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Cost Assessment and Optimization Methods for Multi-Node Radio Access Networks Marc Werner

Peter Moberg and Per Skillermark

Qualcomm Germany Nuremberg, Germany

Ericsson Research Stockholm, Sweden

Mark Naden

Wsewolod Warzanskyj

Nortel Harlow, UK

Telefónica I + D Madrid, Spain

Paulo Jesus and Carlos Silva Portugal Telecom Inovação Aveiro, Portugal Abstract— The expected performance improvements of nextgeneration mobile radio access networks (RANs) can be achieved, e.g., by a flexible deployment of different types of radio access points (RAPs), such as intelligent relay nodes (RNs). To facilitate a cost-vs.-performance assessment of different deployment options, this paper presents a classification of different RAN cost components. Then, a method for deployment cost optimization is introduced which allows a comparison of the cost effectiveness of different RAN deployments by calculating optimum densities of different RAP types, based on a multi-dimensional isoperformance map. This method is first derived in theory and then demonstrated for two different deployment scenario examples. Keywords- cost assessment; cost optimization; deployment costs; iso-performance map; relay nodes

I. INTRODUCTION New mobile cellular communication systems are currently being developed and standardized which promise significantly improved user data rates and an increased range of multimedia services to customers. These systems often employ different kinds of RAPs with dedicated technologies that result in improved spectral efficiencies. Examples of RAPs include macro base stations (BSs) for wide-range coverage, micro BSs for high traffic densities in urban environments, and intelligent fixed relay nodes (RNs) for fast and cost-efficient network deployment. Apart from the increased network performance, the expected deployment and operation costs of future RANs remains a critical guideline in the roll-out and configuration of next-generation cellular radio systems. This paper contributes to the cost-vs.-performance analysis of RANs by providing an extensive cost classification, assessment and optimization framework for RAN deployments in specified environments. Earlier work on the cost analysis of wireless systems was published, e.g., in [1], [2] and [3]. In [1], a model for the total

system cost is proposed accounting for different cost components. The cost structure of wideband wireless networks is further analyzed in [3] with respect to infrastructure and spectrum costs. Cost components of cellular systems are also discussed in [4], comprising operational expenditures (OPEX) like site rental, and transmission costs; and once-only capital expenditures (CAPEX) such as initial hardware investments. The work in [3] and [4] indicates that the cost of wireless networks is proportional to the number of RAPs. The optimization of densities of two RAP types by means of isoperformance curves was studied, e.g., in [3]. The cost assessment framework presented in this paper extends the principles developed in earlier works by providing a procedure for cost optimization with iso-performance maps with an arbitrary number of dimensions, so that deployments with multiple RAP types can be assessed. The outline of this paper is as follows: In Section II, a classification of RAN deployment and operation cost components is presented which serves as the basis for cost assessments. Then, in Section III, a multi-dimensional isoperformance method is introduced for optimizing the performance-vs.-cost ratio by adapting the densities of different RAP types. Two examples of deployment cost assessments and optimizations are given in Section IV. These examples are based on dedicated deployment simulations for an urban radio environment and heterogeneous traffic demand. Conclusions are summarized in Section V. II.

RAN COST COMPONENTS

A complete quantification of RAN deployment costs is difficult to perform. Certain cost components depend, e.g., on the contractual relationship between hardware manufacturer and operator, or on the regulatory and legislative environment in the country of deployment. Furthermore, it is unlikely that in all future systems the network operator will provide all RAN

This work has been carried out within the framework of the IST project IST-2003-507581 WINNER (World Wireless Initiative New Radio), which is partly funded by the European Union.

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cost components. Changed cost allocations can arise, e.g., due to new business entities like service providers, virtual network operators and third-party network maintenance. Furthermore, the RAN deployment cost can be shared between different network operators by employing the concepts of flexible spectrum sharing, roaming, and infrastructure sharing. Nevertheless, the total RAN cost can be calculated from all relevant cost elements, irrespective of the fact that different business entities might effectively pay for these components. For cost-vs.-performance comparisons of various RAN deployments, technical performance figures such as the spectral efficiency have to be used with care as they are only comparable for a constant target quality per radio service. The RAN costs can be classified according to the standard financial categories CAPEX and OPEX. For the calculation of a total deployment cost figure, OPEX costs per time period can be represented by their present value (CAPEX) at the beginning of network deployment. Apart from the CAPEX/OPEX grouping, another classification criterion for the cost components is how they scale with varying network size. Five Cost Groups (CGs) are considered with respect to this criterion. They are listed in Table I which sorts the relevant cost components according to the two mentioned criteria. For CG1 and CG2, if additional types of BSs or RNs are deployed, each one constitutes a new own cost group. In the calculation of total network costs, only CAPEX and OPEX elements directly linked to the RAN are considered. Indirect costs, such as spectrum licenses, marketing, etc.,do not depend on the technology employed and are nearly impossible to predict. From Table I, it becomes clear that aside from indirect costs, all relevant cost components either arise once per network or grow linearly with the number of deployed RAPs. This fact facilitates the assessment and optimization of the total RAN cost with the help of linear cost models. III. DEPLOYMENT COST OPTIMIZATION The basic idea in the deployment cost assessment and optimization is to study a system area with a given user population. We assume that the user density of a certain area element directly translates into a traffic demand in bit/s (e.g., during the busy hour). With a network area and an estimated user demand, the next step is to deploy RAPs to serve the users. Different RAP types, such as macro BSs, micro BSs and RNs, are considered. The deployment procedure should ensure that the entire network area is covered, i.e. all the users in the network are satisfied according to their desired data rates. Certain assumptions regarding the system capacity per RAP, the radio propagation model, and the distribution of transmit powers and interference, must be made for this purpose. Alternatively to full coverage, a certain outage probability could be allowed to reduce cost. The network traffic can be covered in many different ways, i.e., with different combinations and densities of the available RAP types, and different placement strategies. The overall ambition with the cost assessment framework is to enable determination of the best deployment alternative for each scenario from an economical perspective.

TABLE I.

CLASSIFICATION OF COST COMPONENTS

Cost group

CAPEX costs

OPEX costs

CG1 (proportional to no. of BS sites)

BS site acquisition BS equipment BS deployment RAN connectivity (wireline or wireless connections)

BS power supply costs Rent for RAN connectivity BS site rent and maintenance

CG2 (proportional to no. of RNs)

RN site acquisition RN equipment RN deployment

RN power supply costs RN site rent and maintenance

Centralized RRMa servers CG3 Gateways (once-per-network Initial network optimization costs) Spectrum licenseb CG4 (system-specific costs) CG5 (proportional to no. of subscribers)

Network operation Software updates Marketingb Researchb Standardizationb

Subscriber acquisitionb Terminal device costsb a. RRM: Radio Resource Management b. Indirect cost component

Deployment simulations for a given network area provide information regarding the positions and numbers of necessary RAPs to cover the existing traffic demand. Estimating the total cost per RAP then makes it possible to calculate the total deployment cost for the network area. A. Deployment Representation by Iso-performance Maps The cost-optimum deployment densities of different types of RAPs can be represented by a theoretical model. A mathematical cost analysis framework has been developed in [5] and was mostly designed for a cost comparison between a BS-only and a RN based multi-hop deployment, similarly to earlier cost evaluation work in the literature, cf., e.g., [3]. In the following, a multi-dimensional extension to study more and different types of RAPs, including optionally multi-hop nodes, is presented. Note that this cost analysis framework provides optimum deployment densities, depending on specific RAP locations which are chosen by an underlying deployment model. An intelligent RAP placement procedure has a significant positive impact on the cost optimization results. The performance of a RAN, in terms of coverage and capacity density (total information rate per unit geographical area), improves with an increasing density of RAPs of whatever type foreseen in the deployment. As a consequence, a decrease in the density of one RAP type (e.g., BSs) may be compensated by a density increase of another type (e.g., RNs), in order to maintain a constant performance. This trade-off can be illustrated for the case of two RAP types in the form of an iso-performance curve [5]. If three different types are present, the iso-performance curve becomes a surface, and, in the general case of p RAP types, a (p – 1)-dimensional surface in the p-dimensional space.

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The cost benefit Q of the multi-RAP-type system relative to the conventional cellular system with the exclusive use of BSs can now be obtained from the iso-performance map:

Q=

B0 ⋅ K b B0 B = = 0 .(1) A ⋅ K a + B ⋅ Kb + C ⋅ K c A / rba + B + C / rbc B′

The relation B ′ = A / rba + B + C / rbc can also be geometrically derived from Fig. 1: B ′ − B1 = A / rba and B1 − b = db = da / rba = C / rbc because da / db = rba and da / C = rca with rca = rba / rbc . Inspection of the iso-performance map in Fig.1 reveals that the cost benefit increases with the BS/RN cost ratios, rba and rbc , and changes with the curvature of the isoperformance surface.

Figure 1. Illustration of a three-dimensional iso-performance map showing the trade-off between RAP densities a, b, c required to maintain constant capacity density and coverage

The trade-off between deployment densities of macro BSs (b), RNs of type 1 (a), and RNs of type 2 (c), is illustrated in Fig. 1 by the three-dimensional iso-performance map in the subspace {a ≥ 0 ∩ b ≥ 0 ∩ c ≥ 0} . The iso-performance surface is indicated by its intersections with the planes a = 0 , and c = 0 . Each point on the iso-performance surface represents different deployment densities of the three RAP types, under the constraint of identical performance. One such system is shown having BS density B, RN1 density A and RN2 density C. Note that the surface does not intersect with the plane b = 0 because neither RN1 nor RN2 nodes could be operated without any supporting BS. The tangent “equal-cost” plane at the point (A,B,C) of the iso-performance surface exhibits gradients representing the change in RN1 or RN2 density required to compensate for a change in BS density for this system. The A/B gradient of the tangent plane is equal to − rba , and the C/B gradient is equal to − rbc , where rba is the ratio of the total cost K b of a BS, to the cost K a of a RN1, and rbc the ratio of K b to the cost K c of a RN2. Any point on the plane represents the same total cost of all nodes in the network, including the combined CAPEX and OPEX costs for CG1 and CG2 (see Table I). The significance of the system represented by the point at which the equal-cost plane is tangent to the iso-performance surface is that it is the least-cost combination of RAPs capable of providing this performance with the given RAP cost ratios. If the cost ratios were to vary, the corresponding equal-cost plane would be tangent to the iso-performance surface at a different point, corresponding to a system with a different combination of RAPs.

For a network operator, it is also desirable to minimize the total amount of spectrum used by the system to deliver a specified service. This is equivalent to maximizing the spectral efficiency of the system, which is defined as the ratio of the system capacity and the total amount of spectrum used; i.e., the system capacity per unit bandwidth. For the purposes of comparing conventional and multi-node systems, however, it is more useful to define a cost efficiency ηC for a specified service at a particular level of coverage. It is described mathematically as follows:

ηC =

C ( m, n) S (1 + m rba + n rbc )

(2)

where S = total spectrum used by the system, C ( m, n ) = total information rate per BS, including its associated RNs (RN1 and RN2), m = A / B = number of RN1s per BS in the network, n = C / B = number of RN2s per BS in the network.

In the absence of RNs (m = n = 0), this metric becomes equal to spectral efficiency, as used for conventional cellular systems, and is therefore equally applicable to multi-node and conventional cellular systems. The cost benefit Q of a multi-node system relative to a conventional cellular system can be derived using the combined metric: A ⋅ Ka C ⋅ Kc 1 B ⋅ Kb + A ⋅ K a + C ⋅ K c B . (3) + = + = Q B0 ⋅ K b B0 B0 ⋅ K b B0 ⋅ K b B C (0,0) for constant capacity per unit area in = B0 C (m, n ) both points,

With

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1 C (0,0) A C = + + C (m, n) Q C (m, n ) B ⋅ C ( m, n ) ⋅ r B⋅ ⋅ rbc ba C (0,0) C (0,0) η (0,0) C (0,0) (1 + m rba + n rbc ) = C = C (m, n ) η C (m, n )

⇔

1 B0 ηC (m, n ) = = . Q B ′ ηC (0,0)

(4)

(5)

Here, ηC (0,0) represents the conventional cellular system with no RNs and ηC ( m, n) is the metric for the multi-node system with m RNs of type 1 and n RNs of type 2 per BS. Q > 1 if the multi-node network provides comparable spectral efficiency for a lower infrastructure cost. It should be noted that the value of Q only applies to the RAP costs, and does not include once-per-network costs. B. Iso-performance Analysis for Higher Dimensions While the reduction of iso-performance maps to two dimensions is trivial, an extension to more dimensions with RAP densities A, B, C, D, etc., is also straightforward. After an introduction of a corresponding number of cost ratios rba , rbc , rbd etc., the cost benefit can be defined as

Q=

B0 B = 0 . A / rba + B + C / rbc + D / rbd + ! B ′

(6)

With the extended definition of the cost efficiency

ηC =

C ( m, n, o,!) , S (1 + m rba + n rbc + o / rbd + !)

(7)

the cost benefit can still be expressed similar to (5) as

Q=

B0 ηC (m, n, o, !) = B ′ ηC (0,0,0, !)

(8)

(m, n, o, etc. represent the RAP ratios A/B, C/B, D/B, etc.). Note that the cost benefit is again expressed as a ratio in the domain of one reference RAP type (in this case, BSs with density B). It is also possible to select another RAP type as reference. The only constraint is that at least one RAP of the reference type must be present in the network for the above derivation to hold. IV.

COST ANALYSIS FOR DIFFERENT DEPLOYMENT EXAMPLES In this section, the cost optimization procedure using isoperformance maps is performed for the example of two RAN deployments. The system under study is the next-generation

mobile communication system developed within the WINNER project [6]. It is an OFDM-based system with a high degree of adaptivity, incorporating an advanced relaying concept [5]. Apart from identifying optimum deployment densities for BSs and RNs, we demonstrate how a potential cost benefit of RNs compared to an alternative deployment containing macro and micro BSs can be assessed. As a basis for the cost optimization, a dedicated deployment simulation model for certain radio environment scenarios was used which has been described previously, e.g., in [7], [3] and [8], and has been extended with accurate fading and interference modeling. A. RAP Deployment Model To generate a traffic demand map, a user density distribution was assumed with a granularity of 40 m × 40 m where the values of the elements are generated with a lognormal distribution around a large scale mean value. In [9], estimates of the user densities and service usage for urban and rural areas are made. Assuming an operator market share of 30%, a mean traffic density of 7260 kbit/s/km2 for the urban area was derived. Only the downlink is considered. Note that the absolute traffic density figure is not relevant for the cost optimization process as the RAP densities scale linearly with the offered traffic. The initial step of the RAP deployment in the underlying traffic map is to deploy a number of macro BSs, e.g., in a hexagonal grid with specified inter site distance (ISD). Users (or traffic elements) are then connected to the RAP with most favorable radio conditions, accounting for path loss and shadow fading. Every traffic element requires a share of the RAP’s resources. This share depends on the radio conditions and the amount of traffic in that element. Summing the contributions from all the traffic elements within a BS cell gives a measure of the fraction ȡ of the BS’s total amount of consumed resources. If ȡ < 1, the BS can handle all the traffic within its cell. If ȡ > 1, the BS cell is capacity limited. An initially sparse BS deployment will imply that many or all of the BS cells are capacity limited (ȡ > 1), hence requiring additional RAPs to serve the users in the network. These additional RAPs can be of different types. The decision regarding where to deploy the second round of RAPs can be made on several different premises. In our examples below, it is the position of the most unfavorable radio condition for the corresponding macro BS. The network area is covered, and the deployment is finished, if the constraint ȡ < 1 is fulfilled for all RAPs in the network. RNs have a special property regarding their in-band radio connection to the RAN. To transport the traffic of user terminals (UTs) served by a RN, the corresponding BS-RN link consumes a part of the BS radio resources. However, it can be assumed that the BS-RN radio link is a favorable one because the RN can realize a higher antenna gain by exploiting stationary antenna directivity. Furthermore, a LOS connection is assumed for the BS-RN link. Compared to a micro BS, the RN has the drawback of consuming more radio resources but the advantage of saving the CAPEX and OPEX costs for wireline RAN connectivity.

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TABLE II.

DEPLOYMENT SIMULATION ASSUMPTIONS

Parameter Deployment area Mean traffic density Channel model BS-RN link Channel model BS-UT or RN-UT link

Value 7 km × 7 km 7260 kbit/s/km2 B5a LOS [10] C2 NLOS [10]

Transmit power Antenna gain (omnidirectional) Carrier frequency Downlink radio bandwidth Noise psd Receiver noise figure

46 dBm (macro BS); 37 dBm (RN / micro BS) 10 dBi (macro BS); 2 dBi (RN / micro BS) 3.95 GHz 10 MHz -174 dBm/Hz 5 dB (RN); 7 dB (UT)

B. Example Cost Optimizations With the simulation assumptions summarized in Table II, different deployment simulations were carried out. The results of the simulations are given in Fig. 2 as RAP density combinations forming two iso-performance curves for the cases of (1) macro BS and RN (upper curve) and (2) macro BS and micro BS (lower curve). The curves have been fitted to the result points of the deployment simulations, indicated by crosses and circles in Fig. 2. As expected, for a given number of macro BSs, the necessary complementary RN density is always higher than that of micro BSs because of the additional capacity needed for the BS-RN links. As example cost ratios, it was assumed that the total cost of a RN is one third of that for a macro BS, and a micro BS costs half the price of the macro BS. The cost ratios are illustrated by the two lines of constant cost which are tangents to the isoperformance curves. They define the cost-optimum deployment densities of the two deployment scenarios. In this example, the dashed lines indicate the optimum number of RAP types for the deployment area. The corresponding values are given in Table III. It can be observed that both deployment options exhibit significant optimized cost benefits Q over the “macro BS only” deployment. Although the absolute number of RAPs is higher in the case of RNs compared with micro BSs, deployment with RNs is more cost efficient than with micro BSs, because the absolute cost for a RN is lower than that of a micro BS. TABLE III.

COST OPTIMIZATION RESULTS FOR EXAMPLE DEPLOYMENT SCENARIOS

RAP types

Optimum macro BS density, B

Opt. RN or micro BS density, A

(1): Macro BSs & RNs

20.06

67.43

B0

Bƍ

Q

42.53

2.39

46.65

2.18

101.79 (2): Macro & Micro BSs

21.99

41.32

Figure 2. Cost optimization examples with two-dimensional isoperformance curves for two deployment scenarios

The total optimized deployment cost can be derived in each case by multiplying Bƍ from Table III with the absolute cost figure of a macro BS, and adding the RAN costs that arise once per network (see Table I). V. CONCLUSION The two deployment examples demonstrate the applicability of the presented deployment cost optimization framework based on multi-dimensional iso-performance maps. The method can be applied to a wide range of RAN systems, RAP types and radio environments, as long as a suitable deployment model exists. In the example cost optimizations, it was shown how the cost effectiveness of RNs can be compared to alternatively deployed micro BSs in assessments of the resulting total RAN costs. REFERENCES [1]

R. A. Stanley, “A methodology for evaluating and optimizing wireless system infrastructure costs”, Proc. IEEE PIMRC, 1996. [2] J. Zander, “On the cost structure of future wideband wireless access”, Proc. IEEE VTC Spring, 1997. [3] B. Timus, “Cost analysis issues in a wireless multihop architecture with fixed relays”, Proc. IEEE VTC Spring, 2005. [4] K. Johansson, A. Furuskär, P. Karlsson, and J. Zander, “Relation between base station characteristics and cost structure in cellular system”, Proc. IEEE PIMRC, 2004. [5] IST-4-027756 WINNER II, Deliverable D3.5.1, “Relaying concepts and supporting actions in the context of CGs”, version 0.99, Oct. 2006. WINNER deliverables available: https://www.ist-winner.org [6] IST-4-027756 WINNER II, Deliverable D6.13.7, “WINNER II Test scenarios and calibration cases issue 2”, version 1.0, Dec. 2006. [7] P. Moberg, P. Skillermark, N. Johansson, and A. Furuskär, “Performance and cost evaluation of fixed relay nodes in future wide area cellular networks”, Proc. IEEE PIMRC, 2007. [8] A. Furuskär, M. Almgren, and K. Johansson, “An infrastructure cost evaluation of single- and multi-access networks with heterogeneous user behavior”, Proc. IEEE VTC Spring, 2005. [9] IST-4-027756 WINNER II, Deliverable D6.11.2, “Key Scenarios and Implications for WINNER II”, version 1.0, Sept. 2006. [10] IST-4-027756 WINNER II, Deliverable D1.1.1, “WINNER II interim channel models”, version 1.0, Dec. 2006.

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