Interference aware wireless communication link adaptation

Abstract

In various examples, systems and methods for wireless communication link adaptation are described. In some examples, first data may be determined for a first gateway device, the first data representing a plurality of received signal strength (RSS) values of a first signal received from a first end node device. Second data representing interference associated with the first signal may be determined. At least one of a first packet error rate or a first packet success rate may be determined based at least in part on the first data and the second data. A first modulation coding scheme (MCS) associated with at least one of the first packet error rate or the first packet success rate may be determined. Third data may be transmitted to the first end node, the third data instructing the first end node device to use the first MCS to communicate with the first gateway device.

Description

Background Art

[0001] Link adaptation generally refers to adaptive coding and modulation that matches various signal and protocol parameters to the prevailing conditions on a wireless communication link. These techniques are very important for network performance, especially in environments where the noise level is high and / or rapidly changing. Brief Description of the Drawings

[0002] FIG. 1A is a block diagram illustrating an example of interference-aware wireless communication link adaptation in accordance with various aspects of the present disclosure.

[0003] FIG. 1B is a state diagram of packet error rate estimation performed by a wireless receiver device in the presence of interference in accordance with various aspects of the present disclosure.

[0004] FIG. 2 is a block diagram of an example of gateway device selection for communicating with an end node device in accordance with various aspects of the present disclosure.

[0005] FIG. 3 is an exemplary look-up table that can be used to determine the modulation and coding scheme of a gateway device experiencing interference in accordance with various aspects of the present disclosure.

[0006] FIG. 4 is a state diagram of uplink rate adaptation based on interference prediction modeling in accordance with various aspects of the present disclosure.

[0007] FIG. 5 is an exemplary computing device architecture that can be used in accordance with the various techniques described herein.

[0008] FIG. 6 is a diagram illustrating an exemplary system for transmitting and providing data that can be used in accordance with the present disclosure.

[0009] FIG. 7 depicts a curve of packet error rate versus signal-to-noise ratio for a typical environment with a fixed gateway and end node devices in accordance with various aspects of the present disclosure. Detailed Description

[0010] In the following description, reference will be made to the drawings that illustrate several exemplary embodiments of the invention. It should be understood that other examples can be utilized and various operational changes can be made without departing from the scope of the present disclosure. The following detailed description should not be considered restrictive, and the scope of the embodiments of the invention is defined only by the claims of the issued patent.

[0011] Three key performance indicators (KPIs) that can be used to predict the packet success rate (PSR) (e.g., the rate at which a receiver device successfully receives a transmitted packet) in a wireless communication medium are the received signal strength (RSS), the carrier-to-interference ratio (C / I), and the signal-to-noise ratio (SNR). RSS can be expressed in decibels per milliwatt (dBm), while the SNR and C / I metrics are relative numbers that can be expressed in decibel (dB) units. RSS depends on the wireless channel and, in some cases, can be described as representing the power of the desired signal detected by the receiver.

[0012] For example, the received signal strength indication (RSSI) value can be determined based on the voltage level from the baseband signal chain before the baseband amplifier. The output of the RSS circuit can be an analog DC voltage level. This output can be sampled by an analog-to-digital converter (ADC) to determine the RSSI value at a given time. The voltage value (in volts) sampled by the ADC can be converted to a power value for a given impedance Z, e.g., P = V*V / Z. However, to convert it to an RSSI value, it is typically desirable to express this power value in decibels per milliwatt, e.g., P(dbm) = 10*Log10(1000*P). Thus, the voltage value (in volts) sampled by the ADC can be converted to an RSSI value (in decibels per milliwatt) using a formula such as P(dbm) = 10*Log10(1000*V^2 / Z).

[0013] In a system involving an end-node device or a client device communicating with a gateway device, where the gateway device in turn communicates with a remote computing system or the cloud, the communication from the end-node device or client device to the gateway device and then to the cloud can be characterized as uplink communication, while the communication in the opposite direction from the cloud to the gateway device and then to the end-node device or client device can be characterized as downlink communication.

[0014] Due to channel reciprocity, as long as the time between uses of the link is within the coherence time of the channel, the RSS of the uplink (UL) (e.g., the transmission link from a terminal node or client device to a gateway device) and the downlink (DL) (e.g., the transmission link from the gateway device to a terminal node or client device) link can be estimated to be the same. In this case, the RSS determined in the UL can be used to determine the best gateway device (GW) to serve the DL direction. For example, if a message sent from an end-node device is received at two gateway devices, the gateway devices can forward the message along with an indication of the corresponding RSS value associated with the received message to the remote computing system. The remote computing system can compare the corresponding RSS values received from the gateway devices to determine which gateway device to use for downlink communication to the end-node device.

[0015] RSS can depend on terrain type, distance to a given gateway device, channel frequency, antenna gain, and antenna directivity. However, the carrier-to-interference ratio (C / I) mainly depends on the environment in which the receiving device is located at a given time. The term C / I refers to the ratio of the RSS experienced within the desired signal bandwidth to the co-channel interference. In many cases, it is difficult to predict C / I because the interference may be sporadic or bursty during the reception of a given packet, depending on a series of events triggered in the environment. Additionally, unlike RSS channel reciprocity, the C / I observed at the end-node device (EN) in the DL direction is different from the C / I observed at the gateway device in the UL direction. This is because the GW and EN are typically separated by a distance of hundreds of meters and are usually located in different environments with different interference sources around them. Therefore, it may be advantageous to estimate the interference observed in the UL and DL separately. In some examples used herein, the estimated value of the interference may be referred to as the estimated interference level.

[0016] The third metric, SNR, can be characterized as the RSS of the desired signal relative to the integral of the thermal noise of the receiving device over the signal bandwidth. The RSS and SNR can be predicted at the receiver (RX) device without explicit feedback from the transmitter (TX). The rates of change of RSS and SNR are generally much lower than the rate of change of C / I. Generally speaking, the higher the RSS, SNR, and C / I of the desired signal, the higher the packet success rate (PSR) (conversely, the lower the packet error rate (PER)). To estimate the PSR of a communication link, it may be useful to consider RSS, C / I, and SNR.

[0017] PSR can be defined as PSR = 1 - PER, where PER represents the packet error rate. The estimated value of PSR can be determined based on the RSS, C / I, and SNR of the desired signal, each of which can be characterized as a function of distance d, time t, and frequency f, or be affected by these factors. Two receivers located at different geographical locations and at the same distance from the transmitter typically do not result in the receivers experiencing the same RSS and C / I because the geographical location and interference level associated with each receiver vary. Therefore, the RSS and C / I parameters should be monitored by the receiver. In the case of an uplink transmission, the gateway device can be the receiver, while for a downlink transmission, the end node can be the receiver. An RSS greater than the sensitivity of the device does not guarantee packet success because the device may experience severe interference at a given time. PSR also depends on the robustness of the selected modulation and coding scheme (MCS), which can be characterized as a transmission scheme. Different MCSs provide different bit rates and different levels of resilience to impairments such as interference resistance. To attempt to help estimate the packet success rate at a receiver with a given MCS, it may be helpful to consider two conditions:

[0018] 1. RSS(t, d, f) > sensitivity

[0019] 2. C / I(t, d, f) > C / I 阈值 ,

[0020] These are two independent random variables. The term "C / I" 阈值 " represents the carrier - to - interference ratio threshold that can be tolerated without performance degradation, in dB. These conditions depend on the selected MCS. If both conditions are met, then the likelihood of successful wireless packet reception can be estimated to be high.

[0021] Various systems and techniques for estimating the PSR (and / or PER) for each MCS in the presence of interference are described herein. In various examples, the PSR (and / or PER) for each MCS in the presence of interference can be determined for each gateway device based on signals received from end nodes. The various systems and techniques described herein can consider metrics such as RSS and SNR, but can also consider the level of interference experienced by a receiver (e.g., a particular gateway device) over a period of time. This can allow for a more realistic estimate of the PSR value (as compared to traditional techniques that only consider RSS and / or SNR).

[0022] The PSR can be predicted by a cloud service (e.g., based on data received from gateway devices) or by the gateway device.

[0023] The PSR can be predicted across available MCSs and gateway devices via a cloud - based link adaptation service, and / or the PSR can be reported to the cloud - based link adaptation service for decision - making. The cloud - based link adaptation service can select the best gateway device and the best MCS to communicate with a given end node. The selected MCS can be chosen to maximize the PSR and / or reduce the end - node power consumption. It should be noted that PSR and PER can be used interchangeably herein because either metric can be used, and their correspondence is typically defined as PER = 1 - PSR.

[0024] According to one or more preferred embodiments, the PER is minimized via MCS selection, and / or an MCS is selected to minimize the power consumption at the EN.

[0025] The systems and techniques described herein can determine the interference experienced by each GW at different times and then use the interference data to predict the interference experienced by the GW. Each GW can calculate interference data including the determined interference values and transmit it to the cloud for centralized decision-making across gateways. For each MCS and GW, the PSR can be estimated based on the interference and RSS (and / or SNR). Additionally, an optimized MCS can be determined for each GW or each end-node device, which not only improves the PSR but also reduces power consumption. Further, the various systems and techniques described herein provide a compact design of the control information that needs to be transmitted from the cloud to the gateway devices and each end-node device.

[0026] FIG. 1A is a block diagram illustrating an example of interference-aware wireless communication link adaptation in accordance with various aspects of the present disclosure. In various examples, the cloud 104 can include a plurality of networked computing devices (which can include physical and / or virtualized computing devices), and the plurality of networked computing devices can include at least one remote computing device regarding the end-node devices and / or gateway devices. The cloud 104 can provide any desired computing services to the end-node devices such as the EN 102. In some examples, one or more quality of service (QoS) requirements (e.g., quality of service level) can be associated with the network communication of a given computing service. Although only a single end-node device (EN 102) is shown in FIG. 1A, any number of end-node devices can be configured in wireless communication with the cloud 104.

[0027] The end - node device (including EN 102) can communicate with Cloud 104 via one or more gateway devices (including GW 1, p, and P, where p ∈ {1, 2,..., P}). As described in further detail below, each gateway device can estimate or determine the interference and RSS experienced by that gateway device (e.g., periodically, continuously, and / or semi - periodically). This information can be transmitted to Cloud 104. In some other examples, each gateway device can transmit various information (described in further detail below with reference to Figure 1B) to Cloud 104, and Cloud 104 can determine or estimate the interference experienced by each gateway device. Cloud 104 can select the best gateway from all available gateway devices to communicate with a given end - node (e.g., EN 102) (e.g., based on uplink RSSI). Additionally, Cloud 104 can determine the best MCS to be used (block 110) based on the available MCS's best PER estimate for the RSS (and / or SNR value) and interference level reported (or determined for) by a particular gateway device. Additionally, the cloud can select an MCS that meets any relevant Quality of Service (QoS) requirements while minimizing power consumption at the end - node, as described in further detail below. Generally speaking, Cloud 104 can generate a lookup table for each gateway device that specifies the PSR (or PER) for different MCSs for a given SNR (and / or RSS) value, where the PSR / PER estimate is based on the SNR (and / or RSS) value and interference level determined or estimated for the gateway device. The lookup table can also indicate the power - consumption estimate for each MCS index, which can be calculated or affected based on the spreading factor (SF) and / or packet - repetition value (e.g., the number of repeated transmissions per packet or per symbol). The spreading factor can refer to the rate at which the signal frequency varies across the channel bandwidth. The spreading factor controls the chirp rate (i.e., the symbol rate), thus controlling the speed of data transmission. A lower spreading factor results in a faster chirp and thus a higher data - transmission rate. Lower spreading factors reduce the range of LoRa transmissions because they reduce the processing gain and increase the bit rate. The spreading factor and / or packet - repetition value can be adjusted, as described below, to meet the desired QoS. An MCS associated with the minimum power consumption that meets the QoS requirements can be selected, and Cloud 104 can transmit the index value representing the selected MCS to the gateway device. The gateway device can then transmit instructions to EN 102 to use the selected MCS for uplink communication.

[0028] Figure 1B is a state diagram of packet error rate estimation by a wireless receiver device in the presence of interference according to various aspects of the present disclosure. The RSSI and interference level can be continuously (or at a desired rhythm) predicted and monitored by each GW, and explicit feedback from the EN may not be required. The various wireless communications described herein can operate in the unlicensed ISM band or any other desired band. There can be various interference sources within the area where each GW is located. Generally, each GW continuously monitors / listens to the interference level to generate statistics on the interference in the ISM band (or other bands) with respect to thermal noise.

[0029] According to one or more preferred embodiments, to sample the interference periodically, the GW monitors the RSSI in the desired band with a granularity duration of T s seconds. This granularity is less than the packet duration, and thus statistics are generated for several symbols and packets within a period of T seconds.

[0030] Such an estimate not only captures sporadic and bursty interference but also captures an increase in the ambient noise floor, which may be caused by various reasons such as temperature variations and intermittent radio frequency impairments. The various techniques described herein can be easily extended to any number of gateway devices and / or end nodes.

[0031] For a given gateway considering a given bandwidth, the calculation of the thermal noise floor in dBm can be as follows:

[0032] n th = -174 dBm / Hz + 10 log 10 BW + NF, (Equation - 0)

[0033] where the noise figure (NF) in dB and the bandwidth (BW) in Hz are known for each GW. The desired signal strength (RSS) can be defined as: RSS = SNR + n th , in dBm, and its estimated value can be represented by RSSI 真 in dBn per signal bandwidth. With any spread - spectrum modulation (such as Lora modulation), even in the presence of interference, the RSSI can be estimated due to the provided processing gain 真 .

[0034] The estimated KPIs (such as, NF, SNR, etc.) have errors depending on the differences between chips and can be calibrated according to temperature, process, voltage, and / or channel frequency.

[0035] The chipset of the GW reports the SNR (in dB) and RSSI from the packets transmitted by the EN 真As used herein, a chipset refers to a group of one or more integrated circuits and / or electronic components that form a set of circuits / components enabling the GW to operate. The RSSI is measured by the chipset in the time domain, and if there is a co-channel interference source, the RSSI contains not only the power of the RSS but also the interference power I(t).

[0036] The GW can estimate the interference and other noise sources by continuously monitoring the RSSI and derive the interference power:

[0037]

[0038] When there is no interference, the RSSI 真 is linearly correlated with the SNR. Due to the linear correlation on the thermal noise floor, the SNR and RSSI 真 can be used interchangeably. Estimate the interference statistics and then use it to estimate the PER (as shown in Figure 1B).

[0039] The relationship between the estimated value of the SNR and the SINR is as follows:

[0040] SNR 估计 = RSSI 真 - n th , dB, (Equation - 2)

[0041] where RSSI 姜 represents the estimated value of the RSS. Then the estimated value of the SINR can be defined as:

[0042]

[0043] where the SINR is the ratio of the desired signal power to the sum of the undesired noise sources such as thermal noise and interference.

[0044] Substituting Equation - 2 into Equation - 3, Equation - 4 can be obtained:

[0045]

[0046] When I(t) >> n(t), that is, when the interference dominates the receiver performance, Equation - 4 approaches:

[0047] SINR 估计 ≈ SNR 估计 + n th - I(t), dB, (Equation - 5)

[0048] Hereinafter, define ΔI i,dB = n th - I(t), dB, as the difference between the thermal noise floor (in dBn) and the interference level (in dBn). Only when ΔIi，dB Equation 5 is valid only when >>10dB, i.e., the approximation error can be ignored. Otherwise, Equation 1 and Equation 4 (described below) can be used for each iteration to calculate the SINR 估计 without approximation error.

[0049] Figure 1B shows an exemplary method for estimating the PER (or PSR) in the presence of interference. The packet error rate (PER) is estimated based on statistics from an empirical interference prediction model. A look-up table (LUT) is created and stored in cloud 104 for link adaptation decisions. An imaginary illustration of the data in an exemplary LUT is depicted in Figure 3. According to one or more preferred embodiments, the look-up table allows the calculation of the PER (or PSR) value for a given SNR (or RSS) value and a given modulation and coding scheme (MCS) to be looked up. The look-up table can also provide power consumption data for the MCS (e.g., power consumption data associated with a given SNR (or RSS) value and a given MCS).

[0050] According to one or more preferred embodiments, the LUT is generated based on a Monte Carlo simulation of M interference distribution instances. A packet may include N symbols. Taking Lora modulation as an example, each symbol includes SF bits. The bit error rate (BER) can be theoretically calculated based on known approximations as follows:

[0051] Under the condition of additive white Gaussian noise (AWGN).

[0052] This corresponds to box 130 in the state diagram of Figure 1B. Then, the symbol error rate (SER) is calculated as follows:

[0053] Let I i be the interference at the i-th symbol. Shifting the AWGN noise floor by ΔI i may be a good approximation method for modeling the interference on a specific symbol. Then, under the AWGN noise and interference effects, for this specific symbol, the SER can be shifted to SER(SNR + ΔI i , dB).

[0054] In the presence of interference and without using error correction codes, the PER can be estimated as follows:

[0055] where the error probability is equal to 1 minus no errors on all N symbols experiencing interference I i . Additionally, if it is assumed that there are r repetitions for a packet with the same SF, the PER estimate becomes This means that the received packet is in error only if all r repetitions have an error.

[0056] The average PER can be estimated using M iterations of Monte Carlo simulation, as shown in Figure 1B, for example M = 10000. For each symbol, different instances of interference levels are generated using parameters that match the empirical measurements of the suburban (or other) environment. Each Monte Carlo iteration n ∈ {1, 2,..., M} generates an interference vector of size N symbols per packet, and then the average PER over all M iterations is estimated, where each iteration creates a packet. The state diagram in Figure 1B shows the proposed non-repetition algorithm (r = 1). The average PER is ultimately determined based on SF, packet repetition, the number of symbols N (or equivalently packet size / SF), SNR, and interference. The PER is defined as PER p (SF, r, N, SNR, I 统计 ), where PSR = 1 - PER. The term p represents the gateway index, where p ∈ {1, 2,..., P}.

[0057] Once the PSR for a given SNR and modulation and coding scheme is determined based on the above parameters and simulation, the PSR can be saved in a LUT and stored in the cloud 104. The LUT for each GW can be used to make decisions based on time-varying parameters across all GWs. An imaginary description of an exemplary LUT 300 is depicted in Figure 3. The LUT is prepared for a subset of these input parameters and saved to a memory (e.g., the non-transitory computer-readable memory of the cloud 104). If the real-time value of the interference level falls between two points in the LUT, the actual value can be calculated based on linear interpolation. The LUT 300 includes columns for the power consumption levels (e.g., power consumption estimates) for each MCS. The power consumption estimates can be calculated based on the packet size (N) and the corresponding airtime required for UL. During such airtime, the EN is in the active transmission state, which determines the transmission power consumption for each MCS. Higher-bitrate MCSs result in shorter airtimes, thus reducing the transmission power consumption. If there are multiple MCS candidates in the LUT that can meet a certain PER QoS target (e.g., target packet error rate or target packet success rate), the MCS with the highest bitrate can be selected to save power at the end node (the end node may be power-constrained (e.g., a battery-powered device). The target packet error rate or target packet success rate (e.g., QoS target) can be a predefined target packet error rate or target packet success rate (e.g., defined for a specific application and / or computing service).

[0058] FIG. 7 shows the relationship curve of PER versus SNR corresponding to a typical suburban environment where GW and EN are in a stationary state. The channels observed in the field test are log-normally distributed. The PER estimation without considering interference overestimates the system performance, and the decision without considering the interference in the ISM band may lead to QoS problems in the network. The proposed method uses the state diagram in FIG. 1, as shown in FIG. 7. The proposed method is based on online learning of interference and is extended to urban, suburban, and rural areas.

[0059] FIG. 2 is a block diagram of an example of gateway device selection for communicating with an end node device (EN 102) according to various aspects of the present disclosure. As described above, there may be PGμ within the range of EN 102. All Gμ can receive UL signals, but only the best Gμ (e.g., GWp) will be selected for DL, as shown in FIG. 2. The cloud 104 can select Gμp to serve ENu (e.g., EN 102) based on meeting the following conditions:

[0060] p u = arg{max p {SNR p，u}}, where p ∈ {1, 2,..., P}, u ∈ {1, 2,..., U}. Alternatively, the maximum RSSI can be used.

[0061] Therefore, the best GW is selected based on the strongest RSS that the GW experiences in the UL direction of a given EN. The cloud 104 assumes that the RSS will remain unchanged during the DL direction.

[0062] FIG. 3 is an exemplary look-up table (LUT) 300 that can be used to determine the modulation and coding scheme (MCS) of a gateway device experiencing interference according to various aspects of the present disclosure. Once the PER (or PSR) LUT is prepared for a given gateway, the UL rate adaptation can be determined by the cloud 104 using the method presented in FIG. 1B and described above, and the MCS index to be used can be transmitted to the GW. This can be performed for each GW device. The LUT 300 can be prepared daily, weekly, or at any other desired rhythm according to the interference statistics. After receiving the selected MCS, the serving Gμ will then transmit the data of the selected MCS for UL to each end node associated with Gμ.

[0063] Each GW provides the cloud 104 with inputs regarding RSS and the interference levels experienced by that GW. The cloud 104 then determines not only the best GW (e.g., using RSSI and / or SNR as described with reference to FIG. 2), but also the best MCS to be used based on the best PER estimate of the available MCS for a given RSS and interference level. The EN may be oblivious to the cloud 104, and the goal can be to minimize the power consumption of the EN while meeting the quality of service (QoS) requirements. An example of a QoS requirement can be to maintain PSR > 85%. It should be noted that QoS requirements typically depend on the application.

[0064] For example, the cloud 104 can receive interference and SNR information from GW p. The cloud 104 can calculate the PSR associated with these statistics as described above and can generate the LUT 300. Later, the cloud 104 can receive the current SNR value from GW p and can use that SNR value to query the lookup table 300 to determine the PSR for the SNR value (the PSR is determined based on the most recent interference data used to generate the LUT 300). The PSR can be associated with an MCS index in the LUT 300. The MCS can be selected to meet the relevant QoS requirements (e.g., PSR or PER requirements). However, if multiple MCSs meet the QoS requirements, the MCS associated with the lowest power consumption (among the MCSs that meet the QoS requirements) can be used. The cloud 104 can transmit the selected MCS index value to the relevant GW (e.g., the gateway associated with a particular LUT 300). Only the MCS index needs to be sent, and the MCS index can include only 2 bits (in a four-MCS index LUT).

[0065] If the QoS requirement regarding PSR for the selected MCS is not met, the cloud 104 can instruct the GW to instruct the EN102 to increase the SF and the repetition r such that the QoS is met (as shown and described in FIG. 4). Alternatively, if the QoS has been met (e.g., where the EN 102 is in the close proximity of GW p), the GW instructs the EN 102 to decrease the SF and packet repetition, which can save the power of the EN 102 while meeting the QoS requirements. To further explain this, PER(SF, r, N, SNR, I 统计 ) < target PER - γ, where γ is a device-defined threshold for determining the acceptable PER margin, the cloud can instruct the GW to use a lower SF, thus reducing the air time of the packet and saving power while meeting the QoS requirements.

[0066] The indices in the LUT 300 can be used to select the SF and repetition rate r as a function of RSSI (and / or SNR) and interference (as described above). Additionally, the GW only needs to use 2 bits of 4 indices as control information. The lookup table generalizes different SFs and repetitions based on the capabilities of the network. Due to the shorter airtime, the higher bitrate MCSs consume less power on average. Thus, p4 < p3 < p2 (where the higher the SF, the longer the transmission time, as shown in Table 302). Due to packet repetition, p4 < p1 and p1 < p2 (since 72 ms * 5 repetitions = 360 ms (p1) < 578 ms (p2)). The exemplary values in Table 302 are based on a packet size of 10 bytes at a 125 kHz bandwidth.

[0067] Figure 4 is a state diagram of uplink rate adaptation based on interference prediction modeling in accordance with various aspects of the present disclosure. In the example of Figure 4, GW p receives an applied QoS (e.g., target PER, target latency, etc.). Additionally, GW p determines the RSSI associated with the UL communication from ENk. As described above, GWp estimates the interference level experienced by GWp. GW p can transmit the interference estimate and / or RSSI level to the cloud 104, and the cloud 104 can determine the PSR based on the interference estimate and RSSI. The cloud 104 can select an MCS for GWp using a LUT, querying the LUT with the PSR. As shown in Figure 3, the MCS can be associated with a spreading factor SF and a packet repetition value r. At action 404, it can be determined whether the PER estimate associated with the selected MCS is less than the target PER (e.g., from the QoS). If so, the process can proceed to action 406, and GWp can transmit the SF and r values from the LUT to the end node (e.g., EN 102) for UL. Conversely, if the PER estimate value is greater than or equal to the target PER, GW p can modify the SF and r (e.g., by increasing the SF and / or r at action 408), and can return to action 404 until the PER is less than the target PER.

[0068] FIG. 5 is a block diagram illustrating an exemplary architecture 500 of a computing device that may be used in accordance with various aspects of the present disclosure. For example, the architecture may perform one or more of the interference-aware wireless communication link adaptation techniques described above with reference to FIGS. 1-4. In various other examples, one or more components of architecture 500 may be included in an end node device such as EN 102. It should be understood that not all devices include all components of architecture 500, and some user devices may include additional components not shown in architecture 500. Architecture 500 may include one or more processing elements 504 for executing instructions and retrieving data stored in storage element 502. The processing elements 504 may include at least one processor. Any suitable one or more processors may be used. For example, the processing elements 504 may include one or more digital signal processors. The storage element 502 may include one or more different types of memories, data storage devices, or computer-readable storage media within architecture 500 dedicated to different purposes. For example, the storage element 502 may include flash memory, random access memory, disk-based storage devices, and so on. For example, different portions of the storage element 502 may be used for storage of program instructions to be executed by the processing elements 504, storage of images or other digital works, and / or a removable storage device for transferring data to other devices, and so on.

[0069] The storage element 502 may also store software for execution by the processing elements 504. The operating system 522 may provide a user interface for operating the computing device and may facilitate communication and commands between applications executing on architecture 500 and its various hardware. The transmission application 524 may be configured to receive images, audio, and / or video from another device (e.g., a mobile device, an image capture device, and / or a display device) or from an image sensor 532 and / or a microphone 570 included in architecture 500.

[0070] When implemented in some user devices, architecture 500 may also include a display component 506. The display component 506 may include one or more light-emitting diodes (LEDs) or other suitable display lights. Additionally, in some examples, the display component 506 may include, for example, one or more devices such as a cathode ray tube (CRT), a liquid crystal display (LCD) screen, a gas plasma-based flat panel display, an LCD projector, a raster projector, an infrared projector, or other types of display devices, and so on. As described herein, the display component 506 may effectively display input images generated according to the various techniques described herein. In various examples, the display component 506 may be a wearable display (e.g., in a headset, goggles, and / or glasses) that may display various graphical highlighting data, graphical navigation cues, text, other graphical data, and so on described herein. In some examples, architecture 500 may include one or more speakers for effectively outputting audio.

[0071] The architecture 500 may also include one or more input devices 508 operable to receive input from a user. The input devices 508 may include, for example, push buttons, touch pads, touch screens, rollers, joysticks, keyboards, mice, trackballs, keypads, light guns, game controllers, or any other such devices or elements by which a user may provide input to the architecture 500. These input devices 508 may be incorporated into the architecture 500 or operably coupled to the architecture 500 via a wired or wireless interface. In some examples, the architecture 500 may include a microphone 570 or microphone array for capturing sound (such as a voice request). In various examples, the audio captured by the microphone 570 may be streamed to an external computing device via the communication interface 512.

[0072] When the display component 506 includes a touch-sensitive display, the input device 508 may include a touch sensor that operates with the display component 506 to allow a user to interact with an image displayed by the display component 506 using touch input (e.g., with a finger or a stylus). The architecture 500 may also include a power supply 514, such as a wired alternating current (AC) converter, a rechargeable battery operable to be charged by a conventional plug-in method or by other means such as by capacitive or inductive charging.

[0073] The communication interface 512 may include one or more wired or wireless components operable to communicate with one or more other computing devices. For example, the communication interface 512 may include a wireless communication module 536 configured to communicate over a network according to any suitable wireless protocol (such as IEEE 802.11 or another suitable wireless local area network (WLAN) protocol). The short-range interface 534 may be configured to communicate using one or more short-range wireless protocols (e.g., near field communication (NFC), Bluetooth, Bluetooth LE, etc.). The mobile interface 540 may be configured to communicate using a cellular or other mobile protocol. The Global Positioning System (GPS) interface 538 may communicate with one or more Earth-orbiting satellites or other suitable position determination systems to identify the location of the architecture 500. The wired communication module 542 may be configured to communicate according to the USB protocol or any other suitable protocol.

[0074] The architecture 500 may also include one or more sensors 530, such as one or more position sensors, image sensors, and / or motion sensors. An image sensor 532 is shown in FIG. 5. Some examples of the architecture 500 may include multiple image sensors 532. For example, a panoramic camera system may include multiple image sensors 532, thereby generating multiple images and / or video frames that can be stitched and blended to form a seamless panoramic output. Examples of the image sensor 532 may be cameras configured to capture color information, image geometry information, and / or ambient light information. In various examples, the image sensor 532 may effectively capture images and / or video frames that can be used to detect various objects in the user's physical environment.

[0075] As described above, multiple devices may be employed in a single system. In such a multi-device system, each of the devices may include different components to perform different aspects of the system processing. The multiple devices may include overlapping components. As described herein, the components of the various computing devices are exemplary and may be positioned as separate devices or may be included, in whole or in part, as components of a larger device or system.

[0076] Exemplary systems for transmitting and providing data that can be used to perform one or more of the various techniques described herein will now be described in detail. Specifically, FIG. 6 illustrates an exemplary computing environment in which the embodiments described herein can be implemented. For example, the computing environment of FIG. 6 can be an example of a cloud-based environment, in which various end-node devices (e.g., user computers 62a, 62b, etc.) communicate with a backend distributed computing network (e.g., cloud 104) via one or more gateway devices (e.g., gateway 64). FIG. 6 is a diagram schematically illustrating an example of cloud 104 (e.g., a data center), which can provide computing resources to users 60a and 60b (which can be referred to singularly as user 60 or plurally as users 60 in this document) via user computers 62a and 62b (which can be referred to singularly as user computer 62 or plurally as user computers 62 in this document) over computer communication network 604. Cloud 104 can be configured to permanently or as needed provide computing resources for executing applications. The computing resources provided by cloud 104 can include various types of resources, such as gateway resources, load balancing resources, routing resources, networking resources, computing resources, volatile and non-volatile memory resources, content delivery resources, data processing resources, data storage resources, data communication resources, and so on. Each type of computing resource can be available in a variety of specific configurations. For example, data processing resources can be available as virtual machine instances that can be configured to provide various web services. Additionally, combinations of resources can be made available via a network and configured as one or more web services. Instances can be configured to execute applications, including web services, such as application services, media services, database services, processing services, gateway services, storage services, routing services, security services, encryption services, load balancing services, application services, and so on. In various examples, instances can be configured to execute one or more of the various image processing techniques described herein.

[0077] These services can be configured via a set or custom application and can be configured in terms of size, execution, cost, latency, type, duration, accessibility, and any other dimension. These web services can be configured as infrastructure for use by one or more clients and can include one or more applications configured as a platform or software for use by one or more clients. These web services can become available via one or more communication protocols. These communication protocols can include, for example, the Hypertext Transfer Protocol (HTTP) or non-HTTP protocols. These communication protocols can also include, for example, more reliable transport layer protocols such as the Transmission Control Protocol (TCP) and less reliable transport layer protocols such as the User Datagram Protocol (UDP). Data storage resources can include file storage devices, block storage devices, and so on.

[0078] Each type or configuration of computing resource can have different sizes, such as large resources (composed of many processors, large amounts of memory, and / or large storage capacities) and small resources (composed of fewer processors, smaller amounts of memory, and / or smaller storage capacities). For example, a customer can choose to allocate multiple small processing resources as web servers and / or allocate one large processing resource as a database server.

[0079] Cloud 104 can include servers 66a and 66b that provide computing resources (which may be referred to singularly herein as server 66 or plurally as servers 66). These resources can be available as bare-metal resources or as virtual machine instances 68a-d (which may be referred to singularly herein as virtual machine instance 68 or plurally as virtual machine instances 68). In at least some examples, server manager 67 can control the operation of server 66 and / or maintain the server. Virtual machine instances 68c and 68d are replicated switch virtual machine (“RSVM”) instances. RSVM virtual machine instances 68c and 68d can be configured to perform all or any part of the techniques for improved replicated switching according to the present disclosure and described in detail above and / or any other of the disclosed techniques. It should be understood that while the specific example shown in FIG. 6 includes one RSVM virtual machine in each server, this is merely an example. A server can include more than one RSVM virtual machine or may not include any RSVM virtual machines.

[0080] The emergence of computing hardware virtualization technology has facilitated the provision of large-scale computing resources for customers and allowed multiple customers to share computing resources efficiently and securely. For example, virtualization technology can allow multiple users to share a physical computing device by providing each user with one or more virtual machine instances hosted by the physical computing device. A virtual machine instance can be a software emulation of a particular physical computing system that acts as different logical computing systems. Such virtual machine instances provide isolation between multiple operating systems sharing a given physical computing resource. Additionally, some virtualization technologies can provide virtual resources that span one or more physical resources, such as a single virtual machine instance having multiple virtual processors that span multiple different physical computing systems.

[0081] Referring to FIG. 6, network 604 can be, for example, a publicly accessible network that links networks and may be operated by different parties such as the Internet. In other embodiments, network 604 can be a private network, such as a corporate or university network that is not fully or partially accessible to non-privileged users. In other embodiments, network 604 can include one or more private networks that are capable of accessing the Internet and / or being accessed from the Internet.

[0082] The network 604 can provide access to the user computer 62. The user computer 62 can be a computer used by the user 60 or other customers of the cloud 104. For example, the user computers 62a or 62b can be servers, desktop or laptop personal computers, tablet computers, wireless telephones, personal digital assistants (PDAs), e - book readers, game consoles, set - top boxes, or any other computing device capable of accessing the cloud 104. The user computers 62a or 62b can be directly connected to the Internet (e.g., via a cable modem or digital subscriber line (DSL)). Although only two user computers 62a and 62b are depicted, it should be understood that there can be multiple user computers.

[0083] The user computer 62 can also be used to configure various aspects of the computing resources provided by the cloud 104. In this regard, the cloud 104 may provide a gateway or web interface through which various aspects of its operation can be configured by using a web browser application executed on the user computer 62. Alternatively, a stand - alone application executed on the user computer 62 can access an application programming interface (API) presented by the cloud 104 to perform configuration operations. Other mechanisms for configuring the operation of various web services available on the cloud 104 can also be utilized.

[0084] The server 66 shown in FIG. 6 can be a server appropriately configured to provide the aforementioned computing resources and can provide computing resources for executing one or more web services and / or applications. In one embodiment, the computing resources can be virtual machine instances 68. In the example of virtual machine instances, each of the servers 66 in the server 66 can be configured to execute an instance manager 63a or 63b (which can be singularly referred to as the instance manager 63 in this article, or plurally as instance managers 63) capable of executing the virtual machine instance 68. For example, the instance manager 63 can be a virtual machine monitor (VMM) or another type of program configured to enable the execution of the virtual machine instance 68 on the server 66. As described above, each of the virtual machine instances 68 can be configured to execute all or a part of an application.

[0085] It should be understood that although the embodiments disclosed above discuss the context of virtual machine instances, the concepts and techniques disclosed herein can be used to implement other types of embodiments. For example, the embodiments disclosed herein can also be used with computing systems that do not use virtual machine instances.

[0086] In the exemplary cloud 104 shown in FIG. 6, a router 61 can be used to interconnect servers 66a and 66b. The router 61 can also be connected to a gateway 64, which is connected to a network 604. The router 61 can be connected to one or more load balancers and can manage communications within the network in the cloud 104, either individually or in combination, for example by appropriately forwarding packets or other data communications based on characteristics of such communications (e.g., header information including source and / or destination addresses, protocol identifiers, size, processing requirements, etc.) and / or characteristics of the private network (e.g., routing based on network topology, etc.). It should be understood that, for simplicity, certain conventional details are not shown when aspects of the computing systems and other devices in this example are illustrated. In other embodiments, additional computing systems and other devices can be interconnected and can be interconnected in different ways.

[0087] In the exemplary cloud 104 shown in FIG. 6, the cloud 104 is also used to at least partially direct various communications to and from and / or between servers 66a and 66b. Although FIG. 6 depicts a router 61 located between the gateway 64 and the cloud 104, this is merely an exemplary configuration. In some cases, for example, the cloud 104 can be located between the gateway 64 and the router 61. In some cases, the cloud 104 can examine portions of incoming communications from a user computer 62 to determine one or more appropriate servers 66 for receiving and / or processing the incoming communications. The cloud 104 can determine the appropriate server for receiving and / or processing the incoming communications based on factors such as the identity, location, or other attributes associated with the user computer 62, the nature of the task associated with the communication, the priority of the task associated with the communication, the duration of the task associated with the communication, the size of the task associated with the communication, and / or the estimated resource usage, as well as many other factors. The cloud 104 can, for example, collect or otherwise access status information and other information associated with various tasks in order to, for example, assist in managing communications and other operations associated with such tasks.

[0088] It should be understood that the network topology shown in FIG. 6 has been greatly simplified and that more networks and networking devices can be utilized to interconnect the various computing systems disclosed herein. These network topologies and devices should be apparent to those skilled in the art.

[0089] It should also be understood that the cloud 104 depicted in FIG. 6 is merely illustrative, and other embodiments may be used. It should also be understood that a server, gateway, or other computing device may include any combination of hardware or software that can interact and perform functions of the type described, including but not limited to: desktop or other computers, database servers, network storage devices and other network devices, PDAs, tablet computers, cellular phones, wireless phones, pagers, electronic notebooks, Internet devices, television-based systems (e.g., using set-top boxes and / or personal / digital video recorders), and various other consumer products having suitable communication capabilities.

[0090] A network established by an entity (such as a company or a public-sector organization) for providing one or more web services (such as various types of cloud-based computing or storage) that can be accessed via the Internet and / or other networks to a group of distributed clients may be referred to as a provider network. Such a provider network may include numerous data centers hosting various resource pools, such as a collection of physical and / or virtualized computer servers, storage devices, networking equipment, etc., configured to implement and distribute the infrastructure and web services provided by the provider network. In some embodiments, resources may be provided to clients in various units related to web services, such as the amount of storage capacity for storage, processing power for processing, instances, as a collection of related services, and so on. For example, a virtual computing instance may include one or more servers having a specified computing capacity (which may be specified by indicating the type and number of CPUs, main memory size, etc.) and a specified software stack (e.g., a specific version of an operating system, which may in turn run on a hypervisor).

[0091] In different embodiments, multiple different types of computing devices may be used alone or in combination to implement the resources of a provider network, such as computer servers, storage devices, network devices, and so on. In some embodiments, direct access to a resource instance may be provided to a client or user, for example, by providing the user with an administrator login name and password. In other embodiments, a provider network operator may allow a client to specify the execution requirements for a specified client application, and schedule the execution of the application on an execution platform suitable for the application (such as an application server instance, Java TM virtual machine (JVM), a general-purpose or specialized operating system, platform, or high-performance computing platform that supports various interpreted or compiled programming languages (such as Ruby, Perl, Python, C, C++ etc.)) on behalf of the client, without, for example, requiring the client to directly access the instance or the execution platform. In some embodiments, a given execution platform may utilize one or more resource instances; in other embodiments, multiple execution platforms may be mapped to a single resource instance.

[0092] In many environments, an operator of a provider network that implements different types of virtualized computing, storage, and / or other network-accessible functions may allow customers to reserve or purchase access to resources in various resource acquisition modes. A computing resource provider may provide facilities for a customer to select and launch desired computing resources, deploy application components to the computing resources, and maintain the applications executing in the environment. Additionally, as the demand or capacity requirements of an application change, the computing resource provider may provide additional facilities for the customer to quickly and easily increase or decrease the quantity and type of resources allocated to the application, either manually or through auto-scaling. The computing resources provided by the computing resource provider may be available in discrete units, which may be referred to as instances. An instance may represent a physical server hardware platform, a virtual machine instance executing on the server, or some combination of both. Various types and configurations of instances may be provided, including resources of different sizes executing different operating systems (OSs) and / or hypervisors, as well as having various software applications, runtimes, etc. installed. Instances may also be available in specific availability zones representing other geographical locations such as logical regions, fault-tolerant regions, data centers, or underlying computing hardware. Instances may be replicated within or across availability zones to improve instance redundancy, and instances may also be migrated within a specific availability zone or across availability zones. As an example, the latency for a client communicating with a particular server in an availability zone may be less than the latency for a client communicating with a different server. Thus, an instance may be migrated from a server with a higher latency to a server with a lower latency to improve the overall client experience.

[0093] In some embodiments, the provider network may be organized into multiple geographical regions, and each region may include one or more availability zones. An availability zone (which may also be referred to as an availability container) may in turn include one or more different locations or data centers that are configured such that resources in a given availability zone may be isolated or insulated from failures in other availability zones. That is, a failure in one availability zone may not cause a failure in any other availability zone. Thus, the availability profiles of resource instances are intended to be independent of the availability profiles of resource instances in different availability zones. A client may be able to protect its applications from failures at a single location by launching multiple application instances in the corresponding availability zones. At the same time, in some implementations, a low-cost and low-latency network connection may be provided between resource instances residing within the same geographical region (and network transmissions between resources in the same availability zone may even be faster).

[0094] Clause

[0095] The various embodiments of the present disclosure may be described in view of the following terms:

[0096] Clause 1. A method, the method comprising:

[0097] Receiving first data from a gateway device;

[0098] Based on the first data, storing a first interference value associated with the gateway device;

[0099] For a first modulation and coding scheme, determining a first plurality of estimated packet success values based on interference data of the gateway device, each estimated packet success value being associated with a corresponding value of a signal metric, the interference data including the first interference value;

[0100] For a second modulation and coding scheme, determining a second plurality of estimated packet success values based on interference data of the gateway device, each estimated packet success value being associated with a corresponding value of the signal metric;

[0101] Receiving second data from the gateway device;

[0102] Determining a first value of the first signal metric based on the second data;

[0103] Determining a first estimated packet success value of the first modulation and coding scheme based on the first value of the first signal metric and at least one estimated packet success value of the first plurality of estimated packet success values;

[0104] Determining a second estimated packet success value of the first modulation and coding scheme based on the first value of the first signal metric and at least one estimated packet success value of the second plurality of estimated packet success values; and

[0105] Transmitting third data to the gateway device based on the first estimated packet success value of the first modulation and coding scheme and the second estimated packet success value of the second modulation and coding scheme, the third data indicating a switch from the first modulation and coding scheme to the second modulation and coding scheme.

[0106] Clause 2. The method according to clause 1, wherein the first signal metric includes a signal-to-noise ratio, and wherein the method comprises:

[0107] Determining a first bit error rate value of the first modulation and coding scheme based on the first value of the first signal metric;

[0108] Determining a first symbol error rate value of the first modulation and coding scheme based on the first bit error rate value of the first modulation and coding scheme;

[0109] Determining a first interference value based on the interference data;

[0110] Determine a second interference value based on the interference data;

[0111] One of the first plurality of estimated packet success values is determined based on the first interference value, the second interference value, and the first symbol error rate value of the first modulation and coding scheme.

[0112] Clause 3. The method according to clause 1, wherein the first data indicates a first signal strength value determined by the gateway device, and wherein the method includes

[0113] Determine a thermal noise floor value based on the first signal strength value, a noise figure value associated with the gateway device, and a bandwidth value associated with the gateway device; and

[0114] Determine the first interference value based on the first signal strength value and the thermal noise floor value.

[0115] Clause 4. A method, the method comprising:

[0116] For a first transmission scheme, determine a first plurality of estimated packet success values based on interference data of a gateway device, each estimated packet success value being associated with a corresponding value of a signal metric;

[0117] Receive first signal data from the gateway device;

[0118] Determine a first value of the signal metric based on the first signal data;

[0119] Determine a first estimated packet success value of the first transmission scheme based on the first value of the first signal metric and at least one of the first plurality of estimated packet success values;

[0120] Transmit second data indicating the transmission scheme to the gateway device based on the first estimated packet success value of the first transmission scheme.

[0121] Clause 5. The method according to clause 4, wherein the method includes, before determining the first plurality of estimated packet success values,

[0122] Receive third data from the gateway device; and

[0123] Store a first interference value associated with the gateway device based on the third data;

[0124] Wherein the interference data includes the first interference value.

[0125] Clause 6. The method according to clause 5, wherein the third data indicates the first interference value.

[0126] Clause 7. The method as described in Clause 5, wherein the third data indicates a first signal strength value determined by the gateway device, and wherein the method includes determining the first interference value based on the first signal strength value.

[0127] Clause 8. The method as described in Clause 5, wherein the third data indicates a first signal strength value determined by the gateway device, and wherein the method includes

[0128] determining the first interference value based on the first signal strength value and a noise value associated with the gateway device.

[0129] Clause 9. The method as described in Clause 5, wherein the third data indicates a first signal strength value determined by the gateway device, and wherein the method includes

[0130] determining a thermal noise floor value based on the first signal strength value, a noise figure value associated with the gateway device, and a bandwidth value associated with the gateway device; and

[0131] determining the first interference value based on the first signal strength value and the thermal noise floor value.

[0132] Clause 10. The method as described in Clause 4, wherein the method further includes associating and storing each respective packet success value among the first plurality of estimated packet success values with

[0133] the corresponding respective value of the signal metric and

[0134] the first transmission scheme.

[0135] Clause 11. The method as described in Clause 4, wherein determining the first estimated packet success value is based on determining that the first value of the signal metric matches the corresponding value associated with one of the estimated packet success values among the plurality of estimated packet success values.

[0136] Clause 12. The method as described in Clause 4, wherein the method further includes:

[0137] determining the first value of the signal metric

[0138] to be greater than the first corresponding value of the signal metric associated with a second estimated packet success value among the first plurality of estimated packet success values, and

[0139] less than the second corresponding value of the signal metric associated with a third estimated packet success value among the first plurality of estimated packet success values;

[0140] wherein the first estimated packet success value is determined based on the following

[0141] the first corresponding value of the signal metric associated with the second estimated packet success value among the first plurality of estimated packet success values,

[0142] the second corresponding value of the signal metric associated with the third estimated packet success value among the first plurality of estimated packet success values,

[0143] the second estimated packet success value among the first plurality of estimated packet success values,

[0144] the third estimated packet success value among the first plurality of estimated packet success values, and

[0145] the first value of the signal metric.

[0146] Clause 13. The method as described in Clause 4, wherein the method includes:

[0147] determining a first bit error rate value of the first transmission scheme based on the first value of the signal metric;

[0148] determining a first symbol error rate value of the first transmission scheme based on the first bit error rate value of the first transmission scheme;

[0149] determining a first interference value based on the interference data;

[0150] determining a second interference value based on the interference data;

[0151] wherein one of the estimated packet success values among the first plurality of estimated packet success values is determined based on the first interference value, the second interference value, and the first symbol error rate value of the first transmission scheme.

[0152] Clause 14. The method as described in Clause 4, wherein the method includes:

[0153] determining a first bit error rate value of the first transmission scheme based on the first value of the signal metric;

[0154] determining a first symbol error rate value of the first transmission scheme based on the first bit error rate value of the first transmission scheme;

[0155] determining a first set of interference values based on the interference data, the number of interference values in the first set corresponding to the number of symbols in a packet of the first transmission scheme;

[0156] One of the first plurality of estimated packet success values is determined based on the first set of interference values and the first symbol error rate value of the first transmission scheme.

[0157] Clause 15. The method according to clause 4, wherein the method comprises:

[0158] Determining a first bit error rate value of the first transmission scheme based on a first value of the signal metric;

[0159] Determining a first symbol error rate value of the first transmission scheme based on the first bit error rate value of the first transmission scheme;

[0160] Determining a first interference value based on the interference data;

[0161] Determining a second interference value based on the interference data;

[0162] Determining a second estimated packet success value based on the first interference value, the second interference value and the first symbol error rate value of the first transmission scheme;

[0163] Determining a third interference value based on the interference data;

[0164] Determining a fourth interference value based on the interference data; and

[0165] Determining a third estimated packet success value based on the third interference value, the fourth interference value and the first symbol error rate value of the first transmission scheme;

[0166] Wherein the first estimated packet success value is determined based on the second estimated packet success value and the third estimated packet success value.

[0167] Clause 16. The method according to clause 4, wherein the method comprises:

[0168] Determining a first bit error rate value of the first transmission scheme based on a first value of the signal metric;

[0169] Determining a first symbol error rate value of the first transmission scheme based on the first bit error rate value of the first transmission scheme;

[0170] Determining a first set of interference values based on the interference data, the number of interference values in the first set corresponding to the number of symbols in a packet of the first transmission scheme;

[0171] Determining a second set of interference values based on the interference data, the number of interference values in the second set corresponding to the number of symbols in a packet of the first transmission scheme;

[0172] One of the first plurality of estimated packet success values is determined based on the first interference value, the second interference value, and the first symbol error rate value of the first transmission scheme.

[0173] Clause 17. The method as described in Clause 4, wherein the method comprises:

[0174] Determining a first bit error rate value of the first transmission scheme based on a first value of the signal metric;

[0175] Determining a first symbol error rate value of the first transmission scheme based on the first bit error rate value of the first transmission scheme;

[0176] Determining a first set of interference values based on the interference data, the number of interference values in the first set corresponding to the number of symbols in a packet of the first transmission scheme;

[0177] Determining a second estimated packet success value based on the first set of interference values and the first symbol error rate value of the first transmission scheme;

[0178] Determining a second set of interference values based on the interference data, the number of interference values in the second set corresponding to the number of symbols in a packet of the first transmission scheme;

[0179] Determining a third estimated packet success value based on the second set of interference values and the first symbol error rate value of the first transmission scheme;

[0180] One of the first plurality of estimated packet success values is determined based on the second estimated packet success value and the third estimated packet success value.

[0181] Clause 18. The method as described in Clause 17, wherein the first estimated packet success value is an estimated packet error rate, the second estimated packet success value is an estimated packet success rate, and the third estimated packet success value is an estimated packet success rate.

[0182] Clause 19. The method as described in Clause 4, wherein the signal metric is signal-to-noise ratio.

[0183] Clause 20. The method as described in Clause 4, wherein the signal metric is received signal strength.

[0184] Clause 21. The method as described in Clause 4, wherein the first estimated packet success value is an estimated packet error rate.

[0185] Clause 22. The method as described in Clause 4, wherein the first estimated packet success value is an estimated packet success rate.

[0186] Clause 23. The method as described in Clause 4, wherein the first data indicates a first signal strength value determined by the gateway device.

[0187] Clause 24. The method as described in Clause 4, wherein the first data indicates a first signal-to-noise ratio value determined by the gateway device.

[0188] Clause 25. The method as described in Clause 24, wherein the first data indicates the first value of the signal metric.

[0189] Clause 26. The method as described in Clause 4, wherein the second data indicates the first transmission scheme.

[0190] Clause 27. The method as described in Clause 4, wherein the second data indicates a second transmission scheme.

[0191] Clause 28. The method as described in Clause 4, wherein the method includes:

[0192] For the second transmission scheme, determining a second plurality of estimated packet success values based on the interference data of the gateway device, each estimated packet success value being associated with a corresponding value of the signal metric;

[0193] Determining a second estimated packet success value of the second transmission scheme based on the first value of the signal metric and at least one of the second plurality of estimated packet success values; and

[0194] wherein the second data is determined based on the second estimated packet success value of the second transmission scheme.

[0195] Clause 29. The method as described in Clause 4, wherein the method includes determining a power consumption value of the first transmission scheme.

[0196] Clause 30. The method as described in Clause 4, wherein the method includes:

[0197] For the second transmission scheme, determining a second plurality of estimated packet success values based on the interference data of the gateway device, each estimated packet success value being associated with a corresponding value of the signal metric;

[0198] Determining a second estimated packet success value of the second transmission scheme based on the first value of the signal metric and at least one of the second plurality of estimated packet success values;

[0199] Determining a first power consumption value of the first transmission scheme; and

[0200] Determining a second power consumption value of the second transmission scheme;

[0201] wherein the second data is determined based on the second estimated packet success value, the first power consumption value, and the second power consumption value of the second transmission scheme.

[0202] Clause 31. An electronic device, the electronic device comprising:

[0203] a wireless transceiver;

[0204] one or more processors;

[0205] one or more computer-readable media storing computer-executable instructions that, when executed by one or more processors of the electronic device, cause the electronic device to perform operations including the following

[0206] determine a first received signal strength value at a first time,

[0207] determine a first interference value based on the first received signal strength value,

[0208] transmit the first interference value to a remote computing system,

[0209] determine a second received signal strength value at a second time,

[0210] transmit first data to the remote computing system based on the second received signal strength value, and

[0211] receive second data determined based on the first interference value and the first data from the remote computing system, the second data indicating a first transmission scheme.

[0212] Clause 32. The electronic device according to Clause 31, wherein the first data indicates the second received signal strength value.

[0213] Clause 33. The electronic device according to Clause 31, wherein the one or more computer-readable media store computer-executable instructions that, when executed by one or more processors of the electronic device, cause the electronic device to perform operations including the following

[0214] determine a signal-to-noise ratio based on the second received signal strength value, and wherein the first data indicates the signal-to-noise ratio.

[0215] Clause 34. The electronic device according to Clause 31, wherein the one or more computer-readable media store computer-executable instructions that, when executed by one or more processors of the electronic device, cause the electronic device to perform operations including the following

[0216] Determine a thermal noise floor value based on the first signal strength value, a noise figure value associated with the electronic device, and a bandwidth value;

[0217] wherein the first interference value is determined based on the first signal strength value and the thermal noise floor value.

[0218] Clause 35. The electronic device as described in Clause 31, wherein the one or more computer-readable media store computer-executable instructions that, when executed by one or more processors of the electronic device, cause the electronic device to perform operations including: based on the second data indicating the first transmission scheme, transmit third data to another electronic device using the first transmission scheme.

[0219] Clause 36. The electronic device as described in Clause 31, wherein the one or more computer-readable media store computer-executable instructions that, when executed by one or more processors of the electronic device, cause the electronic device to perform operations including: based on the second data indicating the first transmission scheme, transmit third data to another electronic device, indicating the use of the first transmission scheme.

[0220] Clause 37. The electronic device as described in Clause 31, wherein the first transmission scheme is a first modulation and coding scheme.

[0221] Clause 38. The method as described in Clause 4, wherein the first transmission scheme is a first modulation and coding scheme.

[0222] Although the various systems described herein may be embodied in software or code executed by general-purpose hardware as described above, alternatively, they may also be embodied in dedicated hardware or a combination of software / general-purpose hardware and dedicated hardware. If embodied in dedicated hardware, each may be implemented as a circuit or state machine employing any one or combination of a variety of techniques. These techniques may include, but are not limited to, discrete logic circuits having logic gates for implementing various logical functions when applying one or more data signals, application-specific integrated circuits having appropriate logic gates, or other components, and so on. These techniques are generally well-known to those of ordinary skill in the art and thus will not be described in detail herein.

[0223] The flowcharts and methods described herein illustrate the functionality and operations of various embodiments. If embodied in software, each box or step may represent a module, segment, or portion of code that includes program instructions for implementing the specified logical function. The program instructions may be embodied in source code, which includes human-readable statements written in a programming language, or in machine code, which includes digital instructions recognizable by a suitable execution system, such as a processing component in a computer system. If embodied in hardware, each box may represent a circuit or multiple interconnected circuits to implement the specified logical function.

[0224] Although the flowcharts and methods described herein may describe a particular order of execution, it should be understood that the order of execution may be different from the described order. For example, the order of execution of two or more boxes or steps may be out of order relative to the described order. Additionally, two or more boxes or steps may be executed simultaneously or partially simultaneously. Furthermore, in some embodiments, one or more of the boxes or steps may be skipped or omitted. It should be understood that all such variations are within the scope of the present disclosure.

[0225] In addition, any logic or application including software or code described herein may be embodied in any non-transitory computer-readable medium or memory for use by or in conjunction with an instruction execution system, such as a processing component in a computer system. In this sense, the logic may include, for example, statements containing instructions and declarations that can be retrieved from the computer-readable medium and executed by the instruction execution system. In the context of the present disclosure, a "computer-readable medium" may be any medium that can contain, store, or maintain the logic or application described herein for use by or in conjunction with an instruction execution system. The computer-readable medium may include many physical media, such as any of magnetic media, optical media, or semiconductor media. More specific examples of suitable computer-readable media include, but are not limited to, magnetic tape, magnetic floppy disks, magnetic hard disk drives, memory cards, solid-state drives, USB flash drives, or optical discs. Additionally, the computer-readable medium may be random access memory (RAM), including, for example, static random access memory (SRAM) and dynamic random access memory (DRAM), or magnetic random access memory (MRAM). Furthermore, the computer-readable medium may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or other types of memory devices.

[0226] It should be emphasized that the above-described embodiments of the present disclosure are merely possible examples of embodiments presented for a clear understanding of the principles of the present disclosure. Many changes and modifications can be made to the above examples without substantially departing from the spirit and principles of the present disclosure. In this document, all such modifications and variations are intended to be included within the scope of the present disclosure and are protected by the following claims.

Claims

1. A method, the method comprising: For a first transmission scheme, determining a first plurality of estimated packet success values based on interference data of a gateway device, each estimated packet success value being associated with a corresponding value of a signal metric; Receiving first signal data from the gateway device; Determining a first value of the signal metric based on the first signal data; Determining a first estimated packet success value of the first transmission scheme based on the first value of the first signal metric and at least one estimated packet success value of the first plurality of estimated packet success values; Based on the first estimated packet success value of the first transmission scheme, transmitting second data indicating the transmission scheme to the gateway device.

2. The method according to claim 1, wherein the method comprises, before determining the first plurality of estimated packet success values, Receiving third data from the gateway device; and Based on the third data, storing a first interference value associated with the gateway device; Wherein the interference data includes the first interference value.

3. The method according to claim 2, wherein the third data indicates one of the following: (A) The first interference value; (B) A first signal strength value determined by the gateway device, and wherein the method comprises determining the first interference value based on the first signal strength value; (C) A first signal strength value determined by the gateway device, and wherein the method comprises: Determining the first interference value based on the first signal strength value and a noise value associated with the gateway device; Or (D) A first signal strength value determined by the gateway device, and wherein the method comprises determining a thermal noise floor value based on the first signal strength value, a noise figure value associated with the gateway device, and a bandwidth value associated with the gateway device; and determining the first interference value based on the first signal strength value and the thermal noise floor value.

4. The method according to any one of claims 1 to 3, wherein the method further comprises storing each corresponding packet success value of the first plurality of estimated packet success values in association with the corresponding value of the signal metric and The first transmission scheme.

5. The method according to any one of claims 1 to 4, wherein determining the first estimated packet success value is based on determining that the first value of the signal metric matches the corresponding value associated with one of the estimated packet success values of the plurality of estimated packet success values.

6. The method according to any one of claims 1 to 4, wherein the method further comprises determining the first value of the signal metric Greater than a first corresponding value of the signal metric associated with a second estimated packet success value of the first plurality of estimated packet success values, and Less than a second corresponding value of the signal metric associated with a third estimated packet success value of the first plurality of estimated packet success values; Wherein the first estimated packet success value is determined based on the following The first corresponding value of the signal metric associated with the second estimated packet success value among the first plurality of estimated packet success values The second corresponding value of the signal metric associated with the third estimated packet success value among the first plurality of estimated packet success values The second estimated packet success value among the first plurality of estimated packet success values The third estimated packet success value among the first plurality of estimated packet success values, and The first value of the signal metric 7. The method according to any one of claims 1 to 4, wherein the method comprises: Determining a first bit error rate value of the first transmission scheme based on the first value of the signal metric; Determining a first symbol error rate value of the first transmission scheme based on the first bit error rate value of the first transmission scheme; And One of the following: (A) Determining a first interference value based on the interference data; and Determining a second interference value based on the interference data; Wherein one of the estimated packet success values among the first plurality of estimated packet success values is determined based on the first interference value, the second interference value and the first symbol error rate value of the first transmission scheme; (B) Determining a first set of interference values based on the interference data, the number of interference values in the first set corresponding to the number of symbols in the packet of the first transmission scheme; Wherein one of the estimated packet success values among the first plurality of estimated packet success values is determined based on the first set of interference values and the first symbol error rate value of the first transmission scheme; (C) Determining a first interference value based on the interference data; Determining a second interference value based on the interference data; Determining a second estimated packet success value based on the first interference value, the second interference value and the first symbol error rate value of the first transmission scheme; Determining a third interference value based on the interference data; Determining a fourth interference value based on the interference data; And Determining a third estimated packet success value based on the third interference value, the fourth interference value and the first symbol error rate value of the first transmission scheme; Wherein the first estimated packet success value is determined based on the second estimated packet success value and the third estimated packet success value; (D) Determining a first set of interference values based on the interference data, the number of interference values in the first set corresponding to the number of symbols in the packet of the first transmission scheme; And Determining a second set of interference values based on the interference data, the number of interference values in the second set corresponding to the number of symbols in the packet of the first transmission scheme; Wherein one of the estimated packet success values among the first plurality of estimated packet success values is determined based on the first interference value, the second interference value and the first symbol error rate value of the first transmission scheme; Or (E) Determining a first set of interference values based on the interference data, the number of interference values in the first set corresponding to the number of symbols in the packet of the first transmission scheme; Determine a second estimated packet success value based on the first set of interference values and the first symbol error rate value of the first transmission scheme; Determine a second set of interference values based on the interference data, the number of interference values in the second set corresponding to the number of symbols in a packet of the first transmission scheme; And Determine a third estimated packet success value based on the second set of interference values and the first symbol error rate value of the first transmission scheme; Wherein one of the first plurality of estimated packet success values is determined based on the second estimated packet success value and the third estimated packet success value.

8. The method according to claim 7, wherein the first estimated packet success value is an estimated packet error rate, the second estimated packet success value is an estimated packet success rate, and the third estimated packet success value is an estimated packet success rate.

9. The method according to any one of claims 1 to 8, wherein the signal metric is a signal-to-noise ratio or a received signal strength.

10. The method according to any one of claims 1 to 9, wherein the first estimated packet success value is an estimated packet error rate or an estimated packet success rate.

11. The method according to claim 1, wherein the first signal data indicates one of the following: A first signal strength value determined by the gateway device; A first signal-to-noise ratio value determined by the gateway device; or The first value of the signal metric.

12. The method according to any one of claims 1 to 11, wherein the method comprises: For a second transmission scheme, determine a second plurality of estimated packet success values based on the interference data of the gateway device, each estimated packet success value being associated with a corresponding value of the signal metric; Determine a second estimated packet success value of the second transmission scheme based on the first value of the signal metric and at least one of the second plurality of estimated packet success values; And Wherein the second data is determined based on the second estimated packet success value of the second transmission scheme.

13. The method according to any one of claims 1 to 11, wherein the method comprises: For a second transmission scheme, determine a second plurality of estimated packet success values based on the interference data of the gateway device, each estimated packet success value being associated with a corresponding value of the signal metric; Determine a second estimated packet success value of the second transmission scheme based on the first value of the signal metric and at least one of the second plurality of estimated packet success values; Determine a first power consumption value of the first transmission scheme; And Determine a second power consumption value of the second transmission scheme; Wherein the second data is determined based on the second estimated packet success value of the second transmission scheme, the first power consumption value, and the second power consumption value.

14. An electronic device, the electronic device comprising: A wireless transceiver; One or more processors; One or more computer-readable media storing computer-executable instructions that, when executed by one or more processors of the electronic device, cause the electronic device to perform operations including the following Determine a first received signal strength value at a first time, Determine a first interference value based on the first received signal strength value, Transmit the first interference value to a remote computing system, Determine a second received signal strength value at a second time, Transmit first data to the remote computing system based on the second received signal strength value, and Receive second data determined based on the first interference value and the first data from the remote computing system, the second data indicating a first transmission scheme.

15. The electronic device of claim 14, wherein the first data indicates the second received signal strength value.

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