A METHOD FOR UE-ASSISTED ANTENNA PARAMETERS OPTIMIZATION BACKGROUND Field [0001] The present disclosure is related to wireless systems, and more specifically, to the dynamic reconfiguration of base station parameters. Related Art [0002] Related art methods for optimizing network parameters in cellular networks may rely on information exchange and processing by or via the base stations. The base stations choose the network parameters based on individual and independent feedback from their connected user equipment (UE). [0003] In related art cellular networks, the parameters related to transmission such as antenna tilt, transmission mode (TM), number of antennas to use to serve each user, and so on are decided at the time of transmission by the network based on independent feedback by the UEs, or by joint network optimization that considers the base stations and does not involve the UEs in the decision. As the network becomes denser, however, and the backhaul between the base stations becomes less reliable, UE- assisted network optimization promises to yield considerable gains in network performance as compared to traditional optimization and configuration strategies. [0004] For the specific case of vertical antenna tilting, traditionally a fixed mechanical tilt or remote electrical tilt (RET) is set at the base stations to guide the direction of the main lobe. For Adaptive antenna system (AAS)-enabled Full Dimensional Multiple Input Multiple Output (FD-MIMO) base stations, a vertical electrical tilt is expected to be reconfigured. Optimization algorithms in the related art cover optimization of user association and load balancing and user throughput. SUMMARY [0005] Aspects of the present disclosure include a base station, which may involve a plurality of antennas configured to adjust at least one of transmission elevation and beam shape based on transmission parameters; a memory configured to store at least one of a first optimized estimate of the transmission parameters from an associated UE and a second optimized estimate of the transmission parameters from a neighboring base station; and a processor configured to adjust the transmission parameters of the base station based on a processing of at least one of the first optimized estimate and the second optimized estimate. [0006] Aspects of the present disclosure include a method for a base station, which may involve processing of at least one of the first optimized estimate of network parameters from an associated user equipment (UE) and a second optimized estimate of the network parameters from a neighboring base station; and adjusting at least one of transmission elevation and beam shape of a plurality of antennas based on the processing. [0007] Aspects of the present disclosure include a system, which may involve a first base station; and a plurality of user equipment (UE) associated with the first base station and configured to communicate with each other to determine a first optimized estimate of network parameters of the first base station. The first base station may include a plurality of antennas configured to adjust at least one of transmission elevation and beam shape based on network parameters; a memory configured to store at least one of a first optimized estimate of the network parameters from an associated one of the plurality of user equipment (UE) and a second optimized estimate of the network parameters from a neighboring base station; and a processor configured to adjust the network parameters of the base station based on a processing of at least one of the first optimized estimate and the second optimized estimate. BRIEF DESCRIPTION OF THE DRAWINGS [0008] FIG. 1 illustrates an example of a heterogeneous network with different

antenna tilts requirements, in accordance with an example implementation. [0009] FIG. 2(a) illustrates an example of antenna tilt optimization to increase the coverage area of a small cell, in accordance with an example implementation. [0010] FIG. 2(b) illustrates an example of vertical sectorization to serve users in the elevation domain, in accordance with an example implementation. [0011] FIG. 3 illustrates a flowchart of the UE-assisted optimization algorithm, in accordance with an example implementation. [0012] FIG. 4 illustrates an example of antenna tilt that does not take into account interference to users in other cell, and a beam with optimized tilt in accordance with an example implementation. [0013] FIG. 5 illustrates an example of two base stations serving two UEs, in

accordance with an example implementation. [0014] FIG. 6 illustrates an example user equipment upon which example

implementations can be implemented. [0015] FIG. 7 is a flowchart for operation of the algorithm when the optimization is carried out at the base stations, in accordance with an example implementation. [0016] FIG. 8 illustrates an example base station upon which example

implementations can be implemented. DETAILED DESCRIPTION [0017] The following detailed description provides further details of the figures and example implementations of the present application. Reference numerals and descriptions of redundant elements between figures are omitted for clarity. Terms used throughout the description are provided as examples and are not intended to be limiting. For example, the use of the term“automatic” may involve fully automatic or semi-automatic implementations involving user or administrator control over certain aspects of the implementation, depending on the desired implementation of one of ordinary skill in the art practicing implementations of the present application. The terms enhanced node B (eNodeB), small cell (SC), base station (BS) and pico cell may be utilized interchangeably throughout the example implementations. The

implementations described herein are also not intended to be limiting, and can be implemented in various ways, depending on the desired implementation. [0018] The term“optimize” as described in the present disclosure does not require absolute optimization and can depend on the desired implementation. For example, implementations involving thresholds can be utilized for reaching minimum Quality of Service (QoS) levels or for reaching thresholds of other metrics, wherein the parameters of the base station are thereby configured to meet the threshold

requirement for the QoS or other metrics (e.g., configuring beam shape, tilt, elevation, etc. such that the threshold for QoS or other metrics are met). [0019] Distributed estimation and optimization by and via the UEs has not been

utilized in present related art systems. Related art methods do not take interference from neighboring base stations into account, and do not have a UE-assisted distributed solution. [0020] Network densification can play a key role in Long Term Evolution Release 12 (LTE Rel-12) and beyond to meet the growing demand for data and improve the network performance. Densification is evident in deployment of dense small cell networks, with and without ideal backhaul, as well as exploiting FD-MIMO at the base stations. Densification is coupled with dynamic reconfiguration of network parameters to ensure optimized network performance. [0021] Example implementations described herein involve UE-assisted estimation and optimization of network parameters. The proposed distributed estimation and optimization algorithm is based on the principle of distributed stochastic

approximation, and utilizes an exchange of information between neighboring UEs to reach a consensus on the network parameters that are being optimized. This general distributed stochastic approximation approach can be used to estimate and optimize various parameters in the network. Several non-limiting possible example

implementations can be implemented based on the approaches of the present disclosure: (1) more optimally updating the antenna parameters and the transmission strategies at the base stations; (2) more optimally synchronizing the UEs and the base stations in the network; and (3) more optimally detecting desired signals through cooperative distributed UE estimation. [0022] To reach a consensus on the parameters being optimized, an algorithm

involving a local process and a gossip process can be executed among the participating terminals. During the local process, each terminal updates its local estimates of the parameters based on observations related to interference level, signal level, network clock or other related parameters. The local process can utilize a stochastic

approximation algorithm with a decreasing step size that is shown to converge. During the gossiping process, the terminals compute a local weighted average between their estimates and those of their neighbors, where the weights are chosen based on terminals locations, interference levels, association or other related parameters. The weights are such that they can invoke convergence of the algorithm to a consensus. The algorithm can be of low complexity and can be implemented in a distributed manner without excessive overhead. [0023] Network configuration with the aid of the user terminals, made increasingly possible by new UE capabilities such as device-to-device communication, may improve the network performance. UE-assisted network optimization can be used in conjunction with several new features of the wireless network, a non-limiting set of which is given by enhanced downlink MIMO, full-dimensional MIMO (FD-MIMO) and over the air synchronization of small cells. [0024] FD-MIMO using AAS increases capacity and coverage and can facilitate a more flexible deployment of the network. Vertical antenna tilt is one non-limiting example of such network parameters that can be reconfigured in conjunction with FD- MIMO. Reconfiguration takes into account the heterogeneous deployment of users in the plane, the type of base station (macro cell, small cell), the number of simultaneous beams at each base station, formation and shape of the beam, as well as optimized local utilities at the users such as maximizing signal to interference plus noise ratio (SINR) or maximizing energy efficiency at the mobile user equipment (UE). [0025] FIG. 1 illustrates an example of a heterogeneous network with different

antenna tilts requirements, in accordance with an example implementation. In FIG. 1, a heterogeneous cellular network with macro cells and small cells serves a different number and distribution of users. Optimizing the antenna tilt for each of these base stations independently may not be sufficient to optimize the network performance. Optimizing the antenna tilts among the base stations might not be reliable considering the non-ideal backhaul and the lack of global information at the base stations by all the users in the network. Thus in example implementations, a UE-assisted method where neighboring users exchange information to reach a consensus on the optimal tilts at each base station is utilized to improve the network performance. [0026] Although adjusting the antenna tilts for the FD-MIMO array to maximize the received signal at an individual user is optimal for that particular user, such an adjustment might not be the best option in terms of resource optimization at the base station. Optimizing the antenna tilt to serve a group or a cluster of users provides a more feasible scenario. [0027] FIG. 2(a) illustrates an example of antenna tilt optimization to increase the coverage area of a small cell, in accordance with an example implementation.

Consider for example the scenario in FIG. 2(a), where a hot spot small cell is serving a group of users. The SC can transmit assuming a tilt θ1, with a main beam“beam 1” to serve the cluster of users, or it can decrease its tilt to θ2 transmitting with“beam 2”, to serve a larger number of users and extend its coverage range. [0028] FIG. 2(b) illustrates an example of vertical sectorization to serve users in the elevation domain, in accordance with an example implementation. As illustrated in FIG. 2(b), the base station serving users in a high rise building can create sectors in the vertical dimension by serving the users with two or more beams, such that“beam 1” tilt is adjusted to serve the users on the higher floors and“beam 2” is adjusted to serve users on the lower floors. [0029] In example implementations, the general UE-assisted optimization algorithm has the following structure. Consider a network of N nodes (UEs), wherein each node i generates a d-dimensional stochastic process at time n, through an algorithm involving a local process and a gossip process. [0030] During the local process, each node i generates a local estimate of the target stochastic process by solving a local optimization function. The local optimization function is convex, but need not be differentiable. One possible solution to the optimization function is stochastic gradient descent methods. As part of the local estimate process, the algorithm to optimize the local function takes into account communication and estimation noise at node i. The gradient descent method can be solved using the local observation at node i This observation can be based on Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), or any other metric utilized in the optimization of the algorithm, depending on the desired implementation. [0031] During the gossip process, node i observes the values of the local estimates at other neighboring nodes, and computes a weighted average of the estimates. The positive weights are chosen to facilitate convergence to the consensus. The weight per node can depend on the location of node j relative to node i, and the category of node j. For example, if node i for example belongs to the same group as node j, more weight is given to node j. [0032] By implementing a feedback consensus of the network parameters based on the local process and the gossip process, optimized network parameters such as transmission tilt of the base station can be provided to the base station. For example, when the base station receives the optimized transmission tilt estimate, the base station can adjust the transmission elevation based on the optimized transmission tilt. [0033] Other network parameters can be managed and optimized in a similar manner based on the desired implementation. For example, various beam parameters, such as beam shape, transmission power and transmission timing can also be modified depending on the desired implementation. In another example, the associated UE can provide optimized beam parameters based on the location and/or the channel information of the associated UEs. For example, based on location of the UEs, the UEs may determine that channel quality can be improved by widening or narrowing the beam shape, and can thereby provide a feedback consensus of the beam shape to the associated base station. The feedback consensus can include information such as channel estimation or location estimation (e.g., location with respect to the base station) of the associated UE, and the associated UE can use the information to determine a beam shape to address the UE. [0034] In another example, the UEs associated with the base station can, based on a channel or noise estimation, derive a consensus with respect to the desired shape, timing, or power of the beam for the subsequent transmission of the base station. The base station, upon receiving the optimized estimates from either the associated UE or the base station can thereby proceed conducting operations such as adjusting the beam shape and elevation, selecting antenna ports, or muting certain antenna ports. The example implementations described below can therefore be modified to optimize any desired network parameter of the base station based on the feedback consensus. [0035] Further, as described below with respect to the example implementations, the feedback consensus can be provided by the associated UEs or can also be provided by neighboring base stations. The neighboring base stations can involve small cells within the cell of a macro base station, or can also involve base stations within a coordinated multipoint (CoMP) set. [0036] FIG. 3 illustrates a flowchart of the UE-assisted optimization algorithm, in accordance with an example implementation. [0037] For optimizing the vector of different antenna tilts at different neighboring base stations, a distributed stochastic approximation algorithm, as illustrated in the flowchart in FIG. 3 can be utilized. In an example, consider N UEs connected to M neighboring base stations, and applying the algorithm to reach a consensus on a vector of M antenna tilts. [0038] At the start of the algorithm at 300, each UE i extract the initial antenna tilt from its received signal. At the flow at 301, each UE i then calculates a local estimate of the optimal antenna tilt to maximize a local function related to maximizing the target signal or energy efficiency. Other local functions can also be utilized, depending on the desired implementation. [0039] At 302, the UEs share the local estimates of the optimal tilt with its

neighboring UEs. Neighboring UEs can be determined by the range of the UE transmission and the amount of transmit power that is allowed to be spent on this exchange. Or, the local estimates can also be a broadcast to all the UEs within a certain cluster or a geographic location, or the UEs can be determined by other methods, depending on the desired implementation. When the UE updates its local estimate based on information shared from other UEs, it does so based on a weighted average of these shared estimates, as shown in the flow at 303. The choice of weights can dictate the convergence rate of the algorithm. Higher weights can be given to UEs that are closer to UE i, or to those that are connected to the same base station. [0040] At 304, after sharing estimates, if the local estimates at each user match (Y), then the flow proceeds to 305 to indicate that a feedback consensus is reached and no more iterations are needed. If however, more iterations are needed, the local process and the gossip process are carried out at time n+1. The algorithm iterates until a feedback consensus is reached. [0041] FIG. 4 illustrates an example of antenna tilt that does not take into account interference to users in other cell, and a beam with optimized tilt in accordance with an example implementation. A detailed explanation of the UE-assisted optimization algorithm is provided for the case of two transmitting base stations and two UEs. In this example, each base station is assumed to serve one user at a time. If interference is not taken into account for example in designing the antenna tilts at each base station, as shown in FIG. 4, the main lobe will be pointed in a direction that causes major interference to the cell edge user in the neighboring cell. When the antenna tilt is optimized, the interference is minimized. [0042] FIG. 5 illustrates an example of two base stations serving two UEs, in

accordance with an example implementation. For the initial transmission, each base station has a fixed antenna til t, respectively. Let the line of sight angle between the serving base station and its user b e respectively. During the local process of the algorithm, UE estimat

es, and estimate from Termina l then computes a local estimate of the optimal antenna tilts such that its SINR is maximized. Similarl y computes a local estimate o f at time . For the gossip proces

exchange their estimates o f and update their local estimate at time n, the updated estimate at time At time n+1, the process repeats with a local step to updat e and

and a gossip step, until consensus is reached. The weights used in the gossip step ar e [0043] For the example of the two users being on the cell edge, and assuming that the angle between a given base station and is approximately the same as the angle

between the same base station and (similarly for the consensus

can be reached on the second iteration, with the weights in the gossiping step equal to

[0044] FIG. 6 illustrates an example user equipment upon which example

implementations can be implemented. The UE 600 may involve the following modules: the CPU module 601, the RF interface 602, the baseband processor 603, and the memory 604. The CPU module 601 can be configured to perform one or more functions, for example, as described in FIG. 3 and FIG. 7. The CPU 601 can be configured to, for example, implement the estimation of the antenna tilts and the local processes and also store the estimate into memory after the gossip process has completed. The RF interface 602 can be configured to transmit and receive information for communicating with neighboring UEs. The baseband digital signal processing (DSP) module 603 can be configured to perform one or more functions, such as to communicate the estimates to the serving base station after the gossiping process has completed. The memory 604 can be configured to store the most recent local estimate, as well as the estimate obtained after the gossiping process has completed. [0045] FIG. 7 is a flowchart for operation of the algorithm when the optimization is carried out at the base stations, in accordance with an example implementation. [0046] For optimizing the vector of different antenna tilts at the neighboring base stations without UE-assisted optimization a distributed stochastic approximation algorithm at the neighboring base stations, shown in the flowchart in FIG.7 can be implemented. In an example, assume that there are M base stations, each serving N UEs and applying the algorithm to reach a consensus on a vector of M antenna tilts corresponding to the M neighboring base stations. In this non-UE assisted case, the neighboring base stations implement the algorithm themselves. [0047] At 700, base stations send reference signals to the UEs. At 701, the UEs

feedback information about the quality of the received signal and the channel information, which is received by the base station at 702. At 703 the base stations then use this information to find a local estimate of the optimal antenna tilts to maximize a local function related to maximizing the target signal or energy efficiency at its served users. Other local functions can be used, depending on the desired implementation. [0048] At 704, the base stations then share the local estimates of the optimal tilts vector with its neighboring base stations via backhaul. When the base station updates its local estimate based on information shared from other base stations, it does so based on a weighted average of these shared estimates. The choice of weights dictates convergence rate of the algorithm. For example, higher weights can be given to base stations that are dominant interferers to current base station. [0049] At 705, after sharing estimates, if the local estimates at each base station match (Yes), then a consensus is reached, and no more iterations are needed. If however, more iterations are needed (No), the flow reverts to 703 so that the local process and the gossip process are carried out at time n+1. The algorithm iterates among base stations until consensus is reached. [0050] FIG. 8 illustrates an example base station upon which example

implementations can be implemented. The block diagram of a base station 800 in the example implementations is shown in FIG. 8, which could be a macro base station or a pico base station, and can be connected to other base stations via a backhaul. The base station 800 may include the following modules: the Central Processing Unit (CPU) 801, the baseband processor 802, the transmission/receiving (Tx/Rx) array 803, the Xn interface 804, and the memory 805. The CPU 801 is configured to execute one or more processes or flows described, for example, in FIG. 3 and FIG. 7. The estimation of the antenna tilts and the local processing may also be carried out in the CPU 801.The baseband processor 802 generates baseband signaling including the reference signal. The Tx/Rx array 803 contains an array of antennas which are configured to facilitate communications with associated UEs. The antennas may be grouped arbitrarily to form one or more active antenna ports. Associated UEs may

communicate with the Tx/Rx array to submit feedback for the feedback consensus for local functions such as tilt or beam parameters. The Xn interface 804 is used to exchange information between base stations via a backhaul to communicate estimates or perform the gossip processes with other neighboring base stations. The neighboring base stations may be within a Coordinated Multi-Point (CoMP) set. The memory 805 is configured to store estimates from associated UE and neighboring base stations. Memory 805 may take the form of a computer readable storage medium or can be replaced with a computer readable signal medium as described below. The detailed functions of each module are explained below. [0051] Finally, some portions of the detailed description are presented in terms of algorithms and symbolic representations of operations within a computer. These algorithmic descriptions and symbolic representations are the means used by those skilled in the data processing arts to most effectively convey the essence of their innovations to others skilled in the art. An algorithm is a series of defined steps leading to a desired end state or result. In example implementations, the steps carried out require physical manipulations of tangible quantities for achieving a tangible result. [0052] Unless specifically stated otherwise, as apparent from the discussion, it is appreciated that throughout the description, discussions utilizing terms such as “processing,”“computing,”“calculating,”“determ ining,”“displaying,” or the like, can include the actions and processes of a computer system or other information processing device that manipulates and transforms data represented as physical (electronic) quantities within the computer system’s registers and memories into other data similarly represented as physical quantities within the computer system’s memories or registers or other information storage, transmission or display devices. [0053] Example implementations may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may include one or more general-purpose computers selectively activated or reconfigured by one or more computer programs. Such computer programs may be stored in a computer readable medium, such as a computer-readable storage medium or a computer-readable signal medium. A computer-readable storage medium may involve tangible mediums such as, but not limited to optical disks, magnetic disks, read-only memories, random access memories, solid state devices and drives, or any other types of tangible or non-transitory media suitable for storing electronic information. A computer readable signal medium may include mediums such as carrier waves. The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Computer programs can involve pure software implementations that involve instructions that perform the operations of the desired implementation. [0054] Various general-purpose systems may be used with programs and modules in accordance with the examples herein, or it may prove convenient to construct a more specialized apparatus to perform desired method steps. In addition, the example implementations are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the example implementations as described herein. The instructions of the programming language(s) may be executed by one or more processing devices, e.g., central processing units (CPUs), processors, or controllers. [0055] As is known in the art, the operations described above can be performed by hardware, software, or some combination of software and hardware. Various aspects of the example implementations may be implemented using circuits and logic devices (hardware), while other aspects may be implemented using instructions stored on a machine-readable medium (software), which if executed by a processor, would cause the processor to perform a method to carry out implementations of the present application. Further, some example implementations of the present application may be performed solely in hardware, whereas other example implementations may be performed solely in software. Moreover, the various functions described can be performed in a single unit, or can be spread across a number of components in any number of ways. When performed by software, the methods may be executed by a processor, such as a general purpose computer, based on instructions stored on a computer-readable medium. If desired, the instructions can be stored on the medium in a compressed and/or encrypted format. [0056] Moreover, other implementations of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the teachings of the present application. Various aspects and/or components of the described example implementations may be used singly or in any combination. It is intended that the specification and example implementations be considered as examples only, with the true scope and spirit of the present application being indicated by the following claims.