Classifications

• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04W—WIRELESS COMMUNICATION NETWORKS
  • H04W24/00—Supervisory, monitoring or testing arrangements

Definitions

• Embodiments of the invention generally relate to mobile communications networks, such as, but not limited to, the Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access Network (UTRAN), Long Term Evolution (LTE) Evolved UTRAN (E-UTRAN), and/or LTE- A.
• UMTS Universal Mobile Telecommunications System
• UTRAN Universal Mobile Telecommunications System
• LTE Long Term Evolution
• E-UTRAN Evolved UTRAN
• LTE-UTRAN LTE-UTRAN
• Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access Network refers to a communications network including base stations, or Node Bs, and for example radio network controllers (RNC).
• UTRAN allows for connectivity between the user equipment (UE) and the core network.
• the RNC provides control functionalities for one or more Node Bs.
• the RNC and its corresponding Node Bs are called the Radio Network Subsystem (RNS).
• RNS Radio Network Subsystem
• E-UTRAN enhanced UTRAN
• no RNC exists and most of the RNC functionalities are contained in the enhanced Node B (eNodeB or eNB).
• LTE Long Term Evolution
• E-UTRAN refers to improvements of the UMTS through improved efficiency and services, lower costs, and use of new spectrum opportunities.
• LTE is a 3GPP standard that provides for uplink peak rates of at least 50 megabits per second (Mbps) and downlink peak rates of at least 100 Mbps.
• LTE supports scalable carrier bandwidths from 20 MHz down to 1.4 MHz and supports both Frequency Division Duplexing (FDD) and Time Division Duplexing (TDD).
• FDD Frequency Division Duplexing
• TDD Time Division Duplexing
• LTE may also improve spectral efficiency in networks, allowing carriers to provide more data and voice services over a given bandwidth. Therefore, LTE is designed to fulfill the needs for high-speed data and media transport in addition to high-capacity voice support. Advantages of LTE include, for example, high throughput, low latency, FDD and TDD support in the same platform, an improved end-user experience, and a simple architecture resulting in low operating costs.
• LTE-A LTE- Advanced
• LTE-A is directed toward extending and optimizing the 3GPP LTE radio access technologies.
• a goal of LTE-A is to provide significantly enhanced services by means of higher data rates and lower latency with reduced cost.
• LTE- A will be a more optimized radio system fulfilling the international telecommunication union-radio (ITU-R) requirements for I MT- Advanced while keeping the backward compatibility.
• ITU-R international telecommunication union-radio
• a first embodiment is directed to a method comprising evaluating a cell availability (CA) of a cell or an area availability (AA) of an area in a communications network.
• the evaluating comprises calculating a cell availability from an end user perspective (CAS) or an area availability from the end user perspective (AAS).
• the calculating of the CAS or AAS comprises multiplying the cell availability (CA) or area availability (AA) with a weighted coefficient of all neighboring cells to the cell or all neighboring areas to the area, and the weighted coefficient represents an availability ratio of each of the neighboring cells or areas.
• a second embodiment is directed to an apparatus comprising at least one processor and at least one memory comprising computer program code.
• the at least one memory and the computer program code are configured, with the at least one processor, to cause the apparatus at least to evaluate a cell availability (CA) of a cell or an area availability (AA) of an area in a communications network, and calculate a cell availability from an end user perspective (CAS) or an area availability from the end user perspective (AAS) by multiplying the cell availability (CA) or area availability (AA) with a weighted coefficient of all neighboring cells to the cell or all neighboring areas to the area.
• the weighted coefficient represents an availability ratio of each of the neighboring cells or areas.
• a third embodiment is directed to an apparatus comprising evaluating means for evaluating a cell availability (CA) of a cell or an area availability (AA) of an area in a communications network.
• the apparatus also comprises calculating means for calculating a cell availability from an end user perspective (CAS) or an area availability from the end user perspective (AAS) by multiplying the CA or AA with a weighted coefficient of all neighboring cells to the cell or all neighboring areas to the area.
• the weighted coefficient represents an availability ratio of each of the neighboring cells or areas.
• cell availability from the end user perspective may be calculated according to the following equation:
• M may represent a number of attempts
• K may be a factor representing what part of the attempts during the cell unavailability is served by the neighboring cells
• CAM may represent a number of successful attempts during a time the cell is available
• (1-CA) may represent a number of attempts during the cell unavailability.
• Load represents a load of the cell
• Load represents a load of the cell's /-th neighbor
• W may be a weighted factor of the cell to its neighbors and given as:
• AAS ⁇ - ⁇ l - AA)W (6)
• AA may be estimated with the following equation:
• CA j may be a cell availability of the y ' -th cell which is part of the area, and WA, may be its weighted coefficient.
• WA weighted coefficient
• Load j may be a load of the y-th cell from the observed area and J may represent a number of cells belonging to the area.
• the area availability from the end user perspective may be given by the following :
• weighted coefficient may be obtained from measurements provided by neighbor cells or areas.
• weighted coefficient may be obtained from true overlapping of the cell or area with neighboring cells or areas.
• the apparatus may be a performance network management node.
• a fourth embodiment is directed to a computer program, embodied on a computer readable medium, the computer program configured to control a processor to perform a process.
• the process comprises evaluating a cell availability (CA) of a cell or an area availability (AA) of an area in a communications network.
• the evaluating comprises calculating a cell availability from an end user perspective (CAS) or an area availability from the end user perspective (AAS).
• the calculating of the CAS or AAS comprises multiplying the CA or AA with a weighted coefficient of all neighboring cells to the cell or all neighboring areas to the area, and the weighted coefficient represents an availability ratio of each of the neighboring cells or areas.
• a fifth embodiment is directed to a computer program product comprising computer-executable computer program code which, when the program is run on a computer, is configured to cause the computer to carry out the method according to the first embodiment.
• the computer program product of the fifth embodiment wherein the computer program product may comprise a computer-readable medium on which the computer-executable computer program code may be stored, and/or wherein the program may be directly loadable into an internal memory of the processor.
• FIG. 1 illustrates a system according to one example embodiment
• FIG. 2 illustrates a system according to another example embodiment
• Fig. 3 illustrates an example graph showing the weighted factor as a function of area size
• FIG. 4 illustrates an apparatus according to an example embodiment
• FIG. 5 illustrates an apparatus according to another example embodiment
• FIG. 6 illustrates a flow diagram of a method according to an example embodiment.
• some embodiments generally relate to a method for area/cell availability evaluation.
• Cell unavailability directly impacts the quality of services provided to end users. Therefore, the monitoring of cell availability is a requirement for each operator.
• 3GPP 32.450 (chapter 6.4) defines a cell availability ratio key performance indicator (KPI) which provides results from the network's point of view.
• KPI cell availability ratio key performance indicator
• the UE may access one of the neighboring cells such that no problems may be experienced from the point of view of the end user.
• embodiments of the invention provide a method for evaluation of the cell/area availability ratio from an end user point of view in such a way that, on the cell level, the cell availability ratio KPI may be multiplied with a weighted coefficient of all the neighbor cells to the observed cell respecting the availability ratio of each neighbor.
• the cell availability ratio KPI may be multiplied with a weighted coefficient of all the neighbor cells to the observed cell respecting the availability ratio of each neighbor.
• a weighted estimation of the area availability is obtained, which comprise an availability ratio of each cell of the area calculated according to the cell availability ratio KPI, multiplied with a weighted coefficient of all the neighbors to the observed area respecting the availability ratio of each neighbor.
• a cell may be considered available when the UE can request for either signaling or data service in the cell.
• the cell may be unavailable when the UE cannot request for any service in the cell.
• a cell's unavailable may be classified into the following two categories: 1 .
• the cell is planned unavailable (classified as a manual intervention according to 3GPP 32.425); or 2.
• the cell is unplanned unavailable (classified as a fault according to 3GPP 32.425).
• the planned unavailability category may comprise all the causes the operator is aware of, such as a planned operations and maintenance (O&M) with a software (SW) update and/or hardware (HW) upgrade, but may also consider a concept of LTE heterogeneous networks deploying LTE capacity-booster cells on top of cells that give wide-area coverage.
• O&M operations and maintenance
• SW software
• HW hardware
• the function enables the capacity- booster cells to be switched off when their capacity is no longer needed, and to be re-activated from a dormant state on a per-need basis.
• the unplanned unavailability category may comprise all the causes leading to a fault or erroneous situation resulting in an unplanned capacity/traffic and/or services loss.
• Typical examples can comprise when the cell has been suddenly switched off due to some HW or SW problems or due to power down.
• the cell availability ratio KPI was defined in the 3GPP 32.450 (chapter 6.4) with related use cases discussed in 3GPP 32.451 (chapter 5.4), and respective performance measurements introduced in 3GPP 32.425 (chapter 4.5.6).
• the proposed cell availability ratio KPI according to 3GPP 32.450 provides the results from network point of view. It should be noted that today's users take telecommunications- grade service performance for granted and judge operators and service providers by price and the quality of their service portfolios. Thus, for each operator, it is crucial to understand how the quality of the provided services is perceived by the end user. In relation to cell availability, this means measuring availability from the end user's point of view may also be very beneficial for operators to understand how quality is perceived form the user's perspective.
• the cell availability KPI is calculated according to its definition in the 3GPP 32.450 (chapter 6.4), which tends to provide the results from network point of view. From the end user perspective, if the cell is unavailable, then the UE usually accesses one of the neighboring cells and, therefore, no problems may be experienced from the end user's perspective.
• the UE may try to request service on one of them, which in this case may be a visibly worse service as measured by RRC Connection Success Ratio, E-RAB Establishment Success Ratio, and/or E-RAB Drop Ratio. So, if the unavailability of a given cell actually causes issues that are perceived by the end user, these issues should also be visible in the neighboring cells. Thus, the effect of the cell unavailability may be counted twice in the cell itself and in its neighbors.
• the issue of double counting may complicate the obtaining of reliable results of a so called super KPI which combines cell availability, RRC Connection Success Ratio, E- RAB Establishment Success Ratio or even in E-RAB Drop Ratio into one formula.
• certain example embodiments provide a method and apparatus configured to evaluate the cell/area availability ratio from an end user point of view in such a way that, on the cell level, the cell availability ratio KPI may be multiplied with a weighted coefficient of all the neighbor cells to the observed cell respecting the availability ratio of each neighbor.
• the cell availability ratio KPI may be multiplied with a weighted coefficient of all the neighbor cells to the observed cell respecting the availability ratio of each neighbor.
• the cell availability ratio KPI that is calculated according to 3GPP 32.450 may not reflect the real situation perceived by the end user, in addition to the effect of cell unavailability on neighboring cells which may also be expected to partly compensate for the unavailability.
• the cell having N neighboring cells, each with a cell availability CAi with i being from 1 to N.
• the cell availability may be defined from the end user point of view (denoted as CAS) as the number of successful attempts (the UE may camp on the cell or when the UE camps on one of the neighboring ones in case of its unavailability) divided by the number of attempts.
• CAS can be expressed in the following form:
• the attempts are both uniformly distributed in time and geographical area of the cell, where M represents the number of attempts and K is a factor representing what part of the attempts during the cell unavailability is served by its neighboring cells 1 .
• the factor Kma range from 0 to 1 .
• the CAM represents the number of successful attempts as they appear during the time the cell is available while the (1-CA).M represents the number of attempts appearing during the cell unavailability.
• the factor K may then in fact reflect how large the cell is both in terms of its geographical size and from a loading point of view in comparison to its neighboring cells. It is clear that when the observed cell is a small one in comparison to its neighbors and those neighbors are available during its unavailability, then the majority of the traffic/services can be served by them. Therefore, the best approximation of the coefficient K may be given by the following formula:
• the obtained cell availability ratio represents a probability the user can request for a service anywhere and anytime in the observed cell with a uniform probability of distribution, i.e. there are not any sub areas nor time frames that are more prioritized than other ones.
• K l - N (2)
• Load represents a load of the observed cell wile Loadi represents the load of its /-th neighbor.
• the load in general may reflect the number of active UEs, sessions/E-RABs, data volume, etc 2 .
• Equation 2 Inserting Equation 2 into Equation 1 provides the following:
• W is a weighted factor of the observed cell to its neighbors (for example ranging from 0 to 1 ) and given as:
• Equation 4 The Cell Availability for Super KPI purposes (CAS) as expressed in Equation 4 was checked against some extreme scenarios in order to show its reliability and applicability.
• the observed cell is a very small one in comparison to its neighbors, as illustrated in Fig. 1 .
• the observed cell 4 illustrated in Fig. 1 is unavailable for the whole measurement period duration. Due to the above assumption that the observed cell 4 may be considered as having negligible size and loading in comparison to its neighbors (cells 1 , 2, and 3), the weighted factor W according to Equation 5 would lead to zero. Therefore, the CAS according to Equation 4 is one, which is acceptable as all the traffic/sessions of this small cell during its unavailability can be served with its neighbors.
• the approach is to use it as event based, i.e., the number established E-RABs as Load, which represents the easiest way for estimating the weighted factor W.
• the observed cell is a very large cell in comparison to its neighbors, as illustrated in Fig. 2. It is assumed that the observed cell 6 illustrated in Fig. 2 is unavailable for the whole measurement period duration. Due to the above assumption that the observed cell 6 may be considered as having a large size and loading in comparison to its neighbors (cells 1 -5 and 7), the weighted factor W according to Equation 5 would lead to one. As a result, the CAS according to Equation 4 becomes zero which is acceptable as most of the traffic/sessions of this cell during its unavailability cannot be served with its neighbors.
• the Loads from the long period observation periods may be used, as the intention is to catch the relationship between the observed cell and its neighbors from a size and loading perspective. For example, performing the calculation from the night measurement observation period when there is a lack of traffic may not provide a true picture about this relationship.
• Equation 4 The cell availability from an end user point of view (CAS) as expressed in Equation 4 deals with how to perform the calculation on a per cell basis.
• Example embodiments can extend the calculation to provide the CAS for a bigger area than the cell, as discussed below. active users or sessions there can be an error in the obtained results.
• a similar approach as used for the cell level can be applied. For example, the evaluated area can be considered as a cell and then neighbor areas to this area can be considered as they were for the cell level. This approach yields a similar equation to that of Equation 4, which is given as follows:
• AAS ⁇ - ⁇ - AA)W (6) the difference from Equation 4 is that CAS is replaced with AAS (Area Availability from end user point of view) and CA is replaced with AA (Area Availability) which may be estimated with the following formula:
• CA is a cell availability of the y-th cell (calculated according to 3GPP 32.450, chapter 6.4) which is part of the observed area, and WA j is its weighted coefficient and is calculated as:
• Load j is load, e.g., number of established E-RABs, of the y-th cell from the observed area and J represents number of cells belonging to the observed area.
• the factor W in the above Eq. 6 is calculated in the same way as for cell level with the Eq. 5 with the only difference that Load in the numerator of the formula represents the total load (number of E-RABs) of the observed area.
• Equation 6 The area availability from the end user point of view (AAS) as expressed in Equation 6 tends to follow one important pattern in that as the area is increased (as a maximum area we can consider the public land mobile network (PLMN)) the weighted factor W in limit case leads to infinity, i.e., the W is equal to one, as it ranges from 0 to 1 , which transforms the Eq. 6 into the following :
• the weighted factor W for the area availability from the end user point of view may tend to follow the graph illustrated in Fig. 3.
• the example of Fig. 3 illustrates a graph showing the weighted factor W as a function of the area size.
• Size represents the size of the observed area and Sizel ⁇ Size 2 ⁇ Size 6.
• the way that the weighted factors, as given by Equations 5 and 8, are obtained can be modified.
• the weighted factors are not estimated from the Load but instead obtained from neighbouring cells' measurements to the observed cell (or area).
• this embodiment takes into consideration that the eNB creates a service map for each observed cell (area) representing how large a part (geographical area) of the observed cell/area can be covered with its neighbours.
• the eNB may need to know some standard measurements, like reference signal received power (RSRP) or reference signal received quality (RSRQ), in order to determine the boundary of the neighbouring cell(s) able to cover the services within the observed cell (area) in case of its unavailability.
• RSRP reference signal received power
• RSRQ reference signal received quality
• the eNB may also need to know the UE's position.
• the approach provided by this embodiment may be more precise in calculation of the CAS (AAS) using Equation 4 (or Equation 6) when compared to the example embodiments in which the weighted factors are estimated from the Load.
• this embodiment may require some additional algorithm(s) to be implemented within the eNB to create the service maps and also requires running an UE positioning method. It should be noted that the precision of the method used to determine the UE position may be important for applicability of this embodiment.
• weighted factors as given by Equations 5 and 8 are obtained.
• the weighted factors are not estimated from the Load but instead obtained depending on true overlapping of the observed cell (area) with its neighbors (e.g., from network planning phase using the path loss models and drive tests).
• This embodiment may be more precise in calculation of the CAS (AAS) using Equation 4 (or Equation 6) as compared to the example embodiments discussed above, because this embodiment can exactly reflect how large a part of the observed cell (area) can be covered with its neighbouring cells.
• this embodiment may be more costly as the weighted factors are obtained manually and inserted into the system, in addition to being recalculated whenever a new cell has been introduced.
• Example embodiments in which the weighted factors are estimated from the Load may work fully autonomously, in real time, and without any additional operator's intervention. However, some operators may prefer applying the more precise options which calculate the weighted factors from a service map or the overlapping of the observed cell with its neighbors.
• Fig. 4 illustrates an example of an apparatus 10 according to an embodiment.
• apparatus 10 may be a performance network management apparatus.
• the performance network management apparatus may be for example part of a network management system and/or part of a node performing network management functions. Further the performance network management appratus may comprise network management functions.
• apparatus 10 may comprise components or features not shown in Fig. 4. Only those components or feature necessary for illustration of the invention are depicted in Fig. 4.
• apparatus 10 comprises a processor 22 for processing information and executing instructions or operations.
• Processor 22 may be any type of general or specific purpose processor. While a single processor 22 is shown in Fig.
• processor 22 may comprise one or more of general- purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), and processors based on a multi-core processor architecture, as examples.
• DSPs digital signal processors
• FPGAs field-programmable gate arrays
• ASICs application-specific integrated circuits
• Apparatus 10 further comprises a memory 14, which may be coupled to processor 22, for storing information and instructions that may be executed by processor 22.
• Memory 14 may be one or more memories and of any type suitable to the local application environment, and may be implemented using any suitable volatile or nonvolatile data storage technology such as a semiconductor- based memory device, a magnetic memory device and system, an optical memory device and system, fixed memory, and removable memory.
• memory 14 can be comprised of any combination of random access memory (RAM), read only memory (ROM), static storage such as a magnetic or optical disk, or any other type of non-transitory machine or computer readable media.
• the instructions stored in memory 14 may comprise program instructions or computer program code that, when executed by processor 22, enable the apparatus 10 to perform tasks as described herein.
• Apparatus 10 may also comprise one or more antennas 25 for transmitting and receiving signals and/or data to and from apparatus 10.
• Apparatus 10 may further comprise a transceiver 28 configured to transmit and receive information.
• transceiver 28 may be configured to modulate information on to a carrier waveform for transmission by the antenna(s) 25 and demodulates information received via the antenna(s) 25 for further processing by other elements of apparatus 10.
• transceiver 28 may be capable of transmitting and receiving signals or data directly.
• Processor 22 may perform functions associated with the operation of apparatus 10 including, without limitation, precoding of antenna gain/phase parameters, encoding and decoding of individual bits forming a communication message, formatting of information, and overall control of the apparatus 10, including processes related to management of communication resources.
• memory 14 stores software modules that provide functionality when executed by processor 22.
• the modules may comprise, for example, an operating system that provides operating system functionality for apparatus 10.
• the memory may also store one or more functional modules, such as an application or program, to provide additional functionality for apparatus 10.
• the components of apparatus 10 may be implemented in hardware, or as any suitable combination of hardware and software.
• apparatus 10 may be a performance network management node which may be for example part of a network management system and/or part of a node performing network management functions.
• apparatus 10 may be controlled by memory 14 and processor 22 to evaluate a cell availability (CA) of a cell or an area availability (AA) of an area in a communications network, such as E-UTRAN.
• apparatus 10 may be controlled by memory 14 and processor 22 to evaluate the CA or AA by calculating a cell availability from the end user perspective (CAS) or an area availability from the end user perspective (AAS).
• CAS end user perspective
• AAS area availability from the end user perspective
• the calculating of the CAS or AAS may comprise multiplying the CA or AA with a weighted coefficient of all neighboring cells to the cell or all neighboring areas to the area.
• the weighted coefficient represents the availability ratio of each of the neighboring cells or areas.
• apparatus 10 may calculate the CAS according to Equations 1 -4 outlined above.
• the weighted coefficient, W may be calculated according to Equation 5 discussed above.
• apparatus 10 may calculate the AAS according to Equations 6 and 7 outlined above.
• the weighted coefficient, W may be calculated according to Equation 8 discussed above.
• the weighted coefficient, W may be obtained from neighboring cells' or areas' measurements that are provided to the cell, which may be reflected in a service map created by apparatus 10.
• the weighted coefficient, W may be obtained based on the true overlapping of the cell or area with its neighbors.
• Fig. 5 illustrates an example of an apparatus 30 according to an example embodiment.
• apparatus 30 may be a performance network management node.
• the performance network management node may be for example part of a network management system and/or part of a node performing network management functions. Further the performance network management node may comprise network management functions. It should be noted that one of ordinary skill in the art would understand that apparatus 30 may comprise components or features not shown in Fig. 5. Only those components or feature necessary for illustration of the invention are depicted in Fig. 5.
• apparatus 30 comprises processing means 35 for processing information and executing instructions or operations.
• Apparatus 30 may further comprise transceiving means 45 for transmitting and receiving signals and/or data to and from apparatus 30.
• Apparatus 30 can also comprise storing means 50 for storing information and instructions that may be executed by processing means 35.
• processing means 35 may comprise calculating means 40 for calculating a cell availability from the end user perspective (CAS) or an area availability from the end user perspective (AAS).
• the calculating of the CAS or AAS may comprise multiplying the CA or AA with a weighted coefficient of all neighboring cells to the cell or all neighboring areas to the area.
• the weighted coefficient may represent the availability ratio of each of the neighboring cells or areas.
• calculating means 40 of apparatus 30 may calculate the CAS according to Equations 1 -4 outlined above.
• the weighted coefficient, W may be calculated according to Equation 5 discussed above.
• calculating means 40 of apparatus 30 may calculate the AAS according to Equations 6 and 7 outlined above.
• the weighted coefficient, W may be calculated according to Equation 8 discussed above.
• the weighted coefficient, W may be obtained from neighboring cells' or areas' measurements that are provided to the cell, which may be reflected in a service map created by apparatus 30.
• the weighted coefficient, W may be obtained based on the true overlapping of the cell or area with its neighbors.
• Fig. 6 illustrates an example of a flow diagram of a method that comprises, at 600, evaluating, for example by a network management apparatus, a cell availability (CA) of a cell or an area availability (AA) of an area in a communications network, such as E-UTRAN.
• the evaluating may comprise calculating, at 610, a cell availability from the end user perspective (CAS) or an area availability from the end user perspective (AAS).
• the calculating of the CAS or AAS may comprise multiplying the CA or AA with a weighted coefficient of all neighboring cells to the cell or all neighboring areas to the area.
• the weighted coefficient represents the availability ratio of each of the neighboring cells or areas.
• the calculating of the CAS comprises calculating the CAS according to Equations 1 -4 outlined above.
• the weighted coefficient, W may be calculated according to Equation 5 discussed above.
• the calculating of the AAS may comprise calculating the AAS according to Equations 6 and 7 outlined above.
• the weighted coefficient, W may be calculated according to Equation 8 discussed above.
• the weighted coefficient, W may be obtained from neighboring cells' or areas' measurements that are provided to the cell, which may be reflected in a service map created by the network management apparatus.
• the weighted coefficient, W may be obtained based on the true overlapping of the cell or area with its neighbors.
• any of the methods described herein may be implemented by software and/or computer program code stored in memory or other computer readable or tangible media, and executed by a processor.
• the functionality may be performed by hardware, for example through the use of an application specific integrated circuit (ASIC), a programmable gate array (PGA), a field programmable gate array (FPGA), or any other combination of hardware and software.
• ASIC application specific integrated circuit
• PGA programmable gate array
• FPGA field programmable gate array

Landscapes

• Engineering & Computer Science (AREA)
• Computer Networks & Wireless Communication (AREA)
• Signal Processing (AREA)
• Mobile Radio Communication Systems (AREA)

Applications Claiming Priority (2)

Publications (1)

Family

ID=50442527

Family Applications (1)

Country Status (2)

Citations (1)

• 2014
  • 2014-04-08 EP EP14715928.9A patent/EP2989818A1/de not_active Withdrawn
  • 2014-04-08 WO PCT/EP2014/057065 patent/WO2014173677A1/en active Application Filing

Patent Citations (1)

Non-Patent Citations (1)

Also Published As

Similar Documents

Legal Events