Semi-Static Cell Differentiation and Integration with Dynamic BBU-RRH Mapping in Cloud Radio Access Network

Abstract—In this paper, a Self-Organising Cloud Radio Access Network is proposed, which dynamically adapt to varying network capacity demands. A load prediction model is considered for provisioning and allocation of Base Band Units (BBUs) and Remote Radio Heads (RRHs). The density of active BBUs and RRHs is scaled based on the concept of cell differentiation and integration (CDI) aiming efficient resource utilisation without sacrificing the overall QoS. A CDI algorithm is proposed in which a semi-static CDI and dynamic BBU-RRH mapping for load balancing are performed jointly. Network load balance is formulated as a linear integer-based optimisation problem with constraints.The semi-static part of CDI algorithm selects proper BBUs and RRHs for activation/deactivation after a fixed CDI cycle, and the dynamic part performs proper BBU to RRH mapping for network load balancing aiming maximum Quality of Service (QoS) with minimum possible handovers. A Discrete Particle Swarm Optimisation (DPSO) is developed as an Evolutionary Algorithm (EA) to solve network load balancing optimisation problem. The performance of DPSO is tested based on two problem scenarios and compared to Genetic Algorithm (GA) and the Exhaustive Search (ES) algorithm. The DPSO is observed to deliver optimum performance for small-scale networks and near optimum performance for large-scale networks. The DPSO has less complexity and is much faster than GA and ES algorithms. Computational results of a CDI-enabled C-RAN demonstrate significant throughput improvement compared to a fixed C-RAN, i.e., an average throughput increase of 45.53% and 42.102%, and an average blocked users reduction of 23.149%, and 20.903% is experienced for Proportional Fair (PF) and Round Robin (RR) schedulers, respectivel

Mobility prediction and multicasting in wireless networks : performance and analysis

Handoff is a call handling mechanism that is invoked when a mobile node moves from one cell to another. Such movement may lead to degradation in performance for wireless networks as a result of packet losses. A promising technique proposed in this thesis is to apply multicasting techniques aided by mobility prediction in order to improve handoff performance. In this thesis, we present a method that uses a Grey model for mobility prediction and a fuzzy logic controller that has been fine-tuned using evolutionary algorithms in order to improve prediction accuracy. We also compare the self-tuning algorithm with two evolutionary algorithms in terms of accuracy and their convergence times. Our proposed method takes into account signal strengths from the base stations and predicts the signal strength of the next candidate base station in order to provide improved handover performance. The primary decision for mobility prediction is the accurate prediction of signal strengths obtained from the base stations and remove any unwanted errors in the prediction using suitable optimisation techniques. Furthermore, the model includes the procedures of fine-tuning the predicted data using fuzzy parameters. We also propose suitable multicasting algorithms to minimise the reservation of overall network resource requirements during handoff with the mobility prediction information. To be able to efficiently solve the problem, the situation is modelled using a multicast tree that is defined to maintain connectivity with the mobile node, whilst ensuring bandwidth guarantees and a minimum hop-count. In this approach, we have tried to solve the problem by balancing two objectives through putting a weight on each of two costs. We provide a detailed description of an algorithm to implement join and prune mechanisms, which will help to build an optimal multicast tree with QoS requirements during handoff as well as incorporating dynamic changes in the positions of mobile nodes. An analysis of how mobility prediction helps in the selection of potential Access Routers (AR) with QoS requirements - which affects the multicast group size and bandwidth cost of the multicast tree -- is presented. The proposed technique tries to minimise the number of multicast tree join and prune operations. Our results show that the expected size of the multicast group increases linearly with an increase in the number of selected destination AR's for multicast during handoff. We observe that the expected number of joins and prunes from the multicast tree increases with group size. A special simulation model was developed to demonstrate both homogeneous and heterogeneous handoff which is an emerging requirement for fourth generation mobile networks. The model incorporates our mobility prediction model for heterogeneous handoff between the Wireless LAN and a cellular network. The results presented in this thesis for mobility prediction, multicasting techniques and heterogeneous handoff include proposed algorithms and models which aid in the understanding, analysing and reducing of overheads during handoff

Journal of Telecommunications and Information Technology, 2014, nr 3

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Spectrum Sharing, Latency, and Security in 5G Networks with Application to IoT and Smart Grid

The surge of mobile devices, such as smartphones, and tables, demands additional capacity. On the other hand, Internet-of-Things (IoT) and smart grid, which connects numerous sensors, devices, and machines require ubiquitous connectivity and data security. Additionally, some use cases, such as automated manufacturing process, automated transportation, and smart grid, require latency as low as 1 ms, and reliability as high as 99.99\%. To enhance throughput and support massive connectivity, sharing of the unlicensed spectrum (3.5 GHz, 5GHz, and mmWave) is a potential solution. On the other hand, to address the latency, drastic changes in the network architecture is required. The fifth generation (5G) cellular networks will embrace the spectrum sharing and network architecture modifications to address the throughput enhancement, massive connectivity, and low latency. To utilize the unlicensed spectrum, we propose a fixed duty cycle based coexistence of LTE and WiFi, in which the duty cycle of LTE transmission can be adjusted based on the amount of data. In the second approach, a multi-arm bandit learning based coexistence of LTE and WiFi has been developed. The duty cycle of transmission and downlink power are adapted through the exploration and exploitation. This approach improves the aggregated capacity by 33\%, along with cell edge and energy efficiency enhancement. We also investigate the performance of LTE and ZigBee coexistence using smart grid as a scenario. In case of low latency, we summarize the existing works into three domains in the context of 5G networks: core, radio and caching networks. Along with this, fundamental constraints for achieving low latency are identified followed by a general overview of exemplary 5G networks. Besides that, a loop-free, low latency and local-decision based routing protocol is derived in the context of smart grid. This approach ensures low latency and reliable data communication for stationary devices. To address data security in wireless communication, we introduce a geo-location based data encryption, along with node authentication by k-nearest neighbor algorithm. In the second approach, node authentication by the support vector machine, along with public-private key management, is proposed. Both approaches ensure data security without increasing the packet overhead compared to the existing approaches

Mobile Ad Hoc Networks

Guiding readers through the basics of these rapidly emerging networks to more advanced concepts and future expectations, Mobile Ad hoc Networks: Current Status and Future Trends identifies and examines the most pressing research issues in Mobile Ad hoc Networks (MANETs). Containing the contributions of leading researchers, industry professionals, and academics, this forward-looking reference provides an authoritative perspective of the state of the art in MANETs. The book includes surveys of recent publications that investigate key areas of interest such as limited resources and the mobility of mobile nodes. It considers routing, multicast, energy, security, channel assignment, and ensuring quality of service. Also suitable as a text for graduate students, the book is organized into three sections: Fundamentals of MANET Modeling and Simulation—Describes how MANETs operate and perform through simulations and models Communication Protocols of MANETs—Presents cutting-edge research on key issues, including MAC layer issues and routing in high mobility Future Networks Inspired By MANETs—Tackles open research issues and emerging trends Illustrating the role MANETs are likely to play in future networks, this book supplies the foundation and insight you will need to make your own contributions to the field. It includes coverage of routing protocols, modeling and simulations tools, intelligent optimization techniques to multicriteria routing, security issues in FHAMIPv6, connecting moving smart objects to the Internet, underwater sensor networks, wireless mesh network architecture and protocols, adaptive routing provision using Bayesian inference, and adaptive flow control in transport layer using genetic algorithms

Mobile Ad Hoc Networks

Guiding readers through the basics of these rapidly emerging networks to more advanced concepts and future expectations, Mobile Ad hoc Networks: Current Status and Future Trends identifies and examines the most pressing research issues in Mobile Ad hoc Networks (MANETs). Containing the contributions of leading researchers, industry professionals, and academics, this forward-looking reference provides an authoritative perspective of the state of the art in MANETs. The book includes surveys of recent publications that investigate key areas of interest such as limited resources and the mobility of mobile nodes. It considers routing, multicast, energy, security, channel assignment, and ensuring quality of service. Also suitable as a text for graduate students, the book is organized into three sections: Fundamentals of MANET Modeling and Simulation—Describes how MANETs operate and perform through simulations and models Communication Protocols of MANETs—Presents cutting-edge research on key issues, including MAC layer issues and routing in high mobility Future Networks Inspired By MANETs—Tackles open research issues and emerging trends Illustrating the role MANETs are likely to play in future networks, this book supplies the foundation and insight you will need to make your own contributions to the field. It includes coverage of routing protocols, modeling and simulations tools, intelligent optimization techniques to multicriteria routing, security issues in FHAMIPv6, connecting moving smart objects to the Internet, underwater sensor networks, wireless mesh network architecture and protocols, adaptive routing provision using Bayesian inference, and adaptive flow control in transport layer using genetic algorithms

An Intelligent Mobility Prediction Scheme for Location-Based Service over Cellular Communications Network

One of the trickiest challenges introduced by cellular communications networks is mobility prediction for Location Based-Services (LBSs). Hence, an accurate and efficient mobility prediction technique is particularly needed for these networks. The mobility prediction technique incurs overheads on the transmission process. These overheads affect properties of the cellular communications network such as delay, denial of services, manual filtering and bandwidth. The main goal of this research is to enhance a mobility prediction scheme in cellular communications networks through three phases. Firstly, current mobility prediction techniques will be investigated. Secondly, innovation and examination of new mobility prediction techniques will be based on three hypothesises that are suitable for cellular communications network and mobile user (MU) resources with low computation cost and high prediction success rate without using MU resources in the prediction process. Thirdly, a new mobility prediction scheme will be generated that is based on different levels of mobility prediction. In this thesis, a new mobility prediction scheme for LBSs is proposed. It could be considered as a combination of the cell and routing area (RA) prediction levels. For cell level prediction, most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shape cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. Such techniques are the New Markov-Based Mobility Prediction (NMMP) and Prediction Location Model (PLM) that deal with inner cell structure and different levels of prediction, respectively. The NMMP and PLM techniques suffer from complex computation, accuracy rate regression and insufficient accuracy. In this thesis, Location Prediction based on a Sector Snapshot (LPSS) is introduced, which is based on a Novel Cell Splitting Algorithm (NCPA). This algorithm is implemented in a micro cell in parallel with the new prediction technique. The LPSS technique, compared with two classic prediction techniques and the experimental results, shows the effectiveness and robustness of the new splitting algorithm and prediction technique. In the cell side, the proposed approach reduces the complexity cost and prevents the cell level prediction technique from performing in time slots that are too close. For these reasons, the RA avoids cell-side problems. This research discusses a New Routing Area Displacement Prediction for Location-Based Services (NRADP) which is based on developed Ant Colony Optimization (ACO). The NRADP, compared with Mobility Prediction based on an Ant System (MPAS) and the experimental results, shows the effectiveness, higher prediction rate, reduced search stagnation ratio, and reduced computation cost of the new prediction technique