CREaTION

Task description and Expected results
This task provides innovative methodologies for global energy optimization, such as dynamic decision making on sleep modes for radio network elements, on how to price services depending on the availability of renewable energy sources, and on how to manage interference between Small-cell Networks (SC Ns).
It will aim to develop the algorithms and protocols, and has been divided into two tasks. Task 10.1 covers the two right-hand side boxes of Fig. 1 while Task 10.2 is focused on looking at the optimization problem from an upper, i.e., a global level perspective (integration). The algorithms for energy efficiency and the global optimization aspects is milestone M6.
Task 10.1 C haracterization of innovative optimization features in Small Size Green C ell Networks
As mentioned earlier, one of the major aims of this project is to advance the use of energy efficient SC Ns. This task identifies and characterizes the main optimization features to be considered in a joint energy optimization scheme:

1) C ell zooming and relaying
2) Dynamic sleep modes and their impacts – network level policies might decide to switch off certain macro cells, whereby a central entity such as cell zooming server could make these types of decisions. By zooming out the cells during low traffic demand, some equipment in the cell such as relays and pico-cells can be switched off while coverage is still guaranteed by the macro BS.
3) Interference management between macro-cells and the SC s, and also among the SC BSs – having information about cell positions and transmission powers, interference management can be based on a specific model that describes the uplink/downlink interference between macro BSs and SC s, as well as among small-cell BSs.

Task 10.2 Global Energy C onsumption Optimization

In this task, we look at the energy consumption optimization problem from an upper level perspective, i.e., a global perspective, while also keeping in mind renewable energy aspects and parameters.
In green communications, energy management is a multidimensional optimization problem, which consists of dynamically controlling a set of parameters to minimize the average energy consumption under several performance constraints. These constraints are associated with the user traffic demand (data rates and QoS), available transmission technologies, distance and attenuation conditions to the available base stations or femto-cells, cost of the spectrum bands, network features such as the use
of cooperation and the type energy used to supply the network/radio equipment.
The solution of the energy consumption minimization problem is an effective allocation of the available physical resources and a dynamic definition of the optimization parameters (frequency band, cell zooming/interference, active/sleep cells, e.g., by BS wilting). This demands for a global real-time scheduler that selects the best set of available resources.

The optimization scheme will be based on the following aspects:
1) User demand (schedule the resources required by the different users)
2) Energy Pricing (the different types of energy used to supply the plethora of radio and network equipment)
3) Optimization Methodology (analysis of the available radio spectrum opportunities and its energy cost, prioritization of the scheduling of more unoccupied radio bands that are simultaneously supplied by green-energy; definition of the transmitting power of each cell for a specific band by considering the users requirements, the interference management constraints, the energy prioritization/pricing and the specific network topology).
Switching off network equipment is particularly important if the source of energy has a high carbon (C O2) footprint. However, if the power is provided by renewable sources, the network elements may remain operational. Therefore, the decision to switch off certain network elements should also take the source of energy of the associated equipment into account.

Members of the research team in this task: (BI) Bolseiro de Investigação (Mestre) 3; (BPD) Bolseiro de Pós-Doutoramento 1; António Jorge da Silva Morgado; Daniel Luís
Silveira Robalo; Fernando José da Silva Velez; Norberto José Gil Barroca;

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Task description and Expected results

The task on technical and architectural solutions assumes that the trend in planning of future mobile networks is the deployment of an increasing number of small-cells (SC s) within the coverage area of existing macro cells. In this context, it identifies the resources available for the communication and the assignment of resources to the various entities involved in the establishment of a multi-link communication. It defines the schemes that will take advantage of spectral opportunities to reduce energy consumption, such as the use of lower interference spectrum, the dynamic tailoring of communications characteristics to match available bands (e.g., the use of appropriate frequency dependent on propagation characteristics and required coverage range), the use of spectrum aggregation, and the appropriate autonomous configuration of localized coverage elements in shadowed areas (e.g., cognitive femto-cells, cognitive relays, etc.), among others. Moreover, it considers spatial opportunities such as the potential to power down some cells, the use of cell zooming capabilities to maintain coverage with the minimal amount of hardware operational at any one time, the deployment of relays, and the use of cooperative transmissions to save energy. TSK9
& 10 will consider that some cellular elements, e.g., relays, may be powered by renewable energy sources and will therefore attempt to maximize the use of renewable energy whilst ensuring that QoS requirements are maintained.
Finally, this task considers the architectural aspects that are necessary to achieve the identified technical solutions, e.g., frequency adaptability, handover and mobility mechanisms in cellular reconfigurations.

Task 9.2 Identification of energy saving opportunities
This task will develop means to identify spectral/spatial optimization opportunities in SC Ns. This means consider the utilization of renewable energy resources, as well as minimization of the implied necessary hardware density. They aim to satisfy QoS and user experience requirements while still saving energy.

1) Discovery of spectrum opportunities
Radio spectrum real-time usage characterization plays an important role in identifying opportunities to predict future energy consumption reduction in communication systems.
consumption reduction in communication systems.
First, one aims to take advantage of information that is already available to the network as might be obtained through feedback information from terminals and base stations (traffic, QoS, usage of spectrum), and through general network knowledge of the spectral configuration of the network elements.
Second, advanced spectrum sensing schemes (interference awareness, power levels) are considered while learning about sensed spectrum characteristics, so that it is possible to predict better configurations going into the future and match to varying traffic requirements.

2) Discovery of spatial opportunities
It involves the identification of spatial variations in traffic loading and/or locations where traffic load is lower than network capacity, such that cells can be powered down. The spatial solutions identified in Task 9.1 can also be employed. Discovery of these opportunities may involve spectrum sensing solutions in some scenarios, e.g., to ascertain localized power levels hence potential spatial transmission tuning for deployed elements.

Mem bers of the research team in this task: (BI) Bolseiro de Investigação (Mestre) 2; (BI) Bolseiro de Investigação (Mestre) 3; (BI) Bolseiro de Investigação (Mestre) 6;
(BPD) Bolseiro de Pós-Doutoramento 1; António Jorge da Silva Morgado; Daniel Luís Silveira Robalo; Fernando José da Silva Velez; Norberto José Gil Barroca; Nuno Miguel Gonçalves Borges de C arvalho;

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Task description and Expected results
Another very important theme to be dealt with is on the possibility to use the digital capabilities to resolve and to optimize the analog hardware drawbacks, for instance one possibility is to create a post-distortion or a pre-distortion in order to linearize the receiver and/or the transmitter, and then optimize the transmitting/receiving signal quality. Nevertheless for this to be true one
need to extract and to model carefully the analog components on the system, by studying carefully its behavior, either in frequency but also in amplitude variations. Thus a very important point to be dealt with in this project is the behavioral modeling aspects.
In this task some strategies will be evaluated to propose new multi-carrier and multi-standard nonlinear behavior models, in an easy and efficient way. One possibility pass by the study of X-parameters, since they are the natural extension of S-parameters, they can be extracted in real time, and can be used to model, in a black-box approach, the nonlinear and frequency behavior of
analog components.
The team devoted to this task will propose new models combining traditional approaches as Volterra Series with X-parameters in order to maximize the applicability of the extracted models. The models will also be studied in the light of easy parameter extraction, by proposing extraction mechanisms that can be applied easily to real environments and adapt continuously.
In addition, conventional models such as memory polynomial, Wiener, Hammerstein, Murray Hill, etc. will be revisited to apply for the sparse multi-channel case and studied on their performance dependence on hardware structures to be developed in Task 1. This study is expected to be mainly focused on properly modeling long-term memory resulting from the inter-band carrier
aggregation scenario. Advantages of each model will be taken into account when developing new models.
In this task it is expected that a strong collaboration exists between the other TSK, and some strategies will change depending on the findings on other TSK’s.
At the end of this TSK it is expected that the state of the art will be improved in the following topics:
• Behavioral models for multi-carrier signals
• Extraction procedures that can be used in real time
• Instrumentation design and proposals for mixed domain modeling
• X-parameter evaluation to use in mixed-domain systems
The task will be based mainly on the expertise of IT-Aveiro team, since the background in the area will allow a faster evolution time. In this task 3 PhD researchers, 2 PhD students and one of the Bolseiros will devote its time to developed this work.

Mem bers of the research team in this task: (BI) Bolseiro de Investigação (Mestre) 1; (BI) Bolseiro de Investigação (Mestre) 2; Nuno Miguel Gonçalves Borges de C arvalho; Pedro Miguel Duarte C ruz; WONHOON JANG;

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Task description and Expected results
The cognitive radio technique is recognized as the most efficient method to improve the spectrum utilization, by using the
available spectrum effectively amongst the secondary users in an opportunistic manner. Different issues related to the dynamic
spectrum management like spectrum sensing, spectrum decision, spectrum sharing and spectrum scheduling need to be
addressed.
Spectrum Sensing is used to determine the state of the spectrum. A cognitive radio detects an unused spectrum or spectrum
hole. The identification of free frequencies might be viewed as a pattern recognition problem therefore machine learning
techniques are suitable approaches. Detection and classification of very low signal-to-noise ratio signals is one of the most
challenging tasks in C R systems. Various approaches rely on the extraction of cyclostationary features of the signals to perform
detection of the holes on spectrum. The detection task is a simple classification problem: feature extraction is applied to a time
series of the incoming radio channel and each channel is classified as “occupied” if a signal is present and “free” otherwise. The
second step includes classifiers to identify the signals types.
In this task we will study the application of linear dimensionality reduction techniques such as principal component analysis and
nonlinear ones such as the kernel principal component analysis combined with support vector machine. With these techniques we
expect to increase the success rate detection of the presence of primary users when compared with more traditional methods
such as energy detection.
We expect to combine these techniques with the cochlear radio of the task 6 in order to build a system able to perform spectrum
analysis over a large bandwidth.

Mem bers of the research team in this task: (BI) Bolseiro de Investigação (Mestre) 4; Ana Maria Perfeito Tome; José Manuel Neto Vieira; Nuno Miguel Gonçalves Borges de
C arvalho; Pedro Miguel Duarte C ruz; Teófilo José Marques Monteiro;

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