Performance Evaluation and Enhancement in 5G Networks : A Stochastic Geometry Approach
Abstract
The deployment of heterogeneous networks (HetNets), in which low power nodes (LPNs)
and high power nodes (HPNs) coexist, has become a promising solution for extending
coverage and increasing capacity in wireless networks. Meanwhile, several advanced technologies
such as massive multi-input multi-output (MIMO), cloud radio access networks
(C-RAN) and device-to-device (D2D) communications have been proposed as competent
candidates for supporting the next generation (5G) network. Since single technology
cannot solely achieve the envisioned 5G requirements, the e ect of integrating multiple
technologies in one system is worth to be investigated. In this thesis, a thoroughly theoretical
analysis is conducted to evaluate the network performance in di erent scenarios,
where two or more 5G techniques are employed.
First, the downlink performance of massive MIMO enabled HetNets is fully evaluated.
The exact and asymptotic expressions for the probability of a user being associated
with a macro cell or a small cell are presented. The analytical expressions for the
spectrum e ciency (SE) and energy e ciency (EE) in the K-tier network are also derived.
The analysis reveals that the implementation of massive MIMO in the macro cell can
considerably improve the network performance and decrease the demands for small cells
in HetNets, which simpli es the network deployment.
Then, the downlink performance of a massive MIMO enabled heterogeneous C-RAN is
investigated. The exact expressions for the SE and EE of the remote radio heads (RRHs)
tier and a tractable approximation approach for evaluating the SE and EE of the macrocell
tier are obtained. Numerical results collaborate the analysis and prove that massive
MIMO with dense deployment of RRHs can signi cantly enhance the performance of
heterogeneous C-RAN theoretically. Next, the uplink performance of massive MIMO enabled HetNets is exploited with interference
management via derived SE and EE expressions. The numerical results show that
the uplink performance in the massive MIMO macrocells can be signi cantly improved
through uplink power control in the small cells, while more uplink transmissions in the
macrocells have mild adverse e ect on the uplink performance of the small cells. In addition,
the SE and EE of the massive MIMO macrocells with heavier load can be improved
by expanding the small cell range.
Lastly, the uplink performance of the D2D underlaid massive MIMO network is investigated
and a novel D2D power control scheme is proposed. The average uplink achievable
SE and EE expressions for the cellular and D2D are derived and results demonstrate
that the proposed power control can e ciently mitigate the interference from the D2D.
Moreover, the D2D scale properties are obtained, which provide the su cient conditions
for achieving the anticipated SE. The results demonstrate that there exists the optimal
D2D density for maximizing the area SE of D2D tier. In addition, the achievable EE of
a cellular user can be comparable to that of a D2D user.
Stochastic geometry is applied to model all of the systems mentioned above. Monte
Carlo simulations are also developed and conducted to validate the derived expressions
and the theoretical analysis.
Authors
He, AnqiCollections
- Theses [4192]