Cost Efficient Deployment of Heterogeneous Wireless Access Networks Klas Johansson and Jens Zander
Anders Furusk¨ar
Wireless@KTH, Royal Institute of Technology Electrum 418, SE-164 40 Kista, Sweden Email: {klasj, jens.zander}@radio.kth.se
Wireless Access Networks, Ericsson Research Kista, Sweden Email:
[email protected]
Abstract— As data applications become more popular in mobile networks, usage patterns will be different from mobile telephony. Demand for higher bit rates is found in certain areas (“hot spots”), whereas the requirement for wide area coverage remains. Previous work has shown that using classical cellular hot-spot techniques (e.g., micro-cells) with a single radio access technology imply high-cost solutions. Instead, combining several, carefully chosen, access technologies in an efficient manner, leads to reduced infrastructure related costs while retaining a sufficient quality of service. In this paper, we estimate the user data rates achievable by complementing an urban macro cellular High Speed Packet Access (HSPA) system with different hot spot solutions. When deployed at comparable cost levels, “dense macro”, micro, and pico HSPA base stations yield a similar performance in downlink. Thanks to more spectrum bandwidth, IEEE 802.11a WLAN supports higher data rates in downlink both on average and for the lower percentiles of users. For the uplink, which in principal is noise limited already with a few hot spots deployed, indoor (pico and WLAN) base stations outperform outdoor systems, which are subject to severe propagation losses for indoor users.
I. I NTRODUCTION With the introduction of data oriented services in wireless access systems, we see that traffic demand will be increasingly heterogeneous. Applications have differentiated quality of service requirements, and usage patterns vary significantly over the service area. At the same time, traffic volumes and data rates are significantly higher than for voice services, which will put additional requirements on cost efficient network deployments. As a means to resolve this problem, operators may exploit multiple radio access technologies, composed in so-called “heterogeneous networks”1 , in order to match network designs to non-uniform spatial traffic distributions. The objective is principally to take advantage of the distinguishing properties of different radio access technologies. For instance, a macro cellular system should primarily be used to provide area coverage whereas pico cells may be deployed in areas with high traffic density (“hot spots”). However, an integration of multiple technologies could also complicate the system architecture and lead to increased costs and complexity for operation and maintenance, etc. Thus, it is important 1 A heterogeneous network in this context refers to both multi-access networks, where radio access technologies of different standards are accessed with a multi-radio capable terminal, and hierarchical cell structures constituted of a single radio access standard with multiple base station classes.
to understand the basic cost and performance of different combinations of technologies when designing future wireless access networks and that issue is addressed in this paper. From a radio network performance point of view, effects of synergies in multi-radio cellular systems have been analyzed in, e.g., [2]–[5]. These studies, however, consider benefits with common (multi) radio resource management for some given deployment, rather than analyzing economically feasible deployment strategies. Network planning with respect to spatially varying traffic load was studied in [6]–[10]. In particular, combinatorial optimization models have been developed and solved using, e.g., simulated annealing techniques. These studies, however, are focused on developing methods to dimension and plan radio access methods, and not to find cost efficient combinations of radio access technologies. As an initial contribution to this (techno-economical) problem, we have previously proposed an infrastructure cost based approach to evaluate heterogeneous wireless access networks [11]–[13]. A heterogeneous network was dimensioned to serve a spatially non-uniform traffic distribution given an assumed maximum range and aggregate throughput per base station class. These results indicated that costs can be lowered considerably by introducing a hot spot layer. However, for a given macro cellular technology, no significant difference could be seen between different hot spot solutions. Moreover, due to a simplified network capacity modeling, effects of synergies (diversity gains) and achievable user data rates with different system concepts could not be analyzed in detail. In this paper, we extend our previous work by estimating the achievable user data rates for a few commonly used hot spot solutions, including dense macro, micro, and pico base stations and IEEE 802.11a WLAN access points. These are deployed at comparable cost levels to complement an urban HSPA macro cellular system. The maximum feasible guaranteed data rates (at almost full area coverage), as well as the resulting average data rates, are then compared for the different hot spot layers. The paper is outlined as follows. In Section II, the radio network model and network dimensioning method is described. Thereafter, in Section III, numerical results are presented for downlink and uplink, respectively. Furthermore, assumptions on base station cost and performance parameters are summarized. The paper is concluded in Section IV.
II. S YSTEM M ODELING AND P ERFORMANCE M EASURES A method for estimating the achievable user data rates in a heterogeneous networks is presented next. A. Performance Measures The performance of different system configurations is herein evaluated in terms of the user data rates that are achievable at the medium access control layer (for a given outage probability and area throughput density). Let Rl model the rate experienced on average by an arbitrary user l. Then the outage probability ν is defined as ν = Pr[Rl < Rmin ], where Rmin is the minimum data rate that is required for users to be admitted. Hence, the expected total file transfer delay would at most be proportional to 1/Rmin . B. Spatial Traffic Distribution Users are assumed to be distributed over the service area with a density function λu (x, y) [users/km2 ] for a coordinate {x, y}. This is given by a spatially correlated, log-normal distributed, random variable with a correlation equal to e−1 at the correlation distance [12]. The average offered throughput per user during busy hour is assumed to be the same for all users and is equal to s [bits/s]. For example, if a user downloads 10 MB of data during the busy hour, s = 10 × 8 × 10242 /3600 = 2.3 × 103 bps. The area throughput density during busy hour is then given by λs (x, y) = sλu (x, y). C. Network Configuration and User Distribution In the examples to follow, a network consists of one macro cellular layer and one hot spot layer, and all users have access to both. The macro base stations have three sectors and are placed on a regular hexagonal grid with inter-site distance d1 so that n1 base stations are deployed in total. Then, n2 complementary base stations are placed in the hot spots. These are also deployed on a hexagonal grid of “candidate sites” with inter-site distance d2 , of which some fraction is equipped with base stations so that the resulting average hot spot density equals Ω2 [BSs/km2 ]. The total cost of a base station (including investments and running costs) is denoted c1 and c2 for the respective layer. In the numerical examples, we will choose Ω2 such that the total cost of the hot spot layer is comparable for the different system concepts evaluated. Results are then presented for different levels of the cost of the hot spot layer relative to the macro layer (i.e., as a function of c2 n2 /c1 n1 ). D. Network Dimensioning We propose an iterative, greedy, method based on estimated outage probability to dimension the hot spot layer, as illustrated in Figure 1. The idea with this heuristic is to account for both the effect of outage due to a poor path gain and a non-uniform spatial user distribution. In each step, a new snapshot of users is generated and the outage probability νm in the neighborhood of each candidate site m is estimated.
More specifically, νm = Pr[Rl < Rmin |l ∈ M], where the set M represents all users that are closest (with respect to the Euclidean distance) to the site m. The candidate sites are then ranked in a descending order νm , and some fraction Θi of the hot spots are deployed per iteration i. Moreover, for each iteration, a diminishing is required. This way, we minimum inter-site distance dmin i avoid setting a fixed inter-site distance for the complementary base stations, since this is very difficult to determine apriori due to, among other aspects, dependencies on interference, macro-scopic diversity, etc. The accuracy of the deployment method may be tuned by selecting how many iterations to use and the granularity of the candidate site locations. E. Media Access Control Scheduling of packets, etc., is not modeled explicitly due to the snapshot based approach. Instead, the data rate experienced by user l is given by ˆ l 1 − ρtot Rl = R k , ˆ l is the peak data rate (when transmitting), ρtot ∈ where R k [0, 1] is the total load of base station k and ρlk is the load generated by user l only. In this context, “load” measures the fraction of time a user or base station on average transmit data. ˆl Hence, Rl can intuitively be viewed as the peak data rate R multiplied with the fraction of time the channel on average is available. Notice that this model corresponds to a time-fair, work conservative, M/G/1 processor sharing system. It is quite general; files arrive according to a Poisson process and the file size has a general distribution. The model is valid for a system with a large number of users. To achieve a load balancing between carriers serving the same sector, the load generated per user is divided with the number of carriers Nkc . Given that the users connected to base station k constitute the set Lk , 1 s . ρtot k = ˆl Nkc R l∈Lk Users are assigned to base stations one by one in a decreasing order of a predicted downlink peak data rate in the macro cellular layer. This is calculated with maximum transmit power in all base stations. Previously admitted users are neither reallocated, nor dropped. For each user, the candidate base stations are then ordered in a descending order of the predicted downlink peak data rate. Before a user is admitted, it is furthermore checked that the allocation is feasible with respect to base station loads and user data rates: 1) The resulting base station load ρtot ≤ 1 in all base stations. 2) The user data rate Rl ≥ Rmin , for both the new and previously admitted users. A user is considered to be in outage (⇒ Rl = 0) if an allocation is not feasible in any of the subsystems deployed. Notice also that soft handover is not included.
Macro
Hot spot Outage
Fig. 1. Example of the iterative deployment method. Hot spots (circles) are deployed in a descending order of estimated outage probability in the neighborhood, with a gradually decreased minimum inter-site distance. The intensity of the small dots illustrates the outage probability at the respective candidate site.
F. Physical Layer ˆ l is a function of the average received The peak data rate R signal to interference plus noise ratio (SINR) and is approximated as follows for a user l connected to base station k: Γkl w sys max ˆ Rl = min ηk wk log2 1 + γ , rk . ηk Here, rkmax is the maximum supported peak data rate, Γkl is the received SINR, ηkw is a spectral efficiency coefficient, wksys is the carrier bandwidth [Hz], and ηkγ is an offset factor for the SINR. The latter coefficients are determined based on link level simulations for the respective radio access technology. The SINR is (for the downlink case, uplink is treated in a similar fashion) calculated based on average values: Γkl
pdata k Gkl = , tot data + pcontrol )Gjl + pnoise j j∈K\k (ρj pj k
where Gkl denoted the path gain from base station k to user l, the set K includes all base stations at the same channel, and pcontrol are the power transmitted at the data channel pdata j j is the and common control channels respectively, and pnoise k tot with ρ , only the receiver noise power. By multiplying pdata j j average transmit power is accounted for in the interference. Furthermore, power control is applied in uplink for cellular systems to lower interference generated by users that have ˆ l = rmax ). reached the maximum supported peak data rate (R k Only the average path gain is modeled, fast (Rayleigh) fading is included in the link-level results. The deterministic distance dependent path gain is for macro and micro base stations given by the COST-Walfisch-Ikegami model [14], and by a dual slope model for pico and WLAN base stations. For simplicity, the log-normally distributed shadow fading is, motivated by the short correlation distance in urban and indoor scenarios, independent for each user. III. N UMERICAL E XAMPLES We will now use snapshot simulations to estimate the achievable user data rates for a few typical capacity expansion strategies of an urban HSPA system. The purpose is to compare the performance of hot spot layers having different technical characteristics, when deployed at a comparable cost
level to provide almost full (80%) area coverage. The analysis includes: 1) 2) 3) 4)
Dense HSPA macro base stations. HSPA micro base stations. HSPA pico base stations. IEEE 802.11a WLAN access points.
It should be stressed though, that the following results are based on general assumptions and modeling. In practice, more detailed modeling, including both technical and marketing related factors, would be required to determine a viable deployment strategy. A. Simulation Method and Parameter Assumptions For each system concept a binary search is conducted to find the maximum feasible guaranteed data rate Rmin such that the outage probability ν ≤ νmax , where νmax is the target outage probability. Notice that νmax = 0.04 with 80 % area coverage and 4 dB standard deviation of the user distribution. This search is repeated for a number of independent realizations of a synthetically generated user map. The average area throughput density E[λs ] = 50 Mbps/km2 , which would correspond to approximately 1.25 GB per user per month (assuming 3000 subscribers/km2 and that 0.6 % of the traffic is transmitted per busy hour). Common system parameters are given in Table I and base station class specific assumptions are summarized in Table II. The parameters are chosen to emulate the case of a typical mobile network deployed in a low-rise Western European city center, with a regular macro cell network having 500 m intersite distance and 50 % indoor users. Radio related parameters are otherwise in accordance with standard assumptions. It should be noticed, however, that no external interference is included in WLAN and that separate, orthogonal, carrier frequencies are assumed to be available for each cellular network layer (each with reuse factor one). This will lead to slightly optimistic data rates, but simplifies the modeling in terms of frequency and network planning. Empirical data on the costs for cellular base station equipment were provided by the Gartner Group. The other costs are based on the TONIC, ECOSYS, and AROMA projects, see [15]–[17], and our own assumptions. The cost estimates have
TABLE I C OMMON PARAMETERS ASSUMPTIONS ( DOWNLINK / UPLINK ). Parameter Shadow fading, standard deviation Mobile station antenna gain Percentage of indoor users HSPA channel bandwidth, wsys HSPA maximum peak data rate, rmax HSPA spectral efficiency factor η w HSPA SINR offset factor η γ HSPA received noise power, pnoise IEEE 802.11a channel bandwidth, wsys IEEE 802.11a maximum peak data rate, rmax IEEE 802.11a spectral efficiency factor η w IEEE 802.11a SINR offset factor η γ IEEE 802.11a received noise power, pnoise Fraction of hot spots deployed per iteration, Θi Minimum inter-site distance per iteration, dmin i User density, standard deviation User density, correlation distance Peak to average user density Simulated service area Average area throughput Minimum area coverage
Value 8 dB 2 dB 50 % 3.84 MHz (14.4/5.76) Mbps (0.7/0.66) bps/Hz (3/0) dB (-101/-103) dBm 15 MHz 24.8 Mbps 0.25 bps/Hz (6/3) dB (-95/-97) dBm {0, 0.2, 0.3, 0.5} {- -, 3d2 , 2d2 , d2 } 4 dB 500 m 15 6 km2 50 Mbps/km2 80 %
further been verified with representatives from telecom operators, equipment vendors and financial analysts. Resulting cost coefficients for different base stations are given in Table II, normalized with the average total cost per month for a singlecarrier, 3-sector, macro base station (which is assumed to be e 1500). These include capital expenditures for equipment and sites and operational expenditures essentially stemming from operation and maintenance, ‘last-mile’-transmission, electric power and site rental. The network life span is assumed to be 10 years for all radio access technologies and the annual costs are discounted to present value at 10 % rate. To model a roll-out phase, one third of the base stations are deployed per year during the first three years. B. Results The maximum feasible minimum user data rates Rmin are presented in Figure 2 for downlink and uplink, respectively, as a function of infrastructure cost for the hot spot layer. A few general observations can be made from these results. Firstly, slightly higher data rates are feasible in downlink. Furthermore, data rates in the order of 0.5 Mbps and 1.5 Mbps can be offered with almost full area coverage already with a moderate number of hot spots deployed. In addition, the cellular base stations are due to the high transmit power to a great extent interference limited in downlink (i.e., receiver noise can be neglected). For uplink, the systems become more or less noise limited when more hot spots are deployed. This may be useful to have in mind when interpreting the results. 1) A comparison of HSPA single-access concepts: For a certain cost level, the achievable data rate is approximately the same in downlink for all included HSPA hot spot concepts. It ranges between 1.4–3.0 Mbps for the plotted cases, with a slight advantage for micro base stations. In the uplink, though, pico base stations support a significantly higher guaranteed data rate as compared to both micro and dense macro base
stations as the cost of the hot spot layer increases. This owes to the additional propagation loss (here, 15 dB) experienced by indoor users connected to outdoor base stations, which becomes prominent for noise limited systems. Moreover, although not shown in the plots, the average user data rate ranges between 7–12 Mbps in downlink and 4–5 Mbps in uplink for the HSPA systems. Only minor differences can be seen when comparing different base stations. Since the system is interference limited in downlink the base station load can be reduced by deploying more hot spots, which increases the data rates also for users with favorable path loss. In the uplink though, a majority of the users reach the maximum peak data rate supported with HSPA and the average data rate is insensitive to the number of hot spots. 2) Multi-access HSPA + WLAN performance: Introducing IEEE 802.11a access points would primarily bring increased average data rates thanks to a higher supported peak data rate and more spectrum bandwidth. It ranges between 12– 18 Mbps for downlink and 8–16 Mbps for uplink. To some extent, as seen in Figure 2, the guaranteed data rate is also increased in downlink. For the uplink, though, IEEE 802.11a supports higher guaranteed data rate than the outdoor HSPA base stations (dense macro and micro) at first for higher levels of hot spot density. This, again, is because reducing traffic load in the macro cellular system is less useful in systems that are more or less noise limited. For the same reason, HSPA pico base stations (supporting lower minimum SINR and data rates than 802.11a) offer slightly higher guaranteed data rates than IEEE 802.11a in uplink for low and moderate levels of hot spot layer cost. IV. C ONCLUSIONS With a few numerical examples, we compared the performance of an urban HSPA macro-cellular network complemented with dense macro, micro, pico base stations, or IEEE 802.11a WLAN at comparable cost levels. For this purpose a methodology has been proposed, which should be useful to evaluate the cost and achievable user data rates for various heterogeneous wireless network concepts. The results show that there is no significant difference between the evaluated single-access (HSPA) hot spot layers in terms of the average user data rate. With respect to the guaranteed user data rate achievable with almost full area coverage, micro base stations yield slightly better performance than dense macro base and pico base stations in downlink. For the uplink, pico base stations outperform both micro and dense macro base stations, which are subject to severe propagation losses for indoor users. Thanks to the additional (licensed exempt) spectrum available, IEEE 802.11a WLAN offer higher average data rates than all studied single-access HSPA systems. In addition, the guaranteed data rate achieved with WLAN is significantly higher in downlink, and similar to the pico base stations for the uplink. For future work in this area, it would be interesting to probe deeper into the implications of legacy infrastructure on operator migration strategies.
TABLE II BASE STATION CLASS SPECIFIC PARAMETERS ( DOWNLINK / UPLINK ). HSPA Parameter Relative cost c Minimum inter-site distance di [m] Hot spot density Ωh [BSs/km2 ] Data channel power pdata [W] Downlink control channel power pcontrol [W] Maximum base station antenna gain [dBi] Constant path gain [dB] Path loss exponent (before/after breakpoint) Dual slope breakpoint [m] Outdoor to indoor penetration loss [dB] Carriers (per sector/in total)
Regular Macro 1.3 500 – (18/0.25) 2 17 -36 3.8 – 15 (3/3)
Dense Macro 1 200 {1, 2, 3, 4, 5} (9/0.25) 1 17 -36 4.3 – 15 (2/2)
4.5
4
3.5
3
2.5
2
1.5
1 0.1
Pico 0.2 50 {5, 10, 15, 20, 25} (0.24/0.25) 0.06 4 -37 (2/4) 8 0 (1/1)
WLAN IEEE 802.11a 0.1 50 {10, 20, 30, 40, 50} (0.1/0.1) 0 4 -45 (2/4) 8 0 (1/6)
4.5 Hot spot layer Dense HSPA macro (2x5 MHz) HSPA micro (2x5 MHz) HSPA pico (5 MHz) IEEE 802.11a (6x20 MHz)
Guaranteed User Data Rate [Mbps]
Guaranteed User Data Rate [Mbps]
4
Micro 0.33 100 {3, 6, 9, 12, 15} (4.5/0.25) 0.5 8 -21 4.7 – 15 (2/2)
3.5
Hot spot layer Dense HSPA macro (2x5 MHz) HSPA micro (2x5 MHz) HSPA pico (5 MHz) IEEE 802.11a (6x20 MHz)
3 2.5 2 1.5 1 0.5
0.2
0.3
0.4
0.5
0.6
0.7
0.8
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Cost of hot spot layer relative to regular macro layer
(a) Downlink
0 0.1
0.2
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Cost of hot spot layer relative to regular macro layer
(b) Uplink
Fig. 2. The plots depict the maximum feasible guaranteed user data rates with 80 % area coverage as a function of cost for the hot spot layer for downlink and uplink respectively. The lines represent different hot spots layers complementing a regular HSPA macro cellular network with 500 m inter-site distance. User density follow a log-normal spatial distribution and the average area throughput is 50 Mbps/km2 (≈ 1.25 GB/user/month).
ACKNOWLEDGMENT This work was funded by the Swedish Foundation of Strategic Research via the Affordable Wireless Services and Infrastructure project. We would also like to thank Dr. Peter Karlsson and Dr. Christian Bergljung at TeliaSonera for their feedback. R EFERENCES [1] N. Niebert, “Ambient networks: An architecture for communication networks beyond 3G”, IEEE Wireless Commun. Mag., April 2004. [2] O. Yilmas, A. Furusk¨ar, J. Pettersson, and A. Simonsson, “AccessSelection in WCDMA and WLAN Multi-Access Networks”, Proc. IEEE VTC Spring, 2005. [3] J. Kalliokulju et al., “Radio access selection for multistandard terminals”, IEEE Commun. Mag., Oct 2001. [4] A. Furusk¨ar and J. Zander, “Multiservice allocation for multiaccess systems”, IEEE Trans. Wireless Commun., pp. 174-84, Vol. 4, 2005. [5] A. T¨olli, P. Hakalin, and H. Holma, “Performance Evaluation of Common Radio Resource Management (CRRM)”, in Proc. IEEE ICC, 2002. [6] Q. Hao, et al., “A Low-Cost Cellular Mobile Communication System: A Hierarchical Optimization Network Resource Planning Approach”, IEEE J. Selected Areas Commun., Vol. 15, No. 7, Sept. 1997. [7] E. Amaldi, A. Capone, and F. Malucelli, “Planning UMTS Base Station Location: Optimization Models With Power Control and Algorithms”, IEEE Trans. Wireless Commun., Vol. 2, No. 4, Sept. 2003.
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