Hasan Farooq

Coordinated Multi-Point Clustering Schemes: A Survey

Mobile data traffic grew by 74% in 2015 and it is expected to grow eight-fold by 2020. Future wireless networks will need to deploy massive number of small cells to cope with this increasing demand. Dense deployment of small cells will require advanced interference mitigation techniques to improve spectral efficiency and enhance much needed capacity. Coordinated multi-point (CoMP) is a key feature for mitigating inter-cell interference, improve throughput and cell edge performance. However, cooperation will need to be limited to few cells only due to additional overhead required by CoMP due to channel state information (CSI) exchange, scheduling complexity, and additional backhaul limitation. Hence, small CoMP clusters will need to be formed in the network. This paper surveys the state-of-the-art on one of the key challenges of CoMP implementation: CoMP clustering. As a starting point, we present the need for CoMP, the clustering challenge for 5G wireless networks and provide a brief essential background about CoMP and the enabling network architectures. We then provide the key framework for CoMP clustering and introduce self organization as an important concept for effective CoMP clustering to maximize CoMP gains. Next, we present two novel taxonomies on existing CoMP clustering solutions, based on self organization and aimed objective function. Strengths and weaknesses of the available clustering solutions in the literature are critically discussed. We then discuss future research areas and potential approaches for CoMP clustering. We present a future outlook on the utilization of big data in cellular context to support proactive CoMP clustering based on prediction modeling. Finally, we conclude this paper with a summary of lessons learned in this field. This paper aims to be a key guide for anyone who wants to research on CoMP clustering for future wireless networks.

Continuous Time Markov Chain Based Reliability Analysis for Future Cellular Networks

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.

Spatiotemporal Mobility Prediction in Proactive Self-Organizing Cellular Networks

Mobility prediction, one of the key enablers of proactive self-organizing networks, aims at efficient management of future cellular networks, which are envisaged to be extremely dense and complex due to conglomeration of diverse technologies. This paves the way for resource reservation prior to actual handover for seamless handover experience and for forecasting user traffic distribution. In this letter, we have utilized semi-Markov model for spatiotemporal mobility prediction coupled with steady state and gain analysis in cellular networks. Maximum prediction accuracy of 90% is achieved in the experimental evaluation leveraging on the real network traces generated by smartphone application.

Fault prediction and reliability analysis in a real cellular network

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).

A multi-objective performance modelling framework for enabling self-optimisation of cellular network topology and configurations

Cellular system optimisation, a cornerstone of cellular systems paradigm, requires new focus shift because of the emergence of plethora of new features shaping the cellular landscape. These features include self-organising networks with added flavours of heterogeneity of cell sizes and base station types, adaptive antenna radiation patterns, energy efficiency, spatial homogeneity of service levels and focus shift from coverage to capacity. Moreover, to effectively tackle spatiotemporal dynamics of network conditions, a generic low-complexity framework to quantify the key facets of performance, that is, capacity, quality of service and energy efficiency of the various network topology configurations (NTC), is needed for enabling self-organising networks empowered cellular system optimisation on the fly. In this paper, we address this problem and present a performance characterisation framework that quantifies the multiple performance aspects of a given heterogeneous NTC through a unified set of metrics that are derived as function of key optimisation parameters and also present a cross comparison of a wide range of potential NTCs. Moreover, we propose a low-complexity heuristic approach for holistic optimisation of future heterogeneous cellular systems for joint optimality in the multiple desired performance indicators. The performance characterisation framework also provides quantitative insights into the new tradeoffs involved in optimisation of emerging heterogeneous networks and can pave the way for much needed further research in this area. Copyright

Leveraging Intelligence from Network CDR Data for Interference Aware Energy Consumption Minimization

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.

Spectral Efficiency Self-Optimization through Dynamic User Clustering and Beam Steering

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.

Proactive Caching at the Edge Leveraging Influential User Detection in Cellular D2D Networks

Caching close to users in a radio access network (RAN) has been identified as a promising method to reduce a backhaul traffic load and minimize latency in 5G and beyond. In this paper, we investigate a novel community detection inspired by a proactive caching scheme for device-to-device (D2D) enabled networks. The proposed scheme builds on the idea that content generated/accessed by influential users is more probable to become popular and thus can be exploited for pro-caching. We use a Clustering Coefficient based Genetic Algorithm (CC-GA) for community detection to discover a group of cellular users present in close vicinity. We then use an Eigenvector Centrality measure to identify the influential users with respect to the community structure, and the content associated to it is then used for pro-active caching using D2D communications. The numerical results show that, compared to reactive caching, where historically popular content is cached, depending on cache size, load and number of requests, up to 30% more users can be satisfied using a proposed scheme while achieving significant reduction in backhaul traffic load.