Conventional time-series forecasting methods typically aim to minimize overall prediction error, without accounting for the varying importance of different forecast ranges in downstream applications. We propose a training methodology that enables forecasting models to adapt their focus to application-specific regions of interest at inference time, without retraining. The approach partitions the prediction space into fine-grained segments during training, which are dynamically reweighted and aggregated to emphasize the target range specified by the application. Unlike prior methods that predefine these ranges, our framework supports flexible, on-demand adjustments. Experiments on standard benchmarks and a newly collected wireless communication dataset demonstrate that our method not only improves forecast accuracy within regions of interest but also yields measurable gains in downstream task performance. These results highlight the potential for closer integration between predictive modeling and decision-making in real-world systems.

Artificial intelligence will be one of the key pillars of the next generation of mobile networks (6G), as it is expected to provide novel added-value services and improve network performance. In this context, large language models have the potential to revolutionize the telecom landscape through intent comprehension, intelligent knowledge retrieval, coding proficiency, and cross-domain orchestration capabilities. This paper presents Telco-oRAG, an open-source Retrieval-Augmented Generation (RAG) framework optimized for answering technical questions in the telecommunications domain, with a particular focus on 3GPP standards. Telco-oRAG introduces a hybrid retrieval strategy that combines 3GPP domain-specific retrieval with web search, supported by glossary-enhanced query refinement and a neural router for memory-efficient retrieval. Our results show that Telco-oRAG improves the accuracy in answering 3GPP-related questions by up to 17.6% and achieves a 10.6% improvement in lexicon queries compared to baselines. Furthermore, Telco-oRAG reduces memory usage by 45% through targeted retrieval of relevant 3GPP series compared to baseline RAG, and enables open-source LLMs to reach GPT-4-level accuracy on telecom benchmarks.

The application of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems in the telecommunication domain presents unique challenges, primarily due to the complex nature of telecom standard documents and the rapid evolution of the field. The paper introduces Telco-RAG,1an open-source RAG framework designed to handle the specific needs of telecommunications standards, particularly 3rd Generation Partnership Project (3GPP) documents. Telco-RAG addresses the critical challenges of implementing a RAG pipeline on highly technical content, paving the way for applying LLMs in telecommunications and offering guidelines for RAG implementation in other technical domains.

Due to an ever-expansive network deployment, numerous questions are being raised regarding the energy consumption of the mobile network. Recently, Non-Terrestrial Networks (NTNs) have proven to be a useful, and complementary solution to Terrestrial Networks (TN) to provide ubiquitous coverage. In this paper, we consider an integrated TN-NTN, and study how to maximize its resource usage in a dynamic traffic scenario. We introduce BLASTER, a framework designed to control User Equipment (UE) association, Base Station (BS) transmit power and activation, and bandwidth allocation between the terrestrial and non-terrestrial tiers. Our proposal is able to adapt to fluctuating daily traffic, focusing on reducing power consumption throughout the network during low traffic and distributing the load otherwise. Simulation results show an average daily decrease of total power consumption by $45 \%$ compared to a network model following 3GPP recommendation, as well as an average throughput increase of roughly $250 \%$. Our paper underlines the central and dynamic role that the NTN plays in improving key areas of concern for network flexibility.

The increasing interest in Large Language Models (LLMs) within the telecommunications sector underscores their potential to revolutionize operational efficiency. However, the deployment of these sophisticated models is often hampered by their substantial size and computational demands, raising concerns about their viability in resource-constrained environments. Addressing this challenge, recent advancements have seen the emergence of small language models that surprisingly exhibit performance comparable to their larger counterparts in many tasks, such as coding and common-sense reasoning. Phi-2, a compact yet powerful model, exemplifies this new wave of efficient small language models. This paper conducts a comprehensive evaluation of Phi-2’s intrinsic understanding of the telecommunications domain. Recognizing the scale-related limitations, we enhance Phi-2’s capabilities through a Retrieval-Augmented Generation approach, meticulously integrating an extensive knowledge base specifically curated with telecom standard specifications. The enhanced Phi-2 model demonstrates a profound improvement in accuracy, answering questions about telecom standards with a precision that closely rivals the more resource-intensive GPT-3.5. The paper further explores the refined capabilities of Phi-2 in addressing problem-solving scenarios within the telecom sector, highlighting its potentials and limitations.

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.

This work considers a multi-cell, multi-carrier massive MIMO network with carrier aggregation, and tackles the rate versus power consumption trade-off, by jointly optimizing the number of employed component carriers, active antennas, base station density, and transmit power. A provably convergent algorithm is developed together with closed-form results for the individual optimization of the considered resources. Numerical results show how carrier aggregation can effectively reduce the power consumption without sacrificing the rate performance.

We study an industry automation scenario where a central controller broadcasts critical messages to the wireless devices (e.g., sensors/actuators). We devise a stochastic geometry framework where the rate coverage probability of devices is modeled by taking into account the density of roaming blockages over the factory floor. To alleviate the loss in the coverage, we adopt a two-phase transmission policy, where in thebroadcast phase, the central controller broadcasts the messages intended for the devices in the network area. The devices in coverage in the broadcast phase act as decode-and-forward relays in therelay phase, so as to reinforce the signal strength at the devices in outage. The total downlink transmission time is, therefore, partitioned into two phases by a tunable factor. Finally, we study the optimal value of the partitioning factor with varying device densities, blockage densities, and file sizes, and we highlight that a longer transmission time should be allotted to the broadcast phase in the case of larger file sizes or lower transmit power of the controller.

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 study a millimeter wave (mm-wave) wireless network deployed along the roads of an urban area, to support localization and communication services simultaneously for outdoor mobile users. In this network, we propose a mm-wave initial beam-selection scheme based on localization-bounds, which greatly reduces the initial access delay as compared to traditional initial access schemes for standalone mm-wave small cell base station (BS). Then, we introduce a downlink transmission protocol, in which the radio frames are partitioned into three phases, namely, initial access, data, and localization, respectively. We establish a trade-off between the localization and communication performance of mm-wave systems, and show how enhanced localization can actually improve the data-communication performance. Our results suggest that dense BS deployments enable to allocate more resources to the data phase while still maintaining appreciable localization performance. Furthermore, for the case of sparse deployments and large beam dictionary size (i.e., with thinner beams), more resources must be allotted to the localization phase for optimizing the rate coverage. Based on our results, we provide several system design insights and dimensioning rules for the network operators that will deploy the first generation of mm-wave BSs.

Network function virtualization is a promising technology to simultaneously support multiple services with diverse characteristics and requirements in the 5G and beyond networks. In particular, each service consists of a predetermined sequence of functions, called service function chain (SFC), running on a cloud environment. To make different service slices work properly in harmony, it is crucial to appropriately select the cloud nodes to deploy the functions in the SFC and flexibly route the flow of the services such that these functions are processed in the order defined in the corresponding SFC, the end-to-end (E2E) latency constraints of all services are guaranteed, and all cloud and communication resource budget constraints are respected. In this paper, we first propose a new mixed binary linear program (MBLP) formulation of the above network slicing problem that optimizes the system energy efficiency while jointly considers the E2E latency requirement, resource budget, flow routing, and functional instantiation. Then, we develop another MBLP formulation and show that the two formulations are equivalent in the sense that they share the same optimal solution. However, since the numbers of variables and constraints in the second problem formulation are significantly smaller than those in the first one, solving the second problem formulation is more computationally efficient especially when the dimension of the corresponding network is large. Numerical results demonstrate the advantage of the proposed formulations compared with the existing ones.

The dense deployment of millimeter wave small cells combined with directional beamforming is a promising solution to enhance the network capacity of the current generation of wireless communications. However, the reliability of millimeter wave communication links can be affected by severe pathloss, blockage, and deafness. As a result, mobile users are subject to frequent handoffs, which deteriorate the user throughput and the battery lifetime of mobile terminals. To tackle this problem, our paper proposes a deep multi-agent reinforcement learning framework for distributed handover management called RHando (Reinforced Handover). We model users as agents that learn how to perform handover to optimize the network throughput while taking into account the associated cost. The proposed solution is fully distributed, thus limiting signaling and computation overhead. Numerical results show that the proposed solution can provide higher throughput compared to conventional schemes while considerably limiting the frequency of the handovers.

Network virtualization is a key enabler for 5G systems to support the expected use cases of vertical markets. In this context, we study the joint optimal deployment of Virtual Network Functions (VNFs) and allocation of computational resources in a hybrid cloud infrastructure by taking the requirements of the 5G services and the characteristics of the cloud architecture into consideration. The resulting mixed-integer problem is reformulated as an integer linear problem, which can be solved by using a standard solver. Our results underline the advantages of a hybrid infrastructure over a standard cloud radio access network consisting only of a central cloud, and show that the proposed mechanism to deploy VNF chains leads to high resource utilization efficiency and large gains in terms of the number of supported VNF chains. To deal with the computational complexity of optimizing a large number of clouds and VNF chains, we propose a simple low-complexity heuristic that attempts to find a feasible VNF deployment solution with a limited number of functional splits. Numerical results indicate that the performance of the proposed heuristic is close to the optimal one when the edge clouds are well dimensioned with respect to the computational requirements of the 5G services.

Network densification and millimeter-wave technologies are key enablers to fulfill the capacity and data rate requirements of the fifth generation (5G) of mobile networks. In this context, designing low-complexity policies with local observations, yet able to adapt the user association with respect to the global network state and to the network dynamics is a challenge. In fact, the frameworks proposed in literature require continuous access to global network information and to recompute the association when the radio environment changes. With the complexity associated to such an approach, these solutions are not well suited to dense 5G networks. In this paper, we address this issue by designing a scalable and flexible algorithm for user association based on multi-agent reinforcement learning. In this approach, users act as independent agents that, based on their local observations only, learn to autonomously coordinate their actions in order to optimize the network sum-rate. Since there is no direct information exchange among the agents, we also limit the signaling overhead. Simulation results show that the proposed algorithm is able to adapt to (fast) changes of radio environment, thus providing large sum-rate gain in comparison to state-of-the-art solutions.

Finding the optimal association between users and base stations that maximizes the network sum-rate is a complex task. This problem is combinatorial and non-convex, and is even more challenging in millimeter-wave networks due to beamforming, blockages, and severe path loss. Despite the interest that this problem has gained over the last years, the various solutions proposed so far in the literature still fail at being flexible, computationally effective, and suitable to the dynamic nature of mobile networks. This paper addresses these issues with a novel distributed algorithm based on multi-agent reinforcement learning. More specifically, we model each user as an agent, which, at each time step, maps its observations to an action corresponding to an association request to a base station in its coverage range. Our numerical results show that the proposed solution offers near optimal performance and thanks to its flexibility, provides large sum-rate gain with respect to the state-of-art approaches.

Network virtualization is a key enabler for the 5G systems for supporting the novel use cases related to the vertical markets. In this context, we investigate the joint optimal deployment of Virtual Network Functions (VNFs) and the allocation of computational resources in a hybrid cloud infrastructure by taking into account the requirements of the 5G services and the characteristics of the cloud nodes. To achieve this goal, we analyze the relations between functional placement, computational requirements, and latency constraints, and formulate an integer linear programming problem, which can be solved by using a standard solver. Our results underline the advantages of a hybrid architecture over a standard solution with a central cloud, and show that the proposed mechanism to deploy VNFs leads to high resource utilization efficiency and large gains in terms of the number of slice chains that can be supported by the cloud-enhanced 5G networks.

International audience

In this paper, we define a flexible, adaptable, and programmable architecture for 5G mobile networks, taking into consideration the requirements, KPIs, and the current gaps in the literature, based on three design fundamentals: (i) split of user and control plane, (ii) service-based architecture within the core network (in line with recent industry and standard consensus), and (iii) fully flexible support of E2E slicing via per-domain and cross-domain optimisation, devising inter-slice control and management functions, and refining the behavioural models via experiment-driven optimisation. The proposed architecture model further facilitates the realisation of slices providing specific functionality, such as network resilience, security functions, and network elasticity. The proposed architecture consists of four different layers identified as network layer, controller layer, management and orchestration layer, and service layer. A key contribution of this paper is the definition of the role of each layer, the relationship between layers, and the identification of the required internal modules within each of the layers. In particular, the proposed architecture extends the reference architectures proposed in the Standards Developing Organisations like 3GPP and ETSI, by building on these while addressing several gaps identified within the corresponding baseline models. We additionally present findings, the design guidelines, and evaluation studies on a selected set of key concepts identified to enable flexible cloudification of the protocol stack, adaptive network slicing, and inter-slice control and management.

The analyses of cellular network performance based on stochastic geometry generally ignore the traffic dynamics in the network. This restricts the proper evaluation and dimensioning of the network from the perspective of a mobile operator. To address the effect of dynamic traffic, recently, the mean cell approach has been introduced, which approximates the average network load by the zero cell load. However, this is not a realistic characterization of the network load, since a zero cell is statistically larger than a random cell drawn from the population of cells, i.e., a typical cell. In this paper, we analyze the load of a noise-limited network characterized by high signal to noise ratio (SNR). The noise-limited assumption can be applied to a variety of scenarios, e.g., millimeter wave networks with efficient interference management mechanisms. First, we provide an analytical framework to obtain the cumulative density function of the load of the typical cell. Then, we obtain two approximations of the average load of the typical cell. We show that our study provides a more realistic characterization of the average load of the network as compared to the mean cell approach. Moreover, the prescribed closed-form approximation is more tractable than the mean cell approach.

We analyze a millimeter wave network, deployed along the streets of a city, in terms of positioning and downlink data-rate performance, respectively. First, we present a transmission scheme where the base stations provide jointly positioning and data-communication functionalities. Accordingly, we study the trade- off between the localization and the data rate performance based on theoretical bounds. Then, we obtain an upper bound on the probability of beam misalignment based on the derived localization error bound. Finally, we prescribe the network operator a scheme to select the beamwidth and the power splitting factor between the localization and communication functions to address different quality of service requirements, while limiting cellular outage.

5G mobile networks encompass the capabilities of hosting a variety of services such as mobile social networks, multimedia delivery, healthcare, transportation, and public safety. Therefore, the major challenge in designing the 5G networks is how to support different types of users and applications with different quality-of-service requirements under a single physical network infrastructure. Recently, network slicing has been introduced as a promising solution to address this challenge. Network slicing allows programmable network instances which match the service requirements by using network virtualization technologies. However, how to efficiently allocate resources across network slices has not been well studied in the literature. Therefore, in this paper, we first introduce a model for orchestrating network slices based on the service requirements and available resources. Then, we propose a Markov decision process framework to formulate and determine the optimal policy that manages cross-slice admission control and resource allocation for the 5G networks. Through simulation results, we show that the proposed solution is efficient not only in providing slice-as-a-service based on service requirements, but also in maximizing the provider's revenue.

This paper focuses on Inter-Cell Interference Coordination for small cells implementing cell discontinuous transmission (DTX) to enhance the network energy efficiency. The small cell activity is dynamically orchestrated such that the inter-cell interference is limited, the power consumption of Radio Access Network and backhaul is jointly reduced, and the Quality of Service (QoS) constraints are satisfied. To achieve this goal, we develop a Reinforcement Learning framework that achieves network-wise optimization in stochastic environments. Our solution leads to notable energy saving with respect to the state of the art DTX approaches without affecting the QoS. Moreover, the user performance is close to the one experienced when a centralized scheduler is used to limit the inter-cell interference.

This paper focuses on analyzing some key business aspects that arise during the deployment of key novel enabling technologies for 5G systems. Results are taken out of two EU-funded ongoing research projects, namely 5G-MiEdge and Superfluidity, which largely exploit mmWave communications and softwarization concepts for 5G networks. We initially provide a stakeholder analysis of the 5G ecosystem, as well as a Strengths Weaknesses Opportunities Threats (SWOT) analysis of a couple of key and most promising 5G use cases. For one use case also a preliminary business model is provided. Then we detail an economic 5G cost model, which is able to provide indications on the profitability of 5G networks. Finally, we highlight the planned future works.

We characterize a two tier heterogeneous network, consisting of classical sub-6 GHz macro cells, and multi radio access technology (RAT) small cells able to operate in sub-6 GHz and millimeter-wave (mm-wave) bands. For optimizing coverage and to balance loads, we propose a two-step mechanism based on two biases for tuning the tier and RAT selection, where the sub-6 GHz band is used to speed-up the initial access procedure in the mm-wave RAT. First, we investigate the effect of the biases in terms of signal-to-interference-plus-noise ratio (SINR) distribution, cell load, and user throughput. More specifically, we obtain the optimal biases that maximize either the SINR coverage or the user downlink throughput. Then, we characterize the cell load using the mean cell approach and derive upper bounds on the overloading probabilities. Finally, for a given traffic density, we provide the small cell density required to satisfy system constraints in terms of overloading and outage probabilities. Our analysis highlights the importance of deploying dual-band small cells, in particular, when small cells are sparsely deployed or in case of heavy traffic.

The expected diversity of services and the variety of use cases in 5G networks will require a flexible Radio Resource Management able to satisfy the heterogeneous Quality of Service (QoS) requirements. Classical scheduling strategies have been designed to deal mainly with some particular QoS requirements for specific traffic types. To improve the scheduling performance, this paper proposes an innovative scheduler framework, that selects at each transmission time interval, the appropriate scheduling strategy capable to maximize the users' satisfaction measure in terms of distinct QoS requirements. Neural networks and the Reinforcement Learning paradigm are jointly used to learn the best scheduling decision based on the past experiences. The simulation results show very good convergence properties for the proposed policies, and notable QoS improvements with the respect to the baseline scheduling solutions.

We characterize a multi tier network with classical macro cells, and multi radio access technology (RAT) small cells, which are able to operate in microwave and millimeter-wave (mm-wave) bands. The small cells are assumed to be deployed along roads modeled as a Poisson line process. This characterization is more realistic as compared to the classical Poisson point processes typically used in literature. In this context, we derive the association and RAT selection probabilities of the typical user under various system parameters such as the small cell deployment density and mm-wave antenna gain, and with varying street densities. Finally, we calculate the signal to interference plus noise ratio (SINR) coverage probability for the typical user considering a tractable dominant interference based model for mm-wave interference. Our analysis reveals the need of deploying more small cells per street in cities with more streets to maintain coverage, and highlights that mm-wave RAT in small cells can help to improve the SINR performance of the users.

In this paper, we investigate optimal control of cell discontinuous transmission (DTX) for small cells in heterogeneous cellular networks (HetNets). The small cell transmission activity is dynamically orchestrated in a centralized way to jointly minimize the power consumption of Radio Access Network (RAN) and backhaul (BH), while at the same time satisfying user Quality of Service (QoS) constraints. We propose a Fuzzy Q-Learning scheme that combines Reinforcement Learning (RL) and Fuzzy Inference System (FIS) theory to enable system optimization in realistic environments. Our analysis shows that 1) joint RAN and BH optimization is necessary to correctly design network energy saving functions and 2) the proposed controller results in notable energy saving with respect to the classic DTX approach.

With the rapid increase of mobile data demand, Mobile Network Operators (MNOs) have started to pay close attention to wireless network energy consumption and CO2 emissions. In the current context of energy transition, Renewable Energy (RE) represents a great potential for MNOs to use environment-friendly power supply and reduce the energy expenses. Moreover, the Smart Grid (SG) offers important services in terms of Demand Side Management and decentralized production, which make rethinking the power usage within mobile networks a necessity. In this paper, we propose a fuzzy Q-Learning based energy controller for a small cell powered by local RE, local storage, and the SG to simultaneously minimize electricity expenditures of the MNOs and enhance the life span of the storage device. Simulation results show that the proposed solution achieves important cost reduction with respect to simpler approaches and performs very closely to the ideal strategy based on a perfect knowledge of the stochastic variables.

The future demands of high data spectral efficiency and ubiquitous coverage are pushing the next generation of cellular networks towards network densification with massive deployments of small cells to complement macro base stations. In this paper, we study a network comprising of closed access small cells along with macro base stations using stochastic geometry. First, the cell association probability is characterized. Additionally, approximate values of the average downlink signal to interference and noise ratio (SINR) and the downlink spectral efficiency are derived. These derivations are carried out without using the classical approach of solving a Laplace functional. For this, the statistical independence of the useful signals and interference powers is exploited. The obtained results can be used to optimize the small cell network deployment and the inter-cell interference coordination functions.

This paper considers heterogeneous cellular networks, where cluster of small cells are deployed to create local hot spots inside the macro cell. In the past, most of the research in this topic has focused on mitigating inter cell interference; however, wireless backhaul has recently emerged as an urgent challenge to enable ubiquitous broadband wireless services at small cells. Hence, we propose a novel cell selection framework, which associates users and heterogeneous access nodes to improve the efficiency in the overall radio and backhaul resource utilization and avoid load congestions. We also model the relationships amongst cell load, resource management, backhaul capacity constraints, and the overall network capacity. Then, we describe the cell selection problem and we present a heuristic algorithm, named as Evolve, to solve it with limited complexity. Our analysis shows that Evolve achieves near optimal performance leading to notable capacity improvements with respect to the classic SINR based association scheme.

The exponential increase in high rate traffic driven by a new generation of wireless devices is expected to overload cellular network capacity in the near future. Femtocells have recently been proposed as an efficient and cost-effective approach to enhance cellular network capacity and coverage. However, dense and unplanned deployment of additional Base Stations and their uncoordinated operation may increase the system power consumption. Thus, efficient schemes are essential for managing femtocells activity and improving the system performance. In this paper, we investigate the effect of femtocell deployment on the cellular network energy efficiency. The goal is twofold: first, we aim to analyse how classic femtocell access schemes affect the system energy consumption; second, we propose a novel cell selection scheme for Open Access femtocells that allows the effective deployment of femtocells in the cellular network reducing power consumption and limiting the effect of interference.

The exponential increase in high rate traffic driven by the new generation of wireless services is expected to overload cellular network capacity in the near future. Femtocell networks have recently been proposed as an efficient and cost-effective solution to enhance cellular network capacity and coverage. However, dense and unplanned deployment of new Base Stations (BSs) and their uncoordinated operation may increase the system power consumption and rise co-channel interference. Thus, efficient schemes are essential for managing femtocell activity and improving the system performance. Classical cell switch off and discontinuous transmission (DTX) algorithms aim at improving the network Energy Efficiency (EE) in lightly loaded scenarios. In this paper, we propose a novel multi-cell architecture for Open Access femtocell networks, which also enables energy saving at medium and high loads without compromising the end-user performance.

The femtocell deployment in 3GPP/LTE sets new challenges to interference mitigation techniques and Radio Resource Management (RRM). Traditional schemes are mainly designed for classical cellular networks while the ad hoc nature of femtocells notably limits the complexity of possible algorithms. Thus, efficient RRM schemes are essential for limiting the interference impact on end-user performance. The goal of this paper is to achieve effective spectral reuse between macrocells and femtocells while guaranteeing the QoS of users served by both macro and femto base stations. We propose a novel resource management scheme that limits the overall interference per chunk generated outside the coverage range of a femtocell while reducing the transmission power in each Resource Block (RB). Our simulation results show that the proposed RRM scheme enhances the energy efficiency of femtocells and improves both macrocell and femtocell throughput.