PROCESOS DE LAS POLÍTICAS DE INTEGRACIÓN DEL BLOQUE

Although cognitive radio networks have been extensively researched as a po- tential candidate for mitigating the spectrum scarcity and increasing spectrum utilization, there are still a lot of problems that need to be addressed.

First of all, in order to utilize the spectrum effectively with minimum interference to PUs, the SU needs to sense the spectrum efficiently with less chances of error. The reliable spectrum sensing is only guaranteed using the conventional spectrum sensing techniques when signal-to-noise ratio (SNR) is high, however the detection performance degrades, otherwise. For combating this problem, the cooperative spectrum sensing is encouraged. In CSS, the local sensing is performed at each SU and the individual results are sent to the data fusion centre via common control channel [26]. A data fusion cen- tre combines energy measurements from all cooperating cognitive radios to make a final detection decision. In order for CSS to be operative, the lo- cal measurements sensed by each radio should be weighted according to their reliability during data combining. This is because; the received SNR value at each radio can vary intensely due to path loss and shadowing in realis-

tic scenarios. It was observed in [27] that CSS outperforms the standalone energy detector. Similarly [28] derived optimal and sub-optimal weights for a linear combination of measurements in the data fusion centre. A popular weighted energy combining method using CSS is proposed in literature known as ’weighted linear combining (WLC)’. This scheme determines the optimal weighting vector using a heuristic technique proposed in [29] which minimizes the probability of detection error. However there is still a need for investi- gating self-managing, self-configuring energy based combining techniques that can adapt according to the time varying nature of the wireless channels. For that, the bio-inspired approaches, with appealing features like self-adaptation, autonomy and collaborative decision making abilities, can be investigated to address the complexity of the CSS systems. Furthermore, the optimal weights attained for CSS in literature deals with the linear PU signals, however in reality, the PU signal may suffer from non-linear distortions. Therefore, there is a need to consider both linear and non-linear PU signals for CSS analy- sis. Apart from spectrum sensing, there is a need to investigate spectrum allocation method based on spectrum sensing results that can ensure conflict free spectrum allocation. A common method used for spectrum allocation in literature is color-sensitive graph coloring (CSGC) [30]. Three evolutionary algorithms are presented in [31] that outperform CSGC by attaining higher value of the spectrum allocation rewards compared to CSGC. However there is a still a need to analyse the self-adaptable algorithms that can converge quickly and can attain higher value of the spectrum allocation rewards at the same time.

Secondly, many studies have been performed to understand the spec- trum occupancy statistics. For instance, the statistical and spectral occupation analysis of the spectrum measurements was presented in [32] in order to study

the traffic density in all frequency bands. In [33], auto-regressive model was used to predict the radio resource availability using occupancy measurements in order to achieve uninterrupted transmission of the secondary users. Sim- ilarly, in [34] - [36], the occupancy statistics were utilized to select the best channels for control and data transmission purposes so that less time is re- quired for switching transmission from one channel to the other for the case, when the PU appears. All of the aforementioned works have evaluated the spectrum occupancy models by using conventional probabilistic or statistical tools. These tools are often limited to the assumptions required to derive their theories. For example, one has to determine whether the value is a random variable or a random process in order to use the probabilistic and statistical tools. Therefore, there is a need to investigate spectrum occupancy using those techniques that do not have prerequisites on data. The correct modelling of the spectrum occupancy can yield to better spectrum sensing.

Furthermore, the energy consumption due to radio frequency (RF) de- vices is increasing exponentially with an increase in the usage of wireless ap-

plications. It is reported that the energy consumption of the information

and communications technology (ICT) infrastructure is increasing 16 - 20 %

approximately per annum and generates about 2 % of the worldwide CO2

emissions [37], [38]. Therefore, it is important to optimize the energy effi- ciency of the wireless networks as it will not only cut the overall cost of the network but will also decrease the adverse effects on the environment. Energy harvesting devices could be a potential source of energy. In particular, radio frequency (RF) energy harvesting is an upcoming technology that allows am- bient RF signals to be collected by an antenna and converted into DC power using a rectifier [39]. For making CRN efficient, it was proposed in [40] to allow secondary user to harvest energy using renewable energy sources. Powering

mobile devices using harvested energy from ambient sources such as solar, wind, and kinetic activities makes wireless networks not only environmentally friendly but also self-sustaining. On the other hand, the amount of RF energy that could be harvested changes with time and frequency. For example, there are more mobile signals during the day than during the night time in commer- cial areas. Thus, it is very important for RF energy harvesters to choose the right operating time and frequency for harvesting maximum energy.

Last, but not least, energy harvesting in CRN is a hot research topic and the majority of the literature investigates those energy harvesting CRN, where the SU harvests energy from the nearby PU or transmits information, if the PU is far away [41], [42]. There are also few studies that encourages cooperation between PU and SU [43]- [45]. All these works assumed that the PU does not have any energy harvesting capability. Therefore, there is a need to investigate the framework where PU can also have energy harvesting capability and can get benefit from the presence of SU.