Patent Description:
Massive multi-input and multi-out (MIMO) units (MMUs) are much more expensive than legacy remote units (RUs). Although MMUs enable massive MIMO benefits for long term evolution (LTE) release-<NUM> and later user, most of the user equipment that are available in the market are LTE release-<NUM> users who only support transmission mode <NUM> (TM4) with one-dimensional precoding matrix indicator (PMI) feedback. Therefore, it is important to provide enhanced user experience for LTE release-<NUM> and earlier users when MMUs are deployed and not only bring the enhancement for LTE release-<NUM> and later users. Dynamic or adaptive broadcast beam or common beam for transmitting cell specific reference signal (CRS) is needed to improve the performance of TM4 users. The broadcast beam could be used to send the CRS signals. <CIT> discloses mechanisms for precoding over a beam subset.

For an enhanced (or, advanced) cellular communication system, there is a need for a method of antenna parameter configuration.

Embodiments of the present disclosure provide methods and apparatuses for antenna parameter configuration for cellular communication systems.

The term "controller" means any device, system, or part thereof that controls at least one operation.

<FIG> below describe various embodiments implemented in wireless communications systems and with the use of orthogonal frequency division multiplexing (OFDM) or orthogonal frequency division multiple access (OFDMA) communication techniques. The descriptions of <FIG> are not meant to imply physical or architectural limitations to the manner in which different embodiments may be implemented. Different embodiments of the present disclosure may be implemented in any suitably-arranged communications system.

As shown in <FIG>, the wireless network includes a gNB <NUM> (e.g., base station, BS), a gNB <NUM>, and a gNB <NUM>. The gNB <NUM> communicates with the gNB <NUM> and the gNB <NUM>. The gNB <NUM> also communicates with at least one network <NUM>, such as the Internet, a proprietary Internet Protocol (IP) network, or other data network.

The gNB <NUM> provides wireless broadband access to the network <NUM> for a first plurality of UEs within a coverage area <NUM> of the gNB <NUM>. The first plurality of UEs includes a UE <NUM>, which may be located in a small business; a UE <NUM>, which may be located in an enterprise (E); a UE <NUM>, which may be located in a WiFi hotspot (HS); a UE <NUM>, which may be located in a first residence (R); a UE <NUM>, which may be located in a second residence (R); and a UE <NUM>, which may be a mobile device (M), such as a cell phone, a wireless laptop, a wireless PDA, or the like. The gNB <NUM> provides wireless broadband access to the network <NUM> for a second plurality of UEs within a coverage area <NUM> of the gNB <NUM>. The second plurality of UEs includes the UE <NUM> and the UE <NUM>. In some embodiments, one or more of the gNBs <NUM>-<NUM> may communicate with each other and with the UEs <NUM>-<NUM> using <NUM>/NR, LTE, LTE-A, WiMAX, WiFi, or other wireless communication techniques.

Depending on the network type, the term "base station" or "BS" can refer to any component (or collection of components) configured to provide wireless access to a network, such as transmit point (TP), transmit-receive point (TRP), an enhanced base station (eNodeB or eNB), a <NUM>/NR base station (gNB), a macro cell, a femtocell, a WiFi access point (AP), or other wirelessly enabled devices. Base stations may provide wireless access in accordance with one or more wireless communication protocols, e.g., <NUM>/NR 3GPP new radio interface/access (NR), LTE, LTE advanced (LTE-A), high speed packet access (HSPA), Wi-Fi <NUM>. 11a/b/g/n/ac, etc. For the sake of convenience, the terms "BS" and "TRP" are used interchangeably in this patent document to refer to network infrastructure components that provide wireless access to remote terminals. Also, depending on the network type, the term "user equipment" or "UE" can refer to any component such as "mobile station," "subscriber station," "remote terminal," "wireless terminal," "receive point," or "user device. " For the sake of convenience, the terms "user equipment" and "UE" are used in this patent document to refer to remote wireless equipment that wirelessly accesses a BS, whether the UE is a mobile device (such as a mobile telephone or smartphone) or is normally considered a stationary device (such as a desktop computer or vending machine).

As described in more detail below, one or more of the UEs <NUM>-<NUM> include circuitry, programing, or a combination thereof for antenna parameter configuration for cellular communication systems. In certain embodiments, and one or more of the gNBs <NUM>-<NUM> includes circuitry, programing, or a combination thereof for antenna parameter configuration for cellular communication systems.

The controller/processor <NUM> can include one or more processors or other processing devices that control the overall operation of the gNB <NUM>. For example, the controller/processor <NUM> could control the reception of forward channel signals and the transmission of reverse channel signals by the RF transceivers 210a-210n, the RX processing circuitry <NUM>, and the TX processing circuitry <NUM> in accordance with well-known principles. The controller/processor <NUM> could support additional functions as well, such as more advanced wireless communication functions. For instance, the controller/processor <NUM> could support beam forming or directional routing operations in which outgoing/incoming signals from/to multiple antennas 205a-205n are weighted differently to effectively steer the outgoing signals in a desired direction. Any of a wide variety of other functions could be supported in the gNB <NUM> by the controller/processor <NUM>.

For example, when the gNB <NUM> is implemented as part of a cellular communication system (such as one supporting <NUM>/NR, LTE, or LTE-A), the interface <NUM> could allow the gNB <NUM> to communicate with other gNBs over a wired or wireless backhaul connection.

As shown in <FIG>, the UE <NUM> includes an antenna <NUM>, a radio frequency (RF) transceiver <NUM>, TX processing circuitry <NUM>, a microphone <NUM>, and RX processing circuitry <NUM>.

The processor <NUM> is also capable of executing other processes and programs resident in the memory <NUM>, such as processes for beam management. The processor <NUM> can move data into or out of the memory <NUM> as required by an executing process. In some embodiments, the processor <NUM> is configured to execute the applications <NUM> based on the OS <NUM> or in response to signals received from gNBs or an operator. The processor <NUM> is also coupled to the I/O interface <NUM>, which provides the UE <NUM> with the ability to connect to other devices, such as laptop computers and handheld computers. The I/O interface <NUM> is the communication path between these accessories and the processor <NUM>.

To meet the demand for wireless data traffic having increased since deployment of <NUM> communication systems and to enable various vertical applications, efforts have been made to develop and deploy an improved <NUM>/NR or pre-<NUM>/NR communication system. Therefore, the <NUM>/NR or pre-<NUM>/NR communication system is also called a "beyond <NUM> network" or a "post LTE system. " The <NUM>/NR communication system is considered to be implemented in higher frequency (mmWave) bands, e.g., <NUM> or <NUM> bands, so as to accomplish higher data rates or in lower frequency bands, such as <NUM>, to enable robust coverage and mobility support. To decrease propagation loss of the radio waves and increase the transmission distance, the beamforming, massive multiple-input multiple-output (MIMO), full dimensional MIMO (FD-MIMO), array antenna, an analog beam forming, large scale antenna techniques are discussed in <NUM>/NR communication systems.

In addition, in <NUM>/NR communication systems, development for system network improvement is under way based on advanced small cells, cloud radio access networks (RANs), ultra-dense networks, device-to-device (D2D) communication, wireless backhaul, moving network, cooperative communication, coordinated multi-points (CoMP), reception-end interference cancellation and the like.

The discussion of <NUM> systems and frequency bands associated therewith is for reference as certain embodiments of the present disclosure may be implemented in <NUM> systems. However, the present disclosure is not limited to <NUM> systems or the frequency bands associated therewith, and embodiments of the present disclosure may be utilized in connection with any frequency band. For example, aspects of the present disclosure may also be applied to deployment of <NUM> communication systems, <NUM> or even later releases which may use terahertz (THz) bands.

A communication system includes a downlink (DL) that refers to transmissions from a base station or one or more transmission points to UEs and an uplink (UL) that refers to transmissions from UEs to a base station or to one or more reception points.

A time unit for DL signaling or for UL signaling on a cell is referred to as a slot and can include one or more symbols. A symbol can also serve as an additional time unit. A frequency (or bandwidth (BW)) unit is referred to as a resource block (RB). One RB includes a number of sub-carriers (SCs). For example, a slot can have duration of <NUM> milliseconds or <NUM> millisecond, include <NUM> symbols and an RB can include <NUM> SCs with inter-SC spacing of <NUM> or <NUM>, and so on.

DL signals include data signals conveying information content, control signals conveying DL control information (DCI), and reference signals (RS) that are also known as pilot signals. A gNB transmits data information or DCI through respective physical DL shared channels (PDSCHs) or physical DL control channels (PDCCHs). A PDSCH or a PDCCH can be transmitted over a variable number of slot symbols including one slot symbol. For brevity, a DCI format scheduling a PDSCH reception by a UE is referred to as a DL DCI format and a DCI format scheduling a PUSCH transmission from a UE is referred to as an UL DCI format.

A gNB transmits one or more of multiple types of RS including channel state information RS (CSI-RS) and demodulation RS (DMRS). A CSI-RS is primarily intended for UEs to perform measurements and provide channel state information (CSI) to a gNB. For channel measurement, non-zero power CSI-RS (NZP CSI-RS) resources are used. For interference measurement reports (IMRs), CSI interference measurement (CSI-IM) resources associated with a zero power CSI-RS (ZP CSI-RS) configuration are used. A CSI process includes NZP CSI-RS and CSI-IM resources.

A UE can determine CSI-RS transmission parameters through DL control signaling or higher layer signaling, such as radio resource control (RRC) signaling, from a gNB. Transmission instances of a CSI-RS can be indicated by DL control signaling or be configured by higher layer signaling. A DMRS is transmitted only in the BW of a respective PDCCH or PDSCH and a UE can use the DMRS to demodulate data or control information.

<FIG> and <FIG> illustrate example wireless transmit and receive paths according to this disclosure. In the following description, a transmit path <NUM> may be described as being implemented in a gNB (such as the gNB <NUM>), while a receive path <NUM> may be described as being implemented in a UE (such as a UE <NUM>). However, it may be understood that the receive path <NUM> can be implemented in a gNB and that the transmit path <NUM> can be implemented in a UE. In some embodiments, the receive path <NUM> is configured to support the antenna parameter configuration for cellular communication systems as described in embodiments of the present disclosure.

The transmit path <NUM> as illustrated in <FIG> includes a channel coding and modulation block <NUM>, a serial-to-parallel (S-to-P) block <NUM>, a size N inverse fast Fourier transform (IFFT) block <NUM>, a parallel-to-serial (P-to-S) block <NUM>, an add cyclic prefix block <NUM>, and an up-converter (UC) <NUM>. The receive path <NUM> as illustrated in <FIG> includes a down-converter (DC) <NUM>, a remove cyclic prefix block <NUM>, a serial-to-parallel (S-to-P) block <NUM>, a size N fast Fourier transform (FFT) block <NUM>, a parallel-to-serial (P-to-S) block <NUM>, and a channel decoding and demodulation block <NUM>.

As illustrated in FIGURE <NUM>, the channel coding and modulation block <NUM> receives a set of information bits, applies coding (such as a low-density parity check (LDPC) coding), and modulates the input bits (such as with quadrature phase shift keying (QPSK) or quadrature amplitude modulation (QAM)) to generate a sequence of frequency-domain modulation symbols.

The serial-to-parallel block <NUM> converts (such as de-multiplexes) the serial modulated symbols to parallel data in order to generate N parallel symbol streams, where N is the IFFT/FFT size used in the gNB <NUM> and the UE <NUM>. The size N IFFT block <NUM> performs an IFFT operation on the N parallel symbol streams to generate time-domain output signals. The parallel-to-serial block <NUM> converts (such as multiplexes) the parallel time-domain output symbols from the size N IFFT block <NUM> in order to generate a serial time-domain signal. The add cyclic prefix block <NUM> inserts a cyclic prefix to the time-domain signal. The up-converter <NUM> modulates (such as up-converts) the output of the add cyclic prefix block <NUM> to an RF frequency for transmission via a wireless channel. The signal may also be filtered at baseband before conversion to the RF frequency.

A transmitted RF signal from the gNB <NUM> arrives at the UE <NUM> after passing through the wireless channel, and reverse operations to those at the gNB <NUM> are performed at the UE <NUM>.

As illustrated in <FIG>, the down-converter <NUM> down-converts the received signal to a baseband frequency, and the remove cyclic prefix block <NUM> removes the cyclic prefix to generate a serial time-domain baseband signal. The serial-to-parallel block <NUM> converts the time-domain baseband signal to parallel time domain signals. The size N FFT block <NUM> performs an FFT algorithm to generate N parallel frequency-domain signals. The parallel-to-serial block <NUM> converts the parallel frequency-domain signals to a sequence of modulated data symbols. The channel decoding and demodulation block <NUM> demodulates and decodes the modulated symbols to recover the original input data stream.

Each of the gNBs <NUM>-<NUM> may implement a transmit path <NUM> as illustrated in <FIG> that is analogous to transmitting in the downlink to UEs <NUM>-<NUM> and may implement a receive path <NUM> as illustrated in <FIG> that is analogous to receiving in the uplink from UEs <NUM>-<NUM>. Similarly, each of UEs <NUM>-<NUM> may implement the transmit path <NUM> for transmitting in the uplink to the gNBs <NUM>-<NUM> and may implement the receive path <NUM> for receiving in the downlink from the gNBs <NUM>-<NUM>.

Each of the components in <FIG> and <FIG> can be implemented using only hardware or using a combination of hardware and software/firmware. As a particular example, at least some of the components in <FIG> and <FIG> may be implemented in software, while other components may be implemented by configurable hardware or a mixture of software and configurable hardware. For instance, the FFT block <NUM> and the IFFT block <NUM> may be implemented as configurable software algorithms, where the value of size N may be modified according to the implementation.

Furthermore, although described as using FFT and IFFT, this is by way of illustration only and may not be construed to limit the scope of this disclosure. Other types of transforms, such as discrete Fourier transform (DFT) and inverse discrete Fourier transform (IDFT) functions, can be used. It may be appreciated that the value of the variable N may be any integer number (such as <NUM>, <NUM>, <NUM>, <NUM>, or the like) for DFT and IDFT functions, while the value of the variable N may be any integer number that is a power of two (such as <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, or the like) for FFT and IFFT functions.

Although <FIG> and <FIG> illustrate examples of wireless transmit and receive paths, various changes may be made to <FIG> and <FIG>. For example, various components in <FIG> and <FIG> can be combined, further subdivided, or omitted and additional components can be added according to particular needs.

<FIG> and <FIG> are meant to illustrate examples of the types of transmit and receive paths that can be used in a wireless network. Any other suitable architectures can be used to support wireless communications in a wireless network.

In one embodiment, a selection of the common beam from a predefined beam codebook is provided based on the feedback from UEs. The predefined codebook is generated so that the throughput of connected UEs is improved and coverage of idle UEs are maintained. One method to balance the throughput and coverage would be to divide the cell angular coverage into different angular grid as shown in <FIG>.

<FIG> illustrates an example dividing angular domain into grid <NUM> according to embodiments of the present disclosure. An embodiment of the dividing angular domain into grid <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

<FIG> illustrates an example mapping angular domain into read cell <NUM> according to embodiments of the present disclosure. An embodiment of the mapping angular domain into read cell <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

In one embodiment, the predefined cell coverage area is represented by the horizontal angular coverage ϕc and vertical angular coverage θc. In one example, the horizontal angular coverage could be <NUM> degrees and the vertical angular coverage could be <NUM> degrees. The coverage could be the antenna 3dB or any other gain defined beam width. In the angular space, the cell coverage is divided into different parts. One way to divide is to equally divide the horizontal angular into N equal parts and divide the vertical angular into M equal parts as shown in <FIG>. The angular domain angular grid could also be mapped to the cell coverage space as shown in <FIG>.

In one embodiment, to guarantee the coverage and to improve the throughput, different weights may be put to different angular grids when synthesizing the antenna beam pattern. For example, weight w1 may be put on angular grid a0, a1 and put weight w2 on other angular grids. Here the weights may be used to synthesize the beam pattern.

In one embodiment, to generate the beam codebook is to synthesize the beam codebook based on different combinations of assigning the weights w1 w2 to the angular grids. Totally if there are MN grids and two weights, the size of the beam codebook could be 2MN.

<FIG> illustrates a flow chart of a method <NUM> for online beam selection according to embodiments of the present disclosure. An embodiment of the method <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

In one embodiment, to select the beam from the beam codebook, the angular information identified from sounding reference signal (SRS) is provided. As illustrated in <FIG>, First, an eNB at step <NUM> collects SRS signaling from connected UEs for a predefined period T. Second, based on SRS information, the eNB at step <NUM> identifies the angle of arrivals for all connected UEs. Third, the eNB at step <NUM> selects the best beam from the beam codebook based on the beam selection metric. These steps are summarized into the following flowchart.

<FIG> illustrates an example beam selection <NUM> according to embodiments of the present disclosure. An embodiment of the beam selection <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

In the present disclosure, a digital unit (DU) may be considered. After UEs sending SRS and measurement reports to the RU and DU, the DU first call the function in digital signal processing (DSP) to process the SRS and measurement reports so that the angle of arrival (AoA) angular information can be obtained. Then this information may be passed to beam selection module to select a beam from a beam codebook. After that, the selected beam may be sent to RU to configure an antenna configuration.

The network determines a set of beams according to the required spherical coverage area, the UE traffic intensity. During the online beam-selection phase, the beams are looked up and applied.

<FIG> illustrates an example coverage plane <NUM> delimited by a spherical region of interest and partitioned into equally-sized rectangles denoting traffic regions according to embodiments of the present disclosure. An embodiment of the coverage plane <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

In one embodiment, a single beam generation is provided.

In such embodiment, at step <NUM>, the total angular coverage area and the partition of the angular coverage area are identified.

An example of this step is as follows. The spherical coverage plane is set to cover a range of interest <MAT> in the elevation domain and <MAT> in the azimuth domain as illustrated in <FIG>. The angles θC and φC are referred to as the center (boresight) elevation and azimuth and elevation angles, θT the vertical tilt angle, and θW and φW the vertical and horizontal beamwidths. The coverage plain is partitioned along a grid into V × H rectangles of equal dimensions. Rectangle <NUM>≤i<V,<NUM>≤j<H covers the range , in the elevation domain and the range in the azimuth domain.

In such embodiment, at step <NUM>, the traffic intensity of each angular area is determined. For the example given in <FIG>, every rectangle corresponds to regions of traffic with varying levels of intensity. It is required that the beam pointing towards regions of higher traffic intensity to exhibit enhanced coverage according to an arbitrary utility. To accomplish this, the coverage plane is encoded with a set of weights that mirror traffic intensity and thus beamforming gain. Weights are directly related to traffic intensity. The encoded coverage plane is referred to as target coverage pattern. For any coverage pattern, a single-beam codebook is generated by running a K-means algorithm to optimize a utility function (e.g., mean throughput or mean logarithmic throughput) of the coverage plane and corresponding weights.

In such embodiment, at step <NUM>, the beam is generated according to the determined spherical coverage and the weights.

In one embodiment, the beam can be designed to maximize the objective function in the form: Σi aiΣ (θj,φj)∈Ai f(G(θj, φj)) where {Ai} is the coverage area partition, {ai} are the weights applied to each area according to the traffic density, f( · ) is the utility function, and G (θj,φj) is the beamforming gain in the direction (θj,φj). The utility function f( · ) can be the data rate, throughput, received reference signal power, etc..

In one embodiment, an alphabet of size n of weights is specified, e.g. ai ∈ {<NUM>, <NUM>, <NUM>}, where these weights correspond to regions of different traffic intensities and thus target coverage.

<FIG> illustrates a flow chart of a method <NUM> for a beam generation according to embodiments of the present disclosure. An embodiment of the method <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions. The beam generation procedure is summarized as illustrated in <FIG>.

In one embodiment, the above process of single beam generation can be repeated to generate all possible nVH beams. These beams are generated offline and cached for quick access.

In another embodiment, if the network only identifies a subset of traffic density distribution and generate the corresponding beams. The network can choose the subset based on the historical knowledge, building location information, etc..

Example simulation results are provided here to illustrate the design procedure. The base station is assumed to have an 8X4 antenna array. The boresight direction is assumed to the. The down tiltig angle θT is <NUM> degrees.

The required angular coverage area is. The whole area is divided into <NUM> regions. In the following figures, three different cases may be provided.

As illustrated in <FIG>, <NUM> regions with high traffic density only are provided. As illustrated in <FIG>, <NUM> high-traffic regions and <NUM> low-traffic regions are provided. As illustrated in <FIG>, <NUM> regions with similar traffic intensity is provided.

As illustrated in the aforementioned figures, the provided method can adapt the beam radiation pattern according to both the coverage requirement and traffic intensity.

<FIG> illustrates an example coverage pattern <NUM> according to embodiments of the present disclosure. An embodiment of the coverage pattern <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

<FIG> illustrates an example <NUM> decibel (dB) and <NUM> dB cutoff contours (in red and blue) of the common beam <NUM> according to embodiments of the present disclosure. An embodiment of the <NUM> dB and <NUM> dB cutoff contours (in red and blue) of the common beam <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

<FIG> illustrates an example heat map showing the continuum of coverage based on the target coverage pattern <NUM> according to embodiments of the present disclosure. An embodiment of the heat map showing the continuum of coverage based on the target coverage pattern <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

<FIG> illustrates an example coverage pattern where low-traffic regions are given small significance <NUM> according to embodiments of the present disclosure. An embodiment of the coverage pattern where low-traffic regions are given small significance <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

<FIG> illustrates an example coverage contours of the beam generated from the target coverage pattern <NUM> according to embodiments of the present disclosure. An embodiment of the coverage contours of the beam generated from the target coverage pattern <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

<FIG> illustrates an example coverage map of the beam generated from the target coverage pattern <NUM> according to embodiments of the present disclosure. An embodiment of the coverage map of the beam generated from the target coverage pattern <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

<FIG> illustrates an example target coverage pattern indicating a coverage plane targeting uniformly-distributed traffic <NUM> according to embodiments of the present disclosure. An embodiment of the target coverage pattern indicating a coverage plane targeting uniformly-distributed traffic <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

<FIG> illustrates an example coverage contours corresponding to the coverage pattern <NUM> according to embodiments of the present disclosure. An embodiment of the coverage contours corresponding to the coverage pattern <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

<FIG> illustrates an example coverage map corresponding to the coverage pattern <NUM> according to embodiments of the present disclosure. An embodiment of the coverage map corresponding to the coverage pattern <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

In one embodiment, an eNB chooses one beam from a codebook based on SRS signaling. SRS can provide information about the angular direction (horizontal and azimuth) of each UE. Based on this information, the eNB can determine which angular directions contain UEs (traffic directions) and which angular directions do not contain UEs (coverage directions). One metric to select the beam to use is to select beam which can generate the optimal average antenna gain on the traffic direction. Another metric to select the beam is to select the beam which can optimize the gain on traffic direction meanwhile guarantee the gain on coverage directions is reduced within a threshold or maintained unreduced.

In one embodiment, an eNB chooses one beam from a codebook based on SRS and channel quality indicator (CQI) feedback. The selection of the beam depends on many parameters. One of those parameters is SRS which is a subband information sent from the UE to the eNB.

Another parameter is CQI that is also sent from the UE to the eNB and the CQI can be either subband or wideband information. SRS can provide information about the angular direction (horizontal and azimuth) of each UE. Based on this information, the eNB can determine which angular directions contain UEs (traffic directions) and which angular directions do not contain UEs (coverage directions). Moreover, using CQI information can give the eNB some information about the UEs who are well covered by the existing beam (UEs with high CQI values) and the UEs who need to have more coverage (UEs reporting lower CQI values). Combining this information, the eNB can choose the best beam that can be used as a common beam for the cell.

In another embodiment, the eNB also takes into account other KPIs to adjust the beam. One of those KPIs is coverage guarantee which means that even if there are some locations that do not have any users SRS was not received from these angular directions), a minimum amount of coverage has to be provided to those directions so that idle users can find coverage when the idle users become connected. Other KPIs (such as handover frequency, call drops, etc.) are also monitored and used in the decision of the selected beam.

In another embodiment, the beam selection includes initial beam selection and dynamic beam adaptation steps. In the initial beam selection step, the eNB could select the beam based on SRS and/or CQI following aforementioned procedures as shown in <FIG>.

<FIG> illustrates an example initial beam selection <NUM> according to embodiments of the present disclosure. An embodiment of the initial beam selection <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

In the beam adaptation step, after a new beam is applied, an eNB may keep monitoring the network KPIs including throughput, call drop rate, coverage and UE reports. If the KPI degrades larger than a threshold than the eNB may reconfigure the eNB to the previously used beam. Otherwise, the eNB could select a new beam following the beam bisector selection algorithms. In the beam bisector selection algorithm, an eNB may first select an angular grid and select a beam from to enhance the traffic performance on that grid. If the throughput performance is enhanced, then the eNB may decide that grid is a traffic grid, otherwise that grid is decided to be a coverage grid. After deciding the first grid, the eNB may continue to decide the next grid in the angular coverage domain until all grid has been decided to be either a traffic grid or coverage grid. These procedures are shown in <FIG> and <FIG>.

<FIG> illustrates an example bisector beam search algorithm <NUM> according to embodiments of the present disclosure. An embodiment of the bisector beam search algorithm <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

In one embodiment, it is illustrated that how machine learning technique can be used to select the optimal common beam such that the best trade-off between the throughput of connected UEs and the coverage of the cell (including connected UEs and idle UEs) is achieved.

In one example of artificial intelligence (AI) method <NUM>, classification based best beam selection is provided. As shown in figures, the training of classification model can be performed offline.

<FIG> illustrates an example classification based best beam selection <NUM> according to embodiments of the present disclosure. An embodiment of the classification based best beam selection <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

In one embodiment of step1-<NUM>, training model buildup is provided assuming that a finite number of code books is defined. Offline data may be used to classify the UEs based on the best beam indices in terms of optimizing the KPI metric of interest, i.e., a UE class1 corresponds to the beam index <NUM>, a UE class <NUM> corresponds to the beam index <NUM>, etc. Multiple classification algorithm can be used, e.g., ridge, SVM, random forest, etc. The input features for UE classification could be the following but not limited to: UE SRS measurement and statistical moments of the UE SRS; and UE feedback (CQI, PMI, RI, RSRP) and statistical moments of the UE feedback.

In one embodiment of step <NUM>- <NUM>, online selection is provided to uses the classification model obtained via offline training to classify the UEs and apply the corresponding beam indices for each UE category. Note that the trained UEs could include the connected UEs and/or idle UEs.

In one embodiment of AI method <NUM>, RL learning enabled best beam selection (multi-independent state beam selection) is provided as shown in figure below.

<FIG> illustrates an example multi-independent state (MIS) beam selection <NUM> according to embodiments of the present disclosure. An embodiment of the MIS beam selection <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

In one embodiment of step <NUM>-<NUM>, offline classification is provided to use one composite function of multiple parameters to categorize UEs into multiple independent states. This classification can be accomplished by black box (using machine learning classification algorithm) or white box (using domain knowledge to define the classification metric. Different value of classification metric corresponds to different state).

The model of the reward is given as follows: Reward = f(State, Action) where State is a composite function of UE feedbacks and/or their statistical moments, etc. i.e., State = g(UEfeedbacks, UEmeasurement).

Action space is the candidates of all predefined beam books or subset of beam books. Reward is the KPI of interest, e.g., UE throughput, capacity or coverage, predicated throughput (including both connected UEs and idle UEs).

In one embodiment of step <NUM>-<NUM>, online reinforcement learning is provided within each state and multi-bandit arm (MAB) algorithm is employed to maximize the KPI metric of interest as shown in the above equation. Within each state, the action corresponds to the different beams. The beam candidates for each state could be same or different.

In one embodiment, an alternative of AI method <NUM>, a beam selection facilitated by transfer learning enhanced classification is provided. Due to the mismatch of offline data and online data and dynamic change of UE/traffic distributions, transfer learning can be applied to further improve the accuracy of the classification of UEs. The updated training model based on online data can be used to classify UEs.

<FIG> illustrates an example beam selection facilitated by transfer learning enhanced classification <NUM> according to embodiments of the present disclosure. An embodiment of the beam selection facilitated by transfer learning enhanced classification <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

In another embodiment, classification based beam selection with online adjustment is provided. Due to the mismatch of offline data and online data, the offset of best beam for each class of UEs can be calculated online. Then the best beam selection for each class of UEs can be adjusted by the offset calculated online. The offset for each class of UEs could be same or different.

<FIG> illustrates an example classification based beam selection with online adjustment <NUM> according to embodiments of the present disclosure. An embodiment of the classification based beam selection with online adjustment <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

In one embodiment of AI method <NUM>, a size of candidates of beam code books within each state is fixed. However, the candidates could be changed in real time, either some fixed shift of candidates or dynamic adjustment of the candidates. In another embodiment, a size of candidates of beam code books within each state is not same. The candidates could be dynamically changed as well. In yet another embodiment, a candidate of beam code books within each state could be same or different and changed over time.

<FIG> illustrates a flow chart of a method <NUM> for an antenna parameter configuration according to embodiments of the present disclosure, as may be performed by a BS (e.g., <NUM>-<NUM> as illustrated in <FIG>). An embodiment of the method <NUM> shown in <FIG> is for illustration only. One or more of the components illustrated in <FIG> can be implemented in specialized circuitry configured to perform the noted functions or one or more of the components can be implemented by one or more processors executing instructions to perform the noted functions.

As illustrated in <FIG>, the method <NUM> begins at step <NUM>. In step <NUM>, the BS stores a set of beam codebooks including a plurality of angular grids each of which is identified as a first type of angular grid or a second type of angular grid based on a cell angular domain of a cell belonging to the BS.

In step <NUM>, the BS identifies a set of parameters to adjust an antenna beam pattern based on the set of beam codebooks, wherein the set of parameters includes at least one of feedback information received from a UE or a set of KPIs identified by the BS.

In step <NUM>, the set of KPIs includes at least one of a network throughput, a call drop rate, or a number of RRC connected UEs, and the feedback information received from the UE includes at least one of a CQI, a rank indicator (RI), reference signal received power (RSRP) information, reference signal received quality (RSRQ) information, or a SRS.

In step <NUM>, the BS determines, based on the set of parameters, a type of angular grid as the first type of angular grid or the second type of angular grid, wherein the first and second type of angular grids include a first antenna gain and a second antenna gain, respectively.

In step <NUM>, the BS selects a beam codebook, based on the type of angular grid, from the set of beam codebooks, the beam codebook corresponding to the antenna beam pattern.

In one embodiment, the BS identifies the cell angular domain including a high-traffic, traffic grid and a low-traffic, coverage grid, identifies a cell azimuth angular domain including N angles, and identifies a cell elevation angular domain including M angles. In such embodiment, a total of MN grids covers the cell angular domain of the cell.

In one embodiment, the BS identifies the type of angular grid based on an AoA that is determined based on a sounding reference signal or a measurement report received from the UE and assigns a value to each of the plurality of angular grids, the value being increased if the AoA is identified in the plurality of angular grids during a predefined time period.

In one embodiment, the BS determines the type of angular grid as a high-traffic, traffic grid if the value is greater than a predefined threshold or determines the type of angular grid as a low-traffic, coverage grid if the value is less than or equal to the predefined threshold.

In one embodiment, the BS identifies a beam metric based on a weighted antenna gain. In such embodiment, the beam codebook corresponds to the antenna beam pattern such that the beam metric over the first type of angular grid is maximized and the beam metric over the second type of angular grid is reduced within a predefined threshold.

In one embodiment, the BS determines whether the set of KPIs is updated after selecting the beam codebook corresponding to the first type of angular grid, based on the set of KPIs being updated in a first manner, determines the first type of angular grid as a high-traffic, traffic grid, and based on the set of KPIs being updated in a second manner, determines the first type of angular grid as low-traffic, coverage grid. In such embodiment, the beam codebook corresponds to the antenna beam pattern such that an antenna beam gain over the first type of angular grid is maximized.

In one embodiment, the BS determines whether the set of KPIs is updated after selecting the beam codebook corresponding to the second type of angular grid and the first type of angular grid, based on the set of KPIs being updated in a first manner, determining the second type of angular grid as a high-traffic, traffic grid, and based on the set of KPIs being updated in a second manner, determining the second type angular grid as low-traffic, coverage grid. In such embodiment, the beam codebook corresponds to the antenna beam pattern such that a weighted antenna gain for the second type of angular grid and the first type of angular grid is maximized.

Claim 1:
A base station, BS, in a wireless communication system, the BS comprising:
a transceiver;
a memory (<NUM>) operably connected to the transceiver, the memory (<NUM>) configured to store a set of beam codebooks including a plurality of angular grids, wherein each of the plurality of angular grids is identified as a first type of angular grid or a second type of angular grid based on a cell angular domain of a cell provided by the BS; and
a processor (<NUM>) operably connected to the transceiver and the memory (<NUM>), the processor (<NUM>) configured to:
identify a set of parameters to adjust an antenna beam pattern based on the set of beam codebooks, wherein the set of parameters includes at least one of feedback information received from a user equipment, UE, or a set of key performance indicators, KPIs, identified by the BS,
determine, based on the set of parameters, a type of angular grid as the first type of angular grid or the second type of angular grid, wherein the first type of angular grid includes a first antenna gain and the second type of angular grid includes a second antenna gain, and
select a beam codebook, based on the type of angular grid, from the set of beam codebooks, the beam codebook corresponding to the antenna beam pattern.