Patent Description:
<CIT> discloses reducing peak to average power ratio in a wireless communication system. A radio frequency wireless transmitter includes signal peak reduction circuitry configured to reduce peak to average power ratio of a signal to be transmitted by reducing amplitude of the signal to be transmitted that is greater than a predetermined amplitude. The signal peak reduction circuitry includes a bit inverter configured to invert a bit of a symbol identified as causing the amplitude of the signal to exceed the predetermined amplitude.

<CIT> also discloses reducing peak to average power ratio (PAPR) in a wireless communication system. The method involves (i) intentionally inserting error(s) into the time or frequency domain and (ii) employing various bit mapping schemes to provide a significant reduction in the PAPR.

Modern communication systems sometimes suffer from high Peak-to-Average Power ratios (PAPR). Transmissions with large PAPR introduce distortion causing bit error and out-of-band interference when the signal is amplified. Example communication systems that have a tendency to suffer from high PAPR include those that utilize Orthogonal Frequency Division Multiplexing (OFDM) such as <NUM> Long Term Evolution (LTE) networks, <NUM> mobile networks, and some networks based upon the Institute of Electrical and Electronics Engineers (IEEE) <NUM> family of standards. One present technique for reducing high PAPR is clipping - which removes the high amplitude part of the signal that is outside a particular range. Clipping, while reducing PAPR also causes undesired side effects such as distortion and adjacent channel interference. Another solution proposed is to carefully select the codewords used to modulate the signal onto the carrier. This requires large computations and/or storage of large lookup tables.

Modern communication systems face other problems instead of, or in addition to high PAPR, including high bit-error rates in noisy environments. For example, a channel that has poor conditions (e.g., a low received signal strength, a channel with large amounts of interference, etc.) may have a corresponding high error rate when paired with high-bandwidth modulation schemes. The current solutions to this problem force the communication system to reduce bandwidth to lower error rate. The transmitter may downgrade to a lower modulation scheme that is more error tolerant, but also sends fewer bits. For example, a transmitter utilizing a Quadrature Amplitude Modulation (QAM) scheme may prefer to utilize QAM256 which allows for eight data bits to be transmitted per transmission symbol. If the channel is noisy, the transmitter may reduce the coding from QAM256 to a lower QAM level such as QAM64 where each symbol maps to six data bits. Because each symbol in QAM64 is further apart in amplitude and phase as compared to QAM256, it is easier for the receiver to decode in noisy environments (and thus lowers the error rate). However, dropping from QAM256 to QAM64 reduces the channel bandwidth and slows down the user experience.

While the above problems have been discussed with respect to radio frequency communications, high PAPR and bit error rate are also applicable to other communication types. For example, optical communications, such as communication through fiber optics may also use similar modulation schemes. Optical communications may encode a digital bitstream into light pulses or beams through many of the same techniques RF systems modulate data onto radio waves. For example, optical communications may utilize QAM and other techniques.

In some examples, issues such as high PAPR may be caused by a compression applied to the data stream that may be applied before or with modulation. For example, power-based compression may transmit repeated values in the input data stream by transmitting a single bit of the value, but at a power level that is correlated (e.g., positively correlated) with the number of repeated values in the data stream. Thus, a single '<NUM>' may be transmitted at a (peak, or average - depending on the modulation scheme) power level of P1, a value of `<NUM><NUM>' may be transmitted at P2 (where P2 is different than P1), a value of '<NUM><NUM><NUM>' is transmitted at P3 (where P3 is different than P2 and P1), and so on. This technique may cause high PAPR if there is a very large sequence of the repeated value or if there are relatively few repeated bit sequences.

Disclosed in some examples are methods, systems, devices, and machine-readable mediums which optimize one or more metrics of a communication system by intentionally changing symbols in a bitstream after encoding by an error correction coder, but prior to transmission. The symbols may be changed to meet a communication metric optimization goal, such as decreasing a high PAPR, reducing an error rate, reducing an average power level (to save battery), or altering some other communication metric. For example, a symbol that would be transmitted at a high-power level may be changed to a symbol that is transmitted at a lower power level. The symbol that is intentionally changed is then detected by the receiver as an error and corrected by the receiver utilizing the error correction coding. The system thus intentionally introduces errors into the transmission in order to optimize a desired communication metric. The present disclosure thus uses a technical solution - utilizing extra ECC correction capacity to solve the technical problems of improving communication metrics such as PAPR. This solution avoids the drawbacks present in previous solutions such as clipping or downgrading the modulation scheme (which reduces bandwidth).

A symbol, as used herein, is either the one or more bits that are converted to a transmission symbol by modulation (or other processes) or the transmission symbol itself. For example, a symbol may be one or more bits of a received bitstream that would be mapped to a single transmission unit by a modulation scheme or the result of modulating the one or more bits of the received bitstream (e.g., a waveform, a state, or other property of the communication channel that persists for a specified period of time). For example, a QAM16 modulation scheme represents up to four bits per transmission symbol. The actual transmission symbol is described by the combined properties of a carrier phase shift and amplitude. As used herein the symbol refers to the four bits that make up the symbol as well as the phase shift and amplitude that represents those bits.

For optical communication networks with a simple modulation scheme where a light source activates to encode the bits of the bitstream (e.g., "on" represents a '<NUM>' and off represents a '<NUM>') and where the power level represents additional repeating bits (e.g., "on" with power level <NUM> is ` <NUM>' and "on" with power level <NUM> is ` <NUM><NUM>'), the symbol may be either the bits represented by the light source activation and power level or the indication of whether the light source is activated and at the power level it is activated. Thus, the symbol may be a sequence of one or more bits or the activation and/or amplitude of the light source.

Thus, it is contemplated that the symbols may be changed prior to modulation - e.g., as a sequence of one or more bits, or after modulation - when they are described by properties of the communication medium such as a waveform and/or amplitude. For ease of description, the specification will refer to a symbol as a sequence of one or more bits, but it is to be understood that the modulated representation of the sequence of one or more bits is also within the scope of the present disclosure.

In some examples, where the communication metric optimization goal is to reduce a PAPR, symbols which produce high transmission powers when transmitted may be changed to symbols that produce lower transmission powers when transmitted. This reduces high PAPR by reducing transmissions that occur at peak transmission power. The changed symbols are then corrected by the receiver using the ECC. In some examples, the symbols which produce a maximum reduction of a PAPR may be selected and changed. In other examples, symbols which produce a reduction over a threshold value may be selected and changed.

In some examples, where the communication metric optimization goal is to reduce bit errors, symbols of the bitstream may be selected that have adjacent symbols such that the selected symbols and one or more adjacent symbols map to constellation points close to each other (e.g., within a threshold distance) on the constellation map. The selected symbols may be changed such that the selected symbol maps to constellation points that are farther away from their respective adjacent symbols. This makes it easier for the receiver to discriminate between adjacent symbols and thus decreases an overall bit-error rate while maintaining the overall data rate. While this might seem counterintuitive (increasing an error rate to reduce it), consider an example in which three adjacently transmitted symbols are all close together on the constellation map. In a noisy environment, all three symbols may be erroneously received. By changing one or two of these symbols to symbols that are not close to the other remaining symbols (or each other), the possibility of correctly receiving at least one of the three symbols may be increased. Furthermore, these changed symbols are corrected by the receiver using ECC.

One or more symbols may be changed over a specified transmission window (e.g., a time period where a number of symbols are transmitted). A maximum number of symbols that may be changed may be the number of symbols that are correctable via the ECC used over the transmission window. For example, if the ECC can correct <NUM> symbols for every <NUM> symbols, then the system may intentionally change <NUM> symbols to lower PAPR or lower error rate, or the like. This approach may lead to increased bit error rate if the channel experiences any natural errors. This is because any natural errors thus exceed the ECC's ability to correct the combination of naturally occurring errors and intentionally added errors. These errors may be termed uncorrectable errors which may require a retransmission of the data.

To avoid uncorrectable errors and an increase in error rate, the system may utilize less than the maximum number of symbols correctable for the transmission window. The system may utilize a buffer for allowing natural channel errors by intentionally changing fewer symbols than the ECC can correct for. For example, if the ECC can correct <NUM> symbols for every <NUM> symbols transmitted, the system may utilize a one symbol buffer, and so the system may change only nine symbols.

In other examples, the system may determine one or more communication metrics (that may or may not be the communication metrics that are being optimized by the optimization goals), such as a past error rate (e.g., a bit error rate, a number of past symbols corrected during a transmission window, or the like), a received signal strength (RSSI), a signal quality, or the like. The system may calculate a number of expected symbol errors for a current transmission window based upon one or more of these communication metrics and may intentionally change a number of symbols that is based on the difference between the maximum number of symbols that may be changed and the number of expected symbol errors. For example, a number of symbols to be changed may be: <MAT> Where max is the maximum number of symbols that can be changed based upon the ECC used, expected errors is the number of symbols that are expected to be received in error that are not intentionally changed by the transmitter given the current communication metrics, and c is a padding value to ensure that bit error rate is not increased. In some examples, c may be zero.

The communication metrics used may be communication metrics for one or more past transmission time periods or may be for one or more past transmissions in a current time period. Calculating the expected number of symbol errors given the communication metrics may involve one or more machine learning algorithms where models are trained using past communication metrics and labeled with past symbol errors occurring that correspond to those communication metrics. The models predict, given the present communication metrics, the expected number of symbol errors. More information on the machine-learning aspects are disclosed with respect to <FIG> and the later discussion of <FIG>.

In other examples, simple correlations between one or more communication metrics may be utilized. For example, various ranges of RSSI for the recipient device may be expected to have certain corresponding error rates. Similarly, past error rates may be predictive of future error rates if the transmitter and receiver are stationary (such as with a pair of Internet of Things devices). In these use cases, the number of symbols that have errors may be relatively constant and predictable. In these examples the communication metrics may simply be an average symbol error rate for past particular transmission windows. And the expected errors may simply be the average symbol error rate.

In still other examples, the system may learn the average error rate for a particular time of day, a particular day of the week, and the like and may utilize the average error rate in view of the current time of day, particular day of the week, and/or the like to determine the expected number of symbol errors given the average error rate for the particular time of day, a particular day of the week, and the like. In some examples, the expected errors may simply be the average symbol error rate for the particular time of day, a particular day of the week, and the like.

As noted, symbols may be changed to achieve a specified communication metric optimization goal. In some examples, the symbols may be selected to maximize the achievement of the stated communication metric optimization goal. For example, if the number of symbols to change is x, and the objective is to lower PAPR, then the x symbols with the highest transmission powers in a transmission window may be changed to symbols with lower transmission powers. In some examples, instead of just changing the top x symbols with the highest transmission powers, the system may seek to remove large spikes in transmission powers. For example if x is one, and the power levels of respective symbols is: {P1, P2, P2, P6, P2, P1, P2, P3, P4, P4, P5, P6, P7, P6, P5}, where P1 < P2 < P3 < P4 < P5 < P6 < P7, the system may remove the P6 at the fourth symbol as it has a large transmission power jump between successive surrounding symbols (which are transmitted at P2). This may help to provide a more linear transmission and prevent wear on the transmitter.

In another example, the symbols may be selected so as to minimize an error rate, such as a bit-error rate. For example, if the number of symbols that are changed is x, and the objective is to lower bit error rate, then each particular one of x symbols that each have one or more neighboring symbols that map to constellation points that are close (on the constellation chart) to the constellation point of the particular symbol may be changed to symbols that map to points that are further apart. In some examples, closeness may be measured by a distance, or may be determined by a mapping table that specifies a distance between constellation points. In other examples, the system may keep track of error rates for each symbol (e.g., through feedback from the receiver) and may select and change symbols with a high historical error rate.

In some examples, multiple communication metric optimization goals may be pursued at the same time. For example, both a PAPR and a bit-error rate may be lowered. That is, if a symbol sequence of [S1, S2, S3] is examined, and S1, S2, and S3 map to constellation points that are very close to each other, then the symbol with the highest peak power level may be selected to be changed. That is, both reduction in bit error and PAPR may be factors in deciding which symbols to change. Thus, achieving an optimization goal related to a first communication metric may be used to select a first group of symbols and achieving a second optimization goal related to a second communication metric may then be used to select a symbol within that group.

The presently disclosed techniques may be applied to various communication protocols and transmission mediums. For example, optical communications, RF communications, and the like. The present techniques may also be utilized to achieve many different communication metric optimization goals, such as reduction in PAPR, reduce error rate (e.g., a bit-error rate), or the like. Additionally, as used herein, symbol values are changed. However, in other examples, the symbols are punctured (removed) from the bitstream so as not to transmit them at all. This may be beneficial as some ECC schemes can correct more missing data than incorrect data. In yet other examples, some symbols may be removed, some symbols may be changed to achieve a maximum reduction in PAPR and/or error rate.

Turning now to <FIG>, an example transmitter <NUM> and receiver <NUM> are illustrated according to some examples of the present disclosure. Bitstream <NUM> may be received from higher networking layers (e.g., applications) or other components that are not shown. ECC coder <NUM> may apply an ECC to the bitstream such as a forward error correction code. Example ECC may include block codes (e.g., Hamming codes), cyclic codes, BCH (Bose-Chaudhri-Hocquenghem) codes, Reed-Solomon codes, Convolutional codes (including Viterbi codes), and the like.

An optimizer <NUM> may modify one or more symbols of the coded bitstream. For example, the optimizer <NUM> may seek to optimize one or more communication metric optimization goals such as a PAPR and/or an error rate by modifying one or more bits or symbols of the coded bitstream. Optimizer <NUM> may utilize information about the ECC applied by ECC coder <NUM>, and communication metrics <NUM> of the communication medium to determine how many symbols may be changed. For example, the optimizer <NUM> may optimize more than one symbol and may determine, using the coding information <NUM> and communication metrics <NUM> how many symbols to change. Coding information <NUM> may be the type of code applied, the number of symbols for each transmission window that can be corrected, and the like. For example, the optimizer <NUM> may utilize the formula: <MAT> Where max is the maximum number of symbols that can be changed based upon the ECC used, expected errors is the number of symbols that are expected to be received in error by the receiver that are not intentionally changed by the transmitter given the current communication metrics, and c is a padding value to ensure that bit error rate is not increased. In some examples, c may be zero.

After the symbols are changed, the modulator <NUM> applies a modulation scheme, such as a pulse modulation, an orthogonal frequency division multiplexing (OFDM), a Quadrature Amplitude Modulation, or the like. As shown, optimizer <NUM> changes the symbols prior to modulation, but in other examples, the symbols may be changed by optimizer <NUM> after modulation. Thus, optimizer <NUM> may be after the modulator <NUM>. Once the bits are modulated, miscellaneous operations component <NUM> may perform miscellaneous operations to prepare for transmission, such as amplification, clipping, or the like before it is transmitted across the communication medium.

Receiver <NUM> receives the transmitted symbols and miscellaneous operations component <NUM> may perform miscellaneous operations such as equalization, synchronization, and the like. Demodulator <NUM> may demodulate the symbols by applying a detection process for the modulated symbols according to the modulation scheme. ECC decoder <NUM> may find and correct errors by applying an appropriate decoding algorithm corresponding to the ECC algorithm applied by ECC coder <NUM>. The decoded symbols may be checked for errors, and any errors found may be corrected by the ECC decoder <NUM>. Any errors intentionally introduced by optimizer <NUM> may be corrected by ECC decoder <NUM>. Feedback component <NUM> may monitor, measure, or determine various parameters of the communication medium. Feedback component <NUM> may provide one or more communication metrics <NUM> to the transmitter. Communication metrics <NUM> may include a received signal strength, an error rate, a quality metric, or the like. The output bitstream <NUM> is then passed to other components of the receiver that are not shown for clarity.

<FIG> illustrates an example RF transmitter <NUM> and receiver <NUM> according to some examples of the present disclosure. Bitstream <NUM> may be received from higher networking layers or other components that are not shown. ECC Coder <NUM> may apply an ECC to the bitstream <NUM> such as a forward error correction code. Example ECC may include block codes (e.g., Hamming codes), cyclic codes, BCH (Bose-Chaudhri-Hocquenghem) codes, Reed-Solomon codes, Convolutional codes (including Viterbi codes), and the like.

An optimizer <NUM> may modify one or more symbols of the coded bitstream. For example, the optimizer <NUM> may seek to optimize one or more wireless parameters such as a PAPR and/or a bit error rate by modifying one or more bits or symbols of the coded bitstream. Optimizer <NUM> may utilize information about the codes applied by ECC coder <NUM> (coding information <NUM>), and communication metrics <NUM> of the communication medium to determine how many symbols may be changed. For example, the optimizer <NUM> may optimize more than one symbol and may determine, using the coding information <NUM> and communication metrics <NUM> how many symbols to change. Coding information <NUM> may be the type of code applied, the number of symbols for each transmission window that can be corrected, and the like. For example, the optimizer <NUM> may utilize the formula:<MAT>number of symbols to change = max - (expected errors + c) Where max is the maximum number of symbols that can be changed based upon the ECC used, expected errors is the number of symbols that are expected to be received in error by the receiver that are not intentionally changed by the transmitter given the current communication metrics, and c is a padding value to ensure that bit error rate is not increased. In some examples, c may be zero. In some examples, the optimizer <NUM> may be one example of optimizer <NUM>.

After the symbols are changed, the modulator <NUM> applies a radio frequency modulation scheme, such as a pulse modulation, an orthogonal frequency division multiplexing (OFDM), a Quadrature Amplitude Modulation (QAM), or the like. For example, the modulator <NUM> may apply a QAM modulation scheme, which may have an associated constellation diagram <NUM>. The constellation diagram <NUM> shows an example QAM16 constellation diagram <NUM> with a plurality of constellation points representing a phase of the carrier and an amplitude for each given bit combination. As shown, optimizer <NUM> changes the symbols prior to modulation, but in other examples, the symbols may be changed by optimizer after the modulator <NUM>.

Once the bits are modulated, miscellaneous operations component <NUM> may perform miscellaneous operations, such as amplification, clipping, or the like before it is transmitted across the communication medium. The signals may be transmitted using radio frequency waves at antenna <NUM>. The transmitter <NUM> may be a base station (e.g., a cellular base station operating according to a Long-Term Evolution (LTE), LTE-Advanced, or a <NUM> New Radio standard), an access point (e.g., an access point operating according to an <NUM> family of standards, such as <NUM>. 11ax) or another computing device (e.g., peer-to-peer transmitter). In some examples, the transmitter <NUM>, ECC coder <NUM>, coding information <NUM>, optimizer <NUM>, communication metrics <NUM>, modulator <NUM> and miscellaneous operations component <NUM> may be examples of transmitter <NUM>, ECC coder <NUM>, coding information <NUM>, optimizer <NUM>, communication metrics <NUM>, modulator <NUM> and miscellaneous operations component <NUM> of <FIG>.

Receiver <NUM>, in the form of a mobile computing device <NUM> receives the transmitted symbols and miscellaneous operations component <NUM> may perform miscellaneous operations such as equalization, synchronization, and the like. It will be appreciated by a person of ordinary skill in the art with the benefit of this disclosure that other computing devices may be used as receiver <NUM> and that a mobile device is just an example device. Demodulator <NUM> may demodulate the symbols by applying a detection process for the modulated symbols according to the modulation scheme. ECC Decoder <NUM> may find and correct errors by applying an appropriate decoding algorithm corresponding to the ECC algorithm applied by ECC coder <NUM>. The decoded symbols may be checked for errors, and any errors may be corrected by utilizing the ECC decoder <NUM>. Errors intentionally introduced by optimizer <NUM> in changing the symbols transmitted may also be corrected by the ECC decoder <NUM>. Feedback component <NUM> may monitor, measure, or determine various parameters of the communication medium. Feedback component <NUM> may provide one or more communication metrics <NUM> to the transmitter. Communication metrics <NUM> may include a received signal strength, an error rate, a quality metric, or the like.

The output bitstream <NUM> is then passed to other components of the receiver that are not shown for clarity. In some examples, the miscellaneous operations component <NUM>, demodulator <NUM>, ECC decoder <NUM>, and feedback component <NUM> may be examples of miscellaneous operations component <NUM>, demodulator <NUM>, ECC decoder <NUM>, and feedback component <NUM> of <FIG>.

<FIG> illustrates an example optical transmitter <NUM> and optical receiver <NUM> according to some examples of the present disclosure. Bitstream <NUM> may be received from higher networking layers or other components that are not shown. ECC Coder <NUM> may apply an ECC to the bitstream such as a forward error correction code. Example ECC may include block codes (e.g., Hamming codes), cyclic codes, BCH (Bose-Chaudhri-Hocquenghem) codes, Reed-Solomon codes, Convolutional codes (including Viterbi codes), and the like.

Power-based compressor <NUM> may compress repeated values in the encoded bitstream. For example, repeated symbols (e.g., bits) of a same value may be compressed to a single symbol transmitted at a power level that is correlated to the number of repeated symbols. For example, a value of '<NUM>' may be transmitted as a '<NUM>' with a power level of P1. A value of ` <NUM><NUM>' may be transmitted as a '<NUM>' with a power level of P2 (in some examples P2 > P1). A value of ` <NUM><NUM><NUM>' may be transmitted as a ` <NUM>' with a power level of P3 (where in some examples P3>P2). While the power-based compression is shown in the figure as occurring prior to optimization from optimizer <NUM>, one of ordinary skill with the benefit of the present disclosure will appreciate that the power-based compression may be done in a variety of alternative places during processing of the bitstream for transmission. For example, before ECC coder <NUM> or after modulator <NUM>. Power-based compressor <NUM> may utilize a mapping indicating corresponding power levels for a number of repeating values.

An optimizer <NUM> may modify one or more symbols of the coded bitstream. For example, the optimizer <NUM> may seek to optimize one or more optical communication metrics such as a PAPR and/or an error rate by modifying one or more bits or symbols of the coded bitstream. In some examples, the optimizer <NUM> may work with the power-based compressor <NUM> to reduce the peak transmissions. For example, the optimizer <NUM> may select one or more bits in the bitstream to change to reduce a peak transmission power according to the power-based compression applied by the power-based compressor <NUM>.

Optimizer <NUM> may utilize information about the ECC coder <NUM>, and optical communication metrics <NUM> of the communication medium to determine how many symbols may be changed. For example, the optimizer <NUM> may optimize more than one symbol and may determine, using the coding information <NUM> and communication metrics <NUM> to determine how many symbols to change. Coding information <NUM> may be the type of code applied, the number of symbols for each transmission window that can be corrected, and the like. As already noted, in some examples, the optimizer <NUM> may utilize the formula: <MAT> Where max is the maximum number of symbols that can be changed based upon the ECC used, expected errors is the number of symbols that are expected to be received in error that are not intentionally changed by the transmitter given the current communication metrics, and c is a padding value to ensure that bit error rate is not increased. In some examples, c may be zero.

For example, optimizer <NUM> may change a symbol or bit of the bitstream to reduce a PAPR of the transmitter by eliminating long runs of consecutive bits that, because of the power-based compression, produce high transmission powers. For example, if the power level compression mapping was as follows:.

A bitstream of: "<NUM><NUM><NUM><NUM><NUM><NUM><NUM><NUM><NUM><NUM><NUM><NUM>" - would be converted according to the power-based compression into seven separate transmissions of {(<NUM> ,P <NUM>), (<NUM>,P0), (<NUM>,P4), (<NUM>,P0), (<NUM>,P1), (<NUM>,P0), (<NUM>,P3)}. In a simple modulation scheme in which the light source turns on to transmit a '<NUM>' and remains off for a transmission period to transmit a '<NUM>', the actual transmission powers would be P1, P0, P4, P0, P1, P0, P3. In order to reduce the power peak (and thus PAPR), a value of "<NUM>" in the sequence of four consecutive "<NUM>"s may be changed to a zero, thus turning the bitstream to, for example, "<NUM><NUM><NUM><NUM><NUM><NUM><NUM>". This produces transmission powers of P1, P0, P1, P0, P2, P0, P1, P0, P3.

Selecting which of the consecutive "<NUM>"s to change may be done by minimizing the transmission power needed to transmit the sequence (including the changed bits). For example, in the sequence "<NUM><NUM>" changing either the first or last bit to a zero would lower the maximum power level to transmit the sequence to P3 but changing either the second or third bits would change the maximum power level to P2, which is less than P3.

After the symbols are changed, the modulator <NUM> applies an optical modulation scheme, such as a pulse modulation, an orthogonal frequency division multiplexing (OFDM), a Quadrature Amplitude Modulation, or the like. For example, the modulator <NUM> may apply a QAM modulation scheme. Once the bits are modulated, other operations may be performed (not shown) such as amplification, clipping, or the like before it is transmitted across the communication medium by selectively activating the light source <NUM> (by turning the light source on and off; or by varying the amplitude of the light source to produce a carrier wave). The signals may be transmitted using light from light source <NUM> (e.g., a laser or a light emitting diode) through an optical communication medium, such as a fiber optic <NUM>. Other mediums may include air, water, or the like. In some examples, transmitter <NUM>, ECC coder <NUM>, coding information <NUM>, optimizer <NUM>, communication metrics <NUM>, and modulator <NUM> may be examples of transmitter <NUM> and/or <NUM>; ECC coder <NUM> and/or <NUM>; coding information <NUM> and/or <NUM>; optimizer <NUM> and/or <NUM>; communication metrics <NUM> and/or <NUM>; and modulator <NUM> and/or <NUM> respectively of <FIG> and <FIG>.

Receiver <NUM> receives the transmitted symbols, for example, by utilizing a photon detector <NUM>. In some examples, the photon detector may produce one or more photon counts for one or more transmission time periods. These counts are then demodulated by the demodulator <NUM> by applying a detection process for the modulated symbols according to the modulation scheme. For example, using one or more detection models that detect photon distributions at various power levels. ECC decoder <NUM> may find and correct errors by applying an appropriate decoding algorithm corresponding to the ECC algorithm applied by ECC coder <NUM>. The decoded symbols may be checked for errors, and any errors may be corrected by the ECC decoder <NUM>. This includes errors intentionally introduced by the optimizer <NUM>.

Feedback component <NUM> may monitor, measure, or determine various parameters of the communication medium. Feedback component <NUM> may provide one or more communication metrics <NUM> to the transmitter. Communication metrics <NUM> may include a received signal strength, an error rate, a quality metric, or the like.

The output bitstream <NUM> is then passed to other components of the receiver that are not shown for clarity. In some examples, the miscellaneous operations component <NUM>, demodulator <NUM>, ECC decoder <NUM>, and feedback component <NUM> may be examples of miscellaneous operations component <NUM> and/or <NUM>; demodulator <NUM> and/or <NUM>; ECC decoder <NUM> and/or <NUM>; and feedback component <NUM> and/or <NUM> respectively of <FIG> and <FIG>.

<FIG> illustrates an example of an optimizer <NUM> according to some examples of the present disclosure. Optimizer <NUM> is an example implementation of optimizers <NUM>, <NUM>, <NUM>, and the like. Optimizer <NUM> may take as input a bitstream <NUM> (which may be encoded with an ECC), one or more communication metrics <NUM>, and coding information <NUM>. Coding information <NUM> may be the type of code applied, the number of symbols for each transmission window that can be corrected, and the like. Number of symbols determiner <NUM> may determine the number of symbols the optimizer will change. Symbol selector <NUM> may determine the symbols to change, and symbol changer <NUM> may change each symbol to a different value.

The number of symbols determiner <NUM> may use a variety of techniques to determine the number of symbols that are intentionally changed by optimizer <NUM>. In one example, the number of symbols determiner <NUM> may use the maximum number of symbols that may be corrected using the ECC applied. In some examples, the number of symbols determiner <NUM> may reduce the number of symbols that are changed if the receiver reports uncorrectable ECC errors or requests retransmissions. In yet other examples, the number of symbols determiner <NUM> may initially determine that less than the maximum number of symbols is to be changed and then slowly increment the number of symbols that are changed over time until an ECC uncorrectable error is reported by the receiver to the transmitter or a retransmission is requested. The number of symbols determiner <NUM> may then reduce the number of symbols until a maximum number of symbols is found that does not cause uncorrectable ECC errors at the receiver. The number of symbols determiner <NUM> may constantly adjust the number of symbols changed by the optimizer <NUM> to respond to changing channel conditions.

In other examples, the number of symbols determiner <NUM> may start with the maximum number of possible symbols and discount it by a value based upon an expected number of symbols that will be changed by the communication channel. For example, the number of symbols determiner <NUM> may utilize the formula: <MAT> Where max is the maximum number of symbols that can be changed based upon the ECC used, expected errors is the number of symbols that are expected to be received in error by the receiver that are not intentionally changed by the transmitter given the current communication metrics, and c is a padding value to ensure that bit error rate is not increased. In some examples, c may be zero. As previously noted, the max may be based upon the ECC applied to (or to be applied to) bitstream <NUM>. For example, the ECC may have a maximum number of bits that may be corrected for a given transmission size. For example, x symbols may be corrected for every y symbols transmitted.

Number of bits determiner <NUM> may determine the expected errors that indicates the errors caused by the communication channel based upon the communication metrics <NUM>. The expected errors are errors that the transmitting device did not intend to cause but were caused by communication conditions on the communication medium. As noted previously, this may be calculated based upon one or more communication metrics <NUM>. For example, based upon a formula, a past history of symbol errors, an average number of errors over a period of time, a machine-learning model, or the like. In some examples, the communication metrics <NUM> may include one or more of a received signal strength, a bit error rate, a signal quality metric, or the like. In some examples, the communication metrics <NUM> may be historical data of the channel over time. For example, a number of errors that were introduced by the channel (and not the transmitter) over a predetermined period of time, an average error rate, and the like.

As previously described, in some examples, the number of symbols determiner <NUM> may initially start with the maximum number of symbols that can be corrected for a first time period t. In some examples, rather than report a number of uncorrectable ECC the receiver may report a total number of symbols actually corrected. If the number of symbols actually corrected exceeds the number of symbols intentionally introduced by the transmitter, the system may infer that the channel conditions introduced one or more errors and may subsequently reduce the number of symbols that are intentionally changed. For example, the number of symbols determiner <NUM> may calculate a number of symbols received in error that are caused by the communication channel by subtracting the number of symbols actually corrected (as reported by the receiver) by the number of symbols intentionally changed by the transmitter for that time period. The number of symbols to be changed in the current transmission period may be the difference between the maximum number of symbols that can be changed by the ECC and the number of symbols received in error that were caused by the communication channel for a past transmission. In some examples, the number of symbols determiner <NUM> may utilize an average number of symbols received in error that were caused by the communication channel over a particular time period.

As previously described, the number of symbols determiner <NUM> may initially specify that a first (starting) number of symbols may be changed. In some examples, the starting number of symbols may be a maximum number of symbols that may be corrected by the ECC that is applied. In other examples, the starting number may be one symbol. In other examples, the starting number may be a midpoint between one and the maximum number of symbols that may be corrected. As the transmitter transmits and the receiver provides feedback through communication metrics <NUM>, the receiver may increase the number of symbols that are intentionally changed by a value z if the receiver is not reporting any uncorrectable ECC errors (e.g., the number of errors corrected by the ECC does not exceed the maximum number of correctable errors). On the other hand the receiver may decrease the number of symbols that are intentionally changed by z (or some other number) if the receiver reports uncorrectable ECC errors.

The value z may be specified by an administrator or may be calculated based upon the optimization goal. For example, if lowering the PAPR is the optimization goal, then z may be a range of values that may be based upon a current PAPR. If the PAPR is high, then z may be a maximum value in the range, if PAPR is currently low, then z may be the minimum value in the range.

Symbol selector <NUM> selects a number of symbols specified by the number of symbols determiner <NUM> to intentionally change in the bitstream <NUM>. For example, if the number of symbols determiner <NUM> indicates that four symbols may be changed, the symbol selector may select up to four symbols. In some examples, the symbol selector <NUM> may always select exactly the number of symbols to change indicated by the number of symbols determiner <NUM>, but in other examples, the symbol selector <NUM> may select up to the number of symbols to change. For example, the optimization goal may not be served by changing as many symbols as specified by the number of symbols determiner <NUM>. For example, if there are very few maximum power transmission peaks in the bitstream it may not be desirable to increase an error rate.

Symbol selector <NUM> may select symbols of the bitstream <NUM> in a manner that achieves one or more of the communication metric optimization goals. For example, to reduce PAPR, one or more symbols which are transmitted at high peak powers may be changed to symbols that are transmitted at lower peak powers. In some examples, transmission powers are calculated for each of the symbols in the bitstream. If the number of symbols determiner <NUM> indicates that s symbols are capable of being changed, then the s symbols that have a highest peak power level may be selected. In some examples, a minimization function may be applied that may iteratively change each combination of s symbols of the bitstream (which may have > s symbols) to different symbols (e.g., random symbols or symbols that have a minimum transmission power) and recalculate a PAPR for the bitstream. The s symbols that achieve a minimum PAPR may be selected.

For examples in which the communication metric optimization goal seeks to lower error rate, in some examples, the symbol selector <NUM> may select up to s symbols that have neighboring symbols that are a closer than a threshold distance on a constellation map to the selected s symbols. Modulation information <NUM> may be utilized to determine distance between symbols on the constellation map. Modulation information <NUM> may include a constellation map or table that maps symbols to transmission powers, phases, or indicates distances between various symbols. For example, if the constellation diagram <NUM> from <FIG> is used, then a bit sequence of "<NUM>" is a distance of one to bit sequences of "<NUM>," "<NUM>," and "<NUM>" and a distance of two to bit sequences of "<NUM>," "<NUM>," "<NUM>," "<NUM>," and "<NUM>" and so on. The symbol selector may calculate a distance for each symbol between a preceding and next symbol and change the s symbols with the closest distances. In other examples, the receiver may track which symbols and/or symbol sequences have previously caused errors and may provide this to the symbol selector <NUM>. For example, in a QAM environment, certain symbols and/or amplitudes may be subject to interference while other symbols are not. In these examples, symbols that have an error rate (e.g., the number of errors divided by the number of total transmissions of the symbol) above a threshold may be selected to be changed by the symbol selector.

In yet other examples, symbols may be selected by one or more machine-learning algorithms. These examples are described more fully with the discussion of <FIG>.

In examples in which an optical transmitter applies a power-based compression algorithm, the bitstream <NUM> may be the bitstream before or after the ECC is applied. The power-based compression may be applied after the optimizer <NUM> or before the optimizer and may be reflected in the bitstream <NUM>. In the case where the power-based compression is already applied to the bitstream <NUM>, the information about power levels of each bit may be supplied with the bitstream <NUM> or be part of the modulation information <NUM>. The symbol selector <NUM> in these cases may seek to reduce PAPR and may select symbols as previously described.

Symbol changer <NUM> may change the selected symbols to produce a modified bitstream <NUM>. Symbol changer <NUM> may change a symbol from a first value to a second value. The second value may be selected based upon one or more of: the first value, the modulation information <NUM>, communication metrics <NUM>, coding information <NUM>, the optimization goal, or the like. For example, if the optimization goal is to reduce bit error rate, the symbol changer <NUM> may change the selected symbol to be a furthest distance on a constellation map from the symbols that are before and after the selected symbol to maximize the distance for easier detection by the receiver. If the optimization goal is to reduce PAPR, the second symbol may be selected to be a lower power symbol. The symbols are changed to a value that does not represent the corresponding portion of the input bitstream and will be determined to be in error at the receiver.

<FIG> illustrates a flowchart of a method <NUM> of modifying symbols that are to be transmitted to reduce a PAPR according to some examples of the present disclosure. At operation <NUM> the bitstream may be received for transmission. For example, an application on a computing device may send data to be transmitted. Thus, the data may be received over an internal bus, a memory buffer, or the like. In other examples, the data may be received over an external communication link, such as a network connection or the like. The bitstream may have already had an ECC applied to it prior to operation <NUM>.

At operation <NUM> the system may select one or more symbols to change in the bitstream. The symbols may be selected to reduce PAPR. For example, the symbols selected at operation <NUM> may be symbols that would, when transmitted, generate a high peak power level. For example, in a QAM modulation scheme, a symbol that would be transmitted at a high amplitude. In an optical modulation scheme, for example, a modulation scheme with a power-based compression algorithm, a series of repeated consecutive values above a threshold number of values may generate a high peak power level. The selected one or more symbols may be symbols that would produce the highest power in a particular transmission window (which may be equivalent to a block size of an FEC code). In some examples, the selection is for a first n symbols that exceed a threshold transmission power, where n is the number of symbols that are to be changed. In still other examples, symbols with a greatest variation in power level (or a power level variation that exceeds a threshold variation) from neighboring signals may be selected to be changed to minimize power spikes that may damage the transmitter. In these examples, the system may further require that the power level exceed a threshold (as spikes that are low power may not be damaging).

At operation <NUM>, the one or more selected symbols may be changed. The change may be from a first value to a second value, the second value based upon the goal of optimizing the PAPR. The second value is a symbol value that, when received by the receiver, is not converted to the original value of the bitstream as received at operation <NUM> without the use of ECC to correct the value to the original value. In the case of PAPR reduction, the second symbol may be a symbol that is transmitted at a lower peak power, or that would produce a lower PAPR. At operation <NUM> the system may modulate the modified bitstream according to a modulation scheme and transmit the modulated modified bitstream and/or symbols. For example, optically, over radio frequency waves, or the like.

<FIG> illustrates a flowchart of a method <NUM> of modifying symbols that are to be transmitted to reduce an error rate according to some examples of the present disclosure. At operation <NUM> the bitstream may be received for transmission. For example, an application on a computing device may send data to be transmitted. Thus, the data may be received over an internal bus, a memory buffer, or the like. In other examples, the data may be received over an external communication link, such as a network connection or the like. The bitstream may have already had an ECC applied to it prior to operation <NUM>.

At operation <NUM> the system may select one or more symbols to change in the bitstream. The symbols selected at operation <NUM> may be symbols that would, when transmitted, increase an error rate. For example, the transmitter may select a first symbol from the plurality of symbols mapped under a modulation scheme to a first constellation point with an adjacent, second symbol mapped under the modulation scheme to a second constellation point - the second constellation point within a threshold distance to the first constellation point. In some examples, receiving computing devices may have trouble differentiating two symbols that are very close together on the constellation map, especially if the channel is noisy. As noted above, it is possible to downgrade the modulation scheme to achieve higher separation. However, the actual incidence of symbols being mapped to constellation points that are close to each other may be low enough where the symbols can be changed on the transmitter and corrected on the receiver without downgrading the modulation scheme. This allows for continued high data rates while at the same time reducing error rates in noisy environments. In some examples, two constellation points may be within a threshold distance if their distance on the constellation map is within a threshold distance. In other examples, two constellation points may be within a threshold distance if their phase and amplitudes are within a threshold distance of each other.

In other examples, the transmitter may utilize implicit and/or explicit feedback from the receiver on which constellation points are more likely to produce an error. Implicit feedback may include retransmission requests and explicit feedback may include specific symbol and error rates and the like. Since a retransmission request is for an entire packet, with potentially many different symbols, in some examples, the transmitter may correlate, based upon multiple retransmission requests, one or more transmission symbols that are common to the retransmission requests. These symbols may be changed to avoid these errors and to reduce error rate.

At operation <NUM>, the one or more selected symbols may be changed. The change may be from a first value to a second value. The second value is a symbol value that, when received by the receiver, is not converted to the original value of the bitstream as received at operation <NUM> without the use of ECC. In the case of error rate reduction, the second symbol may be a symbol that is not within a threshold distance of the first value and/or neighboring symbols or is not one of the symbols that has historically caused errors at the receiver. At operation <NUM> the system may modulate the modified bitstream and/or symbols according to a modulation scheme and transmit the modulated modified bitstream and/or symbols. For example, optically, over radio frequency waves, or the like.

In some examples, the system may learn patterns of symbols that may cause errors on the receiver. For example, via feedback that indicates symbols were received in error or otherwise corrected. In these examples, the system may store a plurality of symbols before and/or after the symbol that was received in error. The system may apply one or more pattern matching algorithms to determine patterns of symbols that cause errors. These symbols may be changed at the time of transmission to another pattern that is not likely to cause an error.

<FIG> illustrates a flowchart of a method <NUM> of determining how many symbols can be changed according to some examples of the present disclosure. In some examples, more than one symbol may be changed for a particular transmission window. The transmission window may be determined based upon a block size or other size of the ECC used on the bitstream. At operation <NUM>, the transmitter may identify or determine a maximum number of symbols that can be changed for the current transmission window. This may be done using a lookup table that maps ECC algorithms and their parameters to a maximum number of symbols. For example, if the ECC is able to correct up to <NUM>-byte errors per <NUM>-byte block, the transmission window would be <NUM> bytes and the maximum number of symbols that may be changed without causing an uncorrectable error would be <NUM> bytes. For a QAM16 modulation scheme where there are <NUM> bits per symbol, the above ECC could correct <NUM> symbols for every <NUM> symbols received.

In some examples, the transmitter may use all these symbols. This may be sensible where the transmission channel is very good and well controlled. For example, some short-range fiber optic installations may have almost zero error introduced by the channel. For other environments this is not feasible as the channel will introduce errors. If the system intentionally changes all the redundancy in the ECC, any error introduced by the channel will lead to retransmissions. Thus, the maximum number may be reduced by an expected number of errors introduced by the communication channel or interface. At operation <NUM>, the transmitter may identify or determine an expected error rate caused by the communication channel. At operation <NUM>, the number of symbols that may be intentionally changed may be determined based upon the maximum number of symbols that may be changed that was determined in operation <NUM> as well as the expected error rate in operation <NUM>.

For example, based upon past channel conditions, if it may be inferred that an average of <NUM> symbols are changed by the channel for every <NUM> symbols, and the ECC can successfully correct <NUM> symbols for every <NUM> symbols, the number of symbols to intentionally change may be <NUM> symbols for every <NUM> symbols. In some examples, there may be a constant c which may be a buffer against channel variability. For example, while an average error rate may be <NUM> symbols every <NUM> symbols, the channel conditions may fluctuate. To ensure that intentional changing of symbols does not cause uncorrectable ECC errors and thus retransmissions, the system may utilize a buffer c. In the example above, c may be <NUM>. The result of operation <NUM> may thus be that the system may intentionally change one symbol in <NUM> symbols.

In some examples, the maximum number of symbols that may be changed may change if the ECC used changes. The expected error rate determined at <NUM> may change over time as the channel conditions change. Additionally, c, may be static or may change over time. For example, c may be based upon a measure of variability of the expected error rate. If the error rate is relatively static and constant, c may be low (or zero). Conversely, if the channel is volatile and the error rate fluctuates substantially, c may be larger to reflect the variability in conditions. For example, c may be set based upon a deviation of the channel conditions across a sample set from an average condition.

<FIG> illustrates a flowchart of a method <NUM> of modifying symbols that are to be transmitted optically according to a power-based compression scheme according to some examples of the present disclosure. At operation <NUM> the bitstream may be received for transmission. For example, an application on a computing device may send data to be transmitted. Thus, the data may be received over an internal bus, a memory buffer, or the like. In other examples, the data may be received over an external communication link, such as a network connection or the like. The bitstream may have already had an ECC applied to it prior to operation <NUM>.

At operation <NUM> the system may determine that the bitstream includes a sequence of a first number of consecutive bits of a same first value. The sequence associated with a first peak transmission power that is correlated with the first number of consecutive bits in the sequence. For example, if the bitstream is "<NUM><NUM><NUM><NUM><NUM>" then the three repeating <NUM>'s constitutes a sequence of consecutive bits of a first value (` <NUM>'). In a power-based compression scheme, the "<NUM><NUM><NUM>" would be transmitted as a single "<NUM>" but at a peak power level that is correlated to the first number of consecutive bits (in this example, three). In some examples, the power level is positively correlated to the first number of consecutive bits such that increasing numbers of repeated values are transmitted as a single value but at a power level that increases for each additional value. Thus, "<NUM><NUM>" would be transmitted at a greater power level than "<NUM><NUM>".

At operation <NUM>, one or more selected consecutive bits may be changed to a different value to create a modified sequence. The modified sequence including a second number of the one or more consecutive bits from the sequence with the first value, the second number less than the first number, the modified sequence associated with a second peak transmission power to transmit the modified sequence that is correlated with the second number, the second peak transmission power lower than the first peak transmission power. For example, if the bit stream is "<NUM><NUM><NUM><NUM><NUM>" then one of the "<NUM>" bits may be changed to a zero. For example, the bitstream may be changed to "<NUM><NUM><NUM><NUM><NUM>". The change may be from a first value to a second value (e.g., <NUM> to a <NUM>). By changing a value in the sequence identified at operation <NUM> one or more modified sequences are created. For example if the bitstream is "<NUM><NUM><NUM><NUM><NUM><NUM><NUM><NUM><NUM>" and the sequence of repeating values is "<NUM><NUM><NUM><NUM><NUM>" and one of the "<NUM>" values is changed to "<NUM>" this creates one or more modified sequences of "<NUM>"s. For example, if the middle bit is changed to a zero, two modified sequences are created of "<NUM><NUM>" and "<NUM><NUM>". If the first bit is changed, then a single modified sequence is created of "<NUM><NUM><NUM><NUM>", and so on. These modified sequences are transmitted at a lower peak transmission power than the original sequence by the power based compression algorithm (as there are shorter sequences of the same value).

At operation <NUM> the system may transmit the modified sequence optically. For example, the bitstream (as modified) may be transmitted by selectively activating the light source and/or varying an amplitude of the light source according to a modulation scheme. For example, by selectively activating the light source to transmit a single bit of the first value at the second peak transmission power.

<FIG> illustrates a flowchart of a method <NUM> of modifying symbols that are to be transmitted wirelessly using RF to reduce a PAPR according to some examples of the present disclosure. At operation <NUM> the bitstream may be received for transmission. For example, an application on a computing device may send data to be transmitted. Thus, the data may be received over an internal bus, a memory buffer, or the like. In other examples, the data may be received over an external communication link, such as a network connection or the like. The bitstream may have already had an ECC applied to it prior to operation <NUM>. The bitstream may include a symbol that is mapped or correlated to a first transmission power according to a modulation scheme. For example, according to a QAM modulation scheme.

In some examples, the symbol may be selected from a plurality of symbols of the bitstream based upon having an associated transmission power over a threshold. In some examples, the symbol may be selected by having a highest associated transmission power for bits of the bitstream that are to be transmitted over a specified transmission window.

In some examples, multiple symbols may be selected. In these examples, the selection changes a first n symbols that exceed a threshold transmission power, where n is the number of symbols that are to be changed. In still other examples, symbols with a greatest variation in power level (or a power level variation that exceeds a threshold variation) from neighboring signals may be selected to be changed to minimize power spikes that may damage the transmitter. In these examples, the system may further require that the power level exceed a threshold (as spikes that are low power may not be damaging).

At operation <NUM> the system may change the symbol from a first value to a second value, the second value mapped to a transmission power lower than the first transmission power according to the modulation scheme. The change may be from a first value to a second value. The second value is a symbol value that, when received by the receiver, is not converted to the original value of the bitstream as received at operation <NUM> without the use of ECC. In the case of PAPR reduction, the second symbol may be a symbol that is transmitted at a lower peak power. At operation <NUM> the system may transmit the modified bitstream and/or symbols wirelessly using radio frequency waves, including transmitting the symbol as the second value using the second transmission power. For example, the bitstream (as modified) may be modulated using one or more modulation schemes (such as QAM, PSK, and the like) and transmitted.

When selecting symbols to change, it may be desirable to spread the introduced errors out within the transmission window. For example, certain ECC may be faster and may produce fewer uncorrectable ECC when errors are well distributed throughout the transmission window. In some examples, the system may select symbols to change not only based upon achieving communication metric optimization goals, but in addition, or instead, selecting symbols that meet a threshold measure of spread that describes how distributed the positions of the corresponding selected symbols are to other, non-selected symbols from the bitstream.

A measure of spread may be a minimum distance between respective selected symbols, an average distance between respective selected symbols, a variance of the selected symbols, and the like. In some examples, the thresholds may be based upon the ECC properties. That is, ECC algorithms may have varying tolerances for successive or close together errors and by adjusting the threshold of the measurement of spread, the system may tailor its behavior to the ECC algorithm utilized. For example, the less tolerant the ECC algorithm is to errors that are close together, the greater the threshold on the measurement of spread.

In some examples, the measure of spread may be also used to reduce latency at the receiver. For example, a large number of errors in a close span of each other may cause intensive computations at the receiver. In these examples, the threshold may be set based upon the hardware of the receiver and how fast the receiver can correct ECC errors. In these examples, the threshold may be increased if the receiver is using slow hardware. In still other examples, the type of data transmitted may be used to dynamically change the threshold. For example, data that is intolerant of latency may have a high threshold, whereas data that is tolerant of latency may have a lower threshold.

<FIG> illustrates a flowchart of a method <NUM> of modifying symbols that are to be transmitted such that the symbols are well distributed according to the present disclosure. At operation <NUM>, the bitstream is received for transmission. For example, an application on a computing device may send data to be transmitted. Thus, the data may be received over an internal bus, a memory buffer, or the like. In other examples, the data may be received over an external communication link, such as a network connection or the like. The bitstream has already had an ECC applied to it prior to operation <NUM>.

At operation <NUM>, two or more of a plurality of symbols corresponding to the bitstream are selected such that a measure of spread of respective positions within the bitstream corresponding to the selected symbols meets a threshold. The symbols may be selected to meet a communication metric optimization goal and are selected to meet a threshold measure of spread. In some examples, a measure of spread may be a measure of how distributed the selected symbols are within the symbols of the transmission window. In some examples, the measure of spread may be a greatest distance, an average distance, or the like between respective symbols. The measure of spread may be a variance of the selected symbols within the bitstream.

At operation <NUM>, the system changes a value of each of the selected symbols based upon an optimization goal that optimizes a communication metric. The change is from a first value to a second value. The second value is a symbol value that, when received by the receiver, is not converted to the original value of the bitstream as received at operation <NUM> without the use of ECC. In the case of an optimization goal of reduction of a PAPR, the second symbol may be a symbol that is transmitted at a lower peak power. In examples in which the optimization goal is reduction of error rate, the second symbol may be a symbol less likely to produce an error at the receiver. At operation <NUM> the system transmits the modified bitstream and/or symbols over a communication network. For example, the bitstream (as modified) may be modulated using one or more modulation schemes (such as QAM, PSK, and the like) and transmitted. In other examples, the modified bitstream and/or symbols may be transmitted optically.

As previously mentioned, the system may select and change symbols to meet one or more communication metric optimization goals, such as reducing PAPR, reducing errors, or the like. In some examples, the system may attempt to achieve both. For example, if the system can change n symbols per k symbols transmitted, then some of the n symbols may be changed to reduced PAPR, and some of the n symbols may be changed to reduce errors. For example, the system may specify a ratio of the total number of symbols that may be changed that goes towards each optimization goal. For example, in a <NUM>-<NUM> split, n/<NUM> symbols may be changed to lower PAPR and n/<NUM> symbols may be changed to lower error rate.

In other examples, the system may change symbols based upon a plurality of rules. For example, the system may first change symbols that exceed a threshold power during the transmission window. If there are additional symbols that can be changed, symbols with neighbors that are close on the constellation map may be changed (or symbols that have frequent reception errors as reported by the receiver). Conversely, a rule may specify that symbols with neighbors within a threshold distance may be changed first and any left-over symbols that may be changed may be changed to lower PAPR.

In some examples, the system may dynamically determine which communication metric optimization goals to achieve. The system may dynamically change symbols during a same transmission window or for each transmission window. For example, the system may continuously monitor one or more channel metrics. For example, a received signal strength (RSSI) of the signal received by the receiver, an error rate, a PAPR, and the like. The system may apply a set of rules to determine, based upon the metrics, which communication metric optimization goal(s) to select. For example, if the PAPR is low, but the error rate is high, the system may select to optimize the error rate - potentially at the expense of PAPR.

In some examples, the decision rules may output a proportion of symbols to change to achieve multiple communication metric optimization goals. For example, if both PAPR and error rate are high, but PAPR is a more serious problem, then symbols may be changed to achieve both objectives, but more symbols may be adjusted to fix the PAPR issue. The proportions may be determined by formulas given in the rules. For example, based upon a deviation from a threshold. Thus, if both the PAPR and the error rate deviate from their respective thresholds, the proportion of symbols to fix to optimize PAPR may be a ratio that is calculated based upon the amount each communication metric optimization goal deviates from the thresholds.

The rules may be a set of if-then-else statements, one or more formulas, a decision tree, a neural network, a regression, or the like. Models that are supervised learning models may be utilized that are trained with historical metrics and labels with the proportion for each optimization goal. The resulting model determines a proportion of symbols based upon the number of symbols available to change and the channel metrics. In some examples, models may operate directly on the bitstream and the channel metrics to not only determine what symbols to change, but what values to change them to. As before, the models may be decision trees, neural networks, classification, regression, or the like and may be trained with historical bitstreams, and channel metrics with labels that indicate which symbols to change and to what. In some examples, the labels may be calculated using one or more optimization algorithms that optimize all desired channel metrics.

In some examples, a context of the transmitter and/or receiver may be utilized instead of, or in addition to the channel metric. Example contexts include a battery level, a geographic location (e.g., determined by using Global Positioning System (GPS), WiFi signals, inertial navigation, a combination of two or more of GPS, WiFi, and inertial navigation, or the like), an activity of user of the device, data needs of the device, QoS data of the bitstream, and the like. For example, if the transmitter's battery level is low, the device may prioritize lowering a PAPR or even an average power level, a total power level, or the like. Similarly, a user's motion may be used to determine a communication metric optimization goal. For example, if the user is in motion, the system may prioritize error rate to combat changing multipath reflections that may increase an error rate. In other examples, the system may learn communication metric optimization goals for particular locations. Thus, through crowd sourced data the system may determine channel metrics for various locations and ideal optimizations for those locations. The user's location may then be matched to a particular location in a database and the database may have desired optimization goals for that location.

An activity of the user of the device and data needs of the device may also be determined and considered when selecting a communication metric optimization goal. For example, the user may be conducting a video call that prioritizes latency over bandwidth. In these examples, the system may seek to reduce error (which reduces retransmissions). In other examples, the user may be downloading a large file. In these examples the system may seek to reduce a PAPR to preserve device battery (even if the battery currently has plenty of charge). The user's activity may be evidenced by the QoS data, the application that produced the bitstream (e.g., application specific settings may be used that selects a different optimization goal based upon the application source of the bitstream), sensor data that shows the user's activity, and the like.

In some examples, the device context may be used alone to select the communication metric optimization goal and/or proportion of symbols to use for each goal or may be used in combination with the current channel metrics. The various models described above for the channel metrics may also be utilized for the device contexts. For example, a set of rules that may be a set of if-then-else statements, a decision tree, a neural network, a regression, or the like. Models that are supervised learning models may be utilized that are trained with historical contexts and labels with the proportion for each communication metric optimization goal. The resulting model determines a proportion of symbols based upon the number of symbols available to change and the device contexts. In some examples, models may operate directly on the bitstream and the device contexts to not only determine what symbols to change, but what values to change them to. As before, the models may be decision trees, neural networks, classification, regression, or the like and may be trained with historical bitstreams and contexts with labels that indicate which symbols to change and to what.

In examples in which both contexts and channel metrics are utilized, a set of rules may be utilized that may be a set of if-then-else statements, a decision tree, a neural network, a regression, or the like. Models that are supervised learning models may be utilized that are trained with historical contexts, metrics, and labels with the proportion for each optimization goal. The resulting model determines a proportion of symbols based upon the number of symbols available to change and the device contexts. In some examples, models may operate directly on the bitstream, channel metrics, and the device contexts to not only determine what symbols to change, but what values to change them to. As before, the models may be decision trees, neural networks, classification, regression, or the like and may be trained with historical bitstreams, metrics, and contexts with labels that indicate which symbols to change and to what.

<FIG> illustrates a flowchart of a method <NUM> of modifying symbols that are to be transmitted to achieve a dynamically selected goal according to some examples of the present disclosure. At operation <NUM>, the system may receive a bitstream for transmission. For example, an application on a computing device may send data to be transmitted. Thus, the data may be received over an internal bus, a memory buffer, or the like. In other examples, the data may be received over an external communication link, such as a network connection or the like. The bitstream may have already had an ECC applied to it prior to operation <NUM>.

At operation <NUM>, the system may determine one or more optimization criteria comprising communication metrics of a communication channel that the bitstream is transmitted across or a device context of the device. In some examples, the communication metric may include an error rate, a PAPR, a signal strength (as measured by the receiver), a signal quality (as measured by the receiver), or the like. The device context may be any measurable state of the device, including battery level, location, motion, information about the data being transmitted (e.g., Quality of Service properties), or the like. The device contexts may be device contexts of the receiver and/or the transmitter.

At operation <NUM>, a first optimization goal may be selected based upon the optimization criteria. For example, based upon a set of one or more rules, machine-learning models, or the like. As noted previously, both the channel metrics and device contexts may be utilized, or just the device context and not the channel metrics, or just the channel metrics and not the device contexts.

At operation <NUM>, a value of one or more first symbols may be selected and changed to optimize the optimization goal selected at operation <NUM>. For example, if the selected optimization goal is to optimize PAPR, symbols transmitted with a high peak power may be changed to symbols transmitted with lower power levels. If the selected optimization goal is to reduce error rate, symbols that have, in the past, caused errors at the receiver may be changed. In other examples, if the selected optimization goal is to reduce error rate, symbols that have neighbors that are within a threshold distance on a constellation chart of the modulation scheme used may be selected and changed such that the changed symbols are over the threshold distance. At operation <NUM> the one or more first symbols are transmitted with the changed value. For example, the transmitter modulates the symbol and causes it to be transmitted using RF, light waves, or the like.

At operation <NUM>, the one or more optimization criteria may be updated. The channel metrics and/or device contexts may be determined by the transmitter, by the receiver and sent to the transmitter, or the like. At operation <NUM>, a second optimization goal may be selected based upon the updated optimization criteria determined at operation <NUM>. In some examples, the second optimization goal may be a same or a different optimization goal then the one determined at operation <NUM>. The second optimization goal may be selected using a same or a different process as used in operation <NUM> - for example, based upon a set of one or more rules, machine-learning models, or the like.

At operation <NUM>, a value of one or more second symbols may be selected and changed to optimize the second optimization goal selected at operation <NUM>. At operation <NUM>, the transmitter may transmit the one or more second symbols.

One constraint on the number of symbols that may be intentionally changed by the transmitter includes a number of errors introduced by the communication channel. The more symbols that need to be corrected based upon noise or other conditions of the communication channel, the fewer symbols can be intentionally modified by the transmitter. In order to reduce the error rate caused by the channel and increase the number of symbols that may be intentionally changed by the transmitter, the system may increase a transmitter power level. For example, an average power level of the transmissions. This may help the transmitter reduce noise on the receiver and reduce errors. The margin gained by this technique may then be utilized to more effectively achieve one of the desired optimization goals. In some examples, the average power level may be increased by increasing a transmission power level of all symbols.

Turning now to <FIG>, a flowchart of a method <NUM> of increasing a power level of transmissions of a transmitter to increase a number of symbols that are changed is illustrated according to some examples of the present disclosure. At operation <NUM>, a bitstream may be received for transmission. For example, an application on a computing device may send data to be transmitted. Thus, the data may be received over an internal bus, a memory buffer, or the like. In other examples, the data may be received over an external communication link, such as a network connection or the like. The bitstream may have already had an ECC applied to it prior to operation <NUM>.

At operation <NUM>, the average power level to transmit a portion of the bitstream may be increased. The increased average power level is greater than an average power level specified by a communication protocol for transmitting the portion of the bitstream given a current communication channel conditions. For example, if the bitstream would normally be transmitted at an average power level of x operation <NUM> increases x by y. In other words, the bitstream received at operation <NUM> may be associated with an average power level. For example, the bits in the bitstream may be transmitted according to a modulation scheme which assigns a particular amplitude to each of the symbols of the bitstream. The average power level may be the average amplitude of all the symbols of the bitstream. At operation <NUM>, to increase the average power level, the amplitude with which to transmit each symbol at may be increased a same amount. In other examples, each symbol may be increased a different amount such that symbols of the constellation chart may be spaced further apart. For example, in a QAM constellation as shown in <FIG>, the symbols that are assigned to a highest amplitude may have their power levels increased more than symbols that have lower amplitudes to spread the constellation out further.

At operation <NUM>, a first number of symbols to intentionally change may be determined. For example, based upon applying a channel metric to a first function. For example, using the process of <FIG>. The first number of symbols at operation <NUM> is greater than the number of symbols that would have been changed prior to increasing the average power level at operation <NUM>. That is, the number of symbols to change at operation <NUM> is greater than the number of symbols to change that the first function would indicate if the channel metric were input to the first function prior to changing the power level at operation <NUM>.

At operation <NUM>, a value of one or more first symbols may be selected and changed to optimize an optimization goal. For example, if the optimization goal is to optimize PAPR, symbols transmitted with a high peak power may be changed to symbols transmitted with lower power levels. If the optimization goal is to reduce error rate, symbols that have, in the past, caused errors at the receiver may be changed. In other examples, if the selected optimization goal is to reduce error rate, symbols that have neighbors that are within a threshold distance on a constellation chart of the modulation scheme used may be selected and changed such that the changed symbols are over the threshold distance. At operation <NUM> the one or more first symbols are modulated and transmitted with the changed values. For example, the transmitter modulates the symbol and causes it to be transmitted using RF, light waves, or the like.

In some examples, the increased average power level determined at operation <NUM> may be determined based upon a target number of symbols to change. In some examples, the target number of symbols to change may be based upon a target communication metric optimization goal. That is, the system may determine a target average power level based upon a number of symbols the system would like to change. This may be based upon a formula or table that correlates an average power level, the current communication metrics, and the number of symbols that may be changed. At operation <NUM>, the system may increase the transmission power levels to achieve this target. As noted, the target number of symbols to change may be based upon the target communication metric optimization goal. For example, a table or formula may correlate the current communication metric with an improved communication metric and the number of symbols that would need to be changed to meet that goal. This is then used to select an average power level. In some examples, at operation <NUM>, the determining the first number of symbols to change may occur prior to operation <NUM> in order to determine a number of symbols to change to determine what the average power level increase should be.

<FIG> illustrates an example machine learning module <NUM> according to some examples of the present disclosure. The machine learning module <NUM> may be implemented in whole or in part by the transmitter such as transmitter <NUM>. In some examples, the training module <NUM> may be implemented by a different device than the prediction module <NUM>. In these examples, the model <NUM> may be created on a first machine and then sent to a second machine.

Machine learning module <NUM> utilizes a training module <NUM> and a prediction module <NUM>. Training module <NUM> inputs feature data <NUM> into feature determination module <NUM>. Feature determination module <NUM> determines one or more features for feature vector <NUM> from the feature data <NUM>. Features chosen for inclusion in the feature vector <NUM> may be all the feature data <NUM> or in some examples, may be a subset of all the feature data <NUM>. In examples in which the features chosen for the feature vector <NUM> are a subset of the feature data <NUM>, a predetermined list of which feature data <NUM> is included in the feature vector may be utilized. The feature vector <NUM> may be utilized (along with any applicable labels) by the machine learning algorithm <NUM> to produce a model <NUM>.

In the prediction module <NUM>, the current feature data <NUM> of the user may be input to the feature determination module <NUM>. Feature determination module <NUM> may determine the same set of features or a different set of features as feature determination module <NUM>. In some examples, feature determination module <NUM> and <NUM> are the same modules or different instances of the same module. Feature determination module <NUM> produces feature vector <NUM>, which are input into the model <NUM> to produce results <NUM>.

The training module <NUM> may operate in an offline manner to train the model <NUM>. The prediction module <NUM>, however, may be designed to operate in an online manner. It should be noted that the model <NUM> may be periodically updated via additional training and/or user feedback. The machine learning algorithm <NUM> may be selected from among many different potential supervised or unsupervised machine learning algorithms. Examples of supervised learning algorithms include artificial neural networks, convolutional neural networks, Bayesian networks, instance-based learning, support vector machines, decision trees (e.g., Iterative Dichotomiser <NUM>, C4. <NUM>, Classification and Regression Tree (CART), Chi-squared Automatic Interaction Detector (CHAID), and the like),random forests, linear classifiers, quadratic classifiers, k-nearest neighbor, linear regression, logistic regression, and hidden Markov models. Examples of unsupervised learning algorithms include expectation-maximization algorithms, vector quantization, and information bottleneck method. Unsupervised models may not have a training module <NUM> or may not utilize labels on feature data <NUM>.

The model <NUM> may be utilized in one example to select symbols in the bitstream to change and/or the value to change it to. In these examples, the feature data <NUM> and <NUM> may include the bitstream, an optimization goal, and for the training module <NUM>, a label indicating which symbols to change - or an optimization function so that the machine learning algorithm <NUM> can determine a proper symbol and change given the feature data <NUM>. In some examples, the feature data <NUM>, and <NUM> may also include modulation info, such as what each symbol in the bitstream is modulated to, context information of the user, and/or channel metrics. The features of feature vectors <NUM> and <NUM> may be feature data <NUM> and <NUM> determined to be predictive of a symbol and the value to change the symbol to.

In other examples, the model <NUM> may be utilized to select an optimization goal. For example, the feature data <NUM> and <NUM> may channel metrics, device contexts, and the like. The training feature data <NUM> may be labelled with a desired optimization goal. The feature vectors <NUM> and <NUM> may be feature data <NUM> and <NUM> that are predictive of an optimization goal. The output of the model <NUM> may be an optimization goal, or a ratio between various optimization goals (e.g., <NUM>% of symbols changed to reduce PAPR, <NUM>% to reduce error rate), or the like.

In yet other examples, the model <NUM> may be utilized to determine a number of expected symbol errors in the communication channel to assist in determining how many symbols may be intentionally changed. In these examples, the feature data <NUM> and <NUM> may be channel metrics and/or the symbols for transmission during a particular transmission window. The training feature data <NUM> may be labelled with the number of observed symbol errors for that given data caused by the communication channel conditions. The feature vectors <NUM> and <NUM> may be feature data <NUM> and <NUM> that are predictive of a number of symbol errors. The output of the model <NUM> may be a number of expected symbol errors.

As noted herein, symbols are changed intentionally by the transmitter from the value received by upper levels. As previously noted, in some examples, rather than changing symbols from a first value to a second value, some ECC allow more erasures than errors. Thus, in these examples, the symbols may be deleted and not transmitted. This may take advantage of the fact that some ECC algorithms can correct more missing data errors than errors caused by incorrect data. As used herein, transmission power levels static power levels, or may describe amplitudes of a carrier wave that transmits data.

<FIG> illustrates a block diagram of an example machine <NUM> that may implement any one or more of the techniques disclosed herein according to some examples of the present disclosure. The machine <NUM> may be a base station, a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile telephone, a smart phone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Machine <NUM> may implement a transmitter such as transmitters <NUM>, <NUM>, <NUM>, implement optimizer <NUM>, training module <NUM>, prediction module <NUM>. Machine <NUM> may implement a receiver, such as receivers <NUM>, <NUM>, <NUM>. Machine <NUM> may implement the methods of <FIG>.

Examples, as described herein, may include, or may operate on, logic or a number of components, modules, or mechanisms (hereinafter "modules").

For example, a non-transitory machine readable medium.

The term "machine readable medium" may include any medium that is capable of storing, encoding, or carrying instructions for execution by the machine <NUM> and that cause the machine <NUM> to perform any one or more of the techniques of the present disclosure, or that is capable of storing, encoding or carrying data structures used by or associated with such instructions. Machine readable medium includes a non-transitory machine readable medium. Non-limiting machine readable medium examples may include solid-state memories, and optical and magnetic media. Specific examples of machine readable media may include: non-volatile memory, such as semiconductor memory devices (e.g., Electrically Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM)) and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; Random Access Memory (RAM); Solid State Drives (SSD); and CD-ROM and DVD-ROM disks. In some examples, machine readable media may include non-transitory machine readable media. In some examples, machine readable media may include machine readable media that is not a transitory propagating signal.

Claim 1:
A device for transmitting data, the device comprising:
a processor;
a memory, storing instructions, which when executed by the processor, causes the processor to perform the steps of:
receiving a bitstream encoded with an error correction code for transmission;
determining a plurality of symbols based upon values in the bitstream, each symbol corresponding to one or more bits of the bitstream;
selecting two or more of the plurality of symbols corresponding to the bitstream such that a measure of spread of the selected symbols meets a threshold, the threshold being based on properties of the error correction code, based on the hardware of a receiver and how fast the receiver can correct error correction code errors, or based on the type of data transmitted, wherein the measure of spread comprises a minimum distance between respective selected symbols, an average distance between respective selected symbols, or a variance of the selected symbols in the bitstream;
intentionally introducing an error in the bitstream by changing values of each of the selected symbols to a different value chosen based upon an optimization goal that improves a communication metric relative to the communication metric prior to changing the values of each of the selected symbols; and
transmitting the plurality of symbols with the changed values to the receiver over a communication network.