Page 167 - 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

In the context of enabling network slicing in 5G net- Blogowski et al. in [18] deal with the particular scenario
works, there is a myriad of papers that propose archi- when two MNOs have to deploy BSs over a given set of
tectures or test prototypes based on (i) NFV and/or candidate sites. For each site, each MNO has to decide
SDN (see e.g, [35, 36, 85, 86, 113, 119]), (ii) changes to whether to install a BS or not; in the former case, if
the RAN protocol stack (see e.g., [48,116,124]), or (iii) both MNOs decide to install a BS, it is assumed that it
using features of the new 5G radio ([44]) etc. In partic- is profitable for both to install a single shared BS. The
ular, the work in [29] proposes an architecture to sup- problem is formulated as a non-cooperative game where
port network slicing in ultra-dense networks, the one in the payoff of each player (MNO) is given by its total
[74] presents an architecture that supports Internet Of profit (revenues - cost), calculated over all BSs. It is as-
Things (IoT) slices whereas the one in [129] dwells on sumed that each site can serve a given (arbitrary) num-
combining 3GPP specifications for 5G with NFV. ber of users, e.g., those under its coverage area, which
means there are no capacity constraints associated with
3.4 Energy efficiency the sites. Instead, coverage constraints are present and
they are expressed as a minimum percentage of users to
Infrastructure and spectrum sharing allow to reduce the be served by each MNO (a common constraint associ-
energy-consumption OPEX cost particularly in cases ated for spectrum licensees). When the coverage con-
when the aggregated network resources (infrastructure straint is absent, MNOs can decide independently for
and/or spectrum) are redundant. For instance, in ru- each site. Otherwise, the game is no longer separable.
ral areas where capacity is not an issue, MNOs can de- The authors describe the propriety of the Nash equilib-
commission a subset of the aggregated BSs and/or op- ria of the game for different relationships of the payoff
erate at a subset of the aggregated frequency carriers matrix (i.e., by establishing relations between the pay-
[49], which reduces the energy consumption and (indi- offs obtained under different strategy profiles) and also
rectly) the environmental impact. In these lines, since suggest a centralized solution which Pareto dominates
MNOs dimension their networks based on the peak- all Nash Equilibria.
load traffic predictions, there is intrinsically resource
redundancy during the off-peak periods in their indi- [108,109,127] address the problem of infrastructure and
vidual networks. Consequently, MNOs can agree to spectrum sharing arising when a set of MNOs, each
roam users of each other during the off-peak periods, with a given number of users (market share) and own
e.g., overnight, and switch off a subset of their BSs (see spectrum license, plan a greenfield Long-Term Evolu-
e.g., [13, 21]). While the vast majority of infrastruc- tion (LTE) deployment. The strategic problem of coali-
ture (and spectrum) sharing problems revolve around tion formation, namely, which subsets of MNOs volun-
economic and technical aspects, some papers (see e.g., tarily sign long-term infrastructure and spectrum shar-
[12–15,19–22,46,61,84,103,110–112,143]) have taken an ing agreements, is modeled by means of non-cooperative
energy-efficiency/green networking perspective. game theory. We address a very similar problem to
[108, 109, 127] in [25, 26] resorting to cooperative game
3.5 Strategic modeling theory in [26] and non-cooperative game theory in [25].
Unlike in [108,109,127], in [25,26] we (i) account for both
This branch consists of articles that deal with decision- the technical and economic aspects of sharing reflected
making problems such as MNOs deciding whether to en- in the payoff function definition and (ii) do not split the
ter a sharing agreement or not, SPs selecting InPs from shared infrastructure cost among MNOs a priori; how
which to obtain resources etc. In these lines we can these cost are split is an outcome of the model (game).
further split this category into two subcategories: (i) in- In turn in [27], we address a similar scenario to [25,26]
frastructure sharing among conventional MNOs and (ii) but without spectrum pooling. Moreover, in [27] we
infrastructure sharing for decoupled infrastructure from consider two different cases deriving from two different
services (involving InPs and SPs etc.). Such articles perspectives, the one of a regulatory entity favoring the
naturally resort to mathematical programming and to users and the MNOs’ perspective as profit-maximizers.
game theory in particular when the involved actors are We model the former case through Mixed Integer Linear
assumed rational, self-interested and payoff-maximizing Programming and the latter through cooperative game
entities. theory.
The authors in [41] consider the case when a set of
3.5.1 Infrastructure sharing among conven- MNOs agrees to pool together their current individual
tional MNOs RAN networks but make joint decisions for future de-
commissions, network expansion and upgrades of their
The following articles concern either greenfield deploy- shared network; a greedy procedure is proposed to solve
ment of shared networks [18,25–27,108,109,127] or the the multi-period network planning.
case when shared networks are created by pooling to-
gether the existing network infrastructure of at least two Similarly to the “sale-leaseback” approach of Tower
MNOs [39,41,94,126]. Companies (see e.g., [90]), the work in [39] assumes a set

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