28,829 research outputs found
Resource Allocation for Device-to-Device Communications in Multi-Cell Multi-Band Heterogeneous Cellular Networks
Heterogeneous cellular networks (HCNs) with millimeter wave (mm-wave)
communications are considered as a promising technology for the fifth
generation mobile networks. Mm-wave has the potential to provide multiple
gigabit data rate due to the broad spectrum. Unfortunately, additional free
space path loss is also caused by the high carrier frequency. On the other
hand, mm-wave signals are sensitive to obstacles and more vulnerable to
blocking effects. To address this issue, highly directional narrow beams are
utilized in mm-wave networks. Additionally, device-to-device (D2D) users make
full use of their proximity and share uplink spectrum resources in HCNs to
increase the spectrum efficiency and network capacity. Towards the caused
complex interferences, the combination of D2D-enabled HCNs with small cells
densely deployed and mm-wave communications poses a big challenge to the
resource allocation problems. In this paper, we formulate the optimization
problem of D2D communication spectrum resource allocation among multiple
micro-wave bands and multiple mm-wave bands in HCNs. Then, considering the
totally different propagation conditions on the two bands, a heuristic
algorithm is proposed to maximize the system transmission rate and approximate
the solutions with sufficient accuracies. Compared with other practical
schemes, we carry out extensive simulations with different system parameters,
and demonstrate the superior performance of the proposed scheme. In addition,
the optimality and complexity are simulated to further verify effectiveness and
efficiency.Comment: 13 pages, 11 figures, IEEE Transactions on Vehicular Technolog
Resource Allocation for Device-to-Device Communications Underlaying Heterogeneous Cellular Networks Using Coalitional Games
Heterogeneous cellular networks (HCNs) with millimeter wave (mmWave)
communications included are emerging as a promising candidate for the fifth
generation mobile network. With highly directional antenna arrays, mmWave links
are able to provide several-Gbps transmission rate. However, mmWave links are
easily blocked without line of sight. On the other hand, D2D communications
have been proposed to support many content based applications, and need to
share resources with users in HCNs to improve spectral reuse and enhance system
capacity. Consequently, an efficient resource allocation scheme for D2D pairs
among both mmWave and the cellular carrier band is needed. In this paper, we
first formulate the problem of the resource allocation among mmWave and the
cellular band for multiple D2D pairs from the view point of game theory. Then,
with the characteristics of cellular and mmWave communications considered, we
propose a coalition formation game to maximize the system sum rate in
statistical average sense. We also theoretically prove that our proposed game
converges to a Nash-stable equilibrium and further reaches the near-optimal
solution with fast convergence rate. Through extensive simulations under
various system parameters, we demonstrate the superior performance of our
scheme in terms of the system sum rate compared with several other practical
schemes.Comment: 13 pages, 12 figure
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
- …