Improving Coverage and Load Conditions Through Joint Adaptation of Antenna Tilts and Cell Selection Rules in Mobile Networks Henrik Klessig, Albrecht Fehske, and Gerhard Fettweis
Jens Voigt
Vodafone Chair Mobile Communications Systems Dresden University of Technology, Germany Email: {henrik.klessig, albrecht.fehske, fettweis}@ifn.et.tu-dresden.de
Actix GmbH Dresden, Germany Email:
[email protected]
I. I NTRODUCTION
introduced a general framework for a coordination of individual and independent SON use cases in [1]. A policy-based approach to SON use case coordination was given in [2]. This policy-based approach was used for a tailing coordination on top of otherwise independently running mobility load balancing (MLB) and mobility robustness optimization (MRO) use cases in [3]. We apply a different approach to the SON coordination issue. Instead of coordinating the outcomes of independently running use cases as a separate step, we combine the use cases into one algorithm and optimize the cellular network towards a joint target. Thus, the coordination is inherent in the optimization, avoiding the need for an external additional coordination step. In this contribution, we use the example of having a joint coverage and capacity (CCO) and MLB optimization for a cluster of cells in a LTE deployment assuming a centralized SON architecture. This paper is further organized as follows: In Section II our system model is introduced, whereas in Section III the SON optimization problem is formulated and the joint algorithm is presented. In Section IV our verifying simulations are introduced and discussed, before we conclude this contribution in Section V.
Self-organizing networks (SON) is an emerging technology in the field of planning, optimization, and maintenance of wireless cellular networks. One major topic of research into SON technology is the broad field of coordination of otherwise autonomous SON use cases: Closed-loop SON implementations are expected to run on a 24/7 base for multiple SON use cases in multiple geographic areas, on different wireless technologies, and on multiple frequency layers of a wireless network deployment, and so on. Hence, a key objective for the acceptance of a closed-loop implementation of SON use cases (especially optimization use cases) by operators is a coordinated handling of the parameter and configuration changes (proposed by independent SON use case implementations) before they are submitted to the operating network. The first major attention to the SON coordination issue was given by the European research project Socrates. Socrates
We consider the downlink of a cellular network consisting of N base stations (BSs) deployed in a compact region R ⊆ R2 . Users are assumed to be distributed randomly in R according to some given distribution, δ(u), with R δ(u) du = 1. In the following, we are interested in the average resource utilization of all BSs, i. e., their loads, which depend on the traffic demand intensity as well as on the service quality provided by the BSs. Cell loads are essential performance indicators since many relevant quality-of-service (QoS) measures such as the signal-to-interference-and-noise ratio (SINR), user rates, or mean delays are strictly monotonic functions of the load. Network traffic is modeled on flow level, where flows represent individual data transfers of, e. g., web pages, video, audio, or general data files. We assume that the arrival of flow requests to the network take place according to a Poisson
Abstract—One major topic of research into SON technology is the coordination of SON use cases. Network operators expect a coordinated handling of the parameter and configuration changes submitted to the operating network by closed-loop SON use case implementations. Beside a published conceptual framework, SON coordination has already been treated in the literature, especially regarding the mobility load balancing (MLB) and mobility robustness optimization (MRO) use cases. In this paper, we utilize the capacity and coverage optimization (CCO) and MLB use cases. Rather than performing any heading or tailing coordination of separate CCO and MLB algorithms, in our work we concentrate on the optimization considering both use cases in a joint algorithm. Our approach introduces cell-individual loads and the joint treatment of cell selection policies and antenna tilt settings into well-known and previously reported optimization concepts. Using system simulations of a sample LTE real deployment scenario, we verify that our joint optimization of antenna tilts and cell selection rules including the notion of cell-individual loads outperforms the optimization of tilts-only (with and without the notion of cell-individual loads within the algorithm) and of the cell selection rules-only in terms of spectral and energy efficiency. Index Terms—SON; self-optimization; closed-loop optimization; LTE; wireless network planning and optimization; SON coordination
978-1-4673-0762-8/12/$31.00 ©2012 IEEE
II. S YSTEM M ODEL
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process with intensity λ. Flow sizes are assumed to be exponentially distributed with common mean Ω. The terms λ, Ω, and δ(u) determine the traffic intensity distribution σ(u) := λΩδ(u) in Mbps per km2 , which we use in the remainder of the paper.
Eq. (2) thus describes the service that a flow experiences if exposed to average interference conditions as characterized by the load vector η = (η1 , . . . , ηN )T at hand. We further assume that users do not change their locations over the duration of a flow and are scheduled according to a round robin scheme. In addition, we assume that if there is at least one active flow in any cell, the corresponding BS uses all its available resources for service and, in particular, transmits at full power.
A. Radio Link and Resource Sharing Model Several factors determine the signal strength received at a terminal: The distance to the serving BS, the antenna pattern including the antenna tilt, as well as the effects of fast and slow fading, or shadowing. Since serving a data flow takes much longer than the coherence time of a wireless channel, fast fading is observed by its average impact. Shadowing effects happen on a much larger time scale and are constant over the duration of many flows. Consequently, we assume fast and slow fading effects to be part of a location- and tilt-dependent but otherwise constant path loss function. The radio link quality is further governed by the interference scenario, i. e., the collection of BSs that are transmitting at any given point in time. In contrast to slow and fast fading effects, interference scenarios evolve on the same time scale as the flow dynamics. High interference levels reduce a cell’s capacity, leading to longer service times, more active flows, and, consequently, to a higher level of interference being caused to surrounding cells. These neighbors in turn suffer from the same effects, further worsening link-quality amongst each other as well as in the initial cell. As a result, data rates as well as cell loads are strongly connected in the presence of inter-cell interference. Detailed modeling of these effects lead to so-called coupled-processor queuing models, which are intractable analytically [4]. In order to still capture the effect of dynamic interference on data rates, we resort to a simple, yet accurate technique as follows. Let pi (u) denote the power received from BS i at location u, including all described path loss effects. The SINR γi experienced by a data flow at location u ∈ R and connected to BS i is given by pi (u) pi (u) ≥ pmin j=i ηj pj (u)+θ (1) γi (u, η) = 0 otherwise.
B. Cell Selection and Cell Loads In this work, we adopt a cell selection model first proposed in [6]. Generally, users connect to BSs according to the data rate offered as well as the current load situation. A user at location u associates with BS i = s(u, η) according to the rule s(u, η) := argmax (1 − ηj )α cj (u, η). j=1,...,N
The parameter α allows to control the sensitivity of the association rule to the load ηi of BS i. For α = 0, cell selection is purely based on the achievable rate ci (u, η), which depends on the loads of all BSs except BS i (compare Eq. (1), note that in [6] ci is not a function of the loads η). For increasing α the factor (1 − ηi )α forces users to avoid highly loaded BSs even if they provide good achievable rates. In case of ambiguities, users connect to the cell with the lowest index i. The corresponding cell areas are given by Li (η) := {u ∈ L | s(u, η) = i}, i = 1, . . . , N ; the spatial partition on L by P := {L1 , . . . , LN }. The load associated with BS i is then defined as the integral σ(u) du, (4) ηi := c Li (η) i (u, η) where the integrand is denoted as the load density, i. e., ratio of traffic demand provided capacity. Eq. (4) gives only an implicit formulation of the cell load: The right hand side also depends on the load vector η via the cell area Li (η) and the achievable rates ci (u, η). We propose to solve Eq. (4) via the fixed-point iteration
where we take into account that a terminal needs to receive a certain minimum signal power pmin in order to be served by the network. The corresponding served area is given by L := {u ∈ R | ∃i : pi (u) ≥ pmin }. The terms ηi ∈ [0, 1] denote the load of BS i, which according to the underlying queuing theoretic framework can be interpreted as the probability that the corresponding BS is transmitting at any point in time [5]. The corresponding data rate is modeled as the Shannon capacity ci (u, η) = min {ci (u, η), cmax } B log2 (1 + γi (u, η)) , ci (u, η) = 0
γi (u, η) ≥ γmin , otherwise.
(3)
η (n+1) := min {η (n) , 1}.
(5)
Existence and uniqueness of a solution to Eq. (4) as well as convergence of the iteration (5) are discussed in [5] and [6] for the case of fixed cell areas Li and load-independent achievable rates ci (u), respectively. Since here we focus on application aspects of the framework, we leave theoretical discussions of Eqs. (4), and (5) for future publications. Here, we assume = ( there exists a unique solution η η1 , . . . , ηN )T to Eq. (4), which reflects the load distribution that establishes in the network under the given conditions. We further assert that the for any given starting Iteration (5) converges to the solution η (0) value ηi ∈ [0, 1].
(2)
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C. Antenna Tilts And Adaptive Cell Selection
derived from the loads; on the other, a huge amount of KPIs and metrics characterizing large networks can be summarized to a small set of cell loads.
The vertical tilt angle of a BS antenna has a strong effect on the signal propagation conditions throughout the cell. Small tilts provide good reception throughout the cell but cause interference in neighboring cells. A strong down tilt on the other hand reduces interference caused to cells nearby, but also worsens received power at the edge of the cells, baring the danger of coverage holes. The strong impact of BS antenna tilts on received signal strength pi throughout the cell naturally carries through to their load conditions ηi . In addition, the cell selection rule defines the cell areas Li , and can be used to further optimize and fine-tune load distribution among BSs. Let e = (e1 , . . . , eN )T and α denote the vector of antenna tilts applied at all BSs considered and the cell selection parameter as introduced in Eq. (3). The tuple (α, e) represents the degrees of freedom, which we exploit to optimize network performance. A suitable objective function is defined subsequently.
B. Solution Proposed The cell load as a function of the cell selection parameter α and the tilts e are intractable mathematically. The reasons are that the load is only given in an implicit formulation (4) and a mathematical expression for the path loss as a function of the tilt cannot be given due to slow fading effects and realistic, thus complex, antenna patterns. Since the number of possible antenna tilt configurations increases exponentially for increasing number of BSs, an exhaustive search for the tilt to achieve the objective (6) is not practical due to the need for limited computation times. Hence, a structured search according to the Taxi Cab Method (TCM), which is a simpler version of Powell’s method [7], is applied. One can identify two important effects on network statistics by utilizing the cell selection parameter α: The maximum cell load is monotonically decreasing for increasing α, the same holds for the SINR coverage Cγ . To exploit these properties, the optimal α∗ is chosen according to rule (10) for every antenna tilt configuration e that is investigated on during the search, i. e., ⎧ if α : Cγ (α, e) ≥ Cγ,min , ⎪ ⎨0 ∗ (10) α = fα (e) := αmax if αmin > αmax , ⎪ ⎩ αmin otherwise,
III. SON A LGORITHM A. Optimization Problem Formulation The optimization goal, formulated in Eq. (6), is the minimization of the sum of cell loads, i. e., minimization of the amount of radio resources utilized by the network to serve the traffic demand. Consequently, adjusting the cell selection parameter α and the antenna tilts e shall reduce the load densities. Hence, rates and SINRs, as well as the system throughput, shall be increased: N
minimize (α,e)
i=1
Crx (e) :=
(7)
Cγ (α, e) ≥ Cγ,min ,
(8)
∀ ηi (α, e) ≤ ηmax ,
(9)
where αmin and αmax denote the values, for which the maximum cell load and the SINR coverage constraints are barely fulfilled, respectively. If there is no α, for which the SINR coverage constraint is fulfilled, it is chosen to be zero in order to maximize the SINR coverage. If both constraints can be fulfilled but not simultaneously (αmin > αmax ), α∗ is chosen to be αmax to fulfill the SINR coverage constraint and to minimize the maximum load in the network irrespectively of the overload constraint. In all other cases, α∗ is set to αmin to maximize the SINR coverage and to fulfill the overload constraint simultaneously. Note that this results in a higher priority for the SINR coverage compared to the overload constraint.
(6)
Crx (e) ≥ Crx,min ,
subject to
where
ηi (α, e),
L
δ(u) du,
Cγ (α, e) :=
Lγ
δ(u) du.
denote coverages regarding a minimum reference signal received power (RSRP) pmin and minimum SINR γmin , respectively. The coverage area according to the minimum SINR is ) ≥ γmin , i = s(u, η )}. η ) := {u ∈ L | γi (u, η given by Lγ ( Note, that L is a function of the tilt configuration e only and Lγ is a function of both, tilts e and cell selection policy α. Further, the coverages are defined in a hierarchical manner, i. e., Lγ ⊆ L. However, following the objective (6) may not be optimal regarding the individual cell loads. More specifically, users in highly loaded cells probably need longer times to receive their requested data and, therefore, suffer from low quality of service. Thus, the network parameters have to be adjusted with respect to a maximum allowed cell load ηmax , and minimum RSRP and SINR coverages, Crx,min and Cγ,min , respectively. The advantage of using this objective function is twofold. On the one hand, SINRs, rates, and other metrics can be
C. Overview and Practical Aspects Fig. 1 summarizes the proposed joint algorithm. Longterm measured received powers pi and statistics of the traffic generated by the users σ(u) are collected. Cell loads are estimated respectively predicted from these measurements. The optimal antenna tilts e∗ are then derived according to the optimization goal (6) and the Taxi Cab Method, while the optimal cell selection parameter α∗ is chosen according to Rule (10). The optimal partition of cells P ∗ can be derived from the tuple (α∗ , e∗ ) and with the aid of the fixed-point iteration (5). As a last step, P ∗ is transformed into cell individual offsets (CIOs), in order to be compliant with common mobile
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pi (u) e∗ = argmin TCM
σ(u)
e
P∗
i
ηi (fα (e), e)
∗ (5)
P = P(fα (e∗ ), e∗ )
Fig. 1.
C = f (P ∗ )
traffic rel. to peak [%]
100 80 60
2 km
communication standards. Let C = (CIOi,j )N ×N be the matrix of derived CIOs. A user at location u decides in favor of connecting to a BS i, if the weighted received power from that BS is higher than others, i. e., pi (u) · CIOi,j > pj (u). Users moving from cell i to cell j are assumed to request the handover at the same location as users moving from j to i, i. e., −1 holds. the handover zone is symmetric and CIOi,j = CIOj,i In our implementation, we regard the CIOs independently of each other and adjust them to minimize the mismatch between the cell areas obtained by CIOs and the optimal cell selection policy. Finally, if there are notable changes in received powers
40 20 0 0h
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λδ(u)
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Fig. 2. Daily traffic profile [8] (left) and measured spatial traffic distribution at peak hour (right). Overall peak traffic demand σpeak = 20 Mbps 2 . km
adjustments of CIOs and antenna tilts (INITIAL) and the network modified by a different state-of-the-art CCO optimizer (SOTA). The SOTA is not aware of the cell load and only adjusts antenna tilts according to the policy of minimizing the cell overlap area in order to reduce inter-cell interference. The main differences between the model/algorithm introduced in Sections II and III and the simulation settings are provided in Table I. The verification of the proposed joint SON
C
e∗
Algorithm overview.
TABLE I M AIN DIFFERENCES BETWEEN MODEL AND SIMULATION
(they may also be zero, if BSs are shut down) or in the traffic situation, the algorithm calculates new CIOs and antenna tilts, which can be immediately applied to the network.
Model
IV. S IMULATION R ESULTS In order to assess the optimization potential of the algorithm, system level simulations are carried out and performed by a state-of-the-art LTE system level simulator. It implements the full user-plane functionality of LTE release 8 specifications for the PDCP, RLC, MAC, and PHY layers. Also, most of the corresponding control-plane functions are included in the simulations. We simulated at least 60 seconds of real time for each simulated snapshot.
Cell sel.
optimal partition
Traffic
mean arrival intensity, mean file size per 30x30 m pixel
realizations of Poisson point processes
30 %
headers up to layer 4, coding, max. 2 retransmissions, reports, PDCCH and pilots
Shannon capacity
CQI reports, MCS to achieve 10 % BLER
estimated
ratio of occ. and total PRBs
Payload overhead MCS Cell loads
A. Simulation Setup The underlying scenario is a real deployed network using LTE technology comprising 18 sites, which are two-, three-, four-, or six-fold sectorized, yielding 57 cells in an area of 4 km2 and a mean inter-site distance of approximately 500 m. The environment is mainly densely populated. All base stations use a common bandwidth of 10 MHz at 46 dBm maximum output power. Furthermore, realistic 3-dimensional antenna patterns and height data of the environment are considered. A spatially heterogeneous traffic distribution is taken from measurements in that city and is depicted in Fig. 2. Additionally, we use the daily traffic profile from [8] to model daily traffic variations. The following constraints are used: A minimum receive power of pmin = −115 dBm and a minimum SINR of γmin = 0 dB have to be provided for 95 % of the users, i. e., Crx,min = Cγ,min = 0.95. The load of a cell must not exceed 60 %. Besides three flavors of the optimized network according to the proposed algorithm (antenna tilt optimization only: TILT; cell individual offsets only: CIO; and joint CIO and antenna tilt optimization: CIO + TILT), two further reference simulations/optimizations are utilized: The network without any
Simulation P∗
CIOs
algorithm is carried out for two use cases: An event-triggered cell outage compensation (COC) and continuous CCO/MLB in a 24/7 mode. B. Event-triggered use case: COC To simulate a cell outage, two important cells (highest loaded and one of the largest in area) were switched off inevitably. Thus, the trigger was a cell outage detection leading to an overload situation at the remaining cells. We verified how the proposed algorithms can provide a solution to this problem, hereby compensating the cell outage. Fig. 3 depicts the results1 of our simulations. These can be discussed as follows: Irrespectively of the overloaded cells, SOTA reduces inter-cell interference and, thus, improves SINRs and spectral efficiency. TILT primarily tries to fulfill the given constraints by applying the policy of reducing the sum of overloaded cell areas. However, in that scenario, this results in shifting traffic from multiple highly loaded cells to one single cell, which is then even higher loaded than before. Finally, no configuration 1 Q (γ) denotes the 5 %-ile of the SINR in dB. Note that Q (γ) = 0 dB 5 5 corresponds to Cγ = 0.95 with γmin = 0 dB. Hence, it is also an indicator for cell edge performance and user fairness.
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can be found, that fulfills the given overload constraint. CIO does not achieve the overload constraint. However, the maximum cell load could considerably be reduced by forcing the users to connect to cells with lower rates. This yields drastic degradations in SINRs. In this case, CIOs higher than 10 dB have to be utilized in order to reduce the maximum cell load. Applying such CIO values may be very unattractive to operators, since this results in low quality of service at the cell edges. CIO + TILT is the only solution that fulfills both constraints simultaneously. While reducing the maximum cell load by more than 20 %, it is able to guarantee sophisticating SINRs (almost the same as without any adjustments). Due to the additional modification of antenna tilts, smaller CIOs are applied compared to CIO. The optimal cell selection parameter α∗ is determined to be 2, which yields to acceptable CIO values between 2 and 3.75 dB for the cell pairs containing the overloaded cell and between 0 and 1 dB for other cell pairs, respectively. INITIAL
SOTA
CIO
V. C ONCLUSIONS We introduced a joint optimization of the capacity and coverage (CCO) and mobility load balancing (MLB) SON use cases without the necessity of external (heading or tailing) SON coordination. Our approach introduces cell-individual loads and the joint treatment of cell selection rules and antenna tilt settings into well-known and previously reported optimization concepts for CCO. We verified the effectiveness of our approach in terms of spectral and energy efficiency for a cluster of cells in a LTE deployment assuming a centralized SON architecture. As a major practical outcome, our joint CCO and MLB optimization can also be used for the cell outage compensation (COC) SON use case, as it jointly targets coverage, SINR, and load balancing in a cluster of cells.
CIO+TILT
100
max cell load [%]
5
QSINR [dB] 5 (γ) 5%-ile [dB]
TILT
therefore, power consumption. Considering the linearization of the base station power model in [9], enhancements in network energy efficiency can be achieved by up to 9.3 % and 11.1 % by applying SOTA and CIO + TILT, respectively. Concluding, our simulations show that simplifying network statistics down to a small set of cell loads and utilizing that set for network optimization can be superior to more complex optimization approaches as SOTA. This applies to network spectral and energy efficiency, and user throughput as well.
4 3 2 1
80 60 40 20
ACKNOWLEDGMENT The work presented in this paper was partly sponsored by the government of the Free State of Saxony, Germany, within the Cool Silicon Cluster of Excellence under contracts 13919/2367 and 14056/2367. The authors would like to thank Mr. Stefan Funck of Actix GmbH, Dresden, Germany for providing and extending the simulator to meet our needs.
0
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Fig. 3.
0h
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Network behavior during cell outage compensation.
C. Continuous use cases: 24/7 CCO and MLB
INITIAL
SOTA 1
2
0.8
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R EFERENCES [1] L. Schmelz, M. Amirijoo, A. Eisenbl¨atter, R. Litjens, M. Neuland, and J. Turk, “A coordination framework for self-organisation in LTE networks,” IFIP/IEEE International Symposium on Integrated Network Management, Dublin, Ireland, May 2011. [2] T. Bandh, R. Romeikat, H. Sanneck, and H. Tang, “Policy–based coordination and management of Self–Organizing–Network (SON) Functions,” IFIP/IEEE International Symposium on Integrated Network Management, Dublin, Ireland, May 2011. [3] A. Lobinger, S. Stefanskiy, T. Jansen, and I. Balan, “Coordinating Handover Parameter Optimization and Load Balancing in LTE Self– Optimizing Networks,” in IEEE VTC Spring, Budapest, Hungary, 2011. [4] T. Bonald, S. Borst, N. Hegde, and A. Prouti´ere, “Wireless data performance in multi-cell scenarios,” in SIGMETRICS’04, Proceedings of the joint international conference on Measurement and modeling of computer systems, vol. 32, no. 1, Jun. 2004, p. 378. [5] A. J. Fehske and G. P. Fettweis, “Aggregation of variables in load models for interference-coupled cellular data networks,” IEEE International Conference on Communications (ICC), accepted for publication, 2012. [6] H. Kim, G. Veciana, X. Yang et al., “Distributed α-optimal user association and cell load balancing in wireless networks,” IEEE/ACM Transactions on Networking, 2011. [7] M. J. D. Powell, “An efficient method for finding the minimum of a function of several variables without calculating derivatives,” The Computer Journal, no. 7(2), pp. 155–162, 1964. [8] G. Auer, O. Blume et al., “Earth project d2.3 - energy efficiency analysis of the reference systems, areas of improvements and target breakdown,” EARTH FP7, 2011. [9] C. Desset, B. Debaillie et al., “Flexible power modeling of lte base stations,” IEEE Wireless Communications and Networking Conference (WCNC), 2012.
CIO+TILT
2.5
CDF
spect. efficiency [bps/Hz]
Fig. 4 shows that the network spectral efficiency can be increased by a factor of 1.41 already by SOTA over the entire day. The proposed CIO + TILT outperforms SOTA by improvements in spectral efficiency by a factor of 1.60. It also reveals that the experienced user throughputs, i. e., ratios of the amount of transmitted data and the time the user is active, are considerably improved due to increased spectral efficiency and reduced cell loads. For CIO + TILT it can be observed that the number of users, which experience the highest achievable throughput, is significantly higher. This observation indicates increased user rates in the cell centres, since there the SINRs are higher than at cell edges. Further,
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Fig. 4. Improvements in effective spectral efficiency over a day and experienced user rates at 10 pm.
the spectral efficiency enhancements result in a significant reduction of amount of utilized resources, i. e., system load, and,
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