The Access Network

In the UK access, 90% of new systems are copper based and only 10% optical fibre.

Incumbent service operators are finding it difficult to justify the installation of fibre as a replacement technology to support narrow band services carried by the existing copper network. At present, the cost of optical network components is considered too high, especially the electronic Line Termination Unit (LTU). In addition, the introduction of Asymmetric Digital Subscriber Line (ADSL) technology has the potential to greatly

increase the available bandwidth in the copper access network, thus potentially extending its working lifetime.

Consequently, copper bearer platforms still attract significant levels of investment from many telecommunications operators. Although it was previously considered that fibre would be deployed more aggressively in the access sector, copper remains the dominant technology option for the narrow band service sector. The continuous use of copper into the near future has justified the development of a new smart planning tool to automate the planning process for greenfield developments. Although in this instance, the methods described in this chapter have been applied to solve the Copper Planning Problem (CPP), it is believed the approach in general can be successfully tailored to suit a variety of different network problems.

In the UK, an ongoing development programme is in place to provide telephony to new housing estates. It is essential that an estimate of the cost of work is produced, a bill of materials and a resource schedule. This information is passed on to construction team for installation. To produce a satisfactory project plan for the construction team by the manual method proved very difficult, time consuming and tedious because of the many complex factors planners were required to consider.

The smart tool, GenOSys, which has been developed primarily to aid network planners, automates the design of the secondary cable and duct networks necessary to connect new customers to the exchange. GenOSys employs Evolutionary Computation (EC) techniques to facilitate the rapid solution of large and complex network problems.

In order to develop an efficient search strategy yielding optimal or near-optimal solutions, it is essential to understand both the nature of the problem and the structure of the search space. The various merits of using EC methods to address the CPP are discussed in the following section.

5.2.1 An overview of the Greenfield CPP

The copper network provides customer access to a range of narrow and mid-band services.

Most copper architectures comply with a three tier model consisting of the primary, secondary and distribution networks, all implemented in a tree structure.

The primary network connects the secondary network to the exchange via a Primary Connection Point (PCP), as shown in Figure 5.1. The distribution network connects customers to Distribution Points (DPs), which are connected via the secondary network to the PCP.

Access networks are normally accommodated within an underground duct network. The duct network forms the links between the access nodes, such as joint boxes, which are used to accommodate cable joints and DPs. The duct network is normally highly interconnected to provide flexible schemes routing between the exchange and customer. A typical secondary network structure connecting customers to PCPs, is shown in Figure 5.2,

In this context, local access network planning is defined in the following manner. For a predefined geographical area, duct structure and particular demand profile determine the following:

DP

DP

DP

DP

PCP

PCP Main Pairs

Cables

Direct Pairs cables

Primary Network Secondary Network

Distribution Network

= Customer DP

Pairs

DP = Distribution Point

= Primary Connection Point

Exchange

Figure 5.1 Copper access network.

1. location of all access nodes (footway boxes), 2. location of DPs,

3. assignment of customers to DPs, 4. aggregation of DPs into sub-networks, 5. assignment of DPs to cable paths, and

6. route of all cables to satisfy customer demand at the lowest cost.

The distribution network architecture is built from a number of different types of sleeves, joint boxes, cables and ducts. Customers are connected to the network at connection points called sleeves, which are normally accommodated in underground joint boxes. Ducts are used to house the secondary and distribution multicore cables, which range in size from 5 to 100 pairs.

5.2.2 Network Object Model

A model of the network was created using object-oriented development methods, Object oriented analysis OOA and design OOD enable development of flexible, intuitive models.

Object-oriented models are based upon the underlying domain structure. A system built around domain entity structures is normally more stable than one based around functionality. It has been recognised that most software modifications are associated with system functionality rather than the underlying domain (Paul et al., 1994).

PCP

Sleeve

DP

Customer Cable

Duct

Figure 5.2 Secondary copper network.

These principles are then applied to create a model (Paul et al., 1994).

Classification (abstraction) is the operation of grouping together objects with similar features.

Inheritance (generalisation) if there are specialised types of a class which exhibit common features, the principle of inheritance can be used to organise them.

Association represents a relationship between two different classes of objects.

Aggregation (whole/part structure) expresses a special relationship in which one class contains another.

As object modelling is based upon the organisation principles of human thought processes, domain specialists are able to contribute to the design of an object model without requiring specialist computer skills. In an object-oriented model, the objects and their associations correspond directly to real world entities and their relationships. Consequently, the object model is modular and closely resembles the real network. Objects can represent both physical items (copper cables) and abstract concepts (time slots in a frame). These characteristics make object models easy to understand, maintain and extend.

A network object model is required to capture the network topology and its connection rules. Persistent computer based objects capture the functionality of network components and state attributes. The use of object-oriented methods proved successful because they allowed the complex rules relating to network component connectivity to be represented in a flexible and concise manner. This scheme allows the seamless integration of engineering rules into the computer based objects. The majority of constraint checking for engineering rules is therefore carried out implicitly, through the mechanisms provided by the model.

However, practical experience has shown that another level of data abstraction is required to improve optimisation efficiency. The direct manipulation (creation and deletion) of network objects by the solver module during an optimisation run proved to be highly inefficient. To improve the efficiency of the process, relevant data from the object model is now held in the form of tables that can be accessed very rapidly. The design of the object schema employed the Object Modelling Technology (OMT) method developed by Rambaugh. This object model uses the class Nodes to represent access points, equipment cabinets and network components and the class Links to represent the cables and ducts. The high level view of the class schema is shown in Figure 5.3. The general-purpose object model has also been employed to support network performance and reliability analysis.

Figure 5.3 General telecommunications network object model.

5.2.3 Structural Analysis Module

The function of this module is to analyse network structure before an optimisation run. This pre-process generates data held in a distance matrix, tabulating the distance and optimal route between any two nodes. Graph theory is used to calculate the shortest distance between any two nodes and form a modified minimum spanning tree. Floyd’s Algorithm is applied to generate the distance and path matrices. Distance information is required to initialise the search algorithm.