438 research outputs found
Reinforcement Learning in Multiple-UAV Networks: Deployment and Movement Design
A novel framework is proposed for quality of experience (QoE)-driven
deployment and dynamic movement of multiple unmanned aerial vehicles (UAVs).
The problem of joint non-convex three-dimensional (3D) deployment and dynamic
movement of the UAVs is formulated for maximizing the sum mean opinion score
(MOS) of ground users, which is proved to be NP-hard. In the aim of solving
this pertinent problem, a three-step approach is proposed for attaining 3D
deployment and dynamic movement of multiple UAVs. Firstly, genetic algorithm
based K-means (GAK-means) algorithm is utilized for obtaining the cell
partition of the users. Secondly, Q-learning based deployment algorithm is
proposed, in which each UAV acts as an agent, making their own decision for
attaining 3D position by learning from trial and mistake. In contrast to
conventional genetic algorithm based learning algorithms, the proposed
algorithm is capable of training the direction selection strategy offline.
Thirdly, Q-learning based movement algorithm is proposed in the scenario that
the users are roaming. The proposed algorithm is capable of converging to an
optimal state. Numerical results reveal that the proposed algorithms show a
fast convergence rate after a small number of iterations. Additionally, the
proposed Q-learning based deployment algorithm outperforms K-means algorithms
and Iterative-GAKmean (IGK) algorithms with a low complexity
Trajectory Design and Power Control for Multi-UAV Assisted Wireless Networks: A Machine Learning Approach
A novel framework is proposed for the trajectory design of multiple unmanned
aerial vehicles (UAVs) based on the prediction of users' mobility information.
The problem of joint trajectory design and power control is formulated for
maximizing the instantaneous sum transmit rate while satisfying the rate
requirement of users. In an effort to solve this pertinent problem, a
three-step approach is proposed which is based on machine learning techniques
to obtain both the position information of users and the trajectory design of
UAVs. Firstly, a multi-agent Q-learning based placement algorithm is proposed
for determining the optimal positions of the UAVs based on the initial location
of the users. Secondly, in an effort to determine the mobility information of
users based on a real dataset, their position data is collected from Twitter to
describe the anonymous user-trajectories in the physical world. In the
meantime, an echo state network (ESN) based prediction algorithm is proposed
for predicting the future positions of users based on the real dataset.
Thirdly, a multi-agent Q-learning based algorithm is conceived for predicting
the position of UAVs in each time slot based on the movement of users. In this
algorithm, multiple UAVs act as agents to find optimal actions by interacting
with their environment and learn from their mistakes. Additionally, we also
prove that the proposed multi-agent Q-learning based trajectory design and
power control algorithm can converge under mild conditions. Numerical results
are provided to demonstrate that as the size of the reservoir increases, the
proposed ESN approach improves the prediction accuracy. Finally, we demonstrate
that throughput gains of about 17% are achieved
Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks
Unmanned aerial vehicles (UAVs) are capable of serving as aerial base
stations (BSs) for providing both cost-effective and on-demand wireless
communications. This article investigates dynamic resource allocation of
multiple UAVs enabled communication networks with the goal of maximizing
long-term rewards. More particularly, each UAV communicates with a ground user
by automatically selecting its communicating users, power levels and
subchannels without any information exchange among UAVs. To model the
uncertainty of environments, we formulate the long-term resource allocation
problem as a stochastic game for maximizing the expected rewards, where each
UAV becomes a learning agent and each resource allocation solution corresponds
to an action taken by the UAVs. Afterwards, we develop a multi-agent
reinforcement learning (MARL) framework that each agent discovers its best
strategy according to its local observations using learning. More specifically,
we propose an agent-independent method, for which all agents conduct a decision
algorithm independently but share a common structure based on Q-learning.
Finally, simulation results reveal that: 1) appropriate parameters for
exploitation and exploration are capable of enhancing the performance of the
proposed MARL based resource allocation algorithm; 2) the proposed MARL
algorithm provides acceptable performance compared to the case with complete
information exchanges among UAVs. By doing so, it strikes a good tradeoff
between performance gains and information exchange overheads.Comment: 30 pages, 8 figure
Exploiting Multiple Access in Clustered Millimeter Wave Networks: NOMA or OMA?
In this paper, we introduce a clustered millimeter wave network with
non-orthogonal multiple access (NOMA), where the base station (BS) is located
at the center of each cluster and all users follow a Poisson Cluster Process.
To provide a realistic directional beamforming, an actual antenna pattern is
deployed at all BSs. We provide a nearest-random scheme, in which near user is
the closest node to the corresponding BS and far user is selected at random, to
appraise the coverage performance and universal throughput of our system. Novel
closed-form expressions are derived under a loose network assumption. Moreover,
we present several Monte Carlo simulations and numerical results, which show
that: 1) NOMA outperforms orthogonal multiple access regarding the system rate;
2) the coverage probability is proportional to the number of possible NOMA
users and a negative relationship with the variance of intra-cluster receivers;
and 3) an optimal number of the antenna elements is existed for maximizing the
system throughput.Comment: This paper has been accepted by IEEE International Conference on
Communications (ICC), May, USA, 2018. Please cite the format version of this
pape
Modeling and Analysis of MmWave Communications in Cache-enabled HetNets
In this paper, we consider a novel cache-enabled heterogeneous network
(HetNet), where macro base stations (BSs) with traditional sub-6 GHz are
overlaid by dense millimeter wave (mmWave) pico BSs. These two-tier BSs, which
are modeled as two independent homogeneous Poisson Point Processes, cache
multimedia contents following the popularity rank. High-capacity backhauls are
utilized between macro BSs and the core server. A maximum received power
strategy is introduced for deducing novel algorithms of the success probability
and area spectral efficiency (ASE). Moreover, Monte Carlo simulations are
presented to verify the analytical conclusions and numerical results
demonstrate that: 1) the proposed HetNet is an interference-limited system and
it outperforms the traditional HetNets; 2) there exists an optimal pre-decided
rate threshold that contributes to the maximum ASE; and 3) 73 GHz is the best
mmWave carrier frequency regarding ASE due to the large antenna scale.Comment: This paper has been accepted by IEEE International Conference on
Communications (ICC), May, USA, 2018. Please cite the format version of this
pape
Comparative Studies of 10 Programming Languages within 10 Diverse Criteria -- a Team 7 COMP6411-S10 Term Report
There are many programming languages in the world today.Each language has
their advantage and disavantage. In this paper, we will discuss ten programming
languages: C++, C#, Java, Groovy, JavaScript, PHP, Schalar, Scheme, Haskell and
AspectJ. We summarize and compare these ten languages on ten different
criterion. For example, Default more secure programming practices, Web
applications development, OO-based abstraction and etc. At the end, we will
give our conclusion that which languages are suitable and which are not for
using in some cases. We will also provide evidence and our analysis on why some
language are better than other or have advantages over the other on some
criterion.Comment: 139 pages, programming languages comparison table
When Machine Learning Meets Big Data: A Wireless Communication Perspective
We have witnessed an exponential growth in commercial data services, which
has lead to the 'big data era'. Machine learning, as one of the most promising
artificial intelligence tools of analyzing the deluge of data, has been invoked
in many research areas both in academia and industry. The aim of this article
is twin-fold. Firstly, we briefly review big data analysis and machine
learning, along with their potential applications in next-generation wireless
networks. The second goal is to invoke big data analysis to predict the
requirements of mobile users and to exploit it for improving the performance of
"social network-aware wireless". More particularly, a unified big data aided
machine learning framework is proposed, which consists of feature extraction,
data modeling and prediction/online refinement. The main benefits of the
proposed framework are that by relying on big data which reflects both the
spectral and other challenging requirements of the users, we can refine the
motivation, problem formulations and methodology of powerful machine learning
algorithms in the context of wireless networks. In order to characterize the
efficiency of the proposed framework, a pair of intelligent practical
applications are provided as case studies: 1) To predict the positioning of
drone-mounted areal base stations (BSs) according to the specific tele-traffic
requirements by gleaning valuable data from social networks. 2) To predict the
content caching requirements of BSs according to the users' preferences by
mining data from social networks. Finally, open research opportunities are
identified for motivating future investigations.Comment: This article has been accepted by IEEE Vehicular Technology Magazin
Non-Orthogonal Multiple Access for Air-to-Ground Communication
This paper investigates ground-aerial uplink non-orthogonal multiple access
(NOMA) cellular networks. A rotary-wing unmanned aerial vehicle (UAV) user and
multiple ground users (GUEs) are served by ground base stations (GBSs) by
utilizing the uplink NOMA protocol. The UAV is dispatched to upload specific
information bits to each target GBSs. Specifically, our goal is to minimize the
UAV mission completion time by jointly optimizing the UAV trajectory and
UAV-GBS association order while taking into account the UAV's interference to
non-associated GBSs. The formulated problem is a mixed integer non-convex
problem and involves infinite variables. To tackle this problem, we efficiently
check the feasibility of the formulated problem by utilizing graph theory and
topology theory. Next, we prove that the optimal UAV trajectory needs to
satisfy the \emph{fly-hover-fly} structure. With this insight, we first design
an efficient solution with predefined hovering locations by leveraging graph
theory techniques. Furthermore, we propose an iterative UAV trajectory design
by applying successive convex approximation (SCA) technique, which is
guaranteed to coverage to a locally optimal solution. We demonstrate that the
two proposed designs exhibit polynomial time complexity. Finally, numerical
results show that: 1) the SCA based design outperforms the fly-hover-fly based
design; 2) the UAV mission completion time is significantly minimized with
proposed NOMA schemes compared with the orthogonal multiple access (OMA)
scheme; 3) the increase of GUEs' quality of service (QoS) requirements will
increase the UAV mission completion time
Secure Communications in a Unified Non-Orthogonal Multiple Access Framework
This paper investigates the impact of physical layer secrecy on the
performance of a unified non-orthogonal multiple access (NOMA) framework, where
both external and internal eavesdropping scenarios are examined. The spatial
locations of legitimate users (LUs) and eavesdroppers are modeled by invoking
stochastic geometry. To characterize the security performance, new exact and
asymptotic expressions of secrecy outage probability (SOP) are derived for both
code-domain NOMA (CD-NOMA) and power-domain NOMA (PD-NOMA), in which imperfect
successive interference cancellation (ipSIC) and perfect SIC (pSIC) are taken
into account. For the external eavesdropping scenario, the secrecy diversity
orders by a pair of LUs (the n-th user and m-th user) for CD/PD-NOMA are
obtained. Analytical results make known that the diversity orders of the -th
user with ipSIC/pSIC for CD-NOMA and PD-NOMA are equal to zero/K and zero/one,
respectively. The diversity orders of the m-th user are equal to K/one for
CD/PD-NOMA. For the internal eavesdropping scenario, we examine the analysis of
secrecy diversity order and observe that the m-th user to wiretap the n-th user
with ipSIC/pSIC for CD-NOMA and PD-NOMA provide the diversity orders of zero/K
and zero/one, respectively, which is consistent with external eavesdropping
scenario. Numerical results are present to confirm the accuracy of the
analytical results developed and show that: i) The secrecy outage behavior of
the -th user is superior to that of the m-th user; ii) By increasing the
number of subcarriers, CD-NOMA is capable of achieving a larger secrecy
diversity gain compared to PD-NOMA.Comment: 15 pages,10 figure
Reconfigurable Intelligent Surface-assisted Networks: Phase Alignment Categories
The reconfigurable intelligent surface (RIS) is one of the promising
technology contributing to the next generation smart radio environment. The
application scenarios include massive connectivity support, signal enhancement,
and security protection. One crucial difficulty of analyzing the RIS-assisted
networks is that the channel performance is sensitive to the change of user
receiving direction. This paper tackles the problem by categorizing the RIS
illuminated space into four categories: perfect alignment, coherent alignment,
random alignment, and destructive alignment. These four categories cover all
the possible phase alignment conditions that a user could experience within the
overall pi solid angle of RIS-illuminated space. We perform analysis for
each of these categories, deriving analytical expressions for the outage
probability and diversity order. Simulation results are presented to confirm
the effectiveness of the proposed analytical results.Comment: 6 pages, submitted to IEEE ICC'21 - MWN Symposiu
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