Techniques for adaptively determining cell boundary in wireless communications
Described are examples for receiving, from one or more second virtual radio access network (vRAN) workloads operating one or more second cells, an indication of a measurement of at least a first signal transmitted by a first vRAN workload operating a first cell, computing, based on measurements of at least the first signal as received from the one or more second vRAN workloads, a boundary of the first cell, and adjusting, based on the boundary of the first cell, a transmit parameter of the first vRAN workload for transmitting signals in the first cell.
1. A computer-implemented method for adjusting a transmit parameter of a cell operated by a virtual radio access network (vRAN) workload in a cloud-computing environment, comprising:
receiving, by a process that manages multiple vRAN workloads in the cloud-computing environment including a first vRAN workload and one or more second vRAN workloads, indications of measurements, performed by the one or more second vRAN workloads operating one or more second cells, of multiple signals transmitted in different spatial directions via a first cell operated by the first vRAN workload;
computing, by the process and based on the measurements of the multiple signals as received from the one or more second vRAN workloads, a boundary of the first cell; and
adjusting, based on the boundary of the first cell, a transmit parameter of the first vRAN workload for transmitting signals in the first cell.
2. The computer-implemented method of claim 1 , wherein computing the boundary of the first cell is based on a function of the measurements of the multiple signals as determined by the one or more second vRAN workloads.
3. The computer-implemented method of claim 1 , wherein the indications of the measurements correspond to a computed boundary of the first cell computed by each of the one or more second vRAN workloads based on respective measurements of the multiple signals.
4. The computer-implemented method of claim 3 , wherein computing the boundary is based on a function of the computed boundary received from each of the one or more second vRAN workloads.
5. The computer-implemented method of claim 1 , wherein adjusting the transmit parameter includes using machine learning to compare the measurements of the multiple signals based on a trained model of signal measurements that resulted in adjusting transmit parameters of other cells.
6. The computer-implemented method of claim 1 , wherein the transmit parameter is at least one of a transmit power or a modulation for the first vRAN workload to use in providing the first cell.
7. The computer-implemented method of claim 1 , wherein the first VRAN workload and the one or more second vRAN workloads provide a centralized unit (CU), distributed unit (DU), or remote unit (RU) functionality in a fifth generation (5G) mobile edge computing (MEC) environment.
8. A device for adjusting a transmit parameter of a cell operated by a virtual radio access network (vRAN) workload in a cloud-computing environment, comprising:
a memory storing one or more parameters or instructions for executing an operating system; and
at least one processor coupled to the memory, wherein the at least one processor is configured to:
receive, by a process that manages multiple vRAN workloads in the cloud-computing environment including a first vRAN workload and one or more second vRAN workloads, indications of measurements, performed by the one or more second vRAN workloads operating one or more second cells, of multiple signals transmitted in different spatial directions via a first cell operated by the first vRAN workload;
compute, by the process and based on the measurements of the multiple signals as received from the one or more second vRAN workloads, a boundary of the first cell; and
adjust, based on the boundary of the first cell, a transmit parameter of the first vRAN workload for transmitting signals in the first cell.
9. The device of claim 8 , wherein the at least one processor is configured to compute the boundary of the first cell based on a function of the measurements of the multiple signals as determined by the one or more second vRAN workloads.
10. The device of claim 8 , wherein the indications of the measurements correspond to a computed boundary of the first cell computed by each of the one or more second vRAN workloads based on respective measurements of the multiple signals.
11. The device of claim 10 , wherein the at least one processor is configured to compute the boundary based on a function of the computed boundary received from each of the one or more second vRAN workloads.
12. The device of claim 8 , wherein the at least one processor is configured to adjust the transmit parameter at least in part by using machine learning to compare the measurements of the multiple signals based on a trained model of signal measurements that resulted in adjusting transmit parameters of other cells.
13. The device of claim 8 , wherein the transmit parameter is at least one of a transmit power or a modulation for the first vRAN workload to use in providing the first cell.
14. The device of claim 8 , wherein the first VRAN workload and the one or more second vRAN workloads provide a centralized unit (CU), distributed unit (DU), or remote unit (RU) functionality in a fifth generation (5G) mobile edge computing (MEC) environment.
15. A computer-readable medium, comprising code executable by one or more processors for adjusting a transmit parameter of a cell operated by a virtual radio access network (vRAN) workload in a cloud-computing environment, the code comprising code for:
receiving, by a process that manages multiple vRAN workloads in the cloud-computing environment including a first vRAN workload and one or more second vRAN workloads, indications of measurements, performed by the one or more second vRAN workloads operating one or more second cells, of multiple signals transmitted in different spatial directions via a first cell operated by the first vRAN workload;
computing, by the process and based on measurements of the multiple signals as received from the one or more second vRAN workloads, a boundary of the first cell; and
adjusting, based on the boundary of the first cell, a transmit parameter of the first vRAN workload for transmitting signals in the first cell.
16. The computer-readable medium of claim 15 , wherein the code for computing computes the boundary of the first cell based on a function of the measurements of the multiple signals as determined by the one or more second vRAN workloads.
17. The computer-readable medium of claim 15 , wherein the indications of the measurements correspond to a computed boundary of the first cell computed by each of the one or more second vRAN workloads based on respective measurements of the multiple signals, and wherein the code for computing computes the boundary based on a function of the computed boundary received from each of the one or more second vRAN workloads.
18. The computer-readable medium of claim 15 , wherein the code for adjusting adjusts the transmit parameter at least in part by using machine learning to compare the measurements of the multiple signals based on a trained model of signal measurements that resulted in adjusting transmit parameters of other cells.
19. The computer-readable medium of claim 15 , wherein the transmit parameter is at least one of a transmit power or a modulation for the first vRAN workload to use in providing the first cell.
20. The computer-readable medium of claim 15 , wherein the first VRAN workload and the one or more second vRAN workloads provide a centralized unit (CU), distributed unit (DU), or remote unit (RU) functionality in a fifth generation (5G) mobile edge computing (MEC) environment.