21st Telecommunications forum TELFOR 2013
Serbia, Belgrade, November 26-28, 2013.
Algorithmic PON/P2P FTTH Access Network Design for CAPEX Minimization Konstantinos Papaefthimiou
Yonas Tefera
Dimitar Mihaylov
Department of Electronic Systems Aalborg University, Denmark Email:
[email protected]
Department of Electronic Systems Aalborg University, Denmark Email:
[email protected]
Department of Electronic Systems Aalborg University, Denmark Email:
[email protected]
Jose M. Gutierrez
Michael Jensen
Department of Electronic Systems Aalborg University, Denmark Email:
[email protected]
Department of Electronic Systems Aalborg University, Denmark Email:
[email protected]
September 2013 Abstract—Due to the emergence of high bandwidth-requiring services, telecommunication operators (telcos) are called to upgrade their fixed access network. In order to keep up with the competition, they must consider different optical access network solutions with Fiber To The Home (FTTH) as the prevailing one. It provides an obvious advantage for the end users in terms of high achievable data rates. On the other hand, the high initial deployment cost required exists as the heaviest impediment. The main goal of this paper is to study different approaches when designing a fiber access network. More concretely, two different optimizations are alternatively evaluated, fiber and trenching minimization, over two of the most typical fiber access architectures, Point-to-Point (P2P) and Passive Optical Network (PON). These are applied to a real geographical scenario and the best returned output in terms of minimum trenching and fiber length is highlighted. Finally, a physical topology that fits best in one of these architectures will be chosen with regards to minimum total capital expenditure (CAPEX).
I. I NTRODUCTION In the last decade bandwidth requirements have grown tremendously worldwide. Bandwidth hungry services such as file sharing, video streaming, on-line gaming, High Definition Television (HDTV) and cloud-based services are becoming immensely popular and are putting pressure on the existing legacy network infrastructure [1]. Copper-based access networks continue their inevitable decline in deployment [2]. Telcos are trying to integrate new technologies to increase the data rates of the copper cables, such as G.Vector or Phantom, but these are only efficient in a very short range (≤ 1km), so it cannot be regarded as a global future broadband solution [3]. The distance limitation is also a prohibitive factor for hybrid fiber/copper access technologies, such as VDSL or VDSL2 [4]. On the other hand, fiber access networks are regarded as a future proof technology, that can fully cover the expected rise in users’ traffic demands in the upcoming years. Even though many telcos are accepting the need to deploy FTTH networks, some are reluctant to join in due to the incurred high investment costs that are typically dominated by digging works and ducts [5]. Therefore, when considering the deployment of FTTH networks the minimization of the total
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CAPEX should be given a high priority. In case of a full-buried fiber network, the largest share in the initial expenses comes from trenching works [6]. Considering this fact, applying the best-fit topology is of great importance. In this paper, we are comparing two different minimizations, trenching and fiber, using SMT and A* algorithms within a green-field FTTH network deployment in Lolland municipality. The two algorithms are implemented for two architectures, namely P2P and PON. The design utilizes Geographic Information System (GIS) data of the selected service area, i.e. digital maps with road segments and subscriber locations. Digging, duct and fiber costs are calculated, which can be used as an input to a cost effective and reliable network design. The rest of the document is structured as follows, starting with a brief discussion on the access network architectures presented in section II. Section III discusses literature work related to this paper and what is new in here. The methodology behind the total trenching, fiber length and CAPEX calculation is discussed in section IV. Section V discusses extensively the design procedure and the results obtained for the case study region. Finally, a conclusion is given on the results obtained and what is learned from this work. II. FTTH A RCHITECTURES Optical fiber networks have a number of deployment architectures [7], with FTTH as an all fiber access architecture. There are basically two main kinds of FTTH topological structures: Point-to-Point (P2P) and Passive Optical Network (PON). In this section, we briefly present a comprehensive summary for these two mostly deployed access architectures. A. Passive Optical Network In PON topology a single strand of fiber runs from the CO to a passive optical splitter(s), which is further separated into n strands of fiber to serve up to n end users. No active equipment is used within the access network. The maximum coverage distance between the CO and the end user (ONT) should be in range between 10 and 20 km, which also depends on the number of splits [8].
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B. Point-to-Point Optical Network In P2P topology a direct and dedicated link exists between the CO and the end users [9]. This architecture is highly scalable, upgradeable and service transparent. In a P2P network deployment, N number of fibers (this number depends on how many subscribers are connected to the network) and 2xN number of transceivers are needed to provide certain service to N number of end-users [10]. In order to reduce the CAPEX, a number of fibers can be bundled together. To achieve the expected throughput, the maximum distance separating the CO from the end users should not exceed 10km [11]. III. R ELATED W ORK Optical access network design has been the subject of many research works dealing with different versions of the problem depending on the topologies and the particular issues addressed. A number of papers deal with optimizing the network planning in PON and P2P architectures, particularly in finding the best possible positions of splitters and routing of the ducts and fiber [12][13][14]. In addition, most of the published literature related to optimization in the network design are based on meta-heuristic approaches such as genetic algorithms and simulated annealing [15][16][17]. Some other papers use a mixed integer linear programming (MILP) [18] or game theory [19] approach to automate the FTTH designs. Various papers also exist that deal with the economic expenditure of a FTTH rollout from CAPEX and OPEX perspective [20][21][22]. This paper differs from other literature because it focuses at the green-field rollout of FTTH network using GIS data of an existing geographical scenario as an input. The optimization problem studied here compares both A* and SMT algorithm to find out the least expensive one in terms of CAPEX. To the best of our knowledge, such a comparison with exact calculation of the final overall trenching and fiber length needed to connect a certain number of end users to the network is not considered in another optimization literature.
possible cable paths (road segments) and nodes V representing the COs, the NTs, the road segment points (SPs), the splitters and PCPs. In order to create the trenched traces for the fiber to pass, SMT and A* have been used in both architectures. SMT describes the way to connect a set of nodes (segment points - SPs) using the smallest amount of trenching, resulted from picking the shortest edges (road segments) to interconnect these nodes in a tree-formation [24]. A* is a greedy graph traversal and path finding algorithm which lies on the principle of breadth first search algorithms [25]. The design of the access network using A* and SMT algorithms is implemented in two phases. In the first phase, a connection is established between the CO and splitters/PCPs. In the second phase, the remaining connections between the splitters/PCPs and Terminal SPs (TSP)1 are established. The result in both cases is a spanning tree representing the trenched fiber network. 3) Parameters calculation: After forming the minimum trees using the two algorithms, the final trenching length, fiber length and cost in every architecture are calculated, as shown in equations 1, 2, 3 and 4. Trenching length calculation in PON/P2P Design TTL =
J X
I X
j=1
i=1
L(COj , CPji ) +
K X
! L(CPji , T SPk )
k=1
∀ (COj , CPji ) and (CPji , T SPk ) ∈ / P (1) Fiber length calculation in PON/P2P Design TFL =
J X I X j=1
L(COj , CPji ) +
L(CPji , T SPk )
(2)
i=1 k=1
i=1
|
I X K X
{z
TFL 1
}
|
{z
TFL 2
}
CAPEX calculation in PON/P2P Design CAP EXP ON = T T L · (T cost + D cost) + T F L · Single strand (3)
IV. M ETHODOLOGY The main step to perform this analysis is to construct the trees for each of the studied minimization techniques. However, there are a few intermediate steps that must be explained to fully understand the procedure followed: 1) Forming groups of NTs: In PON architecture design, a 1:32 splitter is assumed to be used. In P2P architecture deployment case, instead of splitters primary connection points (PCPs) are used where the bundle of fiber (96 cables for uplink/downlink) is divided between the users connected to the same PCP. The NTs which are closer to each other are served by the same splitter/PCP. In turn, this leads to a reduction in the fiber needed to connect them to the network. This grouping formation of NTs is performed using the k-means clustering algorithm [23]. 2) Tree formation: Typically, network links cannot be built wherever desired but must follow some existing cable paths, the road network and other infrastructure. A network graph G = (V, E) is used, consisting of edges E representing the
CAP EXP 2P = T T L · (T cost + D cost) + T F L 1 · Bundle+ +T F L 2 · Single strand (4)
where: • • • • • •
T cost and D cost are trenching and duct cost respectively, which values can be obtained from Table I TTL and TFL are Total Trenching Length and Total Fiber Length, respectively CP (Connection Point) denotes a PCP and a splitter in P2P and PON cases respectively i, j, k represents the number of CPs, COs and TSPs respectively L(x,y) is the length of the path between node x and y P is the set of unique trenched paths (x,y) or (y,x)
1 TSP: is a segment point available to establish connectivity to a number of NTs
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V. C ASE S TUDY In this section, the described methodology is applied to a real geographical scenario to find the best cost-wise solution for trenching and deploying fiber in both P2P and PON access architectures comparing SMT and A* algorithms in the municipality of Lolland, Denmark. The total area coverage amounts for 889 km2 [4] while its population reaches 44 635 inhabitants, based on the latest statistical data for the 1st quarter of 2013, obtained from Denmark Statistics [26]. The total number of households (GIS available data) is 29 175. Out of the total number of available NT locations, trenching and fiber reached almost 90% coverage, due to the CO-NT distance limitation in P2P architecture as described in section II.
Figure 3: Total PON trench length
Figure 4: Total P2P fiber length
Figure 1: Digital representation of Lolland commune with its corresponding 14 COs and NTs Figure 1 presents the test case area. The x-symbols depict the NT locations and the stars represent the 14 CO locations. The CO locations are selected looking at the density of the NTs in the region. The continuous lines that form the digital road network are used for applying the graph algorithms and calculating the total trenching and fiber length. It is assumed that the fiber is buried, following the existing road infrastructure. Figures 2, 3, 4 and 5 present the final trenching and fiber lengths returned in P2P and PON architecture by applying both SMT and A* algorithms, respectively.
Figure 5: Total PON fiber length The results clearly prove that SMT algorithm reduces the total trenching length in expense of increased total fiber length needed to connect the NTs to the network, while A* algorithm follows the reverse pattern. In addition, the total trenching length is much smaller than the total fiber length in each CO area due to the reuse of trenches by a number of NTs which need separate strands of fiber to be connected to the network. To show the difference from cost perspective, the final CAPEX based on trenching, ducts and fiber cables needed for the whole area, is calculated. Cost assumptions are taken for the different parameters used in the CAPEX calculation as shown in table I on the following page.
Figure 2: Total P2P trench length
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R EFERENCES
Cost(e/m) 20 2 0.6 0.05
Trenching (T cost) Duct (D cost) Fiber cable (Bundle) Fiber cable (Single Strand)
Table I: Sample Cost Values By substituting the values from Table I into equations 3 and 4, the total CAPEX is calculated. The cost results are presented in table II.
Steiner A*
P2P 26 188 927 27 027 705
PON 24 510 654 26 148 842
Table II: Total costs As shown in table II, the initial capital expenditure for P2P architecture, irrespective of the algorithmic design approach used, is higher compared to PON architecture. This high initial rollout expenditure tips the balance in PON implementation direction. However, a fiber access infrastructure planning is a much more complex process that should not be based solely on the economic factor. Various aspects can affect the preference towards a specific architecture, such as geographical scenario, customer prioritization, scalability, upgradability and energy efficiency issues. Looking at the rollout cost difference between the two architectures and clear clarification of the current as well as the future demands and commercial targets set, P2P architecture can prove to be an overall better solution. VI. C ONCLUSION This work compares fiber and trenching minimization process, using two different algorithmic approaches in P2P and PON fiber access architectures. Based on the calculations together with assumed realistic costs for trenching works, ducts and fiber cabling, which occupy the biggest share in the initial rollout, the preferable topology in terms of least CAPEX is highlighted. The case study presented covers the Danish municipality of Lolland. Real GIS data for the households locations and the road network is used to produce the final output. Results prove that minimum spanning tree algorithms are always preferable over shortest path algorithms in terms of least trenching while for the fiber minimization the vice versa is true, irrespective of the architecture picked. This behavior is attributed to the different principle underlying both approaches. Shortest path algorithms always return the minimum weight path connecting two nodes (fiber), regardless of the rest nodes that need to be connected. On the other hand, minimum spanning trees produce an overall minimum weight solution for all the nodes that need to be connected (trenching). Out of the four cases presented, applying SMT in PON architecture returns the least deployment cost. This fact indicates a preference for PON topology from implementation point of view, when minimization of the total CAPEX is prioritized.
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