DE102015106715B3 - Method for connecting and / or disconnecting switchable network elements in a mobile radio network and selbiges - Google Patents

Abstract

The invention relates to a method for connecting and / or disconnecting switchable network elements in a mobile radio network and a mobile radio network using this method. The object of the invention to determine a selection of active switchable network elements based on spatial-temporal characteristics of the data transfer requests is achieved by: determining a spatial and temporal subscriber density, determining a spatial and a temporal network element density of the active switchable network elements, - A relation between the subscriber density and the network element density is formed, from which an optimal number of active disconnectable network elements in the mobile network is calculated and accordingly switched off network elements spatially and temporally switched on and / or off.

Description

The invention relates to a method for connecting and / or disconnecting network elements in a mobile radio network as well as a mobile radio network using this method.

Switchable network elements, wherein among network elements e.g. Micro, pico or femto base stations, as well as relay base stations, antennas, antenna systems, elements or groups of elements, antennas or antenna systems can be understood in low load situations, e.g. at night, when few participants use the radio resources, in a standby state or optionally completely switched off. The various states of the network elements, e.g. "Active", "reduced functionality", "standby" and / or "disabled" depend on the type and hardware of the network elements present. By setting different states, both energy can be saved and the entire network can be operated more efficiently. In general, macro base stations are understood to mean ordinary base stations with a large coverage area, which have a very high transmission power in comparison with micro, pico, femto base stations. In the future, additionally distributed antennas and antenna systems will be used which can not necessarily be counted as a base station or as a base station.

Furthermore, a distinction is therefore made between disconnectable network elements and non-disconnectable network elements. Disconnectable network elements typically add capacity to the network, i. add to the mobile network. In addition, non-disconnectable network elements are also used to signal mobile stations and to cover, e.g. nationwide connectivity, serve. Consequently, non-disconnectable network elements are understood to mean all other network elements that are not to be switched off due to the necessary network coverage and connectivity for mobile subscribers. Such network elements are typically macro base stations or base stations belonging to a signaling network.

It is therefore desirable to determine a selection of already installed network elements that are to be switched on or off depending on the spatially-temporally dynamic data demand and can. This is also of great importance, since future mobile radio networks of the fifth generation (5G) are characterized, inter alia, by a very high number or density of network elements and thus switching on and off is a very complex combinatorial problem.

Heretofore, in the current state of the art, three approaches to the problem are distinguished. First, the optimization potential is characterized by a different number or different densities of base stations. For this purpose, spatially homogeneously distributed users and base stations are often adopted, whereby the assumptions are based on approaches of stochastic geometry.

Or only the temporal but not the spatial dynamics of user data demand are considered. As a rule, these approaches do not include any algorithms for the dynamic de-activation or activation of base stations.

Secondly, there are approaches on how the mobile radio network or individual base stations by means of established protocols even decide on the de- or activation of base stations, such as in the US 2013/0 130 670 A1 , However, revelations about mechanisms and / or algorithms that describe the actual decision process about the de-activation or activation, rarely or not at all to find.

In a third approach, heuristic algorithms or methods are used to make decisions regarding the states (on / off) of base stations. These are often based on exceeding / falling below thresholds, e.g. in terms of resource utilization or base station load. However, it can be assumed that such simple solutions can not lead to optimal results in spatially and temporally highly dynamic traffic demands, especially since it is often unclear how to determine or predict resource utilization after switching on and off of network elements.

Until now, the robustness of such systems has rarely been addressed in relation to the spatial and temporal randomness of data transfer requests. The necessary geolocation of mobile subscribers to decide which network element to turn on or off is usually also subject to random errors, which is rarely considered and disclosed. Geolocation is understood to be the spatial location of the mobile subscribers, with a mobile subscriber representing an autonomously functioning unit which can receive, transmit and transmit data, for example machines, cars and other devices that fulfill these requirements.

For example, in "Sleep Mode Techniques for Small Cell Deployments," IEEE Communications Magazine pp. 72-79, August 2011 describes general strategies for triggering base station activation. either base station-driven, network-driven or user-driven. The decision should be based on the traffic load, eg in US 2013/0 281 049 A1 , However, algorithms that implement this principle are not disclosed.

In "Cluster Analysis Based Switching-off Scheme of Base Stations for Energy Saving" in Journal of Networks, Vol. 3, March 2012 discloses a procedure in which the spatial traffic is classified in terms of its variability and other characteristics. Optimal on and off times for base stations are determined for different selected classes, provided that the traffic behavior follows a typical daily profile. Therefore, it is not possible to address highly dynamic and unscheduled changes in traffic volume. Spatial and temporal components of traffic are only considered separately.

Classic examples of heuristic decision making are based on predefined thresholds for a mean cell load. The cell load is understood to mean the utilization of a radio cell with regard to the amount of data and user requests in a mobile radio network. Such heuristic approaches are described, for example, in "Cell Size Breathing and Possibilities to Introduce Cell Sleep Mode", 2010 European Wireless Conference; "Dynamic Base Station Switching-On / Off Strategies for Green Cellular Networks", IEEE Transaction on Wireless Communications, Vol. 12, No.5 May, 2013 and in US 2013/0 137 446 A1 applied. In the process, base stations are switched off successively, either directly, based on the cell load or based on a quality criterion derived from it. The influence on the network after shutdown is considered here. However, there are no criteria characterizing the robustness of the system to the randomness of traffic, eg, time-varying data requests.

S. Stefanatos and A. Alexiou, "Access Point Density and Bandwidth Partitioning in Ultra-Dense Wireless Networks," Jul. 2013, discloses how base station performance and network densities can best be determined in terms of energy consumption, taking into account characteristics of static but non-dynamic traffic hotspots become. A traffic hotspot refers to spatially limited regions with increased traffic volumes.

CN 103607717 A discloses a system architecture that makes it possible to configure a cellular network energy efficient and with a high data throughput based on a currently prevailing number of mobile users as well as their respective geolocation. Cell sizes within the network are varied and base stations switched off. The decision to switch off base stations is based on exceeding a threshold value. Historical data or trends in spatially varying traffic are not taken directly into account, which may reduce the robustness of the system to inaccuracies in geolocalization and the randomness of traffic. If the robustness of the system is too low, mobile users may or may not be suffering from low data rates.

In EP 2 787 777 A1 discloses a method that serves to avoid conflicts when switching off base stations. In low-load situations, ie when a radio cell is underutilized, several base stations can simultaneously decide when a traffic load threshold is exceeded / undershot, to switch to a standby mode. The method disclosed in EP 2 787 777 A1 and the protocol described prevent the remaining active base stations from being overloaded. The decision on the connection and disconnection is based on thresholds and information, which are determined by the mobile stations.

On the basis of the aforementioned disadvantages of the prior art, it is an object of the present invention to increase the spatiotemporal network element density, e.g. set the number of active base stations per unit area, in an exact relation to the subscriber density and data transfer demand and to determine a selection of active switchable network elements based on the network element density. This is intended in particular to reduce the energy consumption of the entire network while at the same time reducing the number of on / off operations. Another important aspect is the performance of the network. From the point of view of the participants, this should at least remain the same, if not even higher.

A further object of the invention is to be able to take into account the spatial and temporal dynamics of the data transfer demand, while also establishing and increasing the robustness of the system with respect to unforeseen and suddenly occurring changes in the data transfer demand. The terms data transfer request and data transfer request are used synonymously.

• – eine räumlich und zeitlich abhängige Teilnehmerdichte ermittelt wird, d.h. die (zu erwartende) Dichte von gleichzeitig aktiven Mobilfunkteilnehmern oder Datentransfervorgängen,
• – eine räumlich und eine zeitlich abhängige Netzelementdichte der aktiven abschaltbaren Netzelemente ermittelt wird,
• – eine Relation zwischen der Teilnehmerdichte und der Netzelementdichte gebildet wird, wobei daraus eine optimale Anzahl der aktiven abschaltbaren Netzelemente in dem Mobilfunknetzwerk berechnet wird und dementsprechend abschaltbare Netzelemente räumlich und zeitlich zugeschaltet und/oder abgeschaltet werden, wobei die räumlich und zeitlich optimale Anzahl der aktiven abschaltbaren Netzelemente in dem Mobilfunknetzwerk aus einem System aus partiellen Differentialgleichungen berechnet wird.

The object of the present invention is achieved by means of a method for switching on and / or switching off disconnectable network elements in a mobile network comprising at least one network element by

• A spatially and temporally dependent subscriber density is determined, ie the (expected) density of simultaneously active mobile radio subscribers or data transfer processes,
• A spatially and temporally dependent network element density of the active switchable network elements is determined,
• A relation between the subscriber density and the network element density is formed, from which an optimal number of active disconnectable network elements in the mobile radio network is calculated and accordingly switchable network elements spatially and temporally switched on and / or off, the spatially and temporally optimal number of active turn-off Network elements in the mobile network is calculated from a system of partial differential equations.

An inventive form of the system of partial differential equations is described below.

With the relation according to the invention between the determined subscriber density and the determined network element density, both the spatial and the temporal changes of disconnectable network elements to be connected and disconnected can be determined simultaneously. The subscriber density and the network element density are interdependent, i. The network element density is increased via the named relation as soon as the subscriber density increases. As a result, the subscriber density decreases again because data transfer processes can be processed faster due to the additionally introduced capacity. This can be more responsive to changes in a mobile network than in the prior art, since both spatial and temporal changes are recorded and taken into account above all. A subscriber is an autonomous entity that can receive, send and transmit data, such as phones, such as smartphones, but also machines, cars, and other devices that meet those requirements.

In one embodiment of the inventive method, the subscriber density is calculated as a function of a location u and a time t within the mobile radio network from an arrival rate of data transfer requests, a reduction rate of data transfer operations, a subscriber mobility rate and a settlement rate of data transfer operations. The arrival rate is a spatially-temporally variable that indicates how many data transfer requests per time and area units take place in the mobile network. The subscriber density is increased with the arrival rate. The rate of reduction is a spatially-temporally variable which depends on the prevailing subscriber density and indicates the rate at which active data transfers from subscribers are aborted or blocked by a network element (e.g., in the event of an overload). It is specified in participants or data transfer processes per time and area unit. The settlement rate is a spatially-temporally variable variable that indicates how many participants or data transfer processes are successfully processed by network elements. It is specified in participants or data transfer processes per time and area unit.

The data transfer requests, the reduction rate of data transfer operations, the subscriber mobility rate and the handling rate of data transfer operations are determined by a base station or in a central processing unit (unit short) of a mobile radio network. Thus, the spatial and temporal development of changing parameters within the mobile network for the connection and disconnection of network elements can be used.

Furthermore, in the method according to the invention, the processing rate of data transfer processes is determined as a function of the subscriber density, the network element density and a capacity of the individual network elements. The capacity of the individual network elements indicates how many subscribers can be processed / operated by one network element of the mobile radio network per time unit.

In a further embodiment of the invention, the connection and / or the switching off of adjacent network elements by means of a spatially-temporally variable diffusion term and a spatially-temporally variable advection term is taken into account in the determination of the network element density. In this case, the mobility of the participants per network element of the mobile radio network is described by the assumption of a random-walk model (see also Brownian motion) with the diffusion term, which includes, inter alia, a diffusion coefficient. The random walk is a model to describe a random, non-directional movement of a participant (as opposed to a directed movement through the advection term). A possible realization of a radom walk for a participant could provide the following sequence: 1. Random selection of a direction (evenly distributed), 2. Random selection of a distance, 3. The participant moves in the direction of 1. and to a point in Distance to 2. at a certain speed and then in a 4th step back to the starting point. The diffusion coefficient can be determined from the average speed of mobile subscribers in the mobile network at different positions u and at different times t. However, the diffusion coefficient can also be replaced by a scalar constant, with the spatial dependence of average participant speed is neglected. The advection term describes a directional mobility of subscribers in the mobile radio network, with the possibility of describing specific speeds and directions for a "subscriber flow" on roads, tracks or in other urban environments by means of a vector field. By describing the movement and mobility of the mobile subscribers by means of diffusion terms and advection terms, the robustness and reliability of the mobile radio network can be increased.

In a particularly advantageous embodiment of the method according to the invention, the spatial and temporal network element density are determined simultaneously. Information about the data transfer requests is collected as a function of a location u and the time t via network elements which serve exclusively for signaling but not for the actual data transmission and which cover the entire area of a mobile radio network to be viewed. From these data, the spatial-temporal network element density is determined, either directly in these aforementioned network elements or in a central processing unit of the mobile network. Based on these values, it is then decided which network elements are to be switched on or off.

In the method according to the invention, the spatially and temporally optimal number of active switchable network elements in the mobile radio network is calculated from a system of partial differential equations. One possible version of the partial system of differential equations is the following, where u describes a spatial coordinate and t the temporal dimension: ∂T (u, t) / ∂t = α _IT I (u, t) (T ^ (u, t) - T (u, t)) + ∇ (N _T (u, t) ∇T (u, t)) + V → _T (u, t) ∇T (u, t) + m (T ^ (u, t) - T (u, t)) (T ‿ (u, t) - T (u, t)) - s _RT T (u, t) R (u, t) (1) ∂I (u, t) / ∂t = -μ (I (u, t), T (u, t); μ ₀ ) - d _I (u, t) + ∇ (N _I (u, t) ∇ I (u, t)) + V → _I (u, t) ∇I (u, t) + Λ (u, t) (2) ∂R (u, t) / ∂t = α _TR T (u, t) - d _R (u, t) (3)

In this case, T (u, t), I (u, t) and R (u, t) denote the location-dependent and time-dependent densities of network elements, subscribers and regulatory signals. ∇ (·) is the Nabla operator. All the terms on the right-hand side of the equations describe rates, or velocities, as the densities change spatially and temporally. For example, the rate of change of the network element density is proportional to the subscriber density, ie the higher the subscriber density, the faster the network element density increases. The network element density T (u, t) is limited (here by T ‿ (u, t) and T ^ (u, t) downwards and upwards, which by the terms α _IT I (u, t) (T ^ ( u, t) - T (u, t)) and m (T ^ (u, t) - T (u, t)) (T ‿ (u, t) - T (u, t)). A diffusion term ∇ (N _T (u, t) ∇T (u, t)) and a advection term V → _T (u, t) ∇T (u, t) with diffusion coefficients N _T (u, t) and vector field V → _T (u, t) describe a nondirectional (diffusion term) and a directional flux (advection term) of the network element density, which can cause network elements to trigger the deactivation or activation of adjacent network elements, thus allowing network elements to exchange information through it N _T (u, t) and V → _T (u, t) can optionally also represent position- and time-independent constants The term -s _RT T (u, t) R (u, t) describes the rate which is used to switch off network elements, which is both proportional to the network element density and to r density of regulatory signals. The latter is described by the third differential equation. Their rate increases with the network element density and decreases, if and only if α _TR T (u, t) -d _R R (u, t) <0.

The spatial-temporal evolution of the participant density I (u, t) is described by four terms: Λ (u, t) describes the arrival rate of participants or data transfer requests, which is determined by base stations, for example -d _I I (u, t) represents a reduction of active participants or data transfer requests, for example by aborting downloads due to high utilization of the network elements. The diffusion term ∇ (N _I (u, t) ∇I (u, t)) with diffusion coefficient N _I (u, t) describes the mobility of participants by assuming a random-walk model (see also Brownian motion) , N _I (u, t) can be determined from the average speed of mobile subscribers at different positions u and at different times t. N _I (u, t) can optionally be replaced by a scalar constant. In addition, a advection term V → _I (u, t) ∇I (u, t) can describe a directed mobility of the subscribers, wherein the vector field V → _I (u, t) offers the possibility of specifying specific speeds and directions for a "subscriber stream". on roads, railways or in other urban environments. Finally, the negative rate -μ (I (u, t), T (u, t); μ ₀ ) describes how fast data transfer operations (downloads) are handled. This is generally dependent on the subscriber density, the network element density and the capacity of the individual network elements.

A possible definition of this rate is the following: μ (I (u, t) T (u, t); μ ₀ ) = μ ₀ T (u, t) I (u, t) / I (u, t) + q

The rate is proportional to the network element density, μ ₀ describes the average rate with the one Network element can serve mobile subscribers. q is a constant q << 1. The term μ (I (u, t), T (u, t); μ ₀ ) can also be adapted to existing or future mobile technologies.

It is conceivable that the diffusion and advection terms may be omitted to make the algorithm more easily implementable. The system is reduced to a system of ordinary differential equations.

In a further advantageous embodiment of the method, regulatory signals, in particular data streams in the background network (networking of the network elements among one another, including the central arithmetic unit if present) for the purpose of the calculation of the reduction of the network element density, are recorded and used for the calculation.

The regulatory signals serve to trigger the deactivation of network elements. In general, active participants trigger the activation of network elements. Active network elements, described in the model by the network element density, can trigger the activation of adjacent network elements. The set network element density then determines how quickly or effectively mobile radio subscribers can be served, since in general radio resources of individual network elements are distributed via mobile radio subscribers, the so-called scheduling. In addition, information from the past is used for the current optimization due to the nature of the differential equations.

In one embodiment of the method, the network element density is calculated for individual, spatially delimited regions in the mobile radio network, the spatially delimited region coinciding with the base station covering a cell area in the mobile radio network. In the classical cellular concept, each mobile radio network consists of many mobile radio cells, in short called cells or radio cells. A radio cell identifies an area in which the signal transmitted by a network element in the form of a base station of a mobile radio network can be received and decoded without error. The size of a radio cell depends on meteorological and geographical conditions, construction height and type of the antennas used, the transmission power and the mobile radio standard used. For each individual cell of the mobile network, the network element density is determined to be in low load situations, when few participants use the mobile base stations to turn them into a standby state or optionally completely.

Furthermore, in one embodiment of the method, at least one non-disconnectable network element collects the spatial-temporal characteristics of the data transfer requests, and in the at least one non-disconnectable network element, the network element density is calculated from partial differential equations according to the system. Non-disconnectable network elements may be formed by macro base stations or a network of base stations dedicated solely to signaling but not to the actual data transmission. These base stations merely collect information about the data transfer requests, using different geolocation techniques.

In another embodiment of the method, the network element density is calculated according to the system from partial differential equations in a central processing unit.

Regardless of whether non-disconnectable network elements or a central processing unit makes the calculation of the network element density, the calculation result can be translated either at specific times or continuously in the amount of actually active network elements. The translation can be done, for example, by calculating network element densities in individual, spatially delimited regions. These regions can be, for example, the areas covered by the switchable network elements. Based on these values, the central unit or non-disconnectable network elements can then decide which of these network elements to turn on or off. The object of the present invention is on the arrangement side solved by means of a mobile network with at least one non-disconnectable network element and disconnectable network elements, wherein the at least one non-disconnectable network element as a central processing unit, as well as an entire surface to be considered overlapping and formed the disconnectable network elements by the central processing unit the operational readiness state are formed controllable. The central processing unit can be a non-disconnectable network element and serve exclusively for signaling in the mobile radio network, this network element covering the entire area to be covered, which is to be detected by the mobile radio network. The central processing unit can also be a central computing node, which is not involved in the signaling and the data transmission per se. The central processing unit collects information about the data transfer requests from the areas to be covered. Based on this, the network element density is calculated according to the specified partial differential equations and uses the claimed method for connecting and / or disconnecting the disconnectable network elements. Because of the calculated values, the central unit can decide which of these network elements to turn on or off.

The object of the present invention is also solved on the arrangement side by means of a mobile radio network with disconnectable network elements, the disconnectable network elements being designed to calculate a network element density in their respective environment. The switchable network elements calculate the network element density in their environment, e.g. for the surface covering them and, based on the calculated values, decide autonomously about their state, namely whether they turn on or off. It is also conceivable that the disconnectable network elements are in a standby state and thus retain the ability to geolocalize subscribers.

In a further embodiment, the individual switchable network elements are designed to communicate with one another via a background network. Via the background network, the information about geolocalized subscribers can be exchanged between the individual network elements so that the individual network elements can execute the algorithm of the described method.

When selecting the network elements to be added or removed, not only the current but also the overall development of the data transfer request can be taken into account. This can be done for example by derivation terms, delay the de- or activation by means of so-called memory terms. Mechanisms, such as directional advection or diffusion, are added to provide robustness to spatial geolocalization errors of mobile subscribers, as well as the general uncertainty of randomly occurring data transfer requests. This robustness is intended to ensure that the performance of the network, as perceived by subscribers, e.g. data throughput, latencies, mobility restrictions, etc., is not adversely affected. In addition, a diffusion term which describes the mobility of mobile subscribers, a so-called random walk, is also taken into account in the calculation of the subscriber density. This also increases the robustness of the algorithm by allowing mobile mobile subscribers to initiate the activation of neighboring network elements. Another advantage of the invention is that individually occurring mobile subscribers can not necessarily trigger the activation of network elements, provided that their data request isolated events with short-term increased base station load / resource utilization. Thus, increased network efficiency can be achieved. The system of partial differential equations can be implemented in different ways by e.g. Factors, diffusion coefficients, vector fields can be parametrized to control the robustness or effectiveness of reducing energy consumption. These parameters can also be optimized and adapted to prevailing conditions, such as different city characteristics or location-dependent subscriber mobility. For example, self-learning algorithms are conceivable.

The invention will be explained in more detail with reference to embodiments. Show

1 Block diagram for determining the network element density T as a function of a location u and a time t;

2 Block diagram for determining the subscriber density I as a function of a location u and a time t;

3 Block diagram for the determination of regulatory signals R as a function of a location u and a time t;

4 Block diagram of the optimized method for switching on and / or off of disconnectable network elements in a mobile radio network;

5 Schematic representation of the geolocation by a central processing unit or a signaling network;

6 Schematic representation of the de-activation or activation process by a central unit, the signaling network or the network of conventional macro base stations;

7 Schematic representation of the geolocation and determination of the input data by switchable network elements;

8th Schematic representation of the information exchange between disconnectable network elements via a background network;

9 Schematic representation of the activated and deactivated turn-off network elements after execution of the method according to the invention.

The 1 to 3 each show a block diagram for determining the network element density T, the subscriber density I, as well as for regulatory signals R respectively as a function of a location u and a time t.

For the determination of these data, special relationships between the network element and participant density were established, which are expressed by a system of partial differential equations let and with which the reality can be modeled.

One possible version of the partial system of differential equations is the following, where u describes a spatial coordinate and t the temporal dimension: ∂T (u, t) / ∂t = α _IT I (u, t) (T ^ (u, t) - T (u, t)) + ∇ (N _T (u, t) ∇T (u, t)) + V → _T (u, t) ∇T (u, t) + m (T ^ (u, t) - T (u, t)) (T ‿ (u, t) - T (u, t)) - s _RT T (u, t) R (u, t) (1) ∂I (u, t) / ∂t = -μ (I (u, t), T (u, t); μ ₀ ) - d _I (u, t) + ∇ (N _I (u, t) ∇ I (u, t)) + V → _I (u, t) ∇I (u, t) + Λ (u, t) (2) ∂R (u, t) / ∂t = α _TR T (u, t) - d _R (u, t) (3)

In this case, T (u, t), I (u, t) and R (u, t) denote the location-dependent and time-dependent densities of network elements, subscribers and regulatory signals. ∇ (·) is the Nabla operator. All the terms on the right-hand side of the equations describe rates, or velocities, as the densities change spatially and temporally. For example, the rate of change of the network element density is proportional to the subscriber density, ie, the higher the subscriber density, the faster the network element density increases. The network element density T (u, t) is limited here (by T ‿ (u, t) and T ^ (u, t) downwards and upwards, which is represented by the terms α _IT I (u, t) (T ^ ( u, t) - T (u, t)) and m (T ^ (u, t) - T (u, t)) (T ‿ (u, t) - T (u, t)). The diffusion term ∇ (N _T (u, t) ∇T (u, t)) and the advection term V → _T (u, t) ∇T (u, t) with diffusion coefficients N _T (u, t) and vector field V → _T (u, t) describe a nondirectional (diffusion term) and a directional flux (advection term) of the network element density, which can cause network elements to trigger the deactivation or activation of adjacent network elements, thus allowing network elements to exchange information through it N _T (u, t) and V → _T (u, t) can optionally also represent position- and time-independent constants The term -s _RT T (u, t) R (u, t) describes the rate which is used to switch off network elements, which is both proportional to the network element density and to r density of regulatory signals. The latter is described by the third differential equation. Their rate increases with the network element density and decreases, if and only if α _TR T (u, t) -d _R R (u, t) <0.

The spatial-temporal evolution of the participant density I (u, t) is described by four terms: Λ (u, t) describes the arrival rate of participants or is determined eg by network elements, -d _I I (u, t) represents one Reduction of the active participants or data transfer operations is, for example, by aborting downloads due to high utilization of the network elements. The diffusion term ∇ (N _I (u, t) ∇I (u, t)) with diffusion coefficient N _I (u, t) describes the mobility of participants by assuming a random-walk model (see also Brownian motion) , N _I (u, t) can be determined from the average speed of mobile subscribers at different positions u and at different times t. N _I (u, t) can optionally be replaced by a scalar constant. In addition, a advection term V → _I (u, t) ∇I (u, t) can describe a directed mobility of the subscribers, wherein the vector field V → _I (u, t) offers the possibility of specifying specific speeds and directions for a "subscriber stream". on roads, railways or in other urban environments. Finally, the negative rate -μ (I (u, t), T (u, t); μ ₀ ) describes how fast data transfer operations (downloads) are handled.

This is generally dependent on the subscriber density, the network element density and the capacity of the individual network elements.

A possible definition of this rate is as follows: -Μ (I (u, t) T (u, t); μ ₀ ) = μ ₀ T (u, t) I (u, t) / I (u, t) + q The rate is proportional to the network element density, μ ₀ describes the average rate at which a network element can serve mobile subscribers. q is a constant q << 1. The term μ (I (u, t), T (u, t); μ ₀ ) can also be adapted to existing or future mobile technologies.

It is conceivable that the diffusion and advection terms may be omitted to make the algorithm more easily implementable. The system is reduced to a system of ordinary differential equations.

The technical implementation and the translation of the network element density in a set of actually active network elements can be done in many ways. One possible variant is the following:
Non-disconnectable network elements that cover the entire area to be considered independently of disconnectable network elements can collect information about the data transfer requests, represented by Λ (u, t). For example, different geolocation methods can be used. As non-disconnectable network elements, for example, macro base stations or a network of base stations, which only serves the signaling but not the actual data transmission, can be used. These base stations in turn form a signaling network. In these base stations or in a central processing unit 6 based on that, the Calculation of the network element density according to the given partial differential equations. At certain times, or optionally continuously, the network element density can then be converted into a set of actually active network elements 9 to be translated. The translation can be done, for example, by calculating network element densities in individual, spatially delineated regions. These regions can be formed, for example, by the areas covered by the disconnectable network elements. Because of these calculated and translate values, for example, the central unit 6 then decide which of these network elements 9 . 10 are on or off.

In a further embodiment of the subject matter according to the invention, for example, the disconnectable network elements without a central unit can calculate the network element density in its environment, eg for the surface covering it, and autonomously decide on its state (on / off) based on this value. This can be the disconnectable network elements 9 . 10 are in a standby state and retain the ability to geolocalize subscribers. For the individual network elements to execute the algorithm, they can exchange information about geolocalized participants over a background network.

The algorithm for determining the network element density T (u, t) is either in a central processing unit 6 or implemented in a signaling network or in individual switchable network elements. For this, the rate for data transfer requests Λ (u, t) as input data 3 certainly. The parameterization is done either by a memory 2 or a memory, a configurator, the network operator or by an optimizer or optimization algorithm, these components either in the central processing unit 6 or are arranged in a signaling network or in individual disconnectable network elements. This is in 4 shown schematically.

5 shows the determination of the rate of data transfer requests Λ (u, t) as input data 3 for the central unit 6 in which the location and time of the data transfer requests of individual subscribers in the mobile radio network are recorded and processed. The acquisition and processing of the data can also be done by a signaling network or a conventional network of macro base stations.

Calculated after determining the input data 5 the central unit 6 , the signaling network or the network of conventional macro base stations, the network element density and transmits them to a set on or off switchable network elements 9 . 10 , The central unit 6 Finally, the signaling network or the network of conventional macro base stations finally causes the de- or activation of disconnectable network elements 9 . 10 , This is in 6 represented, wherein the marked with a dashed ellipse network elements 10 which are disabled and the active network elements marked with a solid ellipse 9 mark as an example.

In a further embodiment of the present invention, FIGS 7 and 8th the geolocation by the network elements themselves and the exchange of information of the recorded input data Λ (u, t) between the disconnectable network elements 9 . 10 ,

The 9 finally shows the after the execution of in 4 shown system 1 or algorithm activated 9 or deactivated 10 Network elements. The system of partial differential equations or the algorithm was carried out autonomously in each network element. The network elements surrounded by a dashed ellipse represent the deactivated ones 10 and those with a continuous ellipse bordered the active network elements 9 exemplified.

In a further embodiment, other components can be added to the aforementioned configurations 2 added. As a result, not only the current but also the overall development of the data transfer requests can be taken into account in the selection of the switchable network elements to be added or deactivated. This takes place by means of derivation terms, delay of de-activation or activation by means of so-called memory terms. Mechanisms such as directional advection or diffusion can be added to provide robustness to spatial geolocalization errors of mobile subscribers, as well as general uncertainty through random data transfer requests. This robustness is intended to ensure that the performance of the network as perceived by subscribers, eg, through data throughput, latencies, mobility limitations, etc., is not adversely affected. In addition, a diffusion term which describes the mobility of mobile subscribers, the so-called random walk, is also taken into account in the calculation of the subscriber density. This also increases the robustness of the algorithm or of the mobile radio network in that mobile mobile subscribers can trigger the activation of neighboring network elements. However, individually occurring mobile subscribers need not necessarily be able to trigger the activation of network elements, if their data demand represent isolated events with a short-term increase in network element load / resource utilization. Thus, increased network efficiency can be achieved. The system of partial differential equations can be parameterized in different ways by eg factors, diffusion coefficients, vector fields to control the robustness or the effectiveness of the reduction of energy consumption. These parameters can also be optimized and adapted to prevailing circumstances, such as different city characteristics or location-dependent participant mobility. For example, self-learning algorithms are conceivable.

LIST OF REFERENCE NUMBERS

1

Control of a mobile network

2

Components for parameterization

3

entrance

4

output

5

Means for carrying out the calculation of

differential equations

6

central processing unit

61

non-disconnectable network elements

7

Attendees

8th

Mobile network

9

activated network element

10

disabled network element

11

Communication path between participant and network element

12

 network element

13

Communication path between two network elements

I (u, t)

 subscriber density

T (u, t)

 Network element density

Claims (13)

Method for connecting and / or disconnecting switchable network elements ( 12 ) in at least one network element ( 12 ) - a spatially and temporally dependent subscriber density is determined, - a spatially and a temporally dependent network element density of the active switchable network elements ( 12 ), a relation between the subscriber density and the network element density is formed, from which an optimal number of the active switchable network elements ( 9 ) is calculated in the mobile radio network and accordingly switchable network elements ( 12 ) spatially and temporally switched on and / or off, characterized in that the spatially and temporally optimal number of active disconnectable network elements ( 9 ) in the mobile radio network is calculated from a system of partial differential equations. Method for switching on and / or switching off disconnectable network elements according to claim 1, characterized in that the system of partial differential equations has the form: ∂T (u, t) / ∂t = α _IT I (u, t) (T ^ (u, t) - T (u, t)) + ∇ (N _T (u, t) ∇T (u, t)) + V → _T (u, t) ∇T (u, t) + m (T ^ (u, t) - T (u, t)) (T ‿ (u, t) - T (u, t)) - s _RT T (u, t) R (u, t) (1) ∂I (u, t) / ∂t = -μ (I (u, t), T (u, t); μ ₀ ) - d _I (u, t) + ∇ (N _I (u, t) ∇ I (u, t)) + V → _I (u, t) ∇I (u, t) + Λ (u, t) (2) ∂R (u, t) / ∂t = α _TR T (u, t) - d _R (u, t) (3) where T (u, t), I (u, t) and R (u, t) are the location- and time-dependent densities of network elements, subscribers and regulatory signals, ∇ (·) the Nabla operator, ∇N _T (u, t) ∇T (u, t) a diffusion term, V → _T (u, t) ∇T (u, t) a advection term with N _T (u, t) as a diffusion coefficient and V → _T (u, t) as a vector field denote a directional flux of network element density, - _RT T (u, t) R (u, t) the rate at which network elements are turned off, Λ (u, t) the arrival rate of subscribers or data transfer requests, -d _I I (u , t) describe a natural reduction of the active participants or data transfer operations and -μ (I (u, t), T (u, t); μ ₀ ) finally describes how fast data transfer operations are handled. Method for connecting and / or disconnecting switchable network elements according to claim 1, characterized in that the subscriber density depending on a location x and a time t within the mobile network from an arrival rate of data transfer requests, or a reduction rate of data transfer operations, a subscriber mobility rate and a settlement rate is calculated by data transfer operations. Method for connecting and / or disconnecting switchable network elements according to claim 3, characterized in that the processing rate of data transfer operations in dependence on the subscriber density, the network element density and a capacity of the individual network elements ( 12 ) is determined. Method for switching on and / or switching off switchable network elements according to claim 1, characterized in that in the determination of the network element density, the connection and / or the switching off of adjacent network elements ( 12 ) is taken into account by means of a diffusion term and a advection term. Method for connecting and / or disconnecting switchable network elements after a of the preceding claims, characterized in that the spatial and temporal network element density is determined simultaneously. Method for switching on and / or switching off disconnectable network elements according to one of the preceding claims, characterized in that for the calculation of regulatory signals, namely data streams in a background network, recorded and used. Method for connecting and / or disconnecting switchable network elements according to one of the preceding claims, characterized in that the network element density is calculated for each individual spatially delimited regions in the mobile radio network, wherein the spatially delimited region with an area containing a network element ( 12 ) in the mobile radio network matches. Method for switching on and / or switching off disconnectable network elements according to one of the preceding claims 1 or 2, characterized in that at least one non-disconnectable network element ( 61 ) collects the data transfer requests and in the at least one non-disconnectable network element ( 61 ) the network element density is calculated according to the system of partial differential equations. Method for switching on and / or switching off switchable network elements according to one of the preceding claims 1, 2 or 9, characterized in that in a central processing unit ( 6 ) the network element density is calculated according to the system of partial differential equations. Mobile radio network with at least one non-disconnectable network element ( 61 ) and disconnectable network elements ( 12 ), wherein the at least one non-disconnectable network element ( 61 ) as a central processing unit ( 6 ), as well as an entire surface to be considered covering is formed and the disconnectable network elements are formed by the central unit the operational readiness state controllable and the central unit uses the method for connecting and / or switching off the disconnectable network elements according to claims 1 to 10. Mobile network with disconnectable network elements ( 12 ) for carrying out the method according to one of claims 1 to 10, wherein the disconnectable network elements ( 12 ) a network element density are designed to calculate in their respective environment. Mobile radio network with disconnectable network elements according to claim 12, characterized in that the individual disconnectable network elements ( 12 ) communicating with each other via a background network ( 13 ) are formed.

Images