Content Placement in Cache-Enabled Sub-6 GHz and Millimeter-Wave Multi-antenna Dense Small Cell Networks

This paper studies the performance of cache-enabled dense small cell networks consisting of multi-antenna sub-6 GHz and millimeter-wave base stations. Different from the existing works which only consider a single antenna at each base station, the optimal content placement is unknown when the base stations have multiple antennas. We first derive the successful content delivery probability by accounting for the key channel features at sub-6 GHz and mmWave frequencies. The maximization of the successful content delivery probability is a challenging problem. To tackle it, we first propose a constrained cross-entropy algorithm which achieves the near-optimal solution with moderate complexity. We then develop another simple yet effective heuristic probabilistic content placement scheme, termed two-stair algorithm, which strikes a balance between caching the most popular contents and achieving content diversity. Numerical results demonstrate the superior performance of the constrained cross-entropy method and that the two-stair algorithm yields significantly better performance than only caching the most popular contents. The comparisons between the sub-6 GHz and mmWave systems reveal an interesting tradeoff between caching capacity and density for the mmWave system to achieve similar performance as the sub-6 GHz system.Comment: 14 pages; Accepted to appear in IEEE Transactions on Wireless Communication

Advanced Technologies for Device-to-device Communications Underlaying Cellular Networks

The past few years have seen a major change in cellular networks, as explosive growth in data demands requires more and more network capacity and backhaul capability. New wireless technologies have been proposed to tackle these challenges. One of the emerging technologies is device-to-device (D2D) communications. It enables two cellular user equip- ment (UEs) in proximity to communicate with each other directly reusing cellular radio resources. In this case, D2D is able to of oad data traf c from central base stations (BSs) and signi cantly improve the spectrum ef ciency of a cellular network, and thus is one of the key technologies for the next generation cellular systems. Radio resource management (RRM) for D2D communications and how to effectively exploit the potential bene ts of D2D are two paramount challenges to D2D communications underlaying cellular networks. In this thesis, we focus on four problems related to these two challenges. In Chapter 2, we utilise the mixed integer non-linear programming (MINLP) to model and solve the RRM optimisation problems for D2D communications. Firstly we consider the RRM optimisation problem for D2D communications underlaying the single carrier frequency division multiple access (SC-FDMA) system and devise a heuristic sub- optimal solution to it. Then we propose an optimised RRM mechanism for multi-hop D2D communications with network coding (NC). NC has been proven as an ef cient technique to improve the throughput of ad-hoc networks and thus we apply it to multi-hop D2D communications. We devise an optimal solution to the RRM optimisation problem for multi-hop D2D communications with NC. In Chapter 3, we investigate how the location of the D2D transmitter in a cell may affect the RRM mechanism and the performance of D2D communications. We propose two optimised location-based RRM mechanisms for D2D, which maximise the throughput and the energy ef ciency of D2D, respectively. We show that, by considering the location information of the D2D transmitter, the MINLP problem of RRM for D2D communications can be transformed into a convex optimisation problem, which can be ef ciently solved by the method of Lagrangian multipliers. In Chapter 4, we propose a D2D-based P2P le sharing system, which is called Iunius. The Iunius system features: 1) a wireless P2P protocol based on Bittorrent protocol in the application layer; 2) a simple centralised routing mechanism for multi-hop D2D communications; 3) an interference cancellation technique for conventional cellular (CC) uplink communications; and 4) a radio resource management scheme to mitigate the interference between CC and D2D communications that share the cellular uplink radio resources while maximising the throughput of D2D communications. We show that with the properly designed application layer protocol and the optimised RRM for D2D communications, Iunius can signi cantly improve the quality of experience (QoE) of users and of oad local traf c from the base station. In Chapter 5, we combine LTE-unlicensed with D2D communications. We utilise LTE-unlicensed to enable the operation of D2D in unlicensed bands. We show that not only can this improve the throughput of D2D communications, but also allow D2D to work in the cell central area, which normally regarded as a “forbidden area” for D2D in existing works. We achieve these results mainly through numerical optimisation and simulations. We utilise a wide range of numerical optimisation theories in our works. Instead of utilising the general numerical optimisation algorithms to solve the optimisation problems, we modify them to be suitable for the speci c problems, thereby reducing the computational complexity. Finally, we evaluate our proposed algorithms and systems through sophisticated numer- ical simulations. We have developed a complete system-level simulation framework for D2D communications and we open-source it in Github: https://github.com/mathwuyue/py- wireless-sys-sim

An Optimized Multi-Layer Resource Management in Mobile Edge Computing Networks: A Joint Computation Offloading and Caching Solution

Nowadays, data caching is being used as a high-speed data storage layer in mobile edge computing networks employing flow control methodologies at an exponential rate. This study shows how to discover the best architecture for backhaul networks with caching capability using a distributed offloading technique. This article used a continuous power flow analysis to achieve the optimum load constraints, wherein the power of macro base stations with various caching capacities is supplied by either an intelligent grid network or renewable energy systems. This work proposes ubiquitous connectivity between users at the cell edge and offloading the macro cells so as to provide features the macro cell itself cannot cope with, such as extreme changes in the required user data rate and energy efficiency. The offloading framework is then reformed into a neural weighted framework that considers convergence and Lyapunov instability requirements of mobile-edge computing under Karush Kuhn Tucker optimization restrictions in order to get accurate solutions. The cell-layer performance is analyzed in the boundary and in the center point of the cells. The analytical and simulation results show that the suggested method outperforms other energy-saving techniques. Also, compared to other solutions studied in the literature, the proposed approach shows a two to three times increase in both the throughput of the cell edge users and the aggregate throughput per cluster

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