Contribution to the optimization of 4G mobile communications by means of advanced carrier aggregation strategies

Mobile broadband subscriptions and data traffic have increasingly grown in the past years with the deployment of the 3G and 4G technologies and the massive use of mobile devices. In this sense, LTE-A has been presented as the next step in wireless communications where higher data rates are targeted and fully packet switched services are held. The ultimate goal of 4G and the forthcoming 5G technology is to increase the Quality of Experience (QoE) of users. In this context, several challenges open up to face the increased bandwidth demands in both uplink (UL) and downlink (DL). To this end, LTE-A has proposed the use of Carrier Aggregation (CA) which allows the simultaneous data transmission in separate fragments of spectrum. The improvements brought by CA in the DL can be almost straightforward appreciable, since the evolved Node B (eNB) is in charge of transmissions, and power availability is not typically an issue. Conversely, the UL presents many open challenges to introduce aggregated transmissions, since it relies on the user terminal for transmission procedures. Lower transmission power and increased interference variability turn the UL more complex than the DL. For this reason, this Ph.D. thesis provides a contribution to the field of CA for UL mobile systems. The novelties here presented address the main limitations the UL encounters when introducing CA; new methods and strategies are proposed with the final aim of enhancing the UL communications with the use of increased bandwidth transmissions, and reducing the unbalanced data rate between the UL and DL. Throughout an exhaustive literature review, the main research opportunities to successfully implement CA in the UL were identified. In particular, three main blocks can be recognized. First, the need for introducing intelligent Radio Resource Management procedures that provide the user with increased QoE, specially in the cell edge, where users are more likely to be power limited, and CA is typically discarded. Consequently, the first part of this dissertation places emphasis on topics related to scheduling and the power limitations to face the increased bandwidth. In this sense, mechanisms that tackle the throughput improvement are proposed and scheduling schemes that specifically assess the gain or deterioration of CA are designed. Indeed, these strategies strongly rely on an accurate Channel State Information (CSI); it is of utmost importance to possess precise CSI to effectively support these assessments. In this line, the second part deals with the imperfect CSI where the efficient use of reference signals provides a high value. Channel prediction techniques have been proposed with the use of the splines method. However, the increased variability of interferences and the high delay in measurements still impairs the CSI accuracy. In this manner, interference management methods are introduced to support the CSI acquisition process. Finally, since CA constitutes the most transverse topic of the new features added to the 4G standard, the last block of research focuses on the opportunities that emerge with the use of CA in the context of heterogeneous networks, and new system designs are addressed. It is proposed to use dual connectivity in the form of decoupled uplink and downlink connections in a CA context, where aggregated carriers may have different coverage footprints. An analysis of two different cell association cases that arise has been driven. Stochastic geometry is used to study the system analytically, propagation conditions in the different tiers and frequencies are considered and the different association cases are compared to a classical downlink received power association rule. Conclusions show that decoupling the uplink provides the system with outstanding gains, however, being connected to the cell that receives the highest received power may not always be profitable, since issues like interferences or load conditions shall be also considered.El número de usuarios móviles y el tráfico de datos generado han aumentado en los últimos años con el despliegue de redes 3G y 4G y el uso masivo de dispositivos móviles. De este modo, LTE-A surge como el siguiente escalón de las comunicaciones móviles, dónde se apunta a mayores velocidades de transmisión y los servicios se basan en la conmutación de paquetes. El objetivo principal de las redes 4G y de la inminente red 5G es mejorar la experiencia del usuario. En este contexto, se presentan nuevos retos para hacer frente a las demandas de incrementar el ancho de banda en ambos enlaces: ascendente (UL) y descendente (DL). Por ello, LTE-A propone el uso de portadoras agregadas (Carrier Aggregation (CA)), tecnología que permite la transmisión simultánea en dos fragmentos del espectro. Las mejoras que aporta CA en el DL son casi inmediatas dado que las transmisiones corren a cargo de la base, la cual no sufre la falta de potencia. Al contrario, el UL presenta más retos para introducir CA, ya que es el terminal quién se encarga de la transmisión. La baja disponibilidad de potencia y la alta variabilidad de la interferencia lo convierten en un entorno mucho más complejo. Por ello, esta disertación presenta una contribución al campo de CA en el UL de comunicaciones móviles. Las novedades presentadas tratan las principales limitaciones para incorporar CA; se proponen nuevos métodos y estrategias con el objetivo de mejorar las comunicaciones en el UL mediante el uso de CA; todo ello, para reducir el desajuste que existe entre la velocidad de transmisión del UL y DL. Mediante una extensa revisión de la literatura, se han detectado las principales líneas de investigación y potenciales mejoras para incorporar CA exitosamente. Se han identificado tres grandes bloques de investigación. Primero, la necesidad de introducir estrategias de gestión de recursos inteligentes, que proporcionen al usuario una mejora de la experiencia, especialmente en el límite de la celda. Es allí donde los usuarios tienen una mayor probabilidad de estar limitados en potencia, razón por la que se les aparta de CA. Consecuentemente, la primera parte de esta tesis pone énfasis en la asignación de recursos y las limitaciones en potencia por parte del usuario para hacer frente a un incremento del ancho de banda. Se proponen mecanismos que mejoran la velocidad de transmisión evaluando las ganancias o pérdidas de incorporar CA a la transmisión. Para apoyar el funcionamiento de estas estrategias de asignación, y asegurar su máximo rendimiento, es necesario un método que proporcione un conocimiento preciso y fidedigno del estado del canal (Channel State Information (CSI)). De este modo, la segunda parte de la investigación lidia con el CSI, donde el uso eficiente de las señales de referencia es de gran importancia. Se proponen técnicas de predicción de señal mediante el uso de Splines; sin embargo, la alta variabilidad de las interferencias y el gran retardo entre dos muestras de CSI perjudican la precisión. Por ello, se introducen métodos de gestión de interferencias que apoyan el proceso de adquisición del CSI. Finalmente, dado que CA es una de las funciones más transversales de las introducidas por el estándar 4G, la última parte de investigación se centra en las oportunidades que surgen con su uso en las redes heterogéneas. Se propone el uso de la conectividad dual, desacoplando el UL del DL junto con CA, donde el área de cobertura de las portadoras puede ser diferente. Se analizan dos escenarios de asociación posibles. Con el uso de geometría estocástica se estudia analíticamente el sistema, considerando diferentes condiciones de propagación en los distintos tipos de celda y frecuencias; los escenarios de asociación se comparan a uno tradicional, en el cual los usuarios se asocian en función de la potencia recibida de las bases. Las conclusiones destacan que el desacoplo aporta mejoras en el UL. Sin embargo, temas como interferencias o carga deben también considera

Kanavien yhdistämistekniikan suorituskyvyn arviointi edistyneissä LTE-järjestelmissä

Carrier Aggregation (CA) is an essential technology component in LTE-Advanced (LTE-A). CA is capable of combining up to five Long Term Evolution (LTE) carriers to be used for multicarrier transmission in both downlink and uplink. CA provides increased throughputs, additional capacity and possibilities for load balancing. This thesis presents the key features of CA. Furthermore, the results from CA performance measurements are analyzed and presented. The measurements were conducted in live network to evaluate the end-user experience. The objective was to determine whether CA is capable of delivering the performance that could be theoretically expected. The performance was measured in LTE-A radio network using 2x20 MHz bandwidth with 2x2 MIMO configuration and Category 6 User Equipment (UE). Only downlink CA was measured, since uplink CA capable UEs were not commercially available. The performance was evaluated with stationary and mobility measurements. The results indicate that CA is capable of providing the expected performance gain. In good radio conditions the maximum downlink throughput is close to the 300 Mbit/s. Furthermore, CA performs well in poor radio conditions. The performance gain can be more than 100 %, if the additional carrier is on an unused band. In CA, a secondary component carrier is configured for the UE, in addition to the primary carrier. The operation is performed in connected state either after connection setup or radio handover. The delay in secondary carrier addition was measured to evaluate the impact on user experience. The results indicate that the secondary carrier addition after connection setup or handover is sufficiently fast, and do not have an impact to the user experience.Kanavien yhdistäminen (engl. Carrier Aggregation) on oleellinen tekniikka edistyneessä Long Term Evolution järjestelmässä. Sen avulla on mahdollista yhdistää enintään viisi LTE taajuutta käytettäväksi monikanavaiseen ala- ja ylälinkin lähetykseen. CA mahdollistaa aiempaa suuremmat siirtonopeudet, lisää verkon kapasiteettia sekä antaa mahdollisuuden kuormanjakoon eri taajuuksien välillä. Tässä työssä esitellään CA:n keskeiset ominaisuudet. Lisäksi CA:n suorituskykymittauksien tulokset on analysoitu ja esitelty. Mittaukset toteutettiin operaattorin tuotantoverkossa, jotta loppukäyttäjän saamaa kokemusta on mahdollista arvioida. Tavoitteena oli selvittää, pystyykö CA tarjoamaan sellaista suorituskykyä, jota voidaan teorian perusteella odottaa. Suorituskykyä mitattiin LTE-radioverkossa käyttäen 2x20 MHz kaistanleveyttä ja 2x2 MIMO:n kokoonpanoa sekä kategorian 6 päätelaitetta. Mittaukset suoritettiin vain alalinkissä, sillä ylälinkin CA-kykyisiä päätelaitteita ei ollut kaupallisesti saatavilla. Suorituskykyä on arvioitu sekä piste- että mobiliteettimittauksilla. Tulokset osoittavat, että CA pystyy tarjoamaan oletetun suorituskyvyn parannuksen. Hyvissä radio olosuhteissa maksimi datanopeus alalinkissä on lähes 300 Mbit/s. Lisäksi CA toimii hyvin myös huonoissa radio-olosuhteissa. Suorituskyvyn parannus voi olla enemmän kuin 100 %, jos lisätty toinen kanava on vähemmän käytetyllä taajuuskaistalla. CA:ssa toissijainen kanava konfiguroidaan päätelaitteelle ensisijaisen lisäksi. Operaatio suoritetaan yhteystilassa joko yhteyden muodostamisen tai solunvaihdon jälkeen. Toissijaisen kanavan lisäämisen aiheuttama viive mitattiin, jotta sen vaikutus käyttökokemukseen voitiin arvioida. Tulokset osoittavat, että toissijaisen kanavan lisääminen yhteyden muodostamisen tai solunvaihdon jälkeen on riittävän nopea operaatio, eikä sillä ole vaikutusta käyttökokemukseen

Neural network design for intelligent mobile network optimisation

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe mobile networks users’ demands for data services are increasing exponentially, this is due to two main factors: the first is the evolution of smart phones and their application, and the second is the emerging new technologies for internet of things, smart cities…etc, which keeps pumping more data into the network; ‘though most of the data routed in the current mobile network is non-live data’. This increasing of demands arise the necessity for the mobile network operators to keep improving their network to satisfy it, this improvement takes place via adding hardware or increasing the resources or a combination of both. The radio resources are strictly limited due to spectrum licensing and availability, therefore efficient spectrum utilization is a major goal to be achieved for both network operators and developers. Simultaneous and multiple channel access,and adding more cells to the network are ways used to increase the data exchanged between the network nodes. The current 4G mobile system is based on the Orthogonal Frequency Division Multiple Access (OFDMA) for accessing the medium and the intercell interference degrades the link quality at the cell edge, with the introduction of heterogeneity concept to the LTE in Release 10 of the 3GPP the handover process became even more complex. To mitigate the intercell interference at the cell edge, coordinated multipoint and carrier aggregation techniques are utilized for dual connectivity. This work is focused on designing and proposing enhancing features to improve network performance and sustainability, these features comprises of distributing small cells for data only transmission, handover schemes performance evaluation at cell edge with dual connectivity, and Artificial Intelligence technology for balancing and prediction. In the proposed model design the data and controls of the Small eNodeB (SeNodeB) are processed at the network edge using a Mobile Edge Computing (MEC) server and the SeNodeBs are used to boost services provided to the users, also the concept of caching data has been investigated, the caching units where implemented in different network levels. The proposed system and resource management are simulated using the OPNET modeller and evaluated through multiple scenarios with and without full load, the UE is reconfigured to accommodate dual connectivity and have two separate connections for uplink and downlink, while maintaining connection to the Macro cell via uplink, the downlink is dedicated for small cells when content is requested from the cache. The results clearly show that the proposed system can decrease the latency while the total throughput delivered by the network has highly improved when SeNodeBs are deployed in the system, rising throughput will incur the rise of overall capacity which leads to better services being provided to the users or more users to join and benefit from the network. Handover improvement is also considered in this work, with the help of two Artificial Intelligence (AI) entities better handover performance are achieved. Balanced load over the SeNodeBs results in less frequent handover, the proposed load balancer is based on artificial neural network clustering model with self-organizing map as a hidden layer, it’s trained to forecast the network condition and learn to reduce the number of handovers especially for the UEs at the cell edge by performing only necessary ones, and avoid handovers to the Macro cell for the downlink direction. The examined handovers concern the downlinks when routing non live video stored at the small cell’s cache, and a reduction in the frequent handovers was achieved when running the balancer. Keep revolving in the handover orbit, another way to preserve and utilize network resources is by predicting the handovers before they occur, and allocate the required data in the target SeNodeB, the predictor entity in the proposed system architecture combines the features of Radial Basis Function Neural Network and neural network time series tool to create and update prediction list from the system’s collected data and learn to predict the next SeNodeB to associate with. The prediction entity is simulated using MATLAB, and the results shows that the system was able to deliver up to 92% correct predictions for handovers which led to overall throughput improvement of 75%

Aspects of knowledge mining on minimizing drive tests in self-organizing cellular networks

The demand for mobile data traffic is about to explode and this drives operators to find ways to further increase the offered capacity in their networks. If networks are deployed in the traditional way, this traffic explosion will be addressed by increasing the number of network elements significantly. This is expected to increase the costs and the complexity of planning, operating and optimizing the networks. To ensure effective and cost-efficient operations, a higher degree of automation and self-organization is needed in the next generation networks. For this reason, the concept of self-organizing networks was introduced in LTE covering multitude of use cases. This was specifically done in the areas of self-configuration, self-optimization and selfhealing of networks. From an operator’s perspective, automated collection and analysis of field measurements while complementing the traditional drive test campaigns is one of the top use cases that can provide significant cost savings in self-organizing networks. This thesis studies the Minimization of Drive Tests in self-organizing cellular networks from three different aspects. The first aspect is network operations, and particularly the network fault management process, as the traditional drive tests are often conducted for troubleshooting purposes. The second aspect is network functionality, and particularly the technical details about the specified measurement and signaling procedures in different network elements that are needed for automating the collection of the field measurement data. The third aspect concerns the analysis of the measurement databases that is a process used for increasing the degree of automation and self-awareness in the networks, and particularly the mathematical means for autonomously finding meaningful patterns of knowledge from huge amounts of data. Although the above mentioned technical areas have been widely discussed in previous literature, it has been done separately and only a few papers discuss how for example, knowledge mining is employed for processing field measurement data in a way that minimizes the drive tests in self-organizing LTE networks. The objective of the thesis is to use well known knowledge mining principles to develop novel self-healing and self-optimization algorithms. These algorithms analyze MDT databases to detect coverage holes, sleeping cells and other geographical areas of anomalous network behavior. The results of the research suggest that by employing knowledge mining in processing the MDT databases, one can acquire knowledge for discriminating between different network problems and detecting anomalous network behavior. For example, downlink coverage optimization is enhanced by classifying RLF reports into coverage, interference and handover problems. Moreover, by incorporating a normalized power headroom report with the MDT reports, better discrimination between uplink coverage problems and the parameterization problems is obtained. Knowledge mining is also used to detect sleeping cells by means of supervised and unsupervised learning. The detection framework is based on a novel approach where diffusion mapping is used to learn about network behavior in its healthy state. The sleeping cells are detected by observing an increase in the number of anomalous reports associated with a certain cell. The association is formed by correlating the geographical location of anomalous reports with the estimated dominance areas of the cells. Moreover, RF fingerprint positioning of the MDT reports is studied and the results suggest that RF fingerprinting can provide a quite detailed location estimation in dense heterogeneous networks. In addition, self-optimization of the mobility state estimation parameters is studied in heterogeneous LTE networks and the results suggest that by gathering MDT measurements and constructing statistical velocity profiles, MSE parameters can be adjusted autonomously, thus resulting in reasonably good classification accuracy. The overall outcome of the thesis is as follows. By automating the classification of the measurement reports between certain problems, network engineers can acquire knowledge about the root causes of the performance degradation in the networks. This saves time and resources and results in a faster decision making process. Due to the faster decision making process the duration of network breaks become shorter and the quality of the network is improved. By taking into account the geographical locations of the anomalous field measurements in the network performance analysis, finer granularity for estimating the location of the problem areas can be achieved. This can further improve the operational decision making that guides the corresponding actions for example, where to start the network optimization. Moreover, by automating the time and resource consuming task of tuning the mobility state estimation parameters, operators can enhance the mobility performance of the high velocity UEs in heterogeneous radio networks in a cost-efficient and backward compatible manner

Self-organised multi-objective network clustering for coordinated communications in future wireless networks

The fifth generation (5G) cellular system is being developed with a vision of 1000 times more capacity than the fourth generation (4G) systems to cope with ever increasing mobile data traffic. Interference mitigation plays an important role in improving the much needed overall capacity especially in highly interference-limited dense deployment scenarios envisioned for 5G. Coordinated multi-point (CoMP) is identified as a promising interference mitigation technique where multiple base stations (BS) can cooperate for joint transmission/reception by exchanging user/control data and perform joint signal processing to mitigate inter-cell interference and even exploit it as a useful signal. CoMP is already a key feature of long term evolution-advanced (LTE-A) and envisioned as an essential function for 5G. However, CoMP cannot be realized for the whole network due to its computational complexity, synchronization requirement between coordinating BSs and high backhaul capacity requirement. BSs need to be clustered into smaller groups and CoMP can be activated within these smaller clusters. This PhD thesis aims to investigate optimum dynamic CoMP clustering solutions in 5G and beyond wireless networks with massive small cell (SC) deployment. Truly self-organised CoMP clustering algorithms are investigated, aiming to improve much needed spectral efficiency and other network objectives especially load balancing in future wireless networks. Low complexity, scalable, stable and efficient CoMP clustering algorithms are designed to jointly optimize spectral efficiency, load balancing and limited backhaul availability. Firstly, we provide a self organizing, load aware, user-centric CoMP clustering algorithm in a control and data plane separation architecture (CDSA) proposed for 5G to maximize spectral efficiency and improve load balancing. We introduce a novel re-clustering algorithm for user equipment (UE) served by highly loaded cells and show that unsatisfied UEs due to high load can be significantly reduced with minimal impact on spectral efficiency. Clustering with load balancing algorithm exploits the capacity gain from increase in cluster size and also the traffic shift from highly loaded cells to lightly loaded neighbours. Secondly, we develop a novel, low complexity, stable, network-centric clustering model to jointly optimize load balancing and spectral efficiency objectives and tackle the complexity and scalability issues of user-centric clustering. We show that our clustering model provide high spectral efficiency in low-load scenario and better load distribution in high-load scenario resulting in lower number of unsatisfied users while keeping spectral efficiency at comparably high levels. Unsatisfied UEs due to high load are reduced by $68.5\%$ with our algorithm when compared to greedy clustering model. In this context, the unique contribution of this work that it is the first attempt to fill the gap in literature for multi-objective, network-centric CoMP clustering, jointly optimizing load balancing and spectral efficiency. Thirdly, we design a novel multi-objective CoMP clustering algorithm to include backhaul-load awareness and tackle one of the biggest challenges for the realization of CoMP in future networks i.e. the demand for high backhaul bandwidth and very low latency. We fill the gap in literature as the first attempt to design a clustering algorithm to jointly optimize backhaul/radio access load and spectral efficiency and analyze the trade-off between them. We employ 2 novel coalitional game theoretic clustering methods, 1-a novel merge/split/transfer coalitional game theoretic clustering algorithm to form backhaul and load aware BS clusters where spectral efficiency is still kept at high level, 2-a novel user transfer game model to move users between clusters to improve load balancing further. Stability and complexity analysis is provided and simulation results are presented to show the performance of the proposed method under different backhaul availability scenarios. We show that average system throughout is increased by 49.9% with our backhaul-load aware model in high load scenario when compared to a greedy model. Finally, we provide an operator's perspective on deployment of CoMP. Firstly, we present the main motivation and benefits of CoMP from an operator's viewpoint. Next, we present operational requirements for CoMP implementation and discuss practical considerations and challenges of such deployment. Possible solutions for these experienced challenges are reviewed. We then present initial results from a UL CoMP trial and discuss changes in key network performance indicators (KPI) during the trial. Additionally, we propose further improvements to the trialed CoMP scheme for better potential gains and give our perspective on how CoMP will fit into the future wireless networks

Design of a High Capacity, Scalable, and Green Wireless Communication System Leveraging the Unlicensed Spectrum

The stunning demand for mobile wireless data that has been recently growing at an exponential rate requires a several fold increase in spectrum. The use of unlicensed spectrum is thus critically needed to aid the existing licensed spectrum to meet such a huge mobile wireless data traffic growth demand in a cost effective manner. The deployment of Long Term Evolution (LTE) in the unlicensed spectrum (LTE-U) has recently been gaining significant industry momentum. The lower transmit power regulation of the unlicensed spectrum makes LTE deployment in the unlicensed spectrum suitable only for a small cell. A small cell utilizing LTE-L (LTE in licensed spectrum), and LTE-U (LTE in unlicensed spectrum) will therefore significantly reduce the total cost of ownership (TCO) of a small cell, while providing the additional mobile wireless data offload capacity from Macro Cell to small cell in LTE Heterogeneous Networks (HetNet), to meet such an increase in wireless data demand. The U.S. 5 GHz Unlicensed National Information Infrastructure (U-NII) bands that are currently under consideration for LTE deployment in the unlicensed spectrum contain only a limited number of 20 MHZ channels. Thus in a dense multi-operator deployment scenario, one or more LTE-U small cells have to co-exist and share the same 20 MHz unlicensed channel with each other and with the incumbent Wi-Fi. This dissertation presents a proactive small cell interference mitigation strategy for improving the spectral efficiency of LTE networks in the unlicensed spectrum. It describes the scenario and demonstrate via simulation results, that in the absence of an explicit interference mitigation mechanism, there will be a significant degradation in the overall LTE-U system performance for LTE-U co-channel co-existence in countries such as U.S. that do not mandate Listen-Before-Talk (LBT) regulations. An unlicensed spectrum Inter Cell Interference Coordination (usICIC) mechanism is then presented as a time-domain multiplexing technique for interference mitigation for the sharing of an unlicensed channel by multi-operator LTE-U small cells. Through extensive simulation results, it is demonstrated that the proposed usICIC mechanism will result in 40% or more improvement in the overall LTE-U system performance (throughput) leading to increased wireless communication system capacity. The ever increasing demand for mobile wireless data is also resulting in a dramatic expansion of wireless network infrastructure by all service providers resulting in significant escalation in energy consumption by the wireless networks. This not only has an impact on the recurring operational expanse (OPEX) for the service providers, but importantly the resulting increase in greenhouse gas emission is not good for the environment. Energy efficiency has thus become one of the critical tenets in the design and deployment of Green wireless communication systems. Consequently the market trend for next-generation communication systems has been towards miniaturization to meet this stunning ever increasing demand for mobile wireless data, leading towards the need for scalable distributed and parallel processing system architecture that is energy efficient, and high capacity. Reducing cost and size while increasing capacity, ensuring scalability, and achieving energy efficiency requires several design paradigm shifts. This dissertation presents the design for a next generation wireless communication system that employs new energy efficient distributed and parallel processing system architecture to achieve these goals while leveraging the unlicensed spectrum to significantly increase (by a factor of two) the capacity of the wireless communication system. This design not only significantly reduces the upfront CAPEX, but also the recurring OPEX for the service providers to maintain their next generation wireless communication networks

Optimisation of Traffic Steering for Heterogeneous Mobile Networks

Mobile networks have changed from circuit switched to IP-based mobile wireless packet switched networks. This paradigm shift led to new possibilities and challenges. The development of new capabilities based on IP-based networks is ongoing and raises new problems that have to be tackled, for example, the heterogeneity of current radio access networks and the wide range of data rates, coupled with user requirements and behaviour. A typical example of this shift is the nature of traffic, which is currently mostly data-based; further, forecasts based on market and usage trends indicate a data traffic increase of nearly 11 times between 2013 and 2018. The majority of this data traffic is predicted to be multimedia traffic, such as video streaming and live video streaming combined with voice traffic, all prone to delay, jitter, and packet loss and demanding high data rates and a high Quality of Service (QoS) to enable the provision of valuable service to the end-user. While the demands on the network are increasing, the end-user devices become more mobile and end-user demand for the capability of being always on, anytime and anywhere. The combination of end-user devices mobility, the required services, and the significant traffic loads generated by all the end-users leads to a pressing demand for adequate measures to enable the fulfilment of these requirements. The aim of this research is to propose an architecture which provides smart, intelligent and per end-user device individualised traffic steering for heterogeneous mobile networks to cope with the traffic volume and to fulfil the new requirements on QoS, mobility, and real-time capabilities. The proposed architecture provides traffic steering mechanisms based on individual context data per end-user device enabling the generation of individual commands and recommendations. In order to provide valuable services for the end-user, the commands and recommendations are distributed to the end-user devices in real-time. The proposed architecture does not require any proprietary protocols to facilitate its integration into the existing network infrastructure of a mobile network operator. The proposed architecture has been evaluated through a number of use cases. A proof-of-concept of the proposed architecture, including its core functionality, was implemented using the ns-3 network simulator. The simulation results have shown that the proposed architecture achieves improvements for traffic steering including traffic offload and handover. Further use cases have demonstrated that it is possible to achieve benefits in multiple other areas, such as for example improving the energy efficiency, improving frequency interference management, and providing additional or more accurate data to 3rd party to improve their services