For energy efficient operation of the massive multiple-input multiple-output (MIMO) networks, various aspects of energy efficiency maximization have been addressed, where a careful selection of number of active antennas has shown significant gains. Moreover, switching-off physical resource blocks (PRBs) and carrier shutdown saves energy in low load scenarios. However, the joint optimization of spectral PRB allocation and spatial layering in a heterogeneous network has not been completely solved yet. Therefore, we study a power consumption model for multi-cell multi-user massive MIMO 5G network, capturing the joint effects of both dimensions. We characterize the optimal resource allocation under practical constraints, i.e., limited number of available antennas, PRBs, base stations (BSs), and frequency bands. We observe a single spatial layer achieving lowest energy consumption in very low load scenarios, whereas, spatial layering is required in high load scenarios. Finally, we derive novel algorithms for energy efficient user (UE) to BS assignment and propose an adaptive algorithm for PRB assignment and power control. All results are illustrated by numerical system-level simulations, describing a realistic metropolis scenario. The results show that a higher frequency band should be used to support UEs with large rate requirements via spatial multiplexing and assigning each UE maximum available PRBs.

The fifth generation of the Radio Access Network (RAN) has brought new services, technologies, and paradigms with the corresponding societal benefits. However, the energy consumption of 5G networks is today a concern. In recent years, the design of new methods for decreasing the RAN power consumption has attracted interest from both the research community and standardization bodies, and many energy savings solutions have been proposed. However, there is still a need to understand the power consumption behavior of state-of-the-art base station architectures, such as multi-carrier active antenna units (AAUs), as well as the impact of different network parameters. In this paper, we present a power consumption model for 5G AAUs based on artificial neural networks. We demonstrate that this model achieves good estimation performance, and it is able to capture the benefits of energy saving when dealing with the complexity of multi-carrier base stations architectures. Importantly, multiple experiments are carried out to show the advantage of designing a general model able to capture the power consumption behaviors of different types of AAUs. Finally, we provide an analysis of the model scalability and the training data requirements.

The energy consumption of the fifth generation (5G) of cellular technology is concerning for the mobile industry and the entire society. To minimize the environmental footprint and economic costs of 5G, it is necessary to adapt the transmission capabilities of networks to end-users’ quality of service requirements. In this paper, we focus on the carrier shutdown approach that enables a base station (BS) to autonomously switch off during low traffic periods, by transferring its load to neighbouring active BSs. More specifically, we propose a data-driven framework, constructed through real network measurements, which statistically characterizes the user equipment (UE) transfer across neighbouring BSs, when carrier shutdown operates. The implementation of this framework allows the 5G system to determine a poor load distribution due to energy saving mechanisms, prevent drastic reductions in UE performance, and ultimately estimate energy savings when activating carrier shutdown.

To meet the targets of net-zero green house gas (GHG) emissions, future wireless networks must operate highly energy efficient. To this end, various aspects of energy efficiency (EE) maximization have been addressed. On the one hand, careful selection of active number of antennas in massive multiple-input multiple-output (MIMO) systems has shown significant gains. Whereas, switching off physical resource blocks (PRBs) and carrier shutdown saves energy in low load scenarios. However, the joint optimization of both dimensions, the spectral PRB allocation with carrier aggregation (CA) and spatial layering, has not been accounted for. In this paper, we propose a power consumption model that captures the joint effect of CA and spatial layering on the total power consumption of a 5G network. We characterize the optimal resource allocation in spatial and spectral dimensions under practical constraints. Our results show that only in very low load scenarios, a single spatial layer achieves the lowest energy consumption and in most cases with high rate requirements and more users, spatial layering is required with carefully optimized number of active antennas and active PRBs. The gains compared to activating all available antennas and using all available PRB resources are tremendous. Finally, we study the point where switching on another frequency band results in better EE depending on the attenuation model.

The energy consumption of cellular networks, despite the high energy efficiency of the fifth generation (5G) of mobile technology, is still a challenge. The fundamental problem arises due to the complexity of optimising the operation of the available rich set of energy efficiency features in large-scale deployments. To assist such optimisation, a large body of research -with the resulting understanding and algorithms-exists, particularly on the energy efficiency of single-cell massive multiple-input multiple-output systems. However, other funda-mental cellular features, such as those relating to multi-carrier systems, remain largely unexplored. In this paper, we show how multi-carrier features, such as carrier aggregation, can play a significant role in energy savings, and question the need for hundreds of antennas and transceiver chains at the base stations as an urgent solution to increase the energy efficiency of next generation networks.

The energy consumption and carbon footprint of the fifth-generation (5G) of mobile technology is a current concern to mobile network operators (MNOs). These are currently attempting to lower both their carbon emissions and electricity bills by investigating new schemes that allow adapting the network transmission capabilities to the end-users' quality of service (QoS) requirements. Many of such schemes rely on accurate traffic forecasting, and as a consequence, there is a large effort on investigating novel machine learning (ML) algorithms, which fed by network measurement data and empowered by the computing capabilities of dedicated hardware, can help modelling and predicting users' behaviours. Most of the works in the literature, however, focus on predicting the traffic when energy saving features, e.g. carrier shutdown, are not implemented or activated. However, the prediction task becomes much more challenging when energy saving features are adopted due to their impact to the actual measured traffic. In this paper, we consider a scenario in which part of the base stations implement energy saving schemes, which allow them to dynamically switch off part of their hardware to reduce their power consumption. Then, we present a ML framework based on graph convolutional networks (GCNs) for traffic forecasting in such dynamic scenarios, and compare its performance with other statistical and ML prediction algorithms. The proposed GCN framework provides significant accuracy gains. Moreover, we provide an analysis of the impact of spatial correlation-captured by the GCN model-on the achieved performance.

We consider an antenna tuning problem that is quite important to ensure a satisfying user experience in wireless networks. We aim to maximize the coverage ratio of a large service region by properly choosing the antenna angles. In order to embrace the true complexity of the practical networks and the radio channels, a system level simulator is required. The optimization algorithm has to be performed based on the stochastic numerical output of the simulator. We proposed a zeroth order continuation method to solve the challenging stochastic black-box non-convex optimization problem. The basic idea is to use two observations of the simulator output to generate a gradient estimator, that can be applied to optimize the smoothed version of the original objective function. We optimize a series of smoothed functions to make the solution progressively closer to the global optimum. The performance guarantee of the proposed algorithm has been investigated under weaker assumptions compared to those of the state-of-art analysis. Although the proposed algorithm can be applied to general problems, we perform simulations considering an ideal network model and present numerical results to corroborate our claim.

As autonomous-vehicle-related technologies tend to be mature, improving passengers' experience by learning driving styles from human drivers becomes a promising research topic. This study aims at learning human drivers' velocity planning strategies for driving at curvy paths (e.g. negotiating sharp curves, turning at intersections, etc.) on structural road. First, we identified and extracted training trips from the latest naturalistic driving study database. Vehicle trajectories and the disturbances caused by other vehicles were estimated based on sensor data. Road characteristics, environmental parameters were identified from road information database and video clips. Then, neural network based models were developed to fit drivers' speed profiles under different driving situations. Five models with different prediction steps were trained by up to 600 driving trips. Three error criteria were used to evaluate the performance of proposed models. This study verified the possibility of using human drivers' experience to generate velocity recommendations for different driving conditions. The limitations of the models are also documented.