Online gaming is one of the most popular services via Wi-Fi at home, requiring stable, low latency to ensure responsive gameplay. Although recent versions of Wi-Fi offer maximum theoretical bitrates up to 9.6 Gbps, far exceeding throughput requirements for online games, players still experience frustrating moments due to poor Wi-Fi connections during the game. Among other challenges, Wi-Fi is particularly sensitive to its radio environment due to its unlicensed spectrum. In addition to interferences from neighboring Wi-Fi networks in crowded areas, other radio technologies such as Bluetooth, Thread and microwave ovens that share the same frequency as Wi-Fi can also have non-desirable effect for Wi-Fi services. In this paper, we focus on how Wi-Fi coverage and neighboring interference can impact the gaming experience of a well-known multiplayer online game, Counter-Strike: Global Offensive. We conducted gaming experiments and collected metrics from a Wi-Fi access point and gaming device in a controlled and automated Wi-Fi testbed with different attenuation values and available airtime on the 2.4 GHz and 5 GHz frequency bands. Our analysis identifies strong correlations between in-game latency and specific WiFi metrics for each frequency band. This study helps better understand the impact of Wi-Fi quality on in-game latency, and could guide Internet Service Providers and telecommunication operators on diagnostics and future network design.

Currently, the Open Radio Access Network (O-RAN) architecture is the best solution for deploying Radio Access Network (RAN). This architecture presents several challenges, i.e., function splitting, function placement and energy consumption. The aim of this study is to satisfy user demand while minimizing costs. In this work we combine the O-RAN architecture and the multiband system. We consider that the RAN network load varies during the day and we study the optimal placement of the Distributed Unit (DU) function and switch off unnecessary frequency bands. First, we propose an algorithm to find the number of frequency bands needed to satisfy user demand for different smoothing periods. Based on these results, we formulate the problem of DU placement and frequency band switch-off as an Integer Linear Programming (ILP) whose objective is to minimize computing and routing cost while respecting delay and capacity constraints. Evaluation of our model on real topology has shown that our model with frequency band extinction has a much lower system cost than our model without band extinction.

In this paper, we consider a Radio Access Network (RAN) powered by the Smart Grid (SG), which provides monetary incentives to users who respond to the grid's explicit requests to increase or decrease their energy consumption. The typical solution for reducing the RAN's power needs is to employ Base Station (BS) sleep modes, but this may lead to a drop in user coverage. For this reason, we propose dynamically adjusting the bandwidth allocated to users in response to SG requests. Our study considers a realistic urban RAN and evaluates the effectiveness of bandwidth limitation in meeting the SG's power reduction requests. Results indicate that this approach is particularly effective in high-density user environments, reducing power requirements by up to 20% while maintaining sufficient bandwidth for essential applications such as audio and video streaming. In contrast, BS deactivation can lead to substantial coverage losses, with user coverage falling below 90%.

5G networks are moving towards cloud-native architectures and expect higher performances, customizability, and cost efficiency compared to previous generations. Dynamic scaling of Containerized Network Function (CNF) is a major feature that allows telecom operators to find a good balance between cost and Quality of Service (QoS). By accurately determining the necessary resources for CNFs, operators can minimize operational costs without over-provisioning and simultaneously, maintain satisfactory QoS levels without under-provisioning. However, one of the obstacles we identified that can throw off the balance between cost-QoS trade-off is Central Processing Unit (CPU) throttling, which is an underlying system process. Without proper timing in scaling decision-making, it can negatively impact QoS by increasing service response time during scaling events. In this paper, we propose a novel AI-based autoscaling solution to calculate the precise amount of resources and the optimal timing for decision-making, with an awareness of CPU throttling. The solution employs one deep learning model to proactively determine the right amount of resources and another to predict CPU throttling events, which in turn trigger scaling decisions. Our experimental results demonstrate that our new autoscaling solution outperformed the default Horizontal Pod Autoscaling (HPA) in Kubernetes in terms of balancing cost QoS tradeoff.

Open Radio Access Network (O-RAN) is very promising for flexible and efficient 5G and 6G wireless networks. The O-RAN architecture consists of three main units: Radio Unit (RU), Distributed Unit (DU), and Centralized Unit (CU). In this paper, we study the placement of virtualized DUs. This placement has strong consequences on cost and delay, among others, and is thus an important challenge. First, we analyze the throughput between the O-RAN interfaces. Based on our analysis, we propose an efficient Integer Linear Programming (ILP) model. The objective is to minimize the O-RAN cost depending on the DU placement while respecting the delay and capacity constraints. We evaluate our model on a real topology. Our results provide interesting insights into the cost savings with regard to a legacy architecture. Moreover, the proposed model provides solutions in a configuration where a fully centralized Cloud RAN architecture would not. We also estimate the limits of capacity of a given configuration.

5G networks are moving towards cloudification which gives the telecom operators the flexibility to manage their networks efficiently and cost-effectively. Scaling network functions on demand is one of the advantages of using container-based deployment in cloud environments. With the continuously changing network traffic patterns due to the emerging new 5G use cases, there is a need for novel automated network resources management approach in cloud-native environments. Considering the scale and the complexity of the 5G network, managing resources is a challenge. To address this, we propose a deep learning-based resource usage forecasting approach that provides useful insights for decision-making in containerized Network Function (CNF) scaling for the Kubernetes environment. Kubernetes is a container orchestration tool that becoming popular among Telecom operators due to its simplicity. We implemented a testbed in the Kubernetes environment to generate a dataset closer to real-world data for deep learning model training and evaluated the best-performing model for resource usage forecasting. We benchmarked our approach against another deep learning-based resource usage forecasting approach which proved our method can provide a highly accurate forecast for further horizons.

Licensed Shared Access (LSA) is a new concept proposed by the radio spectrum policy group in order to optimize spectrum usage: a Mobile Network Operator (MNO) can access temporarily to other incumbent's spectrum after obtaining a license. The licensing process is made via an auction mechanism. The mechanisms proposed in the literature for the LSA context are one-shot auction mechanisms which allocate all the available spectrum as a unique block. In this paper, we first show how to increase the performance of those auctions (in terms of revenue, efficiency and fairness of the allocation) while preserving truthful bidding, by splitting spectrum and converting single block auctions into multi-block auctions. Simulation results illustrate how appropriately choosing the number of blocks allows to increase the aforementioned metrics. Second, we show how to convert one-shot mechanisms to equivalent ascending mechanisms (in terms of allocations and payments) so that we add transparency and privacy to the auction.

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.

Intelligent reflecting surfaces have been recently introduced as a promising technology to achieve smart and reconfigurable radio environment. Unlike traditional approaches that consider the environment to be uncontrollable in wireless networks, intelligent reflecting surfaces are capable of smartly modifying the wireless channel. This opens a new paradigm with a lot of expected gains that need to be identified and investigated. In this paper, we exploit an intelligent reflecting surface module to reduce the power demand of a base station that is serving multiple users. The problem of managing the intelligent surface's elements is formulated as a non-linear integer problem. Due to the complexity of the problem, the intelligent reflecting surface is discretized into blocks of elements. The blocks have different sizes to assist users with different conditions. We propose a two-stage approach that selects the users to be assisted and then allocates the blocks of elements to the selected users. We also compare the block allocation to the optimal elements allocation for selected users to show the effectiveness of the proposed approach. Results show that the power need of a base station can be decreased by more than 8% using an intelligent reflecting surface module with limited number of elements if the module is well-positioned and assisted users are properly selected.

The high data rates and diversity of services in 5G require a flexible and efficient use of all the available frequencies. In 5G networks, new approaches of dynamic spectrum sharing will be deployed, allowing Mobile Network Operators (MNOs) to access other incumbents' spectrum, after obtaining a license from the regulator. The attribution of licenses will be made via auction mechanisms valid for given geographical areas and durations. To determine how to bid, each MNO has to estimate his valuation for spectrum i.e., how much he is willing to pay for spectrum. In this paper, we propose a model for estimating that valuation. The model is based on Markov chain modeling of user behavior, to compute the MNO satisfaction as a function of the obtained spectrum. We then illustrate our method by applying it to real operator data.

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 article, we propose video delivery schemes insuring around 1s delivery latency with Dynamic Adaptive Streaming over HTTP (DASH), which is a standard version of HTTP Live Streaming (HLS), so as to benefit from the video representation switching between successive video segments. We also propose HTTP/2-based algorithms to apply video frame discarding policies inside a video segment when a selected DASH representation does not match with the available network resources. The current solutions with small buffer suffer from rebuffering events. Rebuffering not only impacts the Quality of Experience (QoE) but also increases the delivery delay between the displayed and the original video streams. In this work, we completely eliminate rebuffering events by developing optimal and practical video frame discarding algorithms to meet the 1s latency constraint. In all our algorithms, we request the video frames individually through HTTP/2 multiple streams, and we selectively drop the least meaningful video frames thanks to HTTP/2 stream resetting feature. Our simulations show that the proposed algorithms eliminate rebuffering while insuring an acceptable video quality with at least a Peak Signal to Noise Ratio (PSNR) of 35dB compared to 25dB of the basic First In First Out (FIFO) algorithm. We also quantify and qualify the resulting temporal distortion of the video segments per algorithm. An important number of missing video frames results in a temporal fluidity break known as video jitter . The displayed video looks like a series of snapshots. We show that both the optimal Integer Linear Program (ILP) and practical algorithms decrease the frequency and duration of the jitters. For example, practical algorithms reduce the number of crashed displayed videos (presenting one jitter longer than 1,350ms) with 22% compared to the basic FIFO algorithm. We also show that requesting video frames separately with HTTP/2 slightly increases the overhead from 4.34% to 5.76%.

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.

360° video streaming is coming up against two major technical challenges: network resource consumption and Quality of Experience (QoE). Dynamically adapting the content delivery process to the user behavior is a promising approach to ensure both important network resource savings and satisfying experiences. In this paper, we propose to leverage HTTP Adaptive Streaming (HAS), tiled-based 360° video encoding and the HTTP/2 protocol to implement this dynamic content delivery process. The 360° video stream is spatially encoded into tiles and temporally divided into segments. The client executes two viewport predictions for each segment, one before and one during its delivery. Upon every prediction, it decides on a priority and a quality level for each tile of the video segment; tiles overlapping with the predicted viewport get higher priorities and quality levels. Then it exploits the priority and stream termination features of the HTTP/2 protocol to enforce its decisions. We compare our proposed solution with four alternative schemes on a set of 360° video streaming sessions corresponding to various types of videos, user behaviors and network conditions. Our solution provides better performances: a higher quality on the viewport pixels, a lower ratio of unreceived viewport pixels in bandwidth-constrained networks, and a reduction of the bandwidth consumption, up to 12% compared to the alternative schemes exploiting 2 viewport predictions per video segment.

Licensed Shared Access (LSA) is a new complementary regulatory framework which aims to optimize wireless spectrum utilization. Under LSA, an incumbent can share his owned spectrum through an “LSA licensee”. This sharing is supervised by the regulator, but the allocation and pricing mechanism to be used is not specified yet. To fill that gap, in this paper we propose PAM: Proportional Allocation Mechanism, which is a truthful auction mechanism offering a good compromise between fairness and efficiency and can generate the highest revenue to the regulator compared to other truthful mechanisms proposed in the literature.

5G systems are close to roll-up, very ambitious and in some points complex and very different from presently-used wireless systems. These systems need extended research for enhancement of performance in different aspects and levels. In this paper, and as previously done in [1], we provide simple framework and models based on the recent proposals for 5G, before providing simple capacity estimations for different use cases and radio channel characteristics. These results can be used as an order of magnitude of more realistic 5G performance results. Our simple model and results can be easily used for more sophisticated radio resource allocation and scheduling studies.

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.

In the last years, Green Mobile Networks that are powered with renewable energy sources have been designed and deployed with the twofold objective of reducing operational costs and providing service in scenarios in which the power grid is not reliable. At the same time, the introduction of Smart Grids is deeply changing the energy market, by effect of the grid actively interacting with its customers. In this paper, we consider a scenario in which a green mobile network is integrated in a smart grid. The mobile network interacts with the smart grid, responding to its requests by adapting its load. Load adaptation is obtained by resource on demand strategies that operate on Base Stations, and by taking decisions about the use of the renewable energy that is locally produced and that can be used for powering the green mobile network, it can be stored or even returned to the grid. The results, derived through a Markovian model, show that the use of resource on demand strategies in the green mobile network improves the interaction between the network and the smart grid: significant cost gain can be achieved, the responsiveness to the smart grid requests increases, low storage probability decreases.

The S-ALOHA (i.e. slotted-ALOHA) protocol is recently regaining interest in Lower Power Wide Area Networks (LPWAN) handling M2M traffic. Despite intensive studies since the birth of S-ALOHA, the special features of M2M traffic and requirements highlight the importance of analytical models taking into account performance-affecting factors and giving a thorough performance evaluation. Fulfilling this necessity is the main focus of this paper: we jointly consider the impact of capture effect, diversity of transmit power levels with imperfect power control. We propose a low-complexity but still accurate analytical model capable of evaluating S-ALOHA in terms of packet loss rate, throughput, energy-efficiency and average number of transmissions. The proposed model is able to facilitate dimensioning and design of S-ALOHA based LPWAN. The comparison between simulation and analytical results confirms the accuracy of our proposed model. The design guides about S-ALOHA based LPWAN deduced from our model are: the imperfect power control can be positive with capture effect and appropriate transmit power diversity strategy. The transmit power diversity strategy should be determined by jointly considering network charges level, power control precision and SINR threshold to achieve optimal performance of S-ALOHA.

Cloud Radio Access Network (C-RAN) is a cost-effective solution for cellular operators which not only offers resource allocation and interference management flexibility, but also helps them enhance the energy efficiency of the network. The key character of the C-RAN architecture is a centralized Baseband Unit (BBU) pool which consists of high-capacity processing units and real-time virtualization. However, this centralized architecture can affect the system latency. As a consequence, the minimization of the execution delay of each user services in the BBU pool can be perceived as a major performance objective. In this paper, we consider different delay sensitive services and optimize their execution time inside the BBU pool. We present a new approach of BBU selection based on queuing theory in order to minimize the response time in the BBU pool under resource cost constraint. Simulation results showed that our proposed approach is more efficient than benchmark algorithms in terms of response time and throughput.

Today, mobile networks are witnessing an exponential growth of traffic volumes, linked to new services, especially for smart cities and smart-grid. The European Celtic-Plus SooGREEN project, started mid 2015, is targeting to reduce the energy consumption of the services in different mobile architectures in interaction with smart-grid. So GREEN is focused on the services energy consumption modelling and measurement, the dynamic optimization of the mobile access network and of the content delivery, the design of an Energy Efficient Virtualized and Centralized Radio Access Network (RAN), and the bi-directional interaction of the mobile network with the smart-grid. This paper presents insight into the project after its first year, and discusses research trends in green communication networks for the future.

Up to recently, two main approaches were used for connecting the “things” in the growing Internet of Things (IoT) - one based on multi-hop mesh networks, using short-range technologies and unlicensed spectrum, and the other based on long-range cellular network technologies using corresponding licensed frequency bands. New type of connectivity used in Low-Power Wide Area networks (LPWAN), challenges these approaches by using low-rate long-range transmission technologies in unlicensed sub-GHz frequency bands. In this paper, we do performance testing on one such star-topology network, based on Semtech's LoRa™ technology, and deployed in the city of Rennes - LoRa FABIAN. In order to check the quality of service (QoS) that this network can provide, generally and in given conditions, we conducted a set of performance measurements. We performed our tests by generating and then observing the traffic between IoT nodes and LoRa IoT stations using our LoRa FABIAN protocol stack. With our experimental setup, we were able to generate traffic very similar to the one that can be used by real application such as sensor monitoring. This let us extract basic performance metrics, such as packet error rate (PER), but also metrics related specifically to the LoRa physical layer, such as the Received Signal Strength Indicator (RSSI) and Signal to Noise ratio (SNR), within various conditions. Our findings provide insight about the performance of LoRa networks, but also about evaluation methods for these type of networks. We gathered measurement data that we make freely available together with the tools we used.

Energy efficiency of wireless cellular networks is a well-studied domain and many proposals have been done following different amelioration paths. In this paper, we analyze the efficient use of small cells in a renewable energy context and considering real- time price of grid energy. This is typically the case when the mobile network is powered by the Smart Grid, in addition to renewable energy when available. We study the use of an algorithm adapted for this context and analyze it in simulation. Our results show that a suitable number or density of small cells gives better cost and energy savings performance, if we take grid energy cost into account.

Machine-to-machine (M2M) communication is expected to enable billions of devices to be connected by the cellular networks in the near future. The large number of devices involved in M2M will create a huge market potential but at the same time pose some challenges for 3GPP cellular networks. The challenges include massive access management, QoS provisioning, energy/power efficiency, etc. Nowadays, energy-related issues are deemed as the key problems in cellular M2M communications. In this paper, we extend the research framework of [1] by taking into account some important issues that were not yet addressed, mainly the existence of machines with different packet lengths and the effect of imperfect power control. With the proposed system model, we evaluate the power efficiency, energy efficiency and system capacity for uncoordinated CDMA and coordinated FDMA. Through numerical results, we conclude that coordinated FDMA is more resistant to various packet lengths of M2M devices packets in terms of power efficiency and is not influenced by imperfect power control. Thus coordinated multiple strategies, especially FDMA, are more suitable for the future M2M-included cellular networks and deserve further optimization works. With respect to uncoordinated CDMA, although its performance is affected by BS load intensity and power control, it is still a considerable choice due to its simplicity and no signaling overhead, when the BS load intensity is not high and the power control policy is suitable.

Small cells and heterogeneous network approaches in mobile networks are very promising as they provide an enhancement of capacity without a significant impact on the energy consumption when properly designed. Yet, recent studies have pointed out that the backhaul energy consumption cannot be neglected when a small cell deployment is introduced. In this paper, we provide a comprehensive study on energy efficiency, cost issues and trade-offs for backhaul infrastructure when the influence of Radio Access Network (RAN) design and growth strategies are considered. For the case study, we use two well-known backhaul architectures, one supported on fiber-to-the-building (FTTB)+ 10Gbps passive optical Network (10GPON) and a second which consists of FTTB+ Microwave backhauling. The numerical results and analysis performed provide significant insights of the potential energy savings, CAPEX, OPEX and challenges for backhaul and RAN architecture design.

During the last years, energy efficiency of wireless networks arises as a dominating concept to answer the huge energy demand due to the increase of mobile traffic. Currently, a new direction appears with powering mobile base stations by renewable energy sources. While most of the studies in this direction are based on simulation or theory, this paper analyzes the usage of renewable energy in a realistic environment. Our study is based on a live scenario, where real traffic measurements of a major European mobile network operator are used. The effects of renewable energy allocation and base stations switching- off are evaluated in terms of on-grid energy saving and electric bill reduction. Moreover, a new algorithm that combines an intelligent renewable energy allocation and a switch-off mechanism based on renewable energy generation, storage state and price of on-grid energy is proposed. Our results show that switching-off bases stations can achieve about 19 % energy demand reduction. Moreover, equipping base stations with renewable energy sources that generate 20 % of the total network energy demand can lead to 51 % reduction in the electric bill.

Recently, energy behaviour is being considered as an important key performance indicator when studying mobile networks. Reducing their operational cost is becoming more essential due to the huge increase of traffic accompanied by the increase in the electric bills. In this paper, we are going a step forward to study the possibility of a new type of energy behaviour of renewable energy-powered base stations in the Smart Grid environment. We use renewable energy and energy storage and we employ the concept of delay tolerant users to provide ancillary services to the Smart Grid. Based on real data from the French power grid, results show that the achieved profit from providing ancillary services exceeds the electric bill and leads to a negative energy operational cost.

Machine Type communication (MTC) in LTE networks is expected to gain a great popularity in the next decade. Due to different traffic characteristics, it poses some challenges for traditional random access procedure and resource allocation algorithm in LTE network. We propose a polling service that avoids contention. This service is integrated into LTE access network, fully compatible with the standard access mechanism and able to manage a large range of polling periods (typically from 1 minute to 28 days). This proposed service reduces the transmission overhead and thus improves the energy efficiency for MTC devices. It also reduces access network overload in radio access network by avoiding random access. Compared with traditional random access mechanism, numerical results show that with proposed service one eNodeB (eNB) can easily support up to 15000 MTC devices without network access collision.

Cell breathing is a mechanism used in cellular networks since many years, which is now proposed for energy efficient purposes. By using this technique, it is possible to save energy during low traffic periods by switching unnecessary BSs off whereas the cell topology is re-adapted supported on some few BSs active. In this article, we analyze a multi-size approach which provide means for an enhanced cell topology adaptation. We divide our approach in two redistribution phases. Each of these two phases is conceived for being efficient at specific traffic levels. A first configuration phase only allows short range cell expansion to cover just direct adjacent sectors, which works well for medium traffic load. Then if low load conditions afford it, a second phase may be executed which allows an extended multi-size expansion making it possible to cover additional sectors at a far distance, which results in some new larger cells and some extra BSs switched-off. The decision mechanism makes it possible to choose the best strategy between running "phase 1 only" or "phase 1 & 2 combined". Our approach is supported on a clustered architecture which distributes the computational load in the network. Each cluster consists of groups of BSs with a BS cluster head in charge of making decisions based on power saving indicators. Simulation and results are provided to analyze each of the potential approaches supported on phase 1 and 2 redistribution. It is shown that some interesting energy savings are obtained when each of our proposed phases is conveniently applied.

Recently, renewable energy (RE) has been introduced as a promising solution to reduce the "non-green" energy consumption of cellular networks. In this paper, we study the electric bill reduction of a cellular network powered by both RE sources and the power grid in a variable electricity price environment. We decompose the problem of electric bill reduction into three sub-problems: RE allocation, energy consumption minimization and radio resource allocation (RRA). In this context, we propose a new algorithm that adjusts the network configuration to increase the utilization of RE. Results show the efficiency of our proposed algorithm, where it achieves 30% reduction of energy consumption and outperforms a benchmark algorithm with a gain of up to 20 percentage points in terms of electric bill reduction.

In the LTE (Long Term Evolution) System, packet scheduling is considered the most important step of RRM (Radio Resource Management) to have better resource utilization. In this context, we proposed a new Uplink scheduling scheme for LTE networks and compared its performances with other well known algorithms such as the RME (Recursive Maximum Expansion), the RR (Round Robin) and the BCQI (Best-CQI) Uplink scheduler's algorithms. The RR scheduler is characterized by a high fairness, but low data rates at cell level. On the other hand, the RME scheduler is characterized by its low complexity, but poor fairness. Also, the BCQI scheme is characterized by high data rates, but poor fairness. The main goal of our proposed scheme was to use the Tabu method for to schedule and optimize the allocation of a PRBs (Physical Resource Blocks) efficiently between different users. Simulation results show that the newly proposed scheduling algorithm allows a fair distribution of available LTE resources while keeping an high system's throughput.

Energy-efficient cell breathing is a mechanism that consists of adapting cell sizes and number of active Base Stations (BS) to the distribution and current levels of traffic. In this article, our goal is to analyse the effect of such a technique on the electromagnetic (EM) radiation levels for mobile phone devices. Although there exist large details in the literature of cell-breathing on energy-efficiency related to the Radio Resource Management (RRM) aspects, to the best of our knowledge, there is still a lack of work on analysing the consequences of cell-breathing and BS switching-off schemes on the EM exposure issues related to the mobile terminal. In such approaches during low-traffic, whereas there are some BSs being switched-off, there are some other BSs that must remain active expanding their cell sizes to guarantee coverage. This requires a transmission power increase for both downlink and uplink side, that for mobile devices implies an increase of the specific absorption rate (SAR) [W/kg] on the mobile user. To conduct our study, we use one of our previously proposed techniques on cell-breathing to analyse the impact of uplink transmission power increase on resulting SAR levels. Here, different BS switching-off aggressiveness levels are considered to observe the resulting exposure levels using a 3G/CDMA scenario. The results show how SAR maximum levels increase as more aggressive switching-off schemes take place as well as the uplink interference rises due to a progressive increase of network load.

In this paper, we analyze energy-efficient cellular networks that are based on renewable energy. We focus on hybrid energy base stations that are powered by renewable energy and power grid. This will provide the opportunity of obtaining green communication and integrating the renewable energy in the existing infrastructure at the same time. We propose a classification of the research problems and highlight different facets that need to be investigated. A new methodology is presented to specify the case study, where we classify the areas, describe different objectives and present several types of constraints. We provide an application of our framework to a specific case study and propose an algorithm for simple price-aware energy management (SPAEMA). The results show that there can be significant cost reduction using SPAEMA in comparison to the normal use of renewable energy. Moreover, this classification can serve as a basis for future work taking into account new cellular network techniques such as cell breathing.

In this paper, we investigate the use of renewable energy in wireless networks. This type of energy directly reduces the greenhouse gases emission and, in many cases, the operator energy costs. A large number of papers have been recently published about energy-efficient wireless networks issues. Some of them proposed analysis about the trade-offs of this new field. In this paper and, to the best of our knowledge for the first time, we introduce a new trade-off: between the user average satisfaction degree and the ratio of green (clean) renewable energy used in green wireless networks. A simple system model is described allowing us to obtain first results illustrating this trade-off. An intelligent battery-based system for a more efficient use of the renewable energy is included. The results show that for the model considered, only 25% of non- renewable energy guarantees more than 75% average user satisfaction.

Pricing plays an important role to efficiently manage the cellular network resources, maintain user required QoS (Quality of Service) levels and to optimize the operator's revenue. In this paper, we analyze some previous pricing schemes proposed for general wireless multimedia systems and for WiMAX and adapt these methods within LTE (Long Term Evolution) networks. We also propose a dynamic pricing strategy for LTE. Most of these methods (including ours) are based on users' price categories: Gold (or high), Silver (or medium) and Bronze (or low). The price charged to these users may also vary with the QoS level. We compare these methods considering the LTE network context. Our proposed strategy provides high revenue for the operator as well as a smaller average price paid for the user, with regard to the flat-rate pricing strategy.

This paper presents an overview of Zewall collaborative project dedicated to proposing an architecture and developing a real-time multimedia streaming application for present day users. This application allows users to film real-time small events (e.g., a local music concert in a village that may not be seen by most of the spectators) and broadcast them 'live' to distant spectators. The technologies like LTE (Long Term Evolution) and HTML-5 (Hyper Text Markup Language 5) are adopted within Zewall project to provide users with high QoS (Quality of Service) and a more dynamic and efficient Web interface. We also present each and every aspect of Zewall's architecture along with the description of one of the basic use case and the project outcomes.

Efficient radio resource usage and quality of service (QoS) provision are important criterions in order to choose the right QoS policy for the LTE operators. In our previous work, we have proposed to add a new database named as BSLRC (Base Station Load and Radio Conditions) to optimize the operator policy with regard to the radio efficiency. Some concrete voice capacity estimations were also proposed. In this paper, we extend our earlier work by addressing some novel concerns. As a first step, we start by calculating the signaling load due to the use of BSLRC. Then, for voice services the BSLRC method is compared with the explicit congestion notification (already proposed for LTE systems). Both BSLRC and ECN (Explicit Congestion Notification) share the same objective of optimizing the choice of codec rate with regard to radio use efficiency. We also propose some capacity estimates for video services as an extension to our previously proposed voice capacity estimations over LTE systems.

InterCell Interference Coordination (ICIC) is a major RRM (Radio Resource Management) feature recently included in wireless OFDMA networks. This paper studies OFDMA/ICIC algorithms. The model of networks considered is based on the LTE and WiMAX physical layers. After a state-of-the-art, we compare the simulation results of some of the proposed ICIC algorithms. We propose some adjustments to these algorithms in the LTE framework. We also propose an ICIC algorithm based on resource exchange between base stations. Our proposed algorithm outperforms often-mentioned ICIC algorithms which are variants of fractional frequency reuse.

WiMAX/IEEE 802.16 Broadband Wireless Access (BWA) is a very open technology with various possible services. While different technical aspects of that technology have been addressed, the pricing of WiMAX has received little attention until now. In this paper, we analyze the performance of a WiMAX pricing scheme that we proposed in previous works. Through extensive simulations, we evaluate the importance of a well-planned WiMAX pricing framework by comparing our proposed scheme with a basic one. Then, we study the sensitivity of the proposed pricing scheme with regard to the parameters values choices. We show that our proposed pricing scheme still provides better revenue than a basic one for a difference up to 30% more or less than the best values chosen.

WiMAX/IEEE 802.16 system is a very open Broadband Wireless Access (BWA) technology with different possible services. Pricing of WiMAX is an important topic with different optimization possibilities. In a previous paper, we investigated WiMAX pricing for real-time applications (Unsolicited Grant Service (UGS) and real-time Polling Service (rtPS) QoS classes of WiMAX). We add the Best Effort (BE) QOS class in this paper in order to have a rather complete WiMAX environment. The system model is described. Then we consider two different BE pricing mechanisms: fixed price and auction-based variable price. The proposed methods are evaluated through simulations in order to have some interesting comments and highlights for WiMAX pricing.

In this paper, we propose some considerations for the WiMAX capacity. The developments realized for a WiMAX NS-2 module are presented. This module allows the evaluation of the WiMAX. We present the spectrum efficiency and mean sojourn time of WiMAX determined through simulations for different scheduling algorithms. It is then possible to compare theoretical values and simulation results of WiMAX spectral efficiency and highlight some interesting comments. This is done for two proposed scenarios.

WiMAX/IEEE 802.16 broadband wireless access (BWA) is a very open technology with different possible services that is likely to be widely used in metropolitan access networks in the near future. While the technical aspects of that technology are now quite well established, the economic implications of the introduction of WiMAX technology have received little attention. In this paper, we consider the problem of pricing WiMAX sessions: we propose some simple pricing mechanisms aimed at coping with congestion while maintaining a sufficient quality of service and/or yielding a sufficient revenue to the operator. Our mechanisms are evaluated under a specific model of user preferences and WiMAX situations, and the implications of some parameter choices are observed.

In this paper, we propose a cross layer design which combines the PHYsical and the MAC layers for a distributed wireless underwater acoustic networks. The PHYsical layer is based on a CDMA transmission as used in UMTS systems, reception is done using a RAKE receiver. The MAC layer is a based on the CSMA/CA protocol. As we work in the case of underwater acoustic networks, the propagation delay is much more significant compared to radio channel. Therefore, the use of RTS/CTS used in the CSMA/CA protocol induces an increase in delay. The use of CSMA techniques allows to several nodes to transmit simultaneously through the common channel and without using control sequences RTS/CTS to reserve the medium. We show in this paper that our proposition solves the problem of collision present in the case of CSMA/CA. The comparison between our cross layer approach and the CSMA/CA is done in term of available throughput of nodes and average delay of transmission.

In a UMTS network, the Radio Access Bearers (RAB) configuration has a direct impact on radio resource usage. The more suited the RAB is to data flows, the more efficient it is. This paper presents an optimal RAB combination for VoIMS (VoIP with IP Multimedia Subsystem in the Core Network). The proposed RAB makes an efficient use of RObust Header Compression (ROHC) which improves the physical layer QoS. The main advantage of the new RAB combination resides in adapting the throughput for SIP (Session Initiation Protocol) signalling and improving the call setup delays for a packet switched transmission. This is achieved via an efficient algorithm of Transport Format selection in conjunction with the flexible rate matching at the physical layer.

In this paper, we propose some simple capacity estimations for WiMAX/ IEEE 802.16 system. The OFDM physical layer of 802.16 is considered. We compare between the now-classical data rates values based on OFDM considerations and data rates obtained in simulation taking into account inter-cell interference. Figures are given for various scenarios of frequency reuse (1times1 and 1times3) and services (real-time and FTP) allowing interesting observations about WiMAX radio efficiency. These results can be used as an order of magnitude of more realistic WiMAX performance results

In this paper, we consider MAC (media access control) protocols appropriate to the underwater acoustic networks. First, we consider a media access method based on the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol used in the wireless local area networks IEEE 802.11 (WiFi). In the underwater acoustic channel, the acoustic wave propagates very slowly with regard to the electromagnetic wave (about 1500 m s ). Therefore the propagation delay is significant and thus collisions present a high cost considering the delay of transmission and the available throughput. To avoid this problem, we consider a second protocol in which we integrate a WAIT command [1]. Finally we integrate to our proposition a power control mechanism in order to increase network performance (delay, throughput). The performance evaluation of the three propositions has been done in terms of available throughput of the underwater sensors, delay of transmission and collision probability. The proposed simulation system model is detailed.

The use of IPv6 in the third generation of cellular networks leads to weak performance of the network systems because the IPv6 header size sent in each packet represents more than twice the payload normally used in video or audio applications. The IETF ROHC (robust header compression) working group has defined a new header compression mechanism to reduce the transmission time and increment the use of the bandwidth in networks with scarce resources. The present version of ROHC considers a static configuration of the ROHC implementation parameters and at the same time does not negotiate some of the link parameters. This paper proposes a dynamic configuration for UMTS radio networks through negotiation. Dynamic configuration enables ROHC to improve its efficiency and robustness based on radio link parameters.

Adaptive power control algorithms has been widely studied for interference reduction, base station radiation decrease and terminal battery saving in 3G cellular systems. We study some of the existent adaptive power control algorithms and we propose an evolution of these algorithms in the forward link. In the proposed algorithm, we try to maintain the intercell interference stable and to reduce intracell interference when it is possible. Therefore, the power control steps of mobiles requesting a power increase may be reduced to limit the total power increase if some constraints are verified. The proposed algorithm uses an adaptive step, which is updated using the instantaneous mobile power control command and the command history. Moreover, a stabilization zone above the SIR target is used to limit oscillations. The proposed algorithm has been compared to the 3GPP standardized power control and to a recently proposed adaptive power control. Simulation results has shown that the proposed algorithm decreases slightly the outage probability as the already proposed adaptive power control and reduces the power consumption.

ROHC Bormann C., et al. (2001) is the new standard for header compression proposed by the IETF (Internet engineering task force) to compress different protocol headers. ROHC needs to be configured to afford the changes in the characteristics of the UMTS radio link and to enable a better compression of the IPv6 flows. In this document we will introduce the characteristics and the behavior of ROHC. Also we study the compression parameters that are recommended as implementation dependent in the ROHC specification and we show how to configure them. This study can help to improve the efficiency and robustness of ROHC when it is used in scarce resources links, such as UMTS.

In this paper, we study the interaction between power control and data rate selection in a wireless CDMA network. Specifically, third-generation cellular WCDMA system is considered. The criterion is the average of the sum of data rates in a variable rates services environment. Simulation results show that the association (or the integration) of power control and data rate selection allows a given network to have better achievable performance. Interesting elements of the proposed algorithm are highlighted and useful comments given.

The issue of power control (PC) in the downlink of a cellular CDMA network is studied. Two approaches are discernible. The first uses the base-to-mobile-distance in order to allocate transmitted powers while the other is based on closed-loop control. The best achievable performance of a PC is evaluated for the downlink of a CDMA network. This paper then studies an almost optimal downlink power control. Numerical examples are presented in order to discuss the downlink PC performance.

In a wireless CDMA network a mobile is classically connected to the base station with the best received power level (R/spl times/L). The association (or the integration) of base station assignment with power control allows a given network to have better achievable performance. We investigate the association of base station assignment and power control in order to study the best achievable signal-to-interference ratio (SIR) behaviour. The existence of a feasible solution to the quality constraints problem, along with the choice of the base station assignment in order to have the minimal sum of transmitted powers, are discussed. This is done for a unique SIR target and for a multi-target environment. We also address two problems: the base station assignment as a function of network quality targets and best achievable performance which is represented by the highest achievable SIR for every possible BS assignment vector. This last value is computed with less computations than when it is directly calculated. Simulations show that appropriate base station assignment may give interesting capacity gains, especially for non-uniform distributions.