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as in the proposed algorithms, yields a rate very similar to the ideal case assuming non-causal knowledge of the macro-users activity. Complementarily to activity 5A1 (see [Freedom-D51]), the metrics measuring system performances have also been tested to different combinations of variable band allocations which could be of interest for a Mobile Network Operator (MNO). Moreover, a temporal variation of the environment and of the users’ positions has been included in the system simulator.

The algorithms developed allow controlling events of transmission, related to the assignment of time slots in the LTE subframe, taking into account also data packets generated by applications running on mobile terminals and their encoding in the PRBs structure by the RRM scheduler.

The packet flow module, complemented with the transmission system dynamics, allows to evaluate the information data passing through the cell and the impact on all transmitters. A measurement of the QoS and traffic load has been put in relation to the energy needed to run a set of applications on UEs mobile terminals. The overall simulator has been tested in several cases of interest which can be summarized as:

- Impact of clustering on possibility to extend optimization methods at system level in a reliable way, in order to manage also high number of users;

- Inclusion of a set of real environments, providing metrics also for the mapped scenarios, produced by SIR, with geographic deployments of buildings and users;

- Mobility of users and temporal changes in the environment around transmitters have been included and evaluated;

- Evaluation of the system performances for different cases of varying band usage: equipartition of band between MBS and femto network, considering a fixed spectrum allocation for the MBS and varying that of second tier, or fixing the band usage of the femto network and increasing that of the macro network;

- Evaluation of system performances when including a GO for power assignment of FUEs/FAPs to mitigate indoor interference;

- Quantification of the MBS offload when routing traffic of a set of Ues indoor through FAPs instead of through the macro network.


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Table of Contents

1 INTRODUCTION ............................................................................................... 12

2 SCENARIOS ........................................................................................................ 14

2.1 BUSINESS SCENARIOS ............................................................................................... 14 2.2 ABSTRACT MODELS .................................................................................................. 14

2.2.1 Buildings deployment ......................................................................................... 15 2.2.2 Distribution of users/femto ................................................................................. 15

2.3 DEPLOYMENT COMPLEXITY .................................................................................... 17 2.3.1 One building, single floor ................................................................................... 17 2.3.2 One building, multiple floors .............................................................................. 17 2.3.3 Multiple buildings ............................................................................................... 17

2.4 REALISTIC DEPLOYMENT MODELS .......................................................................... 19 2.4.1 Geographical map data ...................................................................................... 19 2.4.2 Distribution of macro-BS/users/femtos ............................................................... 20

3 INTERFERENCE ANALYSIS .......................................................................... 22

3.1 SYSTEM ASSUMPTIONS ............................................................................................. 22 3.2 LTE COVERAGE SIMULATION ................................................................................. 22 3.3 SIMULATION WORKFLOW AND PERFORMANCE ANALYSIS .................................... 23 3.4 IDENTIFICATION OF FEMTO-BASED INTERFERENCE IMPACT ................................ 24

4 INTERFERENCE PLANNING ......................................................................... 26

4.1 CLUSTERING METRICS ............................................................................................. 26 4.1.1 Geographical deployment ................................................................................... 26 4.1.2 Interference relation ........................................................................................... 26

4.1.2.1 Pathloss ........................................................................................................... 26 4.1.2.2 SINR ............................................................................................................... 29 4.1.2.3 Methods for evaluating presence and relation to other transmitters ............... 30

4.1.3 Priority of user/service ....................................................................................... 30 4.1.3.1 Type of service ................................................................................................ 31 4.1.3.2 Traffic load/QoS ............................................................................................. 31 4.1.3.3 Latency ............................................................................................................ 31

4.1.4 Interference based on users activity ................................................................... 31 4.2 CLUSTERING AT SYSTEM LEVEL .............................................................................. 31

4.2.1 FAPs clustering .................................................................................................. 31 4.2.2 Centralized clustering ......................................................................................... 33 4.2.3 Local clustering .................................................................................................. 33 4.2.4 Hierarchical clustering: subclusters .................................................................. 33

5 INTERFERENCE MANAGEMENT ................................................................ 34

5.1 SENSING CAPABILITY ............................................................................................... 34 5.2 SYSTEM EVALUATION ............................................................................................... 35

5.2.1 Slowly varying metrics ........................................................................................ 35 5.2.2 Backhaul load ..................................................................................................... 38

5.3 SCALABILITY ............................................................................................................ 38 5.3.1 Cluster level ........................................................................................................ 38 5.3.2 Clusters of clusters ............................................................................................. 39

5.3.2.1 Parallelization topologies ................................................................................ 39 5.3.2.2 Ring Topologies .............................................................................................. 39

5.3.2.2.1 Ring-next, Ring-twist and Ring-generic ................................................... 39 5.4 OPTIMIZATIONS STRATEGIES .................................................................................. 41

5.4.1 Centralized resources allocation ........................................................................ 41


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............... 47

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................. 47................ 48................ 50

................. 50

................. 51................ 51................ 51

............... 52

................. 52................ 52................ 53................ 55................ 55

................. 57

................. 59................ 62

................. 62................ 62................ 64................ 66

............... 68

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ge ............. 71work coveragk coverage76................ 82

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................ 94

................ 96................. 96................. 98................. 98................ 98.............. 100.............. 104

............... 105.............. 105.............. 105

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8.5 MBS OFFLOAD AND QOS ....................................................................................... 107 8.5.1 MBS traffic flow for 120 MUEs ........................................................................ 107 8.5.2 MBS traffic flow for 90 MUEs .......................................................................... 110 8.5.3 MBS offload by 30 FUEs on Femto network .................................................... 113

9 CONCLUSIONS ................................................................................................ 114

9.1 TECHNICAL CONCLUSIONS .................................................................................... 114 9.2 IMPACT ON THE BUSINESS CASE ............................................................................ 115

9.2.1 Scenarios IA-1 and IA-2 – Impact on local urban corporate coverage ........... 115 9.2.2 Scenarios IA-5 and IA-6 – FAP coverage in urban corporate area (no initial macro coverage) ......................................................................................................................... 117 9.2.3 Scenario IA-3 – Impact on urban network coverage ........................................ 118 9.2.4 Scenario IA-4 – Impact on suburban network coverage .................................. 120 9.2.5 Impact of methodologies at system level .......................................................... 122


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[Rosenberg04] J. Rosenberg, “RFC3856 A Presence Event Package for the Session Initiation Protocol SIP,” IETF, 2004. [Online]. Available: http://www.ietf.org/rfc/rfc3856.txt

[Sjodberg02] J. Sjoberg et al., “Real-Time Transport Protocol (RTP) Payload Format and File Storage Format for the Adaptive Multi-Rate (AMR) and Adaptive Multi-Rate Wideband (AMR-WB) Audio Codecs,” IETF, 2002. [Online]. Available at: http://www.ietf.org/rfc/rfc3267.txt

[Yu09] C. Yu, W. Xiangming, L. Xinqi, and Z. Wei, “Research on the modulation and coding scheme in LTE TDD wireless network,” International Conference on Industrial Mechatronics and Automation (ICIMA 2009), May 2009, pp. 468 – 471


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The analysis are based either on a set of scenarios designed in 2A1 for common FAPs deployments, either on some scenarios designed ad hoc for the current tasks. In both cases, the methodologies will be tested highlighting the requirements for the FAP deployment in terms of

- Network hardware (e.g., FAP gateway, IP backhaul) - FAPs software (e.g., local processing load, sensing capabilities and dedicated channel for

signaling).

This task will be based on the analysis and implementation of approaches such as (but not limited to) time/power/subcarriers allocation strategies with low reuse patterns of the radio resources. System-level simulations are completed with an analysis of the femto-based interference impact, in the perspective of dense femto deployments. This is realized with a LTE coverage simulator that makes simple assumptions on the interference modeling and does not consider, in particular, inter-cell interference coordination. Simulations make profit of the path-loss models defined in [Freedom-D21] and enhanced in [Freedom-D31] to provide realistic SINR and throughput maps, then allowing the analysis of the femto interference versus a large number of parameters: FAP deployment scenario, FAP location inside the building, transmit power, traffic load, etc. The simulation methodology is detailed in section 3, supporting both the open- and closed-access modes. The results, given in section 8.1, may be viewed in two different ways: as a characterization of the useful and interference signal levels; but also as a pessimistic reference giving the network performance that would be obtained in absence of any specific interference mitigation technique. Six main scenarios have been investigated:

- Urban corporate FAP deployment within one floor, in presence of co-channel macro coverage;

- Urban corporate FAP deployment within one building, in presence of co-channel macro coverage;

- Urban corporate FAP deployment within a whole co-channel urban macro-cell;

- Suburban residential FAP deployment within a whole co-channel suburban macro-cell;

- Urban corporate FAP deployment within one floor, in absence of any co-channel macro coverage;

- Urban corporate FAP deployment within one building, in absence of any co-channel macro coverage.

Finally, the coverage simulations provide outcomes towards the engineering rules elaborated in activity 6A2 and towards the 2A3 business model analysis. For this later application, network capacity has been evaluated considering different traffic evolution scenarios (section 8.1).


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Several aspects of interference relations among transmitters can be inferred by direct measurements at PHY level. A complementary approach will be devoted to identify possible range of values of characteristic parameters, such as for example pathlosses, which will be useful for the purposes of the activity, devised in 5A2 and related to deployment peculiarities (e.g., FAP installed in several buildings, geographic location, etc.). In the first case, transmitters can perform either standard evaluations, such as SNR measurement, or by implementing targeted procedures (e.g. enable listening and identification phases for each FAP) to obtain a list of other transmitters and pathloss evaluation. Possible methods to address this task will be developed in another section of this document (see 4.1.2).

2.2.1 Buildings deployment

The basic parameters about the buildings deployment for residential suburban and corporate urban scenarios, are described in details in Section 5.2 of 2D1 [Freedom-D21]. Appropriate issues regarding the activity 5A2 have been included in that document. The realistic building deployments are

- Residential suburban scenario (from [Freedom-D21]); Femtocell areas are dropped with a random uniform distribution subject to a minimum separation. The density of femtocells is a variable in the simulations. Each femtocell area has one FAP that is assumed active, i.e. there is at least one active call/service. Each femtocell area is modeled as a 2-D rectangular house plus a surrounding lot. FAPs are randomly dropped within each house. FUEs are randomly dropped within each house or outdoor in the surrounding lot. MUEs are dropped uniformly and randomly within the macrocell. It is possible that some MUEs are dropped within a FAP area. MUEs are assumed to be indoor or outdoor.

- Corporate urban scenario (from [Freedom-D21]; Femtocells are deployed in urban building blocks, which are randomly dropped within the macrocell. Each femtocell block is represented by two stripes of offices separated by a street. Each femtocell block has L floors, where L is uniformly randomly distributed between 1 and 10.

- Inhomogeneous deployment: urban (used in clustering evaluation, i.e. devoted to investigate

properties of several kinds of transmitters grouping in different ways); Femtocells are deployed in a strongly inhomogeneous scenario with four nearby building (4 to 8 floors) at the vertices of a square. Size of buildings and dimensions of the square and streets in between can be varied in the simulator. Deployment of femtocells is homogeneous within each building along floors and households. Part of MUEs is deployed indoor on different floors of the buildings and part is deployed homogeneously outdoor in the cell. FUEs are attached to FAPs indoor only. Number of FAPs, number of FUEs, number of MUEs (indoor and outdoor) are among simulation parameters.

2.2.2 Distribution of users/femto

Rules for random users and femtocells distribution are described in [Freedom-D21]. A program is implemented in MatLab to automatically generate random building blocks, FAPs and UEs deployments. Figure 1 gives an example of residential deployment using the house model (i.e. 24m24m FAP areas) and distribution rules defined in Table 17 of [Freedom-D21]. Figure 2 illustrates a corporate deployment using the two-buildings-stripe model and reference distribution values defined in Table 18 of [Freedom-D21]. For clarity, the figure shows only the elements at street-level or ground-floor level; however FAPs and UEs are distributed within all


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23

The mean path-loss and shadowing component are provided either by the analytical models (for theoretical deployment scenarios) or site-specific models (for realistic deployment scenarios) defined in [Freedom-D21] and [Freedom-D31].

3.3 Simulation workflow and Performance analysis

The simulation workflow is divided in the three successive steps illustrated in Figure 9. The first step defines the network topology. The macro layout is fixed, organized as two rings around a central three-sector site, as shown in Figure 10. The FAPs are randomly deployed into buildings within the three central cells. In studies based on a synthetic environment (i.e. buildings are represented by rectangular blocks separated by a 20m wide street), the buildings are randomly dropped in the study area. In study based on a real environment, a 3D geographical map data is used. Corporate buildings where FAPs are installed are randomly selected among the buildings located in the study area, using an average corporate building ratio. In both cases, buildings are divided in 100m² small areas, where at most one FAP is authorized to be installed. Then small areas with a FAP are randomly selected depending on a FAP density, i.e. percentage of small areas owning a FAP. Finally, the location of the FAP within the small area is generated from a random distribution law. A FAP is not necessarily active, meaning that there may be no user in communication with this FAP, except the user for which the coverage is simulated. The activation of a FAP is random, based on an average activation ratio in range from 0 to 100%. Coverage is simulated in the second step from respectively the macro-only and random two-tier networks. Outputs are 3D maps providing the service coverage area, the best-server and the available spectral efficiency at any possible UE location in the streets or in building floors. Two different FAP access modes are simulated: open or closed. In case of the closed access mode, two different types of user are simulated: FAP subscriber or non-subscriber. This simulation step includes a part of randomness, as a spatially-correlated lognormal shadowing term is added to the predicted mean path-loss. This lognormal term actually represents the unpredicted or mis-predicted part of the shadowing. This is an alternative to the error margin that is usually accounted in the radio planning simulation methodology. It permits a more realistic evaluation of the network performance evolution from the macro-only to the two-tier network topology, however at the expense of a time costly Monte-Carlo computation (composed of N random realizations as shown in Figure 9). The third step provides statistics on the coverage simulation outputs, and in particular compares the coverage performance in the macro-only and two-tier networks. Remark the second step (coverage simulation) is implemented in the VolcanoLAB platform, while the generation of the network topology and the LTE output processing are run in MatLab. This workflow has been facilitated by the development of VolcanoLAB-Matlab interface features that allows the simulation chain to be mostly automatic.


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25

mode, FAP location into the building (random, close to window, regular deployment, etc.), FAP distance to MBS, MUE distance to FAP (inside same FAP area, inside same floor, …) and network traffic load. Several inter-site distances are simulated to address both noise-limited and interference-limited macro networks. Co-channel macro and femtocell layers are considered. Highest priority is given to the investigation of the DL femto-to-macro interference in urban area. This is expected to be the most critical issue, where a dense femto deployment can significantly degrade the macro-layer coverage. Six distinct simulation scenarios have been designed so far, based on DL network coverage, and covering all 3 FREEDOM business scenarios: - Scenario IA-1: Evaluating the local impact of a single-floor corporate FAP deployment in an

urban macro-cell (addressing BM1). - Scenario IA-2: Evaluating the local impact of a multi-floor corporate FAP deployment in an urban

macro-cell (addressing BM1). - Scenario IA-3: Evaluating the impact of a dense corporate FAP deployment on the coverage

quality of an urban network (addressing BM1). - Scenario IA-4: Evaluating the impact of a dense residential FAP deployment on the coverage

quality of a suburban network (addressing BM3). - Scenario IA-5: Evaluating the local impact of a single-floor corporate FAP deployment in absence

of macro-cell signal (addressing BM2). - Scenario IA-6: Evaluating the local impact of a dense corporate FAP deployment in absence of of

macro-cell signal (addressing BM2). Results are given in section 8.1.


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27

Case 2 outlooks the whole cell, thus, due to its complexity, further details will be provided in the second part of current Section. Case 1 The analysis of the pathloss matrix, providing the pathloss relation among all transmitters in the network, provide a method to group users on the basis of their capability to interfere each other and for transmitting with their intended counterpart (i.e., FAP-FUE or FUE-FAP). Since it is natural that a physical deployment of transmitters provides a scenario in which not all transmitters have appreciable interference from all others, several realistic cases are considered in relation to explicit scenarios, which allow inferring a clustered structure. We will address several methods to provide an estimate or a measurement of the pathloss matrix, i.e. a matrix with a number of rows (columns) corresponding to the index of MUEs (FAPs) whose elements are the relative pathlosses (e.g. in dB). Elements on the diagonal represent the pathloss of the signal from the transmitter to the intended receiver, off-diagonal elements represent the loss of power for the interfering components. For example, a computation ab initio shows that, for a regular type of building with several floors (the approach has been tested from 2 to 5 floors), the pathloss matrix shows a peculiar pattern with squares reflecting the floors distribution, as is visible in Figure 11.

Figure 11. Examples of pathloss distribution, in dB, for a single building with different number of floors: left, 27 FAPs, 3 floors; center, 64 FAPs, 3 floors ; right, 125 FAPs, 5

floors.

Figure 12. Example of pathloss distribution, in dB, for 3 nearby buildings, assuming a

homogeneous FAPs distribution within each one.


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31

we can for example distinguish among voice, web browsing, video streaming and so on. Priority for an operator would focus on guaranteeing QoS first on basic services, than on delay tolerant applications. Similarly, resources allocation can rely on the type of service requested by a terminal, such that an optimization strategy can assign priority of a service (e.g., voice) with respect to others. An overall fairness criterion can depend on the quantity of services running on each terminal and on the requirements that such services have to run.

4.1.3.1 Type of service

Simulations will consider VoIP, web browsing, streaming as the main types of service to simulate. Different types of data flux can be represented as FTP or video/audio streaming by tuning parameters of generation (e.g., average packets dimensions depending on coding, interarrival time) which are proper of the specific application and can be considered more or less relevant applications with respect to others, in view of the different requirements on latency.

4.1.3.2 Traffic load/QoS

Different services can be grouped on the basis of traffic load and/or QoS to run. A fairness policy could consider this characteristic as a reference, thus leading to a different fairness criterion.

4.1.3.3 Latency

Some services are not delay-tolerant and pose the most stringent requirements on packet queues. Evaluation of backhaul quality will be considered for its impact on packets delay and jitter affecting IP traffic from the FAPs, providing input to the business model treated in WP2. This aspect can be dealt at radio level (considering link quality improvement) or at scheduler level, assigning more time slots for transmission to impaired users with longer queues to digest.

4.1.4 Interference based on users activity

Different activity profiles can be defined from a selection of characteristics above mentioned: for example, a type of activity can be a high latency tolerant application with high traffic load (e.g., ftp).

4.2 Clustering at system level

The mentioned distances and the related metrics can be treated at a local or at centralized level, as well as with different levels of hierarchy. In fact, a distributed algorithm can be locally run to identify a set of transmitters interfering each other in view of feeding back to the RRM a slowly varying metric describing an aggregate measure of the transmission properties characterizing such local cluster. This is provided by:

- Overall capacity/throughput of the local cluster - Distribution of throughput among transmitters belonging to such cluster - Distribution of SINRs among such transmitters - Fairness of the resources allocation - Number of disconnections.

The possible metrics will allow the comparison among different solutions, thus considering the benefits of the clustering strategy on the overall network.

4.2.1 FAPs clustering

Given a deployment of N FAPs, the system can be represented as p clusters 1 , …, n. Each cluster

a, a=1,…,p, is a set of FAPs including a FAPs each, so that 1

p

aa

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33

Figure 15. Scheme of clustering with different hierarchy levels. Different colors

represent difference clustering criteria adopted (abstract distances) whose knowledge can be available to the network through dedicated tables.

4.2.2 Centralized clustering

The RRM can collect all users’ information and transmission parameters, although for a huge deployment of FAPs this would lead to shortcomings related to the amount of data and to requirements on processing. A more realistic approach for the network to have control on possible clusters of users supporting adaptation of heterogeneous users requirements arises from the possibility to deal with aggregated slowly varying metrics got from local subclusters or measured with a timing of order of minutes, or tens of minutes, to have an overall scheme of the traffic levels and services.

4.2.3 Local clustering

Users are aware of others transmitters in range and implement one (or more) of the clustering strategies among mentioned.

4.2.4 Hierarchical clustering: subclusters

Once a cluster is identified, the value of a metric (as mentioned above) considered over the current cluster is available. A cluster can be isolated if only transmitters belonging to it are in range. If some transmitter belonging to that cluster receives the interference of transmitters external to that cluster, one can identify the presence of other clusters. Using the information running through the femto gateways, it is possible to identify and classify clusters of clusters. This way, aggregated information can be fed back to the RRM with limited load on the network in a semi-centralized way.


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38

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39

Considering the GA case, the constrained search for (sub)-optimal parameters starts from random (satisfying the system constraints) initial values and outputs the (sub)-optimum value for the transmission power to adopt for transmission between each FUE and the corresponding FAP. Given the power levels pscl1 and pscl2 coming out from two GAs running on the sub-cluster scl1 and scl2, before next iteration the system can implement the two mentioned options to select which value will serve as initial condition for the next run. The average or the switch are expressed as

1 2 1 21 2, ,

2 2out outscl scl scl scl

scl scl

p p p pp p

(1)

1 2 2 1,out outscl scl scl sclp p p p

, (2)

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sclp and 2out

sclp will be adopted as initial values, and so on, until the constraint is fulfilled. The choices both for setting the output power value and for the constraint type (threshold value or maximum number of iterations), provide similar results and do not affect the computing time. Simulations show that both criteria bring to convergence of the optimization algorithm.

5.3.2 Clusters of clusters

5.3.2.1 Parallelization topologies

The analysis of the cluster structures leads to identify several topologies of interest to investigate the scalability of the system optimization methods.

We will describe a ring topology and a blocks topology. The first is an abstraction to tackle a system of FAPs which interfere with the closest ones only, whose number can be indeed high (several tens) and the optimization is split among NFAPs/2 very fast sub-processes. The second topology proposed represents several sets of FAPs. The interference to be considered can occur within FUEs belonging to the same set, and for some of them at the borders it can occur also with transmitters in an adjacent set.

5.3.2.2 Ring Topologies

Dealing with interference among nearby FAPs can be successfully approached on the basis of the topology underlying the propagation distances characterizing the environment. This case has been tested for the GA optimization in a scenario in which the frequency bands have already been assigned by other resources, such for example by a former GA-based algorithm parameters search, thus corresponding to the case of different transmitters competing within one frequency chunk.

The idea is to propose a method to identify some sub-clusters (typically including 3 FAPs each) over which running fast GA algorithms. A precise strategy has been implemented to deal with FAPs belonging to two adjacent mini-clusters and is detailed by Eq. (1) or Eq. (2).

5.3.2.2.1 Ring-next, Ring-twist and Ring-generic

The whole set of FAPs can be considered as belonging to a cluster schematized as a ring where every FAP interferes only with few nearby ones. There are several levels of complexity in the interaction between FAPs increasing from a pathloss matrix with the diagonal form as for example the one shown in Figure 23, to less regular ones.


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42

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44

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ocks of a me. These uler. The uration of at), which

tion 7.3.2 scheduled

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onding to ate game

on for the

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he FAP’s

intervals,

y, so that transmits

als of equal

45

the estimate interference to the associated FAP so that 2 FapsN OFDM symbol intervals are

spent for each iteration of the algorithm.

We are now in the position to evaluate the loss on the data transmission rate due to the time spent for the algorithm to converge. To this end, we assume that all the slots in the 200 frames can be potentially used for data transmission. Under this assumption the rate loss depends on how often the resource allocation algorithm is run. More specifically if we assume that the maximum-rate game is run Tn times on 200 frames the overall time slots available for data transmission are given by

4000 T cn n where Tn can be also expressed as 4000 / ( )T d cnn n denoting with dn the

number of time slots over which the FAPs allocate every time that the algorithm is run. Therefore, the algorithm works as follows:

1. Each FAP observes for a single time slot the macro-user activity; 2. cn time slots are spent for running the maximum rate game, assuming that for this time

interval the macro-user activity does not change. The game predicts the time-frequency power allocation over the next dn time slots.

3. After dn time slots return to step 1.

As a consequence, there exists a trade-off between the overall rate and Tn : for a fixed cn by

increasing Tn the available data transmission time slots dn decrease. In order to quantify this

behaviour we introduce the parameter c

c d

n

n n

, representing the loss in terms of data time slots

due to the running time ( / 2cn ms) of the game. Observe that 0 when cdn n , i.e. if the

convergence time can be neglected with respect to the data transmission time, while 1 if the loss in the transmission time due to the convergence time is non-negligible. Therefore we report in Figure 30 the sum of the rate per-FAP over 200 frames and 25 frequency resource blocks versus the parameter . We can observe that as the rate loss is low, i.e. for small values the achievable rate is higher and it decreases as we increase the frequency with which the resource allocation game is run over 200 frames. Furthermore it can be noted that as the number of active FAPs is increased (lower subplot) a lower rate can be achieved due to the higher interference levels among the FAPs. Finally, from Figure 31 we can assess a fundamental feature of the proposed algorithms. As extensively discussed in section 7 of [Freedom-D3.2] the predictive behaviour of the resource allocation strategy assuming that the macro-users activity can be modelled by a first order Markov chain is more effective when the number of time slots dn composing the block over which the

algorithm is performed is low. As we increase dn the mismatching between what is predicted and the

real interference increases.

According to these observations, in Figure 31 we report versus dn the relative gap nck sk

nck

R R

R

between the rate with non-causal knowledge of the interference activity, denotes as nckR , and the rate

obtained with statistical knowledge of the macro-user activity skR . We can note that, as expected,

such performance measure is an increasing function of dn . What is interesting to notice from Figure

31 is that the knowledge of interference statistical parameters, if properly exploited as in the method


Freedom

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46

edge (the

te with

47

6 METHODOLOGY FOR SYSTEM LEVEL SIMULATION In this section the methodologies introduced in the previous part will be considered in view of a comparison of the network properties (SNRs of users, traffic load, fairness, etc.). The dynamic simulator has been designed to evaluate system performances to address problems for both activities 5A1 (reported in [Freedom-D51]) and 5A2. The objective of this part of activity is to complete and merge the evaluations at system level regarding the impact of a femto network in a cell with focus on traffic and applications running on mobile terminals. Several system level evaluations are a follow up of 5A1: the output measurements and QoS indicator for the activities in 5A1 could be summarized by SINR levels over time and their impact on transmission events, while the “event-unit” is the transmission by a MS of FAP. On the other hand, in the present activity the SINR (and its time variation) can be considered as the input parameter at the basis of the system processing whose basic “event” is the single packet exchanged by a certain application running on the mobile terminal.

6.1 System simulator

The dynamic simulator manages the evolving aspects of the network. The dynamics is given mainly by two factors:

- different applications running on a smartphone generate a variation over time of packet flux to/from that unit. Such data flow is encoded and transmitted following a scheduling function which manages queues and simulates retransmission of NACK packets, thus request of resources varies from subframe to subframe;

- the environment changes over time due to motion of transmitters or motion of object around transmitters (e.g., a car passing by, a door opens or closes) on the timescale of human movements, providing significant changes in the pathloss (and consequently on the SINR) on the timescale of few frames (i.e., some tens of ms).

The system simulator addresses both cases, focusing attention depending on the problem under analysis. The output is expressed, for example, in terms of advantage or disadvantage of the MBS offload measured in terms of resources otherwise made available or energy consumption. Different evaluations about possible choices are left as business case issues under the MNO decision At this level, system simulator is developed in parallel for 5A1 and 5A2. We refer to [Freedom-D51] for the dynamic simulator structure.

6.2 Simulator principles

Simulator developed for this activity is based on the dynamic simulator elaborated in WP5 (see also [Freedom-D51]), which is an event-driven system level simulator. Overall simulator scheme is represented in Figure 32. One aspect is given by the evaluation of the clustering strategies and their impact on network performances. The simulator has a modular structure in which every block corresponds to a functional aspect of system: structure, geometry, propagation, interferences, management, applications etc. The system evaluation is based on a set of system-level metrics allowing the assessment of network performances from a system-level perspective.


Freedom

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51

- Propagation models; - Interference power models; - Spectrum usage;

- Traffic models;

- Synchronization error models;

- PHY-layer abstraction and backhaul abstraction (link quality);

- Mobility and handover; The simulation parameters are directly related to the aspects listed above. The details about these simulation parameters are given in paragraph 5 of [Freedom-D21].

6.4 Performance metrics

A detailed description of selected metrics is given in section 5.2 of [Freedom-D21]. The focus is towards system oriented metrics, describing the efficiency of the overall network. The performance evaluation of a system-level network based on LTE-A standard will allow also to comment on the targets of LTE-A.

6.4.1 System-level metrics

Metrics used for performance evaluation at system level (according to the definitions given in paragraph 5.2 of [Freedom-D21]) are:

- Overall cell throughput

- Clusters cell throughput

- Overall spectral efficiency

- Clusters distribution of spectral efficiency

- Overall QoS

- Cluster distribution of QoS

- Fairness

- Call drop rate

- Latency

- Backhaul overhead

- Average power per user.

6.4.2 User-level metrics

As a reference, system can evaluate also user level metrics, in order to assess a comparison with a network without implementation of optimization strategies for interference management. For the performance evaluation at user-level the following metrics are selected:

- Distribution of users throughput

- Distribution of users spectral efficiency

- Distribution of users QoS

- Call drop rate per user

- Distribution of latency

- Power per user.

Such metrics can be measured for both MUEs and FUEs. The objective for this type of metrics is to give the performance evaluation of the network as seen by a single user (UE).


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7.1.3 Low level abstraction models

The PHY-MAC layer abstraction model and the backhaul abstraction model are represented by block E. The system simulator is based on PDU-ER vs SNR curves for different MCS, allowing the assignment to each transmitter of the appropriate modulation for the propagation conditions. The MCS is assigned or changed to each user after measurement of the current PDU-ER which varies with interference levels around each transmitter. To avoid excessive changes of MCS, such measurement is performed over a number of transmission opportunities which for the simulator has been fixed to 50 as a compromise for data fluxes with scarce transmission requests and necessity to ensure reliable QoS.

7.1.4 Resource management

The radio resource management (RRM) includes the core of the packet scheduler (PS) allocation. It has been implemented both for the MBS where several MUEs compete for radio resources, and for the FAPs where much less users (1 to 4) have to share the corresponding radio resources. The user scheduler is a function, implemented at BS level (MBS or FAP) which manages the data packets allocations within one single time slot, i.e. one column of the sequence reported in Figure 35. The scheduler has been implemented taking into account

- Frequency selective channels. The band is divided in PRBs and the central unit knows which are the most favorable channels for each users’ transmission.

- Traffic level. A priority engine takes into account the number of packets queued for each user and the priority of the traffic type. A score is assigned following such criteria and a classification is fed to the scheduler. Priority is given to users with longer queues, and VoIP, delay sensitive applications.

- Optimization strategies. If an optimization strategy has been implemented (for the femto network) simulator takes into account possible clustering strategies and slowly-varying metrics describing parts of the network.

In particular, the core unit is functional for transmitting aggregate slowly-varying metrics across the network to implement hierarchical clustering, including the scheduler engine for the single FAPs which is also affected by the presence of clustering. On the basis of information about the links (if requested by the optimization algorithms implemented) and the UEs requirements (services requested by UEs), the RRM assigns the radio resources in terms of system efficiency criteria (time-frequency elements) and power to every single transmission. In case of distributed algorithms, the action of RRM can be reduced to scheduling traffic on the basis of competitive resources assignment run in a decentralized way. Figure 39 represents one of the two temporal slots which constitute the basic unit of the LTE PRBs resources scheme. The scheduler has to fill the PRB texture by assigning the most favorable frequency interval to all users.


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7.1.4.1 Adaptive MCS

The architecture allocation of data packets is summarized in the block scheme of Figure 41: the traffic requests are synthesized at the millisecond scale coherently with the LTE standard. Packets are transmitted in the order set by the scheduler and correctness receipt is evaluated. Packets received with errors are postponed and added to the transmitter’s queue. At the beginning of the simulation, a MCS is assigned on the basis of the initial SINR. As the flux of packets proceeds with time, PDU-ER changes when SINR varies due to the environment evolution and activity (e.g., dynamics of the surrounding transmitters) and each unit measures the current PDU-ER by evaluating the number of packets with negative ACK from the companion receiver (PDU-ER is indeed evaluated at level of the radio link, and not at application level). The current PDU-ER is computed averaging the last 50 transmission events, whose effective time lapse depends on the application type. Such value is chosen in order to prevent excessive changes of MCS, although maintaining a sufficient QoS. The MCS module takes into account both improved or worsened link conditions by setting an upper and a lower threshold. At each simulation time epoch, PDU-ER is updated on the basis of the last transmission opportunities (of order of 50) and eventually the MCS is adapted if the measured PDU-ER is above (or below) an upper (lower) threshold. When the measured PDU-ER decreases under the lower value, and lasts for at least 50 transmission events, the scheduler changes MCS to a more favorable level. The opposite happens when the PDU-ER is higher than the upper value. The considered PDU-ER is an averaged quantity, which in normal conditions is assumed to vary in the range [0.05-0.6], as adopted for the simulations.

Figure 41: Scheme of the realization of traffic flow for the system simulator.

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15 40 120 15 40 120 15 150 250 15 250

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d in grey) vFAPs and w

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5 160 110

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posed of lessthe overlaps

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>

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225 6400

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vs. not clust

66

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o get GO in some

ng nearby

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olutions; y the

aluate the

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e

terd

67

N FAPs parameters Iterations Population Generations clusters Convergence time

T=JxPxG

30

N FAPs per block

N blocks overlap

10 3 3

8 25 10 3 2000

1 100 230 1 23000

75

N FAPs per block

N blocks overlap

25 3 7

10 25 10 3 2500

1 100 250 1 25000

105

N FAPs per block

N blocks overlap

35 3

10

15 25 10 3 6250

1 100 >400 1 >40000

Table 4: Comparison of optimization performances for clustered vs. not clusterd implementation. Blocks topology.


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Networksummaritools and The simspectral exploits multiple


m_5D2_DUN

ERFORM

P-based netwalysis) and ould take als

s of the inteation techniqon 3.

s in Sectionin activities 3

formance anendations in

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ected resultslevel and theption of the phis implies al

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k parametersized in Tabld methodolog

mulation toolsefficiency athe MIMO xing; therefo


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MANCE E

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have to asseeir effect on proposed straso some cost

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allow evaluthe deploym

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etwork simuill provide ren a strategy i

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mplement any outputs mon to generatmum LTE sp

system level

TION

wo different dual UE anaomical aspect

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uating netwoment type. Th

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s

simulation mSee Section

ny interferenmust be conste average dipectral effici

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s: network pconsideratio

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of users?

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clustering crwork implemeg. Complexi

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n, but do notps/Hz.

performance ons made w

ecific coopesimulator in

set of the al

ons and giviollowing que

s reduced?

planning algoresults will a

network perfoes or fairness

riteria with entation, higity issues wi

ation paramion of the si

outputs expThe simulatt consider an

68

(system-ithin this

eration or ntroduced

lgorithms

ing some stions:

orithms at address if ormances .

solutions ghlighting ll also be

meters are imulation

ressed as ion tools ny spatial

69

System

Technology LTE FDD

Frequency band 2.0GHz

Channel bandwidth 210MHz

Macro layout (optional)

Sectorisation Three sectors (or cells) per site

Macro network design - Hexagonal site deployment

- Two rings around the central site, i.e. 19 sites corresponding to 57 cells

Environment - Urban

- Suburban

Inter-site distance (ISD) - Urban: 500m

- Suburban: 1732m

Average MBS (macro base station) density

- ISD=500m: 4.62 sites/km², 13.86 MBS/km²

- ISD=1732m: 0.38 sites/km², 1.15 MBS/km²

MBS antenna height 32m

MBS total transmit power 46dBm

MBS antenna Directional, 14dBi gain

MBS antenna elect. down-tilt - ISD=500m: 6°

- ISD=1732m: 2°

MBS nb antennas 4

Femto layout

Deployment zone Within the study area shown in Figure 57

Deployment type - Urban: in corporate buildings

- Suburban: in houses

Spectrum usage Co-channel

Access mode - Closed

- Open

FAP (femto access point) antenna height 1m above floor

FAP max transmit power - 10dBm

- 20dBm

FAP antenna Omnidirectional, 5dBi gain

FAP nb antennas 2

User

Indoor / Outdoor distribution - Indoor: 80%

- Outdoor: 20%

UE antenna height 1.5m

UE antenna Omnidirectional, 0dBi

UE noise figure 9dB

Table 5: Network parameters.


Freedom

Random FA

Path-loss (i

LTE cover


m_5D2_DUN

AP deployment

incl. shadowing)

age


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57. Macro n

Urban

uburban

AP density

AP activation rat

ynthetic environm

Real environment

Link

HY/MAC layers

Coverage predictio

Table 6: Si

system level

network layo

tio

ment

abstraction

on

imulation m

out and Stu

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- At mo

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Variabl

Variabl

Analyti

Site-spe

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methodology.

dy area.

om distribution ofgs: iformly

gularly in staggeren uniformly – disn uniformly – dis

ost 1 FAP per 10m

om uniform distriost one FAP per h

le

le

cal models define

ecific models defi

y

om RB allocationterference managatial multiplexing

age downlink traffBS or FAP (TL =llocated by the cer MBS: 50% by dr FAP: variable

age inter-cell inter–MCS mapping

multi-floor) covera

f FAPs in corpora

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ibution of FAPs ihouse

ed in [D21] and [

fined in [D21] and

n gement g, only average d

ffic load (TL) assi= average percentell) default

rference calculati

age matrices

70

ate

ure 58) wall < 1m wall > 5m

ea

n houses

[D31]

d [D31]

diversity

igned to tage of

ion

MBS Study area

a

71

8.1.2 S

Scenarioarea (com Scenarioin scena20m40 Several Table 7.otherwis Three di

F C

w N

Figure 5

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os IA-1 and mposed of a

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ates FAP depAPs are depl0m).

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20% and 50% o

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for IA-1 an

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all areas

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rio IA-2 - Im

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rio IA-2 – Imorate FAP s

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101

deployment 'SIR_N1' 'SIR_N1_a' 'SIR_N2' 'SIR_N3' 'SIR_N4'FUEs 98 98 98 154 151 UEs 153 153 151 211 213

FAP_on_balcony 0 0 0 0 0 MUE_indoor 47 47 46 50 52

MUE_outdoor 8 8 7 7 10 capaMUEs_overall 95.3 97.0 69.8 65.7 77.5

capaMUEs_out 192.5 187.7 182.6 98.7 185.6 capaMUEs_in 78.7 81.6 52.7 61.0 56.6 outageMUE 0.42 0.42 0.42 0.39 0.48

outageMUEin 0.49 0.49 0.46 0.42 0.58 outageMUEout 0 0 0.1429 0.1429 0

capaFUEs 254.2 233.3 255.7 219.7 187.3 outageFUE 0.18 0.17 0.13 0.16 0.19 capaFAPs 61.3 57.5 62.9 54.4 45.1 outageFAP 0.18 0.17 0.13 0.1558 0.19 outageUE 0.27 0.26 0.23 0.2180 0.28

capa_overallUE 167.2 158.4 168.4 153.1 128.9 UE 153 153 151 211 213

FAP 98 98 98 154 151

Table 16. System performances for different realizations of a mapped scenario, based on SIR realizations. The meaning of the adopted variables is explained in the following

Table 17.

FUEs Number of FUEs

UEs Number of overall UEs FAP_on_balcony Number of FAPs on balcony

MUE_indoor Number of MUEs indoor MUE_outdoor Number of MUEs outdoor

capaMUEs_overall Capacity of all MUEs, in Kbit/s/PRB capaMUEs_out Capacity of MUEs outdoor only, in Kbit/s/PRB capaMUEs_in Capacity of MUEs indoor only, in Kbit/s/PRB outageMUE Outage of all MUEs (ratio, percentage/100)

outageMUEin Outage of MUEs indoor only (ratio, percentage/100) outageMUEout Outage of MUEs outdoor only (ratio, percentage/100)

capaFUEs Capacity of FUEs, in Kbit/s/PRB outageFUE Outage of FUEs (ratio, percentage/100) capaFAPs Capacity of FAPs, in Kbit/s/PRB outageFAP Outage of FAPs (ratio, percentage/100) outageUE Outage of overall UEs (ratio, percentage/100)

capa_overallUE Capacity of overall MUEs, in Kbit/s/PRB UE Number of UEs (MUEs and FUEs)

FAP Number of FAPs

Table 17. Meaning of system variables. The plots in Figure 105, Figure 106 and Figure 107 report the temporal variation of MUEs, FAPs and FUEs SINR and capacity for SIR scenario N1, with a summary of average throughput per user in Figure 108. The same can be said for Figure 109, Figure 110 and Figure 111 for the SIR scenario N1a. The change in the environment and thus on all pathloss relations among transmitters reflects in the


Freedom

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enario – SIR

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107

A difference between the tested scenarios is that the design of synthetic ones (i.e., homogeneous and inhomogeneous) is characterized by a deep penetration of indoor MUEs which are much closer to FAPs, while in the mapped developed by SIR indoor MUEs are more sparse. Analyzing the right panels of the Figure 114, Figure 115, emerges that in the synthetic in more dense deployments scenarios outage is reduced of up to 80%, while from Figure 117 referring to SIR scenario outage is improved of about 10%, as it is sparser.

8.5 MBS offload and QoS

In this Section we report the system performances evaluation in a scenario deployment where traffic is produced by UEs running specific applications. For this test, the inhomogeneous scenario (150 UEs, 60 FUEs, 4 buildings) has been chosen as a reference case. The system simulator takes into account:

- Deployment

- SINR of UEs

- Packet data flow.

In order to quantify the impact on QoS of each unit (both MUEs and FUEs) when deploying FAPs, this activity has been designed by comparing the system performances of a macro network when offloading traffic by attaching some (indoor) MUEs to (indoor) FAPs. The simulations take into account different traffic flows:

- in a macro network with 120 MUEs

- in the same macro network with 90 MUEs

- data transmitted when the 30 MUEs are FUEs attached to nearby FAPs.

The comparison is carried out by considering the different levels of SINR for indoor UEs when attached to the MBS or to a FAP, its impact on PDU-ER and thus on QoS. Finally, we provide an estimate on the extra energy consumption as a consequence of NACK packets that need retransmissions, evaluated in both cases of UE attached to the MBS or to a FAP.

8.5.1 MBS traffic flow for 120 MUEs

In this Section is reported the dynamic simulator output of traffic flow through the MBS. 120 MUEs are equally divided in three types, depending on the number and type of applications running on mobile devices:

- VoIP - VoIP and homogeneous stream (FTP-like) - VoIP, FTP, and bursty (HTTP-like).

Figure 118 shows a snapshot at certain instant of time users’ queues, while the right panel reports the instantaneous PDU-ER of the first four MUEs.


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8.5.3 MBS offload by 30 FUEs on Femto network

The effect of attaching 30 indoor MUEs to a FAP has several outcomes, which are summarized in Table 18. In this simulation, the FUEs have transmission power set to 15 dBm, while when they are MUEs it is 20 dBm, thus the overall power per user is reduced. The average power increase per user, due to the necessity to transmit NACK packets is significantly less than in other cases; the same is valid for the average power increase per user. Thus simulations confirm that FAPs allow MBS offload and ensure comparable or better transmission conditions to indoor users attached to them. MBS with

120 MUEs MBS with 90

MUEs FEMTO layer

30 FUEs

Traffic increase due to rescheduled packets 124 % [total]

97 % 58%

Overall Power expenditure [tot]/user 99.85 mW 99.85 mW 5.42 mW Tx Power dBm 20 20 15 Average power increase – percentage/user 163.5 % 88.9 % 37% Average power increase - mW/user 0.23 mW 0.13 mW 0.029 mW Overall power increase 27.99 mW 11.42 mW 0.88 mW 30 usrs – overall power expenditure [tot] mW 748.9 329.2 Overall power expenditure/user mW 24.96 10.9 30 usrs – power increase – percentage/user 25.5 % 37.9 % 30 usrs - power increase - mW/user 0.036 0.036 30 usrs - Traffic increase due to rescheduled packets 89 % 53% 30 usrs - Traffic increase/user due to rescheduled packets (avg.)

2.9 % 1.95 %

90 usrs - overall power [tot] 26.9 mW 90 usrs - power increase – percentage/user 210 % 90 usrs - power increase - mW/user 0.299 mW 90 usrs - Traffic increase due to rescheduled packets 140 % Flux tot - th MBps 6.1 4.3 1.0 Flux tot - out MBps 23.3 14.1 5.3 Flux tot - gross MBps 26.3 15.9 6.2

Table 18. Comparison of system performances when offloading the traffic of 30 MUEs and attaching them to FAPs.


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tem level he overall

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115

The dynamic system simulator has been complemented including packet flow module to evaluate the information data passing through the cell and the impact of all transmitters on the QoS and energy needed to run a set of applications on UE mobile terminals. The overall simulator has been tested in several cases of interest that can be summarized as:

- Impact of clustering on possibility to extend optimization methods at system level in a reliable way, in order to manage also high number of users;

- A set of real environments have been included, providing metrics also for a mapped scenario with geographic deployments of buildings and users;

- Mobility of users and temporal changes in the environment where transmitters are positioned have been included and evaluated;

- Evaluation on the system performances for different cases of varying band usage: equipartition of band between MBS and femto network, considering a fixed spectrum allocation for the MBS and varying that of second tier, or fixing the band usage of the femto network and increasing that of the macro network;

- Evaluation of system performances when including a GO for power assignment of FUEs/FAPs to mitigate indoor interference;

- Quantification of the MBS offload when routing traffic of a set of UEs indoor through FAPs instead of through the macro network.

9.2 Impact on the business case

This section summarizes the outcomes used in 2A3 activity for business model analysis. All inputs and key methodology aspects of the LTE coverage simulations presented in sections 9.2.1 to 9.2.4 are given section 3. These results are extension of those provided in section 8.1. The development of some activities (clustering and optimization methods) developed in 3A2, evaluated at system level in 5A1 and 5A2 and their impact on the business case are reported in section 9.2.5. The overall conclusions that can be drawn from a list of simulations are also presented for their impact on possible choices for a MNO.

9.2.1 Scenarios IA-1 and IA-2 – Impact on local urban corporate coverage

Scenarios IA-1 and IA-2 evaluate the impact of a FAP deployment in one restricted urban corporate area (composed of a few buildings only) in presence of existing macro coverage. Figure 130 gives spectral efficiency statistics for a FAP subscriber located into the same small 10m10m area as the FAP. These statistics are compared to the spectral efficiency provided by the macro-only network. These figures will help a LTE network operator to assess the gain in quality of coverage it can offer to its corporate customers, in case a closed-access FAP is installed for subscribers located in a restricted area. Remark that the simulation outputs are highly sensitive to the realized FAP deployments (generated from a random process); therefore only statistics given in one same table may be directly compared in order to assess gains or degradations in spectral efficiency.


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