Cooperative radio resource allocation in a wireless communication network and methods for use therewith
A cooperative radio resource manager operates by: obtaining constraint data indicating a plurality of radio network constraints and channel state information (CSI) corresponding to wireless communications between a plurality of client devices and a plurality of remote radio heads; performing a first sub-problem optimization, subject to the plurality of radio network constraints, to generate an remote radio head (RRH) to client device association that associates a corresponding one of the plurality of remote radio heads to each of the plurality of client devices and further to generate a physical resource block (PRB) allocation that allocates PRBs of each RRH among the plurality of client devices. The cooperative radio resource manager can also operate by performing a second sub-problem optimization, subject to a subset of the plurality of radio network constraints, to generate a power allocation (PA) that allocates power for uplink and downlink transmissions between the plurality of remote radio heads and the plurality of client devices, based on the RRH to client device association and the PRB allocation generated via the first sub-problem optimization, for example. In either case, the cooperative radio resource manager can facilitate fronthaul communications via a radio network interface to control the wireless communications between the plurality of remote radio heads and the plurality of client devices via the RRH to client device association, the PRB allocation and/or the PA.
1. A network element comprises:
a core network interface configured to communicate backhaul communications with one or more network elements of a core communication network;
a radio network interface configured to communicate fronthaul communications with a plurality of remote radio heads of a radio network, wherein the plurality of remote radio heads are configured to engage in wireless communications with a plurality of client devices;
a radio resource manager that includes a processor and a memory, wherein the memory stores operational instructions that, when executed by the processor, cause the processor to perform operations that include:
obtaining constraint data indicating a plurality of radio network constraints;
obtaining channel state information (CSI) corresponding to the wireless communications between the plurality of client devices and the plurality of remote radio heads;
performing an optimization that includes a binary-valued sub-problem optimization subject to a first subset of the plurality of radio network constraints, wherein the binary-valued sub-problem optimization jointly optimizes a remote radio head (RRH) to client device association together with a physical resource block (PRB) allocation by associating a corresponding one of the plurality of remote radio heads to each of the plurality of client devices and further by optimizing uplink and downlink scheduling via selection, by the binary-valued sub-problem optimization, of either uplink or downlink transmission for each PRB of each RRH of the plurality of remote radio heads; and
facilitating the fronthaul communications via the radio network interface to control the wireless communications between the plurality of remote radio heads and the plurality of client devices via the RRH to client device association and the PRB allocation.
2. The network element of claim 1 , wherein the optimization further includes a real-valued sub-problem optimization, subject to a second subset of the plurality of radio network constraints, to generate a power allocation (PA) that allocates power for uplink and downlink transmissions between the plurality of remote radio heads and the plurality of client devices; and
wherein facilitating the fronthaul communications via the radio network interface to control the wireless communications between the plurality of remote radio heads and the plurality of client devices is also via the PA.
3. The network element of claim 2 , wherein the real-valued sub-problem optimization is based on the RRH to client device association and the PRB allocation generated via the binary-valued sub-problem optimization.
4. The network element of claim 2 , wherein the real-valued sub-problem optimization includes performing a modified particle swarm optimization.
5. The network element of claim 2 , wherein the second subset of the plurality of radio network constraints, include a plurality of real-valued constraints.
6. The network element of claim 5 , wherein the plurality of real-valued constraints, include one or more of power budget constraint and one or more quality of service constraint.
7. The network element of claim 1 , wherein the first subset of the plurality of radio network constraints, include a plurality of binary-valued constraints.
8. The network element of claim 7 , wherein the plurality of binary-valued constraints include at least one unified architecture variable.
9. The network element of claim 7 , wherein the plurality of binary-valued constraints include at least one time slot constraint and at least one time slot allocation constraint.
10. The network element of claim 1 , wherein the binary-valued sub-problem optimization includes performing a modified genetic algorithm.
11. A method comprises:
obtaining constraint data indicating a plurality of radio network constraints;
obtaining channel state information (CSI) corresponding to wireless communications between a plurality of client devices and a plurality of remote radio heads;
performing an optimization that includes a binary-valued sub-problem optimization subject to a first subset of the plurality of radio network constraints, wherein the binary-valued sub-problem optimization jointly optimizes a remote radio head (RRH) to client device association together with a physical resource block (PRB) allocation by associating a corresponding one of the plurality of remote radio heads to each of the plurality of client devices and further by optimizing uplink and downlink scheduling via selection, by the binary-valued sub-problem optimization, of either uplink or downlink transmission for each PRB of each RRH of the plurality of remote radio heads; and
facilitating a fronthaul communications via a radio network interface to control the wireless communications between the plurality of remote radio heads and the plurality of client devices via the RRH to client device association and the PRB allocation.
12. The method of claim 11 , wherein the optimization further includes a real-valued sub-problem optimization, subject to a second subset of the plurality of radio network constraints, to generate a power allocation (PA) that allocates power for uplink and downlink transmissions between the plurality of remote radio heads and the plurality of client devices; and
wherein facilitating the fronthaul communications via the radio network interface to control the wireless communications between the plurality of remote radio heads and the plurality of client devices is also via the PA.
13. The method of claim 12 , wherein the real-valued sub-problem optimization is based on the RRH to client device association and the PRB allocation generated via the binary-valued sub-problem optimization.
14. The method of claim 12 , wherein the real-valued sub-problem optimization includes performing a modified particle swarm optimization.
15. The method of claim 12 , wherein the second subset of the plurality of radio network constraints, include a plurality of real-valued constraints.
16. The method of claim 15 , wherein the plurality of real-valued constraints, include one or more of power budget constraint and one or more quality of service constraint.
17. The method of claim 11 , wherein the first subset of the plurality of radio network constraints, include a plurality of binary-valued constraints.
18. The method of claim 17 , wherein the plurality of binary-valued constraints include at least one unified architecture variable.
19. The method of claim 17 , wherein the plurality of binary-valued constraints include at least one time slot constraint and at least one time slot allocation constraint.
20. The method of claim 11 , wherein the binary-valued sub-problem optimization includes performing a modified genetic algorithm.