Haut PDF Radio Resource Management for Green Wireless Networks

Radio Resource Management for Green Wireless Networks

Radio Resource Management for Green Wireless Networks

ing the channel dynamics according to a stochastic auto-regressive (AR) model [51, 148]. As a rate constraint, we consider that each AP-UE pair must complete a given data packet transmission within a deadline. The users are also sharing common resources (bandwidth, time spectral resources, etc.) and compete when transmitting. The competition between users is expressed through interference, due to other users attempting to transmit at the same time on an adjacent cell. The decentralized power control problem is then naturally formulated as a multiuser non-cooperative stochastic game, [53]: each user seeks its optimal transmission powers strategy, i.e. the strategy that minimizes its utility func- tion, which consists of the total power consumption, while ensuring a complete transmission of a data packet of initial size known, within a predefined deadline. In mathematical terminology, the set of power strategies that will minimize the considered utility function corresponds to a Nash Equilibrium (NE) [54] of the stochastic non-cooperative game. When the NE is reached, no user wishes to deviate independently from its own power strategy, since any deviation would lead in the end, to a worse utility for this user (incomplete transmission or, higher power consumption). Studying NE in such a context is then relevant. In such multiuser stochastic games, it is possible to prove the existence of a NE and to define sets of N Partial Differential Equations (PDE), namely the Hamilton- Jacobi-Bellman (HJB) equations, one for every single user of the system [53, 149, 150]. However, solving these sets of equations, in order to characterize the NE of the game, becomes complicated and even impossible, when the number of AP-UE pairs N grows large (especially when N > 2).
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Survey of radio resource management issues and proposals for energy-efficient cellular networks that will cover billions of machines

Survey of radio resource management issues and proposals for energy-efficient cellular networks that will cover billions of machines

Qipeng Song 1,2* , Loutfi Nuaymi 1,2 and Xavier Lagrange 1,2 Abstract A huge increase of machines attached to wireless networks is expected in the next few years. A large part of these machines will be covered by some wireless wide area networks. The arrival of cellular M2M (machine-to-machine) communication poses new requirements due to its specific characteristics. For most of the cellular M2M applications, the essential requirement is low energy consumption level or high energy efficiency. This survey provides a global view of the network technologies previewed for cellular M2M. In this survey, we study the existing classifications of M2M applications according to different criteria in the literature. The comparison of traffic characteristics between M2M and human-to-human is also proposed. Quality of service (QoS) requirements for typical M2M applications are resumed. The advance of reference M2M network architectures proposed by the Standard Development Organization (SDO) is investigated. We identify two possible effort directions to improve the energy efficiency for cellular M2M. The first one is to evolve the current existing 3rd Generation Partnership Project (3GPP) Consortium cellular networks to effectively support MTC (Machine Type Communication). The other direction is to design M2M-dedicated networks from scratch, which are often called low-power wide-area (LPWA) networks. We review, compare and categorize the proposals related to energy issues of cellular M2M mainly over the period 2011–2015 for the first direction. We introduce the development of LPWA networks for the other research directions. We highlight that the cooperative relaying, the design of energy-efficient signaling and operation, the new radio resource allocation schemes, and the energy-efficient random access procedure are the main points of improvement. It is important to jointly use the aforementioned approaches, for example, joint design of random access control and radio resource allocation, to seek for a trade-off between energy efficiency and other system performances.
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Metrology in wireless mobile networks for enhanced radio resource management

Metrology in wireless mobile networks for enhanced radio resource management

In Chapter 1 , we focus on current standardized radio measurements in radio access technologies and we propose an innovative approach that aims at processing the reported measurements so as to extract additional information on the external environment. We experiment this idea on reported power measurements in GSM and UMTS networks using non-parametric regres- sion and smoothing methods. As a result, we develop a dynamic estimator of the attenuation components of the received radio signal (namely pathloss, shadowing and fast fading), given a set of consecutive reported radio measurements. The main originality of this estimator (filed as a patent) is its almost real-time functioning and the production of results from existing mea- surements with no further hardware implementation. Adding to that, we propose a method for the determination of the mobile user situation (incar, pedestrian and unmoving) as well as a proposal of handover procedure improvement through enhanced target cell determination. To conclude, in this first part of the thesis, we begin with a "low level" analysis by proposing appropriate processing methods on existing radio measurements, employed by the network in RRM mechanisms. Our goal is to demonstrate that our approach can be implemented in future wireless networks for an efficient use of radio measurements and for the improvement of radio resource management in cognitive networks. During this work, the main challenging issue is the set up of a database of real radio measurements in different contexts and situa- tions so as to validate the proposed processing methods and applications in wireless networks. Thus, our approach can be improved by testing it on various different measurements databases
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Radio resource management for high-speed wireless
cellular networks.

Radio resource management for high-speed wireless cellular networks.

Several other papers focus on precoding/decoding design for CoMP considering different design aspects of Cloud-RAN. In particular, in [140], the joint optimization of antenna selection, regular- ization, and power allocation was studied to maximize the average weighted sum rate. The random matrix theory was utilized to decompose the considered nonconvex problem into subproblems that can be tackled more easily. In [92] and [93], the precoding vectors were optimized for all RRHs to minimize the total network power consumption; the downlink was considered in [92], whereas whereas both downlink and uplink communications were addressed in [93]. Total power to support radio transmission and operations of fronthaul links and RRHs is accounted for in these research works, where the authors show how to transform the underlying problems into the sparse beam- forming problems. Then, these papers combine the solution techniques employed to address the traditional power minimization problem in [117] and [165] and the fronthaul capacity minimization problem in [94] to tackle the transformed problem. This solution technique is also closely related to that employed in [69]. In fact, the design problems considered in [94] and [69] for CoMP and in [92] and [93] for Cloud-RAN with standard convex constraints do not explicitly model the fronthaul ca- pacity constraints. Consequently, they can be solved directly by employing the compressed sensing techniques [96–98]. We would like to emphasize that the related work [94] aims at minimizing the number of active links between base stations (RRHs) and users. Therefore, the limited fronthaul capacity is not explicitly imposed as constraints in [94], but it is rather considered in the design objective. Note that the greedy principle proposed in [94] can be employed to solve our problem (even though our problem is not the same with the problem in [94]); however, such greedy approach may not achieve very good performance.
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Services KPI-based Energy Management Strategies for Green Wireless Networks

Services KPI-based Energy Management Strategies for Green Wireless Networks

IV. ADAPTATION OF ENERGY MANAGEMENT STRATEGIES Enhancing the EE-KPI requires maximizing the throughput of the network while minimizing the on-grid energy consumption. The two parameters that directly affect this improvement are traffic load and RE. SPAEMA, an energy management algorithm described in [9], is an online algorithm that decides how to manage RE (store it in battery or use it) based on the battery state of charge (SoC) and the price of electricity, in order to reduce the electric bill of the operator. Thus, it does not require any future knowledge of RE nor traffic load. In [9], we showed that SPAEMA outperforms the traditional algorithm that uses RE whenever it is available. However, since the objective has shifted from reducing the electric bill of the operator to increasing our EE-KPIs, adaptation of our previously proposed algorithm (SPAEMA) is needed to match our new objective. Thus, we propose a new algorithm that adapts SPAEMA to satisfy the newly introduced objective. Then we extend the proposed algorithm for further enhancement. A. Proposed algorithm 1 - STAEMA
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Reinforcement Learning for Radio Resource Management of Hybrid-Powered Cellular Networks

Reinforcement Learning for Radio Resource Management of Hybrid-Powered Cellular Networks

Index Terms—Cellular Networks, Reinforcement learn- ing, Renewable energy, Smart grid I. I NTRODUCTION Cellular networks are witnessing exponential increase in mobile traffic with no sign of slowing down. Based on CISCO VNI, it is forecasted that the global mobile data traffic will witness an increase of seven-fold between 2016 and 2021 [1]. As a fast solution, mobile operators are deploying more base stations (BSs). This imposes serious challenges on mobile operators in terms of both operational and capital expenditures. Focusing on operation expenditure, the energy cost can reach more than 32% of the total operational cost with BSs as the most consuming part of the network [2].
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Joint radio and power resource optimal management for wireless cellular networks interconnected through smart grids

Joint radio and power resource optimal management for wireless cellular networks interconnected through smart grids

Power and spectrum allocation can be divided into two families: short time scale scheduling (frame and subframe level) and long time scale scheduling (network-level). In the first category, Derrick Wing Kwan et al. [ Ng 2013 ] have designed algorithms for power and sub-carrier allocation for an Orthogonal Frequency-Division Multiple Access (OFDMA) downlink network with energy harvesting base station. By taking into account circuit energy consumption, a finite energy storage capacity, and a minimum required data rate, an offline problem has been formulated to maximize the weighted energy efficiency of the net- work and solved by using Dinkelbach method. In another study, Gong et al. [ Gong 2014 ] have formulated the problem of grid power minimization for a downlink cellular network with RE as a two-stage dynamic programming which determines the on-off state of the BSs and assigns the resource block. The dynamic resource allocation and BS activation is constrained by the blocking probability, which serve as the QoS metric. Concerning longer time scales, traffic offloading among BSs is considered as a network-level solu- tion, wherein the cell-level traffic load is dynamically adjusted to balance the energy supply and demand of BSs. Zhang et al. [ Zhang 2016 ] have proposed energy-aware traffic offloading for HetNets with multiple SBSs powered by diverse energy sources. The aim is to minimize the on-grid network power consumption through user associations and on-off states of SBSs, while satisfying the QoS requirement in terms of rate outage probability. Also, Wei et al. [ Wei 2016 ] have proposed an offloading model to reduce the energy consumption of a network of SBS with RE. The model enables to obtain the maximum number of users that each SBS can offload theoretically by predicting the value of green energy collected and the level of the energy storage. None of these works has proposed an efficient local energy management that includes time- varying energy pricing and the battery aging mechanisms. Also, all the proposed approaches are off-line and need therefore to be adapted to more realistic conditions of RE generation, data traffic, and electricity price.
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Quality-aware Resource Management inWireless Networks

Quality-aware Resource Management inWireless Networks

Thesis contributions 39 1.3 Thesis contributions Topics concerning problems stated previously have been investigated. First of all, state of the art in resource management is studied. According to the literature, multimedia application is the problematic issue. This type of application has restricted require- ments and it is difficult to guarantee a level of service quality. Many management schemes are proposed but very few of them are interested in user experience. As men- tioned earlier that final objective of a service is user satisfaction and thus quality of experience is the most important factor. Meanwhile, network operators should also be satisfied of their profit by optimizing resource utilization. According to that, manage- ment mechanisms that aim to satisfy user experience and at the same time to optimize resource utilization are studied in this thesis. For network side, QoE-oriented mech- anism such as admission control, rate adaptation, and scheduling have been proposed and network selection mechanisms for user side. Most of them take into account in- formation from both user and network to cover all criteria. The studies have been conducted in different wireless technologies (IEEE 802.11 and Cellular Network) in both homogeneous and heterogeneous way. The obtained results demonstrate that it is feasible and beneficial to use quality of experience as metric to improve network management in the future.
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Radio Access Technology Selection in Heterogeneous Wireless Networks

Radio Access Technology Selection in Heterogeneous Wireless Networks

On the one hand, when the average rate maximization method is used, mobiles select the RAT that offers them the best throughput. Therefore, load balancing is achieved: Mobile WiMAX and LTE are similarly occupied with respect to their maximum capacity. As a result, the network utility can likely follow the throughput demand increase. On the other hand, when our hybrid approach is employed, the network modulates the broadcasted QoS parameters as a function of its load conditions. It tries to push future arrivals to less loaded RATs, thus enhancing resource utilization. By integrating their needs and prefer- ences, mobiles can avoid oversized decisions, and so improve their perceived satisfaction. Typically, at low traffic load, when both RATs can perfectly meet user QoS needs, mobile WiMAX will be preferred since it charges less. This explains why, when using our hybrid method, user utility is constantly higher than when adopting the average rate maximiza- tion method. The latter ignores user preferences (i.e., its willingness to pay for better performance or to save up money) and mainly deals with load balancing. However, be- cause the proportion of users that are connected to the LTE technology is almost constant and the user throughput is always close to R max , user utility hardly changes as a function
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Data collection and management solution for wireless sensor networks

Data collection and management solution for wireless sensor networks

Correspondent author: gil.de-sousa@cemagref.fr Abstract Wireless sensors networks (WSN) use can be very interesting in agricultural and environmental data collection. The first WSN generations operated in a continuous data stream mode which generates high energy consumption. This article presents a new WSN platform that limits data exchanges and has an increased lifetime. All of its components are designed in a resource aware mode in relation with energy, memory and processing. This platform is built on wireless sensors, implementing a hardware component-based concept, that allow them to be combined to form a more evolved wireless device. To manage this wireless sensor, a hybrid operating system, both multithreading and based on events, has been developed and is associated to a micro-file system. A WSN management tool is in charge of monitoring the wireless sensors and of the data collection. A first evaluation of this WSN platform has been realised in an agricultural context data collection.
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Adaptive Distributed Resource Allocation in Wireless Sensor Networks

Adaptive Distributed Resource Allocation in Wireless Sensor Networks

C. Results and Discussion Figure 5 shows the coverage area against time for the three cases measured on our 16-node testbed. The unit of time in the x-axis is in terms of time cycles. A short time cycle duration makes packet collision reduction and power management difficult to control, whereas a long time cycle hampers the sensibility of target detection. In our implementation, we tried different durations and eventually decided to make each time cycle last for 5 seconds to obtain acceptable performance. Each MICA2 mote is powered by a pair of AA batteries which can last for days. To expedite the data collection and analysis process, we consider only the first 250 cycles as shown in Figure 5.
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Bounds on Contention Management in Radio Networks

Bounds on Contention Management in Radio Networks

This problem has been studied in multiple wireless network models. The most com- mon such model is the classical model, introduced by Chlamatac and Kutten [8], in which links are reliable and concurrent broadcasts by neighbors always generate col- lisions. The dominant local broadcast strategy in this model is the Decay routine in- troduced by Bar-Yehuda et al. [9]. In this strategy, nodes cycle through an exponential distribution of broadcast probabilities with the hope that one will be appropriate for the current level of contention (e.g., [9, 11–17, 22]). To solve local broadcast with high probability (with respect to the network size n), the Decay strategy requires O(∆ log n) rounds, where ∆ is the maximum contention in the network (which is at most the max- imum degree in the network topology). It has remained an open question whether this bound can be improved to O(∆ + polylog(n)). In this paper, we resolve this open ques- tion by proving the Decay bound optimal (notice, throughout this paper, when we call an upper bound “optimal” or a lower bound “matching,” we mean within poly-log log factors). This result also proves for the first time that existing constructions of ad hoc selective families [15, 16]—a type of combinatorial object used in wireless network algorithms—are optimal.
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2016 — Mobility and resource management for 5G heterogeneous networks

2016 — Mobility and resource management for 5G heterogeneous networks

40 2.2 Introduction The conventional topology of current cellular networks is a star-shaped structure with central control points. This structure makes it simple to provide quality of service (QoS) guarantees. In next generation networks, this topology will not help to increase efficiency in bandwidth utilization, especially when all user data need to go through the core network. Thus, the cellular topology needs to be hybrid with different kinds of connections using new technologies such as relay nodes, small cells and device-to-device communications, which can play important roles in overcoming the difficulties and challenges of next generation networks (Shanzhi & Jian, 2014). Relaying is considered to be one of the key functionalities for 3GPP releases 10 and 11 of Long Term Evolution-Advanced (LTE-A) in order to improve the cell-edge user throughput, and to extend coverage to new areas by flexible and easy deployment (Parkvall et al., 2011). With relaying, the User Equipment (UE) connects to the network via a relay station (RS) that is wirelessly connected to a base station using the LTE radio interface technology (Parkvall et al., 2011). The base station (BS) or evolved NodeB (eNB) may serve one or several relays in addition to directly serving the macro UEs (M-UEs) (3GPP, 2012a).
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Power-Delay Tradeoffs in Green Wireless Access Networks

Power-Delay Tradeoffs in Green Wireless Access Networks

† University of Versailles - PRISM, France Abstract—Targeting energy efficiency while meeting user Qual- ity of Service (QoS) is one of the most challenging problems in green wireless networks. In this paper, we propose an opti- mization model based on finding a tradeoff between reducing the number of active radio cells and increasing the transmit power of base stations (BSs) to better serve all users in the system. The main contribution of the paper is the formulation of a multiobjective optimization problem that jointly minimizes the network power consumption and the sum of the network user transmission delay. Our proposed problem is solved using an exhaustive search algorithm to obtain the optimal solution. Solving the optimization problem at hand is very challenging due to the high computational complexity of the exhaustive search. Therefore, we run simulations in a small network to give insights into the optimal solution. Specifically, we study different cases by tuning the respective weights of the power and delay costs. This is a distinctive and important feature of our model allowing it to reflect various decision preferences. Regarding these preferences and under various spatial distribution of users, results show that our solution allows the optimal network configuration to be selected in terms of power consumption while guaranteeing minimal delay for all users in the network.
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Energy and Resource Allocations for Battery Aging-Aware Green Cellular Networks

Energy and Resource Allocations for Battery Aging-Aware Green Cellular Networks

Fig. 7: On-grid price reduction with different harvested energy prediction errors. VIII. C ONCLUSION In this paper, we have addressed the problem of grid energy consumption in hybrid BS cellular network equipped with RE. In contrast to most studies that focus only on reducing the opex cost of the operator by bringing down the grid energy consumption of the network, our work takes into account the battery degradation model that represents a significant cost to the operator. We proposed BAPA, an energy management algorithm that brings down the grid energy consumption of the operator, while preserving the life of the battery. Our results show that BAPA can reach up to 99% grid energy savings compared to the optimal solution and outperforming a benchmark algorithm.
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Competition between wireless service providers sharing a radio resource

Competition between wireless service providers sharing a radio resource

Keywords: Game Theory, wireless networks, pricing, shared spectrum, Wardrop equilibrium 1 Introduction Internet access has become almost compulsory in everyday life, and each user has to decide through which provider to access the network. While there are areas with a single access point and provider, in most cases the Internet access has become highly competitive and users can choose the provider they prefer depending on a combination of price, offered quality of service (QoS), and rep- utation. Studying the outcome of the association choices from users is of major importance for providers, in order to decide if they have an interest in competing for the access offers, and if so, to define at which price.
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Designing dynamic resource allocation mechanisms for wireless powered communication networks.

Designing dynamic resource allocation mechanisms for wireless powered communication networks.

inductive coupling, magnetic resonant coupling, laser power beaming, and radio-frequency wireless power transfer (RF-WPT). Table 1.1 compares these four WPT technologies from different aspects. Since our research focus is on RF-WPT only, henceforth we simply call it WPT for convenience. WPT provides an attractive solution by powering devices with continuous and stable energy over the air. By leveraging the far-field radiative properties of EM waves, wireless receivers can harvest energy remotely from RF signals radiated by an energy transmitter (ET) 4 . WPT enjoys many practical advantages due to being stable, fully controllable in its transmit power, waveforms, and occupied time/frequency dimensions to power energy receivers (ERs)—in contrast to intermittent and uncontrollable energy resources like solar, wind, or ambient EM radiations. Hence, WPT is a very good candidate for applications requiring deployment of low-power devices as in wireless sensor networks (WSNs), wireless body area networks (WBANs), and the Internet of things (IoT). The typical operation range for WPT systems is from several meters to hundreds of kilometers [3]. WPT has advantages like long-range operation, small receiver form factor, deployment flexibility, power multicasting, and not necessarily requiring a line-of-sight (LoS) link; whereas having low end-to-end power transfer efficiency and safety issues are among the main bottlenecks of applying this technology[4].
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Radio Access Selection Approaches in Heterogeneous Wireless Networks

Radio Access Selection Approaches in Heterogeneous Wireless Networks

In fact, when the instantaneous rate maximization method is used, mobiles select the RAT that offers them the best throughput. Therefore, a kind of load balancing is achieved: Mobile WiMAX and LTE are similarly occupied with respect to their maximum capacity. As a result, the network utility can likely follow the throughput demand increase. On the other hand, when our hybrid approach is employed, the network modulates the broadcasted QoS parameters as a function of its load conditions. It tries to push future arrivals to less loaded RATs, thus enhancing resource utilization. By integrating their needs and preferences, mobiles can avoid oversized decisions and so improve their perceived-satisfaction. Typically, at low traffic load, when both RATs can perfectly meet users QoS needs, mobile WiMAX will be preferred since it charges less. This explains why, when using our hybrid method, user utility is constantly higher than when adopting the instantaneous rate maximization method. The latter ignores user preferences (i.e., its willingness to pay for better performances or to save up money) and mainly deals with load balancing. However, because the proportion of users that are connected to the LTE technology is almost constant and the user-perceived through- put is always close to R max , user utility hardly changes as a
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Resource management for enabling heterogeneous services and applications in wireless cellular systems.

Resource management for enabling heterogeneous services and applications in wireless cellular systems.

and resource allocation with 5G NR frame structure show a promising way to enhance the future system performance. Besides, an important aspect of 5G wireless network is the application’s view. Indeed, with recent breakthroughs in artificial intelligence (AI), new emerging applications enabling new ways of interactions among things and humans have been created to enhance the quality of life. Many of them are compute-intensive applications such as e-health, object recognition/detection/monitoring. When only communications-related issues are concerned in network design and management, it is impossible to enable these compute-intensive applications on many different kinds of devices, espe- cially low-cost IoT devices. Therefore, 5G wireless networks must support not only communication, but also computation, control, and content delivery (4C) functions. Mobile Edge Computing (MEC) has been recently proposed as an important technology in 5G wireless networks to enable a variety of new compute-intensive applications even on low-cost IoT devices. In general, MEC is a network architecture concept defined by ETSI [20], that enables cloud computing capabilities and an IT service environment at the edge of the cellular network. Different design aspects of MEC, such as task partitioning and resource allocation, have been investigated in both academic and industry communities to enable them and support future system scenarios and applications [21, 22]. One typical network architecture for the 5G wireless system is shown in Fig. 3.1, which employs vari- ous enabling technologies and novel network architectures for efficient support of various wireless applications.
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Q-Learning for Policy Based SON Management in wireless Access Networks

Q-Learning for Policy Based SON Management in wireless Access Networks

of SON solutions as well as the use of advanced network optimization tools has kept the OPEX at the same level even while the network is required to handle more radio access technologies (2G, 3G, 4G), more traffic, and a higher diversity of users and services. As the network complexity is drastically increasing, due to network heterogeneity, the traffic growth and the high user expectation, the automation of RAN operation has become a necessity. Hence, automation is gaining momentum, and it is commonly agreed today that SON is required for an efficient RAN operation. It is also expected to fully realize the initial promise of SON concept: to provide the operator with a Self Organized Network, capable of reaching as a whole the operator high level objectives. These high level objectives reflect the operator strategy in terms of e.g. network capacity, user satisfaction... As stated before, PBSM framework defined in Semafour project aims at building a Self Organized Network out of a RAN enabled with different SON functions. This PBSM system is supposed to be independent from the SON functions, i.e., considering SON functions as black boxes and acting only on the SON configuration parameters. These parameters are supposed to be provided by RAN vendors to orient the behavior of the SON, whereas the internal SON optimization algorithm remains proprietary [1].
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