237 research outputs found
Generalized Area Spectral Efficiency: An Effective Performance Metric for Green Wireless Communications
Area spectral efficiency (ASE) was introduced as a metric to quantify the
spectral utilization efficiency of cellular systems. Unlike other performance
metrics, ASE takes into account the spatial property of cellular systems. In
this paper, we generalize the concept of ASE to study arbitrary wireless
transmissions. Specifically, we introduce the notion of affected area to
characterize the spatial property of arbitrary wireless transmissions. Based on
the definition of affected area, we define the performance metric, generalized
area spectral efficiency (GASE), to quantify the spatial spectral utilization
efficiency as well as the greenness of wireless transmissions. After
illustrating its evaluation for point-to-point transmission, we analyze the
GASE performance of several different transmission scenarios, including
dual-hop relay transmission, three-node cooperative relay transmission and
underlay cognitive radio transmission. We derive closed-form expressions for
the GASE metric of each transmission scenario under Rayleigh fading environment
whenever possible. Through mathematical analysis and numerical examples, we
show that the GASE metric provides a new perspective on the design and
optimization of wireless transmissions, especially on the transmitting power
selection. We also show that introducing relay nodes can greatly improve the
spatial utilization efficiency of wireless systems. We illustrate that the GASE
metric can help optimize the deployment of underlay cognitive radio systems.Comment: 11 pages, 8 figures, accepted by TCo
UAV Trajectory Optimization for Directional THz Links Using Deep Reinforcement Learning
As an alternative solution for quick disaster recovery of backhaul/fronthaul
links, in this paper, a dynamic unmanned aerial vehicles (UAV)-assisted
heterogeneous (HetNet) network equipped with directional terahertz (THz)
antennas is studied to solve the problem of transferring traffic of distributed
small cells. To this end, we first characterize a detailed three-dimensional
modeling of the dynamic UAV-assisted HetNet, and then, we formulate the problem
for UAV trajectory to minimize the maximum outage probability of directional
THz links. Then, using deep reinforcement learning (DRL) method, we propose an
efficient algorithm to learn the optimal trajectory. Finally, using
simulations, we investigate the performance of the proposed DRL-based
trajectory method
On Modeling Heterogeneous Wireless Networks Using Non-Poisson Point Processes
Future wireless networks are required to support 1000 times higher data rate,
than the current LTE standard. In order to meet the ever increasing demand, it
is inevitable that, future wireless networks will have to develop seamless
interconnection between multiple technologies. A manifestation of this idea is
the collaboration among different types of network tiers such as macro and
small cells, leading to the so-called heterogeneous networks (HetNets).
Researchers have used stochastic geometry to analyze such networks and
understand their real potential. Unsurprisingly, it has been revealed that
interference has a detrimental effect on performance, especially if not modeled
properly. Interference can be correlated in space and/or time, which has been
overlooked in the past. For instance, it is normally assumed that the nodes are
located completely independent of each other and follow a homogeneous Poisson
point process (PPP), which is not necessarily true in real networks since the
node locations are spatially dependent. In addition, the interference
correlation created by correlated stochastic processes has mostly been ignored.
To this end, we take a different approach in modeling the interference where we
use non-PPP, as well as we study the impact of spatial and temporal correlation
on the performance of HetNets. To illustrate the impact of correlation on
performance, we consider three case studies from real-life scenarios.
Specifically, we use massive multiple-input multiple-output (MIMO) to
understand the impact of spatial correlation; we use the random medium access
protocol to examine the temporal correlation; and we use cooperative relay
networks to illustrate the spatial-temporal correlation. We present several
numerical examples through which we demonstrate the impact of various
correlation types on the performance of HetNets.Comment: Submitted to IEEE Communications Magazin
A Distributed Approach for Networked Flying Platform Association with Small Cells in 5G+ Networks
The densification of small-cell base stations in a 5G architecture is a
promising approach to enhance the coverage area and facilitate the ever
increasing capacity demand of end users. However, the bottleneck is an
intelligent management of a backhaul/fronthaul network for these small-cell
base stations. This involves efficient association and placement of the
backhaul hubs that connects these small-cells with the core network.
Terrestrial hubs suffer from an inefficient non line of sight link limitations
and unavailability of a proper infrastructure in an urban area. Seeing the
popularity of flying platforms, we employ here an idea of using networked
flying platform (NFP) such as unmanned aerial vehicles (UAVs), drones, unmanned
balloons flying at different altitudes, as aerial backhaul hubs. The
association problem of these NFP-hubs and small-cell base stations is
formulated considering backhaul link and NFP related limitations such as
maximum number of supported links and bandwidth. Then, this paper presents an
efficient and distributed solution of the designed problem, which performs a
greedy search in order to maximize the sum rate of the overall network. A
favorable performance is observed via a numerical comparison of our proposed
method with optimal exhaustive search algorithm in terms of sum rate and
run-time speed.Comment: Submitted to IEEE GLOBECOM 2017, 7 pages and 4 figure
A Stochastic Geometric Analysis of Device-to-Device Communications Operating over Generalized Fading Channels
Device-to-device (D2D) communications are now considered as an integral part
of future 5G networks which will enable direct communication between user
equipment (UE) without unnecessary routing via the network infrastructure. This
architecture will result in higher throughputs than conventional cellular
networks, but with the increased potential for co-channel interference induced
by randomly located cellular and D2D UEs. The physical channels which
constitute D2D communications can be expected to be complex in nature,
experiencing both line-of-sight (LOS) and non-LOS (NLOS) conditions across
closely located D2D pairs. As well as this, given the diverse range of
operating environments, they may also be subject to clustering of the scattered
multipath contribution, i.e., propagation characteristics which are quite
dissimilar to conventional Rayeligh fading environments. To address these
challenges, we consider two recently proposed generalized fading models, namely
and , to characterize the fading behavior in D2D
communications. Together, these models encompass many of the most widely
encountered and utilized fading models in the literature such as Rayleigh, Rice
(Nakagami-), Nakagami-, Hoyt (Nakagami-) and One-Sided Gaussian. Using
stochastic geometry we evaluate the rate and bit error probability of D2D
networks under generalized fading conditions. Based on the analytical results,
we present new insights into the trade-offs between the reliability, rate, and
mode selection under realistic operating conditions. Our results suggest that
D2D mode achieves higher rates over cellular link at the expense of a higher
bit error probability. Through numerical evaluations, we also investigate the
performance gains of D2D networks and demonstrate their superiority over
traditional cellular networks.Comment: Submitted to IEEE Transactions on Wireless Communication
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