Arsalan Saeed

Energy efficiency in heterogeneous wireless access networks

In this article, we bring forward the important aspect of energy savings in wireless access networks. We specifically focus on the energy saving opportunities in the recently evolving heterogeneous networks (HetNets), both Single- RAT and Multi-RAT. Issues such as sleep/wakeup cycles and interference management are discussed for co-channel Single-RAT HetNets. In addition to that, a simulation based study for LTE macro-femto HetNets is presented, indicating the need for dynamic energy efficient resource management schemes. Multi-RAT HetNets also come with challenges such as network integration, combined resource management and network selection. Along with a discussion on these challenges, we also investigate the performance of the conventional WLAN-first network selection mechanism in terms of energy efficiency (EE) and suggest that EE can be improved by the application of intelligent call admission control policies.

Controlling self healing cellular networks using fuzzy logic

Wireless cellular communication networks is undergoing a transition from being a simply optional voice communication to becoming a necessity in our everyday lives. In order to ensure uninterrupted high Quality of Experience for subscribers, network operators must ensure 100% reliability of their networks without any discontinuity either for planned maintenance or breakdown. This paper demonstrates self healing capability to the fault recovery process for each cell. It is proposed to compensate cells in failure by neighboring cells optimizing their coverage with antenna reconfiguration and power compensation resulting in filling the coverage gap and improving the QoS for users. The right choice of these reconfigured parameters is determined through a process involving fuzzy logic control and reinforcement learning. Results show an improvement in the network performance for the area under outage as perceived by each user in the system.

Data-driven analytics for automated cell outage detection in Self-Organizing Networks

In this paper, we address the challenge of autonomous cell outage detection (COD) in Self-Organizing Networks (SON). COD is a pre-requisite to trigger fully automated self-healing recovery actions following cell outages or network failures. A special case of cell outage, referred to as Sleeping Cell (SC) remains particularly challenging to detect in state-of-the-art SON, since it triggers no alarms for Operation and Maintenance (O&M) entity. Consequently, no SON compensation function can be launched unless site visits or drive tests are performed, or complaints are received by affected customers. To address this issue, we present and evaluates a COD framework, which is based on minimization of drive test (MDT) reports, a functionality recently specified in third generation partnership project (3GPP) Release 10, for LTE Networks. Our proposed framework aims to detect cell outages in an autonomous fashion by first pre-processing the MDT measurements using multidimensional scaling method and further employing it together with machine learning algorithms to detect and localize anomalous network behaviour. We validate and demonstrate the effectiveness of our proposed solution using the data obtained from simulating the network under various operational settings.

A SON solution for sleeping cell detection using low-dimensional embedding of MDT measurements

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.

A learning-based approach for autonomous outage detection and coverage optimization

To be able to provide uninterrupted high quality of experience to the subscribers, operators must ensure high reliability of their networks while aiming for zero downtime. With the growing complexity of the networks, there exists unprecedented challenges in network optimization and planning, especially activities such as cell outage detection COD and mitigation that are labour-intensive and costly. In this paper, we address the challenge of autonomous COD and cell outage compensation in self-organising networks SON. COD is a pre-requisite to trigger fully automated self-healing recovery actions following cell outages or network failures. A special case of cell outage, referred to as sleeping cell, remains particularly challenging to detect in state-of-the-art SON, because it triggers no alarms for operation and maintenance entity. Consequently, no SON compensation function can be launched unless site visits or drive tests are performed, or complaints are received by affected customers. To address this issue, our COD solution leverages minimization of drive test functionality, recently specified in third generation partnership project Release 10 for LTE networks, in conjunction with state-of-the art machine learning methods. Subsequently, the proposed cell outage compensation mechanism utilises fuzzy-based reinforcement learning mechanism to fill the coverage gap and improve the quality of service, for the users in the identified outage zone, by reconfiguring the antenna and power parameters of the neighbouring cells. The simulation results show that the proposed framework can detect cell outage situations in an autonomous fashion and also compensate for the detected outage in a reliable manner. Copyright

Energy efficient resource allocation for 5G Heterogeneous Networks

This paper investigates the downlink resource allocation problem in Orthogonal Frequency Division Multiple Access (OFDMA) Heterogeneous Networks (HetNets) consisting of macrocells and small cells sharing the same frequency band. The focus of this study is to devise an energy efficient scheme that allows shared spectrum access to small cells, while ensuring a certain level of quality of service for the macro cell users. It further enable us to minimize the overall energy consumption by switching the underutilized small cells to sleep mode. To devise such a mechanism, we have used a combination of linear binary integer programming and progressive analysis based heuristic algorithm. We evaluated our proposed solution by comparing the macrocell served users performance against Reuse 1 case. Moreover, we provide an analytical comparison of the network power consumption with and without the sleep mode capabilities. It has been shown that our proposed algorithm not only reduces the overall network energy consumption but also minimizes the interference caused by smalls cells to macrocell served users.

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.

Control and data channel resource allocation in macro-femto Heterogeneous Networks

This paper investigates the downlink resource allocation problem in Orthogonal Frequency Division Multiple Access (OFDMA) Heterogeneous Networks (HetNets) consisting of macro cells and small cells sharing the same frequency band. Dense deployment of small cells overlaid by a macro layer is considered to be one of the most promising solutions for providing hotspot coverage in future 5G networks. The focus is to devise an optimised policy for small cells’ access to the shared spectrum, in terms of their transmissions, in order to keep small cell served users sum data rate at high levels while ensuring that certain level of quality of service (QoS) for the macro cell users in the vicinity of small cells is provided. Both data and control channel constraints are considered, to ensure that not only the macro cell users’ data rate demands are met, but also a certain level of Bit Error Rate (BER) is ensured for the control channel information. Control channel reliability is especially important as it holds key information to successfully decode the data channel. The problem is addressed by our proposed linear binary integer programming heuristic algorithm which maximises the small cells utility while ensuring the macro users imposed constraints. To further reduce the computational complexity, we propose a progressive interference aware low complexity heuristic solution. Discussion is also presented for the implementation possibility of our proposed algorithms in a practical network. The performance of both the proposed algorithms is compared with the conventional Reuse-1 scheme under different fading conditions and small cell loads. Results show a negligible drop in small cell performance for our proposed schemes, as a trade-off for ensuring all macro users data rate demands, while Reuse-1 scheme can even lead up to 40 % outage when control region of the small cells in heavily loaded.

Outage Detection Framework for Energy Efficient Communication Network

In this chapter, we present a Cell Outage Detection (COD) framework for Heterogeneous Networks (HetNets) with split control and data planes. COD is a pre-requisite to trigger fully automated self-healing recovery actions following cell outages or network failures not only to ensure reliable recovery of services but also to significantly minimize wastage of energy. To cope with the idiosyncrasies of both the data and control planes, our proposed framework incorporates control COD and data COD mechanisms. The control COD leverage the relatively larger number of UEs in the control cell to gather large scale Minimize Drive Testing (MDT) reports data. These measurements are further pre-processed using multidimensional scaling method and are employed together with state-of-the art machine learning algorithms to detect and localize anomalous network behaviour. On the other hand, for data cells COD, we propose a heuristic Grey-Prediction based approach, which can work with the small number of UEs in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity, by receiving a periodic update of the Received Signal Reference Power (RSRP) statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the fourier series of residual error that is inherent to grey prediction model. We validate and demonstrate the effectiveness of our proposed solution for detecting cell outages in both data and control planes via performing network simulations under various operational settings.

Femtocell Collaborative Outage Detection (FCOD) With Built-In Sleeping Mode Recovery (SMR) Technique

Self-Organizing Networks (SONs) have an important role in the development of the next generation mobile networks by introducing automated schemes. Cell outage detection is one of the main functionalities in self-healing mechanism. Outage detection for small cells has not been discussed in literature with greater emphasis yet. The Femtocell Collaborative Outage Detection (FCOD) algorithm with built-in Sleeping Mode Recovery (SMR) is introduced in this paper. The proposed algorithm is mainly based on the femtocell collaborative detection with incorporated sniffer. It compares the current Femtocell Access Points FAPs’ Reference Signal Received Power (RSRP) statistics with a benchmark data. An outage decision is autonomously taken by each FAP depending on a certain threshold value. Moreover, the FCOD algorithm is capable of differentiating between the outage and sleeping cells due to the presence of the built-in SMR technique.