Page 164 - ITU Journal, Future and evolving technologies - Volume 1 (2020), Issue 1, Inaugural issue

ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1

users. This radical change of the technology scenario, decide to pool together their respective networks. Ki-
mainly due to the new architectural solutions and ser- bilda et al. [81] resort to stochastic geometry to cal-
vice definition of 5G, did not cancel the main issues culate the gains of sharing for the cases of infrastruc-
analyzed by the early works on infrastructure sharing, ture and/or spectrum pooling. Their key finding is that
such as the economic aspects of cost sharing and their the infrastructure and spectrum sharing gains do not
relation with resource allocation or partitioning. sum up when combined since full sharing (infrastruc-
ture+spectrum) introduces a trade-off between the data
3. MORE RECENT AND UP-TO- rate and coverage.
DATE WORKS As 5G is expected to make use of the millimeter wave

In the more recent and up-to-date literature, there is a (mmWave) frequencies [11], the gains of infrastructure
tendency to address specific problems, e.g., the problem and/or spectrum in these frequencies have become the
of resource management, for specific sharing scenarios, object of several recent articles. For instance, Gupta
e.g., infrastructure and spectrum sharing at the RAN. et al. in [54] provide a stochastic geometry-based the-
There are at least two ways to go about the classifi- oretical analysis on the gains of spectrum sharing us-
cation of this literature, one being problem-centric and ing a simplified antenna and channel model for the
the other being methodology-centric. We have opted mmWave frequency range. In particular, in [54] it is
for the first one in order to highlight the fact that there shown how narrow beams are key for spectrum sharing
are many aspects to infrastructure sharing and hence in the mmWaves. A very similar investigation to [81]
provide the reader with the bigger picture on the topic. is carried by Rebato et al. in [120] for mmWaves; the
Methodology details are discussed only when deemed authors highlight the impact of the channel model ac-
necessary. curacy when carrying out a quantitative analysis of the
Under the problem-centric classification, we have iden- sharing gains. The recent work in [71] also addresses
tified the following research branches/categories for the infrastructure and spectrum sharing at mmWaves and
revised articles: (i) performance evaluation, (ii) resource it resorts to stochastic geometry to derive the proba-
management, (iii) enablers and architectures, (iv) energy bility of Signal-to-Interference-plus-Noise Ratio (SINR)
efficiency, (v) strategic modeling and (vi) miscellaneous. coverage as a performance metric.
In Table 2 we provide a visual overview of the classi-
It is worth pointing out that some of the articles may fication of the different articles that were included in
fit in more than one category, but for each such article, the performance evaluation category. As can be seen
we have opted for a single category, the one we believe from the table, methodologically-wise, the authors use
is the most salient. mainly stochastic geometry, simulation and optimiza-
tion. The other method found was empirical analysis.
With respect to the type of measures used to evalu-
3.1 Performance evaluation ate performance, we can see that network measures are
fairly diverse: even though most work in this category
Several authors have addressed the gains of particu- deals with physical layer measures such as SINR, net-
lar infrastructure and/or spectrum sharing scenarios in working measures such as traffic load, sector overload
terms of network performance metrics, such as through- or packet drop probability are also considered. Not sur-
put, coverage probability etc. (see e.g., [71, 114, 139, prisingly, less diversity can be found in the economic
145]) and/or economic ones such as CAPEX/OPEX re- measures’ category.
duction (see e.g., [67, 75, 80, 106]). The common ap-
proach is to benchmark such scenarios against the base- 3.2 Resource management
line case when no sharing takes place and the involved
MNOs build individual networks instead. Methodology- Problems of resource management arise whenever infras-
wise, both theoretical, mainly stochastic geometry anal- tructure sharing is combined with spectrum sharing, as
ysis (see e.g., [54,71,81,145]), and simulation approaches users of multiple MNOs/MVNOs have to be assigned
(see e.g., [40,114,120]) have been adopted. For instance, resources from a shared pool.
the work in [114] proposes a virtualized architecture to
enable two types of spectrum sharing other than the Several studies ([34, 53, 99, 137]) have proposed algo-
classical one and capacity sharing (national roaming) rithms for a multi-operator scheduler, namely when
and compares the different sharing alternatives with no users of multiple MNOs have to be scheduled in the
sharing case. The performance metrics considered in finite resources available in a shared Base Station (BS).
[114] are the sector load and packet drop probability. Assuming MNOs agree a priori on the resource shares,
i.e., how to split the available BS resources among them,
The authors in [40] analyse how the time and space cor- the work in [137] adopts the concept of Generalized Pro-
relation of the MNO individual traffic loads impacts the cessor Sharing for a multi-operator scheduler. For the
gains of infrastructure sharing in the case when MNOs same setting, Malanchini et al. [99] explore the trade-

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