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Title:
ARRANGEMENT FOR REMOVING TRANSMITTER POWER AMPLIFIER DISTORTION AT A RECEIVER
Document Type and Number:
WIPO Patent Application WO/2022/073615
Kind Code:
A1
Abstract:
A method, apparatus, and a computer-readable storage medium are provided for compensating UE power amplifier distortion at a base station receiver. In an example implementation, the method may include a network node receiving a plurality of training signals from at least a user equipment and training a power amplifier model, the training based at least on the plurality of training signals and generating a trained power amplifier model. The method may further include generating a correction signal for at least a signal received from the user equipment based at least on the trained power amplifier model, wherein the signal received from the user equipment includes at least one of a data signal, a control signal, and a reference signal. In an additional example implementation, the method may include a user equipment receiving configuration information for a plurality of training signals associated with a power amplifier model from a network node and transmitting the plurality of training signals to the network node, the plurality of training signals generated based on at least on the configuration information received from the network node.

Inventors:
PAJUKOSKI KARI PEKKA (FI)
TIIROLA ESA TAPANI (FI)
HOOLI KARI JUHANI (FI)
KARJALAINEN JUHA PEKKA (FI)
KAIKKONEN JORMA JOHANNES (FI)
TERVO OSKARI (FI)
Application Number:
PCT/EP2020/078335
Publication Date:
April 14, 2022
Filing Date:
October 08, 2020
Export Citation:
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Assignee:
NOKIA TECHNOLOGIES OY (FI)
International Classes:
H04B17/13; H03F1/32; H04B17/00; H04B17/21
Foreign References:
US9973225B12018-05-15
US20190058545A12019-02-21
Other References:
FERNANDO H. GREGORIO ET AL: "Receiver-side nonlinearities mitigation using an extended iterative decision-based technique", SIGNAL PROCESSING., vol. 91, no. 8, 23 March 2011 (2011-03-23), NL, pages 2042 - 2056, XP055233987, ISSN: 0165-1684, DOI: 10.1016/j.sigpro.2011.03.011
Attorney, Agent or Firm:
NOKIA EPO REPRESENTATIVES (FI)
Download PDF:
Claims:
WHAT IS CLAIMED IS:

1. A method of communications, comprising: receiving, at a network node, a plurality of training signals from at least a user equipment; training, at the network node, a power amplifier model, the training based at least on the plurality of training signals and generating a trained power amplifier model; and generating, at the network node, a correction signal for at least a signal received from the user equipment based at least on the trained power amplifier model, wherein the signal received from the user equipment includes at least one of a data signal, a control signal, and a reference signal.

2. The method of claim 1, further comprising: applying the correction signal to the at least one of a data signal, a control signal, and a reference signal to generate a corrected signal.

3. The method of any of claims 1-2, wherein the training of the power amplifier model comprises: determining correction signals for the plurality of training signals, wherein a correction signal for a first training signal of the plurality of training signals received from the user equipment is generated at the network node based at least on a difference between a second training signal generated at the network node and the first training signal as received at a receiver of the network node after a channel correction.

4. The method of any of claims 1-3, wherein a training signal of the plurality of training signals is received multiple times.

5. The method of any of claims 1-4, wherein the plurality of training signals is scheduled by the network node with one or more pre-defined backoff values.

6. The method of any of claims 1-5, wherein a training signal of the plurality of training signals comprises one or more pseudo-random sequences.

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7. The method of any of claims 1-6, wherein the plurality of training signals as received at the receiver of the network node includes non-linear power amplifier distortion.

8. The method of any of claims 1-7, wherein the network node configured antenna ports associated with resources for the plurality of training signals received from the user equipment.

9. The method of any of claims 1-8, further comprising: receiving information of antenna ports associated with resources of the plurality of training signals, wherein one-to-one association exists between the antenna ports associated with resources of the plurality of training signals and antenna ports of demodulation reference signal resources.

10. The method of any of claims 1-9, wherein a training signal of the plurality of training signals received from the user equipment introduces higher non-linear distortion than an associated demodulation reference signal received from the user equipment.

11. The method of any of claims 1-10, wherein the training signals are triggered to be transmitted with multiple transmission occasions in time and/or frequency.

12. The method of any of claims 1-11, wherein the generating of the correction signal is further based on a backoff value of the associated training signal.

13. The method of any of claims 1-12, wherein the generating of the correction signal includes generating an amplitude error and/or a phase error.

14. The method of any of claims 2-13, wherein applying the correction signal to the signal further comprises: applying the amplitude error and/or phase error to the at least one of a data signal, a control signal, and a reference signal received at the receiver after channel correction to compensate for non-linear distortions introduced at the receiver of the user equipment.

15. The method of any of claims 1-14, further comprising: receiving, from the user equipment, an indication that the user equipment is capable of sending training signals to be used for training the power amplifier model at the network node.

16. The method of any of claims 1-15, wherein the correction signal for at least the signal received from the user equipment is generated further based on the signal received from the user equipment.

17. The method of any of claims 1-16, wherein at least some of the plurality of training signals are based on different power backoff values.

18. A method of communications, comprising: receiving, by a user equipment, configuration information for a plurality of training signals associated with a power amplifier model from a network node; and transmitting, by the user equipment, the plurality of training signals to the network node, the plurality of training signals generated based on at least on the configuration information received from the network node.

19. The method of claim 18, wherein the configuration information includes at least one of backoff values, antenna ports, modulation schemes, and/or transmission occasions in time and/or frequency.

20. The method of any of claims 18-19, wherein the plurality of training signals is based on one or more power backoff values.

21. The method of any of claims 18-20, wherein a training signal of the plurality of training signals introduces a higher power amplifier distortion than an associated demodulation reference signal.

22. The method of any of claims 18-21, wherein at least one first training signal of the plurality of training signals causes a higher power amplifier distortion than at least one second training signal of the plurality of training signals.

23. The method of any of claims 18-22, wherein at least one of: the plurality of training signals is scheduled by the network node with one or more pre-defined backoff values; a training signal of the plurality of training signals comprises one or more pseudorandom sequences; and the plurality of training signals includes non-linear power amplifier distortion.

24. The method of any of claims 18-23, further comprising: transmitting an indication to the network node that the user equipment is capable of sending the plurality of training signals to be used for training the power amplifier model at the network node.

25. The method of any of claims 1-24, wherein the user equipment is in a radio resource control connected state.

26. An apparatus comprising means for performing the method of any of claims 1-25.

27. A non-transitory computer-readable storage medium comprising instructions stored thereon that, when executed by at least one processor, are configured to cause a computing system to perform the method of any of claims 1-25.

28. An apparatus comprising: at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to perform the method of any of claims 1-24.

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Description:
ARRANGEMENT FOR REMOVING TRANSMITTER POWER AMPLIFIER

DISTORTION AT A RECEIVER

TECHNICAL FIELD

[0001] This description relates to wireless communications, and in particular, power amplifier distortion.

BACKGROUND

[0002] A communication system may be a facility that enables communication between two or more nodes or devices, such as fixed or mobile communication devices. Signals can be carried on wired or wireless carriers.

[0003] An example of a cellular communication system is an architecture that is being standardized by the 3rd Generation Partnership Project (3GPP). A recent development in this field is often referred to as the long-term evolution (LTE) of the Universal Mobile Telecommunications System (UMTS) radio-access technology. E-UTRA (evolved UMTS Terrestrial Radio Access) is the air interface of 3GPP's Long Term Evolution (LTE) upgrade path for mobile networks. In LTE, base stations or access points (APs), which are referred to as enhanced Node AP or Evolved Node B (eNBs), provide wireless access within a coverage area or cell. In LTE, mobile devices, or mobile stations are referred to as user equipments (UE). LTE has included a number of improvements or developments.

[0004] 5G New Radio (NR) development is part of a continued mobile broadband evolution process to meet the requirements of 5G, similar to earlier evolution of 3G & 4G wireless networks. In addition, 5G is also targeted at the new emerging use cases in addition to mobile broadband. A goal of 5G is to provide significant improvement in wireless performance, which may include new levels of data rate, latency, reliability, and security. 5G NR may also scale to efficiently connect the massive Internet of Things (loT), and may offer new types of mission-critical services. Ultra reliable low latency communications (URLLC) devices may require high reliability and very low latency.

SUMMARY

[0005] Various example implementations are described and/or illustrated. The details of one or more examples of implementations are set forth in the accompanying drawings and the description below. Other features will be apparent from the description and drawings, and from the claims.

[0006] A method, apparatus, and a computer-readable storage medium are provided for compensating UE power amplifier distortion at a base station receiver. In an example implementation, the method may include a network node receiving a plurality of training signals from at least a user equipment and training a power amplifier model, the training based at least on the plurality of training signals and generating a trained power amplifier model. The method may further include generating a correction signal for at least a signal received from the user equipment based at least on the trained power amplifier model, wherein the signal received from the user equipment includes at least one of a data signal, a control signal, and a reference signal.

[0007] In an additional example implementation, the method may include a user equipment receiving configuration information for a plurality of training signals associated with a power amplifier model from a network node and transmitting the plurality of training signals to the network node, the plurality of training signals generated based on at least on the configuration information received from the network node.

BRIEF DESCRIPTION OF THE DRAWINGS

[0008] FIG. l is a block diagram of a wireless network according to an example implementation.

[0009] FIG. 2 illustrates a mechanism for compensating UE power amplifier distortion at a base station receiver, according to an example implementation.

[0010] FIG. 3 illustrates transmit powers of a training reference signal and a demodulation reference signal, according to an example implementation.

[0011] FIG. 4 illustrates amplitudes of a quadrature amplitude modulation (QAM) based power amplifier modulation training signal and associated demodulation reference signal based on a lower PAR sequence, according to an example implementation.

[0012] FIG. 5 illustrates block diagrams for learning and compensation phases (or processes) at a base station, according to an example implementation.

[0013] FIG. 6 is a flow chart illustrating compensating of UE power amplifier distortion at a base station receiver, according to an example implementation.

[0014] FIG. 7 is a flow chart illustrating transmission of training reference signals from a UE to a base station for compensating UE power amplifier distortion at the base station, according to an additional example implementation.

[0015] FIG. 8 is a block diagram of a node or wireless station (e.g., base station/access point or mobile station/user device/UE), according to an example implementation.

DETAILED DESCRIPTION

[0016] FIG. 1 is a block diagram of a wireless network 130 according to an example implementation. In the wireless network 130 of FIG. 1, user devices (UDs) 131, 132, 133 and 135, which may also be referred to as mobile stations (MSs) or user equipment (UEs), may be connected (and in communication) with a base station (BS) 134, which may also be referred to as an access point (AP), an enhanced Node B (eNB), a next-generation Node B (gNB) or a network node. At least part of the functionalities of an access point (AP), base station (BS), (e)Node B (eNB), or gNB may also be carried out by any node, server or host which may be operably coupled to a transceiver, such as a remote radio head. BS (or AP) 134 provides wireless coverage within a cell 136, including to user devices 131, 132, 133 and 135. Although only four user devices are shown as being connected or attached to BS 134, any number of user devices may be provided. BS 134 is also connected to a core network 150 via a SI interface 151. This is merely one simple example of a wireless network, and others may be used.

[0017] A user device (user terminal, user equipment (UE)) may refer to a portable computing device that includes wireless mobile communication devices operating with or without a subscriber identification module (SIM), including, but not limited to, the following types of devices: a mobile station (MS), a mobile phone, a cell phone, a smartphone, a personal digital assistant (PDA), a handset, a device using a wireless modem (alarm or measurement device, etc.), a laptop and/or touch screen computer, a tablet, a phablet, a game console, a notebook, and a multimedia device, as examples, or any other wireless device. It should be appreciated that a user device may also be a nearly exclusive uplink only device, of which an example is a camera or video camera loading images or video clips to a network.

[0018] In LTE (as an example), core network 150 may be referred to as Evolved Packet Core (EPC), which may include a mobility management entity (MME) which may handle or assist with mobility /handover of user devices between BSs, one or more gateways that may forward data and control signals between the BSs and packet data networks or the Internet, and other control functions or blocks.

[0019] In addition, by way of illustrative example, the various example implementations or techniques described herein may be applied to various types of user devices or data service types, or may apply to user devices that may have multiple applications running thereon that may be of different data service types. New Radio (5G) development may support a number of different applications or a number of different data service types, such as for example: machine type communications (MTC), enhanced machine type communication (eMTC), Internet of Things (loT), and/or narrowband loT user devices, enhanced mobile broadband (eMBB), and ultra-reliable and low-latency communications (URLLC).

[0020] loT may refer to an ever-growing group of objects that may have Internet or network connectivity, so that these objects may send information to and receive information from other network devices. For example, many sensor type applications or devices may monitor a physical condition or a status, and may send a report to a server or other network device, e.g., when an event occurs. Machine Type Communications (MTC or machine to machine communications) may, for example, be characterized by fully automatic data generation, exchange, processing and actuation among intelligent machines, with or without intervention of humans. Enhanced mobile broadband (eMBB) may support much higher data rates than currently available in LTE.

[0021] Ultra-reliable and low-latency communications (URLLC) is a new data service type, or new usage scenario, which may be supported for New Radio (5G) systems. This enables emerging new applications and services, such as industrial automations, autonomous driving, vehicular safety, e-health services, and so on. 3 GPP targets in providing up to e.g., 1 ms U-Plane (user/data plane) latency connectivity with l-le-5 reliability, by way of an illustrative example. Thus, for example, URLLC user devices/UEs may require a significantly lower block error rate than other types of user devices/UEs as well as low latency. Thus, for example, a URLLC UE (or URLLC application on a UE) may require much shorter latency, as compared to an eMBB UE (or an eMBB application running on a UE).

[0022] The various example implementations may be applied to a wide variety of wireless technologies or wireless networks, such as LTE, LTE-A, 5G, loT, MTC, eMTC, eMBB, URLLC, etc., or any other wireless network or wireless technology. These example networks, technologies or data service types are provided only as illustrative examples.

[0023] Multiple Input, Multiple Output (MIMO) may refer to a technique for increasing the capacity of a radio link using multiple transmit and receive antennas to exploit multipath propagation. MIMO may include the use of multiple antennas at the transmitter and/or the receiver. MIMO may include a multi-dimensional approach that transmits and receives two or more unique data streams through one radio channel. For example, MIMO may refer to a technique for sending and receiving more than one data signal simultaneously over the same radio channel by exploiting multipath propagation. According to an illustrative example, multi-user multiple input, multiple output (multi-user MIMIO, or MU-MIMO) enhances MIMO technology by allowing a base station (BS) or other wireless node to simultaneously transmit or receive multiple streams to different user devices or UEs, which may include simultaneously transmitting a first stream to a first UE, and a second stream to a second UE, via a same (or common or shared) set of physical resource blocks (PRBs) (e.g., where each PRB may include a set of time-frequency resources).

[0024] Also, a BS may use precoding to transmit data to a UE (based on a precoder matrix or precoder vector for the UE). For example, a UE may receive reference signals or pilot signals, and may determine a quantized version of a DL channel estimate, and then provide the BS with an indication of the quantized DL channel estimate. The BS may determine a precoder matrix based on the quantized channel estimate, where the precoder matrix may be used to focus or direct transmitted signal energy in the best channel direction for the UE. Also, each UE may use a decoder matrix may be determined, e.g., where the UE may receive reference signals from the BS, determine a channel estimate of the DL channel, and then determine a decoder matrix for the DL channel based on the DL channel estimate. For example, a precoder matrix may indicate antenna weights (e.g., an amplitude/gain and phase for each weight) to be applied to an antenna array of a transmitting wireless device. Likewise, a decoder matrix may indicate antenna weights (e.g., an amplitude/gain and phase for each weight) to be applied to an antenna array of a receiving wireless device. This applies to UL as well when a UE is transmitting data to a BS.

[0025] For example, according to an example aspect, a receiving wireless user device may determine a precoder matrix using Interference Rejection Combining (IRC) in which the user device may receive reference signals (or other signals) from a number of BSs (e.g., and may measure a signal strength, signal power, or other signal parameter for a signal received from each BS), and may generate a decoder matrix that may suppress or reduce signals from one or more interferers (or interfering cells or BSs), e.g., by providing a null (or very low antenna gain) in the direction of the interfering signal, in order to increase a signal-to interference plus noise ratio (SINR) of a desired signal. In order to reduce the overall interference from a number of different interferers, a receiver may use, for example, a Linear Minimum Mean Square Error Interference Rejection Combining (LMMSE-IRC) receiver to determine a decoding matrix. The IRC receiver and LMMSE-IRC receiver are merely examples, and other types of receivers or techniques may be used to determine a decoder matrix. After the decoder matrix has been determined, the receiving UE/user device may apply antenna weights (e.g., each antenna weight including amplitude and phase) to a plurality of antennas at the receiving UE or device based on the decoder matrix. Similarly, a precoder matrix may include antenna weights that may be applied to antennas of a transmitting wireless device or node. This applies to a receiving BS as well.

[0026] In mmWave deployments, the architecture of a power amplifier (PA) at a user equipment (UE) is generally based on low-cost complementary metal-oxide-semiconductor (CMOS) technology with several limitations, e.g., limited transmit (Tx) power, poor PA efficiency, and/or poor linearity.

[0027] In the current systems, PA efficiency decreases when a backoff value (e.g., a power backoff value) increases resulting in increased power consumption or reduced transmitted power for similar power consumption. A backoff value in an amplifier is reducing transmit power below the saturation point of the amplifier to enable the amplifier operate in a linear region even when input power level increases. This is done to fulfil RF requirements such as error vector magnitude requirement. Therefore, some techniques to reduce backoff values to lower the power consumption or increase transmitted power and increase coverage may be used. However, if the backoff values are reduced, the distortion (e.g., error vector magnitude, EVM) increases which may be caused by PA non-linearities resulting in signal EVM above the tolerable limits for modulation. For example, with the higher order modulations, the transmitted signals are EVM limited and the backoff values have to be very large so that the EVM requirements, e.g., in 3GPP Specifications can be satisfied.

[0028] For example, for CMOS based PAs, a higher backoff value (e.g., ~10 dB) may be typically needed for meeting the EVM requirements (or other RF requirements such as in- band emission (IBE), occupied bandwidth (OCB), etc.). However, this may limit cell coverage and/or increase UE power consumption. The power added efficiency (PAE) is only a few percent which means that the antenna array has a higher power dissipation. The power consumption of PA will dominate the transmitter power consumption in the near future, because the baseband power consumption comes down with the increased level of integration. In other words, it’s not possible to increase the transmit power per PA in a way that EVM requirements (or other requirements) are fulfilled and the transmitter power consumption remains acceptable. This is limiting the coverage for corresponding waveform/modulation schemes.

[0029] Therefore, there is a desire and/or need to remove the distortions at the receiving end, for example, at a receiver of a network node, so that the transmitter at the UE may be allowed to increase EVM (e.g., to transmit above the EVM requirements) using higher transmit power and/or use of simpler/less-expensive PAs.

[0030] The present disclosure proposes a mechanism to enable correction and/or compensation at the receiving end. A method, apparatus, and a computer-readable storage medium are provided for compensating UE power amplifier distortion at a base station receiver. In an example implementation, the method may include a network node receiving a plurality of training signals from at least a user equipment and training a power amplifier model, the training based at least on the plurality of training signals and generating a trained power amplifier model. The method may further include generating a correction signal for at least a signal received from the user equipment based at least on the trained power amplifier model, wherein the signal received from the user equipment includes at least one of a data signal, a control signal, and a reference signal. In an additional example implementation, the method may include a user equipment receiving configuration information for a plurality of training signals associated with a power amplifier model from a network node and transmitting the plurality of training signals to the network node, the plurality of training signals generated based on at least on the configuration information received from the network node.

[0031] FIG. 2 illustrates a mechanism 200 for compensating UE power amplifier distortion at a base station receiver, according to an example implementation.

[0032] In an example implementation, optionally, at 210, a UE, e.g., UE 204, which may be same as or similar to UD 133 of FIG. 1, may send an indication to indicate capability of the UE to a gNB, e.g., gNB 202, which may be same as or similar to BS 134 of FIG. 1. In some implementations, for example, the indication sent by the UE may indicate to the gNB that the UE has the capability to support compensation of the non-linear distortion by the receiver. The indication at 210 may be considered optional. In 5G/NR, not all UEs may support this capability. However, in 6G and beyond, all UEs may support this capability. The capability may be signaled independently, or it may be combined with one or more UE functionalities. The signaling may be done via higher layer signaling, e.g., radio resource control (RRC) signaling.

[0033] For example, a 5G/NR UE may have the capability to determine its transmit (Tx) power in several ways. In an example implementation, UE Tx power may meet error vector magnitude (EVM), in-band emission (IBE), occupied bandwidth, spectrum mask, etc. requirements. In another example implementation, UE Tx power may meet these requirements with the exception of EVM (or may meet relaxed EVM requirements). For 6G, all UEs may have the capability to support compensation of the non-linear power amplifier distortion by the receiver.

[0034] In FIG. 2, 220 and 230 illustrate learning phase and inference (correction) phases, respectively, for compensating UE power amplifier distortion at a BS receiver. In an example implementation, when the gNB becomes aware of UE’s capability, the gNB may start the learning process at 220.

[0035] At 220, gNB 202, upon learning (or becoming aware) of UE’s capability, may initiate the learning phase (also referred to as a training phase). In an example implementation, the goal of the learning phase is to train a power amplifier model (e.g., a machine learning (ML) model) at a gNB receiver to learn the impact of UE’s non-linear distortion to the transmitted signal such that the gNB (e.g., gNB receiver) may compensate for such non-linear distortion.

[0036] In an example implementation, at 222, gNB 202 may schedule (or trigger) UE 204 to transmit a training signal to the gNB. In some implementations, for example, the training signal may be a training reference signal, where a plurality of them may be used to train the power amplifier model at the gNB.

[0037] In some implementations, for example, the training reference signals may be predefined or configured with different backoff values so that the PA model may be trained properly for better results. In other words, the PA model may be trained for wide range of power amplifier distortions. This may be achieved using different sequences from the training reference signals which may be defined with different PAPR characteristics and may be transmitted with different modulations and/or backoff values to have different levels of distortions.

[0038] At 224, UE 204 may transmit training reference signals, as scheduled by the gNB, to gNB 202. In some implementations, for example, the UE may transmit the training reference signals with the pre-defined (or configured) backoff values. In other words, the training reference signals may be transmitted with different transmit powers corresponding to the different backoff values. The training reference signals may be periodical, or they can be triggered aperiodically by uplink resource allocation grant. The triggering may be “one- shot” or “multi-shot.”

[0039] In some implementations, for example, UE 204 may transmit the antenna ports associated with resources for the training reference signals such that one-to-one association is present with demodulation reference signal (DMRS) antenna ports for data/control signals based on the scheduling. Alternatively, in some implementations, for example, the gNB may configure (e.g., explicitly) antenna ports of training reference signal resources to be a subset of associated DMRS antenna ports. In addition, in some implementations, for example, the training reference signal and DMRS symbols may be transmitted with different transmit powers (e.g., with different backoff values). In addition, in some implementations, for example, the training reference signal resources may be transmitted with significantly higher PAPR than the DMRS resources.

[0040] In some implementations, for example, the UE may follow modified requirements (e.g., relaxed EVM requirements or tightened maximum power reduction (MPR) requirements) for transmitting the training reference signals. For example, MPR is the amount that a UE is allowed reduce its maximum transmit power from the nominal maximum transmit power for the corresponding transmission signal or type to fulfill RF requirements like EVM. In an example implementation, DMRS resources may include low peak-to-average ratio (PAR) sequences, e.g., ZC-sequences or time domain pi/2 BPSK - modulated Gold sequences. In additional example implementation, the antenna ports of training reference signal resources may use the same modulation and/or waveform as the antenna port of uplink data/control resources to be transmitted. In an additional example implementation, the antenna port of training reference signal resources may be associated with a pseudo-random sequence using high PAR modulations like orthogonal frequency division multiplexing (OFDM), higher order quadrature amplitude modulation (QAM), or other high PAR sequence.

[0041] In some implementations, for example, the antenna port(s) of training reference signals may be transmitted with a high Tx power such that it experiences non-linear distortion. In an example implementation, the antenna port(s) of training reference signal resources may be time and/or frequency domain multiplexed with antenna port(s) of data and/or control resources. In an additional example implementation, antenna port(s) of DMRS resources may be transmitted with a lower transmission power (or lower PAPR) such that the antenna port(s) do not experience non-linear distortion. This may allow the gNB to differentiate (e.g., isolate) the distortion caused by the radio channel (e.g., based on DMRS) and distortion caused by the power amplifier (to the training reference signal). In addition, in some implementations, for example, the training reference signals may be triggered to be transmitted with multiple transmission occasions in time and/or frequency (e.g., with multiple different PA backoff values). In addition, in some implementations, for example, a training reference signal with a pre-defined backoff value may be sent multiple times.

[0042] At 226, gNB 202 may determine the impact of non-linear distortion for the UE transmitter and the given backoff value. In some implementations, for example, the gNB may determine (or generate) a correction signal for the training signal based at least a difference (e.g., using a subtracting operation) between a corresponding training signal generated at the gNB and the training signal as received at a receiver of the gNB after channel correction. In other words, the gNB may generate the same training signal that is generated at the UE and compute a difference between the training signal generated at the gNB and the training signal as received at the gNB. It should be noted that the correction signal is generated based on the difference between the training signal generated at the gNB and the training signal as received at the gNB after channel correction to isolate the channel distortion so that the channel distortion is not included in the power amplifier distortion.

[0043] In some implementations, for example, the learning phase may be performed multiple times with different backoff values for training the power amplifier model. In addition, in some implementations, for example, learning phase 220 and inference phase may be performed in parallel or in an interleaving manner (e.g., interleaved in time).

[0044] At 230, during the inference (or correction) phase, the gNB may determine correction signals for signals (e.g., data, control, reference signals) received from the UE based at least on the learning of the model performed at 220.

[0045] At 232, gNB 202 may trigger an uplink transmission with a pre-defined backoff value. In some implementations, the uplink transmission may be a control signal, data signal, or a reference signal. In an example implementation, the signal may be pre-defined or configured with a backoff value. In addition, in some implementations, for example, the pre-defined backoff value may be a maximum backoff value (e.g., MPR). Further, the UE may take into account the power control parameters (e.g., current power control parameters) and the signaled backoff/maximum backoff values when determining the actual Tx power.

[0046] At 234, UE 204 may transmit the signal, as scheduled by the gNB, to gNB 202. In some implementations, for example, the UE may transmit the signal with the pre-defined (or configured) backoff value. As described above at 232, the signal transmitted by the UE may include control signal, data signal, or a reference signal.

[0047] At 236, gNB 202 may determine a correction signal for the signal received at the receiver of the gNB. In some implementations, for example, the gNB may determine the correction based at least on the trained model generated during the learning phase at 220. This will allow the gNB to generate the corrected signal which removes the distortion introduced at the UE power amplifier.

[0048] In some implementations, for example, data associated control signaling may be applied in uplink providing information about the Tx power used for data. In such a scenario, data associated control signaling may be transmitted with smaller Tx power and/or PAPR such that it may be received without including much PA distortions and thus can be decoded without PA distortion compensation.

[0049] In some implementations, for example, the UE may be configured to report power backoff value per (logical) antenna port. Based on this report information, the network may be enabled to efficiently utilize the learned model.

[0050] In addition, in some implementations, for example, during the inference phase, the gNB receiver may utilize the learned model and compensate the non-linear distortion based on the transmitted signal of the UE and the given backoff value. In addition, the UE may be allowed to use higher EVM (e.g., lower MPR) when operating in non-linear distortion conditions as described in the present disclosure.

[0051] In some implementations, for example, the UE may indicate capability for assisting gNB in the receiver learning and compensation. The capability signalling may indicate that UE is capable of performing the steps needed for gNB to learn the UEs PA model. The capability signalling may also indicate the extent to which the UE can increase Tx power beyond normal operations / regulatory rules (if not directly defined by the RAN4 specifications for certain UE classes). For example, the UE can indicate max power level or max power increase per modulation or amount of MPR that can be reduced. This may be limited by spectrum mask or some other factor at the transmitter. This may be defined based on a predetermined reference resource allocation or allocations due to dependency on resource allocation width and frequency location on carrier. In some implementations, for example, the indication may also contain associated EVM level per modulation. In addition, in some implementations, for example, the capability signalling may also include additional information on UE Tx structure, e.g., on PA design or model. The gNB may use this information as a priori information in the learning phase.

[0052] In some implementations, for example, the gNB may configure the UE to assist in the receiver operation. The configuration may take place in two phases; one before training/leaming, and before actual operation. The configuration provides information necessary for learning (e.g., ML training or algorithms) as well as limits to max Tx power increase (if not defined by the Specification). The configuration may also include, for example, configuration of training signals or training signal generation and the associated DMRS. In some implementations, for example, the configuration may also include max power level, max power increase per modulation, max MPR reduction, max EVM per modulation. The value may be configured also after training or indicated in DCI for each UL allocation. Further, the configuration (or indication) may depend on the used frequency domain allocation location, size or structure (e.g., contiguous/non-contiguous symbols). In addition, in some implementations, for example, the configuration may also include configuration of downlink control information (DCI) fields needed for ML Rx assistance (e.g., for training reference signals).

[0053] In addition, in some implementations, for example, the scheduling may contain training signal type (e.g., modulation) and Tx power level or Tx power increase or MPR reduction beyond normal MPR in addition to resource allocation.

[0054] In some implementations, for example, the scheduling may optionally include indication whether UE can increase Tx power beyond normal limits or how much it can increase Tx power beyond normal limits (this can be seen also as selection for the UE regulatory framework to follow: existing or new). This may allow gNB dynamically turn off the feature, for example, when the compensation performs poorly.

[0055] Optionally, in some implementations, for example, the transmission may include ML Rx assisting control information. It may, for example, provide gNB with information of used Tx power which may be in a form of power headroom report indicating the used Tx power relative to the configured new max power level. In some implementations, it may be simple indication that UE is using Tx power causing additional distortions at Tx. This data associated control information is sent at low modulation order (e.g., QPSK) or via sequence on predetermined resource elements so that it can be reliably & easily detected even with additional distortions at received signal.

[0056] The present disclosure applies to relay nodes as well, for example, integrated access and backhaul (IAB) node, which may facilitate related functionalities: MT (mobile termination) functionality of IAB node may provide the backhaul connection (e.g., connection between parent distributed unit (DU) and IAB node). The DU (distributed unit) functionality of IAB node may provide the access link connections (e.g., connections between IAB node and UEs, as well as connection(s) between the child backhaul connections for the next-hop IAB nodes).

[0057] FIG. 3 illustrates transmit powers 300 of a training reference signal and a demodulation reference signal, according to an example implementation.

[0058] A receiver at a gNB may not be able to separate power amplifier distortions and channel distortions from the same reference signal (e.g., a training reference signal). In an example implementation, the training reference signal may be associated with a demodulation reference signal. The demodulation reference signal may be transmitted with larger backoff value, for example, as illustrated at 320, to minimize (or eliminate) power amplifier distortion by the demodulation reference signal while the training reference signals are transmitted with smaller backoff values (and/or higher PAPR), for example, as illustrated at 310. In other words, association means that there is a pre-defined DMRS available for training reference signal 310. In an example implementation, each training reference signal may contain two separate parts (e.g., 310 and 320). In an additional example implementation, training reference signal 310 may not involve a dedicated DMRS block, for example, DMRS 320, but the training reference signal 310 may be associated to some existing DMRS (e.g., DMRS used for demodulating physical uplink shared channel, PUSCH). In another additional example implementation, the associated DMRS (e.g., 320) may be transmitted using different OFDM symbol than training reference signal 310.

[0059] In some implementations, for example, the training reference signal and demodulation reference signal may be transmitted in consecutive or non-consecutive symbols. In an example implementation, gNB 202 may determine whether to transmit the training and demodulation reference signals in consecutive or non-consecutive symbols based on subcarrier spacing as, for example, large subcarrier spacing has smaller cyclic prefix and some guard time (e.g., transient time) may be needed to switch the transmit power between the training and demodulation reference signals. In addition, in some implementations, for example, the power difference between a training reference signal and demodulation reference signal may be controlled by gNB 202 to support training for different backoff values.

[0060] In some implementations, for example, the training reference signal may be a pseudo-random sequence using pre-defined or configured higher-order modulations. Therefore, a set of initialization values for a pseudo-random generator may be pre-defined or the set of initialization values may be determined based on time (e.g., slot) index, etc. to generate sufficient number of training reference signals for training the power amplifier model. Further, in some implementations, for example, the training reference signal may occupy a part of a symbol, a full symbol, a plurality of full symbols, etc., and different training reference signals may be transmitted with different backoff values.

[0061] FIG. 4 illustrates amplitudes 400 of a QAM (e.g., 256QAM) based power amplifier modulation training signal and associated demodulation reference signal based on a lower PAR sequence, according to an example implementation.

[0062] As illustrated in FIG. 4, an amplitude 410 of a training reference signal is greater than an amplitude 420 of a demodulation reference signal. This allows for minimizing or eliminating power amplifier distortion due to the channel at a receiver of the gNB. In other words, this allows the receiver to separate power amplifier distortions from distortions caused by the channel.

[0063] FIG. 5 illustrates block diagrams 500 and 550 for learning and compensating phases (or processes) at a base station, according to an example implementation.

[0064] In an example implementation, during learning phase 500, a receiver at a base station, e.g., gNB 202, learns (or trains) a distortion function (e.g., of a PA model) by comparing received training reference signals at the receiver of the gNB to known training reference signals generated at the gNB using a pseudo-random generator. In some implementations, for example, the learning may be performed after estimating the channel distortion and compensating for the channel distortion.

[0065] In some implementations, for example, the amount of learning (or training) may be observed by, for example, testing the learned compensation for the next training reference signal and observing the resulting residual EVM or mean squared error between the compensated (or corrected) and known training reference signal. As the training signals may change between transmissions, this may provide sufficient verification for the learning and control before starting to compensate for the actual data. The correction may be performed for data symbols after sufficient number of learning steps (e.g., after satisfying a threshold value for correction accuracy).

[0066] During inference phase 550, the receiver at the base station applies a correction signal which may be determined based at least on learned distortion function, to the received signal at the receiver of the base station (after channel correction). For example, during the learning phase 500, the receiver at the gNB, at 510, may receive a training reference signal (e.g., after channel correction).

[0067] At 512, the receiver at the gNB may generate the training reference signal using a pseudo-random generator. In some implementations, for example, the training reference signal generated at 512 may be same/similar to the training reference signal generated at the UE.

[0068] At 520, the receiver at the gNB may determine the error signal based at least on the difference between the training reference signal generated at 512 and the training reference signal received at 510. In an example implementation, the difference may be calculated based on a subtraction operation.

[0069] At 530, the learning (or training) of the power amplifier model may include determining the amplitude and/or phase errors based at least on the error signal generated at 520 and the training reference signal received at 510.

[0070] At 540, the trained power amplifier model is generated which may be used to compensate for power amplifier distortion at a base station receiver when receiving signals (e.g., data, control, or reference signals) from the UE.

[0071] In addition, during the inference phase 550, the gNB may, at 560, receive a signal (e.g., data, control, or reference signal).

[0072] At 570, the gNB may analyze the received signal and determine the correction signal 580 based at least on the trained power amplifier model 540.

[0073] At 590, the gNB may generate the corrected signal.

[0074] Thus, the UE power amplifier distortion at a base station receiver may be compensated to improve performance.

[0075] FIG. 6 is a flow chart 600 illustrating compensating UE power amplifier distortion at a base station receiver, according to an example implementation.

[0076] In an example implementation, at block 610, a network node, e.g., gNB 202, may receive a plurality of training signals (e.g., training reference signals) from a user equipment, e.g., UE 204. In some implementations, for example, some of the plurality of training signals may be based on different power backoff values.

[0077] At block 620, the gNB, may train a power amplifier model. In some implementations, for example, the training of the power amplifier model may be based at least on the plurality of training signals and generate a trained power amplifier model which may be used by the gNB for compensating power amplifier distortion at a receiver of the gNB.

[0078] At block 630, the gNB may generate a correction signal for at least a signal received from the UE based at least on the trained power amplifier model. In an example implementation, the signal received from the UE may include at least one of a data signal, control signal, or a reference signal.

[0079] Optionally, at block 640, the gNB may apply the correction signal to the at least one of a data signal, a control signal, and a reference signal to generate a corrected signal.

[0080] In some implementations, for example, the training of the power amplifier model may include receiving a plurality of training reference signals from the UE. The plurality of training signals may be configured with different backoff values so that the power amplifier model is trained with diverse training reference signals. In addition, in some implementations, for example, a correction signal for a training reference signal during the learning phase may be determined based at least on a difference between a corresponding training reference signal generated at the gNB and the training reference signal as received at a receiver of the gNB, for example, after channel correction to remove channel distortion.

[0081] Thus, the UE power amplifier distortion at a base station receiver may be removed to improve performance.

[0082] FIG. 7 is a flow chart 700 illustrating transmission of training reference signals from a UE to a base station for compensating UE power amplifier distortion at the base station, according to an additional example implementation.

[0083] In an example implementation, at block 710, a UE, e.g., UE 204 may receive configuration information for a plurality of training signals associated with a power amplifier model from a network node, e.g., gNB 202. In some implementations, for example, the configuration information may include resource information (e.g., bandwidth, start and end point in terms of PRBs), mapping information (e.g., how resources are associated with sequences), initialization of sequences, backoff values, etc.

[0084] In some implementations, for example, UE 204 may be in a radio resource control (RRC) CONNECTED mode when the UE receives the configuration information and/or transmitting the training signals.

[0085] At block 720, the UE may transmit the plurality of training signals to the gNB. In some implementations, for example, the training signals may be generated based on at least on the configuration information received from the gNB.

[0086] Additional example implementations are described herein.

[0087] Example 1. A method of communications, comprising: receiving, at a network node, a plurality of training signals from at least a user equipment; training, at the network node, a power amplifier model, the training based at least on the plurality of training signals and generating a trained power amplifier model; and generating, at the network node, a correction signal for at least a signal received from the user equipment based at least on the trained power amplifier model, wherein the signal received from the user equipment includes at least one of a data signal, a control signal, and a reference signal. [0088] Example 2. The method of Example 1, further comprising: applying the correction signal to the at least one of a data signal, a control signal, and a reference signal to generate a corrected signal.

[0089] Example 3. The method of any of Examples 1-2, wherein the training of the power amplifier model comprises: determining correction signals for the plurality of training signals, wherein a correction signal for a first training signal of the plurality of training signals received from the user equipment is generated at the network node based at least on a difference between a second training signal generated at the network node and the first training signal as received at a receiver of the network node after a channel correction.

[0090] Example 4. The method of any of Examples 1-3, wherein a training signal of the plurality of training signals is received multiple times.

[0091] Example 5. The method of any of Examples 1-4, wherein the plurality of training signals is scheduled by the network node with one or more pre-defined backoff values.

[0092] Example 6. The method of any of Examples 1-5, wherein a training signal of the plurality of training signals comprises one or more pseudo-random sequences.

[0093] Example 7. The method of any of Examples 1-6, wherein the plurality of training signals as received at the receiver of the network node includes non-linear power amplifier distortion.

[0094] Example 8. The method of any of Examples 1-7, wherein the network node configured antenna ports associated with resources for the plurality of training signals received from the user equipment.

[0095] Example 9. The method of any of Examples 1-8, further comprising: receiving information of antenna ports associated with resources of the plurality of training signals, wherein one-to-one association exists between the antenna ports associated with resources of the plurality of training signals and antenna ports of demodulation reference signal resources.

[0096] Example 10. The method of any of Examples 1-9, wherein a training signal of the plurality of training signals received from the user equipment introduces higher nonlinear distortion than an associated demodulation reference signal received from the user equipment.

[0097] Example 11. The method of any of Examples 1-10, wherein the training signals are triggered to be transmitted with multiple transmission occasions in time and/or frequency.

[0098] Example 12. The method of any of Examples 1-11, wherein the generating of the correction signal is further based on a backoff value of the associated training signal.

[0099] Example 13. The method of any of Examples 1-12, wherein the generating of the correction signal includes generating an amplitude error and/or a phase error.

[0100] Example 14. The method of any of Examples 2-13, wherein applying the correction signal to the signal further comprises: applying the amplitude error and/or phase error to the at least one of a data signal, a control signal, and a reference signal received at the receiver after channel correction to compensate for non-linear distortions introduced at the receiver of the user equipment.

[0101] Example 15. The method of any of Examples 1-14, further comprising: receiving, from the user equipment, an indication that the user equipment is capable of sending training signals to be used for training the power amplifier model at the network node.

[0102] Example 16. The method of any of Examples 1-15, wherein the correction signal for at least the signal received from the user equipment is generated further based on the signal received from the user equipment.

[0103] Example 17. The method of any of Examples 1-16, wherein at least some of the plurality of training signals are based on different power backoff values.

[0104] Example 18. A method of communications, comprising: receiving, by a user equipment, configuration information for a plurality of training signals associated with a power amplifier model from a network node; and transmitting, by the user equipment, the plurality of training signals to the network node, the plurality of training signals generated based on at least on the configuration information received from the network node.

[0105] Example 19. The method of Example 18, wherein the configuration information includes at least one of backoff values, antenna ports, modulation schemes, and/or transmission occasions in time and/or frequency.

[0106] Example 20. The method of any of Examples 18-19, wherein the plurality of training signals is based on one or more power backoff values.

[0107] Example 21. The method of any of Examples 18-20, wherein a training signal of the plurality of training signals introduces a higher power amplifier distortion than an associated demodulation reference signal.

[0108] Example 22. The method of any of Examples 18-21, wherein at least one first training signal of the plurality of training signals causes a higher power amplifier distortion than at least one second training signal of the plurality of training signals, wherein the plurality of training signals is scheduled by the network node with one or more pre-defined backoff values, wherein a training signal of the plurality of training signals comprises one or more pseudo-random sequences, and wherein the plurality of training signals includes non-linear power amplifier distortion.

[0109] Example 23. The method of any of Examples 18-22, further comprising: transmitting an indication to the network node that the user equipment is capable of sending the plurality of training signals to be used for training the power amplifier model at the network node.

[0110] Example 24. The method of any of Examples 1-23, wherein the user equipment is in a radio resource control connected state.

[0111] Example 25. An apparatus comprising means for performing the method of any of Examples 1-24.

[0112] Example 26. A non-transitory computer-readable storage medium comprising instructions stored thereon that, when executed by at least one processor, are configured to cause a computing system to perform the method of any of Examples 1-24.

[0113] Example 27. An apparatus comprising: at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to perform the method of any of Examples 1-24.

[0114] FIG. 8 is a block diagram of a wireless station (e.g., user equipment (UE)/user device or AP/gNB/MgNB/SgNB) 800 according to an example implementation. The wireless station 800 may include, for example, one or more RF (radio frequency) or wireless transceivers 802A, 802B, where each wireless transceiver includes a transmitter to transmit signals and a receiver to receive signals. The wireless station also includes a processor or control unit/entity (controller) 804/806 to execute instructions or software and control transmission and receptions of signals, and a memory 808 to store data and/or instructions.

[0115] Processor 804 may also make decisions or determinations, generate frames, packets or messages for transmission, decode received frames or messages for further processing, and other tasks or functions described herein. Processor 804, which may be a baseband processor, for example, may generate messages, packets, frames or other signals for transmission via wireless transceiver 802 (802A or 802B). Processor 804 may control transmission of signals or messages over a wireless network, and may control the reception of signals or messages, etc., via a wireless network (e.g., after being down-converted by wireless transceiver 802, for example). Processor 804 may be programmable and capable of executing software or other instructions stored in memory or on other computer media to perform the various tasks and functions described above, such as one or more of the tasks or methods described above. Processor 804 may be (or may include), for example, hardware, programmable logic, a programmable processor that executes software or firmware, and/or any combination of these. Using other terminology, processor 804 and transceiver 802 together may be considered as a wireless transmitter/receiver system, for example.

[0116] In addition, referring to FIG. 8, a controller 806 (or processor 804) may execute software and instructions, and may provide overall control for the station 800, and may provide control for other systems not shown in FIG. 8, such as controlling input/output devices (e.g., display, keypad), and/or may execute software for one or more applications that may be provided on wireless station 800, such as, for example, an email program, audio/video applications, a word processor, a Voice over IP application, or other application or software. Moreover, a storage medium may be provided that includes stored instructions, which when executed by a controller or processor may result in the processor 804, or other controller or processor, performing one or more of the functions or tasks described above.

[0117] According to another example implementation, RF or wireless transceiver(s) 802A/802B may receive signals or data and/or transmit or send signals or data. Processor 804 (and possibly transceivers 802A/802B) may control the RF or wireless transceiver 802A or 802B to receive, send, broadcast or transmit signals or data.

[0118] The aspects are not, however, restricted to the system that is given as an example, but a person skilled in the art may apply the solution to other communication systems. Another example of a suitable communications system is the 5G concept. It is assumed that network architecture in 5G will be quite similar to that of the LTE-advanced. 5G is likely to use multiple input - multiple output (MIMO) antennas, many more base stations or nodes than the LTE (a so-called small cell concept), including macro sites operating in co-operation with smaller stations and perhaps also employing a variety of radio technologies for better coverage and enhanced data rates.

[0119] It should be appreciated that future networks will most probably utilize network functions virtualization (NFV) which is a network architecture concept that proposes virtualizing network node functions into “building blocks” or entities that may be operationally connected or linked together to provide services. A virtualized network function (VNF) may comprise one or more virtual machines running computer program codes using standard or general type servers instead of customized hardware. Cloud computing or data storage may also be utilized. In radio communications this may mean node operations may be carried out, at least partly, in a server, host or node operationally coupled to a remote radio head. It is also possible that node operations will be distributed among a plurality of servers, nodes or hosts. It should also be understood that the distribution of labor between core network operations and base station operations may differ from that of the LTE or even be non-existent.

[0120] Implementations of the various techniques described herein may be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. Implementations may be implemented as a computer program product, i.e., a computer program tangibly embodied in an information carrier, e.g., in a machine-readable storage device or in a propagated signal, for execution by, or to control the operation of, a data processing apparatus, e.g., a programmable processor, a computer, or multiple computers. Implementations may also be provided on a computer readable medium or computer readable storage medium, which may be a non-transitory medium. Implementations of the various techniques may also include implementations provided via transitory signals or media, and/or programs and/or software implementations that are downloadable via the Internet or other network(s), either wired networks and/or wireless networks. In addition, implementations may be provided via machine type communications (MTC), and also via an Internet of Things (IOT).

[0121] The computer program may be in source code form, object code form, or in some intermediate form, and it may be stored in some sort of carrier, distribution medium, or computer readable medium, which may be any entity or device capable of carrying the program. Such carriers include a record medium, computer memory, read-only memory, photoelectrical and/or electrical carrier signal, telecommunications signal, and software distribution package, for example. Depending on the processing power needed, the computer program may be executed in a single electronic digital computer or it may be distributed amongst a number of computers.

[0122] Furthermore, implementations of the various techniques described herein may use a cyber-physical system (CPS) (a system of collaborating computational elements controlling physical entities). CPS may enable the implementation and exploitation of massive amounts of interconnected ICT devices (sensors, actuators, processors microcontrollers,...) embedded in physical objects at different locations. Mobile cyber physical systems, in which the physical system in question has inherent mobility, are a subcategory of cyber-physical systems. Examples of mobile physical systems include mobile robotics and electronics transported by humans or animals. The rise in popularity of smartphones has increased interest in the area of mobile cyber-physical systems. Therefore, various implementations of techniques described herein may be provided via one or more of these technologies. [0123] A computer program, such as the computer program(s) described above, can be written in any form of programming language, including compiled or interpreted languages, and can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit or part of it suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network.

[0124] Method steps may be performed by one or more programmable processors executing a computer program or computer program portions to perform functions by operating on input data and generating output. Method steps also may be performed by, and an apparatus may be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).

[0125] Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer, chip or chipset. Generally, a processor will receive instructions and data from a read only memory or a random access memory or both. Elements of a computer may include at least one processor for executing instructions and one or more memory devices for storing instructions and data. Generally, a computer also may include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. Information carriers suitable for embodying computer program instructions and data include all forms of non volatile memory, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks. The processor and the memory may be supplemented by, or incorporated in, special purpose logic circuitry.