
summarize lessons learned, and present ten essential concerns to open
discussions about future research opportunities on energy-efficient softwarized
networks.Comment: Accepted draft for publication in TNSM with minor updates and editin
Will SDN be part of 5G?
For many, this is no longer a valid question and the case is considered
settled with SDN/NFV (Software Defined Networking/Network Function
Virtualization) providing the inevitable innovation enablers solving many
outstanding management issues regarding 5G. However, given the monumental task
of softwarization of radio access network (RAN) while 5G is just around the
corner and some companies have started unveiling their 5G equipment already,
the concern is very realistic that we may only see some point solutions
involving SDN technology instead of a fully SDN-enabled RAN. This survey paper
identifies all important obstacles in the way and looks at the state of the art
of the relevant solutions. This survey is different from the previous surveys
on SDN-based RAN as it focuses on the salient problems and discusses solutions
proposed within and outside SDN literature. Our main focus is on fronthaul,
backward compatibility, supposedly disruptive nature of SDN deployment,
business cases and monetization of SDN related upgrades, latency of general
purpose processors (GPP), and additional security vulnerabilities,
softwarization brings along to the RAN. We have also provided a summary of the
architectural developments in SDN-based RAN landscape as not all work can be
covered under the focused issues. This paper provides a comprehensive survey on
the state of the art of SDN-based RAN and clearly points out the gaps in the
technology.Comment: 33 pages, 10 figure
Energy Efficiency and Coverage Trade-Off in 5G for Eco-Friendly and Sustainable Cellular Networks
Recently, cellular networks’ energy efficiency has garnered research interest from academia
and industry because of its considerable economic and ecological effects in the near future. This study
proposes an approach to cooperation between the Long-Term Evolution (LTE) and next-generation
wireless networks. The fifth-generation (5G) wireless network aims to negotiate a trade-off between
wireless network performance (sustaining the demand for high speed packet rates during busy traffic
periods) and energy efficiency (EE) by alternating 5G base stations’ (BSs) switching off/on based
on the traffic instantaneous load condition and, at the same time, guaranteeing network coverage
for mobile subscribers by the remaining active LTE BSs. The particle swarm optimization (PSO)
algorithm was used to determine the optimum criteria of the active LTE BSs (transmission power,
total antenna gain, spectrum/channel bandwidth, and signal-to-interference-noise ratio) that achieves
maximum coverage for the entire area during the switch-off session of 5G BSs. Simulation results
indicate that the energy savings can reach 3.52 kW per day, with a maximum data rate of up to
22.4 Gbps at peak traffic hours and 80.64 Mbps during a 5G BS switched-off session along with
guaranteed full coverage over the entire region by the remaining active LTE BSs
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