The deployment of large antenna arrays in Massive Multiple Input Multiple Output (M-MIMO) systems substantially improves mobile networks’ performance. Nevertheless, the increase in performance brought by additional antennas is often counterbalanced by an increase in Power Consumption (PC) caused by the use of supplemental hardware resources supporting additional Radio Frequency (RF) channels. In M-MIMO networks, switching on/off RF channels and muting the associated antennas according to load conditions is known to be an efficient Energy Saving (ES) mechanism. This paper introduces a new RF channels switch on/off solution to maximize the Energy Efficiency (EE) under Quality of Service (QoS) constraints. The proposed solution is based on a MAB algorithm, which appears simpler than state of the art approaches and can be easily implemented in real systems. The algorithm leverages the quasiconcave shape of the EE metric to sequentially select the optimal antenna array configuration - i.e., the number of RF channels in both azimuth and elevation - from a predefined set of configurations. Extensive system-level simulations demonstrate that the proposed algorithm achieves a significant EE gain over a baseline solution.

Massive-MIMO (M-MIMO) allows to schedule users with high gain narrow beams and to reduce intra-cell inter-beam interference. Close to cell edge, users may experience high interference from beams of neighboring cells which degrades their performance. This paper introduces low complexity inter-cell interference coordination between neighboring cells for M-MIMO systems implementing Grid of Beams (GoB) using Radio Environment Maps (REMs). The REMs are designed using the Kriging with the Covariance Tapering spatial interpolation technique and are created for the serving and the interfering beams. The interference coordination is introduced as constraints to the schedulers that avoid simultaneous scheduling of users served by highly interfering beams from the neighboring cells. The coordination decision is based on information retrieved from REMs and its quality depends on the REMs’ precision. Around 72 percent performance gain in terms of mean user throughput is achieved by the REM based coordination with respect to a baseline solution without coordination, and around 40 percent gain with respect to a state of the art solution that implements coordination using information at beam level resolution.

The deployment of RISs in future 6G networks is expected to substantially improve mobile network coverage. This paper introduces a new cross-layer low-complexity scheme for the online optimization of RIS-assisted communication systems. It jointly combines BS and RIS configuration and fair UEs’ scheduling which is critical for high-performance deployments. A RIS beam synthesis method is especially proposed for RIS configuration. The proposed solution embeds two nested control loops: i) a fast control loop working at the OFDMA slot scale and consisting in a standard UEs proportional fair scheduler, and ii) a slow control loop operating at the OFDMA frame scale which adapts the RIS’ configuration to the UEs’ spatial distribution and maximizes the UEs’ aggregated performance. The slow control loop is based on an online stochastic approximation algorithm whose convergence to the optimal restpoint is proved. In a reference scenario, the proposed scheduler achieves a gain of 47% in mean spectral efficiency for NLOS UEs over a baseline scheme.

We study in this paper optimal control strategy for Advanced Sleep Modes (ASM) in 5G networks. ASM correspond to different levels of sleep modes ranging from deactivation of some components of the base station for several micro-seconds to switching off of almost all of them for one second or more. ASMs are made possible in 5G networks thanks to the definition of so-called lean carrier radio access which allows for configurable signaling periodicities. We model such a system using Markov Decision Processes (MDP) and find optimal sleep policy in terms of a trade-off between saved power consumption versus additional incurred delay for user traffic which has to wait for the network components to be woken-up and serve it. Eventually, for the system not to oscillate between sleep levels, we add a switching component in the cost function and show its impact on the energy reduction versus delay trade-off.

Advanced Sleep Modes (ASMs) are defined as a progressive shutdown of the Base Station (BS) depending on the activation and the deactivation times of the different components. This transition duration defines different levels of sleep modes that can be implemented in future 5G networks. We propose in this paper a management strategy based on Q-learning approach which will enable to find the best combination and durations of ASM levels depending on the traffic load and the network operator's policy regarding energy reduction versus latency. Our results show that even in delay-sensitive scenarios, high energy gains can be achieved in low and moderate traffic loads, respectively 55% and 10%, without inducing an extra latency. Starting from a certain traffic load (approximately 30%), ASM should not be implemented in case of stringent latency constraint.

Advanced Sleep Modes (ASMs) correspond to a gradual deactivation of the Base Station (BS)'s components in order to reduce its Energy Consumption (EC). Different levels of Sleep Modes (SMs) can be considered according to the transition time (deactivation and activation durations) of each component. We propose in this paper a management solution for ASMs based on Q-learning approach. The target is to find the optimal durations for each SM level according to the requirements of the network operator in terms of EC reduction and delay constraints. The proposed solution shows that even with a high constraint on the delay, we can achieve high energy savings in a low load scenario (up to 57% of EC reduction) without inducing any impact on the delay. When the delay constraint is relaxed, we can achieve up to almost 90% of energy savings.

The concept of Advanced Sleep Modes (ASMs) corresponds to a gradual deactivation of the Base Station (BS)'s components in order to decrease the Energy Consumption (EC). We propose in this paper a strategy to implement the ASMs and to manage users whose service requests occur whilst the BS is sleeping. As the BS has to wake up periodically to send signaling bursts, we increase this periodicity in order to extend the sleep durations and hence maximize the energy gains. We study several scenarios with various traffic profiles and different periodicities of signaling bursts and quantify the outcomes of this solution, both in terms of EC reduction and quality perceived by the users (mainly throughput and latency). Our results show that high energy gains (up to 90%) can be obtained using ASMs, albeit some degradation in throughput (ranging from 3% to 19% in low loads depending on the signaling periodicity) and latency (less than 1ms for low periodicities and around 5ms for larger ones).

In future networks, Radio Resource Management (RRM) could benefit from Geo-Localized Measurements (GLM) thanks to the Minimization of Drive Testing (MDT) feature introduced in Long Term Evolution (LTE). Such measurements can be processed by the network and be used to optimize its performance. The purpose of this paperais to use GLM to significantly improve scheduling. We introduce the concept of forecast scheduler for users in high mobility that exploits GLM. It is assumed that a Radio Environment Map (REM) can provide interpolated Signal to Interference plus Noise Ratio (SINR) values along the user trajectories. The diversity in the mean SINR values of the users during a time interval of several seconds allows to achieve a significant performance gain. The forecast scheduling is formulated as a convex optimization problem namely the maximization of an α-fair utility function of the cumulated rates of the users along their trajectories. Numerical results for thee different mobility scenarios illustrate the important performance gain achievable by the forecast scheduler.

Cellular networks are witnessing an exponential traffic growth leading to an increase in Energy Consumption (EC), and having both environmental and economic impact. Recently, different approaches have been studied to build Green cellular networks focusing mainly on the Base Stations (BSs) as the access network represents 80% of the total wireless network consumption [1] [2]. One of the promising solutions for increasing throughput and reducing EC is the deployment of large antenna arrays, known as Large Scale Antenna Systems (LSAS) [3] [4] [5] [6], that can transmit highly focused beams. In the present work, we focus on the use of an advanced BS power model developed within the GreenTouch project [7] [8]. This model allows to quantify the power consumption of a reference scenario comprising multiple sites with standard BS antennas, and then compare it to a LSAS solution implementing multilevel beamforming. We then exploit the LSAS merits to get greener deployment with less BSs, some of which can be turned off as a function of the traffic demand. The coverage areas of each cell can be modified, by updating the codebook of beams. We finally investigate different network configurations which represent distinct trade-offs between EC and capacity. We propose a methodology to represent and design green policies for managing the network which select the desired operating points. Detailed simulation results illustrate the proposed methodology.

The aim of this paperais to study new antenna array technologies in order to manage efficiently heterogeneous, fixed and mobile traffic. Traffic light close to the cell edge is introduced to generate non stationary mobility pattern in the cell. A car following model is used to model the mobility behavior of the vehicles. A heterogeneous antenna system with different large antenna array technologies is considered: Virtual Small Cell (VSC), virtual small cell with Self-Organizing Network (VSC-SON) and beamforming with multilevel global codebook that manages the heterogeneous antenna system at the Base Station (BS). The first two technologies improve the cell performance due to the capability to focus the signal at the traffic concentration near the traffic light. The novel beamforming solution with global codebook can further and significantly improve performance due to the capability to focus the signal along the road and to implicitly balance the traffic between the different antennas. Numerical simulations illustrate the benefits brought about by the different antenna technologies.

Virtual Sectorization (ViSn) aims at covering a confined area such as a traffic hot-spot using a narrow beam. The beam is generated by a remote antenna array located at- or close to the Base Station (BS). This paper develops the ViSn model and provides the guidelines for designing the Virtual Sector (ViS) antenna. In order to mitigate interference between the ViS and the traditional macro sector covering the rest of the area, a Dynamic Spectrum Allocation (DSA) algorithm that self-optimizes the frequency bandwidth split between the macro cell and the ViS is also proposed. The Self-Organizing Network (SON) algorithm is constructed to maximize the proportional fair utility of all the users throughputs. Numerical simulations show the interest in deploying ViSn, and the significant capacity gain brought about by the self-optimized bandwidth sharing with respect to a full reuse of the bandwidth by the ViS.

The aim of this paper is to study resource allocation and control in a LTE-Advanced heterogeneous network with vehicular traffic in the presence of traffic light. A car following model is used to model the cars' speed and their interactions. A small cell is deployed near the traffic light to relieve periodic congestion and QoS degradation. Three resource allocation and control schemes are investigated: a full frequency reuse, a static and a dynamic frequency splitting algorithm that are optimized with respect to a throughput based _-fair utility. Through numerical simulations, it is shown that the frequency splitting algorithms outperform the full frequency reuse scheme in term of user throughput and file transfer time. Furthermore, it is shown that dynamic control scheme is of particular interest for non-stationary traffic as the one introduced by a periodic traffic light.

Vertical Sectorization (VS) consists in creating vertically separated sectors in the original cell using an Active Antenna Systems (AAS) supporting two distinct beams with different downtilts. The total transmit power is split between the two sectors, while the frequency bandwidth can be reused by each sector, creating additional interference between the two sectors. For low traffic demand, VS may lead to performance degradation, while for high traffic demand in both sectors, VS is likely to bring about important capacity gains. Hence intelligent activation policy of VS is needed to fully benefit from this feature. In this paper, we propose an approach taking advantage of the more focused downtilted beam. A dynamic alpha-fair bandwidth sharing is proposed for low and medium load. It is autonomously replaced by full bandwidth reuse for high load scenarios using a threshold-based controller. A flow-level dynamic simulator is used to numerically validate the proposed mechanisms.

Developments in autonomic network management offer many promises, but its economic benefits are hard to assess. This paper proposes a method to calculate the OPEX gains of a typical network on the basis of a scenario, for which a management framework and autonomous mechanisms have been developed. It makes use of a novel approach, a Toy model, taking into account expert opinions as well as simulation results. The new approach allows assessment of the OPEX impact of both individual mechanisms as well as the overall impact, which, in the present scenario results in an expected OPEX saving of 11 to 13 per cent. © 2014 IEEE.

This paper focuses on the exposure to Radio Frequency (RF) Electromagnetic Fields (EMF) and on optimization methods to reduce it. Within the FP7 LEXNET project, an Exposure Index (EI) has been defined that aggregates the essential components that impact exposure to EMF. The EI includes, among other, downlink (DL) exposure induced by the base stations (BSs) and access points, the uplink (UL) exposure induced by the devices in communication, and the corresponding exposure time. Motivated by the EI definition, this paper develops stochastic approximation based self-optimizing algorithm that dynamically adapts the network to reduce the EI in a heterogeneous network with macro- and small cells. It is argued that the increase of the small cells' coverage can, to a certain extent, reduce the EI, but above a certain limit, will deteriorate DL QoS. A load balancing algorithm is formulated that adapts the small cell' coverage based on UL loads and a DL QoS indicator. The proof of convergence of the algorithm is provided and its performance in terms of EI reduction is illustrated through extensive numerical simulations.

Small cells have been identified as an effective solution for coping with the important traffic increase that is expected in the coming years. But this solution is accompanied by additional interference that needs to be mitigated. The enhanced Inter Cell Interference Coordination (eICIC) feature has been introduced to address the interference problem. eICIC involves two parameters which need to be optimized, namely the Cell Range Extension (CRE) of the small cells and the ABS ratio (ABSr) which defines a mute ratio for the macro cell to reduce the interference it produces. In this paper we propose self-optimizing algorithms for the eICIC. The CRE is adjusted by means of load balancing algorithm. The ABSr parameter is optimized by maximizing a proportional fair utility of user throughputs. The convergence of the algorithms is proven using stochastic approximation theorems. Numerical simulations illustrate the important performance gain brought about by the different algorithms.

Three dimensional (3D) beamforming (also elevation beamforming) is now gaining interest among researchers in wireless communication. The reason can be attributed to its potential for enabling a variety of strategies such as sector or user specific elevation beamforming and cell-splitting. Since these techniques cannot be directly supported by current LTE releases, the 3GPP is now working on defining the required technical specifications. In particular, a large effort is currently being made to get accurate 3D channel models that support the elevation dimension. This step is necessary as it will evaluate the potential of 3D and full dimensional (FD) beamforming techniques to benefit from the richness of real channels. This work aims at presenting the on-going 3GPP study item “study on 3D-channel model for elevation beamforming and FD-MIMO studies for LTE” and positioning it with respect to previous standardization works.

In dense wireless networks, inter-cell interference highly limits the capacity and quality of service perceived by users. Previous work has shown that approaches based on frequency reuse provide important capacity gains. We model a wireless network with Inter-Cell Interference Coordination (ICIC) at the flow level where users arrive and depart dynamically, in order to optimize quality of service indicators perceivable by users such as file transfer time for elastic traffic. We propose an algorithm to tune the parameters of ICIC schemes automatically based on measurements. The convergence of the algorithm to a local optimum is proven, and a heuristic to improve its convergence speed is given. Numerical experiments show that the distance between local optima and the global optimum is very small, and that the algorithm is fast enough to track changes in traffic on the time scale of hours. The proposed algorithm can be implemented in a distributed way with very small signaling load.

The adoption of autonomic features in network management becomes a necessity to cope with the increasing complexity of the telecommunications ecosystem. The purpose of this paper is to present the concept of Unified Management Framework (UMF) developed in the FP7 UniverSelf project. The core of UMF comprises three functional blocks, namely Governance, Coordination and Knowledge which allow to efficiently operate autonomic mechanisms such as SON functionalities in the radio access network. These are denoted as Network Empowerment Mechanisms (NEMs) that, by means of policies, enforce operator business and operation objectives. The paper focuses on the Coordination core block of UMF, and its instantiation to the problem of SON coordination for managing LTE-Advanced a Heterogeneous Network (HetNet) with relays. Prototype description and results are described so as to illustrate the value and applicability of the proposed framework.

This paper introduces self-optimization for wireless networks taking into account flow-level dynamics. Users arrive and leave the network according to a traffic model. Elastic traffic is considered here. The developed solutions self-optimize the network stability region using user feedback (measurements). The use case considered is cell size optimization. An algorithm is given, and its convergence is proven using stochastic approximation techniques. Convergence points are characterized, allowing performance gains to be evaluated rigorously. Performance gains are evaluated numerically, showing an important increase of the network capacity.

As plug-and-play devices, femtocells are expected to be self-managed, empowered by self-organization functionalities. This paper presents a Self-organizing networks (SON) process for off-loading macrocell traffic towards Open Subscriber Group (OSG) femtocells. The off-loading process comprises two SON functionalities: the first configures the femtocell transmitted pilot power, which depends on the received macrocell pilot power and the density of femtocells. The femtocell pilot powers are chosen using a look-up table generated through an off-line queuing theory based analysis. To mitigate interference from femtocells with different transmission powers, a self-optimizing Inter-Cell Interference Coordination (ICIC) functionality is activated. The performance gain of the self-organizing mechanisms is evaluated using a large scale network simulator. It is shown that in dense femtocell deployment, the SON off-loading can bring about considerable capacity gain.

Relay stations are an important component of heterogeneous networks introduced in the LTE-Advanced technology as a means to provide very high capacity and QoS all over the cell area. This paper develops a self-organizing network (SON) feature to optimally allocate resources between backhaul and station to mobile links. Static and dynamic resource sharing mechanisms are investigated. For stationary ergodic traffic we provide a queuing model to calculate the optimal resource sharing strategy and the maximal capacity of the network analytically. When traffic is not stationary, we propose a load balancing algorithm to adapt both the resource sharing and the zones covered by the relays based on measurements. Convergence to an optimal configuration is proven using stochastic approximation techniques. Self-optimizing dynamic resource allocation is tackled using a Markov Decision Process model. Stability in the infinite buffer case and blocking rate and file transfer time in the finite buffer case are considered. For a scalable solution with a large number of relays, a well-chosen parameterized family of policies is considered, to be used as expert knowledge. Finally, a model-free approach is shown in which the network can derive the optimal parameterized policy, and the convergence to a local optimum is proven.

This paper shows a Self-organizing networks (SON) algorithm for interference coordination in downlink Orthogonal Frequency-Division Multiple Access (OFDMA) networks. A distributed algorithm is introduced with a proof of convergence for a static user population. The algorithm uses closed-form formulas for the transmit powers update, and is therefore computationally light. The proposed algorithm is applied to a 117 cells dynamic network simulator with a File Transfer Protocol (FTP) service, showing significant performance gains over a Reuse 1. The Quality of Service (QoS) of cell-edge users improves without degrading the QoS of other users. The trade-off between Block Call Rate (BCR), which is the proportion of users rejected by admission control, and cell-edge users throughput is shown, and a simple method for the network operator to manage it is provided.

This paper proposes two novel contributions: a fast statistical method for scheduling gain calculation is introduced, and we show how it can be combined with queuing theory and real network measurements in order to calculate the flow-level performance of a cellular network that uses Proportional Fair (PF) scheduling. A statistical method to evaluate the scheduling gain is proposed and a proof of its convergence is given. This method is three orders of magnitude faster than full system simulation. Based on these results, a queuing theory analysis is performed and is applied to the pedestrian A 3km/h channel model and LMMSE receiver. The evaluation uses drive tests measurements to reflect the realistic distribution of radio conditions. The derived flow-level performance of PF scheduling, including blocking rate, outage rate and flow throughput, is of major interest for network operators.

Distributing Radio Resource Management (RRM) in heterogeneous wireless networks is an important research and development axis that aims at reducing network complexity. In this context, RRM decision making can be delegated to mobiles by incorporating cognitive capabilities into mobile handsets, resulting in the reduction of signalling and processing burden. This may however result in inefficiencies (such as those known as the "tragedy of commons") that are inherent to equilibria in non-cooperative games. Due to the concern for efficiency, centralized network architectures and protocols keep being considered and being compared to decentralized ones. From the point of view of the network architecture, this implies the co-existence of network-centric and terminal-centric RRM schemes. Instead of taking part within the debate among the supporters of each solution, we propose in this paper hybrid schemes where the wireless users are assisted in their decisions by the network that broadcasts aggregated load information. At some system's states, the network manager may impose his decisions on the network users. In other states the mobiles may take autonomous actions in reaction to information sent by the network. In order to improve the performance of the non-cooperative scenario, we investigate the properties of an alternative solution concept named Stackelberg game, in which the network tries to control the users' behavior by broadcasting appropriate information, expected to maximize its utility, while individual users maximize their own utility. We derive analytically the utilities related to the Quality of Service (QoS) perceived by mobile users and develop a Bayesian framework to obtain the equilibria. Numerical results illustrate the advantages of using our hybrid game framework in an association problem in a network composed of HSDPA and 3G LTE system that serve streaming and elastic flows.

Motivated by the fact that when selfish users choose their policies independently without any coordination mechanism, Nash equilibria could result in a network collapse, we develop in this paper a hierarchical distributed learning framework for decision-making in heterogeneous cognitive networks. We introduce the Nash-Stackelberg fuzzy Q-learning, with the network as leader that aims at maximizing its utility (revenue) and the mobiles as followers that have their individual objectives (maximizing their QoS). We validate our results through extensive simulations of the algorithm in a practical setting of a geographical area covered by a global HSDPA and 3G LTE system that serves both streaming and elastic traffic.

The area of networking games has had a growing impact on wireless networks. This reflects the recognition in the important scaling advantages that the service providers can benefit from by increasing the autonomy of mobiles in decision making. This may however result in inefficiencies that are inherent to equilibria in non-cooperative games. Due to the concern for efficiency, centralized protocols keep being considered and compared to decentralized ones. From the point of view of the network architecture, this implies the co-existence of network-centric and terminal centric radio resource management schemes. Instead of taking part within the debate among the supporters of each solution, we propose in this paper hybrid schemes where the wireless users are assisted in their decisions by the network that broadcasts aggregated load information. We derive the utilities related to the Quality of Service (QoS) perceived by the users and develop a Bayesian framework to obtain the equilibria. Numerical results illustrate the advantages of using our hybrid game framework in an association problem in a network composed of HSDPA and 3G LTE systems.

Troubleshooting of wireless networks is a challenging network management task. We have developed, in a previous work, a new troubleshooting methodology, which we named Statistical Learning Automated Healing (SLAH). This methodology uses statistical learning, in particular logistic regression, to extract the functional relationships between the noisy Key Performance Indicators (KPIs) and Radio Resource Management (RRM) parameters. These relationships are then processed by an optimization engine so as to calculate the optimized RRM parameters which improve the KPIs of a degraded cell. The process is iterative and converges to the optimum RRM parameter value in few iterations, which makes it suitable for wireless networks. The present work focuses on the adaptation of SLAH for troubleshooting the mobility parameter, namely the handover margin, in 3G Long Term Evolution (LTE) networks. The simulation results, which we obtain for a practical use case, show the advantage of this new, automated troubleshooting methodology.

Self organizing network (SON) functionalities are currently developed to improve network performance and management tasks. SON functionalities require efficient utilization of data extracted from the network. In this context, the paper has two objectives. First it is shown that one can use simple statistical learning techniques such as regression to extract a model from data. The model comprises closed form expressions that approximate the functional relations between measured key performance indicators (KPIs) and radio resource management (RRM) parameters. Second, it is shown how the model can be integrated in a monitoring process and be used to devise an efficient auto-tuning algorithm. To this end, two case studies of handover monitoring and handover auto-tuning in a LTE network are described and illustrate the application of the proposed approach.

The problem of dynamic load balancing in heterogeneous networks is investigated. The sub-systems considered are a UMTS network with WLAN hotspots. A load balancing algorithm is first proposed that can push traffic between the two sub-systems according to load conditions and predetermined load thresholds. To adapt the algorithm to traffic condition, the load threshold for the UMTS to WLAN vertical handover is auto-tuned. The auto-tuning process is orchestrated by a fuzzy logic controller that is optimized using the fuzzy Q-learning algorithm. Numerical simulations using a semi-dynamic network simulator illustrate the effectiveness of the proposed approach.

Troubleshooting (TS) in UMTS networks are basic management tasks required to guarantee efficient usage of the network infrastructure. This paper presents a methodology for automating TS tasks based on Bayesian networks (BN). In a first learning phase, data relating symptoms and alarms to faults in the network is extracted and used to create the TS model. In a second phase, symptoms and alarms are used to identify faults with the highest probabilities. A TS case study using a dynamic system simulator illustrates the effectiveness of the proposed approach

With the evolution of wireless services and the increasing popularity of multimedia applications, setting radio parameters and sharing resources between different services are more than ever key issues for providing high-quality services. In this paper, we propose a dynamic and autonomic approach to sharing radio resources between RT and NRT services in order to achieve optimal trade-offs between their qualities of service. Fuzzy Q-learning controller (FQLC) is used to adapt the resource reserved for the RT traffic according to perceived quality indicators of each service. Each base station is fitted out with a controller which manages its resources. Learning results from each base station are supplied to a central FQLC of the network. Simulations have shown the effectiveness of the FQLC in terms of balancing the quality between RT and NRT services

IEEE 802.11 wireless local area networks (WEAN) widely deployed in recent years work mostly with distributed coordinated function (DCF) at medium access control (MAC) layer without QoS (quality of service) support. This paper aims at modelling WLAN network for a semi-dynamic simulator: Bianchi's DCF model is extended to include asymmetric TCP traffic for mobile stations with different radio conditions and modulations; radio resource management (RRM) algorithms are devised to guarantee QoS; adaptation of the model for a semi-dynamic simulator is described. Simulation results illustrate the effectiveness of the proposed modelling

In this article, we address the problem of auto-tuning of soft handover (SHO) parameters in WCDMA networks. The auto-tuning process uses a fuzzy Q-learning controller to adapt SHO parameters to varying network situations such as traffic fluctuation. The fuzzy Q-learning controller combines both fuzzy logic theory and reinforcement learning method. The cooperation of these two mechanisms simplifies the task of the online optimization of fuzzy logic rules and consequently leads to a better online SHO parameterization of each base station in the network. The proposed scheme improves the system capacity compared to a classical network with fixed parameters, balances the load between base stations and minimizes human intervention in network management and optimization tasks

This paper presents indicator matrices for assessing the quality of UMTS networks, the interference and the macrodiversity matrices. A design methodology based on these indicators is developed to guide the network designer in complex design tasks. The indicator matrices are of particular interest for both assessing of network performance and for design purposes since they allow one to identify the interaction between any couple of stations in the network. Hence, the identification of problematic sectors as well as possible curative solutions is made much simpler. An example of network design illustrates the effectiveness of the indicator matrix approach.