Analysis and evaluation of a practical downlink multiuser MIMO scheduler over LTE advanced massive MIMO systems
View Patent ↗Methods and a system are provided for enhanced long-term evolution scheduling. A ranking is constructed for one or more users scheduled on one or more resource blocks. A layer mapping is generated, using a finite modulation and coding scheme, utilizing the ranking of each of the one or more users for the one or more resource blocks. An enhanced ranking is determined, using a finite constraint on a buffer for each of the one or more users, for the one or more resource blocks utilizing the layer mapping. The enhanced ranking is deployed into a schedule for the one or more resource blocks being utilized by each of the one or more users. Bandwidth usage is optimized in the one or more resource blocks by utilizing the schedule.
1. A computer-implemented method for enhanced scheduling on a long-term evolution network, the method comprising:
constructing, by a processor, a ranking for one or more users scheduled on one or more resource blocks;
generating, by the processor using a finite modulation and coding scheme, a layer mapping utilizing the ranking of each of the one or more users for the one or more resource blocks;
determining, by the processor using a finite constraint on a buffer for each of the one or more users, an enhanced ranking for the one or more resource blocks utilizing the layer mapping;
deploying, by the processor, the enhanced ranking into a schedule for the one or more resource blocks being utilized by each of the one or more users; and
optimizing, by the processor, bandwidth usage in the one or more resource blocks by utilizing the schedule,
wherein said generating step processes each of the one or more users in a descending sequential order according to the ranking of each of the one or more users, by sorting weighted sum rates of each of the one or more users across all of the one or more resource blocks.
2. The computer-implemented method of claim 1 , wherein said constructing step includes ranking each of the one or more users on each of the one or more resource blocks and balancing the ranking for each of the one or more users across the one or more resource blocks to provide a balanced ranking.
3. The computer-implemented method of claim 1 , wherein said sorting step sorts the one or more resource blocks into a subset, with any of the one or more resource blocks that are absent from the subset being removed from consideration for use by the one or more users.
4. The computer-implemented method of claim 1 , wherein the layer mapping includes all of the one or more resources blocks that each of the one or more users is scheduled to utilize.
5. A non-transitory article of manufacture tangibly embodying a computer readable program which when executed causes a computer to perform the steps of claim 1 .
6. A computer-implemented method for enhanced scheduling on a long-term evolution network, the method comprising:
constructing, by a processor, a ranking for one or more users scheduled on one or more resource blocks;
generating, by the processor using a finite modulation and coding scheme, a layer mapping utilizing the ranking of each of the one or more users for the one or more resource blocks;
determining, by the processor using a finite constraint on a buffer for each of the one or more users, an enhanced ranking for the one or more resource blocks utilizing the layer mapping;
deploying, by the processor, the enhanced ranking into a schedule for the one or more resource blocks being utilized by each of the one or more users; and
optimizing, by the processor, bandwidth usage in the one or more resource blocks by utilizing the schedule,
wherein said determining step includes utilizing a signal-to-interference-plus-noise ratio for each of the one or more users in the layer mapping, and
wherein said utilizing step includes re-computing the signal-to-interference-plus-noise ratio for the one or more users absent from the layer mapping.