154. Via the temperature data provided by the temperature sensors, the antenna controller 154 may implement various techniques disclosed herein to balance thermal load experienced by the antenna panel 152 in real-time and/or dynamically. For example, based on real-time temperature data received from the temperature sensors, the antenna controller 154 causes the ESA to hop between different power modes (each having a unique pattern of activated tiles).

In some embodiments, the antenna controller 154 is configured to operate the antenna panel 152 based on activating electrical power to antenna tiles 156 of the antenna panel 152. In some embodiments, the antenna controller 154 is configured to selectively activate power to certain antenna tiles 156 of the antenna panel 152 without activating power to other antenna tiles 156 of the antenna panel 152. In some embodiments, the antenna controller 154 further implements a modem that processes raw signals received by the antenna panel 152 and that generates raw signals to be emitted by the antenna panel 152. In doing so, the antenna controller 154 may be configured to selectively apply phase offsets to different antenna tiles 156 to “steer” or direct an emitted signal and/or to receive a steered or directed signal.

In some embodiments, the antenna panel 152 is an ESA, and the antenna tiles 156 are operated to electronically steer the ESA. While the following description in this document may refer to ESAs and tiles of an ESA, it will be appreciated that the disclosed concepts are applicable to antenna arrays and elements of an antenna array, generally.

FIG. 2 illustrates an example of a computing device 200 that implements various embodiments disclosed herein. According to example implementations, the computing device 200 may embody the antenna controller 154 of an antenna system 104 and/or a server 106 of a vehicle 102. For example, the computing device 200 performs various example operations to select an optimized sequence of tile patterns, determine a dwelling time for specific patterns in the optimized sequence, and operate an antenna panel according to optimized sequence and dwelling times.

In FIG. 2, the device 200 includes at least one processor 202 and a memory 204 having instructions stored thereupon. The memory 204 may store instructions to be executed by the processor 202. In other embodiments, additional, fewer, and/or different elements may be used to configure the device 200. The memory 204 is an electronic holding place or storage for information or instructions so that the information or instructions can be accessed by the processor 202. The memory 204 can include, but is not limited to, any type of random-access memory (RAM), any type of read-only memory (ROM), any type of flash memory, etc. Such as magnetic storage devices (e.g., hard disk, floppy disk, magnetic strips, etc.), optical disks (e.g., compact disk (CD), digital versatile discs (DVD), etc.), smart cards, flash memory devices, etc. The instructions upon execution by the processor 202 configure the device 200 to perform the example operations described in this patent document.

The instructions executed by the processor 202 may be carried out by a special purpose computer, logic circuits, or hardware circuits. The processor 202 may be implemented in hardware, firmware, software, or any combination thereof. The term “execution” is, for example, the process of running an application or the carrying out of the operation called for by an instruction. The instructions may be written using one or more programming language, scripting language, assembly language, etc. By executing the instruction, the processor 202 can perform the operations called for by that instruction. The processor 202 operably couples with the memory 204 and transceiver 206 to receive, to send, and to process information and to control the operations of the device 200. The processor 202 may retrieve a set of instructions from a permanent memory device such as a ROM device and copy the instructions in an executable form to a temporary memory device that is generally some form of RAM. In some implementations, the device 200 can include a plurality of processors that use the same or a different processing technology.

The transceiver 206 transmits and receives information or data to another device (e.g., a server 106, passenger devices 110, an antenna controller 154). For example, the transceiver 206 provides demodulated signals received via the antenna system 104 to the server 106 and/or passenger devices 110 and receives raw signals to be sent via the antenna system 104. The transceiver 206 may be comprised of a transmitter and a receiver; in some embodiments, the device 200 comprises a transmitter and a receiver that are separate from another but functionally form a transceiver. In some embodiments, the computing device 200 (and/or the transceiver 206) implements a network router or switch that connects multiple devices to the antenna system 104, such that external communication capability is provided to the multiple devices.

As illustrated, the computing device 200 further includes an optimization module 208. In some embodiments, the optimization module 208 is embodied by a combination of at least a portion of the processor 202 and the memory 204. In some embodiments, the optimization module 208 includes a dedicated processor and a dedicated memory. In some embodiments, the optimization module 208 is a computing device that is included in or connected to the computing device 200. The optimization module 208 is configured to perform (or cause the computing device 200 to perform) example operations disclosed herein for optimizing ESA operation and balancing thermal load experienced by an ESA. In particular, the optimization module 208 may be configured to construct graph structures relating different patterns of antenna tiles 156, determine optimized paths through said graph structures, determine optimal dwelling times for certain tile patterns in a sequence of tile patterns, and/or the like. In order to implement optimization techniques, the optimization module 208 may cooperate with other components of the computing device 200 and/or other systems/devices to obtain current temperature data of an ESA, obtain communication channel information, and/or the like. Thus, the optimization module 208 is configured for various optimization techniques that are implemented by technical solutions disclosed herein.

Turning now to FIGS. 3A-3C, optimization techniques performed by the optimization module 208 for thermal load balancing an ESA are demonstrated. According to example embodiments, the demonstrated optimization techniques may be performed to select an optimized sequence for powering specific antenna tiles 156 of an ESA in a manner that maximizes expected time for the ESA to reach shutdown temperature. For ease of description, the following description is provided with respect to a transmission chain via the ESA; however, it will be understood that the disclosed concepts are applicable to the receive chain of the ESA.

Thermal load balancing according to example embodiments may be performed according to a reduced service mode being set for the ESA, and the optimization module 208 may perform the following operations for the reduced service mode. In some embodiments, the reduced service mode is a mode of operation for the ESA in which thermal load balancing is implemented, and the reduced service mode may be set based on a determination that thermal load balancing is necessary. For example, the reduced service mode may be set in response to a determination that an ambient temperature of an environment in which the ESA is located has exceed a pre-defined threshold, and that the ESA is at risk of quickly reaching its shutdown temperature without thermal load balancing being performed.

In some embodiments, the optimization module 208 determines a minimum number of antenna tiles to be powered to meet a target signal-to-noise ratio (SNR). The minimum number of antenna tiles is dependent at least in part on a relative location of a target system (e.g., a LEO satellite). For example, the minimum number of antenna tiles may be lower when communicating with a target system that is normal to a planar area or surface of the ESA, while the minimum number of antenna tiles may be higher for a target system that is near the horizon with respect to the planar area or surface of the ESA. Accordingly, in some embodiments, the optimization module 208 is configured to obtain position information for a target system to determine the minimum or reduced number of antenna tiles. In some embodiments, the minimum number of antenna tiles is determined based on at least one of a transmit power requirement or a receive power requirement, said requirements corresponding to the target SNR.

FIG. 3A illustrates an example of an ESA 300 that includes a total N number of tiles 302, with N=6 in the illustrated example. According to the illustrated example, the optimization module 208 has determined that the minimum or reduced M number of tiles that meets the target SNR is four (M=4), with the ESA 300 having four activated tiles 302A and two in-active tiles 302B.

In some embodiments, the optimization module 208 generates an exhaustive list of all possible M-from-N tile patterns. The number of possible tile patterns or permutations may be defined as

S
=

N
!

M
⁢

!

(

N
-
M

)

!

.

Accordingly, in the illustrated example in which N=6 and M=4, then the number of possible tile patterns is fifteen (S=15). FIG. 3B illustrates each of the fifteen possible tile patterns 304, with each possible tile pattern 304 specifying a unique permutation or arrangement of activated tiles 302A.

In some embodiments, the optimization module 208 analyzes each possible tile pattern 304 to determine whether a possible tile pattern 304 complies with one or more requirements and constraints. In particular, in some embodiments, the requirements and constraints include a symmetry requirement, in which an arrangement of activated tiles 302A in a possible tile pattern 304 must be symmetrical. Because of this symmetry requirement, resulting beam geometry may be compliant and reliable. The symmetry requirement may be associated with polarization and power constraints associated with the ESA, due to asymmetrical tile patterns requiring more power to emit a signal without significant noise, in some examples. FIG. 3B illustrates an example of an asymmetrical tile pattern 304A and a symmetrical tile pattern 304B. In some embodiments, the optimization module 208 classifies certain ones of the possible tile patterns 304 (e.g., the symmetrical tile patterns 304B) as feasible and eliminates the remaining ones of the possible tile patterns 304 (e.g., the asymmetrical tile patterns 304A). In the illustrated example of FIG. 3B, the optimization module 208 eliminates ten of the possible tile patterns 304 due to non-compliance thereof with the symmetrical requirement.

As shown in FIG. 3C, the optimization module 208 then constructs a graph 310, in some embodiments. The graph 310 includes nodes 312 and edges 314. The nodes 312 of the graph 310 correspond to the possible tile patterns 304 classified as feasible (e.g., symmetrical tile patterns 304B). The edges 314 of the graph 310 define a number of shared or common tiles between respective tile patterns. For example, between the nodes 312 labelled “1” and “4,” the respective tile patterns share two common tiles, and the particular edge between Node 1 and Node 4 accordingly indicates the value 2. Likewise, the particular edge between Node 9 and Node 8 indicates that the respective tile patterns of Node 9 and Node 8 share three common tiles. Accordingly, if two tile patterns are non-overlapping, an edge between the corresponding two nodes would define a value of zero. The edges 314 of the graph 310 are directionless.

In some embodiments, the optimization module 208 then calculates the shortest path that traverses the graph. Specifically, the optimization module 208 searches for a path that visits each node exactly once and returns to the original node, with the path having a minimized “distance” or total edge value. It may be appreciated that this path resembles a solution to the Traveling Salesman problem. Thus, for example, one solution is to exhaustively compare lengths for all possible paths. For a graph 310 with X number of nodes 312, there exist at most (X−1)! possible paths. Other path optimization solutions may be implemented.

The shortest path solution then may describe a cyclic power sequence among the feasible tile patterns by which the ESA can be operated. In FIG. 3C, the shortest path 316 visits in order Node 1, Node 4, Node 9, Node 7, Node 8, and back to Node 1, and each of these nodes corresponds to a tile pattern. Due to the shortest path 316 being minimized with respect to edge values, the number of shared tiles between consecutive tile patterns in the sequence is minimized. Accordingly, when switching from a given tile pattern to the subsequent tile pattern, a maximum number of tiles are inactivated to allow for said tiles to cool and rest. Any given tile of the ESA has its cooling or resting time maximized by way of the shortest path 316. In some embodiments, the ESA is operated according to the cyclic power sequence in a cyclic or repeated manner while in the reduced service mode and/or until a stop command is received. In some embodiments, after exiting the reduced service mode and/or receiving a stop command, the ESA is operated under normal conditions, for example, with all of the tiles being activated or powered.

Therefore, in some embodiments, the optimization module 208 determines a dynamic power sequence for an ESA according to graph minimum path. In some embodiments, this dynamic power sequence is pre-determined by the optimization module 208 and stored in association with the M and N parameters. In some embodiments, the optimization module 208 pre-determines an optimized sequence of tile patterns for each value of M for a given N total number of tiles in a given ESA. In some embodiments, the optimization module 208 is provided with optimized sequences corresponding to one or more values of M by a ground server, another vehicle, and/or the like.

In some embodiments, the ESA is powered according to a given tile pattern in the optimized power sequence for a respective dwelling time. For example, when the ESA powers up, the ESA enters a first power mode (that is, apply power to a first tile pattern in the optimized power sequence). The ESA dwells in the first power mode for a first time period T1, and then the ESA switches to a second power mode (that is, stop power to the first tile pattern, apply power to a second tile pattern in the optimized power sequence). The ESA dwell in the second power mode for a second time period T2, and continues through the optimized power sequence.

In some embodiments, the respective dwelling times (e.g., first time period T1, second time period T2) are determined based on information associated with a communication channel (e.g., a radio frequency channel) within which the ESA transmits or receives signals. For example, for time-slotted waveforms (e.g., TDMA, Mx-DMA), the dwelling time may be set equal to the integer number of time slots, as indicated by time division information associated with the communication channel, or channel characteristic information generally. For example, when deployed with Mx-DMA, T1=T2= . . . =n seconds. In some embodiments, the respective dwelling times are determined based on the shutdown temperature of the ESA. For example, for continuous time waveforms (e.g., CDMA, DVB-SX), the dwelling time in a particular tile pattern may be calculated as a function of estimated time for the tile set to reach shutdown temperature. Tile temperatures are sampled periodically, for example, via the temperature sensors of the antenna system, and the tile pattern's temperature is the highest temperature among all its tiles. Accordingly, in some embodiments, the antenna system may include a temperature sensor for each tile. For example, at the start of a first power mode, the temperature of a first tile pattern is 50° C., and the shutdown temperature of the antenna system is 75° C. The ESA may dwell in the first tile pattern until the temperature of the first tile pattern is 55° C., based on the total number of patterns in the power sequence, an offset from the shutdown temperature, the ambient temperature, the predicted temperature gradient, and/or the like. In some embodiments, the dwelling times for power sequences for the TX aperture and for the RX aperture may be independent.

Turning now to FIG. 4, an example method 400 for determining an optimization for ESA operation is illustrated. For example, the method 400 may be performed by an antenna controller that operates an ESA. In some embodiments, the method 400 may be performed in advanced of the ESA requiring thermal load balancing, such that the optimization for ESA operation is pre-determined. In some embodiments, the method 400 is performed on demand to determine the optimization for ESA operation based on current states and usages of the ESA.

At 402, the antenna controller identifies a subset of feasible, permissible, or compliant tile patterns out of a plurality of possible tile patterns, with each possible tile pattern having a reduced number of antenna tiles within an antenna configuration. For example, the antenna controller identifies the feasible, permissible, or compliant tile patterns based on a spatial symmetry (or lack thereof) across the specific tiles of each pattern. In some embodiments, alternative or in addition to spatial symmetry requirements, the subset of patterns is identified based on a number of shared tiles. For example, a maximum shared tile count is defined, and any tile pattern of the possible tile pattern that has at least the maximum shared tile count with any other tile pattern is eliminated from the subset. FIG. 3B demonstrates the identification of a subset of tile patterns out of a plurality of possible tile patterns. In some embodiments, the identification of a subset of tile patterns promotes efficiency and feasibility of subsequent graph construction and path optimization operations.

At 404, the antenna controller constructs a graph with nodes corresponding to the permissible patterns and edges corresponding to a shared tile count between respective permissible patterns. In order to do so, in some embodiments, each feasible or permissible tile pattern is associated with a unique identifier, which the antenna controller correlates or associates with a node of the graph. FIG. 3C illustrates an example of a graph constructed by the antenna controller at 404.

At 406, the antenna calculates an optimized path that traverses the graph with minimized distance. Because edge distances or lengths are defined according to shared tile count, minimization of path length corresponds to a minimization of shared tiles between consecutive tile patterns in the path. The optimized path may visit each node of the graph exactly once before returning to an original node. By including every node of the graph (or, every permissible tile pattern), there are more unique patterns to hop between, which maximizes inactivity time for a given tile. In some embodiments, a Traveling Salesman solution is used to calculate the optimized path. FIG. 3C indicates an example optimized path that traverses a graph with minimized distance.

At 408, the antenna controller stores the optimized path in a database (e.g., database 108 in FIG. 1A) in association with the reduced number of tiles and/or a particular antenna configuration. For example, the antenna controller pre-calculates or pre-determines the optimized path and stores the optimized path for later use in ESA operation when triggered. In some embodiments, the database stores a plurality of optimized paths. Because the optimized path is unique to the graph which is constructed primarily based on a reduced number of antenna tiles and an antenna configuration (e.g., a total number of antenna tiles and geometric layout or spatial arrangement thereof), the database may store the optimized path in reference to the reduced number of antenna tiles and the antenna configuration. Therefore, given a reduced number of antenna tiles and an antenna configuration, a specific optimized path may be retrieved from the database. In some embodiments, the antenna controller further determines dwelling times for patterns in the optimized path and stores the dwelling times with the optimized path in the database.

FIG. 5 illustrates an example method 500 for thermal load balancing an ESA. The example method 500 may be implemented by an antenna system to maximize an expected time before the antenna system reaches a shutdown temperature. In particular, the antenna system includes an ESA that includes antenna tiles operable in coordination with one another to emit and/or receive directed communication signals, and the antenna system further includes an antenna controller device that performs the method 500 to maximize an expected time before the ESA reaches a shutdown temperature. In some embodiments, the ESA is located on an aircraft, and the method 500 may be performed to improve communication capability between the aircraft and target systems, including ground stations and satellites.