OBJECTIVE: Cardiac diseases are a leading cause of global mortality. Electrocardiograms (ECGs) are essential for detecting abnormal cardiac rhythms. Smartwatches can record ECGs, similar to lead I ECGs recorded by a patient vitals monitor in a hospital, potentially helping clinicians in early diagnosis and improved management of cardiovascular diseases. While AI models have classified arrhythmias with human-level accuracy, their potential for broad screening remains underutilized. METHODS: We propose a deep learning based framework for diagnosing various cardiac arrhythmias using 10-second lead I ECG recordings, demonstrating lead I's utility in remote monitoring. Robustness was tested by introducing noise to simulate real-world conditions. Additionally, a novel data similarity assessment metric was developed to enhance transfer learning and external dataset validation. RESULTS: Using over 60,000 ECGs from the PhysioNet Challenge 2021, the trained model classified clean lead I ECGs in one dataset with a test-fold area under receiver operating characteristic curve (AUC), sensitivity, and specificity of 0.915, 0.867 and 0.858 respectively. For signals with 0 dB signal-to-noise ratio from the same dataset, the respective performance metrics dropped slightly to 0.899, 0.862 and 0.818. External validation across three separate datasets showed a minimum AUC of 0.807. The data similarity metric outperformed an existing method in improving classification, particularly with limited target dataset samples, i.e. 50. CONCLUSION: The proposed Cardiac Arrhythmia Risk Evaluation from Lead-I ECG (CARE-I) framework enables accurate arrhythmia detection across diverse populations in real-world noisy environments, thus enhancing model generalisation and early diagnosis.

State-Of-the-art deep transfer learning methods depend on exhaustive, trial-and-error fine-tuning of pre-trained models—a process that is both computationally expensive and unreliable when data in target domain are scarce. To overcome these limitations, we propose a domain-informed fine-tuning strategy built upon a novel Innately-Intelligent Neural Network (IINN) architecture. Unlike how state-of-the-art deep learning models are heuristically constructed, IINN constructs each layer in a domain informed manner by directly mapping the mathematical operations of analytical equations (e.g., 3GPP propagation models) into it’s network architecture prior to any training. This "innate" design strategy inherently aligns each layer with specific physical parameters, making the model fully interpretable. As a result, we can pre-identify the exact layers associated with parameters that change between source and target domains and fine-tune only those—eliminating the need for iterative layer-by-layer retraining. This targeted fine tuning approach reduces computational overhead and data requirements. We validated IINN on radio-propagation modelling for cellular networks, achieving faster adaptation and higher accuracy than the conventional fine-tuning approach. Experimental evaluations demonstrate that our proposed domain-aware transfer learning framework achieves up to 16.4% improvement in sector-based performance and approximately 10.3% gain in adapting to varying base station heights, with overall average gains in the 10–15% range over state-of-the-art DNN transfer learning approaches. The proposed framework offers a promising direction for data-efficient learning in next-generation wireless systems.

Despite significant efforts, AI powered automation in wireless networks remains hindered by several challenges, including lack of interpretability of traditional ML models, lack of resiliency against training data size and training data errors, and the resource-intensive process of determining suitable neural network architecture and hyper-parameters. To address these challenges, we pioneer a novel method by incorporating domain knowledge into the neural network design even before exposure to training data, akin to the innate intelligence in a newborn’s connectome design. We test our approach on both real data and simulated data and benchmark it against two common alternatives for system modeling, analytical modeling and conventional neural networks. Traditional neural networks perform well with abundant training data but struggle significantly when faced with realistically limited data, with the proposed domain-inspired approach achieving a 47.6% reduction in test MSE compared to traditional neural networks, while the pure analytical model-based approach performs the worst.

The trend towards denser base station deployment and multi-band operations in emerging cellular networks has made mobility management and handover (HO) optimization a formidable challenge. The challenge is further aggravated by the scarcity of practical multi-objective mobility management solutions optimizing both intra and inter frequency HO. This paper presents a holistic multi-objective mobility management solution for both intra and inter frequency HO employing multiple parameters of standardized HO events A2, A3, and A5. We formulate a multi-objective optimization problem to determine the optimal parameter settings that jointly optimize four key performance indicators: number of HO failures, HO latency, signaling overhead and number of radio link failures. We leverage soft actor-critic reinforcement learning (RL) to solve the multi-objective problem. To mitigate the risk of performance deterioration resulting from direct interactions between live network and RL-agent during training, this paper proposes a mobility management framework that develops and employs a digital twin (DT) as the training environment. To develop a cellular network DT for mobility management and HO optimization, we present a tri-pronged approach including realistic network deployment, realistic user mobility and 3GPP HO events. Results show that the proposed DT-trained RL solution for the multi-objective optimization can converge 7x faster than the brute force method with negligible loss in the value of the objective function. An analysis of the individual KPI values reveal a strong trade-off between HO signaling overhead and radio link failures.

The conventional network dimensioning and optimization approaches prioritize coverage and capacity as the most vital components. However, handover signaling overhead has emerged as a critical concern in the emerging cellular networks. This is particularly evident with the proliferation of network densification leading to a higher number of handovers. Hence, an optimal cell overlap is vital to ensure retainability and service continuity for the ever-growing fraction of mobile users and the expected cell densification. It is also crucial because the unprecedented signaling overhead can clog both the core network and air interface. To address this challenge, this paper presents an analytical model built on the control data separation architecture (CDSA) to quantify the handover signaling overhead as a function of cell overlap, user speed and cell density. We first compute probabilities for handover failures and successes and model the handover signaling overhead as a Markov chain. Numerical results demonstrate that for a given cell density and user velocity, a suitable cell overlap yields substantial reductions in handover signaling by improving handover success rate. The proposed model has the potential to become an integral element in the network planning process for emerging cellular networks.

The evolution of wireless cellular networks to support Digital Twins (DTs) requires robust propagation models. Traditional propagation modeling methods, though fundamental, lack the realism, completeness, and computational efficiency required for effective DT synchronization. This inadequacy underscores the need for models that can seamlessly integrate with real-world network dynamics. Therefore, this work critically examines the resilience of conventional machine learning based models and highlights their vulnerabilities to data scarcity and the inherent dynamism of wireless networks. To address these challenges, we propose an innovative approach that leverages a multi-stage GAN for the generation and augmentation of tabular synthetic data, coupled with an Attention through Segmentation training strategy. This strategy is based on partitioning the data distribution based on the histogram of important features and replacing the single complex model with multiple simpler models focused on specific parts of the histogram. This dual approach significantly improves the resilience of the model, and our evaluations in realistic scenarios show an impressive recovery of more than $90 \%$ performance loss compared to traditional models and this improvement is achieved with a notable reduction in model complexity. Our research marks a significant advancement in the development of resilient and efficient propagation models for the next generation of wireless networks.

Data-driven Machine Learning (ML) based propagation models are essential for modern wireless network planning and optimization. However, their effectiveness is limited by scarse data conditions. Generative Adversarial Networks (GANs) often considered as a viable approach for data augmentation, struggle in these conditions because they also require large datasets for effective training. To address this challenge, we propose a novel approach that incorporates domain knowledge directly into GAN training. Using an analytical propagation equation based on 3GPP recommendations, we generate pseudo-random data to train a neural network, which then initializes the GAN generator network. This initialization improves the GAN's learning ability in extreme data scarcity. The framework enhances data generation quality by up to 52% and machine learning applicability by 60%, providing a robust solution to the scarse data problem in wireless network modeling with demonstrating the potential of integrating domain knowledge within ML methodologies.

In the rapidly evolving landscape of wireless networks, accurate and resilient propagation models are essential to achieve optimal performance and reliability. This paper presents a novel domain-aware framework for interpretable and resilient propagation models. The proposed approach represents an innovative architecture framework that is not only interpretable but can also deal with training data size scarcity. Bridges domain knowledge with machine learning. The proposed approach leverages a combination of domain expertise, analytical modeling, and customized neural networks to construct interpretable models that excel in both identical distribution and non-identical distribution test-train dataset scenarios. Through a comprehensive analysis, we demonstrate the proposed approach's ability to adapt and refine models in response to real-world variations, ensuring consistent, high-quality performance. The proposed framework not only enhances our understanding of complex systems but also paves the way for the creation of digital twins for wireless networks. Furthermore, the root mean square error of the performance metric for the proposed approach is reported as 6.97 dB, further confirming its effectiveness in accurately predicting the results of wireless propagation.

This paper presents and evaluates a simple but effective approach for substantially reducing inter-frequency handover (HO) failure rate. We build a machine learning model to forecast inter-frequency HO failures. For improved accuracy compared to the state-of-the-art models, we use domain knowledge to identify and leverage the model input features. These features include reference signal received power (RSRP) of the source and target base stations as well as the RSRP of the interferers for both the source and the target layers. Six machine learning classifiers are tested with the highest accuracy of 93% observed for the XGBoost classifier. The novel idea to include the RSRP of the interferes improved the accuracy of XGBoost by 10%.

Existing SINR models are based on best server associations, which represent a network dominated by static users. During the handover (HO), users are not camped on the best-server, resulting in negative SINR. This is important because the emerging networks are likely to have a much higher HO rate due to higher base station density and an increased proportion of mobile devices. We derive a model that characterizes spatio-temporal downlink SINR as a function of BS density, user velocity, and HO delay duration.

The performance of user-centric ultra-dense networks (UCUDNs) hinges on the Service zone (Szone) radius, which is an elastic parameter that balances the area spectral efficiency (ASE) and energy efficiency (EE) of the network. Accurately determining the Szone radius requires the precise location of the user equipment (UE) and data base stations (DBSs). Even a slight error in reported positions of DBSs or UE will lead to an incorrect determination of Szone radius and UE- D BS pairing, leading to degradation of the UE-DBS communication link. To compensate for the positioning error impact and improve the ASE and EE of the UCUDN, this work proposes a data-driven optimization and error compensation (DD-OEC) framework. The framework comprises an additional machine learning model that assesses the impact of residual errors and regulates the erroneous data-driven optimization to output Szone radius, transmit power, and DBS density values which improve network ASE and EE. The performance of the framework is compared to a baseline scheme, which does not employ the residual, and results demonstrate that the DD-OEC framework outperforms the baseline, achieving up to a 23% improvement in performance.

Existing works on user mobility in unmanned aerial vehicle (UAV)/drone-based aerial networks consider either fixed height drone base stations (DBSs), or are limited to two-tier networks only, and do not take into account tier association biasing, which is important for developing intelligent traffic offloading and load balancing schemes to support the diverse use cases enabled by emerging networks. This paper addresses these gaps by analyzing the impact of user mobility in a multi-tier UAV heterogeneous network at varying heights serving ground users and taking into account the biased average receive power association, where each tier has an independent cell range extension (CRE) factor. We evaluate the handover probability of a mobile user by first deriving the distance distributions to the serving DBS and the probability of user association with a DBS of a specific tier. The quantification and insights from our theoretical analysis, corroborated with numerical simulations reveal that the probability of handover (dependent on the CRE factor of the tiers, height of the DBSs and velocity) must be optimized in tandem with coverage probability for optimal network performance.

With highly heterogeneous application requirements, 6G and beyond cellular networks are expected to be demand-driven, elastic, user-centric, and capable of supporting multiple services. A redesign of the one-size-fits-all cellular architecture is needed to support heterogeneous application needs. This paper addresses this need by proposing an intelligent, demand-driven, elastic user-centric cloud radio access network (UCRAN) architecture capable of providing services to a diverse set of use cases ranging from augmented/virtual reality to high-speed rails to industrial robots to E-health applications, and more. The proposed framework leverages deep reinforcement learning to adjust the size of a user-centered virtual cell based on each application’s heterogeneous throughput and latency requirements. Finally, numerical results are presented to validate the convergence and network adaptability of the proposed approach against the brute-force method.

In modern wireless communication systems, radio propagation modeling to estimate pathloss has always been a fundamental task in system design and optimization. The state-of-the-art empirical propagation models are based on measurements in specific environments and limited in their ability to capture idiosyncrasies of various propagation environments. To cope with this problem, ray-tracing based solutions are used in commercial planning tools, but they tend to be extremely time-consuming and expensive. We propose a Machine Learning (ML)-based model that leverages novel key predictors for estimating pathloss. By quantitatively evaluating the ability of various ML algorithms in terms of predictive, generalization and computational performance, our results show that Light Gradient Boosting Machine (LightGBM) algorithm overall outperforms others, even with sparse training data, by providing a 65% increase in prediction accuracy as compared to empirical models and 13x decrease in prediction time as compared to ray-tracing. To address the interpretability challenge that thwarts the adoption of most ML-based models, we perform extensive secondary analysis using SHapley Additive exPlanations (SHAP) method, yielding many practically useful insights that can be leveraged for intelligently tuning the network configuration, selective enrichment of training data in real networks and for building lighter ML-based propagation model to enable low-latency use-cases.

The fifth generation of cellular network (5G) can facilitate in-ambulance patient monitoring, diagnosis, and treatment by a remote specialist. However, 5G coverage and link quality can vary in time and location. The ambulance route selection can help meet the communication requirements of the in-ambulance applications. In this paper, we propose an innovative ambulance route selection framework which combines the communication requirements along with the network coverage and resources. The framework leverages the minimization of drive test (MDT) data to estimate the network coverage along the ambulance routes. To address the uneven distribution of location-based user-generated MDT data, we examine the performance and trustworthiness of several interpolation techniques to enrich the global MDT map for route selection. A simulated analysis shows that the proposed framework can dynamically adapt to varying application requirements as well as rapidly changing network conditions such as outages. Results also reveal that nearest neighbor and kriging interpolation techniques help complement the proposed framework by addressing the data sparsity problem.

In the wake of network densification and multi-band operation in emerging cellular networks, mobility and handover management is becoming a major bottleneck. The problem is further aggravated by the fact that holistic mobility management solutions for different types of handovers, namely inter-frequency and intra-frequency handovers, remain scarce. This paper presents a first mobility management solution that concurrently optimizes inter-frequency related A5 parameters and intra-frequency related A3 parameters. We analyze and optimize five parameters namely A5-time to trigger (TTT), A5-threshold1, A5-threshold2, A3-TTT, and A3-offset to jointly maximize three critical key performance indicators (KPIs): edge user reference signal received power (RSRP), handover success rate (HOSR) and load between frequency bands. In the absence of tractable analytical models due to system level complexity, we leverage machine learning to quantify the KPIs as a function of the mobility parameters. An XGBoost based model has the best performance for edge RSRP and HOSR while random forest outperforms others for load prediction. An analysis of the mobility parameters provides several insights: 1) there exists a strong coupling between A3 and A5 parameters; 2) an optimal set of parameters exists for each KPI; and 3) the optimal parameters vary for different KPIs. We also perform a SHAP based sensitivity to help resolve the parametric conflict between the KPIs. Finally, we formulate a maximization problem, show it is non-convex, and solve it utilizing simulated annealing (SA). Results indicate that ML-based SA-aided solution is more than 14x faster than the brute force approach with a slight loss in optimality.

The user-centric ultra-dense networks (UUDNs) confront the challenge of performance degradation because of the erroneous user equipment (UE), and data base station (DBS) positions estimated at the central controller (CC). This paper adopts the database aided approach to quantify the error impact on system-level key performance indicators (KPIs) under various configuration and optimization parameters (COPs). Although the performance fall is consistent with the increase in error radius of both UEs and DBS positions, its impact can be alleviated by extrapolating on the erroneous database and adopting to new COP values. To realize this, time-series (TS) forecasting is utilized to determine the extent of compensation and COP variations. Results compared for two TS based schemes show that a significant portion, more than 50%, of the decreased performance can be recovered by the suggested adoption in the COP values.

Fault diagnosis is turning out to be an intense challenge due to the increasing complexity of the emerging cellular networks. The root-cause analysis of coverage-related network anomalies is traditionally carried out by human experts. However, due to the vast complexity and the increasing cell density of the emerging cellular networks, it is neither practical nor financially viable. To address this, many studies are proposing artificial intelligence (AI)-based solutions using minimization of drive test (MDT) reports. Nowadays, the focus of existing studies is either on diagnosing faults in a single base station (BS) only or diagnosing a single fault in multiple BS scenarios. Moreover, they do not take into account training data sparsity (varying user equipment (UE) densities). Inspired by the emergence of convolutional neural networks (CNN), in this paper, we propose a framework combining CNN and image inpainting techniques for root-cause analysis of multiple faults in multiple base stations in the network that is robust to the sparse MDT reports, BS locations and types of faults. The results demonstrate that the proposed solution outperforms several other machine learning models on highly sparse UE density training data, which makes it a robust and scalable solution for self-healing in a real cellular network.

Minimization of Drive Test (MDT) reports are a key enabler for Machine Learning (ML)-based zero-touch automation envisioned for emerging cellular networks. However, due to numerous factors, the MDT reports are spatially sparse in nature. This sparsity undermines the performance of ML models that are built on the MDT data to estimate and optimize network KPIs. In this paper, we present and evaluate a framework to address this challenge. We leverage generative models, specifically, Gener-ative Adversarial Networks (GAN) and Variational Autoencoders (VAE) to augment the sparse multi-dimensional MDT data. Unlike image data where the quality of synthetic images produced by the generative models can be evaluated visually, establishing the authenticity of tabular synthetic data is a more complex problem. We address this problem by leveraging a tripartite approach: 1) We use several statistical measures to quantify the resemblance of synthetic data with original data. 2) We compare the performance of an ensemble learning model trained on augmented data, with that of trained on original data only 3) We benchmark the performance of the generative models with several classical ML models. This analysis is carried out for varying levels of sparsity and reveals insights about robustness of generative models against training data sparsity as well as on suitability of various methods for evaluating the quality of the generated synthetic tabular data. Results show GAN performs considerably better compared to other approaches. The presented solution thus can be used to overcome the sparsity problem in MDT reports thereby enabling ML-based automation use cases.

Complexity is an inherent property in wireless heterogeneous networks (HetNets). In this paper, we investigate the application of the agent-based modeling (ABM) tool for optimization of complex and dynamic HetNets. The proposed framework contains a diversity of game-theoretic, machine learning, and rule-based algorithms within the same model. We present and analyze a HetNet ABM model that runs parallel reinforcement learning (RL) algorithms for spectrum deployment, interference management, resource allocation, and load balancing at both micro and macrocell levels. In our proposed model, two RL-based algorithms work jointly to manage the co-tier and cross-tier interferences. The macrocell runs the first algorithm to control the transmission power of the small cells. The second RL algorithm is run by small cells to assign the users to the sub-bands with less interference levels. Simultaneously, the user association is decided by the users depending on the available resources at the cells and user preferences. The model is then evaluated under various network load conditions to deduce relationships between the cell loads, aggregate bit rate, latency, and user association. Moreover, the system is assessed in a dynamic network scenario with moving users and is confirmed to possess the ability to attain convergence with sufficient performance levels.

5G is considered as the ecosystem to abet the ever growing number of mobile devices and users requiring an unprecedented amount of data and highly demanding Quality of Experience (QoE). To accommodate these demands, 5G requires extreme densification of base station deployment, which will result in a network that requires overwhelming efforts to maintain and manage. User mobility prediction in wireless communications can be exploited to overcome these foregoing challenges. Knowledge of where users will go next enables cellular networks to improve handover management. In addition, it allows networks to engage in advanced resource allocation and reservation, cell load prediction and proactive energy saving. However, anticipating the movement of humans is, in itself, a challenge due to the lack of realistic mobility models and insufficiencies of cellular system models in capturing a real network dynamics. In this paper, we have evaluated Artificial Intelligence (AI)-assisted mobility predictors. We model mobility prediction as a multi-class classification problem to predict the future base station association of the mobile users using Extreme Gradient Boosting Trees (XGBoost) and Deep Neural Networks (DNN). Using a realistic mobility model and a 3GPP-compliant cellular network simulator, results show that, XGBoost outperforms DNN with prediction accuracy reaching up to 95% in a heterogeneous network (HetNet) scenario with shadowing varied from OdB to 4dB.

Densification and multi-band operation in 5G and beyond pose an unprecedented challenge for mobility management, particularly for inter-frequency handovers. The challenge is aggravated by the fact that the impact of key inter-frequency mobility parameters, namely A5 time to trigger (TTT), A5 threshold1 and A5 threshold2 on the system's performance is not fully understood. These parameters are fixed to a gold standard value or adjusted through hit and trial. This paper presents a first study to analyze and optimize A5 parameters for jointly maximizing two key performance indicators (KPIs): Reference signal received power (RSRP) and handover success rate (HOSR). As analytical modeling cannot capture the system-level complexity, a data driven approach is used. By developing XGBoost based model, that outperforms other models in terms of accuracy, we first analyze the concurrent impact of the three parameters on the two KPIs. The results reveal three key insights: 1) there exist optimal parameter values for each KPI; 2) these optimal values do not necessarily belong to the current gold standard; 3) the optimal parameter values for the two KPIs do not overlap. We then leverage the Sobol variance-based sensitivity analysis to draw some insights which can be used to avoid the parametric conflict while jointly maximizing both KPIs. We formulate the joint RSRP and HOSR optimization problem, show that it is non-convex and solve it using the genetic algorithm (GA). Comparison with the brute force-based results show that the proposed data driven GA-aided solution is 48x faster with negligible loss in optimality.

In the first phase of 5G network deployment, User Equipment (UE) will camp traditionally on LTE network. Later on, if the UE requests a 5G service, it will be made to camp simultaneously on LTE and 5G. This dual-camping is enabled through a 3GPP-standardized approach known as E-UTRAN New-Radio Dual-Connectivity (EN-DC). Unlike single-network-camping, where poor RF conditions of only one network affect user Quality-of-Experience (QoE), in EN-DC, poor RF condition in either LTE or 5G network can be detrimental to user QoE. Sub-optimal parameter configuration to activate EN-DC can hamper retainability KPI as UE may observe increased radio link failure (RLF). While the need to maximize the EN-DC activation is obvious for 5G network maximum utility, RLF avoidance is equally important to maintain the QoE requirements. We address this problem by first using Tomek Link to counter data imbalance problem and then building an AI model to predict RLF from real network low level measurements. We then propose and evaluate an RLF risk-aware EN-DC activation scheme that draws on insights from the developed RLF prediction model. Simulation using a 3GPP-compliant 5G simulator show that compared to no-conditioning on EN-DC activation, in the evaluated cell cluster, the proposed scheme can help reduce the potential RLF instances by 99%. This RLF reduction happens at the cost of 50% reduction in EN-DC activation. This is first study to present a framework and insights for operators to optimally conFigure the EN-DC activation parameters to achieve desired trade-off between maximizing 5G sites utility and QoE.

5G is expected to serve diverse applications and users due to the popularity of Internet of Things (IoT), big data and industrial applications. Many of these IoT and industrial applications have inherent loss tolerance that can be used to enable energy efficient uplink communication. The uplink energy efficient system will increase the battery life of devices enabling new use cases in industrial IoT. In this paper, we map the effects of application loss tolerance to the rate requirements of the user. We then mathematically model an energy minimization problem for the uplink user association and resource allocation in heterogeneous networks. We aim to provide acceptable quality of service (QoS) with improved energy efficiency by exploiting the loss tolerance and bandwidth expansion simultaneously. A distributed uplink joint user association and resource allocation strategy for uplink energy per bit minimization is presented. We conduct extensive simulation based study for a heterogeneous network to evaluate the performance of our proposed schemes. Average energy per bit consumption in the proposed scheme is -74 dB compared to -53 dB in state-of-the-art channel individual offset (CIO) scheme.

Escalating cell outages and congestion-treated as anomalies-cost a substantial revenue loss to the cellular operators and severely affect subscriber quality of experience. State-of-the-art literature applies feed-forward deep neural network at core network (CN) for the detection of above problems in a single cell; however, the solution is impractical as it will overload the CN that monitors thousands of cells at a time. Inspired from mobile edge computing and breakthroughs of deep convolutional neural networks (CNNs) in computer vision research, in this article we split the network into several 100-cell regions each monitored by an edge server; and propose a framework that preprocesses raw call detail records having user activities to create an image-like volume, fed to a CNN model. The framework outputs a multilabeled vector identifying anomalous cell(s). Our results suggest that our solution can detect anomalies with up to 96% accuracy, and is scalable and expandable for industrial Internet of Things environment.

In modern wireless communication systems, radio propagation modeling has always been a fundamental task in system design and performance optimization. These models are used in cellular networks and other radio systems to estimate the pathloss or the received signal strength (RSS) at the receiver or characterize the environment traversed by the signal. An accurate and agile estimation of pathloss is imperative for achieving desired optimization objectives. The state-of-the- art empirical propagation models are based on measurements in a specific environment and limited in their ability to capture idiosyncrasies of various propagation environments. To cope with this problem, ray-tracing based solutions are used in commercial planning tools, but they tend to be extremely time consuming and expensive. In this paper, we propose a Machine Learning (ML) based approach to complement the empirical or ray tracing-based models, for radio wave propagation modeling and RSS estimation. The proposed ML-based model leverages a pre-identified set of smart predictors, including transmitter parameters and the physical and geometric characteristics of the propagation environment, for estimating the RSS. These smart predictors are readily available at the network-side and need no further standardization. We have quantitatively compared the performance of several machine learning algorithms in their ability to capture the channel characteristics, even with sparse availability of training data. Our results show that Deep Neural Networks outperforms other ML techniques and provides a 25% increase in prediction accuracy as compared to state-of-the-art empirical models and a 12x decrease in prediction time as compared to ray tracing.

Millimeter wave spectrum utilization and network densification are two of the fundamental technologies that will enable high user quality of experience required in 5th Generation mobile cellular networks. However, user sparsity in ultra-dense heterogeneous networks and coverage limitations of millimeter wave cells means reliability of such networks will become a key operational challenge. Recent studies have explored the use of machine learning techniques for outage detection in legacy and heterogeneous mobile cellular networks. However, machine learning techniques are highly susceptible to noise in the training data which can affect their outage detection accuracy. To counter these challenges, we present a novel outage detection method based on entropy field decomposition technique first introduced in [1]. The proposed method is able to detect cell outages with at least 96% accuracy even as the level of shadowing in the network is increased which makes it ideal for practical implementation in emerging ultra-dense heterogeneous networks with millimeter wave cells. The proposed solution is compared against k-means clustering for outage detection with results showing that not only does entropy field decomposition return higher true positive results, it also returns fewer false positive results compared to k-means clustering.

To meet the accuracy, latency and energy efficiency requirements of modern healthcare systems during real-time collection and analysis of health data, a distributed edge computing environment is the answer, combined with 5G speeds and modern AI techniques. Using the state-of-the-art machine learning based classification techniques plays a crucial role in creating the optimal healthcare system on the edge. This work first provides a background on the current and emerging edge computing classification techniques for healthcare applications, specifically for electrocardiogram (ECG) beat classification. After implementing these classification techniques on a Raspberry Pi- based platform we perform a comparison of the performance of these classification techniques with respect to three key performance indicators (KPI) of interest for health care applications namely accuracy, energy efficiency, and latency. Benefiting from the results of the comparative analysis presented in this work, a distilled neural network algorithm can be selected for optimal deployment and over 90% accuracy in given scenario in healthcare system depending on the specific requirements of the given scenario.

It is well known that current reactive network management would be unable to support the exponential increase in complexity and rapidity of change in future cellular networks. Keeping this in perspective, the goal of this paper is to investigate applicability of machine learning and predictive models to assess cell-level user quality of experience (QoE) in real-time. For this purpose, we leverage a 5 week LTE metrics data collected at cell level granularity for a national LTE network operator. Domain knowledge is applied to assess user QoE with network key performance indicators (KPIs), namely scheduled user throughput, inter-frequency handover success rate and intra-frequency handover success rate. Results indicate that applying boosted trees model on a subset of carefully selected non-collinear features allows high accuracy threshold-based estimation of user throughput and inter-frequency handover success rate. We also exploit the periodic nature of cell data characteristics and apply a recently developed time series prediction model known as PROPHET for future QoE estimation. By employing machine learning and data analytics on network data within an end-to-end framework, network operators can proactively identify low performance cell sites along with the influential factors that impact the cell performance. Based on the root cause analysis, appropriate corrective measures may then be taken for low performance cell sites.

The proliferation of mobile devices and big data has made it possible to understand the human movements and forecasts of precise and intelligent short and long-term data consumption of services like call, sms, or internet data which has interesting and promising applications in modern cellular networks. Human nature and moods are known to be synonymous with the physical attributes of mother nature such as temperature. The change in those physical features affects the human routines and activities such as cellular data consumptions. The future of telecommunication lies in the exploration of heap of information and data available to companies and inferring the valuable results through extensive analysis. In this paper, we analyze three main traits of cellular activity: sms, call, and internet. This paper investigates whether the relationship between the temperature and the cellular data consumption exits or not. This work introduces a novel approach to identify the strength of relationship between the temperature and cellular activity (sms, call, internet) and discuss the methods to quantify the relationship using correlation method. The real network CDR big data set - Milano Grid data set is used to analyze the behavior of the cellular activity with respect to temperature.

Increased network wide energy consumption is a paramount challenge that hinders wide scale ultra-dense networks (UDN) deployments. While several Energy Saving (ES) enhancement schemes have been proposed recently, these schemes have one common tenancy. They operate in reactive mode i.e., to increase ES, cells are switched ON/OFF reactively in response to changing cell loads. Though, significant ES gains have been reported for such ON/OFF schemes, the inherent reactiveness of these ES schemes limits their ability to meet the extremely low latency and high QoS expected from future cellular networks vis-a-vis 5G and beyond. To address this challenge, in this paper we propose a novel user mobility prediction based AUtonomous pROactive eneRgy sAving (AURORA) framework for future UDN. Instead of observing changes in cell loads passively and then reacting to them, AURORA uses past hand over (HO) traces to determine future cell loads. This prediction is then used to proactively schedule small cell sleep cycles. AURORA also incorporates the effect of Cell Individual Offsets (CIOs) for balancing load among cells to ensure QoS while maximizing ES. Extensive system level simulations leveraging realistic SLAW model based mobility traces show that AURORA can achieve significant energy reduction gain without noticeable impact on QoS.

The popularity of cellular internet of things (IoT) is increasing day by day and billions of IoT devices will be connected to the internet. Many of these devices have limited battery life with constraints on transmit power. High user power consumption in cellular networks restricts the deployment of many IoT devices in 5G. To enable the inclusion of these devices, 5G should be supplemented with strategies and schemes to reduce user power consumption. Therefore, we present a novel joint uplink user association and resource allocation scheme for minimizing user transmit power while meeting the quality of service. We analyze our scheme for two-tier heterogeneous network (HetNet) and show an average transmit power of -2.8 dBm and 8.2 dBm for our algorithms compared to 20 dBm in state-of-the-art Max reference signal received power (RSRP) and channel individual offset (CIO) based association schemes.

In this paper, we present a detailed analysis of the coverage and spectral efficiency of a downlink cellular network. Rather than relying on the first order statistics of received signal-to- interference-ratio (SIR) such as coverage probability, we focus on characterizing its meta- distribution. Our analysis is based on the alpha- beta-gamma (ABG) path-loss model which provides us with the flexibility to analyze urban macro (UMa) and urban micro (UMi) deployments. With the help of an analytical framework, we demonstrate that selection of underlying degrees-of-freedom such as BS height for optimization of first order statistics such as coverage probability is not optimal in the network-wide sense. Consequently, the SIR meta-distribution must be employed to select appropriate operational points which will ensure consistent user experiences across the network. Our design framework reveals that the traditional results which advocate lowering of BS heights or even optimal selection of BS height do not yield consistent service experience across users. By employing the developed framework we also demonstrate how available spectral resources in terms of time slots/channel partitions can be optimized by considering the meta-distribution of the SIR.

The growing subscriber Quality of Experience demands are posing significant challenges to the mobile cellular network operators. One such challenge is the autonomic detection of sleeping cells in cellular networks. Sleeping Cell (SC) is a cell degradation problem, and a special case in Cell Outage Detection (COD) because it does not trigger any alarm due to hardware or software problems in the BS. To minimize the effect of such outages, researchers have proposed autonomous outage detection and compensation solutions. State-of-the-art SC detection depends on drive tests and subscriber complaints to identify the effected cells. However, this approach is quickly becoming unsustainable due to rising operational expenses. To address this particular issue, we employ a Deep Learning based framework which uses Minimization of Drive Tests (MDT) functionality introduced in LTE networks. In our proposed framework, MDT measurements are used to train the deep learning model. Anomalies or cell outages in the network can be then quickly detected and localized, thus significantly reducing the duty cycle of self-healing process in SON. In our simulation setup, we also quantitatively compare and demonstrate superior performance of our proposed approach with state of the art machine learning algorithm such as One Class SVM using multiple performance metrics.

Ambitious targets for aggregate throughput, energy efficiency and ubiquitous user experience are propelling the advent of ultra- dense networks. Intercell interference and high energy consumption in an ultra-dense network are the prime hindering factors in pursuit of these goals. To address the aforementioned challenges, in this paper, we propose a novel user-centric network orchestration solution for Cloud RAN based ultra-dense deployments. In this solution, a cluster (virtual disc) is created around users depending on their service priority. Within the cluster radius, only the best remote radio head (RRH) is activated to serve the user, thereby decreasing interference and saving energy. We follow a stochastic geometry based approach to quantify the area spectral efficiency (ASE) and RRH power consumption models to quantity energy(EE) efficiency of the proposed user-centric Cloud RAN (UCRAN). Through extensive analysis, we observe that the cluster sizes that yield optimal ASE and EE are quite different. Subsequently, we propose a game theoretic self-organizing network (GT-SON) framework that can orchestrate the network between ASE and EE focused operational modes in real-time in response to changes in network conditions and the operator's revenue model, to achieve a Pareto optimal solution. A bargaining game is modeled to investigate the ASE-EE tradeoff through adjustment in the exponential efficiency weightage in the Nash bargaining solution (NBS). Results show that compared to current non user-centric network design, the proposed solution offers the flexibility to operate the network at multiple folds higher ASE or EE along with significant improvement in user experience.

Optimizing parameters to achieve optimal trade-off between coverage and capacity is a well known research problem in 5th Generation mobile cellular networks. Introduction of ultra dense heterogeneous networks is bound to exacerbate this problem by adding a third conflicting objective i.e., load balancing between macro and small cells. Existing solutions on load balancing are not suited for this purpose since they balance cell loads at the cost of spectral efficiency, a key measure of resource efficiency in 5G networks. To tackle these challenges, we propose a novel solution for joint optimization of coverage, capacity and load in ultra dense heterogeneous networks that does not compromise spectral efficiency or subscriber satisfaction. The proposed solution incorporates antenna tilt, transmit power and cell offset parameters into a single objective. We compare two different versions of our proposed solution with existing coverage optimization [1], and coverage, capacity and load optimization [2] algorithms, as well as coverage optimization and load balancing solutions for HetNets to highlight its advantages. Simulation results show that the proposed solution improves service quality while also offering higher residual capacity compared to nearest benchmark.

User-centric network architectures are a key proponent to enable the uniform Quality of Experience (QoE) requirement for future dense heterogeneous network (HetNet) deployments. However, catering to spatio-temporally varying user service demands arising from the plethora of diverse mobile applications remains a challenge in such network architectures. In this paper, we propose a QoE-centric elastic framework for a dense multi-tier cellular network deployment. The framework leverages the control and data plane separation architecture (CDSA) for enabling selective data base station (DBS) activation within user equipment (UE)-centric virtual cells (also referred to as service zones). The allocation of these virtually elastic service zones around selected UEs is conducted via a central control base station (CBS) and modeled through two game techniques, namely evolutionary and auction games. Both the games are based on a utility minimization problem which is a function of weighted mean UE throughput and usage based UE service demands. To illustrate the trade-offs between the game models, network level performance is compared in terms of aggregate throughput, energy efficiency, algorithm convergence speed and mean UE scheduling probabilities.

Providing broadband connectivity to airborne systems using ground based cellular networks is a promising solution as it offers several advantages over satellite-based solutions. However, limited range of terrestrial base stations is a key challenge in full realization of this approach. This paper addresses this problem by proposing a mathematical framework for range extension leveraging large number of antennas at the base station. In contrast to prior works where range is not considered as a design parameter, we model the signal to noise ratio as a function of both number of antennas as well as the range in line-of-sight ground-to-air systems. This allows us to derive analytical expressions to determine the number of antennas required to increase range in different frequency bands and tracking and non tracking scenarios.

Understanding of mobile internet traffic patterns and capacity to estimate future traffic, particularly at high spatiotemporal granularity, is crucial for proactive decision making in emerging and future cognizant cellular networks enabled with self-organizing features. It becomes even more important in the world of `Internet of Things' with machines communicating locally. In this paper, internet activity data from a mobile network operator Call Detail Records (CDRs) is analysed at high granularity to study the spatiotemporal variance and traffic patterns. To estimate future traffic at high granularity, a Support Vector Regression (SVR) based traffic model is trained and evaluated for the prediction of maximum, minimum and average internet traffic in the next hour based on the actual traffic in the last hour. Performance of the model is compared with that of the State-of-the-Art (SOTA) deep learning models recently proposed in the literature for the same data, same granularity, and same predicates. It is concluded that this SVR model outperforms the SOTA deep and non-deep learning methods used in the literature.

Cell densification is being perceived as the panacea for the imminent capacity crunch. However, high aggregated energy consumption and increased inter-cell interference (ICI) caused by densification, remain the two long-standing problems. We propose a novel network orchestration solution for simultaneously minimizing energy consumption and ICI in ultra-dense 5G networks. The proposed solution builds on a big data analysis of over 10 million CDRs from a real network that shows there exists strong spatio-temporal predictability in real network traffic patterns. Leveraging this, we develop a novel scheme to pro-actively schedule radio resources and small cell sleep cycles yielding substantial energy savings and reduced ICI, without compromising the users QoS. This scheme is derived by formulating a joint Energy Consumption and ICI minimization problem and solving it through a combination of linear binary integer programming, and progressive analysis based heuristic algorithm. Evaluations using: 1) a HetNet deployment designed for Milan city where big data analytics are used on real CDRs data from the Telecom Italia network to model traffic patterns, 2) NS-3 based Monte-Carlo simulations with synthetic Poisson traffic show that, compared to full frequency reuse and always on approach, in best case, the proposed scheme can reduce energy consumption in HetNets to 1/8th while providing same or better QoS.

Today, the importance of cellular networks is ever-growing. The increasing complexity of networks is expected to decrease reliability. In order to continue reliable operation in a cost-efficient manner, previous literature has explored Proactive Self-Healing methods, but actual application to cellular networks has been lacking. Thus, in this paper, we aim to institute a proactive approach for failure prediction of time series data by surveying a wide range of techniques. To determine the best in predicting network failures, Support Vector Machine (SVM) Regression and multiple Neural Network variants were utilized along with a Continuous Time Markov Chain (CTMC) analytical model to provide reliability analysis. All results are derived from actual network data. We conclude the pattern of these failures is most likely non-linear, and the most promising technique is a Deep Neural Network utilizing Autoencoders. The CTMC analysis demonstrates that current networks barely reside in a healthy state, so the goal is that this paper will lead to improvements, especially in Self-Organizing Networks (SON).

Ultra-dense Heterogeneous networks (UDHN) are emerging as the inevitable approach to cope with the imminent cellular network capacity crunch. However, load imbalance and widely disproportionate SINR distribution between macro and small cells, remains the key hurdle in harnessing the full potential of UDHN. In this paper we address this problem by proposing and analysing a novel load-aware user association methodology that offers a mechanism to simultaneously optimize network capacity, load distribution and coverage. The solution concurrently leverages the three key optimization parameters for Coverage and Capacity Optimization (CCO) and Load Balancing (LB) SON functions i.e. antenna tilts, transmit powers and cell individual offsets (CIOs). The method incorporates exponential-weighting based prioritization of CCO and LB SON functions within the user association process. The results suggest that the proposed approach offers a distribution of load between macro and small cells that yields more gain in terms of both network capacity and user quality of service than conventional max signal strength or max SINR based association methods.

With increasing cell density and the heterogeneity in the network, optimal user-cell association which is a well known open problem, will become an even more challenging issue. Contrary to the current studies that address user-cell association problem for convectional HetNets with massive MIMO deployments in HF (high frequencies) ranges, in this paper we investigate user-cell association problem for dense two-tier networks with massive MIMO deployment both at macro and femto-tier operating in HF and mmWave spectrum, respectively. We evaluate the performance of four user-cell association algorithms for massive MIMO deployment in a two-tier network under two different deployment scenarios: 1) HF-HF (both tiers operating in HF band); 2) HF-mmWave (MBSs operating in HF while FBSs in mmWave bands. To this end, we model the association problem in form of a convex network utility maximization problem as a function of the downlink user throughput. Contrary to the existing load aware association schemes that preclude the effect of bandwidth disparity in HF and mmWave bands, we propose a modified utility function that takes into account the effect of large bandwidth at mmWave bands. The problem is solvable through centralized as well as distributed or user centric load aware user association schemes.

Frequent handovers (HOs) in dense small cell deployment scenarios could lead to a dramatic increase in signaling overhead. This suggests a paradigm shift toward a signaling conscious cellular architecture with intelligent mobility management. In this direction, a futuristic radio access network with a logical separation between control and data planes has been proposed in research community. It aims to overcome limitations of the conventional architecture by providing high data rate services under the umbrella of a coverage layer in a dual connection mode. This approach enables signaling efficient HO procedures since the control plane remains unchanged when the users move within the footprint of the same umbrella. Considering this configuration, we propose a core-network efficient radio resource control signaling scheme for active state HO and develop an analytical framework to evaluate its signaling load as a function of network density, user mobility, and session characteristics. In addition, we propose an intelligent HO prediction scheme with advance resource preparation in order to minimize the HO signaling latency. Numerical and simulation results show promising gains in terms of reduction in HO latency and signaling load as compared with conventional approaches.

Database-aided user association, where users are associated with data base stations (BSs) based on a database which stores their geographical location with signal-to-noise-ratio tagging, will play a vital role in the futuristic cellular architecture with separated control and data planes. However, such approach can lead to inaccurate user-data BS association, as a result of the inaccuracies in the positioning technique, thus leading to sub-optimal performance. In this paper, we investigate the impact of database-aided user association approach on the average spectral efficiency (ASE). We model the data plane base stations using its fluid model equivalent and derive the ASE for the channel model with pathloss only and when shadowing is incorporated. Our results show that the ASE in database-aided networks degrades as the accuracy of the user positioning technique decreases. Hence, system specifications for database-aided networks must take account of inaccuracies in positioning techniques.

The demand for increasingly challenging data rates in cellular networks has motivated the pursuit to exploit abundant bandwidth at the millimeter waves (mmWave) spectrum, which offers large bandwidths and near free-space path loss for line of sight links. However, this solution comes at the cost of limited communication range. Thus, mmWave basestations (BS) are expected to be densely deployed to maximize offered service. As the energy consumed in a network is roughly proportional to the number of nodes, how energy efficient a mmWave network will be is a question that remains unanswered so far. In this paper, we compare the performance of mmWave cellular networks in terms of energy efficiency (EE) to that of networks operating at 2 GHz. We start from the link budget to determine the average cell radius of two mmWave systems operating at 28 GHz and 60 GHz. Afterwards, intensity of a Poisson Point Process that models mmWave BS locations pertaining to expected operational network parameters is calculated. The probability of coverage offered by investigated systems is evaluated using analytical expressions. Finally, EE is calculated using consumed power model that assumes actual mmWave components. Results suggest that when the deployment environment allows for high signal to interference and noise ratio (SINR) to be achieved at the receiver, mmWave system EE outperforms a 2 GHz system. Conversely, when only low SINR is achievable, 2 GHz system EE is superior to mmW system.

In this paper, we propose a user centric network parameter optimization approach that utilizes the information contained in subscriber call detail records generated in a cellular network. To be able to maximize average user throughput on a cell level, we perform optimization of sector azimuth angles based on user centric weighted grids. The grid pattern is formulated by identifying spatially distributed points that correspond to user activity quantified by user location and service utilization information obtained from the call detail records. In this study, we present numerical and cell level simulation over a system of 30 cells to evaluate the proposed solution. In comparison to an optimal brute-force method that takes into account the location of every user to optimize the azimuth angles in a cell, we show that the proposed grid based self-optimization approach yields matching results in performance with substantial reduction in computational complexity.

It is anticipated that the future cellular networks will consist of an ultra-dense deployment of complex heterogeneous Base Stations (BSs). Consequently, Self-Organizing Networks (SON) features are considered to be inevitable for efficient and reliable management of such a complex network. Given their unfathomable complexity, cellular networks are inherently prone to partial or complete cell outages due to hardware and/or software failures and parameter misconfiguration caused by human error, multivendor incompatibility or operational drift. Forthcoming cellular networks, vis-a-vis 5G are susceptible to even higher cell outage rates due to their higher parametric complexity and also due to potential conflicts among multiple SON functions. These realities pose a major challenge for reliable operation of future ultra-dense cellular networks in cost effective manner. In this paper, we present a stochastic analytical model to analyze the effects of arrival of faults in a cellular network. We exploit Continuous Time Markov Chain (CTMC) with exponential distribution for failures and recovery times to model the reliability behavior of a BS. We leverage the developed model and subsequent analysis to propose an adaptive fault predictive framework. The proposed fault prediction framework can adapt the CTMC model by dynamically learning from past database of failures, and hence can reduce network recovery time thereby improving its reliability. Numerical results from three case studies, representing different types of network, are evaluated to demonstrate the applicability of the proposed analytical model.

This paper presents a novel scheme for spectral efficiency (SE) optimization through clustering of users. By clustering users with respect to their geographical concentration we propose a solution for dynamic steering of antenna beam, i.e., antenna azimuth and tilt optimization with respect to the most focal point in a cell that would maximize overall SE in the system. The proposed framework thus introduces the notion of elastic cells that can be potential component of 5G networks. The proposed scheme decomposes large-scale system-wide optimization problem into small-scale local sub- problems and thus provides a low complexity solution for dynamic system wide optimization. Every sub- problem involves clustering of users to determine focal point of the cell for given user distribution in time and space, and determining new values of azimuth and tilt that would optimize the overall system SE performance. To this end, we propose three user clustering algorithms to transform a given user distribution into the focal points that can be used in optimization; the first is based on received signal to interference ratio (SIR) at the user; the second is based on received signal level (RSL) at the user; the third and final one is based on relative distances of users from the base stations. We also formulate and solve an optimization problem to determine optimal radii of clusters. The performances of proposed algorithms are evaluated through system level simulations. Performance comparison against benchmark where no elastic cell deployed, shows that a gain in spectral efficiency of up to 25% is possible depending upon user distribution in a cell.

In this paper, we develop a game theoretic formulation for empowering cloud enabled HetNets with adaptive Self Organizing Network (SON) capabilities. SON capabilities for intelligent and efficient radio resource management is a fundamental design pillar for the emerging 5G cellular networks. The C-RAN system model investigated in this paper consists of ultra-dense remote radio heads (RRHs) overlaid by central baseband units that can be collocated with much less densely deployed overlaying macro base-stations (BSs). It has been recently demonstrated that under a user centric scheduling mechanism, C-RAN inherently manifests the trade-off between Energy Efficiency (EE) and Spectral Efficiency (SE) in terms of RRH density. The key objective of the game theoretic framework developed in this paper is to dynamically optimize the trade-off between the EE and the SE of the C- RAN. More specifically, for an ultra-dense C- RAN based HetNet, the density of active RRHs should be carefully dimensioned to maximize the SE. However, the density of RRHs which maximizes the SE may not necessarily be optimal in terms of the EE. In order to strike a balance between these two performance determinants, we develop a game theoretic formulation by employing a Nash bargaining framework. The two metrics of interest, SE and EE, are modeled as virtual players in a bargaining problem and the Nash bargaining solution for RRH density is determined. In the light of the optimization outcome we evaluate corresponding key performance indicators through numerical results. These results offer insights for a C-RAN designer on how to optimally design a SON mechanism to achieve a desired trade-off level between the SE and the EE in a dynamic fashion.

In research community, a new radio access network architecture with a logical separation between control plane (CP) and data plane (DP) has been proposed for future cellular systems. It aims to overcome limitations of the conventional architecture by providing high data rate services under the umbrella of a coverage layer in a dual connection mode. This configuration could provide significant savings in signalling overhead. In particular, mobility robustness with minimal handover (HO) signalling is considered as one of the most promising benefits of this architecture. However, the DP mobility remains an issue that needs to be investigated. We consider predictive DP HO management as a solution that could minimise the out-of-band signalling related to the HO procedure. Thus we propose a mobility prediction scheme based on Markov Chains. The developed model predicts the user's trajectory in terms of a HO sequence in order to minimise the interruption time and the associated signalling when the HO is triggered. Depending on the prediction accuracy, numerical results show that the predictive HO management strategy could significantly reduce the signalling cost as compared with the conventional non-predictive mechanism.

Most of the wireless systems such as the long term evolution (LTE) adopt a pilot symbol-aided channel estimation approach for data detection purposes. In this technique, some of the transmission resources are allocated to common pilot signals which constitute a significant overhead in current standards. This can be traced to the worst-case design approach adopted in these systems where the pilot spacing is chosen based on extreme condition assumptions. This suggests extending the set of the parameters that can be adaptively adjusted to include the pilot density. In this paper, we propose an adaptive pilot pattern scheme that depends on estimating the channel correlation. A new system architecture with a logical separation between control and data planes is considered and orthogonal frequency division multiplexing (OFDM) is chosen as the access technique. Simulation results show that the proposed scheme can provide a significant saving of the LTE pilot overhead with a marginal performance penalty.

In this paper, we investigate the optimal inter-frequency small cell discovery (ISCD) periodicity for small cells deployed on carrier frequency other than that of the serving macro cell. We consider that the small cells and user terminals (UTs) positions are modelled according to a homogeneous Poisson Point Process (PPP). We utilize polynomial curve fitting to approximate the percentage of time the typical UT missed small cell offloading opportunity, for a fixed small cell density and fixed UT speed. We then derive analytically, the optimal ISCD periodicity that minimizes the average UT energy consumption (EC). Furthermore, we also derive the optimal ISCD periodicity that maximizes the average energy efficiency (EE), i.e. bit-per-joule capacity. Results show that the EC optimal ISCD periodicity always exceeds the EE optimal ISCD periodicity, with the exception of when the average ergodic rates in both tiers are equal, in which the optimal ISCD periodicity in both cases also becomes equal.

In this work, we address the antenna tilt optimization problem for a two-tier cellular network consisting of macrocells and femtocells, where both tiers share the same spectrum and their positions are modeled via two independent Poisson point processes (PPPs). First, we derive the coverage probability for a traditional cellular network consisting only of macrocells and obtain the optimum tilt angle that maximizes the overall energy efficiency (EE). Gains of up to 400% in EE were found for a scenario (approximately) equivalent to a hexagonal cell deployment with cell radius of 200 m when the optimum tilt was selected. We then proceed to model the heterogeneous network (HetNet) scenario where femtocells are also deployed in the network's area. We observe that the macrousers performance is highly sensitive to the interference emanating from the femtocell tier. In order to circumvent this issue, interference coordination employing a guard zone for the macrocell user is proposed. Subsequently, we formulate a joint optimization problem where we derive both, the radius of a guard zone protecting the macrouser and the tilt angle that maximize the EE of the network.

This paper presents a novel framework for joint self-optimization of backhaul as well as coverage links spectral efficiency in relay enhanced heterogeneous networks. Considering a realistic heterogeneous network deployment, where some cells contain Relay Station (RS), while others do not, we develop an analytical framework for self-optimisation of macrocell Base Station (BS) antenna tilts. Our framework exploits a unique system level perspective to enable dynamic maximization of system-wide spectral efficiency of the BS-RS backhaul links as well as that of the BS-user coverage links. A distributed and practical self-organising solution is obtained by decomposing the large scale system-wide optimization problem into local small scale optimization problems, by mimicking the operational principles of self-organisation in biological systems. The local problems are non-convex but have very small scale and can be solved via appropriate numerical methods, such as sequential quadratic programming. The performance of developed solution is evaluated through extensive system level simulations for LTE-A type networks and compared against conventional tilting benchmarks. Numerical results show that up to 50% gain in average spectral efficiency is achievable through the proposed solution depending on users geographical distributions.

Automatic detection of cells which are in outage has been identified as one of the key use cases for Self Organizing Networks (SON) for emerging and future generations of cellular systems. A special case of cell outage, referred to as Sleeping Cell (SC) remains particularly challenging to detect in state of the art SON because in this case cell goes into outage or may perform poorly without triggering an alarm for Operation and Maintenance (O&M) entity. Consequently, no SON compensation function can be launched unless SC situation is detected via drive tests or through complaints registered by the affected customers. In this paper, we present a novel solution to address this problem that makes use of minimization of drive test (MDT) measurements recently standardized by 3GPP and NGMN. To overcome the processing complexity challenge, the MDT measurements are projected to a low-dimensional space using multidimensional scaling method. Then we apply state of the art k-nearest neighbor and local outlier factor based anomaly detection models together with pre-processed MDT measurements to profile the network behaviour and to detect SC. Our numerical results show that our proposed solution can automate the SC detection process with 93% accuracy.

Despite years of physical-layer research, the capacity enhancement potential of relays is limited by the additional spectrum required for Base Station (BS)-Relay Station (RS) links. This paper presents a novel distributed solution by exploiting a system level perspective instead. Building on a realistic system model with impromptu RS deployments, we develop an analytical framework for tilt optimization that can dynamically maximize spectral efficiency of both the BS-RS and BS-user links in an online manner. To obtain a distributed self-organizing solution, the large scale system-wide optimization problem is decomposed into local small scale subproblems by applying the design principles of self-organization in biological systems. The local subproblems are non-convex, but having a very small scale, can be solved via standard nonlinear optimization techniques such as sequential quadratic programming. The performance of the developed solution is evaluated through extensive simulations for an LTE-A type system and compared against a number of benchmarks including a centralized solution obtained via brute force, that also gives an upper bound to assess the optimality gap. Results show that the proposed solution can enhance average spectral efficiency by up to 50% compared to fixed tilting, with negligible signaling overheads. The key advantage of the proposed solution is its potential for autonomous and distributed implementation.

The next generation Long Term Evolution (LTE) & LTE-Advanced cellular networks will be equipped with numerous Self-Organizing (SO) functions. These SO functions are being envisioned to be inevitable for technical as well as commercial viability of LTE/LTE-Advanced networks. Therefore, a lot of research effort is currently being channeled to the design of various SO functions. However, given the convoluted and complex interrelationships among cellular system design and operational parameters, a large number of these SO functions are highly susceptible to parametric or logical inter-dependencies. These inter-dependencies can induce various types of conflicts among them, thereby undermining the smooth and optimal network operation. Therefore, an implicit or explicit self-coordination framework is essential, not only to avoid potential objective or parametric conflicts among SO functions, but also to ensure the stable operation of wireless networks. In this paper we present such a self-coordination framework. Our framework builds on the comprehensive identification and classification of potential conflicts that are possible among the major SO functions envisioned by Third Generation Partnership Project (3GPP) so far. This classification is achieved by analyzing network topology mutation, temporal and spatial scopes, parametric dependencies, and logical relations that can affect the operation of SO functions in reality. We also outline a solution approach for a conflict-free implementation of multiple SO functions in LTE/LTE-Advanced networks. Moreover, as an example, we highlight future research challenges for optimum design of Mobility Load Balancing (MLB) and Mobility Robustness Optimisation (MRO).

Uneven traffic load among the cells increases call blocking rates in some cells and causes low resource utilisation in other cells and thus degrades user satisfaction and overall performance of the cellular system. Various centralised or semi centralised Load Balancing (LB) schemes have been proposed to cope with this time persistent problem, however, a fully distributed Self Organising (SO) LB solution is still needed for the future cellular networks. To this end, we present a novel distributed LB solution based on an analytical framework developed on the principles of nature inspired SO systems. A novel concept of super-cell is proposed to decompose the problem of “system-wide blocking minimization” into the local sub-problems in order to enable a SO distributed solution. Performance of the proposed solution is evaluated through system level simulations for both macro cell and femto cell based systems. Numerical results show that the proposed solution can reduce the blocking in the system close to an Ideal Central Control (ICC) based LB solution. The added advantage of the proposed solution is that it does not require heavy signalling overheads.

A mass deployment of femtocells is anticipated to affect a number of areas more adversely, especially in the cell-edge of the macrocell network. In this paper, we propose a flexible frequency-partitioning scheme for OFDMA network based on cyclic difference sets. The cyclic property of these sets allows a quick construction of orthogonal patterns for the macrocell network with an emphasis on tri-sector sites. The impact and the co-deployment of femtocells is also investigated. Unlike, with existing works in the literature, the novel scheme can adaptively control the level of coverage in the cell-edges areas and additionally enables coexistence of femtocells in the network. Simulation results confirm the effectiveness of the proposed scheme in both macrocell and femtocell networks compared to the legacy soft frequency reuse and universal frequency reuse.

Performance of next generation OFDM/OFDMA based Distributed Cellular Network (ODCN) where no cooperation based interference management schemes are used, is dependent on four major factors: 1) spectrum reuse factor, 2) number of sectors per site, 3) number of relay station per site and 4) modulation and coding efficiency achievable through link adaptation. The combined effect of these factors on the overall performance of a Deployment Architecture (DA) has not been studied in a holistic manner. In this paper we provide a framework to characterize the performance of various DA's by deriving two novel performance metrics for 1) spectral efficiency and 2) fairness among users. These metrics are designed to include the effect of all four contributing factors. We evaluate these metrics for a wide set of DA's through extensive system level simulations. The results provide a comparison of various DA's for both cellular and relay enhanced cellular systems in terms of spectral efficiency and fairness they offer and also provide an interesting insight into the tradeoff between the two performance metrics. Numerical results show that, in interference limited regime, DA's with highest spectrum efficiency are not necessarily those that resort to full frequency reuse. In fact, frequency reuse of 3 with 6 sectors per site is spectrally more efficient than that with full frequency reuse and 3 sectors. In case of relay station enhanced ODCN a DA with full frequency reuse, six sectors and 3 relays per site is spectrally more efficient and can yield around 170% higher spectrum efficiency compared to counterpart DA without RS.

Recent research on Frequency Reuse (FR) schemes for OFDM/OFDMA based cellular networks (OCN) suggest that a single fixed FR cannot be optimal to cope with spatiotemporal dynamics of traffic and cellular environments in a spectral and energy efficient way. To address this issue this paper introduces a novel Self Organizing framework for adaptive Frequency Reuse and Deployment (SO-FRD) for future OCN including both cellular (e.g. LTE) and relay enhanced cellular networks (e.g. LTE Advance). In this paper, an optimization problem is first formulated to find optimal frequency reuse factor, number of sectors per site and number of relays per site. The goal is designed as an adaptive utility function which incorporates three major system objectives; 1) spectral efficiency 2) fairness, and 3) energy efficiency. An appropriate metric for each of the three constituent objectives of utility function is then derived. Solution is provided by evaluating these metrics through a combination of analysis and extensive system level simulations for all feasible FRD's. Proposed SO-FRD framework uses this flexible utility function to switch to particular FRD strategy, which is suitable for system's current state according to predefined or self learned performance criterion. The proposed metrics capture the effect of all major optimization parameters like frequency reuse factor, number of sectors and relay per site, and adaptive coding and modulation. Based on the results obtained, interesting insights into the tradeoff among these factors is also provided.