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

Interference management and system optimisation for Femtocells technology in LTE and future 4G/5G networks

Femtocells are seen to be the future of Long Term Evaluation (LTE) networks to improve the performance of indoor, outdoor and cell edge User Equipments (UEs). These small cells work efficiently in areas that suffer from high penetration loss and path-loss to improve the coverage area. It is said that 30% of total served UEs in LTE networks are vehicular, which poses challenges in LTE networks due to their high mobility, high vehicular penetration loss (VPL), high path loss and high interference. Therefore, self-optimising and dynamic solutions are required to incorporate more intelligence into the current standard of LTE system. This makes the network more adaptive, able to handle peak data demands and cope with the increasing capacity for vehicular UEs. This research has drawn a performance comparison between vehicular UEs who are served by Mobile-Femto, Fixed-Femto and eNB under different VPL scales that range between highs and lows e.g. 0dB, 25dB and 40dB. Deploying Mobile-Femto under high VPLs has improved the vehicular UE Ergodic capacity by 1% and 5% under 25dB and 40dB VPL respectively as compared to other eNB technologies. A noticeable improvement is also seen in signal strength, throughput and spectral efficiency. Furthermore, this research discusses the co-channel interference between the eNB and the Mobile-Femto as both share the same resources and bandwidth. This has created an interference issue from the downlink signals of each other to their UEs. There were no previous solutions that worked efficiently in cases where UEs and base stations are mobile. Therefore, this research has adapted an efficient frequency reuse scheme that worked dynamically over distance and achieved improved results in the signal strength and throughput of Macro and Mobile-Femto UE as compared to previous interference management schemes e.g. Fractional Frequency Reuse factor1 (NoFFR-3) and Fractional Frequency Reuse factor3 (FFR-3). Also, the achieved results show that implementing the proposed handover scheme together with the Mobile-Femto deployment has reduced the dropped calls probability by 7% and the blocked calls probability by 14% compared to the direct transmission from the eNB. Furthermore, the outage signal probabilities under different VPLs have been reduced by 1.8% and 2% when the VPLs are 25dB and 40dB respectively compared to other eNB technologies

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