Evolutionary algorithms for geographic load balancing using a distributed antenna system
Methods and apparatuses are presented for balancing non-uniformly distributed network traffic in a wireless communications system having a plurality of digital remote units (DRUs). In some embodiments, a method comprises partitioning the plurality of DRUs into a plurality of DRU sectors, and dynamically repartitioning the plurality of DRU sectors depending on traffic conditions in at least one of the DRU sectors, such that the repartitioning satisfies at least one of a soft capacity constraint or a hard capacity constraint. The dynamic repartitioning may be based on at least one optimization algorithm.
1. A method comprising:
providing a digital access unit (DAU) associated with a plurality of sectors of a base station, wherein the DAU is operable to receive signals from the base station;
providing a plurality of digital remote units (DRUs) associated with the DAU, wherein the DAU assigns a plurality of radio resources associated with the plurality of sectors to the plurality of DRUs based on a measure of network traffic;
partitioning the plurality of DRUs into a plurality of DRU groupings, wherein partitioning balances non-uniformly distributed network traffic;
measuring at least one metric associated with the plurality of DRU groupings;
comparing the at least one metric to at least one threshold; and
iterating the partitioning, measuring and comparing steps.
2. The method of claim 1 , wherein measuring the at least one metric further comprises determining a compactness index of each sector of the plurality of DRU sectors.
3. The method of claim 2 , wherein partitioning the plurality of DRUs comprises maximizing a compactness index of each sector of the plurality of DRU sectors.
4. The method of claim 1 , wherein the at least one metric further comprises traffic conditions.
5. The method of claim 1 , wherein the at least one metric further comprises a maximum number of users associated with a predetermined signal to noise ratio (SNR).
6. The method of claim 1 , wherein iterating comprises utilizing at least one optimization algorithm.
7. The method of claim 6 , wherein the at least one optimization algorithm is at least one of a Genetic Algorithm (GA) or an Estimation Distribution Algorithm (EDA).
8. The method of claim 1 , wherein each sector of the plurality of DRU sectors comprises connected DRUs.
9. A system comprising:
digital access unit (DAU) associated with a plurality of sectors of a base station, wherein the DAU is operable to receive signals from the base station, and the DAU is configured to dynamically repartition cells of a distributed antenna system and to receive signals from a base station, the DAU comprising a data processor coupled to a non-transitory computer-readable storage medium comprising a plurality of computer-readable instructions tangibly embodied on the computer-readable storage medium, which, when executed by the data processor cause to be performed a method comprising:
providing a plurality of digital remote units (DRUs) associated with the DAU, wherein the DAU assigns a plurality of radio resources associated with the plurality of sectors to the plurality of DRUs based on a measure of network traffic;
partitioning the plurality of DRUs into a plurality of DRU groupings, wherein partitioning balances non-uniformly distributed network traffic;
measuring at least one metric associated with the plurality of DRU groupings;
comparing the at least one metric to at least one threshold;
determining that the at least one metric is greater than the at least one threshold; and
iterating the above steps.
10. The DAU of claim 9 , wherein the at least one metric further comprises traffic conditions.
11. The DAU of claim 9 , wherein the instructions that cause the data processor to measure the at least one metric further comprise instructions for determining a compactness index of each sector of the plurality of DRU sectors.
12. The DAU of claim 11 , wherein the instructions that cause the data processor to partition the plurality of DRUs comprise instructions for maximizing the compactness index of each sector of the plurality of DRU sectors.
13. The DAU of claim 9 , wherein the at least one metric further comprises a maximum number of users associated with a predetermined signal to noise ratio (SNR).
14. The DAU of claim 9 , wherein iterating comprises utilizing at least one optimization algorithm.
15. The DAU of claim 14 , wherein the at least one optimization algorithm is at least one of a Genetic Algorithm (GA) or an Estimation Distribution Algorithm (EDA).
16. The DAU of claim 9 , wherein each sector of the plurality of DRU sectors comprises connected DRUs.