DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
No information disclosure statement (IDS) was submitted with the application.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 6, 9-11, 13, 16-18 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sundaresan et al. ( U.S. PGPUB 2014/0031049), Sundaresan hereinafter.
Regarding Claims 1, 9 and 16, Sundaresan teaches a system comprising: a processor configured to: (paragraph [0126] … the C-RAN concept (depicted in FIG. 14) migrates the BBUs to a datacenter (i.e., the BBU pool) hosting high performance general purpose and DSP processors, while providing high-bandwidth optical transport (called the front-haul) to the remote antennas called remote radio heads (RRHs). ...)
a method comprising: creating, in association with a network, a set of sector types, each sector type comprising information related to components and characteristics of the network; (paragraph [0029] ... The method comprises obtaining, from each small cell in said each sector, aggregate traffic demand, determining a minimum set of resources needed for distributed antenna systems (DAS) and fractional frequency reuse (FFR) configurations based on first traffic demand from mobile traffic and second traffic demand from sector-exterior traffic, respectively, determining optimal multiplexing of the DAS and FFR configurations for said each sector, determining baseband processing unit (BBU) resource usage metric (RU metric) for said each sector, clustering the plurality of sectors two at a time based on the RU metrics of the two sectors, and applying, through a front-haul configuration on allocated resources, DAS and FFR strategies to each small cell in the two sectors. ... [0115] Referring to FIG. 32, each of the steps can be elaborated as follows: [0116] Step 1a (block 3201): Each sector aggregates the radio resource (traffic) demands from mobile traffic in each of its small cells. The minimum radio resource demand needed for its DAS configuration is then the smallest number of OFDMA resources needed to satisfy the net mobile traffic demand.)
inputting, for each of the sector types, a network traffic model comprising information related to user equipment (UE) and applications operating on the network; ([0118] Step 2 (block 3202): Since users of multiple profiles may be inter-twined in a sector, FluidNet enables hybrid configurations that allow joint application of both DAS and FFR strategies to cater to all kinds of user and traffic profiles simultaneously. FluidNet multiplexes DAS and FFR strategies in each sector in either time and/or frequency resources and determines the optimal split of spectral resources between these two strategies for the hybrid configuration in each sector.)
executing, based on the set of sector types and the network traffic model, a Radio Frequency (RF) simulator to estimate uplink (UL) and downlink (DL) throughputs delivered by each of the set of sector types; ([0117] Step 1b (block 3201): Similarly, to determine the minimum radio resource demand for FFR, it aggregates the cell-exterior traffic from all its small cells that are on the edge of the sector. … [0150] … By appropriately employing FFR and DAS in combination in different parts of the network, FluidNet's goal is to strike a fine balance between them. Specifically, subject to the primary requirement of supporting as much traffic (D) as the optimal configuration (D.sub.OPT), … The optimal configuration would depend on the relative composition of mobile and static traffic and their priorities (D.sub.OPT=D.sub.FFR when there is only static traffic demand). We assume mobile traffic to be prioritized over static traffic, albeit other models are also possible. Also note that minimization of compute resource consumption is only subject to satisfying as much of the traffic demand as possible and does not come at the expense of the latter. ... [0198] ... Set-up: We use a 3GPP-calibrated system simulator to create a outdoor heterogeneous cellular network, with 19 macrocell sites) ... [0201] Traffic Heterogeneity: We first simulate a network where no clients are mobile. ...)
creating, in association with the network, a set of baseband unit (BBU) types, wherein each BBU type is suited to serve a set of sector types; ([0038] Still another aspect of the present invention includes a method used in a wireless communications system comprising a baseband processing unit (BBU) pool including one or more baseband processing units (BBUs), and a plurality of remote radio heads (RRHs) connected to the BBU pool through a front-haul network, the method comprising applying to each sector a combination of the one-to-one configuration and the one-to-many configuration, wherein the wireless communications system has a plurality of sectors, each of which includes one or more small cells, each of which is deployed by one of the plurality of RRHs, and wherein a BBUs is mapped to two or more RRHs in a sector in a one-to-many configuration, and a BBU is mapped to a single RRH in a sector in a one-to-many configuration.)
determining a number of each sector type served by each BBU in the set of BBUs; ([0120] Step 3a (block 3203): We define a metric called the BBU resource usage (RU) metric. This captures the effective number of BBUs needed to run and provide signals to DAS and FFR strategies in each sector. While a single BBU is sufficient for realizing a DAS configuration (since one-many signal mapping from BBU to RAUs in DAS), as many BBUs as the number of small cells in the sector is needed for a FFR configuration (one-one mapping in FFR). The RU is determined by, for example, the following formula: RU(b.sub.i, n.sub.i)=b.sub.i1+(B-b.sub.i)n.sub.i, where n.sub.i is the number of small cells in sector i and b.sub.i is the number of spectral resources allocated to its DAS configuration, while B is the total number of available spectral resources. ... [0158] To capture the BBU resource usage for a hybrid configuration in a sector, we define the resource usage metric, RU: where, n.sub.i is the number of small cells in sector i and b.sub.i the number of carriers (out of B total) allocated to its DAS configuration. In every carrier, the number of BBU units needed for DAS is one, while it is equal to the number of small cells (n) for FFR. Thus, RU captures the effective number of BBU units needed to support the offered load on the given spectral resources (OFDMA resources in B carriers).)
determining uplink (UL) and downlink (DL) throughputs delivered by each BBU in the set of BBUs based on the determined number of each sector type associated with the particular BBU; and ([0094] In a small cell configuration, where different signals are sent to different RAUs, an FFR-based approach is needed to address interference between cells. To determine the frequency reuse factor, we first need to measure the interference dependencies between the 3 cells. We serve RAU 2 and 3 from two different BBUs (BBU 1 and 2, respectively) on the same frequency band (2.61 GHz with 10 MHz BW). Then an MS associated with BBU1 through RAU2 is moved towards RAU3 to test the interference from RAU3 especially at the cell edge. The measured throughput results are shown in FIG. 7(a). Similarly an MS associated with BBU2 through RAU3 is moved towards RAU2 to test the resulting interference as shown in FIG. 7(b). Comparing with individual RAU coverage results (FIG. 5)), we find that the impact of interference between RAU 2 and 3 is very small as long as the client remains in the same corridor as the RAU, but is severe otherwise. ... [0190] ... Each experiment takes 180 seconds and is repeated multiple times with varying client locations. Impact of rate adaptation is isolated by picking the MCS that delivers maximum throughput for a client (we try all MCSs). The fraction of the offered load supported and the effective number of BBU units consumed in the process are the metrics of evaluation.)
configuring the network based on the determined UL and DL throughputs. ([0072]A system called FluidNet allows for a functional decoupling (in addition to the already existing physical decoupling) of the BBU pool from the RAUs. Further, it designs a backhaul (also called front-haul) architecture that is re-configurable to allow the mapping of signals between BBUs and RAUs to be changed dynamically and intelligently (based on network feedback) so as to not just optimize RAN performance but also energy consumption in the BBU pool. Specifically, it employs a combination of one-to-one and one-to-many mapping of BBU signals to RAUs to execute DAS (distributed antenna systems) and FFR (fractional frequency reuse) strategies and tailor them appropriately for heterogeneous traffic load and user profiles. The end result is better performance on the RAN and reduced use of computing/energy resources in the BBU pool. ... [0105] From the measured throughput in FIG. 9(b), we can see that the two mobile users experience very stable throughput from their DAS configuration, while moving from one end of the network to the other. Sharing of the 10 MHz bandwidth between the two MSs, reduces their individual absolute throughput. With FFR used by operator 2, since there is no interference between RAU 2 and 3, both the static MSs can reuse the whole 10 MHz bandwidth at 2.59 GHz, resulting in an increased individual and hence system throughput as shown in FIG. 9(c). ... [0198] Set-up: We use a 3GPP-calibrated system simulator to create a outdoor heterogeneous cellular network, with 19 macrocell sites (each has three sectors) and ten small cells per sector. Thus, the network has a total of 627 cells (57 macro+570 small) based on the scenarios defined in 3GPP 36.814 ... [0200] ... When the total load of neighboring sectors is less than a frame's worth of resources (i.e., the max. capacity of DAS), they are merged in a DAS cluster and thus served by one BBU. ...)
Regarding Claims 2, 10 and 17 Sundaresan teaches claim 1, 19 and 16.
Sundaresan further teaches further comprising: determining UL and DL bandwidths for a backhaul link on the network based on the UL and DL throughputs delivered by each BBU in the set of BBUs; ([0072] A system called FluidNet allows for a functional decoupling (in addition to the already existing physical decoupling) of the BBU pool from the RAUs. Further, it designs a backhaul (also called front-haul) architecture that is re-configurable to allow the mapping of signals between BBUs and RAUs to be changed dynamically and intelligently (based on network feedback) so as to not just optimize RAN performance but also energy consumption in the BBU pool. Specifically, it employs a combination of one-to-one and one-to-many mapping of BBU signals to RAUs to execute DAS (distributed antenna systems) and FFR (fractional frequency reuse) strategies and tailor them appropriately for heterogeneous traffic load and user profiles. The end result is better performance on the RAN and reduced use of computing/energy resources in the BBU pool.)
and performing the configuration of the network based on the UL and DL backhaul bandwidths. ([0073] The proposed FluidNet C-RAN solution incorporates a dynamically reconfigurable backhaul to provide better performance on the RAN by catering transmission strategies like DAS, FFR, CoMP, etc. (through backhaul configurations) to both spatio-temporal traffic distribution as well as heterogeneous user profiles (static, nomadic, mobile users). In addition, it also minimizes the use of computing resources and hence provides cost/energy savings in the BBU pool.)
Regarding Claim 3, Sundaresan teaches claim 1. Sundaresan further teaches further comprising: creating a set of core types for the network; and ([0181] 1) Resource Manager: The resource manager is responsible for two key functionalities: (i) determining the appropriate number of BBU units (using FluidNet's algorithms) needed to generate distinct frames and how these frames from BBUs are mapped to specific RRHs, and (ii) assigning compute resources (DSPs, cores, etc.) to each BBU unit.)
determining a number of each BBU type served by each core in the set of cores. ([0181] 1) Resource Manager: The resource manager is responsible for two key functionalities: (i) determining the appropriate number of BBU units (using FluidNet's algorithms) needed to generate distinct frames and how these frames from BBUs are mapped to specific RRHs, and (ii) assigning compute resources (DSPs, cores, etc.) to each BBU unit.)
Regarding Claim 4, Sundaresan teaches claim 3. Sundaresan further teaches
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determining, based on the number of each BBU type served by each core in the set of cores, UL and DL throughputs for each core; and ([0125] … FluidNet's algorithms determine configurations that maximize the traffic demand satisfied on the RAN, while simultaneously optimizing the compute resource usage in the BBU pool. We prototype FluidNet on a 6 BBU, 6 RRH WiMAX C-RAN testbed. Prototype evaluations and large-scale simulations reveal that FluidNet's ability to re-configure its front-haul and tailor transmission strategies provides a 50% improvement in satisfying traffic demands, while reducing the compute resource usage in the BBU pool by 50% compared to baseline transmission schemes.)
performing the configuration of the network based on the UL and DL throughputs for each core in the set of cores. ([0178] ... FluidNet's algorithms yield network-wide transmission configurations with a RU that is within a factor of ... and 2 from the optimal for sector and general graphs respectively. … [0181] 1) Resource Manager: The resource manager is responsible for two key functionalities: (i) determining the appropriate number of BBU units (using FluidNet's algorithms) needed to generate distinct frames and how these frames from BBUs are mapped to specific RRHs, and (ii) assigning compute resources (DSPs, cores, etc.) to each BBU unit. FluidNet focuses on the former functionality and is complementary to the processor scheduling problem addressed by studies with the latter functionality [1].)
Regarding Claims 6, 13 and 20, Sundaresan teaches claim 1, 9 and 16.
Sundaresan further teaches further comprising an RF simulator that is executed for each sector type in accordance with the network traffic model. ([0198] Set-up: We use a 3GPP-calibrated system simulator to create a outdoor heterogeneous cellular network, with 19 macrocell sites (each has three sectors) and ten small cells per sector. Thus, the network has a total of 627 cells (57 macro+570 small) based on the scenarios defined in 3GPP 36.814. We distribute 3600 small cell clients according to the `4b` distribution. We assume that the macrocells and their clients use pre-determined spectral resources orthogonal to the ones used by the small cells and their clients, and thus ignore the interference from/to the macrocell network.)
Regarding Claims 8 and 15, Sundaresan in view of Fertonani teaches claims 7 and 14.
Sundaresan further teaches wherein the mix of public and private networks is enabled via Radio Access Network (RAN) sharing. ([0197] ... FluidNet's support for multiple operators and technologies are very useful features in a C-RAN, given the growing popularity of RAN-sharing and dual carrier small cells (for WiFi offload).)
Regarding Claim 11, Sundaresan teaches claim 9.
Sundaresan further teaches wherein the processor is further configured to: create a set of core types for the network; ([0181] 1) Resource Manager: The resource manager is responsible for two key functionalities: (i) determining the appropriate number of BBU units (using FluidNet's algorithms) needed to generate distinct frames and how these frames from BBUs are mapped to specific RRHs, and (ii) assigning compute resources (DSPs, cores, etc.) to each BBU unit.)
determine a number of each BBU type served by each core in the set of cores; ([0181] 1) Resource Manager: The resource manager is responsible for two key functionalities: (i) determining the appropriate number of BBU units (using FluidNet's algorithms) needed to generate distinct frames and how these frames from BBUs are mapped to specific RRHs, and (ii) assigning compute resources (DSPs, cores, etc.) to each BBU unit.)
determine, based on the number of each BBU type served by each core in the set of cores, UL and DL throughputs for each core in the set of core types; and ([0125] … FluidNet's algorithms determine configurations that maximize the traffic demand satisfied on the RAN, while simultaneously optimizing the compute resource usage in the BBU pool. We prototype FluidNet on a 6 BBU, 6 RRH WiMAX C-RAN testbed. Prototype evaluations and large-scale simulations reveal that FluidNet's ability to re-configure its front-haul and tailor transmission strategies provides a 50% improvement in satisfying traffic demands, while reducing the compute resource usage in the BBU pool by 50% compared to baseline transmission schemes.)
perform the configuration of the network based on the UL and DL throughputs for each core in the set of cores. ([0178] ... FluidNet's algorithms yield network-wide transmission configurations with a RU that is within a factor of ... and 2 from the optimal for sector and general graphs respectively. … [0181] 1) Resource Manager: The resource manager is responsible for two key functionalities: (i) determining the appropriate number of BBU units (using FluidNet's algorithms) needed to generate distinct frames and how these frames from BBUs are mapped to specific RRHs, and (ii) assigning compute resources (DSPs, cores, etc.) to each BBU unit. FluidNet focuses on the former functionality and is complementary to the processor scheduling problem addressed by studies with the latter functionality [1].)
Regarding Claim 18, Sundaresan teaches claim 16.
Sundaresan further teaches the non-transitory computer-readable storage medium of claim 16, further comprising: creating a set of core types for the network; ([0181] 1) Resource Manager: The resource manager is responsible for two key functionalities: (i) determining the appropriate number of BBU units (using FluidNet's algorithms) needed to generate distinct frames and how these frames from BBUs are mapped to specific RRHs, and (ii) assigning compute resources (DSPs, cores, etc.) to each BBU unit.)
determining a number of each BBU type served by each core in the set of cores; ([0181] 1) Resource Manager: The resource manager is responsible for two key functionalities: (i) determining the appropriate number of BBU units (using FluidNet's algorithms) needed to generate distinct frames and how these frames from BBUs are mapped to specific RRHs, and (ii) assigning compute resources (DSPs, cores, etc.) to each BBU unit.)
determining, based on the number of each BBU type served by each core in the set of cores, UL and DL throughputs for each core; and ([0125] … FluidNet's algorithms determine configurations that maximize the traffic demand satisfied on the RAN, while simultaneously optimizing the compute resource usage in the BBU pool. We prototype FluidNet on a 6 BBU, 6 RRH WiMAX C-RAN testbed. Prototype evaluations and large-scale simulations reveal that FluidNet's ability to re-configure its front-haul and tailor transmission strategies provides a 50% improvement in satisfying traffic demands, while reducing the compute resource usage in the BBU pool by 50% compared to baseline transmission schemes.)
performing the configuration of the network based on the UL and DL throughputs for each core in the set of cores. ([0178] ... FluidNet's algorithms yield network-wide transmission configurations with a RU that is within a factor of ... and 2 from the optimal for sector and general graphs respectively. … [0181] 1) Resource Manager: The resource manager is responsible for two key functionalities: (i) determining the appropriate number of BBU units (using FluidNet's algorithms) needed to generate distinct frames and how these frames from BBUs are mapped to specific RRHs, and (ii) assigning compute resources (DSPs, cores, etc.) to each BBU unit. FluidNet focuses on the former functionality and is complementary to the processor scheduling problem addressed by studies with the latter functionality [1].)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
In event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 5, 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Sundaresan et al. ( U.S. PGPUB 2014/0031049), Sundaresan hereinafter, in view of Hedge et al. (U.S. PGPub 2025/0008429), Hedge hereinafter.
Regarding Claims 5, 12 and 19 Sundaresan teaches claims 1, 9 and 16.
Yet, Sundaresan does not expressly teach wherein the network traffic model information comprises UL and DL data rates input for the UEs and applications.
However, in the analogous art, Hedge explicitly discloses wherein the network traffic model information comprises UL and DL data rates input for the UEs and applications ([0017] Optionally, the processing system 105 includes and executes a management system (MS) 105A used to manage each pair of RAT base station and RAT core network end to end, e.g., RRHs, RUs, BBU(s), DU(s), CU(s), and/or core network(s). ... [0019] … The deterministic model may be a function of parameters.sup.4 (for example, key performance indicators of a RAT base station) which are obtained or generated by the RAT base station. Optionally, the model includes a threshold level and/or a weighting for one or more of the parameters. A parameter value, e.g., downlink traffic throughput, is compared, e.g., to ascertain if it is greater than or less than, a parameter threshold level, e.g., downlink traffic throughput threshold level. The model may optionally include a second parameter threshold level to facilitate hysteresis so that the model does not cause a component of the RAT base station to rapidly alternate between an unreduced power state and a reduced power state. .sup.4Parameters may include without limitation a number of UE attached to a RAT base station, location(s) of such attached UE (e.g., based on UE downlink measurements reported by the attached UE), and/or an amount of traffic conveyed in an uplink path and/or downlink path between the UE and the RAT base station (“traffic throughput in an uplink path and/or downlink path).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine Sundaresan’s Cloud-based Radio Access Network for Small Cells to include Hedge's model that uses uplink and downlink throughput in order to adapt BBU resource to maximize throughput.
Claims 7, 8, 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Sundaresan et al. ( U.S. PGPUB 2014/0031049), Sundaresan hereinafter, in view of Fertonani et al. (U.S. PGPub 2017/0373890), Fertonani hereinafter.
Regarding Claims 7 and 14 Sundaresan teaches claims 1 and 9.
Yet, Sundaresan does not expressly teach further comprising the network being a network selected from a group consisting of: a public network, a private network, and a mix of public and private networks.
However, in the analogous art, Fertonani explicitly discloses 7. The method of claim 1, further comprising the network being a network selected from a group consisting of: a public network, a private network, and a mix of public and private networks ([0061] … The BBUs 360 in the data center 310 are coupled to a network 390 to provide access to a core network 399, such as an EPC of an E-UTRAN. The network 390 may be a part of the core network 399 in some embodiments, or may be a private managed network, or the public internet, depending on the embodiment. In some installations, the fronthaul link 335 may be coupled to the network 390 by active network equipment, such as the router 320. In some embodiments, the fronthaul link 335 may be a part of the network 390. In some embodiments, the distributed RAN 300 may be referred to as a Cloud-RAN 300 which includes a BBU 361 coupled to the network 390 and a RRU 340 with antenna 341 coupled to the network 390. … ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine Sundaresan’s Cloud-based Radio Access Network for Small Cells to include Fertonani's public and private network to provide communication services to diverse subscriber.
Regarding Claims 8 and 15, Sundaresan in view of Fertonani teaches claims 7 and 14.
Sundaresan further teaches wherein the mix of public and private networks is enabled via Radio Access Network (RAN) sharing. ([0197] ... FluidNet's support for multiple operators and technologies are very useful features in a C-RAN, given the growing popularity of RAN-sharing and dual carrier small cells (for WiFi offload).)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. This includes:
U.S. PGPUB 2024/0031850 which describes cell site energy utilization management
U.S. PGPUB 2024/0098575 which describes methods and devices for determination of an update timescale for radio resource management algorithms
U.S. PGPUB 2024/0223462 which describes NRDU data path simulation server for testing data throughput capacity in 5G communication network
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nicholas A. Jensen can be reached at 571-270-5443. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A.L.O./Examiner, Art Unit 2472
/NICHOLAS A JENSEN/Supervisory Patent Examiner, Art Unit 2472
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