Towards 5G Cellular: Understanding 3D In-Building Single Band and Multi-band Small Cells with Control/User-plane Coupled and Separation Architectures with a Novel Resource Reuse Approach

In this paper, we present numerous small cell base station, i.e. femtocell base station (FCBS), with control-/user-plane coupled and separation architectures based on the number of transceivers and operating frequency bands to serve control-/user-plane traffic. A single transceiver enabled FCBS can operate at either a co-channel microwave of the overlaid macrocell or a millimeter wave band. For multiple transceivers, dual transceivers are considered operating at both bands. FCBSs are deployed in a number of buildings with each floor modeled as 5×5 square-grid apartments. The co-channel interference with FCBSs is avoided using enhanced intercell interference coordination techniques. We propose a static frequency reuse approach and develop an algorithm by avoiding adjacent channel interferences from reusing frequencies in FCBSs. We also develop a resource scheduling algorithm for FCBSs with CUCA and CUSA to evaluate system level performances with a multi-tier network. It is found that a single transceiver co-channel microwave enabled FCBS with CUCA provides the worse, whereas a single or dual transceivers millimeter wave enabled FCBS with CUSA provides the best overall system capacity and FCBSs’ energy efficiency performances. Besides, we show the outperformances of the proposed resource reuse approach over an existing approach in literature in terms of system capacity and fairness among FCBSs with CUCA. Finally, we point out the applicability of a multi-band enabled FCBS and several features and issues of FCBSs with CUCA and CUSA.In this paper, we present numerous small cell base station, i.e. femtocell base station (FCBS), with control-/user-plane coupled and separation architectures based on the number of transceivers and operating frequency bands to serve control-/user-plane traffic. A single transceiver enabled FCBS can operate at either a co-channel microwave of the overlaid macrocell or a millimeter wave band. For multiple transceivers, dual transceivers are considered operating at both bands. FCBSs are deployed in a number of buildings with each floor modeled as 5 by 5 square-grid apartments. The co-channel interference with FCBSs is avoided using enhanced intercell interference coordination techniques. We propose a static frequency reuse approach and develop an algorithm by avoiding adjacent channel interferences from reusing frequencies in FCBSs. We also develop a resource scheduling algorithm for FCBSs with CUCA and CUSA to evaluate system level performances with a multi-tier network. It is found that a single transceiver co-channel microwave enabled FCBS with CUCA provides the worse, whereas a single or dual transceivers millimeter wave enabled FCBS with CUSA provides the best overall system capacity and FCBSs' energy efficiency performances. Besides, we show the outperformances of the proposed resource reuse approach over an existing approach in literature in terms of system capacity and fairness among FCBSs with CUCA. Finally, we point out the applicability of a multi-band enabled FCBS and several features and issues of FCBSs with CUCA and CUSA

Project Final Report – FREEDOM ICT-248891

This document is the final publishable summary report of the objective and work carried out within the European Project FREEDOM, ICT-248891.This document is the final publishable summary report of the objective and work carried out within the European Project FREEDOM, ICT-248891.Preprin

Interference Aware Cognitive Femtocell Networks

Femtocells Access Points (FAP) are low power, plug and play home base stations which are designed to extend the cellular radio range in indoor environments where macrocell coverage is generally poor. They offer significant increases in data rates over a short range, enabling high speed wireless and mobile broadband services, with the femtocell network overlaid onto the macrocell in a dual-tier arrangement. In contrast to conventional cellular systems which are well planned, FAP are arbitrarily installed by the end users and this can create harmful interference to both collocated femtocell and macrocell users. The interference becomes particularly serious in high FAP density scenarios and compromises the ensuing data rate. Consequently, effective management of both cross and co-tier interference is a major design challenge in dual-tier networks. Since traditional radio resource management techniques and architectures for single-tier systems are either not applicable or operate inefficiently, innovative dual-tier approaches to intelligently manage interference are required. This thesis presents a number of original contributions to fulfill this objective including, a new hybrid cross-tier spectrum sharing model which builds upon an existing fractional frequency reuse technique to ensure minimal impact on the macro-tier resource allocation. A new flexible and adaptive virtual clustering framework is then formulated to alleviate co-tier interference in high FAP densities situations and finally, an intelligent coverage extension algorithm is developed to mitigate excessive femto-macrocell handovers, while upholding the required quality of service provision. This thesis contends that to exploit the undoubted potential of dual-tier, macro-femtocell architectures an interference awareness solution is necessary. Rigorous evidence confirms that noteworthy performance improvements can be achieved in the quality of the received signal and throughput by applying cognitive methods to manage interference

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Recent advances in radio resource management for heterogeneous LTE/LTE-A networks

As heterogeneous networks (HetNets) emerge as one of the most promising developments toward realizing the target specifications of Long Term Evolution (LTE) and LTE-Advanced (LTE-A) networks, radio resource management (RRM) research for such networks has, in recent times, been intensively pursued. Clearly, recent research mainly concentrates on the aspect of interference mitigation. Other RRM aspects, such as radio resource utilization, fairness, complexity, and QoS, have not been given much attention. In this paper, we aim to provide an overview of the key challenges arising from HetNets and highlight their importance. Subsequently, we present a comprehensive survey of the RRM schemes that have been studied in recent years for LTE/LTE-A HetNets, with a particular focus on those for femtocells and relay nodes. Furthermore, we classify these RRM schemes according to their underlying approaches. In addition, these RRM schemes are qualitatively analyzed and compared to each other. We also identify a number of potential research directions for future RRM development. Finally, we discuss the lack of current RRM research and the importance of multi-objective RRM studies

Efficient radio resource management for future generation heterogeneous wireless networks

The heterogeneous deployment of small cells (e.g., femtocells) in the coverage area of the traditional macrocells is a cost-efficient solution to provide network capacity, indoor coverage and green communications towards sustainable environments in the future fifth generation (5G) wireless networks. However, the unplanned and ultra-dense deployment of femtocells with their uncoordinated operations will result in technical challenges such as severe interference, a significant increase in total energy consumption, unfairness in radio resource sharing and inadequate quality of service provisioning. Therefore, there is a need to develop efficient radio resource management algorithms that will address the above-mentioned technical challenges. The aim of this thesis is to develop and evaluate new efficient radio resource management algorithms that will be implemented in cognitive radio enabled femtocells to guarantee the economical sustainability of broadband wireless communications and users' quality of service in terms of throughput and fairness. Cognitive Radio (CR) technology with the Dynamic Spectrum Access (DSA) and stochastic process are the key technologies utilized in this research to increase the spectrum efficiency and energy efficiency at limited interference. This thesis essentially investigates three research issues relating to the efficient radio resource management: Firstly, a self-organizing radio resource management algorithm for radio resource allocation and interference management is proposed. The algorithm considers the effect of imperfect spectrum sensing in detecting the available transmission opportunities to maximize the throughput of femtocell users while keeping interference below pre-determined thresholds and ensuring fairness in radio resource sharing among users. Secondly, the effect of maximizing the energy efficiency and the spectrum efficiency individually on radio resource management is investigated. Then, an energy-efficient radio resource management algorithm and a spectrum-efficient radio resource management algorithm are proposed for green communication, to improve the probabilities of spectrum access and further increase the network capacity for sustainable environments. Also, a joint maximization of the energy efficiency and spectrum efficiency of the overall networks is considered since joint optimization of energy efficiency and spectrum efficiency is one of the goals of 5G wireless networks. Unfortunately, maximizing the energy efficiency results in low performance of the spectrum efficiency and vice versa. Therefore, there is an investigation on how to balance the trade-off that arises when maximizing both the energy efficiency and the spectrum efficiency simultaneously. Hence, a joint energy efficiency and spectrum efficiency trade-off algorithm is proposed for radio resource allocation in ultra-dense heterogeneous networks based on orthogonal frequency division multiple access. Lastly, a joint radio resource allocation with adaptive modulation and coding scheme is proposed to minimize the total transmit power across femtocells by considering the location and the service requirements of each user in the network. The performance of the proposed algorithms is evaluated by simulation and numerical analysis to demonstrate the impact of ultra-dense deployment of femtocells on the macrocell networks. The results show that the proposed algorithms offer improved performance in terms of throughput, fairness, power control, spectrum efficiency and energy efficiency. Also, the proposed algorithms display excellent performance in dynamic wireless environments