Methods, systems, and computer readable media for simulating channel conditions
View Patent ↗Methods, systems, and computer readable media for simulating channel conditions are disclosed. According to one method, the method includes storing a plurality of system level metrics associated with various channel conditions. The method also includes receiving data from a device under test (DUT). The method further includes for each of a plurality of user devices or simulated user devices during a test period: identifying, using information about the received data and a precomputed channel type configuration, a first system level metric from the plurality of system level metrics, and providing the first system level metric to the DUT.
1. A method for simulating channel conditions, the method comprising:
storing a plurality of system level metrics associated with various channel conditions;
receiving data from a device under test (DUT);
for each of a plurality of user devices or simulated user devices during a test period:
identifying, using information about the received data and a precomputed channel type configuration, a first system level metric from the plurality of system level metrics; and
providing the first system level metric to the DUT,
wherein identifying the first system level metric from the plurality of system level metrics includes retrieving, based on the information about the received data and the precomputed channel type configuration, the first system level metric from the plurality of system level metrics and performing a bounded randomization function on the first system level metric using statistical properties associated with the first system level metric.
2. The method of claim 1 wherein storing the plurality of system level metrics associated with various channel conditions includes generating the plurality of system level metrics based on one or more physical layer simulations.
3. The method of claim 2 wherein the one or more physical layer simulations are performed prior to the test period and wherein each of the one or more physical layer simulations are based on one or more channel models.
4. The method of claim 1 wherein storing the plurality of system level metrics associated with various channel conditions includes generating the statistical properties associated with the plurality of system level metrics and storing the statistical properties in a memory.
5. The method of claim 1 wherein the statistical properties include a mean, a variance, a standard deviation, a minimum value, or a maximum value.
6. The method of claim 1 wherein performing the bounded randomization function includes modifying the first system level metric to a value within a range using a standard deviation, a variance value, or a minimum value and a maximum value.
7. The method of claim 1 wherein the first system level metric includes an uplink parameter, a channel quality indicator (CQI), a block error rate, a pre-coding matrix indicator (PMI), a rank indicator (RI), a downlink or receive signal strength (RxLev), or a downlink or receive signal quality (RxQual).
8. The method of claim 1 wherein each of the plurality of user devices or simulated user devices is associated with a precomputed channel type configuration, wherein at least two user devices of the plurality of user devices or simulated user devices are associated with different precomputed channel type configurations.
9. The method of claim 1 wherein the precomputed channel type configuration includes an additive white Gaussian noise (AWGN) model, an extended pedestrian A (EPA) model, an extended vehicular A (EVU) model, an extended typical urban (ETU) model, an extended pedestrian A five hertz (EPA-5) model, an extended vehicular A five hertz (EVA-5) model, an extended vehicular A seventy hertz (EVA-70) model, a high-speed train (HST) model, an extended typical urban seventy hertz (ETU-70) model, or an extended typical urban three hundred hertz (ETU-300) model.
10. The method of claim 1 wherein the DUT includes an evolved Node B, a Node B, an evolved packet core (EPC) network, an EPC network node, or a base transceiver station (BTS).
11. The method of claim 1 wherein the information about the received data includes a channel condition metric, a modulation and coding scheme (MCS) value, a downlink channel property, a signal to noise ratio (SNR), a signal to interference-noise ratio (SINR), or an energy per bit to thermal noise ratio.
12. The method of claim 1 wherein the first system level metric is a block error rate and wherein providing the first system level metric to the DUT includes providing negative acknowledgements (NACKs) in correlation with the block error rate for an appropriate period.
13. The method of claim 1 wherein providing the first system level metric to the DUT includes providing the first system level metric as an uplink parameter in an uplink data frame to the DUT.
14. A system for simulating channel conditions, the system comprising:
a memory configurable for storing a plurality of system level metrics associated with various channel conditions;
a communications interface configured to receive data from a device under test (DUT); and
a channel condition simulator (CCS) module embodied in a non-transitory computer readable medium configured to for each of a plurality of user devices or simulated user devices during a test period:
identify, using information about the received data and a precomputed channel type configuration, a first system level metric from the plurality of system level metrics, and
provide the first system level metric to the DUT,
wherein the CCS module is configured to identify the first system level metric from the plurality of system level metrics by retrieving, based on the information about the received data and the precomputed channel type configuration, the first system level metric from the plurality of system level metrics and performing a bounded randomization function on the first system level metric using statistical properties associated with the first system level metric.
15. The system of claim 14 wherein the CCS module is configured to generate the plurality of system level metrics based on one or more physical layer simulations and to store the plurality of system level metrics in the memory.
16. The system of claim 15 wherein the one or more physical layer simulations are performed prior to the test period and wherein each of the one or more physical layer simulations are based on one or more channel models.
17. The system of claim 14 wherein the CCS module is configured to generate the statistical properties associated with the plurality of system level metrics and to store the statistical properties in the memory.
18. The system of claim 14 wherein the statistical properties include a mean, a variance, a standard deviation, a minimum value, or a maximum value.
19. The system of claim 14 wherein the CCS module is configured to perform the bounded randomization function by modifying the first system level metric to a value within a range using a standard deviation, a variance value, or a minimum value and a maximum value.
20. The system of claim 14 wherein the first system level metric includes an uplink parameter, a channel quality indicator (CQI), a block error rate, a pre-coding matrix indicator (PMI), a rank indicator (RI), a downlink or receive signal strength (RxLev), or a downlink or receive signal quality (RxQual).
21. The system of claim 14 wherein each of the plurality of user devices or simulated user devices is associated with a precomputed channel type configuration, wherein at least two user devices of the plurality of user devices or simulated user devices are associated with different precomputed channel type configuration.
22. The system of claim 14 wherein the precomputed channel type configuration includes an additive white Gaussian noise (AWGN) model, an extended pedestrian A (EPA) model, an extended vehicular A (EVU) model, an extended typical urban (ETU) model, an extended pedestrian A five hertz (EPA-5) model, an extended vehicular A five hertz (EVA-5) model, an extended vehicular A seventy hertz (EVA-70) model, a high-speed train (HST) model, an extended typical urban seventy hertz (ETU-70) model, or an extended typical urban three hundred hertz (ETU-300) model.
23. The system of claim 14 wherein the DUT includes an evolved Node B, a Node B, an evolved packet core (EPC) network, an EPC network node, or a base transceiver station (BTS).
24. The system of claim 14 wherein the information about the received data includes a channel condition metric, a downlink channel property, a modulation and coding scheme (MCS) value, a signal to noise ratio (SNR), a signal to interference-noise ratio (SINR), or an energy per bit to thermal noise ratio.
25. The system of claim 14 wherein the first system level metric is a block error rate and wherein the system is configured to provide the first system level metric to the DUT by providing negative acknowledgements (NACKs) in correlation with the block error rate for an appropriate period.
26. The system of claim 14 wherein the system is configured to provide the first system level metric to the DUT by providing the first system level metric as an uplink parameter in an uplink data frame to the DUT.
27. A non-transitory computer readable medium comprising computer executable instructions embodied thereon that when executed by a processor of a computer control the computer to perform steps comprising:
storing a plurality of system level metrics associated with various channel conditions;
receiving data from a device under test (DUT);
for each of a plurality of user devices or simulated user devices during a test period:
identifying, using information about the received data and a precomputed channel type configuration, a first system level metric from the plurality of system level metrics; and
providing the first system level metric to the DUT,
wherein identifying the first system level metric from the plurality of system level metrics includes retrieving, based on the information about the received data and the precomputed channel type configuration, the first system level metric from the plurality of system level metrics and performing a bounded randomization function on the first system level metric using statistical properties associated with the first system level metric.