A method for making handover decisions in a radio communications network

Abstract: The invention relates to a method for making handover decisions in a radio communication network comprising a number of fixed base stations and a number of mobile units. The method utilises an artificial neural network which is an image of the real network of a respective base station and which exhibit a behaviour pattern learnt through the acquisition of information from the network. Thereafter, simulation is carried out in the neural network through the generation of a list of eligible base stations to which handover can be effected, every one of the eligible stations being given points. Thereafter, a decision is made whether, or not, a handover will be effected by the network.

Description: The invention relates to a method for making handover decisions in a radio communications network comprising radio base stations and mobile units. Such a method is disclosed in GB-A-2 242 805. In accordance with the method of the present invention, decisions will be made concerning whether, and when, the handover of a mobile unit from one base station to another base station unit will occur. It is important that such decisions are correct because a wrong decision can result in communication being interrupted. Furthermore, wrong decisions concerning handover could also have the result that other mobile units are deprived of the possibility to communicate. Neural networks are generally described in US Patent Nos. 4 796 199, 4 918 617 and 4 983 962. These patents are concerned with the organisation of a proposed architectural structure and provide a good source of information in relation to neural networks. In the following text, this information concerning neural networks will be utilised in connection with mobile telephony. In mobile radio systems, communication is established between a mobile unit and a fixed unit, i.e. a base station. In practice, a mobile unit makes contact with a base station which is the most suitable for the purpose of establishing effective communication. The selection of a base station can be preprogrammed in relation to the geographic position of the mobile unit with respect to the base stations. Another possibility is for measurement to be made at the terminal, or at the base station. The parameters which will be considered are, for example, signal strength, distance and so forth. When a mobile unit has to change base station, it will normally be done in accordance with one of the above-mentioned principles. In the case where a wrong decision is made in respect of such a change, i.e. handover from one base station to another base station, an inferior transmission channel will be obtained, or alternatively, the communication will be interrupted. Any lack of handover can result in other connections being broken due to interference. It is thus of great significance that decisions concerning handover are made with the correct assumptions. It is an object of the present invention to provide a method for making handover decisions in a radio communication network at the correct time. To this end, an artificial neural network has been created. The artificial neural network represents a model of the real radio communication network. The present invention provides a method for making handover decisions in radio communication network which comprises a number of fixed base stations and a number of mobile units, and in which communication between a mobile unit and a first radio base station is discontinued to enable the communication to be handed over to a second radio base station, characterised in that, the method includes the steps of causing an artificial neural network which is an image of the real network of a respective base station, to exhibit a behaviour pattern learnt through the acquisition of information from the network, the information acquired by the artificial neural network being correlated to the position of the mobile unit relative to the fixed base stations in the radio communication network with which the mobile unit can communicate; providing a list of fixed base stations with which the mobile unit can communicate, the artificial neural network determining the degree of suitability for communication with a respective fixed base station; providing information for the radio communication network concerning the most suitable fixed base station for communicating with the mobile unit; and making a decision concerning the connection of the mobile unit to a selected fixed base station. The term "handover" means that a mobile unit in a radio communication network changes from communication with one radio base station of the network to another radio base station of the network which is better placed from the point of view of communication. According to one aspect of the present invention, the neural network is allocated a number of layers comprising an input layer, an output layer and a number of intermediate layers, the input layer being allocated a number of nodes representing selected measurement data units, and the output layer being allocated a number of nodes representing a fixed base station to which the mobile unit is presently communicating and the fixed stations to which handover is possible, the nodes in the intermediate layers being utilised for simulation. According to another aspect of the present invention, each node of the neural network is a neuron, and weights input data from all neurons in the preceding layer, all weighted signals being added together and thereafter subjected to transformation. According to a further aspect of the present invention, the neural network is allocated training data, which is normalised, in that the training data is selected to be as representative as possible of network behaviour, in that training data is allocated for the entire reception area to be served by the fixed base station and for the adjoining areas, and in that a desired structure for the neural network is determined for each set of training data. During the training process, weights are randomly selected whereafter the neural network operates with received data. The result at the output nodes of the neural network is compared with a desired result, the difference between the desired result and the received result constituting an error relating to the selection of the weights. New weights are calculated and the process is repeated until the result at the output nodes of the neural network converges towards a desired value. The training process is terminated when the difference between the desired and received results falls below a predetermined level. According to a further aspect of the present invention, input data, in vector form, is transferred to a locating block for the mobile unit, and the locating block transfers the said data to a decision block, which produces a vector with a number that relates to the suitability for handover to another fixed base station. A handover decision is based on historical information and whether handover to the said another fixed station would result in improved communication, by a certain measure, in relation to the suitability number of the fixed base station with which the mobile unit is presently communicating. The present invention also provides a radio communication network including a plurality of fixed base stations, and a plurality of mobile units, in which each one of said base station is connected to a central telecommunications exchange and adapted to communicate with, and receive information from, a number of said mobile units, information received from a mobile unit including, inter alia, its position and received signal strength, and in which said communication network is adapted to discontinue a communication between a mobile unit and a first radio base station to enable the communication to be handed over to a second base station, characterised in that said communication network includes an artificial neural network for each of said base stations, each artificial neural network being an image of a real network of a respective base station, and adapted to exhibit a behaviour pattern learnt through acquisition of information from said communication network; correlation means for correlating the information acquired by an artificial neural network to the position of a mobile unit relative to the base stations in said communication network with which the mobile unit can communicate; listing means for providing a list of fixed base stations with which the mobile unit can communicate, the artificial neural network determining the location information for a mobile unit relative to the fixed base stations; information means for providing information for said communication network concerning the most suitable fixed base station for communicating with the mobile unit; and decision means for making a decision concerning the connection of the mobile unit to a selected fixed base station. The central telecommunications exchange includes a locating method using artifical neural network (LMANN) function for each of said fixed base stations, and each of said artificial neural networks (ANN) forms part of a LMANN function of a respective fixed base station. According to one aspect of the radio communication network of the present invention, an artificial neural network (ANN) is allocated a number of layers comprising an input layer, an output layer and a number of intermediate layers. The input layer is allocated a number of nodes representing selected measurement data units, the output layer is allocated a number of nodes representing a fixed base station to which the mobile unit is presently communicating and the fixed base stations to which handover is possible, and the nodes in the intermediate layers are utilised for simulation. According to another aspect of a radio communication network of the present invention, each node of the neural network (ANN) is a neuron and is adapted to weight input data from all neurons in the preceding layer, all weighted signals being added together and thereafter subjected to transformation. According to a further aspect of the radio communication network of the present invention, the communication network includes training means adapted to allocate training data to an artificial neural network (ANN), said training data being normalised, and selected to be as representative as possible of network behaviour. The training means are also adapted to allocate said training data for the entire reception area to be served by a fixed base station and for the adjoining areas. A desired structure for the neural network is determined for each set of training data. During a training process, to the training means are adapted to randomly select weights, whereafter the neural network is adapted to operate with received data, the results at the output nodes of the neural network being compared with a desired result, a difference between a desired result and the received result constituting an error relating to the selection of the weights. In this situation, the training means are adapted to calculate new weights and the training process is repeated until the result at the output nodes of the neural network converges towards a desired value, the training means being adapted to terminate the training process when the difference between the desired and received results falls below a predetermined level. According to a further aspect of a radio communication network of the present invention, the communication network includes a locating block to which measured input data, in vector form, for a mobile unit, is transferred; and a decision block to which said measured input data is transferred from said locating block. The decision block is adapted to produce a vector with a number that relates to suitability for handover to another fixed base station, a handover decision being based on historical information, and whether handover to the said another fixed station would result in improved communication, by a certain measure, in relation to a suitability number of the fixed base station with which the mobile unit is presently communicating. Thus, the present invention solves handover problems with the aid of neural networks, which are characterised by being able to learn certain behaviour through the acquisition of information. The neural network is an image of the real radio communication network and every one of the radio base stations of the network obtain access to a neural network that reflects the network of the base station concerned. A mobile unit identifies its geographic position and communicates this information to the radio base station which thereafter utilises the neural network for deciding whether, or not, there should be a change of radio base station, i.e. handover. The foregoing and other features according to the present invention will be better understood from the following description with reference to the accompanying drawings, in which: Figure 1 illustrates, in a pictorial view, a radio communication network including mobile radio units and a number of base stations connected to a central exchange. Figure 2 shows, in the form of a block diagram, the manner in which measurement data in the mobile radio system is transferred to a locating block and thereafter to a decision block which forwards a decision regarding handover to the mobile radio system, Figure 3 shows, in the form of a block diagram, the manner in which the measurement data is introduced into a normalisation block; the normalised data being transferred to an ANN (Artificial Neural Network), the output of which is the locating information for a mobile radio unit, Figure 4 illustrates the structure of an artificial neural network, Figure 5 illustrates the manner in which input data from all nodes (neurons) in a layer of the neural network are weighted and added together and thereafter transformed, and Figure 6 illustrates the measurement area for a radio base station, and the adjacent base stations, of a radio communication network. The radio communication network illustrated in Figure 1 of the drawings includes a number of base stations 1, each one of which is connected to a central exchange and adapted to communicate with a number of mobile units. The central exchange includes a number of LMANN functions, one for each base station. The LMANNs contain, inter alia, the artificial neural networks hereinbefore referred to. As illustrated, in Figure 1 of the drawings, in a pictorial view, the mobile radio units communicate with a respective one of the radio base stations 1 over the airwaves. The radio base station 1 continuously receives information from a mobile radio unit about its position, received signal strength, and so forth. As shown in Figure 2 of the drawings, this information, i.e. the measurement data from the mobile radio system, is fed into a locating block, associated with the radio base station 1, for locating the mobile radio unit from which the information is received. The information received by the locating block is then processed, consideration being given to the geographic position and the direction of movement of the mobile radio unit concerned. This processing results in the establishment of a list of radio base stations 1 which are eligible for communication with the mobile radio unit. This list is a priority list, in that, it specifies an order of priority relating to the suitability of the eligible radio base stations for communicating with the mobile radio unit. As shown in Figure 2 of the drawings, the priority list is then transferred to a decision block which decides, and provides an output indicative of, whether, or not, the mobile radio unit should change radio base stations. The decision is then forwarded to the mobile radio system. In order to obtain the best possible decision with such an arrangement, it is necessary, as shown in Figure 3 of the drawings, to create an artificial neural network (ANN) which forms part of the locating block, and the output of which is the locating information for a mobile unit, i.e. the information which is fed to the decision block of Figure 2. With this arrangement, the measurement data, applied to the locating block, is fed to a normalisation block and the normalised data at the output of this block is transferred to the artificial neural network. The output of the artificial neural network is the locating information for a mobile radio unit and is applied to the input of the decision block. As stated above, the artificial neural network (ANN) is an image of the actual radio network of the respective radio base station. The artificial neural network is given information regarding the limits of its own coverage area and the overlap with adjoining base stations. In order to obtain the best possible decisions, the artificial neural network will have to learn a desired behaviour depending on the position and direction of movement of the mobile radio units. As illustrated in Figure 4 of the drawings, the artificial neural network is structured in a number of layers, i.e. an input layer, a number of intermediate layers and an output layer. The input layer has a node, shown as a circle, for each selected measurement unit. As illustrated in Figure 4, every one of the nodes in the input layer transfers its information to all nodes, shown as circles, in the first intermediate layer. Respective nodes in the first intermediate layer transfer their information to the nodes of the next intermediate layer and so forth. Only the first and last intermediate layers are illustrated in Figure 4. The number of intermediate layers in the neural network is selected freely which is why one or a number of intermediate layers can be utilised. Finally, every one of the nodes in the last intermediate layer transfers its information to all nodes in the output layer which represents possible candidates for handover. This includes the base station to which the mobile radio unit is currently connected. The structure of the artificial neural network varies with measurement data which is why different radio environments result in different optimum structures. Each node of the neural network is a neuron which weights input data from all neurons in the previous layer. Figure 5 of the drawings illustrates the manner in which input data from each neuron is weighted and added together, and thereafter subjected to a transformation. The transformation is carried out in all layers of the neural network except the input layer which only contains one input per neuron. In order to obtain a desired behaviour, a nonlinear transfer function is selected. In order to obtain correct decisions regarding handover, it is necessary for the neural network to be taught certain behaviour through the acquisition of information. This is effected by supplying training data to the system. The training data needs to be provided for the entire reception area to be handled by the base station, and the adjoining areas. Figure 6 of the drawings illustrates the measurement area for a base station. The solid lines show the desired reception area for a respective base station 1. The dotted lines represent measurement points for training data and the dashed areas illustrate the coverage area of the base station 1. The adjoining base stations 2 and 3 are also illustrated, as is the areas of overlap between the three base stations. The construction of the artificial neural network is determined for each set of training data. Normalisation (see Figure 3) is carried out for a value between 0 and 1 with the aid of a maximum value for each data item in the complete set of training data. During the learning process, a mobile radio unit travels within the coverage area of the base station and the adjoining areas. In the mobile radio unit, measurements are made with respect to the relevant parameters referred to above. The mobile unit sends the results to the radio base station which further processes the information. During the training phase, different geographic positions are defined which can also be defined for other mobile radio units. Also, during the learning process, weights, referred to above in relation to Figure 5 of the drawings, are first randomly selected whereafter the network works with training data. For each set of training data, the result at the output nodes of the artificial neural network is checked against a desired result. The difference between the desired result and the result obtained with the training data gives rise to an error. Calculation of weight is carried out, for example, by the optional gradient method and the error is spread back through the network. By changing the different weights, the result converges towards a desired result. When the error drops below a predetermined level, the neural network is considered to be trained and learning is terminated. As stated above, the measurement data from the mobile radio system is fed to the locating block wherein the geographic position of a mobile radio unit can be unambiguously determined by the artificial neural network, when network training has been completed. The information regarding the position of a mobile radio unit is then transferred by the locating block to the decision block. The decision block produces a vector with numbers which relate to the suitability for handover to a respective fixed radio base station. The number varies within limits which depend on the selected transfer function. A decision is made with respect to history and hysteresis. 'History' means that the decision block stores a number of the latest locating vectors. A demand is made that a certain number of these locating vectors should show the same result for handover to occur. A short history provides a fast decision, but a long history provides a more reliable decision at the cost of speed. Thus, an appropriate balance between these extreme cases must be carried out in the individual cases where different aspects get balanced against one another. "Hysteresis", referred to above, means that a candidate for handover would result in improved communication, by a certain measure, in relation to the suitability number of the existing base station. For handover to be possible, the hysteresis value must lie within the transfer function. Hysteresis relates the handover decision to the suitability number for the present base station and provides the same result in the handover function as the history.

Claims: A method for making handover decisions in a radio communication network which comprises a number of fixed base stations (1) and a number of mobile units, and in which communication between a mobile unit and a first radio base station is discontinued to enable the communication to be handed over to a second radio base station, characterised in that, the method includes the steps of causing an artificial neural network (ANN) which is an image of the real network of a respective base station, to exhibit a behaviour pattern learnt through the acquisition of information from the network, the information acquired by the artificial neural network (ANN) being correlated to the position of the mobile unit relative to the fixed base stations (1) in the radio communication network with which the mobile unit can communicate; providing a list of fixed base stations with which the mobile unit can communicate, the artificial neural network (ANN) determining the degree of suitability for communication with a respective fixed base station (1); providing information for the radio communication network concerning the most suitable fixed base station (1) for communicating with the mobile unit; and making a decision concerning the connection of the mobile unit to a selected fixed base station (1). A method as claimed in claim 1, characterised in that the neural network is allocated a number of layers comprising an input layer, an output layer and a number of intermediate layers, in that the input layer is allocated a number of nodes representing selected measurement data units, in that the output layer is allocated a number of nodes representing a fixed base station to which the mobile is presently communicating and the fixed stations to which handover is possible, and in that nodes in the intermediate layers are utilised for simulation. A method as claimed in claim 2, characterised in that each node of the network is a neuron, in that each node weights input data from all neurons in the preceding layer, and in that all weighted signals are added together and are thereafter subjected to transformation. A method as claimed in any one of the preceding claims, characterised in that the neural network is allocated training data, which is normalised, in that the training data is selected to be as representative as possible of network behaviour, in that training data is allocated for the entire reception area to be served by the fixed base station and for the adjoining areas, and in that a desired structure for the neural network is determined for each set of training data. A method as claimed in claim 4, characterised in that, during the training process, weights are randomly selected whereafter the neural network operates with received data, in that the result at the output nodes of the neural network is compared with a desired result, the difference between the desired result and the received result constituting an error relating to the selection of the weights, in that new weights are calculated and the process is repeated until the result at the output nodes of the neural network converges towards a desired value, and in that the training process is terminated when the difference between the desired and received results falls below a predetermined level. A method as claimed in any one of the preceding claims, characterised in that input data in vector form is transferred to a locating block for the mobile unit, in that the locating block transfers the said data to a decision block, in that the decision block produces a vector with a number that relates to the suitability for handover to another fixed base station, and in that a handover decision is based on historical information and whether handover to the said another fixed station would result in improved communication, by a certain measure, in relation to the suitability number of the fixed base station with which the mobile unit is presently communicating. A radio communication network including a plurality of fixed base stations (1), and a plurality of mobile units, in which each one of said base stations (1) is connected to a central telecommunications exchange and adapted to communicate with, and receive information from, a number of said mobile units, information received from a mobile unit including, inter alia, its position and received signal strength, and in which said communication network is adapted to discontinue a communication between a mobile unit and a first radio base station (1) to enable the communication to be handed over to a second base station (1), characterised in that said communication network includes: an artificial neural network (ANN) for each of said base stations (1), each artificial neural network (ANN) being an image of a real network of a respective base station (1), and adapted to exhibit a behaviour pattern learnt through acquisition of information from said communication network; correlation means for correlating the information acquired by an artificial neural network (ANN) to the position of a mobile unit relative to the base stations (1) in said communication network with which the mobile unit can communicate; listing means for providing a list of fixed base stations (1) ) with which the mobile unit can communicate, the artificial neural network (ANN) determining the location information for a mobile unit relative to the fixed base stations (1); information means for providing information for said communication network concerning the most suitable fixed base station (1) for communicating with the mobile unit; and decision means for making a decision concerning the connection of the mobile unit to a selected fixed base station (1). A radio communication network as claimed in claim 7, characterised in that said central telecommunications exchange includes a locating method using artifical neural network (LMANN) function for each of said fixed base stations (1), and in that each of said artificial neural networks (ANN) forms part of a LMANN function of a respective fixed base station (1). A radio communication network as claimed in claim 7, or claim 8, characterised in that an artificial neural network (ANN) is allocated a number of layers comprising an input layer, an output layer and a number of intermediate layers, in that the input layer is allocated a number of nodes representing selected measurement data units, in that the output layer is allocated a number of nodes representing a fixed base station (1) to which the mobile unit is presently communicating and the fixed base stations (1) ) to which handover is possible, and in that nodes in the intermediate layers are utilised for simulation. A radio communication network as claimed in claim 9, characterised in that each node of the neural network (ANN) is a neuron, in that each node is adapted to weight input data from all neurons in the preceding layer, all weighted signals being added together and thereafter subjected to transformation. A radio communication network as claimed in any one of claims 7 to 10, characterised in that said communication network includes training means adapted to allocate training data to an artificial neural network (ANN), said training data being normalised, and selected to be as representative as possible of network behaviour, in that said training means are adapted to allocate said training data for the entire reception area to be served by a fixed base station (1) ) and for the adjoining areas, and in that a desired structure for the neural network (ANN) is determined for each set of training data. A radio communication network as claimed in claim 11, characterised in that said training means are adapted, during a training process, to randomly select weights, whereafter the neural network (ANN) is adapted to operate with received data, the results at the output nodes of the neural network (ANN) being compared with a desired result, the difference between the desired result and the received result constituting an error relating to the selection of the weights, in that said training means are adapted to calculate new weights and said training process is repeated until the result at the output nodes of the neural network (ANN) converges towards a desired value, and in that said training means are adapted to terminate said training process when a difference between a desired and received results falls below a predetermined level. A radio communication network as claimed in any one of claims 7 to 12, characterised in that said communication network includes: a locating block to which measured input data, in vector form, for a mobile unit, is transferred; and a decision block to which said measured input data is transferred from said locating block; and in that said decision block is adapted to produce a vector with a number that relates to suitability for handover to another fixed base station (1), a handover decision being based on: historical information; and whether handover to the said another fixed station (1) would result in improved communication, by a certain measure, in relation to a suitability number of the fixed base station (1) ) with which the mobile unit is presently communicating.

Inventors: KAELLMAN ROBERT, KAELLMAN, ROBERT, Källman, Robert
Applicants: TELIA AB
Application year: 1993
Main classification: H04Q7
IPC codes: H04W36