The number of connected mobile devices is increasing rapidly with more than 10 billion expected by 2022. Their total aggregate energy consumption poses a significant concern to society. The current 3gpp (3rd Generation Partnership Project) LTE/LTE-Advanced standard incorporates an energy saving technique called discontinuous reception (DRX). It is expected that 5G will use an evolved variant of this scheme. In general, the single selection of DRX parameters per device is non trivial. This paper describes how to improve energy efficiency of mobile devices by selecting DRX based on the traffic profile per device. Our particular approach uses a two phase data-driven strategy which tunes the selection of DRX parameters based on a smart fast energy model. The first phase involves the off-line selection of viable DRX combinations for a particular traffic mix. The second phase involves an on-line selection of DRX from this viable list. The method attempts to guarantee that latency is not worse than a chosen threshold. Alternatively, longer battery life for a device can be traded against increased latency. We built a lab prototype of the system to verify that the technique works and scales on a real LTE system. We also designed a sophisticated traffic generator based on actual user data traces. Complementary method verification has been made by exhaustive off-line simulations on recorded LTE network data. Our approach shows significant device energy savings, which has the aggregated potential over billions of devices to make a real contribution to green, energy efficient networks.

Modern telecommunication and mobile networks are increasingly complex from a resource management perspective, with diverse combinations of software and infrastructure elements that need to be configured and tuned for efficient operation with high quality of service. Increased real-time automation at all levels and time-frames is a critical tool in controlling this complexity. A key component in automation is practical and accurate simulation methods that can be used in live traffic scenarios. This paper introduces a new method with supporting algorithms for sampling key parameters from live or recorded traffic which can be used to generate large volumes of synthetic traffic with very similar rate distributions and temporal characteristics. Multiple spatial renewal processes are used to generate fractional Gaussian noise, which is scaled and transformed into a log-normal rate distribution with discrete arrival events, fitted to the properties observed in given recorded traces. This approach works well for modelling large user aggregates but is especially useful for medium sized and relatively small aggregates, where existing methods struggle to reproduce the most important properties of recorded traces. The technique is demonstrated through experimental comparisons with data collected from an operational LTE network to be highly useful in supporting self-learning and automation algorithms which can ultimately reduce complexity, increase energy efficiency, and reduce total network operation costs.

Modern mobile networks are increasingly complex from a resource management perspective, with diverse combinations of software, infrastructure elements and services that need to be configured and tuned for correct and efficient operation. It is well accepted in the communications community that appropriately dimensioned, efficient and reliable configurations of systems like 5G or indeed its predecessor 4G is a massive technical challenge. One promising avenue is the application of machine learning methods to apply a data-driven and continuous learning approach to automated system performance tuning. We demonstrate the effectiveness of policy-gradient reinforcement learning as a way to learn and apply complex interleaving patterns of radio resource block usage in 4G and 5G, in order to automate the reduction of cell edge interference. We show that our method can increase overall spectral efficiency up to 25% and increase the overall system energy efficiency up to 50% in very challenging scenarios by learning how to do more with less system resources. We also introduce a flexible phased and continuous learning approach that can be used to train a bootstrap model in a simulated environment after which the model is transferred to a live system for continuous contextual learning. 

As design, deployment and operation complexity increase in mobile systems, adaptive self-learning techniques have become essential enablers in mitigation and control of the complexity problem. Artificial intelligence and, in particular, reinforcement learning has shown great potential in learning complex tasks through observations. The majority of ongoing reinforcement learning research activities focus on single-agent problem settings with an assumption of accessibility to a globally observable state and action space. In many real-world settings, such as LTE or 5G, decision making is distributed and there is often only local accessibility to the state space. In such settings, multi-agent learning may be preferable, with the added challenge of ensuring that all agents collaboratively work towards achieving a common goal. We present a novel cooperative and distributed actor-critic multi-agent reinforcement learning algorithm. We claim the approach is sample efficient, both in terms of selecting observation samples and in terms of assignment of credit between subsets of collaborating agents.

This paper presents a method for load balancing heterogeneous networks by dynamically assigning values to the LTE cell range expansion (CRE) parameter. The method records hand-over events online and adapts flexibly to changes in terminal traffic and mobility by maintaining statistical estimators that are used to support autonomous assignment decisions. The proposed approach has low overhead and is highly scalable due to a modularised and completely distributed design that exploits self-organisation based on local inter-cell interactions. An advanced simulator that incorporates terminal traffic patterns and mobility models with a radio access network simulator has been developed to validate and evaluate the method.