Providing 5G seamless and secure services for train passengers is very challenging. In addition to the challenges related to high-speed, many others, such as low signal quality due to 5G coverage holes, tunnels, and complex orography, may compromise the Quality of Service (QoS) provided to train passengers, especially in trains crossing international borders. To overcome these challenges, the EU-funded Horizon 2020 5GMED project has devised a cross-border 5G network architecture that allows train passengers to have the same QoS they have at home in whichever country they are. The network architecture is based on the concept of train neutral host, which operates as a service provider for other mobile network operators. The communication between the train and the ground is facilitated by a heterogeneous network infrastructure comprised of 5G StandAlone (SA), IEEE 802.11ad, and satellite networks to minimize the probability of having low quality signals along the rail track. The seamless switching between these network technologies is performed using Adaptive Communication System-Gateways (ACS-GW) developed in the project. In this paper, we propose the train neutral host architecture, and present the performance evaluation results obtained in a realistic environment over 5G SA networks deployed on the Mediterranean cross-border corridor between Spain and France. Experimental results show satisfying results in terms of service throughput, latency and train inter-cell handover.

Network Function Virtualization (NFV) simplifies the deployment of network services by leveraging virtualization technologies to make the management of network functions more flexible and cost efficient. The deployment of these services requires the allocation of Virtual Network Function - Forwarding Graphs (VNF-FGs), which implies placing and chaining VNFs according to the requests of VNF-FGs. In this paper, we consider the offline allocation of VNF-FGs problem to improve resource utilization and reduce total costs. We focus on how VNF-FG demands are routed so as to optimize resource utilization without adding capacity to the infrastructure. Given a non-linear cost function associated to each network resource, we formulate the problem as a non-linear single-path routing problem in an extended graph. Then, we propose to adapt a single-path routing heuristic algorithm inspired from game theory to solve it. We show that this algorithm converges and establishes its approximation ratio in a number of cases. Experimental results obtained for different network topologies and different cost functions show that this algorithm provides very good quality solutions with substantially lower computing times compared to the optimal solution.

The massive deployment of small cells in 5G networks represents an alternative to meet the ever increasing mobile data traffic and to provide very-high throughout by bringing the users closer to the Base Stations (BSs). This large increase in the number of network elements demands a significant increase in the energy consumption and carbon footprint followed by complex interference management. In order to address these challenges, we consider multi-level Sleep Mode (SM) where BS components with similar activation/deactivation times can be put to sleep. The deeper and higher energy efficient the SM is, the longer it will take the BS to activate, which might impose degradation in the Quality of Service (QoS). While this adds operational flexibility to the BS, it brings complex management to the operator. In this paper, we consider a heterogeneous network architecture where small cells can switch to different SM levels to save energy and reduce dropping rate. We propose a reinforcement learning algorithm for small cells that adapts their activities subject to service delay constraint. In this regard, the algorithm intelligently learns from the environment based on the co-channel interference, the cell buffer size and the expected cell throughput in order to decide the best SM policy. Numerical values show that important energy savings can be obtained with an acceptable dropping rate. Moreover, we show that while offloading users to the macro cell can significantly reduce their delay, dropping rate and the cluster energy consumption, it comes at a cost of decreasing the network energy efficiency up to 5 times compared with the case of no offload.

We consider the SFC embedding (SFCE) problem in the Slice as a Service (SlaaS) model. In this model, a slice provider leases resources from multiple cloud and network providers in order to instantiate the Service Function Chain (SFC) requested by a slice tenant. As the slice provider has no visibility on the infrastructures of the resource providers, in which resources may be purchased and released quite rapidly, it has to query them to determine what are the possible allocations and their costs. We show that when there are many resource providers and many VNFs composing the SFC, the number of queries to be made for discovering a minimum cost SFC embedding grows quickly, leading to excessively long deployment times. In order to reduce the latter quantity, we propose to query resource providers strategically, rather than collecting the information on all possible allocations at once. We provide bounds on the number of queries to be made in this approach, and propose to exploit a Shortest Path Discovery algorithm in order to reduce this number of queries and thus the SFC deployment time. Our numerical results suggest that this algorithm is fairly efficient, and that the deployment times can be significantly shortened, in particular when initial estimates of allocation costs can be provided by the slice provider.

The increase of energy demand in cellular networks imposes big economic and ecological challenges. Answering to these challenges requires complex energy frameworks that consider Smart Grid, renewable energy, battery systems and employs efficient management of radio as well as energy resources. In this context, lithium batteries present very good performance indicators (e.g., high energy density, large service life and environmental friendliness) but can also have very poor use duration when the energy management system is not suitable. This is linked to the important consequences of radio resource allocation on energy consumption and operational cost. In this article, we study and propose energy and radio allocation mechanisms for cellular networks supplied with hybrid energy sources (grid and renewable). We propose a Battery Aging and Price-Aware (BAPA) algorithm that brings down the grid energy consumption of the operator while including battery degradation constraints. We decompose the problem into three subproblems: radio resource allocation problem, grid energy purchase problem and power allocation problem. We show that our algorithm performs very close to the optimal solution and outperforms a benchmark algorithm, allowing more efficient battery use, energy savings and network operation cost reduction with no impact on the QoS of users. Finally, we provide some insights into how the advancement in base station technology will help reducing the investment cost in future cellular networks.

In this paper, we propose a sleep strategy for energy-efficient 5G Base Stations (BSs) with multiple Sleep Mode (SM) levels to bring down energy consumption. Such management of energy savings is coupled with managing the Quality of Service (QoS) resulting from waking up sleeping BSs. As a result, a tradeoff exists between energy savings and delay. Unlike prior work that studies this problem for binary state BS (ON and OFF), this work focuses on multi-level SM environment, where the BS can switch to several SM levels. We propose a Q-Learning algorithm that controls the state of the BS depending on the geographical location and moving velocity of neighboring users in order to learn the best policy that maximizes the tradeoff between energy savings and delay. We evaluate the performance of our proposed algorithm with an online suboptimal algorithm that we introduce as well. Results show that the Q-Learning algorithm performs better with energy savings up to 92% as well as better delay performance than the heuristic scheme.

In 5G networks, specific requirements are defined on the periodicity of Synchronization Signaling (SS) bursts. This imposes a constraint on the maximum period a Base Station (BS) can be deactivated. On the other hand, BS densification is expected in 5G architecture. This will lead to an energy crunch if kept ignored. In this paper, we propose a distributed algorithm based on Reinforcement Learning (RL) that controls the states of the BSs while respecting the requirements of 5G. By considering different levels of Sleep Modes (SMs), the algorithm chooses how deep a BS can sleep according to the best switch-off SM level policy that maximizes the trade-off between energy savings and system delay. The latter is calculated based on the wake-up time required by the different SM levels. Results show that our algorithm performs better than the case of using only one type of SM. Furthermore, our simulations show a gain in energy savings up to 90% when the users are delay tolerant while respecting the periodicity of the SS bursts in 5G.

In this paper, we consider cellular networks powered by both renewable energy and the Smart Grid. We study the problem of minimizing the cost of on-grid energy while maximizing the satisfaction of users with different requirements. We consider patterns of renewable energy generation, traffic variation and real-time price of grid energy. Knowing that these patterns are all time related, we use Q-learning to extract a common pattern as well as to decide the number of radio resource blocks activated to maximize the users' satisfaction and minimize the on-grid energy cost. Results show that using Q-learning achieves a good tradeoff with more than 75% reduction in energy cost and negligible degradation in users' satisfaction.

In this paper, we study the grid energy consumption of a hybrid energy powered base station system equipped with a renewable source and a battery. We focus on the energy storage element that is prone to irreversible aging mechanisms, thus requiring precise management that takes into account both energy cost and constraints preventing fast battery degradation. We propose a Battery Aging and Price-Aware (BAPA) algorithm that brings down the grid energy consumption of the operator while including battery degradation constraints. We decompose the problem into three subproblems: Resource allocation problem, grid energy purchase problem and power allocation problem. We show that our algorithm performs very close to optimality (up to 99%) and outperforms a benchmark algorithm.

Hybrid energy powered cellular networks are key for proposing green and cost-efficient wireless networks. Yet, the related energy management imposes severe challenges to efficiently manage the power allocation between the Renewable Energy (RE) source, the batteries and the smart power grid. The energy storage element (i.e., battery) is prone to irreversible aging mechanisms, requiring intelligent management that takes into account both the energy cost and requirements preventing battery degradation. In this work, we include this important constraint before proposing an energy management and base stations switch off algorithm for a partially RE-equipped cellular network in a variable electricity price environment. Results show that our algorithm outperforms a benchmark algorithm with a gain up to 20% in terms of electric bill reduction and enhances the battery life time by 35%.

Complex green wireless networks including smart grid, renewable energy and batteries need precise energy management strategies in order to realize some of the defined objectives. Although these networks have different services (streaming, web, voice call, etc.) with different requirements, most research work slightly covered the specific contribution of each of these services and their effect on the network energy consumption. In this paper, we consider advanced KPIs (Key Performance Indicators) putting forward each service contribution to energy consumption. Using these new KPIs, we propose to adapt some energy management strategies, leading to performance amelioration and new energy savings, under renewable energy and smart grid environment. Simulations indicate the efficiency of our proposed adaptation, achieving 11.45% enhancement in these new KPIs, and outperforming several benchmark algorithms with a gain up to 8% points in terms of energy savings reduction. We analyze the benefits of the services KPIs and also their limits, leading to possible new services-based efficient energy management strategies in the considered framework.

Wireless networks have important energy needs. Many benefits are expected when the base stations, the fundamental part of this energy consumption, are equipped with renewable energy (RE) systems. Important research efforts have been done to enhance the utilization of RE. However, to the best of our knowledge, these efforts did not take into consideration partially RE-equipped systems. The latter is of great importance considering the high cost of these systems and the feasibility of implementing RE systems at all base station sites. Thus, it is interesting to study the percentage of sites to be equipped with RE systems. In this work, we analyze the energy and cost savings for a defined energy management strategy of a RE hybrid system. Our study of the relationship between cost savings and percentage of sites equipped with RE show significant results. For example, our simulation shows that a cost gain of 60% is realized when 30% of the base stations are equipped with solar panels that harvest only 35% of the total network energy demand at full load. Results also show an upper limit for the battery capacity at which the cost gain is maximized.