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.

This study investigates the downlink resource allocation problem in orthogonal frequency division multiple access heterogeneous networks consisting of macrocells and femtocells sharing the same frequency band. The focus is to devise optimised policies for femtocells' access to the shared spectrum, in terms of femtocell transmissions, in order to maximise femto‐users (FUEs) sum data rate while ensuring that certain level of quality of service (QoS) for the macro‐cell users in the vicinity of femtocells is provided. The optimal solution to this problem is obtained by employing the well‐known dual Lagrangian method and the optimal femtocell transmit power and resource allocation solution is derived in detail. However, the optimal solution introduces high computational complexity. To this end, a heuristic solution to the problem is proposed. The algorithms to implement both optimal and efficient suboptimal schemes in a practical system are also given in detail while their complexity is compared. Simulation results show that proposed dynamic resource allocation scheme (a) ensures the macro‐users QoS requirements compared with the Reuse‐1 scheme, where femtocells are allowed to transmit at full power and bandwidth; (b) can maintain FUE data rates at high levels; (c) provides performance close to the optimal solution, while introducing much lower complexity.

This paper investigates the downlink resource allocation problem in Orthogonal Frequency Division Multiple Access (OFDMA) Heterogeneous Networks (HetNets) consisting of macrocells and femtocells sharing the same frequency band. The focus is to devise an optimised policy for femtocells' access to the shared spectrum, in terms of femtocell transmissions, in order to keep femto 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 femtocells is provided. Both data and control channel constraints are considered, to insure that not only the macro-cell users' data rate demands are meet, but also a certain level of Bit Error Rate (BER) is ensured for the control channel information. The problem is addressed by our proposed linear binary integer programming heuristic algorithm and the performance is compared with the conventional Reuse-1 scheme. Results show a negligible drop in femtocell performance for our proposed scheme, as a trade-off for ensuring all macro users data rate demands when Reuse-1 scheme can even lead up to 40% outage. Discussion is also presented for the implementation possibility of our proposed in a practical LTE network.

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.

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.