Systems and methods for wireless communications interference mitigation

Abstract

Systems and methods include mitigating interference in satellite communications, using predicted in-line event (ILE) detection between satellites of different systems operating on the same frequency, each prediction being generated via change detection algorithms based on machine learning models or ephemeris-based predictions so as to anticipate reductions in signal quality before they occur. Upon detection, proactive adaptive coding and modulation (P-ACM) adjusts signal characteristics in accordance with the predicted ILEs, the adjustments being sufficient to mitigate interference prior to degradation. Additionally, successive interference cancellation (SIC) and cooperative spectrum sharing techniques are employed to further enhance performance, wherein the combination of P-ACM, SIC, and cooperative spectrum sharing is sufficient to improve signal reliability, reduce latency, and optimize spectral efficiency in dense satellite networks, particularly in low Earth orbit (LEO) systems.

Description

SYSTEMS AND METHODS FOR WIRELESS COMMUNICATIONS INTERFERENCE MITIGATION FIELD OF TECHNOLOGY

[0001] The present disclosure generally relates to computer-based systems and methods for wireless communications interference mitigation, including proactive adaptation of coding and modulation scheme to mitigate adjacent wireless system interference. BACKGROUND OF TECHNOLOGY

[0002] Mobile wireless relays are playing an increasing role in providing wireless communication and networking to users across the world. Such mobile wireless relays may operate on communication with one or more backhaul terminals and / or user terminals, often via two-way communication whereby a user’s data requests (such as loading a webpage) are sent from the user terminal to the mobile wireless relay, which then relays the request to the backhaul terminal. The backhaul terminal processes the request and sends the data back through the mobile wireless relay to the user terminal. BACKGROUNDOFTECHNOLOGY

[0003] Mobile wireless relays are playing an increasing role in providing wireless communication and networking to users across the world. Such mobile wireless relays may operate on communication with one or more backhaul terminals and / or user terminals, often via two-way communication whereby a user’s data requests (such as loading a webpage) are sent from the user terminal to the mobile wireless relay, which then relays the request to the backhaul terminal. The backhaul terminal processes the request and sends the data back through the mobile wireless relay to the user terminal. SUMMARY OF THE INVENTION

[0004] In some aspects, the techniques described herein relate to a method including: receiving, by at least one processor of a mobile wireless relay-enabled communications transceiver, a series of communications over a mobile wireless relay communications link, wherein each communication in the series of communications includes at least one signal quality metric; inputting, by the at least one processor, the at least one signal quality metric of a plurality ofcommunications in the series of communication into at least one change detection algorithm to detect, for a next communication in the series of communications, at least one predicted reduction in signal quality resulting from in-line event with at least one other mobile wireless relay; applying, by the at least one processor, in response to the at least one predicted reduction in signal quality prior to the next communication, at least one signal adjustment to at least one characteristic of at least one signal for carrying the next communication, wherein the at least one signal adjustment is configured to mitigate interference from the at least one other mobile wireless relay; generating, by the at least one processor, the at least one signal to carry the next communication to the mobile wireless relay based at least in part on the at least one signal adjustment; and transmitting, by the at least one processor, the at least one signal via the mobile wireless relay communications link.

[0005] In some aspects, the techniques described herein relate to a method, further including: receiving, by the at least one processor, from the mobile wireless relay, at least one subsequent received communication including at least one subsequent signal quality metric; inputting, by the at least one processor, the at least one subsequent signal quality metric of the at least one subsequent received communication into the at least one change detection algorithm to detect, for another next communication in the series of communications, at least one predicted improvement in signal quality resulting from an end to the in-line event with the at least one other mobile wireless relay; applying, by the at least one processor, in response to the at least one predicted improvement in signal quality prior to the next communication, at least one subsequent signal adjustment for generating at least one other next signal to carry the other next communication, wherein the at least one signal adjustment is configured to return the signal quality to an original state.

[0006] In some aspects, the techniques described herein relate to a method, wherein the at least one change detection algorithm includes at least one machine learning model trained to classify communications as one of nominal or not-nominal based at least in part on the at least one signal quality metric of each communication in the series of communications; wherein the at least one machine learning model is trained based at least in part on training data including training communications pre-labeled as having nominal signal quality.

[0007] In some aspects, the techniques described herein relate to a method, further including: obtaining, by the at least one processor, ephemeris data associated with the mobile wireless relay; obtaining, by the at least one processor, ephemeris data associated with the at least one othermobile wireless relay; determining, by the at least one processor, a position of a ground terminal associated with the mobile wireless relay communication; determining, by the at least one processor, at least one predicted in-line event between the at least one mobile wireless relay and the at least one other mobile wireless relay based at least in part on the ephemeris data of the mobile wireless relay, the ephemeris data of the at least one other mobile wireless relay and the position of the ground station; and validating, by the at least one processor, the at least one detected in-line event based at least in part on the at least one predicted in-line event.

[0008] In some aspects, the techniques described herein relate to a method, further including: applying, by the at least one processor, in response to the at least one predicted improvement in signal quality prior to the next communication, the at least one subsequent signal adjustment an orthogonal multiple access (OMA) scheme with cooperative spectrum sharing.

[0009] In some aspects, the techniques described herein relate to a method, wherein the at least one change detection algorithm includes at least one of: at least one edge detection algorithm, at least one correlator filter, at least one matched filter, or at least one filter bank including at least one of: at least one correlator filter, or at least one matched filter.

[0010] In some aspects, the techniques described herein relate to a method, further including: determining, by the at least one processor, an average signal quality based at least in part on the at least one signal quality metric of each communication in the series of communications; determining, by the at least one processor, for at least one most recent communication in the series of communications, a signal quality metric change relative to the average signal quality based at least in part on the at least one signal quality metric of the at least one most recent communication; determining, by the at least one processor, a filtered signal quality metric change based at least in part on the signal quality metric change and a change pattern; and determining, by the at least one processor, the at least one predicted reduction in signal quality based at least in part on the filtered signal quality metric change exceeding a predetermined threshold.

[0011] In some aspects, the techniques described herein relate to a method, further including: determining, by the at least one processor, a signal-to-interference-plus-noise ratio (SINR) of each communication in the series of communications based at least in part on the at least one signal quality metric of each communication in the series of communications; wherein the at least one signal quality metric including at least one of: bit error rate, packet error rate, Block Error Rate, error count, forward error correction (FEC) decoder iteration count, change of modulation andcoding (MODCOD) level, spectral efficiency, automatic repeat request (ARQ), hybrid ARQ (HARQ), or received power.

[0012] In some aspects, the techniques described herein relate to a method, further including: applying, by the at least one processor, an interference cancellation approach in a power-domain within a Non-orthogonal Multiple Access (NOMA) scheme.

[0013] In some aspects, the techniques described herein relate to a method, wherein the interference cancellation approach includes successive interference cancellation (SIC).

[0014] In some aspects, the techniques described herein relate to a method, wherein the NOMA scheme is cooperative or non-cooperative.

[0015] In some aspects, the techniques described herein relate to a method, the at least one processor is part of at least one of: the mobile wireless relay, at least one ground station, at least one user terminal, at least one gateway, or at least one high-altitude platform (HAP) station.\

[0016] In some aspects, the techniques described herein relate to a method, wherein the mobile wireless relay includes a transparent communication mobile wireless relay or a regenerative communication mobile wireless relay.

[0017] In some aspects, the techniques described herein relate to a system including: at least one processor of a mobile wireless relay communications gateway; at least one non-transitory computer-readable medium having computer instructions stored thereon, wherein the at least one processor, upon execution of the computer instructions, is configured to perform steps including: receiving, by at least one processor of a mobile wireless relay-enabled communications transceiver, a series of communications over a mobile wireless relay communications link, wherein each communication in the series of communications includes at least one signal quality metric; inputting, by the at least one processor, the at least one signal quality metric of a plurality of communications in the series of communication into at least one change detection algorithm to detect, for a next communication in the series of communications, at least one predicted reduction in signal quality resulting from in-line event with at least one other mobile wireless relay; applying, by the at least one processor, in response to the at least one predicted reduction in signal quality prior to the next communication, at least one signal adjustment to at least one characteristic of at least one signal for carrying the next communication, wherein the at least one signal adjustment is configured to mitigate interference from the at least one other mobile wireless relay; generating, by the at least one processor, the at least one signal to carry the next communication to the mobilewireless relay based at least in part on the at least one signal adjustment; and transmitting, by the at least one processor, the at least one signal via the mobile wireless relay communications link.

[0018] In some aspects, the techniques described herein relate to a system, wherein the steps further include: receiving, from the mobile wireless relay, at least one subsequent received communication including at least one subsequent signal quality metric; inputting the at least one subsequent signal quality metric of the at least one subsequent received communication into the at least one change detection algorithm to detect, for another next communication in the series of communications, at least one predicted improvement in signal quality resulting from an end to the in-line event with the at least one other mobile wireless relay; applying, in response to the at least one predicted improvement in signal quality prior to the next communication, at least one subsequent signal adjustment for generating at least one other next signal to carry the other next communication, wherein the at least one signal adjustment is configured to return the signal quality to an original state.

[0019] In some aspects, the techniques described herein relate to a system, wherein the at least one change detection algorithm includes at least one machine learning model trained to classify communications as one of nominal or not-nominal based at least in part on the at least one signal quality metric of each communication in the series of communications; wherein the at least one machine learning model is trained based at least in part on training data including training communications pre-labeled as having nominal signal quality.

[0020] In some aspects, the techniques described herein relate to a system, wherein the steps further include: obtaining, by the at least one processor, ephemeris data associated with the mobile wireless relay; obtaining, by the at least one processor, ephemeris data associated with the at least one other mobile wireless relay; determining, by the at least one processor, a position of a ground terminal associated with the mobile wireless relay communication; determining, by the at least one processor, at least one predicted in-line event between the at least one mobile wireless relay and the at least one other mobile wireless relay based at least in part on the ephemeris data of the mobile wireless relay, the ephemeris data of the at least one other mobile wireless relay and the position of the ground station; and validating, by the at least one processor, the at least one detected in-line event based at least in part on the at least one predicted in-line event.

[0021] In some aspects, the techniques described herein relate to a system, wherein the steps further include: applying, in response to the at least one predicted improvement in signal qualityprior to the next communication, the at least one subsequent signal adjustment an orthogonal multiple access (OMA) scheme with cooperative spectrum sharing.

[0022] In some aspects, the techniques described herein relate to a system, wherein the at least one change detection algorithm includes at least one of: at least one edge detection algorithm, at least one correlator filter, at least one matched filter, or at least one filter bank including at least one of: at least one correlator filter, or at least one matched filter.

[0023] In some aspects, the techniques described herein relate to a system, wherein the steps further include: determining an average signal quality based at least in part on the at least one signal quality metric of each communication in the series of communications; determining, for at least one most recent communication in the series of communications, a signal quality metric change relative to the average signal quality based at least in part on the at least one signal quality metric of the at least one most recent communication; determining a filtered signal quality metric change based at least in part on the signal quality metric change and a change pattern; and determining the at least one predicted reduction in signal quality based at least in part on the filtered signal quality metric change exceeding a predetermined threshold.

[0024] In some aspects, the techniques described herein relate to a system, wherein the steps further include: determining a signal-to-interference-plus-noise ratio (SINR) of each communication in the series of communications based at least in part on the at least one signal quality metric of each communication in the series of communications; wherein the at least one signal quality metric including at least one of: bit error rate, packet error rate, Block Error Rate, error count, forward error correction (FEC) decoder iteration count, change of modulation and coding (MODCOD) level, spectral efficiency, automatic repeat request (ARQ), hybrid ARQ (HARQ), or received power.

[0025] In some aspects, the techniques described herein relate to a system, further including: applying, by the at least one processor, an interference cancellation approach in a power-domain within a Non-orthogonal Multiple Access (NOMA) scheme.

[0026] In some aspects, the techniques described herein relate to a system, wherein the interference cancellation approach includes successive interference cancellation (SIC).

[0027] In some aspects, the techniques described herein relate to a system, wherein the NOMA scheme is cooperative or non-cooperative.

[0028] In some aspects, the techniques described herein relate to a system, the at least one processor is part of at least one of: the mobile wireless relay, at least one ground station, at least one user terminal, at least one gateway, or at least one high-altitude platform (HAP) station.

[0029] In some aspects, the techniques described herein relate to a system, wherein the mobile wireless relay includes a transparent communication mobile wireless relay or a regenerative communication mobile wireless relay.

[0030] In some aspects, the techniques described herein relate to a non-transitory computer- readable medium having computer instructions stored thereon, wherein at least one processor of a mobile wireless relay-enabled communications transceiver, upon execution of the computer instructions, is configured to perform steps including: receiving a series of communications over a mobile wireless relay communications link, wherein each communication in the series of communications includes at least one signal quality metric; inputting the at least one signal quality metric of a plurality of communications in the series of communication into at least one change detection algorithm to detect, for a next communication in the series of communications, at least one predicted reduction in signal quality resulting from in-line event with at least one other mobile wireless relay; applying, in response to the at least one predicted reduction in signal quality prior to the next communication, at least one signal adjustment to at least one characteristic of at least one signal for carrying the next communication, wherein the at least one signal adjustment is configured to mitigate interference from the at least one other mobile wireless relay; generating the at least one signal to carry the next communication to the mobile wireless relay based at least in part on the at least one signal adjustment; and transmitting the at least one signal via the mobile wireless relay communications link.

[0031] In some aspects, the techniques described herein relate to a non-transitory computer- readable medium, wherein the steps further include: receiving, from the mobile wireless relay, at least one subsequent received communication including at least one subsequent signal quality metric; inputting the at least one subsequent signal quality metric of the at least one subsequent received communication into the at least one change detection algorithm to detect, for another next communication in the series of communications, at least one predicted improvement in signal quality resulting from an end to the in-line event with the at least one other mobile wireless relay; applying, in response to the at least one predicted improvement in signal quality prior to the next communication, at least one subsequent signal adjustment for generating at least one other nextsignal to carry the other next communication, wherein the at least one signal adjustment is configured to return the signal quality to an original state.

[0032] In some aspects, the techniques described herein relate to a non-transitory computer- readable medium, wherein the at least one change detection algorithm includes at least one machine learning model trained to classify communications as one of nominal or not-nominal based at least in part on the at least one signal quality metric of each communication in the series of communications; and wherein the at least one machine learning model is trained based at least in part on training data including training communications pre-labeled as having nominal signal quality.

[0033] In some aspects, the techniques described herein relate to a non-transitory computer- readable medium, wherein the steps further include: obtaining, by the at least one processor, ephemeris data associated with the mobile wireless relay; obtaining, by the at least one processor, ephemeris data associated with the at least one other mobile wireless relay; determining, by the at least one processor, a position of a ground terminal associated with the mobile wireless relay communication; determining, by the at least one processor, at least one predicted in-line event between the at least one mobile wireless relay and the at least one other mobile wireless relay based at least in part on the ephemeris data of the mobile wireless relay, the ephemeris data of the at least one other mobile wireless relay and the position of the ground station; and validating, by the at least one processor, the at least one detected in-line event based at least in part on the at least one predicted in-line event.

[0034] In some aspects, the techniques described herein relate to a non-transitory computer- readable medium, wherein the steps further include: applying, in response to the at least one predicted improvement in signal quality prior to the next communication, the at least one subsequent signal adjustment an orthogonal multiple access (OMA) scheme with cooperative spectrum sharing.

[0035] In some aspects, the techniques described herein relate to a non-transitory computer- readable medium, wherein the steps further include: determining an average signal quality based at least in part on the at least one signal quality metric of each communication in the series of communications; determining, for at least one most recent communication in the series of communications, a signal quality metric change relative to the average signal quality based at least in part on the at least one signal quality metric of the at least one most recent communication;determining a filtered signal quality metric change based at least in part on the signal quality metric change and a change pattern; and determining the at least one predicted reduction in signal quality based at least in part on the filtered signal quality metric change exceeding a predetermined threshold.

[0036] In some aspects, the techniques described herein relate to a non-transitory computer- readable medium, wherein the steps further include: determining a signal-to-interference-plus- noise ratio (SINR) of each communication in the series of communications based at least in part on the at least one signal quality metric of each communication in the series of communications; wherein the at least one signal quality metric including at least one of: bit error rate, packet error rate, Block Error Rate, error count, forward error correction (FEC) decoder iteration count, change of modulation and coding (MODCOD) level, spectral efficiency, automatic repeat request (ARQ), hybrid ARQ (HARQ), or received power.

[0037] In some aspects, the techniques described herein relate to a non-transitory computer- readable medium, further including: applying, by the at least one processor, an interference cancellation approach in a power-domain within a Non-orthogonal Multiple Access (NOMA) scheme.

[0038] In some aspects, the techniques described herein relate to a non-transitory computer- readable medium, wherein the interference cancellation approach includes successive interference cancellation (SIC).

[0039] In some aspects, the techniques described herein relate to a non-transitory computer- readable medium, wherein the NOMA scheme is cooperative or non-cooperative.

[0040] In some aspects, the techniques described herein relate to a non-transitory computer- readable medium, the at least one processor is part of at least one of: the mobile wireless relay, at least one ground station, at least one user terminal, at least one gateway, or at least one high- altitude platform (HAP) station.

[0041] In some aspects, the techniques described herein relate to a non-transitory computer- readable medium, wherein the mobile wireless relay includes a transparent communication mobile wireless relay or a regenerative communication mobile wireless relay.BRIEFDESCRIPTIONOFTHEDRAWINGS

[0042] Various embodiments of the present disclosure can be further explained with reference to the attached drawings, wherein like structures are referred to by like numerals throughout the several views. The drawings shown are not necessarily to scale, with emphasis instead generally being placed upon illustrating the principles of the present disclosure. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ one or more illustrative embodiments.

[0043] FIG. 1 is a block diagram of an exemplary mobile wireless relay communication framework in accordance with one or more embodiments of the present disclosure.

[0044] FIG. 2 is a block diagram of an exemplary communication loop using mobile wireless relay-enabled communication transceivers exchanging mobile wireless relay communication signals over a mobile wireless relay communication link in accordance with one or more embodiments of the present disclosure.

[0045] FIG. 3 illustrates an exemplary conjunction event where (a) depicts an interfering mobile wireless relay’s 14 antenna pointing direction not being aligned to a terminal’s (ground station terminal 11, user terminal 13, or both) antenna boresight toward the desired mobile wireless relay 12 such that interference is reduced due to spatial filtering / beamforming, whereas (b) depicts a conjunction event in which the terminal’s 11 / 13 antenna pointing direction to the desired mobile wireless relay 12 is close to the interfering mobile wireless relay’s 14 antenna pointing direction in accordance with one or more embodiments of the present disclosure.

[0046] FIG. 4(a) illustrates an exemplary ACM concept in operation in accordance with one or more embodiments of the present disclosure.

[0047] FIG. 4(b) illustrates an exemplary schematic of ACM feedback control loop assuming a transparent mobile wireless relay payload in accordance with one or more embodiments of the present disclosure.

[0048] FIG.5 depicts two example cases (a) and (b) of variation in change to signal-to-interference (power) ratio (SIR), differential SIR (∆κ), during two simulated conjunction events in accordance with one or more embodiments of the present disclosure.

[0049] FIG.6 depicts a simulated predictive algorithm detecting the onset and the end of an event (e.g., ILE-induced) in which the SINR reduces in accordance with one or more embodiments of the present disclosure.

[0050] FIG. 7 depicts latency of the HARQ with IR and ACM-ARQ compared to the latency of the proposed P-ACM scheme of zero in accordance with one or more embodiments of the present disclosure.

[0051] FIG. 8 depicts an example of probability of bit error as a function of SNR in an example of the successive interference cancellation (SIC) applied to an OFDM waveform interference scenario in accordance with one or more embodiments of the present disclosure.

[0052] FIG. 9 depicts exemplary achievable throughputs (or spectral efficiencies) of Mobilewireless relay A and Mobile wireless relay B signals in a co-operation scenario when ^^^^ / ^^ =^^^^ / ^^ in accordance with one or more embodiments of the present disclosure.

[0053] Various detailed embodiments of the present disclosure, taken in conjunction with the accompanying FIGs., are disclosed herein; however, it is to be understood that the disclosed embodiments are merely illustrative. In addition, each of the examples given in connection with the various embodiments of the present disclosure is intended to be illustrative, and not restrictive. DETAILEDDESCRIPTION

[0054] Throughout the specification, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise. The phrases “in one embodiment” and “in some embodiments” as used herein do not necessarily refer to the same embodiment(s), though it may. Furthermore, the phrases “in another embodiment” and “in some other embodiments” as used herein do not necessarily refer to a different embodiment, although it may. Thus, as described below, various embodiments may be readily combined, without departing from the scope or spirit of the present disclosure.

[0055] In addition, the term "based on" is not exclusive and allows for being based on additional factors not described, unless the context clearly dictates otherwise. In addition, throughout the specification, the meaning of "a," "an," and "the" include plural references. The meaning of "in" includes "in" and "on."

[0056] As used herein, the terms “and” and “or” may be used interchangeably to refer to a set of items in both the conjunctive and disjunctive in order to encompass the full description of combinations and alternatives of the items. By way of example, a set of items may be listed with the disjunctive “or”, or with the conjunction “and.” In either case, the set is to be interpreted as meaning each of the items singularly as alternatives, as well as any combination of the listed items.

[0057] FIGs.1 through 9 illustrate systems and methods of mitigating adjacent system interference in LEO satellite systems. The following embodiments provide technical solutions and technical improvements that overcome technical problems, drawbacks and / or deficiencies in the technical fields involving interference between satellites of different satellite systems that arise due to the increasing number of conjunction events and / or ILEs. As multiple satellite systems are deployed, the likelihood of an in-line event (ILE, or a conjunction) between the satellites of different system providers operating on the same frequency but from different satellite systems increases. The ILE causes heightened interference at the victim receiver. Embodiments herein include schemes for mitigating the ILE-induced interference.

[0058] In some embodiments, ILE-induced interference may be mitigated using adaptive coding and modulation (ACM) through which the transmitter of the affected system can adjust transmission, e.g., by lowering the modulation and coding scheme (MCS) rate, decreasing forward error correction (FEC) or other adjustment or any combination thereof, to counteract the reduced signal to interference ratio (SIR). To reduce the latency due to the round-trip time (RTT) over the links, aspects of some embodiments may include a Proactive ACM (P-ACM) scheme that detects the onset of an ILE and promptly takes corrective action without the delay of multiple RTTs. P- ACM may facilitate more agile response to the ILE, thereby mitigating the impact of the ILE in terms of spectral efficiency (SE) and the latency.

[0059] In some embodiments, ILE-induced interference may be mitigated using SIC to address the impact of the interference at the physical layer receiver. This SIC scheme may facilitate interference mitigation in a worst-case scenario, such as when the interference is stronger in the received power compared to the desired signal. In an example SIC scheme, the receiver may first demodulate and decode the stronger power signal, treating the weaker signal as interference (this added interference lowers the SINR, with the assumption that channel coding can compensate for it). Once the stronger signal is successfully decoded, it may be reconstructed and subtracted from the received signal, effectively eliminating interference for the weaker signal. In someembodiments, in the above example case, the SIC may be of most benefit for the weaker signal for which the signal is lower in power than the interference. Without the SIC, the weaker signal may suffer from poor detection error rates, while with the SIC, the weaker signal may be extracted as if the interference is not present.

[0060] In some embodiments, ILE-induced interference may be mitigated using cooperation between the co-channel satellite systems that can mutually interfere.

[0061] Thus, as explained in more detail, below, technical solutions and technical improvements herein include aspects of improved satellite communication technology by reducing error rates, signal dropouts and other errors that arise due to interference, particularly from ILEs. Based on such technical features, further technical benefits become available to users and operators of these systems and methods. Moreover, various practical applications of the disclosed technology are also described, which provide further practical benefits to users and operators that are also new and useful improvements in the art.

[0062] FIG. 1 is a block diagram of an exemplary mobile relay communication framework in accordance with one or more embodiments of the present disclosure.

[0063] In some embodiments, communications between a ground station terminal 11 and a user terminal 13 may be enabled by a mobile relay link via a mobile relay 12. Communications between a ground station terminal 11 and a user terminal 13 are described here for simplicity to illustrate principles of the invention, but such principles may apply to communications between any two or more endpoints, including user terminal to user terminal, ground station terminal to ground station terminal, ground station terminal to other terminal type, user terminal to other terminal type, in a one-to-one, one-to-many, many-to-one or other configuration or any combination thereof.

[0064] For example, the ground station terminal 11 may include a connection to a backhaul network or data source with which a user at the user terminal 13 is attempting to communicate. Such communications may be effectuated with a suitable communications mobile relay 13 by a series of uplinks and downlinks. Communications via the mobile relay 13 from an initiating endpoint to a responding endpoint may be termed a “forward” link, while communications from the responding endpoint (e.g., the response) to the initiating endpoint may be termed the “reverse link.”

[0065] FIG. 1 depicts an example scenario where the ground station terminal 11 is the initiating endpoint and the user terminal 13 is the responding terminal, though this relationship may bereversed. In some embodiments, the mobile relay 12 may be equipped with a transponder, e.g., an integrated receiver and transmitter of radio signals, which may receive signals from Earth and retransmit them back to the planet. Thus, the ground station terminal 11 may communicate to the user terminal 13 via a forward uplink 1 to the mobile relay 12, which may relay the communication to the user terminal 13 via a forward downlink 2. In reply, the user terminal 13 may communicate a reverse uplink 3 to the mobile relay 12, which may relay such reply to the ground station terminal 11 via a reverse downlink 4.

[0066] In some embodiments, the mobile relay 12 may relay and amplify the uplinks 1, 3 via the transponder to create a communication link between the ground station terminal 11 and the user terminal 13 at different locations on Earth. In some embodiments, the mobile relay 12 may be used for television, telephone, radio, internet, and military applications.

[0067] In some embodiments, the mobile relay 12 may be configured for transparent payload communications where the mobile relay 12 acts as a simple repeater, without performing onboard signal processing (e.g., “bent-pipe” or “non-regenerative” payload mode). Such a configuration may receive signals from the ground station terminal 11, amplifies them, and retransmits them back to Earth without altering the content. As such, transparent payload communications do not need complex processing equipment, and is generally cheaper to build and launch. Additionally, by avoiding onboard processing, transparent payloads can reduce the latency associated with signal transmission.

[0068] In some embodiments, the mobile relay 12 may be configured for regenerative payload communications that performs signal processing onboard the mobile relay itself, rather than relying solely on ground stations. In some embodiments, the mobile relay 12 can handle tasks such as demodulation, decoding, switching, routing, mesh networking, among other tasks or any combination thereof directly which may reduce the need for extensive ground infrastructure and can improve the efficiency and speed of data transmission. Additionally, in some embodiments, by processing signals onboard, regenerative payloads can significantly reduce the latency associated with sending data back and forth between the mobile relay and ground stations and support more complex communication protocols to provide better overall performance compared to transparent designs.

[0069] In some embodiments, the mobile relay 12 may include one or more vehicles that may include, but is not limited to, a high-altitude platform (HAP), an aerial drone system, a ground-based drone system, a satellite system, a vehicle (e.g., a personal vehicle or commercial vehicle such as a car, truck, van or other automobile), an aircraft (e.g., personal or commercial helicopter, airplane, etc.) or other vehicle carrying wireless communication relay components configured to operate as a mobile relay 12, or any combination thereof.

[0070] For example, the mobile relay 12 may be a satellite of a satellite system. The satellite may be in geostationary orbit (GEO) or non-geostationary orbit (NGSO) (e.g., MEO or LEO). In some embodiments, the mobile relay 12 and mobile relay system are not limited to space-based or orbital systems. Indeed, as mobile relays in MEO and LEO orbit the Earth faster, a particular mobile relay (e.g., mobile relay 12) does not remain visible in the sky to a fixed point on Earth continually like a geostationary mobile relay but appear to a ground observer to cross the sky and "set" when they go behind the Earth beyond the visible horizon.

[0071] To provide continuous communications capability with lower altitude mobile relays 12, such as an MEO or LEO satellite, drone, HAP, aircraft, etc., require a larger number of mobile relays in a mobile relay system that move in a coordinated fashion, so that one of these mobile relays will always be visible in the sky for transmission of communication signals. Thus, the mobile relay 12 and others in its mobile relay system, as well as the mobile relays of other mobile relay systems may be moving relative to the Earth.

[0072] However, as the number of mobile relay systems proliferate, the instances of ILEs, there is an increased likelihood of a mobile relay from a first mobile relay system blocking or otherwise interfering with the mobile relay links of one or more mobile relays of a second mobile relay system. Such ILEs impair or impede performance of a mobile relay system in providing communication capabilities between endpoints, where the impairments may include, but are not limited to, signal loss, dropped packets, errors in the signal and data carried therein, reduced bandwidth, among other degradations of performance or any combination thereof.

[0073] FIG.2 is a block diagram of an exemplary communication loop using mobile relay-enabled communication transceivers exchanging mobile relay communication signals over a mobile relay communication link in accordance with one or more embodiments of the present disclosure.

[0074] FIG. 3 illustrates an exemplary conjunction event where (a) depicts an interfering mobile relay’s 14 antenna pointing direction not being aligned to a terminal’s (ground station terminal 11, user terminal 13, or both) antenna boresight toward the desired mobile relay 12 such that interference is reduced due to spatial filtering / beamforming, whereas (b) depicts a conjunctionevent in which the terminal’s 11 / 13 antenna pointing direction to the desired mobile relay 12 is close to the interfering mobile relay’s 14 antenna pointing direction in accordance with one or more embodiments of the present disclosure.

[0075] FIG. 4(a) illustrates an exemplary ACM concept in accordance with one or more embodiments of the present disclosure in accordance with one or more embodiments of the present disclosure. As shown, higher data rates can be achieved when ACM is employed (purple curve) compared to without ACM (any of the other curves).

[0076] FIG. 4(b) illustrates an exemplary schematic of ACM feedback control loop assuming a transparent mobile relay in accordance with one or more embodiments of the present disclosure in accordance with one or more embodiments of the present disclosure.

[0077] In some embodiments, the ILE occurs when two mobile relays operating on the same frequency but from different system operators are in the Field of View (FoV) of a User Terminal (UT) antenna. During the ILE, the UT antenna boresight toward the desired mobile relay is also aligned to the interfering mobile relay (see FIG. 3(b)). Whilst this scenario is for the user link, it can also occur for the feeder link, e.g., between a gateway and mobile relay. For the case of regenerative payloads, the user and feeder links are independent from each other, but for transparent mobile relays, ILE on one link impacts the other. Specifically, the ILE on the feeder link can potentially disturb all the user links.

[0078] In some embodiments, the impairment due to the ILE, whose probability of occurrence increases as the mobile relay systems become denser, occurs (i) on the downlink since the UT with a directional antenna pointed toward the desired mobile relay receives the downlink signal not only from the desired mobile relay but also from the interfering mobile relay, and (ii) on the uplink because a mobile relay’s directional receiving antenna on the uplink may receive interfering signal transmitted by a UT of an adjacent system. In either case, the signal to interference ratio (SIR) may significantly reduce and possibly result in a link outage.

[0079] In some embodiments, a remedial mechanism for operating the communication links over a range of Signal to Interference plus Noise Ratio (SINR) is adaptive coding and modulation (ACM) operating at the data link control (DLC) layer. The ACM responds to a reduction in the SIR by reducing the rate of the forward error correction (FEC) code and additionally and optionally by reducing the modulation index. The lower the modulation index and the coding rate (MODCOD), the greater the resiliency of the corresponding waveform transmitted by the physical(PHY) layer to interference and noise. The ACM reduces the probability of link outage due to a decreased SIR (or in general, a decreased SINR). This is, however, at the expense of a reduced spectral efficiency (SE). Conversely, as the SINR increases, the ACM throttles the waveform selection upward, e.g., to a waveform that has a higher SE, with higher coding rate and modulation index, is selected at the PHY layer. An example ACM operation is shown in FIG. 4(a), where, as the SNR (C / N_0) changes over a range, the achievable throughput (shown in the solid purple) stays on the envelope of a family of individual C / N_0 versus throughput curves for different modulation and coding schemes.

[0080] In some embodiments, communications via mobile relay 12 may be performed between a first mobile relay-enabled communications transceiver 210 (e.g., the ground station terminal 11 or other originating endpoint) and a second mobile relay-enabled communications transceiver 220 (e.g., the user terminal 13 or other receiving endpoint). The mobile relay-enabled communications transceiver 210 may transmit a mobile relay communication signal via a mobile relay communication link 230 to the second mobile relay-enabled communications transceiver 220, which may reply with another mobile relay communication signal via the mobile relay communication link 230, where the reply may include a signal quality metric representing a quality of the signal via the mobile relay communication link 230.

[0081] In some embodiments, the first mobile relay-enabled communications transceiver 210 may include hardware components such as a processor 211, which may include local or remote processing components. In some embodiments, the processor 211 may include any type of data processing capacity, such as a hardware logic circuit, for example an application specific integrated circuit (ASIC) and a programmable logic, or such as a computing device, for example, a microcomputer or microcontroller that include a programmable microprocessor. In some embodiments, the processor 211 may include data-processing capacity provided by the microprocessor. In some embodiments, the microprocessor may include memory, processing, interface resources, controllers, and counters. In some embodiments, the microprocessor may also include one or more programs stored in memory, on external storage, in a cloud platform (e.g., software-as-a-service or download as a package, or a combination thereof), among other storage locations or any combination thereof.

[0082] Similarly, the first mobile relay-enabled communications transceiver 210 may include storage (not shown), such as one or more local and / or remote data storage solutions such as, e.g.,local hard-drive, solid-state drive, flash drive, database or other local data storage solutions or any combination thereof, and / or remote data storage solutions such as a server, mainframe, database or cloud services, distributed database or other suitable data storage solutions or any combination thereof. In some embodiments, the storage may include, e.g., a suitable non-transient computer readable medium such as, e.g., random access memory (RAM), read only memory (ROM), one or more buffers and / or caches, among other memory devices or any combination thereof.

[0083] In some embodiments, the first mobile relay-enabled communications transceiver 210 may implement computer engines for ILE detection 216 and signal adjustment 218. In some embodiments, the terms “computer engine” and “engine” identify at least one software component and / or a combination of at least one software component and at least one hardware component which are designed / programmed / configured to manage / control other software and / or hardware components (such as the libraries, software development kits (SDKs), objects, etc.).

[0084] Examples of hardware elements may include processors, microprocessors, circuits, circuit elements (e.g., transistors, resistors, capacitors, inductors, and so forth), integrated circuits, application specific integrated circuits (ASIC), programmable logic devices (PLD), digital signal processors (DSP), field programmable gate array (FPGA), logic gates, registers, semiconductor device, chips, microchips, chip sets, and so forth. In some embodiments, the one or more processors may be implemented as a Complex Instruction Set Computer (CISC) or Reduced Instruction Set Computer (RISC) processors; x86 instruction set compatible processors, multi- core, or any other microprocessor or central processing unit (CPU). In various implementations, the one or more processors may be dual-core processor(s), dual-core mobile processor(s), and so forth.

[0085] Examples of software may include software components, programs, applications, computer programs, application programs, system programs, machine programs, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, procedures, software interfaces, application program interfaces (API), instruction sets, computing code, computer code, code segments, computer code segments, words, values, symbols, or any combination thereof. Determining whether an embodiment is implemented using hardware elements and / or software elements may vary in accordance with any number of factors, such as desired computational rate, power levels, heat tolerances, processing cycle budget, input data rates, output data rates, memory resources, data bus speeds and other design or performance constraints.

[0086] In some embodiments, the first mobile relay-enabled communications transceiver 210 may include a transmitter 214 for transmitting the mobile relay communication signal via the mobile relay communication link 230. In some embodiments, the first mobile relay-enabled communications transceiver 210 may include a receiver 212 for receiving the reply mobile relay communication signal via the mobile relay communication link 230. In some embodiments, the transmitter 214 and receiver 212 may be implemented as separate circuitry, as integrated together to form an integrated transceiver, or may be software-defined, as in a software-defined radio implemented by the processor 211 or other processing circuitry, or any combination thereof.

[0087] In some embodiments, the second mobile relay-enabled communications transceiver 220 may include hardware components such as a processor 221, which may include local or remote processing components. In some embodiments, the processor 221 may include any type of data processing capacity, such as a hardware logic circuit, for example an application specific integrated circuit (ASIC) and a programmable logic, or such as a computing device, for example, a microcomputer or microcontroller that include a programmable microprocessor. In some embodiments, the processor 221 may include data-processing capacity provided by the microprocessor. In some embodiments, the microprocessor may include memory, processing, interface resources, controllers, and counters. In some embodiments, the microprocessor may also include one or more programs stored in memory.

[0088] Similarly, the second mobile relay-enabled communications transceiver 220 may include storage (not shown), such as one or more local and / or remote data storage solutions such as, e.g., local hard-drive, solid-state drive, flash drive, database or other local data storage solutions or any combination thereof, and / or remote data storage solutions such as a server, mainframe, database or cloud services, distributed database or other suitable data storage solutions or any combination thereof. In some embodiments, the storage may include, e.g., a suitable non-transient computer readable medium such as, e.g., random access memory (RAM), read only memory (ROM), one or more buffers and / or caches, among other memory devices or any combination thereof.

[0089] In some embodiments, the second mobile relay-enabled communications transceiver 220 may also implement computer engines, e.g., for ILE detection and signal adjustment, or may omit such computer engines.

[0090] In some embodiments, the second mobile relay-enabled communications transceiver 220 may include a transmitter 224 for transmitting the reply mobile relay communication signal viathe mobile relay communication link 230. In some embodiments, the second mobile relay-enabled communications transceiver 220 may include a receiver 222 for receiving the mobile relay communication signal via the mobile relay communication link 230. In some embodiments, the transmitter 224 and receiver 222 may be implemented as separate circuitry, as integrated together to form an integrated transceiver, or may be software-defined, as in a software-defined radio implemented by the processor 221 or other processing circuitry, or any combination thereof.

[0091] In some embodiments, as detailed above, as the number of mobile relay systems proliferate, the instances of ILEs increase, thus posing greater risk to the performance and reliability of each mobile relay system. Such ILEs impair or impede performance of a mobile relay system in providing communication capabilities between endpoints, including signal loss, dropped packets, errors in the signal and data carried therein, reduced bandwidth, among other degradations of performance or any combination thereof. Aspects of embodiments herein may mitigate the ILE- induced impairment based on a prediction of the ILE event and proactively making the ACM decisions. In the normal mode of operation, as shown in FIG. 4B, detailed below, the out-route transmitter at the GW (Gateway) sends the signal quality indicator (SQI) (measured by the GW’s in-route receiver) to the UT on the downlink. When this feedback SQI falls below a threshold, the UT in-route transmitter may regress to a lower-order modulation or a reduced-rate coding scheme. Compared to the onset of an interference event on the in-route link, the time for the UT’s in-route transmitter to react and adjust its transmission (e.g., the modulation and coding rate) is at least one roundtrip time (RTT) in addition to the signal processing time. The packets transmitted by the UT’s in-route receiver may be lost during this time window. The ARQ (automatic repeat request) process at the medium access control (MAC) layer redundantly retransmits these data packets, which reduces the spectral efficiency.

[0092] In contrast, the efficiency of the ACM and ARQ operation is enhanced if the UT predicts the ILE event and autonomously reduces the target spectral efficiency without waiting for the signal quality indicator (SQI) feedback from the GW. With this, not only the ACM and ARC signalling overhead is reduced but also the probability of successful packet reception increases, thereby avoiding ARQ retransmissions. If the prediction is correct, this pre-emptively reduced code rate would provide the needed correction before the channel deteriorates, thereby reducing the chance of re-transmission. If the prediction is incorrect, the reduced code rate would momentarily lower the throughput until the ACM increases it back based on the actual (notpredicted) channel quality. Since the ACM with ARQ or the Hybrid ARQ operate post-facto (i.e., these processes are inherently reactive and they act after the channel quality already deteriorates), they do not achieve the above goal. Unlike the terrestrial channel, where the channel quality variations are comparatively more chaotic, the channel quality variation during an ILE over the mobile relay links is likely to be more predictable. Furthermore, the RTT for terrestrial networks is negligible compared to that for the mobile relay communication systems, hence they do not suffer from this latency penalty.

[0093] The RTT for a signal to be sent from a UT to the MAC controller and back, as required to request and make changes to the selected MODCOD level, depends on whether the mobile relay is transparent or regenerative, and it is a function of the altitude and elevation of the mobile relay compared to the UT and gateway. If the mobile relay is regenerative, the signalling is only between the UT and the mobile relay; and if the mobile relay has a transparent payload then the signalling is from the UT to the mobile relay and then to the GW.

[0094] For example, at an altitude of 1000 km, the propagation time from the ground to the mobile relay when the mobile relay is at zenith equals of 3.3 ms. For a transparent mobile relay, when both the UT and the Gateway are close to the submobile relay point, the one-way propagation time increases to nearly 6.6 ms. Now to consider the potential impact of using pro-active ACM (P- ACM). There are two performance measures that determine the impact of P-ACM compared to reactive ACM (R-ACM). These are (i) reduction in latency due to ARQ not being triggered, which results a suitable modulation and coding being selected before errors are incurred, and (ii) reduction in packet errors due to less errors being incurred, which also results from a suitable MODCOC being selected before errors are incurred. In either case the aim is to adjust the MODCOD level before errors occur. In some embodiments, it may be assumed that a particular MODCOD level is resilient over an SINR range of, for example, 1 dB. Errors are likely to occur if the SINR reduces by 1 dB faster than the new MODCOD can be selected.

[0095] Accordingly, in some embodiments, for the communication loop between the first and second mobile relay-enabled communication transceivers 210 and 220, the receiver 212 of the first mobile relay-enabled communication transceiver 210 may receive a communication, carried in a mobile relay communication signal, e.g., from a series of communications, over the mobile relay communications link 230. In some embodiments, the communication may include or be accompanied by an SQI or other signal quality representation that specifies one or more values forone or more signal quality metrics. The signal quality metric(s) may measure the quality of the signal over the mobile relay communication link 230, including, e.g., bit error rate, packet error rate, Block Error Rate, error count, forward error correction (FEC) decoder iteration count, change of modulation and coding (MODCOD) level trigger, spectral efficiency, automatic repeat request (ARQ) trigger, hybrid ARQ (HARQ), or received power, or other metric or any combination thereof.

[0096] In some embodiments, the signal quality metric(s) may be measured at the mobile relay, the second mobile relay-enabled communications transceiver 220, the first mobile relay-enabled communication transceiver 210, or other device, system, relay, etc. in the mobile relay communication link 230. For example, where the mobile relay is a transparent mobile relay, the second mobile relay-enabled communications transceiver 220 may measure the signal quality and transmit the signal quality metric(s) with the mobile relay communication signal. In another example, the mobile relay may be regenerative and thus may measure the signal quality and transmit the signal quality metric(s) with the mobile relay communication signal to the first mobile relay-enabled communications transceiver 210.

[0097] In some embodiments, using the signal quality metric(s), the ILE detection 216 may determine whether there is likely to be an ILE with another mobile relay that may impair the quality of signals via the mobile relay communication link 230, e.g., using a change detection algorithm configured to identify a change in quality of signals that is indicative of an ILE. To do so, the ILE detection 216 may input the signal quality metric(s), a signal-to-interference-plus-noise ratio (SINR) derived from the signal quality metric(s), or other characteristics of the signal or any combination thereof, into the change detection algorithm to detect, for a next communication in the series of communications, at least one predicted reduction in signal quality resulting from in- line event with at least one other mobile relay. In some embodiments, the change detection algorithm may detect a pattern in the changes to signal quality to detect the ILE. Thus, the signal quality, as measured by the signal quality metric, SINR, or other characteristic, for each signal in the series of signals between the first mobile relay-enabled communications transceiver 210 and the second mobile relay-enabled communications transceiver 220 may be input in change detection algorithm. Thus, based on time-varying patterns, the change detection algorithm may identify characteristics in the changes to the signal quality that is indicative of an ILE.

[0098] In some embodiments, the change detection algorithm may employ a pattern matching algorithm, such as, e.g., edge detection, gradient detection, matched filtering, correlator filtering, or other filtering or any combination thereof, e.g., in one or more filter banks of match and / or correlator filters.

[0099] For example, to do so, the ILE detection 216 may determine an average signal quality based at least in part on the at least one signal quality metric of each communication in the series of communications. Using the average signal quality, the ILE detection 216 may, for at least one most recent communication in the series of communications, determine a signal quality metric change relative to the average signal quality based at least in part on the signal quality metric(s) of received with the mobile relay communication signal. The ILE detection 216 may apply a filter to the change of each communication in the series of communications, and compare the filtered changes to a change pattern, e.g., via a matched filter, correlator filter, or a bank of one or more match and / or correlator filters. The change pattern may be a predefined or learned pattern representative of the pattern of changes to signal quality in an ILE (see, for example, FIG. 5, (a) and (b), showing example patterns of change to signal quality during an ILE).

[0100] In another example, the ILE detection 216 may apply a change detection algorithmincluding an edge detection method to the real-time measurements of the received SINR. Letdenote the SINR estimate formed by the receiver at frame index ^ relativeto the average SINR ^^̅. Thus, ∆^^^^^is a zero-mean random process. The edge detector mayconvolve the temporal samples ∆^^^^^ with an edge impulse response denoted as ↳ ^^^ to obtainthe filtered output ∆^^^^^^ = ^∆^^ ∗↳^^^^. The filtered samples ∆^^^^^^ may be compared to athreshold ^, and when ∆^^^^^^ < ^, the ILE is declared to have occurred. Similarly, the ILE isdeclared to have concluded when ∆^^^^^^ > ^.

[0101] In some embodiments, the ILE detection 216 may also or instead detect that an ILE has concluded. When applying the change detection algorithm to a new communication received during an ILE, if the change detection algorithm fails to match the signal quality metric(s) thereof to the change pattern, the ILE detection 216 may determine that the ILE is over and that signaling can resume according to the coding scheme and other signal parameters configured for when no ILE is present.

[0102] In some embodiments, ILE detection based on the signal quality (e.g., SINR) estimation may be considered a pattern recognition problem and a machine learning algorithm, e.g., a neuralnetwork (NN), can be applied to perform this detection. In some embodiments, the ILE detection 216 may utilize the machine learning model to predict an ILE.

[0103] In some embodiments, the machine learning model ingests a feature vector that encodes features representative of the signal quality. In some embodiments, the machine learning model processes the feature vector with parameters to produces a prediction of whether there is likely an impending ILE. In some embodiments, the parameters of the machine learning model may be implemented in a suitable machine learning model including a classifier machine learning model, such as, e.g., a convolutional neural network (CNN), a Naive Bayes classifier, decision trees, random forest, support vector machine (SVM), K-Nearest Neighbors, or any other suitable algorithm for a classification model. In some embodiments, for computational efficiency while preserving accuracy of predictions, the machine learning model may advantageously include a random forest classification model.

[0104] In some embodiments, the machine learning model processes the features encoded in the feature vector by applying the parameters of the classifier machine learning model to produce a model output vector. In some embodiments, the model output vector may be decoded to generate one or more labels indicative of whether there is likely an impending ILE. In some embodiments, the model output vector may include or may be decoded to reveal a numerical output, e.g., one or more probability values where each probability value indicates a degree of probability that a particular label correctly classifies the signal quality metric(s) and / or changes thereof. For example, the probability values may be on a scale of, without limitation, between 0 and 1, from 0 to 10, from 0 to 100 or from any two values ranging from a minimum value for lowest probability to a maximum value for high probability. In some embodiments, the machine learning model may test each probability value against a respective probability threshold. In some embodiments, each probability value has an independently learned and / or configured probability threshold. Alternatively, or additionally, in some embodiments, one or more of the probability values of the model output vector may share a common probability threshold. In some embodiments, where a probability value is greater than the corresponding probability threshold, the signal quality metric(s) and / or changes thereof is labeled according to the corresponding label. For example, the probability threshold can be, e.g., greater than 0.5, greater than 0.6, greater than 0.7, greater than 0.8, greater than 0.9, or other suitable threshold value. Therefore, in some embodiments, the machine learning model may produce whether there is likely an impending ILE for a particularsignal quality metric(s) and / or changes thereof based on the probability value(s) of the model output vector and the probability threshold(s).

[0105] In some embodiments, the parameters of the machine learning model may be trained based on known outputs. For example, the signal quality metric(s) and / or changes thereof may be paired with a target classification or known classification to form a training pair, such as a historical signal quality metric(s) and / or changes thereof and an observed result and / or human annotated classification denoting whether the historical signal quality metric(s) and / or changes thereof is whether there is likely an impending ILE. In some embodiments, the signal quality metric(s) and / or changes thereof may be provided to the machine learning model, e.g., encoded in a feature vector, to produce a predicted label. In some embodiments, an optimization function associated with the machine learning model may then compare the predicted label with the known output of a training pair including the historical signal quality metric(s) and / or changes thereof to determine an error of the predicted label. In some embodiments, the optimization function may employ a loss function, such as, e.g., Hinge Loss, Multi-class SVM Loss, Cross Entropy Loss, Negative Log Likelihood, or other suitable classification loss function to determine the error of the predicted label based on the known output.

[0106] In some embodiments, the known output may be obtained after the machine learning model produces the prediction, such as in online learning scenarios. In such a scenario, the machine learning model may receive the signal quality metric(s) and / or changes thereof and generate the model output vector to produce a label classifying the signal quality metric(s) and / or changes thereof. Subsequently, a user may provide feedback by, e.g., modifying, adjusting, removing, and / or verifying the label via a suitable feedback mechanism, such as a user interface device (e.g., keyboard, mouse, touch screen, user interface, or other interface mechanism of a user device or any suitable combination thereof). The feedback may be paired with the signal quality metric(s) and / or changes thereof to form the training pair and the optimization function may determine an error of the predicted label using the feedback.

[0107] In some embodiments, based on the error, the optimization function may update the parameters of the machine learning model using a suitable training algorithm such as, e.g., backpropagation for a classifier machine learning model. In some embodiments, backpropagation may include any suitable minimization algorithm such as a gradient method of the loss function with respect to the weights of the classifier machine learning model. Examples of suitable gradientmethods include, e.g., stochastic gradient descent, batch gradient descent, mini-batch gradient descent, or other suitable gradient descent technique. As a result, the optimization function may update the parameters of the machine learning model based on the error of predicted labels in order to train the machine learning model to model the correlation between signal quality metric(s) and / or changes thereof and whether there is likely an impending ILE in order to produce more accurate labels of signal quality metric(s) and / or changes thereof.

[0108] For example, a neural network may be trained with a labelled dataset including real-time SINR measurements (or simulation output in case the real-time measurements are not available) during and outside the conjunction events. The neural network training may be performed off-line, and the trained neural network may be applied in real time to detect the onset of the ILE. Rather than ILE decisions being based on a data base of ILE profiles, this could be based on the SINR of nominal operation, and P-ACM triggered when operation is detected away from nominal. This saves trying to train for all ILE profiles, hence making training easier, however it also runs the risk of triggering for reasons other than an ILE, but these may prove to be minimal false positives.

[0109] In some embodiments, to improve the reliability of ILE predictions, whether by filtering / pattern matching or machine learning, the ILE detection 216 may include orbit determination-based ILE prediction using the ephemeris of the mobile relay(s) of the mobile relay communication link 230 and the ephemeris of the other mobile relay(s). Where the ILE detection 216 has the knowledge of the ephemeris vectors of Mobile relay A and Mobile relay B and of its position. In this case, the measurement-based ILE detection can be compared with the ILE predicted using the mobile relay orbital models to increase the probability of correct detection and to reduce the false alarm rate. Thus, the ILE detection 216 may obtain ephemeris data associated with that at least one other mobile relay, determine at least one predicted in-line event between the mobile relay and the other mobile relay, and validate in-line event detected by the change detection algorithm based on the ephemeris-based prediction, or vice versa.

[0110] In some embodiments, where the ILE detection 216 detects an ILE, the signal adjustment 218 may apply at least one signal adjustment to at least one characteristic of a signal to be transmitted by the transmitter 214 for carrying the next mobile relay communication signal via the mobile relay communication link 230. In some embodiments, the signal adjustment may be configured to mitigate interference from the at least one other mobile relay. For example, the signaladjustment 218 may include modulating the coding scheme, e.g., via ACM of the MODCOD, reducing forward error correction (FEC), among other adjustments or any combination thereof.

[0111] In some embodiments, upon adjusting the characteristic(s) of the next mobile relay communication signal, the transmitter 214 may transmit the next mobile relay communication signal to the receiver 222 via the mobile relay communication link 230.

[0112] In some embodiments, the signal adjustment 218 may include one or more alternative or additional approaches to mitigating the detected ILE. For example, the signal adjustment 218 may employ an interference cancellation approach, e.g., utilizing SIC, which is leveraged in the power- domain within Non-orthogonal Multiple Access (NOMA) schemes. SIC is applicable when the signals received from different sources are disparate in the power. For example, a radio receiver may receive the mobile relay-enabled communication signal from another endpoint. The radio receiver may first demodulate and decode a stronger power signal (the interfering signal), while treating the weaker signal (the intended signal) as interference (this added interference lowers the SINR, with the assumption that channel coding can compensate). Once the stronger signal is successfully decoded, it may be reconstructed and subtracted from the received signal, effectively eliminating interference for the weaker signal. In some embodiments, in the above example case, the SIC may be of most benefit for the weaker signal for which the signal is lower in power than the interference. Without the SIC, the weaker signal may suffer from poor detection error rates, while with the SIC, the weaker signal may be extracted as if the interference is not present.

[0113] In some embodiments, the received signal can be written as ^^^^^ = ^^^,^s^^t^ +where $^^^^ and $^^^^ are – e.g., for a Physical Layer based on the 4G or5G standard – OFDM signals (The average symbol energies of $^^^^ and $^^^^ may be unity) transmitted by Mobile relays A and B, respectively, and ^^^^ is AWGN with unit power. In someembodiments, $^^^^ =where *^^+^ may represent the complex-valued ,-aryPSK or QAM informative symbol, + represents the frequency-axis over which the symbol are located at the transmitter, and %&'()^°^ represents the OFDM modulator (IFFT and the cyclic prefix insertion).

[0114] In some embodiments, the SIC approach may be applicable to mitigate the ILE impactwhen 2 / 0 ≪ 1 for which 3 = 2 / 56 / 4 0 < 1. In this scenario, the normalized version of ^^^^^ mayb 78e written as ^^^^ = ^9^^:;,5 = s^^t^^t^ + ^>6=^ ^^^^. In some embodiments, since36=4 > 1, the receiver demodulates the stronger Mobile relay B signal in the SIC Step 1 andestimates the information symbols for Mobile relay B. For example, the signal ^^^^ may be 6=^ ^ demodulated using the standard OFDM demodulator %&'()° (cyclic prefix removal, and FFT)to obtain an estimate *? ^+^ = %6=^ &'() ^^^^^^ of the information bearing symbol at subcarrierfrequency + for the interfering mobile relay. In SIC Step 2, the contribution of Mobile relay B 6=^ ^ signal to ^^^^ may be regenerated using the estimated symbols as ^3̂ $̂ t , where 3̂ is the^ 44^ estimated SIR, and $̂ t is the regenerated OFDM signal of mobile relay B? ^ ^ the demodulated symbols * + . In SIC Step 3, the regenerated Mobile relay B signal may be^^ ^ ^ removed from ^^^^ to obtain ^ ^ = ^ ^ which is sent to Mobile relaydemodulator for an interference-free or interference-reduced reception.

[0115] In some embodiments, since SIC is applicable for scenarios when a negative SNR occurs,it may be deployed in the forward link when D < D . Conversely, it may be deployed in the return^ ^link when the aggressor mobile relay has a higher altitude than the victim mobile relay.

[0116] Alternatively, or in addition, the signal adjustment 218 may employ an interference coordination approach. In some embodiments, mitigating the impact of the ILE may be based on cooperation between Mobile relay A and Mobile relay B system operators. In this approach, the ILE occurrences for a given service area on the ground may be determined a-priori using the respective mobile relay ephemeris data. During the ILE events, the OFDM subcarriers may be shared in a non-overlapping manner by the two mobile relay operators. In some embodiments, this approach may be viewed as orthogonal multiple access (OMA). As a result, the signal adjustment 218 may include providing one or more commands, instructions, triggers, alerts, etc. to a system operator and / or ground station to cause the mobile relays to adjust signal parameters and / or trajectory adjustments to avoid signal interference between the mobile relays.

[0117] Accordingly, in some embodiments, the first mobile relay-enabled communications transceiver 210 may use the ILE detection 216 to proactively identify an impending ILE using one or more of: pattern matching based on matched filters, correlator filters and / or edge detection; machine learning-based ILE prediction; and / or ephemeris-based conjunction determination. Based on the ILE detection 216, signal adjustment 218 may perform or otherwise trigger one or more adjustments to mitigate interference associated with the impending ILE, e.g., via proactive ACM,interference coordination, interference cancellation, or other adjustments or any combination thereof. EXAMPLE – INTERFERENCE MITIGATION IN SIMULATED CONJUNCTION EVENTS

[0118] FIG.5 depicts two example cases (a) and (b) of variation in change to signal-to-interference (power) ratio (SIR), differential SIR (∆κ), during two simulated conjunction events in accordance with one or more embodiments of the present disclosure.

[0119] FIG.5 shows the variation in ∆3 for two simulated conjunction events. These conjunctionevents may be simulated by considering that the aggressor satellite orbital plane altitude may be1000 km, the orbital inclination may be 55G, and the eccentricity may be zero. The simulatedsample duration may be 0.1 seconds. The UT may be placed at the equator and the angles H andI, which denote the elevation and the azimuth of the UT measured in the satellite antenna plane(with the antenna boresight forming the J axis) are evaluated for each satellite pass (multiple passes of different satellites in the UT’s FoV are simulated). For the two simulation scenarios, the result may be shown in three panels. The upper left panel shows the elevation angle H which – as the satellite comes directly overhead the UT, whose antenna may be assumed to be pointed directly G upward – approaches 0 (i.e., the UT may be aligned to the satellite antenna boresight). The panelon the right shows the aggressor satellite antenna radiation pattern plotted in the antenna [L, M]plane, where L = H cos I, and M = H sin I.

[0120] The radiation pattern may be defined as

[0121] The maximum of the radiation pattern, which occurs when H = 0, may be given asS = D Here, D may be the antenna efficiency, V ^c^ may be the Bessel function of[\]=the first kind, *^#Xmay be the antenna diameter in meters, Y may be the wavelength at the radio frequency + calculated asd^' efg, where h may be the speed of light in vacuum in meters / second.

[0122] For these results, the aggressor satellite antenna diameter *^#Xmay be taken as 1 meter,the RF frequency + = 11.95 GHz, and antenna efficiency D = 0.55. The [L, M] trajectory of the^'UT as it appears in the satellite antenna plane may be shown with a dark red trace (line) in thek0lnmnnn8panel on he r okplnmnnn8t ight. The differential SIR ∆3 = = ok lq qm ok lm o0 pZ_0 which may be shown in the panel on the left bottom (this assumes = ∆^ = 1). An B C^ 8_0observation from the lower left panel of this figure may be (i) that the differential SIR ∆3 deteriorates rapidly (i.e., in two to three seconds) as the interfering satellite comes in the line of sight of the UT antenna, (ii) the worst-case SIR occurs only when the UT and the satellite antenna boresights are aligned to a fraction of a degree as shown in FIG. 5(a). In contrast, when the alignment is off by more than a degree, as shown in FIG.5(b), the SIR may be comparatively much greater.

[0123] P-ACM Scheme:

[0124] FIG.6 depicts a simulated predictive algorithm detecting the onset and the end of an event (e.g., ILE-induced) in which the SINR reduces in accordance with one or more embodiments of the present disclosure.

[0125] FIG. 6 shows an example of implementation of the edge detection scheme as detailed ^ ^ above. A zero-mean random process representing ∆^ ^ may be simulated. The SIR impairment^due to ILE may be modelled as negative-valued bell shaped (Gaussian) profile 602 of peak intensity 6 dB and a duration over, e.g., −4 seconds to +4 seconds. This random process may be passed through an edge-detector and its output may be compared against a threshold. This threshold detector may be observed to declare the onset and the offset of the interference event in a timely manner, e.g., as depicted at curve 601.

[0126] One or more benefits of the techniques detailed herein, e.g., compared to either HARQ or ACM-based ARQ, may be that it eliminates the round-trip latency on the LEO link when a frame may be decoded in error. The proposed scheme pre-emptively lowers the MODCOD so that the probability of frame error reduces to near zero. The HARQ and the ACM schemes are reactive in comparison.

[0127] FIG. 7 depicts latency of the HARQ with IR and ACM-ARQ compared to the latency of the proposed P-ACM scheme of zero in accordance with one or more embodiments of the present disclosure.The result in FIG.7 shows a preliminary analysis of the latency performance of HARQ and ACM ARQ schemes, where the solid lines denote HARQ-IR latency, and the dotted lines denote ACM-ARQ latency, with each solid line and each dotted line being associated with a givennumber of transmissions. The impact of multiple transmissions, where the number of transmissions is denoted by ^, may be to result in increased latency in mitigate the ILE. The latency of the proposed scheme may be one RTT in comparison.

[0128] Interference Cancellation Scheme

[0129] FIG. 8 depicts an example of probability of bit error as a function of SNR in an example of the SIC applied to an OFDM waveform interference scenario in accordance with one or more embodiments of the present disclosure.

[0130] In some embodiments, the result of a simulation of the SIC method may be based on assumptions including (i) the subcarrier spacing and the bandwidth of satellite B signal are known, (ii) the estimate 3̂4may be accurate, (iii) the channel offset (especially the phase) of satellite B signal may be estimated near-perfectly, (iv) The delay-dispersive channels corresponding to both satellite A and satellite B signals are frequency non-selective (i.e., the OFDM frequency domain equalization may be not performed for either of the two satellite signals), and (v) Satellite A and B signals are synchronized in time and frequency (alternatively, for the asynchronous reception, the timing / frequency and Doppler offsets of both the satellite signals are known).

[0131] In the simulation, SNR > may be varied from 0 to 10 dB and the SIR ∆ 6=s= 34 dB may bevaried from 0 to 9 dB. The results in Erreur ! Source du renvoi introuvable.IG. 8 show theefficacy of the SIC and provides a proof of concept when the SIR may be low, i.e., ∆s≥ 6 dB.

[0132] Interference Coordination Scheme

[0133] FIG. 9 depicts exemplary achievable throughputs (or spectral efficiencies) of Satellite Aand Satellite B signals in a co-operation scenario when ^^ ^^^ / ^^ = ^^^ / ^^ in accordance with oneor more embodiments of the present disclosure. The blue pentagon and the green curve represent the achievable SEs with NOMA or OMA, respectively.

[0134] In some embodiments, when the two satellite systems agree to cooperate, both the systems benefit in terms of achieved throughputs. FIG. 9 is based on an example which shows the achievable spectral efficiencies for Satellite A and Satellite B links when these two systems use either a NOMA-based or an OMA-based cooperative strategy. This example may be for a specific 50scenario in which the SNRs of the two satellite links are equal to 0 dB i.e.,sfu = sfus` s` = 1.

[0135] In some embodiments, in the NOMA cooperation, one of the two systems can operate at amaximum achievable rate of 1 bpcu, provided the other system agrees to operate at a lower rate of0.58 bpcu. This corresponds to the operation at two of the corners of the pentagon in the blue.Similarly, the achievability of all the points along the blue-coloured pentagon corresponding to SIC-based NOMA cooperation scheme can be shown.

[0136] In some embodiments, in the NOMA cooperation, one of the two systems can operate at a maximum achievable rate using the SIC, one of the two satellite signals, say, Satellite A signal, 5can be transmitted at a spectral efficiency of log B1 + sfu x=C = log BzZ s0fu x=ys`x= Z ZC = 0.58 bpcu. Thisassumes that Satellite A receiver considers Satellite B signal as noise. After Satellite A signal may be decoded, Satellite B receiver subtracts the regenerated Satellite A signal from the received signal and it may be able to receive information at a maximum achievable rate of bpcu.

[0137] In some embodiments, the orthogonal multiple access (OMA) based cooperative transmission scheme, in comparison, can be shown to achieve any operational point along the green-coloured curve in FIG.9.

[0138] In some embodiments, both NOMA and OMA strategies achieve the same performance when the two satellite signals are received at the same power. However, in general case, the NOMA-based cooperation provides a greater combined throughput to the two satellite systems at an expense of a greater complexity of implementation.

[0139] It is understood that at least one aspect / functionality of various embodiments described herein can be performed in real-time and / or dynamically. As used herein, the term “real-time” is directed to an event / action that can occur instantaneously or almost instantaneously in time when another event / action has occurred. For example, the “real-time processing,” “real-time computation,” and “real-time execution” all pertain to the performance of a computation during the actual time that the related physical process (e.g., a user interacting with an application on a mobile device) occurs, in order that results of the computation can be used in guiding the physical process.

[0140] As used herein, the term “dynamically” and term “automatically,” and their logical and / or linguistic relatives and / or derivatives, mean that certain events and / or actions can be triggeredand / or occur without any human intervention. In some embodiments, events and / or actions in accordance with the present disclosure can be in real-time and / or based on a predetermined periodicity of at least one of: nanosecond, several nanoseconds, millisecond, several milliseconds, second, several seconds, minute, several minutes, hourly, several hours, daily, several days, weekly, monthly, etc.

[0141] In some embodiments, exemplary inventive, specially programmed computing systems and platforms with associated devices are configured to operate in the distributed network environment, communicating with one another over one or more suitable data communication networks (e.g., the Internet, satellite, etc.) and utilizing one or more suitable data communication protocols / modes such as, without limitation, IPX / SPX, X.25, AX.25, AppleTalk(TM), TCP / IP (e.g., HTTP), near-field wireless communication (NFC), RFID, Narrow Band Internet of Things (NBIOT), 3G, 4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, satellite, ZigBee, and other suitable communication modes.

[0142] The material disclosed herein may be implemented in software or firmware or a combination of them or as instructions stored on a machine-readable medium, which may be read and executed by one or more processors. A machine-readable medium may include any medium and / or mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device). For example, a machine-readable medium may include read only memory (ROM); random access memory (RAM); magnetic disk storage media; optical storage media; flash memory devices; electrical, optical, acoustical or other forms of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.), and others.

[0143] Computer-related systems, computer systems, and systems, as used herein, include any combination of hardware and software. Examples of software may include software components, programs, applications, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, procedures, software interfaces, application program interfaces (API), instruction sets, computer code, computer code segments, words, values, symbols, or any combination thereof. Determining whether an embodiment is implemented using hardware elements and / or software elements may vary in accordance with any number of factors, such as desired computational rate, power levels, heat tolerances, processing cycle budget, input data rates, output data rates, memory resources, data bus speeds and other design or performance constraints.

[0144] One or more aspects of at least one embodiment may be implemented by representative instructions stored on a machine-readable medium which represents various logic within the processor, which when read by a machine causes the machine to fabricate logic to perform the techniques described herein. Such representations, known as “IP cores,” may be stored on a tangible, machine readable medium and supplied to various customers or manufacturing facilities to load into the fabrication machines that make the logic or processor. Of note, various embodiments described herein may, of course, be implemented using any appropriate hardware and / or computing software languages (e.g., C++, Objective-C, Swift, Java, JavaScript, Python, Perl, QT, etc.).

[0145] In some embodiments, as detailed herein, one or more of the computer-based systems of the present disclosure may obtain, manipulate, transfer, store, transform, generate, and / or output any digital object and / or data unit (e.g., from inside and / or outside of a particular application) that can be in any suitable form such as, without limitation, a file, a contact, a task, an email, a message, a map, an entire application (e.g., a calculator), data points, and other suitable data. In some embodiments, as detailed herein, one or more of the computer-based systems of the present disclosure may be implemented across one or more of various computer platforms such as, but not limited to: (1) FreeBSD, NetBSD, OpenBSD; (2) Linux; (3) Microsoft Windows™; (4) OpenVMS™; (5) OS X (MacOS™); (6) UNIX™; (7) Android; (8) iOS™; (9) Embedded Linux; (10) Tizen™; (11) WebOS™; (12) Adobe AIR™; (13) Binary Runtime Environment for Wireless (BREW™); (14) Cocoa™ (API); (15) Cocoa™ Touch; (16) Java™ Platforms; (17) JavaFX™; (18) QNX™; (19) Mono; (20) Google Blink; (21) Apple WebKit; (22) Mozilla Gecko™; (23) Mozilla XUL; (24) .NET Framework; (25) Silverlight™; (26) Open Web Platform; (27) Oracle Database; (28) Qt™; (29) SAP NetWeaver™; (30) Smartface™; (31) Vexi™; (32) Kubernetes™ and (33) Windows Runtime (WinRT™) or other suitable computer platforms or any combination thereof. In some embodiments, illustrative computer-based systems or platforms of the present disclosure may be configured to utilize hardwired circuitry that may be used in place of or in combination with software instructions to implement features consistent with principles of the disclosure. Thus, implementations consistent with principles of the disclosure are not limited to any specific combination of hardware circuitry and software. For example, various embodiments may be embodied in many different ways as a software component such as, without limitation, astand-alone software package, a combination of software packages, or it may be a software package incorporated as a “tool” in a larger software product.

[0146] For example, exemplary software specifically programmed in accordance with one or more principles of the present disclosure may be downloadable from a network, for example, a website, as a stand-alone product or as an add-in package for installation in an existing software application. For example, exemplary software specifically programmed in accordance with one or more principles of the present disclosure may also be available as a client-server software application, or as a web-enabled software application. For example, exemplary software specifically programmed in accordance with one or more principles of the present disclosure may also be embodied as a software package installed on a hardware device.

[0147] In some embodiments, illustrative computer-based systems or platforms of the present disclosure may be configured to handle numerous concurrent users that may be, but is not limited to, at least 100 (e.g., but not limited to, 100-999), at least 1,000 (e.g., but not limited to, 1,000- 9,999 ), at least 10,000 (e.g., but not limited to, 10,000-99,999 ), at least 100,000 (e.g., but not limited to, 100,000-999,999), at least 1,000,000 (e.g., but not limited to, 1,000,000-9,999,999), at least 10,000,000 (e.g., but not limited to, 10,000,000-99,999,999), at least 100,000,000 (e.g., but not limited to, 100,000,000-999,999,999), at least 1,000,000,000 (e.g., but not limited to, 1,000,000,000-999,999,999,999), and so on.

[0148] In some embodiments, illustrative computer-based systems or platforms of the present disclosure may be configured to output to distinct, specifically programmed graphical user interface implementations of the present disclosure (e.g., a desktop, a web app., etc.). In various implementations of the present disclosure, a final output may be displayed on a displaying screen which may be, without limitation, a screen of a computer, a screen of a mobile device, or the like. In various implementations, the display may be a holographic display. In various implementations, the display may be a transparent surface that may receive a visual projection. Such projections may convey various forms of information, images, or objects. For example, such projections may be a visual overlay for a mobile augmented reality (MAR) application.

[0149] In some embodiments, illustrative computer-based systems or platforms of the present disclosure may be configured to be utilized in various applications which may include, but not limited to, gaming, mobile-device games, video chats, video conferences, live video streaming,video streaming and / or augmented reality applications, mobile-device messenger applications, and others similarly suitable computer-device applications.

[0150] As used herein, the term “mobile electronic device,” or the like, may refer to any portable electronic device that may or may not be enabled with location tracking functionality (e.g., MAC address, Internet Protocol (IP) address, or the like). For example, a mobile electronic device can include, but is not limited to, a mobile phone, Personal Digital Assistant (PDA), Blackberry ™, Pager, Smartphone, or any other reasonable mobile electronic device.

[0151] As used herein, terms “cloud,” “Internet cloud,” “cloud computing,” “cloud architecture,” and similar terms correspond to at least one of the following: (1) a large number of computers connected through a real-time communication network (e.g., Internet); (2) providing the ability to run a program or application on many connected computers (e.g., physical machines, virtual machines (VMs)) at the same time; (3) network-based services, which appear to be provided by real server hardware, and are in fact served up by virtual hardware (e.g., virtual servers), simulated by software running on one or more real machines (e.g., allowing to be moved around and scaled up (or down) on the fly without affecting the end user).

[0152] In some embodiments, the illustrative computer-based systems or platforms of the present disclosure may be configured to securely store and / or transmit data by utilizing one or more of encryption techniques (e.g., private / public key pair, Triple Data Encryption Standard (3DES), block cipher algorithms (e.g., IDEA, RC2, RC5, CAST and Skipjack), cryptographic hash algorithms (e.g., MD5, RIPEMD-160, RTR0, SHA-1, SHA-2, Tiger (TTH),WHIRLPOOL, RNGs).

[0153] As used herein, the term “user” shall have a meaning of at least one user. In some embodiments, the terms “user”, “subscriber” “consumer” or “customer” should be understood to refer to a user of an application or applications as described herein and / or a consumer of data supplied by a data provider. By way of example, and not limitation, the terms “user” or “subscriber” can refer to a person who receives data provided by the data or service provider over the Internet in a browser session or can refer to an automated software application which receives the data and stores or processes the data.

[0154] The aforementioned examples are, of course, illustrative and not restrictive.

[0155] Publications cited throughout this document are hereby incorporated by reference in their entirety. While one or more embodiments of the present disclosure have been described, it isunderstood that these embodiments are illustrative only, and not restrictive, and that many modifications may become apparent to those of ordinary skill in the art, including that various embodiment of the inventive methodologies, the illustrative systems and platforms, and the illustrative devices described herein can be utilized in any combination with each other. Further still, the various steps may be carried out in any desired order (and any desired steps may be added and / or any desired steps may be eliminated).

Claims

CLAIMS1. A method comprising: receiving, by at least one processor of a satellite-enabled communications transceiver, a series of communications over a satellite communications link, wherein each communication in the series of communications comprises at least one signal quality metric; inputting, by the at least one processor, the at least one signal quality metric of a plurality of communications in the series of communication into at least one change detection algorithm to detect, for a next communication in the series of communications, at least one predicted reduction in signal quality resulting from in-line event with at least one other satellite; applying, by the at least one processor, in response to the at least one predicted reduction in signal quality prior to the next communication, at least one signal adjustment to at least one characteristic of at least one signal for carrying the next communication, wherein the at least one signal adjustment is configured to mitigate interference from the at least one other satellite; generating, by the at least one processor, the at least one signal to carry the next communication to the satellite based at least in part on the at least one signal adjustment; and transmitting, by the at least one processor, the at least one signal via the satellite communications link.

2. The method of claim 1, further comprising: receiving, by the at least one processor, from the satellite, at least one subsequent received communication comprising at least one subsequent signal quality metric; inputting, by the at least one processor, the at least one subsequent signal quality metric of the at least one subsequent received communication into the at least one change detection algorithm to detect, for another next communication in the series of communications, at least one predicted improvement in signal quality resulting from an end to the in-line event with the at least one other satellite; applying, by the at least one processor, in response to the at least one predicted improvement in signal quality prior to the next communication, at least one subsequent signal adjustment for generating at least one other next signal to carry the other next communication,wherein the at least one signal adjustment is configured to return the signal quality to an original state.

3. The method of claim 1, wherein the at least one change detection algorithm comprises at least one machine learning model trained to classify communications as one of nominal or not-nominal based at least in part on the at least one signal quality metric of each communication in the series of communications; wherein the at least one machine learning model is trained based at least in part on training data comprising training communications pre-labeled as having nominal signal quality.

4. The method of claim 1, further comprising: obtaining, by the at least one processor, ephemeris data associated with the satellite; obtaining, by the at least one processor, ephemeris data associated with the at least one other satellite; determining, by the at least one processor, a position of a ground terminal associated with the satellite communication; determining, by the at least one processor, at least one predicted in-line event between the at least one satellite and the at least one other satellite based at least in part on the ephemeris data of the satellite, the ephemeris data of the at least one other satellite and the position of the ground station; and validating, by the at least one processor, the at least one detected in-line event based at least in part on the at least one predicted in-line event.

5. The method of claim 4, further comprising: applying, by the at least one processor, in response to the at least one predicted improvement in signal quality prior to the next communication, the at least one subsequent signal adjustment an orthogonal multiple access (OMA) scheme with cooperative spectrum sharing.

6. The method of claim 1, wherein the at least one change detection algorithm comprises at least one of: at least one edge detection algorithm,at least one correlator filter, at least one matched filter, or at least one filter bank comprising at least one of: at least one correlator filter, or at least one matched filter.

7. The method of claim 5, further comprising: determining, by the at least one processor, an average signal quality based at least in part on the at least one signal quality metric of each communication in the series of communications; determining, by the at least one processor, for at least one most recent communication in the series of communications, a signal quality metric change relative to the average signal quality based at least in part on the at least one signal quality metric of the at least one most recent communication; determining, by the at least one processor, a filtered signal quality metric change based at least in part on the signal quality metric change and a change pattern; and determining, by the at least one processor, the at least one predicted reduction in signal quality based at least in part on the filtered signal quality metric change exceeding a predetermined threshold.

8. The method of claim 1, further comprising: determining, by the at least one processor, a signal-to-interference-plus-noise ratio (SINR) of each communication in the series of communications based at least in part on the at least one signal quality metric of each communication in the series of communications; wherein the at least one signal quality metric comprising at least one of: bit error rate, packet error rate, Block Error Rate, error count,forward error correction (FEC) decoder iteration count, change of modulation and coding (MODCOD) level, spectral efficiency, automatic repeat request (ARQ), hybrid ARQ (HARQ), or received power.

9. The method of claim 1, further comprising: applying, by the at least one processor, an interference cancellation approach in a power- domain within a Non-orthogonal Multiple Access (NOMA) scheme.

10. The method of claim 9, wherein the interference cancellation approach comprises successive interference cancellation (SIC).

11. The method of claim 9, wherein the NOMA scheme is cooperative or non-cooperative.

12. The method of claim 1, the at least one processor is part of at least one of: the satellite, at least one ground station, at least one user terminal, at least one gateway, or at least one high-altitude platform (HAP) station.\ 13. The method of claim 1, wherein the satellite comprises a transparent communication satellite or a regenerative communication satellite.

14. A system comprising: at least one processor of a satellite communications gateway; at least one non-transitory computer-readable medium having computer instructions stored thereon, wherein the at least one processor, upon execution of the computer instructions, is configured to perform steps comprising:receiving, by at least one processor of a satellite-enabled communications transceiver, a series of communications over a satellite communications link, wherein each communication in the series of communications comprises at least one signal quality metric; inputting, by the at least one processor, the at least one signal quality metric of a plurality of communications in the series of communication into at least one change detection algorithm to detect, for a next communication in the series of communications, at least one predicted reduction in signal quality resulting from in-line event with at least one other satellite; applying, by the at least one processor, in response to the at least one predicted reduction in signal quality prior to the next communication, at least one signal adjustment to at least one characteristic of at least one signal for carrying the next communication, wherein the at least one signal adjustment is configured to mitigate interference from the at least one other satellite; generating, by the at least one processor, the at least one signal to carry the next communication to the satellite based at least in part on the at least one signal adjustment; and transmitting, by the at least one processor, the at least one signal via the satellite communications link.

15. The system of claim 14, wherein the steps further comprise: receiving, from the satellite, at least one subsequent received communication comprising at least one subsequent signal quality metric; inputting the at least one subsequent signal quality metric of the at least one subsequent received communication into the at least one change detection algorithm to detect, for another next communication in the series of communications, at least one predicted improvement in signal quality resulting from an end to the in-line event with the at least one other satellite; applying, in response to the at least one predicted improvement in signal quality prior to the next communication, at least one subsequent signal adjustment for generating at least one othernext signal to carry the other next communication, wherein the at least one signal adjustment is configured to return the signal quality to an original state.

16. The system of claim 14, wherein the at least one change detection algorithm comprises at least one machine learning model trained to classify communications as one of nominal or not-nominal based at least in part on the at least one signal quality metric of each communication in the series of communications; wherein the at least one machine learning model is trained based at least in part on training data comprising training communications pre-labeled as having nominal signal quality.

16. The system of claim 16, wherein the steps further comprise: obtaining, by the at least one processor, ephemeris data associated with the satellite; obtaining, by the at least one processor, ephemeris data associated with the at least one other satellite; determining, by the at least one processor, a position of a ground terminal associated with the satellite communication; determining, by the at least one processor, at least one predicted in-line event between the at least one satellite and the at least one other satellite based at least in part on the ephemeris data of the satellite, the ephemeris data of the at least one other satellite and the position of the ground station; and validating, by the at least one processor, the at least one detected in-line event based at least in part on the at least one predicted in-line event.

17. The system of claim 16, wherein the steps further comprise: applying, in response to the at least one predicted improvement in signal quality prior to the next communication, the at least one subsequent signal adjustment an orthogonal multiple access (OMA) scheme with cooperative spectrum sharing.

18. The system of claim 14, wherein the at least one change detection algorithm comprises at least one of: at least one edge detection algorithm,at least one correlator filter, at least one matched filter, or at least one filter bank comprising at least one of: at least one correlator filter, or at least one matched filter.

19. The system of claim 18, wherein the steps further comprise: determining an average signal quality based at least in part on the at least one signal quality metric of each communication in the series of communications; determining, for at least one most recent communication in the series of communications, a signal quality metric change relative to the average signal quality based at least in part on the at least one signal quality metric of the at least one most recent communication; determining a filtered signal quality metric change based at least in part on the signal quality metric change and a change pattern; and determining the at least one predicted reduction in signal quality based at least in part on the filtered signal quality metric change exceeding a predetermined threshold.

20. The system of claim 14, wherein the steps further comprise: determining a signal-to-interference-plus-noise ratio (SINR) of each communication in the series of communications based at least in part on the at least one signal quality metric of each communication in the series of communications; wherein the at least one signal quality metric comprising at least one of: bit error rate, packet error rate, Block Error Rate, error count, forward error correction (FEC) decoder iteration count, change of modulation and coding (MODCOD) level,spectral efficiency, automatic repeat request (ARQ), hybrid ARQ (HARQ), or received power.

21. The system of claim 14, further comprising: applying, by the at least one processor, an interference cancellation approach in a power- domain within a Non-orthogonal Multiple Access (NOMA) scheme.

22. The system of claim 21, wherein the interference cancellation approach comprises successive interference cancellation (SIC).

23. The system of claim 21, wherein the NOMA scheme is cooperative or non-cooperative.

24. The system of claim 14, the at least one processor is part of at least one of: the satellite, at least one ground station, at least one user terminal, at least one gateway, or at least one high-altitude platform (HAP) station.

25. The system of claim 14, wherein the satellite comprises a transparent communication satellite or a regenerative communication satellite.

26. A non-transitory computer-readable medium having computer instructions stored thereon, wherein at least one processor of a satellite-enabled communications transceiver, upon execution of the computer instructions, is configured to perform steps comprising: receiving a series of communications over a satellite communications link, wherein each communication in the series of communications comprises at least one signal quality metric; inputting the at least one signal quality metric of a plurality of communications in the series of communication into at least one change detection algorithm to detect, for a next communicationin the series of communications, at least one predicted reduction in signal quality resulting from in-line event with at least one other satellite; applying, in response to the at least one predicted reduction in signal quality prior to the next communication, at least one signal adjustment to at least one characteristic of at least one signal for carrying the next communication, wherein the at least one signal adjustment is configured to mitigate interference from the at least one other satellite; generating the at least one signal to carry the next communication to the satellite based at least in part on the at least one signal adjustment; and transmitting the at least one signal via the satellite communications link.

27. The non-transitory computer-readable medium of claim 26, wherein the steps further comprise: receiving, from the satellite, at least one subsequent received communication comprising at least one subsequent signal quality metric; inputting the at least one subsequent signal quality metric of the at least one subsequent received communication into the at least one change detection algorithm to detect, for another next communication in the series of communications, at least one predicted improvement in signal quality resulting from an end to the in-line event with the at least one other satellite; applying, in response to the at least one predicted improvement in signal quality prior to the next communication, at least one subsequent signal adjustment for generating at least one other next signal to carry the other next communication, wherein the at least one signal adjustment is configured to return the signal quality to an original state.

28. The non-transitory computer-readable medium of claim 26, wherein the at least one change detection algorithm comprises at least one machine learning model trained to classify communications as one of nominal or not-nominal based at least in part on the at least one signal quality metric of each communication in the series of communications; and wherein the at least one machine learning model is trained based at least in part on training data comprising training communications pre-labeled as having nominal signal quality.

29. The non-transitory computer-readable medium of claim 26, wherein the steps further comprise: obtaining, by the at least one processor, ephemeris data associated with the satellite;obtaining, by the at least one processor, ephemeris data associated with the at least one other satellite; determining, by the at least one processor, a position of a ground terminal associated with the satellite communication; determining, by the at least one processor, at least one predicted in-line event between the at least one satellite and the at least one other satellite based at least in part on the ephemeris data of the satellite, the ephemeris data of the at least one other satellite and the position of the ground station; and validating, by the at least one processor, the at least one detected in-line event based at least in part on the at least one predicted in-line event.

30. The non-transitory computer-readable medium of claim 29, wherein the steps further comprise: applying, in response to the at least one predicted improvement in signal quality prior to the next communication, the at least one subsequent signal adjustment an orthogonal multiple access (OMA) scheme with cooperative spectrum sharing.

31. The non-transitory computer-readable medium of claim 26, wherein the steps further comprise: determining an average signal quality based at least in part on the at least one signal quality metric of each communication in the series of communications; determining, for at least one most recent communication in the series of communications, a signal quality metric change relative to the average signal quality based at least in part on the at least one signal quality metric of the at least one most recent communication; determining a filtered signal quality metric change based at least in part on the signal quality metric change and a change pattern; and determining the at least one predicted reduction in signal quality based at least in part on the filtered signal quality metric change exceeding a predetermined threshold.

32. The non-transitory computer-readable medium of claim 26, wherein the steps further comprise: determining a signal-to-interference-plus-noise ratio (SINR) of each communication in the series of communications based at least in part on the at least one signal quality metric of each communication in the series of communications;wherein the at least one signal quality metric comprising at least one of: bit error rate, packet error rate, Block Error Rate, error count, forward error correction (FEC) decoder iteration count, change of modulation and coding (MODCOD) level, spectral efficiency, automatic repeat request (ARQ), hybrid ARQ (HARQ), or received power.

33. The non-transitory computer-readable medium of claim 26, further comprising: applying, by the at least one processor, an interference cancellation approach in a power- domain within a Non-orthogonal Multiple Access (NOMA) scheme.

34. The non-transitory computer-readable medium of claim 33, wherein the interference cancellation approach comprises successive interference cancellation (SIC).

35. The non-transitory computer-readable medium of claim 33, wherein the NOMA scheme is cooperative or non-cooperative.

36. The non-transitory computer-readable medium of claim 26, the at least one processor is part of at least one of: the satellite, at least one ground station, at least one user terminal, at least one gateway, or at least one high-altitude platform (HAP) station.

37. The non-transitory computer-readable medium of claim 26, wherein the satellite comprises a transparent communication satellite or a regenerative communication satellite.

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