15 research outputs found
Distributed algorithms for optimized resource management of LTE in unlicensed spectrum and UAV-enabled wireless networks
Next-generation wireless cellular networks are morphing into a massive Internet
of Things (IoT) environment that integrates a heterogeneous mix of wireless-enabled
devices such as unmanned aerial vehicles (UAVs) and connected vehicles.
This unprecedented transformation will not only drive an exponential growth in
wireless traffic, but it will also lead to the emergence of new wireless service
applications that substantially differ from conventional multimedia services. To
realize the fifth generation (5G) mobile networks vision, a new wireless radio
technology paradigm shift is required in order to meet the quality of service
requirements of these new emerging use cases. In this respect, one of the major
components of 5G is self-organized networks. In essence, future cellular networks
will have to rely on an autonomous and self-organized behavior in order to manage
the large scale of wireless-enabled devices. Such an autonomous capability can be
realized by integrating fundamental notions of artificial intelligence (AI) across
various network devices.
In this regard, the main objective of this thesis is to propose novel self-organizing
and AI-inspired algorithms for optimizing the available radio resources
in next-generation wireless cellular networks. First, heterogeneous networks that
encompass licensed and unlicensed spectrum are studied. In this context, a deep
reinforcement learning (RL) framework based on long short-term memory cells is
introduced. The proposed scheme aims at proactively allocating the licensed assisted
access LTE (LTE-LAA) radio resources over the unlicensed spectrum while
ensuring an efficient coexistence with WiFi. The proposed deep learning algorithm
is shown to reach a mixed-strategy Nash equilibrium, when it converges.
Simulation results using real data traces show that the proposed scheme can yield
up to 28% and 11% gains over a conventional reactive approach and a proportional
fair coexistence mechanism, respectively. In terms of priority fairness, results
show that an efficient utilization of the unlicensed spectrum is guaranteed when
both technologies, LTE-LAA and WiFi, are given equal weighted priorities for
transmission on the unlicensed spectrum. Furthermore, an optimization formulation
for LTE-LAA holistic traffic balancing across the licensed and the unlicensed
bands is proposed. A closed form solution for the aforementioned optimization
problem is derived. An attractive aspect of the derived solution is that it can be
applied online by each LTE-LAA small base station (SBS), adapting its transmission behavior in each of the bands, and without explicit communication with
WiFi nodes. Simulation results show that the proposed traffic balancing scheme
provides a better tradeoff between maximizing the total network throughput and
achieving fairness among all network
ows compared to alternative approaches
from the literature. Second, UAV-enabled wireless networks are investigated. In
particular, the problems of interference management for cellular-connected UAVs
and the use of UAVs for providing backhaul connectivity to SBSs are studied.
Speci cally, a deep RL framework based on echo state network cells is proposed
for optimizing the trajectories of multiple cellular-connected UAVs while minimizing
the interference level caused on the ground network. The proposed algorithm
is shown to reach a subgame perfect Nash equilibrium upon convergence. Moreover,
an upper and lower bound for the altitude of the UAVs is derived thus
reducing the computational complexity of the proposed algorithm. Simulation
results show that the proposed path planning scheme allows each UAV to achieve
a tradeoff between minimizing energy efficiency, wireless latency, and the interference
level caused on the ground network along its path. Moreover, in the context
of UAV-enabled wireless networks, a UAV-based on-demand aerial backhaul network
is proposed. For this framework, a network formation algorithm, which is
guaranteed to reach a pairwise stable network upon convergence, is presented.
Simulation results show that the proposed scheme achieves substantial performance
gains in terms of both rate and delay reaching, respectively, up to 3.8 and
4-fold increase compared to the formation of direct communication links with the
gateway node. Overall, the results of the different proposed schemes show that
these schemes yield significant improvements in the total network performance
as compared to current existing literature. In essence, the proposed algorithms
can also provide self-organizing solutions for several resource management problems
in the context of new emerging use cases in 5G networks, such as connected
autonomous vehicles and virtual reality headsets
Holistic Small Cell Traffic Balancing across Licensed and Unlicensed Bands
Due to the dramatic growth in mobile data traffic on one hand and the
scarcity of the licensed spectrum on the other hand, mobile operators are
considering the use of unlicensed bands (especially those in 5 GHz) as
complementary spectrum for providing higher system capacity and better user
experience. This approach is currently being standardized by 3GPP under the
name of LTE Licensed-Assisted Access (LTE-LAA). In this paper, we take a
holistic approach for LTE-LAA small cell traffic balancing by jointly
optimizing the use of the licensed and unlicensed bands. We pose this traffic
balancing as an optimization problem that seeks proportional fair coexistence
of WiFi, small cell and macro cell users by adapting the transmission
probability of the LTE-LAA small cell in the licensed and unlicensed bands. The
motivation for this formulation is for the LTE-LAA small cell to switch between
or aggregate licensed and unlicensed bands depending on the
interference/traffic level and the number of active users in each band. We
derive a closed form solution for this optimization problem and additionally
propose a transmission mechanism for the operation of the LTE-LAA small cell on
both bands. Through numerical and simulation results, we show that our proposed
traffic balancing scheme, besides enabling better LTE-WiFi coexistence and
efficient utilization of the radio resources relative to the existing traffic
balancing scheme, also provides a better tradeoff between maximizing the total
network throughput and achieving fairness among all network flows compared to
alternative approaches.Comment: Accepted for publication at MSWiM 201