375 research outputs found
Distributed Resource Allocation and Performance Analysis in 5G Wireless Cellular Networks
This thesis focuses on the study of Heterogeneous Networks (HetNets), Device-to-device (D2D) communication networks, and unmanned aerial vehicle (UAV) networks in fifth generation wireless communication (5G) systems. HetNets that consist of macro-cells and small-cells have become increasingly popular in current wireless networks and 5G systems to meet the exponentially growing demand for higher data rates. Compared to conventional homogeneous cellular networks, the disparity of transmission power among different types of base stations (BSs), the relatively random deployment of SBSs, and the densifying networks, bring new challenges, such as the imbalanced load between macro and small cells and severe inter-cell interference. In the other hand, with the skyrocketing number of tablets and smart phones, the notion of caching popular content in the storage of BSs and users' devices is proposed to reduce duplicated wireless transmissions. To fulfill multi-fold communication requirements from humans, machine, and things, the 5G systems which include D2D communications, UAV communications, and so on, can improve the network performance. Among them, the performance analyses of these emerging technologies are attracting much attention and should be investigated first. This thesis focuses on these hot issues and emerging technologies in 5G systems, analyzing the network performance and conducting the allocation of available resources, such as serving BSs, spectrum resources, and storage resources. Specifically, three main research focuses are included in the thesis. The first focus of this thesis is the impact of the BS idle mode capacity (IMC) on the network performance of multi-tier and dense HCNs with both line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions. I consider a more practical set-up with a finite number of UEs in the analysis. Moreover, the SBSs apply a positive power bias in the cell association procedure, so that macrocell UEs are actively encouraged to use the more lightly loaded SBSs. In addition, to address the severe interference that these cell range expanded UEs may suffer, the MBSs apply enhanced inter-cell interference coordination (eICIC), in the form of almost blank subframe (ABS) mechanism. For this model, I derive the coverage probability and the rate of a typical UE in the whole network or a certain tier. The impact of the IMC on the performance of the network is shown to be significant. In particular, it is important to note that there will be a surplus of BSs when the BS density exceeds the UE density, and thus a large number of BSs switch off. As a result, the overall coverage probability, as well as the area spectral efficiency (ASE), will continuously increase with the BS density, addressing the network outage that occurs when all BSs are active and the interference becomes LoS dominated. Finally, the optimal ABS factors are investigated in different BS density regions. One of major findings is that MBSs should give up all resources in favor of the SBSs when the small cell networks go ultra-dense. This reinforces the need for orthogonal deployments, shedding new light on the design and deployment of the future 5G dense HCNs. The second focus of this thesis is the content caching in D2D communication networks. In practical deployment, D2D content caching has its own problem that is not all of the user devices are willing to share the content with others due to numerous concerns such as security, battery life, and social relationship. To solve this problem, I consider the factor of social relationship in the deployment of D2D content caching. First, I apply stochastic geometry theory to derive an analytical expression of downloading performance for the D2D caching network. Specifically, a social relationship model with respect to the physical distance is adopted in the analysis to obtain the average downloading delay performance using random and deterministic caching strategies. Second, to achieve a better performance in more practical and specific scenarios, I develop a socially aware distributed caching strategy based on a decentralized learning automaton, to optimize the cache placement operation in D2D networks. Different from the existing caching schemes, the proposed algorithm not only considers the file request probability and the closeness of devices as measured by their physical distance, but also takes into account the social relationship between D2D users. The simulation results show that the proposed algorithm can converge quickly and outperforms the random and deterministic caching strategies. With these results, the work sheds insights on the design of D2D caching in the practical deployment of 5G networks. The third focus of this thesis is the performance analysis for practical UAV-enabled networks. By considering both LoS and NLoS transmissions between aerial BSs and ground users, the coverage probability and the ASE are derived. Considering that there is no consensus on the path loss model for studying UAVs in the literature, in this focus, three path loss models, i.e., high-altitude model, low-altitude model, and ultra-low-altitude model, are investigated and compared. Moreover, the lower bound of the network performance is obtained assuming that UAVs are hovering randomly according to homogeneous Poisson point process (HPPP), while the upper bound is derived assuming that UAVs can instantaneously move to the positions directly overhead ground users. From the analytical and simulation results for a practical UAV height of 50 meters, I find that the network performance of the high-altitude model and the low-altitude model exhibit similar trends, while that of the ultra-low-altitude model deviates significantly from the above two models. In addition, the optimal density of UAVs to maximize the coverage probability performance has also been investigated
Efficient offloading and load distribution based on D2D relaying and UAVs for emergent wireless networks
The device to device (D2D) and unmanned aerial vehicle (UAV) communications are considered as enabling technologies of the emergent 5th generation of wireless and cellular system (5G). Consequently, it is important to determine their corresponding performance with respect to the 5G requirements. In particular, we focus on enhancing the offloading and load balancing performance in three directions.
In the first direction, we study the achievable data rate of user relay assisting other users in two-tier networks. We propose a novel heuristic communication scheme called device-for-device (D4D). The D4D enables moving users to share their resource by taking advantage of cooperative communication. We study the moving user rate sensitivity to the relay selection and blocking probability.
In the second direction, we study the offloading from macrocell to small cell and load balancing among small cell. Also, we design a new utility weight function that enables a balanced relay assignment. We propose a novel low complexity algorithm for centralized scheme maximizing the load among small cells as well as users subject to SINR threshold constraints. The simulations show that our proposed schemes achieve performance in load balancing compared to those obtained with the previous or traditional method.
In the third direction, we study the 3D deployment of multiple UAVs for emergent on-demand offloading. We propose a novel on-demand deployment scheme based on maximizing both the operator’s profit and the quality of service. The proposed scheme is based on solving a non-convex problem by combining k-means clustering with pattern search to find the suboptimal location of UAVs. The simulation results show that our proposed scheme maximizes the operator’s profit and improves offloading traffic efficiency.
Our global contribution was the development of a scheme to improve the quality of service and the performance in emergent networks through the improvement of the load distribution and resource sharing using D2D and UAV
Cache Enabled UAV HetNets Access xHaul Coverage Analysis and Optimal Resource Partitioning
We study an urban wireless network in which cache-enabled UAV-Access points
(UAV-APs) and UAV-Base stations (UAV-BSs) are deployed to provide higher
throughput and ad-hoc coverage to users on the ground. The cache-enabled
UAV-APs route the user data to the core network via either terrestrial base
stations (TBSs) or backhaul-enabled UAV-BSs through an xHaul link. First, we
derive the association probabilities in the access and xHaul links.
Interestingly, we show that to maximize the line-of-sight (LoS) unmanned aerial
vehicle (UAV) association, densifying the UAV deployment may not be beneficial
after a threshold. Then, we obtain the signal to interference noise ratio
(SINR) coverage probability of the typical user in the access link and the
tagged UAV-AP in the xHaul link, respectively. The SINR coverage analysis is
employed to characterize the successful content delivery probability by jointly
considering the probability of successful access and xHaul transmissions and
successful cache-hit probability. We numerically optimize the distribution of
frequency resources between the access and the xHaul links to maximize the
successful content delivery to the users. For a given storage capacity at the
UAVs, our study prescribes the network operator optimal bandwidth partitioning
factors and dimensioning rules concerning the deployment of the UAV-APs
Deployment Strategies of Multiple Aerial BSs for User Coverage and Power Efficiency Maximization
Unmanned aerial vehicle (UAV) based aerial base stations (BSs) can provide
rapid communication services to ground users and are thus promising for future
communication systems. In this paper, we consider a scenario where no
functional terrestrial BSs are available and the aim is deploying multiple
aerial BSs to cover a maximum number of users within a certain target area. To
this end, we first propose a naive successive deployment method, which converts
the non-convex constraints in the involved optimization into a combination of
linear constraints through geometrical relaxation. Then we investigate a
deployment method based on K-means clustering. The method divides the target
area into K convex subareas, where within each subarea, a mixed integer
non-linear problem (MINLP) is solved. An iterative power efficient technique is
further proposed to improve coverage probability with reduced power. Finally,
we propose a robust technique for compensating the loss of coverage probability
in the existence of inaccurate user location information (ULI). Our simulation
results show that, the proposed techniques achieve an up to 30% higher coverage
probability when users are not distributed uniformly. In addition, the proposed
simultaneous deployment techniques, especially the one using iterative
algorithm improve power-efficiency by up to 15% compared to the benchmark
circle packing theory
Wireless Communication using Unmanned Aerial Vehicles (UAVs): Optimal Transport Theory for Hover Time Optimization
In this paper, the effective use of flight-time constrained unmanned aerial
vehicles (UAVs) as flying base stations that can provide wireless service to
ground users is investigated. In particular, a novel framework for optimizing
the performance of such UAV-based wireless systems in terms of the average
number of bits (data service) transmitted to users as well as UAVs' hover
duration (i.e. flight time) is proposed. In the considered model, UAVs hover
over a given geographical area to serve ground users that are distributed
within the area based on an arbitrary spatial distribution function. In this
case, two practical scenarios are considered. In the first scenario, based on
the maximum possible hover times of UAVs, the average data service delivered to
the users under a fair resource allocation scheme is maximized by finding the
optimal cell partitions associated to the UAVs. Using the mathematical
framework of optimal transport theory, a gradient-based algorithm is proposed
for optimally partitioning the geographical area based on the users'
distribution, hover times, and locations of the UAVs. In the second scenario,
given the load requirements of ground users, the minimum average hover time
that the UAVs need for completely servicing their ground users is derived. To
this end, first, an optimal bandwidth allocation scheme for serving the users
is proposed. Then, given this optimal bandwidth allocation, the optimal cell
partitions associated with the UAVs are derived by exploiting the optimal
transport theory. Results show that our proposed cell partitioning approach
leads to a significantly higher fairness among the users compared to the
classical weighted Voronoi diagram. In addition, our results reveal an inherent
tradeoff between the hover time of UAVs and bandwidth efficiency while serving
the ground users
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