1,721,107 research outputs found
Impact of Small Cells Overlapping on Mobility Management
Get PDFThe mobility management will be more complex and will have a great impact on the quality of service (QoS) in the future cellular networks, as these networks will have to handle a huge number of user equipment (UEs) and their frequent handoffs due to very dense short-footage small cells. This paper presents a framework to model and derive the coverage of small cells, the cell sojourn time and the handoff rate in multi-tier small cell networks. The distribution of the small cells around a reference UE’s path is studied by taking into consideration
the overlaps among the small cells. Two types of handoff rates are introduced to estimate the load managed by different cells, where inter-frequency handoff (IRH) rate and intra-frequency handoff (IAH) rate represent the fraction of handoffs managed by the first tier and the other tiers, respectively. Our analysis shows that ignoring the overlaps among the small cells affects the accuracy of the results ignificantly. The simulation results validate the accuracy of the analytical results and also show the impact of different parameters such as the small cell density, the number of tiers and the size of the small cells on the small cell sojourn time, the macro cell sojourn time and the handoff rate
Resource Allocation and Performance Analysis of Cellular-assisted OFDMA Device-to-Device Communications
Get PDFResource allocation of cellular-assisted device-todevice (D2D) communication is very challenging when frequency reuse is considered among multiple D2D pairs within a cell, as intense inter D2D interference is difficult to tackle and generally causes extremely large signaling overhead for channel state information (CSI) acquisition. In this paper, a novel resource allocation framework for cellular-assisted D2D communication is developed with low signaling overhead while maintaining high system capacity. By utilizing the spatial dispersion property
of D2D pairs, a geography-based sub-cell division strategy is proposed to divide the cell into multiple sub-cells and D2D pairs within one sub-cell are formed into one group. Then, sub-cell resource allocation is performed independently among sub-cells without the need of any prior knowledge of inter D2D interference. Under the proposed resource allocation framework, a tractable approximation for the inter D2D interference modelling is obtained and a computationally efficient expression for the average ergodic sum capacity of the cell is derived. The expression further allows us to obtain the optimal number of sub-cells, which is an important parameter for maximizing the average ergodic sum capacity of the cell. It is shown that with small CSI feedback, system capacity can be improved significantly by adopting the
proposed resource allocation framework, especially in dense D2D deployed systems
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Performance Analysis of Small Cell and Distributed Antenna Systems for Indoor Mobile Communications
Get PDFFifth generation (5G) mobile communication systems aim to provide high data rate wireless transmissions and ubiquitous coverage to users but achieving this objective remains a challenge particularly in regions with high user density, such as urban/metropolitan areas and within buildings. To support users in indoor environments, indoor wireless communication systems are required, but frequency spectrum allocations are limited and requires reuse of the limited spectrum. Frequency reuse causes co-channel interference, which is detrimental to the performance and capacity of
indoor systems. In order to design efficient and reliable indoor systems, a thorough understanding of co-channel interference within buildings is vital. This paper presents a comparative study of the performance of indoor deployed small cell base station (SBS) and indoor distributed antenna systems (DAS) in an isolated multi-storey building. Each floor of the building is equipped with either an indoor SBS or an indoor DAS where geographically distributed remote radio heads (RRHs) are connected to a central unit (CU), and frequency reuse
is employed among floors. Signal propagation characteristics within multi-storey buildings and the impact of inter floor interference on system performance is analysed and compared for both systems. The effect of different reuse distances, pathloss exponents, penetration loss and co-channel interference on achievable rate is analysed over a wide range of potential mobile equipment (ME) locations
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Performance Analysis of Non-Orthogonal Multiple Access (NOMA) in C-RAN, H-CRAN and F-RAN for 5G Systems
Get PDFThe world of telecommunication is witnessing a swift transformation towards fifth generation (5G) cellular networks. The future networks present requisite needs in ubiquitous throughput, low latency, and high reliability. They are also envisioned to provide diversified services such as enhanced Mobile BroadBand (eMBB) and ultra-reliable low-latency communication (URLLC) as well as improved quality of
user experience. More interestingly, a novel mobile network architecture allowing centralized processing and cloud computing has been proposed as one of the best candidates for fifth generation. It is denoted as Cloud Radio Access Network (CRAN) and Heterogeneous Cloud Radio Access Network (H-CRAN). Furthermore, the 5G architecture will be fog-like, namely fog radio access networks (F-RAN) enabling a functional split of network functionalities between cloud and edge nodes
with caching and fog computing capabilities.
Meanwhile non-orthogonal multiple access (NOMA) has been proposed as an promising multiple access (MA) technology for future radio access networks (RANs) to meet the heterogeneous demands for high throughput, low latency and massive connectivity. One of the main challenges of NOMA is that how well it is to be compatible with other emerging techniques for meeting the requirements of 5G. However, comprehensive performance analysis on NOMA and practical resource allocation designs in co-existence with other emerging networks have not been fully studied and investigated in the literature. This thesis focuses on potential performance enhancement brought by NOMA for the C-RAN, H-CRAN and F-RAN and is expected to
address some of the aforementioned key challenges of 5G. The research work of this thesis can be divided into three parts.
In the first part of our research, we focus on investigating the performance analysis of NOMA in a C-RAN. The problem of jointly optimizing user association, muting and power-bandwidth allocation is formulated for NOMA-enabled C-RANs. To
solve the mixed integer programming problem, the joint problem is decomposed into two subproblems as 1) user association and muting 2) power-bandwidth allocation optimization. To deal with the first subproblem, we propose a centralized and heuristic algorithm to provide the optimal and suboptimal solutions to the remote radio head (RRH) muting problem for given bandwidth and transmit power, respectively.
The second subproblem is then reformulated and we propose an optimal solution to bandwidth and power allocation subject to users data rate constraints. Moreover, for given user association and muting states, the optimal power allocation is derived in a closed-form. Simulation results show that the proposed NOMA-enabled C-RAN outperforms orthogonal multiple access (OMA)-based C-RANs in terms of total achievable rate, interference mitigation and can achieve significant fairness improvement.
Our second work investigates the performance of NOMA in H-CRAN, where coordination of macro base station (MBS) and remote radio heads (RRHs) for H-CRAN with NOMA is introduced to improve network performance. We formulate the problem of jointly optimizing user association, coordinated scheduling and power allocation for NOMA-enabled H-CRANs. To efficiently solve this problem, we decompose the joint optimization problem into two subproblems as 1) user association and scheduling 2) power allocation optimization. Firstly the users are divided based on
different interference they suffer. This interference-aware NOMA approach account for the inter-tier interference. Proportional fairness (PF) scheduling for NOMA is utilized to schedule users with a two-loop optimization method to enhance throughput and fairness. Based on the user scheduling scheme, optimal power allocation optimization is performed by the hierarchical decomposition approach. It is then followed by algorithm for joint scheduling and power allocation. Simulation results show that the proposed NOMA-enabled H-CRAN outperforms OMA-based H-CRANs in terms of total achievable rate and can achieve significant fairness improvement.
In the third part of our research, we propose a NOMA-enabled fog-cloud structure in a novel density-aware F-RAN to tackle different aspects such as throughput and latency requirements of high and low user-density regions, in order to meet the heterogeneous requirements of eMBB and URLLC traffic. A framework of the multi-objective problem is formulated to cater the high throughput and low-latency requirements in a high and low user-density mode respectively. In the first problem, we study the joint caching placement and association strategy aiming at minimizing the average delay. To deal with the first problem, we apply McCormick envelopes and Lagrange partial relaxation method to transform it into three convex sub-problems, which is then solved by proposed distributed algorithm. The second problem is to jointly optimize transmission mode selection, subchannel assignment and power allocation to maximize the sum data rate of all fog user equipments
(F-UEs) while satisfying fronthaul capacity and fog-computing access point (F-AP) power constraints. Moreover, for given transmission mode selection and subchannel assignment, the optimal power allocation is derived in a closed-form. Simulation
results are provided for the proposed NOMA-enabled F-RAN framework and reveal that the ultra-low latency and high throughput can be achieved by properly utilizing the available resources
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Resource Allocation in Drone-Assisted Emergency Communication Systems
Get PDFDue to low cost and high mobility, drones are considered important in emergency communications. In this thesis, we consider a unique drone assisted emergency communication system used in disaster scenarios, where the drone with limited power acts as a relay to improve the downlink sum rate through rational resource allocation. The wireless channel model between drones and ground users in emergency communications is different from conventional relay networks, while drones have their coverage area and data rate limits. Considering these specific characteristics, we formulate a joint power and subcarrier allocation problem to maximize data rate of users, which is limited by the transmit power budget per drone and the number of users on each subcarrier in emergency communications.
However, resource allocation in a unique drone assisted emergency communication system is a nondeterministic polynomial time (NP)-hard problem requiring brute force search, which has prohibitive computational complexity. Instead, efficient algorithms that provide a good trade-off between system performance and implementation practicality are needed.
The contributions of this thesis are proposing two different resource allocation schemes. Both schemes divide users into high-priority(HP) users and low-priority(LP) users and both guarantee minimum guaranteed rate for HP users.
The first scheme is an adaptive algorithm with low complexity. In this scheme, a suboptimal solution is proposed by dividing users into two priority groups: HP users (rescuers) and LP users (affected people). This procedure achieves quasi-linear complexity in terms of the number of users. Finally, the data of the brute force search method and this method were collected through simulation experiments. The data shows that the data rate of the proposed scheme was very close to the optimal data rate when there was a lack of resources.
The second scheme is an adaptive algorithm. In the proposed scheme, we formulate a joint power and subcarrier allocation problem to maximize data rate of users, which is limited by the transmit power budget per drone and the number of users on each subcarrier in emergency communications. Due to the intractability of the formulated problem, it is decomposed into two sub-problems: power allocation optimisation and subcarrier allocation optimization. Then a joint resource allocation algorithm is proposed. The simulation results show that the performance of the proposed method is close to that of the optimal solution but with much lower complexity
Deep Learning Applications in Beyond 5G Mobile Networks
No full textThe thesis is motivated by the evolution of mobile networks needing to match novel applications. Cloud radio access network (C-RAN) is a network architecture that solves power consumption issues, improves scaling, and reduces the operational expenditures for operators. Due to capacity limitations of fronthaul links, C-RAN fails to meet the expectations placed on it in solving the network objectives. Fog radio access network (F-RAN) has been proposed to solve the fronthaul loading. By placing radio processing and caching functionality at the edge nodes latency can be reduced and fronthaul experiences less load, increasing performance gains of the cloud infrastructure.
F-RAN faces multiple issues since the fog access points (FAPs) are a novel addition to the network architecture. FAPs have to communicate with each other to solve optimisation problems and provide a satisfactory quality of service (QoS). Further, compared to the baseband unit (BBU), FAPs are equipped with much smaller caches, so algorithms have to proactively cache content that users would like to access. Due to the stringent low latency requirement set for the future generation of mobile networks, some information cannot be exchanged, thus FAPs have to make assumptions about the global state of the environment. For this reason, the thesis looks at machine learning options, which are known to be able to predict and generalise outcomes based on historical data.
Reinforcement learning (RL) is chosen as it learns from an environment that follows a Markov decision process (MDP). Double deep Q-network (DDQN) is known to be one of the best performers, as it provides convergence guarantees for MDP environments and improved learning stability through the use of experience replay. The thesis uses C-RAN architecture as an environment for a DDQN deployment to control flexible ethernet (FlexE) calendar slot allocation as well as remote unit (RU) load-balancing. The operation of the method is assessed and its performance is compared to auto regressive integrated moving average (ARIMA) algorithm, which uses statistical network information to perform resource allocation for FlexE links.
Assuming information about the global state is unknown, the environment becomes partially observable. This is expected to be the case for F-RAN considering the latency constraints. In a partially observable Markov decision process (POMDP) this leads to uncertainty in making decisions, as the outcomes are perceived as stochastic by the agent. With the goal of maximising the number of users served by multiple FAPs in a POMDP under stringent constraints, DDQN fails to converge to a solution. For this problem, a multi-agent double deep Q-network (MA-DDQN) solution is proposed, in which FAPs exchange information about their experiences after every learning step. The method is tested in a simulated F-RAN environment and its performance is discussed.
While focusing on allocating resources provides satisfactory performance it has been shown to only be a half of the complete solution. Since FAPs are capable of caching data or applications close to the user, controlling what is being cached is equally as important. This makes the problem a capacitated facility location problem (CFLP). A method is proposed that chooses which services to cache in the fog layer in the first step and how to distribute the content to the users in the second step. An algorithm based on MA-DDQN, termed focused batch DDQN (FB-DDQN) is used to solve the optimisation problem of improving the cache hit ratio in the fog layer. FB-DDQN focuses on areas of the state space that the agents know they are uncertain with and requests other agents to share their solutions to similar states, which improves the generalisation of the less knowledgeable agent. The results are compared to the base MA-DDQN algorithm, as well as a RL algorithm that is written for this task
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