System and method of automatically optimizing an operational radio access network

Systems and methods of automatically optimizing an operational radio access network are provided. Objective and operational cost functions for the radio access network are generated, and a deviation between the generated cost functions is determined. At least one aspect of the radio access network is adjusted in order to minimize the determined deviation between cost functions.

BACKGROUND OF THE INVENTION

Wireless communication networks are very complicated, and expensive to deploy. Prior to deploying a wireless communication network, extensive computer simulations are performed in order to optimize the parameters and placement of networks elements. Computer simulations cannot, however, account for the real-world conditions in which the network is deployed. Accordingly, once a wireless communication network is installed, additional testing is performed in order to account for real-world conditions. Furthermore, as wireless communication networks are expanded with additional base stations to provide additional capacity or coverage, the network must again be tested to optimize the base stations.

SUMMARY OF THE INVENTION

Exemplary embodiments of the present invention are directed to systems and methods of automatically optimizing an operational radio access network using the system's key performance parameters and desired service outcome. An exemplary method involves receiving information related to operational coverage, capacity and handoffs in a radio access network and generating an operational cost function based on the received information related to operational coverage, capacity and handoffs. Information related to objective coverage, capacity and handoffs in a radio access network is received and an objective cost function based on the received information related to objective coverage, capacity and handoffs is generated. A deviation between the operational and objective cost functions is determined and an aspect of at least one of the operational coverage, capacity and handoffs is automatically adjusted to minimize the determined deviation.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 1is a block diagram of an exemplary network in accordance with the present invention. The illustrated network is a WiMAX network that includes a radio access network105and a connectivity service network150. The radio access network includes a plurality of cells110A-110n. Each cell includes a plurality of base stations115A1-115nxcoupled to an application service node-gateway (ASN-GW)120A-120n. Each ASN-GW120A-120nis coupled to the connectivity service network150.

Connectivity service network150includes a home agent152, Internet Protocol multimedia system (IMS)154, customer relationship manager (CRM)156, billing component158, authentication, authorization and accounting component160and dynamic host control protocol/domain name server (DHCP/DNS)162. Additionally, connectivity service network150includes service optimizer system (SOS)164, which as will be described in more detail below, receives information from ASN-GWs120A-120nand provides information back that is used to control the radio access network. Although exemplary embodiments are described in connection with the WiMAX network, the present invention is equally applicable to other types of networks, such as CDMA, GSM, iDEN, EV-DO, EDGE, etc. Furthermore, althoughFIG. 1illustrates a particular number of base stations within each cell and a particular number of cells, the present invention can be employed with a different number of base stations per cell and a different number of cells.

FIG. 2is a block diagram of an exemplary service optimizer system164in accordance with the present invention. Service optimizer system164includes a communication interface205for communicating with ASN-GWs120A-120n, as well as other components of the connectivity service network150. Communication interface is coupled to processor210, which in turn is coupled to memory250. Processor210includes logic212-220, which will be described in more detail below in connection with the method ofFIG. 3. Processor210can be a microprocessor, field programmable gate array (FPGA) and/or application specific integrated circuit (ASIC). When processor210is a microprocessor, logic212-220can be processor-executable code loaded from memory250.

FIG. 3is a flow diagram of an exemplary method in accordance with the present invention. Initially, service optimizer system164receives information related to operational parameters from one or more ASN-GWs120A-120nvia communication interface205(step305). Logic212generates an operational cost function using the received information (step310). An exemplary cost function (CFOp) is
CFOp=A*XCov+B*YCap+C*ZHO

where XCovis the coverage function, YCapis the capacity function, ZHOis a handoff function, and A, B and C are weights that can be adjusted depending upon whether the optimization is being performed during the busy hour or normal operating hours. Thus, the cost function during normal hours is expressed as CFnhand the cost function during busy hours is expressed as CFbh.

The coverage function includes some parameters that can be adjusted by the network administrator and other parameters that are dependent upon the network environment. The administrator adjustable parameters include BTS EIRP (which is a function of the transmit power minus the cable loss plus the antenna gain), MAP repetition (a constant that can be dynamic), Paging Cycle (a constant that can be dynamic), ARQ Block Size (a constant that can be dynamic), and CPE EIRP (which is a function of the transmit power plus the antenna gain). The environmental dependent parameters include CINR, RSSI, user throughput and average sector throughput. Although particular parameters are described above, the cost function can include a greater or less number of parameters or different parameters.

The administrator adjustable parameters include the sleep timer, idle timer, MAP repetition (a constant that can be dynamic) and ARQ block size (a constant that can be dynamic). The environmental-dependent parameters include CINR, RSSI and throughput capacity. Although particular parameters are described above, the cost function can include a greater or less number of parameters or different parameters.

The HO function ZHOdepends on several parameters as follow:ZHO=Fbh[HO Delay Timer, Add Threshold, Delete Threshold, Trigger CINR, RSSI, Neighbor List]

The administrator adjustable parameters include handover delay timer, neighbor add threshold, neighbor delete threshold, trigger CINR and neighbor list. The environmental dependent parameters include CINR and RSSI. Although particular parameters are described above, the cost function can include a greater or less number of parameters or different parameters.

Referring again toFIGS. 2 and 3, processor210then receives information related to objective parameters (step315) and logic214generates an objective cost function using the received information (step320). The objective cost function is as follows:
CFObj=A*XCov+B*YCap+C*ZHO

The coverage, capacity and handoff functions for the objective cost functions use the same parameters as those described above in connection with the operational cost function, but the data for the parameters is based on objective values. The objective values can be derived by a simulation of the network and/or one or more of the values can be set by a network administrator.

The objective parameters can be received from memory250. Logic216then determines a deviation between the operational and objective cost functions (step325) and logic218automatically adjusts at least one parameter of the cost functions in order to minimize the deviation (step330).

The minimization of the deviations of the cost functions can be expressed as [CFOp−CFObj]2, where the minimized cost function for the busy hour is:
CF=ΣiAbhXi+ΣiBbhYi+ΣiCbhZi

Accordingly, the minimization of the cost function becomes

Processor210then receives updated information related to the operational parameters that account for the automatic adjustment (step335) and logic212generates an updated operational cost function (step340). Logic216then determines a deviation between the updated operational cost function and the objective cost function (step345). Logic220then determines whether the deviation is minimized (step350). When the deviation is not minimized (“No” path out of decision step350), then the process is immediately repeated. When the deviation is minimized, then the process is repeated after a predetermined delay or an event trigger from the service network (step355). Accordingly, the present invention provides an iterative technique for automatically adjusting parameters of, and in turn performance in, a live radio access network in order to converge the parameters to optimum values. The method ofFIG. 3can be performed on an entire network basis, and/or on a per sector, cell or location area basis.

Although the present invention has been described above in connection with particular parameters used in the cost functions, the present invention can use other parameters in addition to, or as an alternative to, those discussed above. These additional parameters can be, for example:operational and performance information, including number of radio channels, size of radio channels, transmitted power, coding, modulation;system statistics, including call admissions, dropped calls/sessions, network entry attempts;operator parameters, including backhaul capacity, available spectrum, service policy;geographical information, including ground cover, land use, location of base stations, minimum acceptable service quality; andhistorical performance information, including utilization of each of the network resources, faults and alarms indicating service quality issues, number of active and dormant users during each time interval, location of served mobile stations relative to the base station, amount of traffic demand, traffic successfully delivered to the mobile stations.