Patent Description:
As resources are limited, it is desirable to optimize the usage of network resources. <CIT> discloses adaptation of the location of points in a constellation in response to channel conditions. The receiver uses knowledge of the geometrically shaped symbol constellation used by the transmitter. A geometrically shaped symbol constellation is used, which optimizes the capacity of the constellation. Performance equivalent or better than that enabled by traditional PSK is achieved by using optimized PSK-<NUM> in conjunction with a single tuning parameter that controls where constellation points should be selected based on SNR.

<CIT> discloses transmitting training symbols to a receiver over a channel, generating a loss function at the receiver and generating updated constellation point positions, by updating the neural network to minimise the loss function. Both geometric (position of the constellation point) and probabilistic (how often constellation point used) shaping are used. End-to-end learning is used and the rpobability function for probabilistic shaping depends on channel information. The transmitter algorithm may be implemented as a look-up table in operational mode.

<NPL> discloses machine learning (ML) used to optimize a waveform to minimize out-of-band emissions and learn constellation shape that facilitates pilotless detection by a simultaneously learned receiver. The constellation is asymmetric.

<NPL> discloses a learned constellation (i.e., geometric shaping(GS)) at the transmitter side, which is jointly optimized together with the neural receiver. No pilots are transmitted. End-to-end learning could hence remove the need and control overhead for demodulation reference signals.

In the following, various exemplary embodiments will be described in greater detail with reference to the accompanying drawings, in which.

In the following, different exemplary embodiments will be described using, as an example of an access architecture to which the exemplary embodiments may be applied, a radio access architecture based on long term evolution advanced (LTE Advanced, LTE-A), new radio (NR, <NUM>), or beyond <NUM>, without restricting the exemplary embodiments to such an architecture, however. It is obvious for a person skilled in the art that the exemplary embodiments may also be applied to other kinds of communications networks having suitable means by adjusting parameters and procedures appropriately. Some examples of other options for suitable systems may be the universal mobile telecommunications system (UMTS) radio access network (UTRAN or E-UTRAN), long term evolution (LTE, substantially the same as E-UTRA), wireless local area network (WLAN or Wi-Fi), worldwide interoperability for microwave access (WiMAX), Bluetooth®, personal communications services (PCS), ZigBee®, wideband code division multiple access (WCDMA), systems using ultra-wideband (UWB) technology, sensor networks, mobile ad-hoc networks (MANETs) and Internet Protocol multimedia subsystems (IMS) or any combination thereof.

<FIG> depicts examples of simplified system architectures showing some elements and functional entities, all being logical units, whose implementation may differ from what is shown. It is apparent to a person skilled in the art that the system may also comprise other functions and structures than those shown in <FIG>.

The exemplary embodiments are not, however, restricted to the system given as an example but a person skilled in the art may apply the solution to other communication systems provided with necessary properties.

<FIG> shows user devices <NUM> and <NUM> configured to be in a wireless connection on one or more communication channels in a cell with an access node (such as (e/g)NodeB) <NUM> providing the cell. The physical link from a user device to a (e/g)NodeB may be called uplink or reverse link and the physical link from the (e/g)NodeB to the user device may be called downlink or forward link. It should be appreciated that (e/g)NodeBs or their functionalities may be implemented by using any node, host, server or access point etc. entity suitable for such a usage.

A communication system may comprise more than one (e/g)NodeB, in which case the (e/g)NodeBs may also be configured to communicate with one another over links, wired or wireless, designed for the purpose. These links may be used for signaling purposes. The (e/g)NodeB may be a computing device configured to control the radio resources of communication system it is coupled to. The (e/g)NodeB may also be referred to as a base station, an access point or any other type of interfacing device including a relay station capable of operating in a wireless environment. The (e/g)NodeB may include or be coupled to transceivers. From the transceivers of the (e/g)NodeB, a connection may be provided to an antenna unit that establishes bidirectional radio links to user devices. The antenna unit may comprise a plurality of antennas or antenna elements. The (e/g)NodeB may further be connected to core network <NUM> (CN or next generation core NGC). Depending on the system, the counterpart on the CN side may be a serving gateway (S-GW, routing and forwarding user data packets), packet data network gateway (P-GW) for providing connectivity of user devices (UEs) to external packet data networks, mobility management entity (MME), access and mobility management function (AMF), or location management function (LMF), etc..

The user device (also called UE, user equipment, user terminal, terminal device, etc.) illustrates one type of an apparatus to which resources on the air interface may be allocated and assigned, and thus any feature described herein with a user device may be implemented with a corresponding apparatus, such as a relay node.

An example of such a relay node may be a layer <NUM> relay (self-backhauling relay) towards the base station. The self-backhauling relay node may also be called an integrated access and backhaul (IAB) node. The IAB node may comprise two logical parts: a mobile termination (MT) part, which takes care of the backhaul link(s) (i.e., link(s) between IAB node and a donor node, also known as a parent node) and a distributed unit (DU) part, which takes care of the access link(s), i.e., child link(s) between the IAB node and UE(s), and/or between the IAB node and other IAB nodes (multi-hop scenario).

Another example of such a relay node may be a layer <NUM> relay called a repeater or a smart repeater. The repeater may amplify a signal received from a base station and forward it to a UE, and/or amplify a signal received from the UE and forward it to the base station.

The user device 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 (mobile phone), smartphone, personal digital assistant (PDA), handset, device using a wireless modem (alarm or measurement device, etc.), laptop and/or touch screen computer, tablet, game console, notebook, and multimedia device. It should be appreciated that a user device may also be a nearly exclusive uplink only device, of which an example may be a camera or video camera loading images or video clips to a network. A user device may also be a device having capability to operate in Internet of Things (IoT) network which is a scenario in which objects may be provided with the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. In some applications, a user device may comprise a small portable device with radio parts (such as a watch, earphones or eyeglasses) and the computation may be carried out in the cloud. The user device (or in some exemplary embodiments a layer <NUM> relay node) may be configured to perform one or more of user equipment functionalities. The user device may also be called a subscriber unit, mobile station, remote terminal, access terminal, user terminal, terminal device, or user equipment (UE) just to mention but a few names or apparatuses.

Mobile cyber physical systems, in which the physical system in question may have inherent mobility, are a subcategory of cyber-physical systems.

<NUM> enables using 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 employing a variety of radio technologies depending on service needs, use cases and/or spectrum available. <NUM> mobile communications may support a wide range of use cases and related applications including video streaming, augmented reality, different ways of data sharing and various forms of machine type applications (such as (massive) machine-type communications (mMTC), including vehicular safety, different sensors and real-time control. <NUM> may be expected to have multiple radio interfaces, namely below <NUM>, cmWave and mmWave, and also being integrable with existing legacy radio access technologies, such as the LTE. Integration with the LTE may be implemented, at least in the early phase, as a system, where macro coverage may be provided by the LTE, and <NUM> radio interface access may come from small cells by aggregation to the LTE. In other words, <NUM> may support both inter-RAT operability (such as LTE-<NUM>) and inter-RI operability (inter-radio interface operability, such as below <NUM> - cmWave, below <NUM> - cmWave - mmWave). One of the concepts considered to be used in <NUM> networks may be network slicing in which multiple independent and dedicated virtual sub-networks (network instances) may be created within the substantially same infrastructure to run services that have different requirements on latency, reliability, throughput and mobility.

The current architecture in LTE networks may be fully distributed in the radio and fully centralized in the core network. The low latency applications and services in <NUM> may need to bring the content close to the radio which leads to local break out and multi-access edge computing (MEC). <NUM> may enable analytics and knowledge generation to occur at the source of the data. This approach may need leveraging resources that may not be continuously connected to a network such as laptops, smartphones, tablets and sensors. MEC may provide a distributed computing environment for application and service hosting. It may also have the ability to store and process content in close proximity to cellular subscribers for faster response time. Edge computing may cover a wide range of technologies such as wireless sensor networks, mobile data acquisition, mobile signature analysis, cooperative distributed peer-to-peer ad hoc networking and processing also classifiable as local cloud/fog computing and grid/mesh computing, dew computing, mobile edge computing, cloudlet, distributed data storage and retrieval, autonomic self-healing networks, remote cloud services, augmented and virtual reality, data caching, Internet of Things (massive connectivity and/or latency critical), critical communications (autonomous vehicles, traffic safety, real-time analytics, time-critical control, healthcare applications).

The communication system may also be able to communicate with other networks, such as a public switched telephone network or the Internet <NUM>, or utilize services provided by them.

Using edge cloud may mean access node operations to be carried out, at least partly, in a server, host or node operationally coupled to a remote radio head (RRH) or a radio unit (RU), or a base station comprising radio parts. It may also be possible that node operations will be distributed among a plurality of servers, nodes or hosts. Carrying out the RAN real-time functions at the RAN side (in a distributed unit, DU <NUM>) and non-real time functions in a centralized manner (in a central unit, CU <NUM>) may be enabled for example by application of cloudRAN architecture.

Some other technology advancements that may be used may be Big Data and all-IP, which may change the way networks are being constructed and managed. <NUM> (or new radio, NR) networks may be designed to support multiple hierarchies, where MEC servers may be placed between the core and the base station or nodeB (gNB). It should be appreciated that MEC may be applied in <NUM> networks as well.

<NUM> may also utilize non-terrestrial communication, for example satellite communication, to enhance or complement the coverage of <NUM> service, for example by providing backhauling. Possible use cases may be providing service continuity for machine-to-machine (M2M) or Internet of Things (IoT) devices or for passengers on board of vehicles, or ensuring service availability for critical communications, and future railway/maritime/aeronautical communications. Satellite communication may utilize geostationary earth orbit (GEO) satellite systems, but also low earth orbit (LEO) satellite systems, in particular mega-constellations (systems in which hundreds of (nano)satellites are deployed). At least one satellite <NUM> in the mega-constellation may cover several satellite-enabled network entities that create on-ground cells.

It is obvious for a person skilled in the art that the depicted system is only an example of a part of a radio access system and in practice, the system may comprise a plurality of (e/g)NodeBs, the user device may have an access to a plurality of radio cells and the system may also comprise other apparatuses, such as physical layer relay nodes or other network elements, etc. At least one of the (e/g)NodeBs or may be a Home(e/g)nodeB.

Furthermore, the (e/g)nodeB or base station may also be split into: a radio unit (RU) comprising a radio transceiver (TRX), i.e., a transmitter (Tx) and a receiver (Rx); one or more distributed units (DUs) that may be used for the so-called Layer <NUM> (L1) processing and real-time Layer <NUM> (L2) processing; and a central unit (CU) (also known as a centralized unit) that may be used for non-real-time L2 and Layer <NUM> (L3) processing. The CU may be connected to the one or more DUs for example by using an F1 interface. Such a split may enable the centralization of CUs relative to the cell sites and DUs, whereas DUs may be more distributed and may even remain at cell sites. The CU and DU together may also be referred to as baseband or a baseband unit (BBU). The CU and DU may also be comprised in a radio access point (RAP).

The CU may be defined as a logical node hosting higher layer protocols, such as radio resource control (RRC), service data adaptation protocol (SDAP) and/or packet data convergence protocol (PDCP), of the (e/g)nodeB or base station. The DU may be defined as a logical node hosting radio link control (RLC), medium access control (MAC) and/or physical (PHY) layers of the (e/g)nodeB or base station. The operation of the DU may be at least partly controlled by the CU. The CU may comprise a control plane (CU-CP), which may be defined as a logical node hosting the RRC and the control plane part of the PDCP protocol of the CU for the (e/g)nodeB or base station. The CU may further comprise a user plane (CU-UP), which may be defined as a logical node hosting the user plane part of the PDCP protocol and the SDAP protocol of the CU for the (e/g)nodeB or base station.

Cloud computing platforms may also be used to run the CU and/or DU. The CU may run in a cloud computing platform, which may be referred to as a virtualized CU (vCU). In addition to the vCU, there may also be a virtualized DU (vDU) running in a cloud computing platform. Furthermore, there may also be a combination, where the DU may use so-called bare metal solutions, for example application-specific integrated circuit (ASIC) or customer-specific standard product (CSSP) system-on-a-chip (SoC) solutions. It should also be understood that the distribution of labour between the above-mentioned base station units, or different core network operations and base station operations, may differ.

Additionally, in a geographical area of a radio communication system, a plurality of different kinds of radio cells as well as a plurality of radio cells may be provided. Radio cells may be macro cells (or umbrella cells) which may be large cells having a diameter of up to tens of kilometers, or smaller cells such as micro-, femto- or picocells. In multilayer networks, one access node may provide one kind of a cell or cells, and thus a plurality of (e/g)NodeBs may be needed to provide such a network structure.

For fulfilling the need for improving the deployment and performance of communication systems, the concept of "plug-and-play" (e/g)NodeBs may be introduced. A network which may be able to use "plug-and-play" (e/g)NodeBs, may include, in addition to Home (e/g)NodeBs (H(e/g)nodeBs), a home node B gateway, or HNB-GW (not shown in <FIG>). A HNB Gateway (HNB-GW), which may be installed within an operator's network, may aggregate traffic from a large number of HNBs back to a core network.

Machine learning (ML) and/or other artificial intelligence (AI) algorithms may be applied in various areas of a communication system. For example, various deep learning-based solutions may be used for enhancing the physical layer performance of wireless communication systems. Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input. Deep learning may be suitable for implementing tasks, for which the optimal solution is very complex or unknown. In the context of the sixth generation (<NUM>) and beyond, end-to-end learning of the whole wireless link may be considered as one possible application for deep learning.

<FIG> provides a simplified illustration of end-to-end learning of a wireless link, where the transmitter <NUM> and receiver <NUM> are trained jointly to communicate over a wireless channel <NUM>. This can be done in a supervised manner by considering the transmitted information bits as the input, and the received bits as the output. The received bits should ideally be equal to the transmitted bits. In <FIG>, LLR is an abbreviation for log-likelihood ratio.

In principle, treating the problem in this way may be based on differentiable models of the components considered during training, including at least the transmitter <NUM>, wireless channel <NUM>, and the receiver <NUM>. In addition, the prominent hardware impairments, such as those stemming from a nonlinear power amplifier (PA), may also need to be included as a differentiable implementation. However, there are also techniques for incorporating non-differentiable components into the learned (trained) system, and some exemplary embodiments may also be applicable to such scenarios. However, it should be noted that some exemplary embodiments may also be applicable with fully differentiable models.

It is also possible to incorporate elements of modulation and waveform schemes into this type of a framework. For instance, the learned (trained) transmitter <NUM> may utilize orthogonal frequency-division multiplexing (OFDM) modulation before and/or after the learned layers, while the receiver <NUM> may demodulate the OFDM symbols before and/or after the learned processing.

A specific example of an end-to-end learned system is one where the transmitter learns the signal constellation to be used for data transfer. A signal constellation is a mapping between bits and complex-valued symbols (to be modulated onto a waveform). The symbols may be represented by constellation points in the signal constellation, wherein a given constellation point represents a pre-defined number of bits. The signal constellation may also be referred to as a constellation or a constellation diagram. This learned constellation may be modulated similarly to a legacy constellation (e.g., quadrature amplitude modulation, QAM, constellation), using any modulation scheme (e.g., OFDM). Learning the constellation jointly with the receiver may facilitate pilot-less transmissions, as the transmitter learns an asymmetric constellation, based on which the receiver may learn to detect the signal without any reference signals (i.e., pilots). This results in a considerably improved spectral efficiency, since more resources can be dedicated to data transmission.

However, a challenge with this approach is to extend it to a practical cellular network. For example, it may not be feasible that the UEs and base stations learn to communicate with a new constellation whenever they encounter one another for the first time. Therefore, the transmitter and receiver should by some means agree on the constellation to be used, without inducing excessive signaling overhead. Moreover, it may be beneficial to support legacy QAM transmissions with pilots as well. Pilots may also be referred to as reference signals herein.

A problem to be solved by some exemplary embodiments is how to ensure that the transmitter and receiver can agree on the used constellation with minimal overhead, while still achieving the spectral efficiency gains of an end-to-end learned system and legacy support. The overhead stemming from communicating full constellation points may result in overhead comparable to that of using pilots in the first place, meaning that a more refined solution may be needed.

Some exemplary embodiments are based on parameterized constellation shapes, which facilitate pilot-less detection by a deep learning-based receiver. The constellation shape may be fully defined by one or more parameters that are efficient to communicate.

A constellation shape, i.e., a shape of a signal constellation, refers to a distribution of the corresponding individual constellation points in the utilized space. For example, the utilized space may be the complex plane, where the complex-valued constellation points represent the phases and amplitudes of individual subcarriers. However, it should be noted that this is just one example, and it is possible to encode the information in other ways as well.

Some exemplary embodiments provide a method for signaling parameters that transform the constellation, thus facilitating pilot-less transmissions to an advanced receiver for improved spectral efficiency. For example, a QAM constellation may be transformed for an ML receiver, i.e., a receiver capable of machine learning. Herein the transformation may be any kind of mapping from one set of values to another.

Furthermore, some exemplary embodiments provide a method for quickly adjusting the constellation shape based on prevailing conditions, by communicating said parameters to the UE. For instance, the parameters can be adjusted based on a predicted or estimated UE velocity.

In one exemplary embodiment, two new downlink control information (DCI) formats are described, which can be used to inform the UE about which constellation shape it should use in the forthcoming physical uplink shared channel (PUSCH) transmissions. For example, the optimal constellation parameters may be obtained from a pre-defined table, or by running the training at the gNB by using a differentiable surrogate UE model and a differentiable channel model matched to the prevailing conditions. Values for the prevailing conditions may be obtained from any relevant source, for example from UEs, access points (e.g., gNBs), or elsewhere.

By jointly learning a constellation and a deep neural network-based receiver, it may be possible to transfer data over a multipath channel without including any pilot symbols in the data transmission(s). The reason for this is the asymmetric nature of the learned constellation, which makes it possible for the receiver to blindly deduce which part of the constellation the received symbols originally resided in. That is, while a legacy QAM constellation is ambiguous in terms of the rotation angle (a <NUM>-degree rotation will result in the original constellation shape), a properly designed asymmetric constellation has no such ambiguity. Since the blind detection is based on investigating the statistics of the received symbols, it should be ensured that this asymmetry exhibits itself throughout the complex plane (or whichever space the data is modulated into).

Some exemplary embodiments consider a case, where the asymmetric constellation follows a pre-defined and parameterized shape, with certain design parameters that can be used to modify the constellation for various channel conditions.

In one exemplary embodiment, the following transformation may be defined for a legacy constellation (e.g., QPSK, <NUM>-QAM, <NUM>-QAM, etc.) in the complex plane, which results in the desired asymmetric properties of the transformed constellation in the complex plane: <MAT> <MAT> where, C is the transformed form of the legacy constellation (e.g., QPSK, <NUM>-QAM, <NUM>-QAM, etc.) in the complex plane, Clegacy is the corresponding constellation point in the legacy constellation in the complex plane, and α, β > <NUM> are the adjustable constellation parameters.

For example, with a legacy QAM constellation in the complex plane, the following transformation may be defined for the legacy QAM constellation, which results in the desired asymmetric properties of the transformed QAM constellation in the complex plane: <MAT> <MAT> where, C is the transformed form of the legacy QAM constellation (e.g., <NUM>-QAM, <NUM>-QAM, etc.), CQAM is the corresponding constellation point in the legacy QAM constellation, and α, β > <NUM> are the adjustable constellation parameters.

This transformation results in a type of convex isosceles trapezoid in the complex plane, whose angle and dimensions may be adjusted based on the prevailing channel conditions. For example, the first parameter value α may be used to adjust the scaling of the imaginary axis of the constellation, while the second parameter value β may be used to control the level of asymmetry of the constellation. The level of asymmetry may also be referred to as slope herein. It should be noted that the ensuing or transformed constellation may be normalized to ensure that the overall transmission power remains substantially the same. This type of a constellation shape is non-ambiguous in terms of both amplitude and phase distortion, facilitating blind detection at the receiver side after being distorted by a multipath channel.

<FIG> illustrates a system block diagram for this type of scheme according to an exemplary embodiment. In <FIG>, blocks <NUM> and <NUM> depict the trainable or adjustable components. The learned parameters <NUM> refer to the parameters α and β described above. For example, a DeepRx-type learned receiver <NUM> may be used, which has been trained to operate with certain constellation shapes without any pilots, processing a single slot at a time (e.g., a slot comprising <NUM> OFDM symbols). DeepRx is a deep learning receiver comprising a deep fully convolutional artificial neural network. In the considered example case, the constellation corresponds to the transformed QAM constellation defined above, where the parameters α and β can be learned and adjusted. Examples of the constellation shape, along with performance comparisons to legacy systems, are presented in <FIG> and <FIG>. Note that it is possible to (i) learn the parameters of the constellation jointly with the DeepRx receiver, or (ii) consider a pre-defined set of constellation shapes and train the DeepRx receiver to support those. Some exemplary embodiments can be applied for the signaling involved in both of these approaches, although the case (ii) of having certain pre-defined values may be a more efficient approach.

With these two adjustable parameters, the parameterized constellation may facilitate efficient signaling of a constellation to be used by the transmitter (e.g., upon initial access by a UE, to be used for PUSCH transmissions), as well as real-time updates to the constellation shape, for example under changing velocity, or under online learning.

For instance, consider a case where an arbitrary constellation is communicated using <NUM> bits per quantity (e.g., <NUM> bits for a single complex-valued constellation point). Assuming a <NUM>-point constellation, <NUM> bits would need to be transmitted to agree on the constellation (or to change the constellation points). Considering the parameterized constellation of some exemplary embodiments, which can be characterized for example with the two parameter values (i.e., <NUM> bits in total), this overhead can be reduced to just <NUM> %. However, in practice, the overhead of the two constellation parameters may be even smaller, for example if they are communicated as indices of a look-up table.

In one exemplary embodiment, the two parameters may have <NUM> discrete values per parameter, meaning that <NUM> bits are used to communicate the values of α and β. Table <NUM> below shows an example for the possible discrete values of α and β according to this exemplary embodiment.

Signaling procedures according to some exemplary embodiments are described in the following. To provide concrete examples, <NUM> uplink (UL) is used as the basis, and the signaling is incorporated by modifying the <NUM> procedures. However, it should be noted that some exemplary embodiments are not limited to <NUM>, and they may also be applicable to <NUM> and beyond.

<FIG> illustrates a signaling diagram according to an exemplary embodiment for the procedure for agreeing on the used constellation at initial access. This exemplary embodiment is illustrated based on the <NUM> random-access procedure, for example.

Referring to <FIG>, in step <NUM>, a UE transmits a physical random-access channel (PRACH) preamble to a base station such as a gNB. The PRACH preamble may also be referred to as Msg1.

In step <NUM>, the gNB transmits physical downlink control channel (PDCCH) downlink control information (DCI) indicating a random-access radio network temporary identifier (RA-RNTI).

In step <NUM>, the gNB transmits a random-access response (RAR) to the UE. The RAR may also be referred to as Msg2. The RAR may comprise information regarding UL grant and a temporary cell radio network temporary identifier (C-RNTI), as well as the values of the constellation parameters α and β.

That is, the RAR UL grant may comprise the following elements in this exemplary embodiment: frequency hopping flag (<NUM> bit), Msg3 PUSCH frequency resource allocation (<NUM> bits), Msg3 PUSCH time resource allocation (<NUM> bits), modulation and coding scheme (MCS) (<NUM> bits), a transmit power control (TPC) command for Msg3 PUSCH (<NUM> bits), a channel state information (CSI) request (<NUM> bit), and the values of the constellation parameters (<NUM> bits).

In step <NUM>, the UE uses the transformed constellation corresponding to the values of the constellation parameters to transmit an RRC setup request to the gNB, omitting the pilots (e.g., DMRS and PTRS) from the transmitted signal.

In step <NUM>, the gNB transmits PDCCH DCI indicating a C-RNTI to the UE without including the pilots in the PDCCH transmission.

In step <NUM>, the gNB transmits an RRC setup message to the UE. The RRC setup message may also be referred to as Msg4.

After the UE has successfully received the RRC setup message and the access procedure is finalized, the UE continues to use the values of the constellation parameters α and β, when communicating with the gNB, unless instructed otherwise. It should also be noted that, if the constellation parameters are such that the transformation results in legacy QAM symbols, the UE should resort to the legacy QAM symbols and include the demodulation reference signal (DMRS) and phase-tracking reference signal (PTRS) pilots to the signal to be transmitted. As a non-limiting example, parameter values α = <NUM> and β = <NUM> may result in the legacy QAM symbols.

It may also be possible to adapt the constellation parameters after initial access, for example if the velocity of the UE changes. There may be also other changes in the environment that can be used to trigger a change in the constellation parameters (such as a reduction in performance not explained by signal-to-noise-ratio, SNR, or a significant reduction in the average SNR). In this case, the constellation parameters may be communicated for example with DCI, which is illustrated in <FIG>.

<FIG> illustrates a signaling diagram according to an exemplary embodiment. Using again <NUM> signaling as the basis, a new DCI Format 4_1 is introduced herein, which is otherwise similar to DCI Format 0_1, but instead of the DMRS and PTRS fields (PTRS - DMRS association & DMRS sequence init. ), it comprises the values of the constellation parameters (two <NUM>-bit fields for α and β, respectively). This format may be used whenever the base station detects a need to adjust the constellation parameters. <FIG> illustrates PUSCH scheduling with DCI Format 4_1, when constellation parameter values are provided to the UE.

Referring to <FIG>, in step <NUM>, a base station such as a gNB detects a change in UE context. The change may comprise at least one of: a change in a velocity of the UE, a change in channel conditions between the gNB and the UE, and/or a change in average received signal power from the UE. In other words, the gNB detects a need to adjust the constellation parameters.

In step <NUM>, in response to the detection, the gNB transmits, to the UE, DCI Format 4_1 comprising new values for the constellation parameters α and β.

In step <NUM>, the UE uses the transformed constellation corresponding to the new values of the constellation parameters to transmit one or more PUSCH transmissions to the gNB.

In case there is no need to adjust the constellation parameters, but the PUSCH transmissions still use the previously agreed transformed constellation without using pilots, DCI Format 4_0 may be used for scheduling PUSCH. This signaling is illustrated in <FIG>.

<FIG> illustrates a signaling diagram according to an exemplary embodiment for PUSCH scheduling with DCI Format 4_0, when no changes in constellation parameters are needed. DCI Format 4_0 is otherwise similar to DCI Format 4_1, but it does not contain the fields for α and β. In other words, DCI Format 4_0 is similar to the currently specified DCI Format 0_1, but without the DMRS and PTRS related fields.

Referring to <FIG>, in step <NUM>, a base station such as a gNB transmits DCI Format 4_0 to a UE, when no change is needed in the constellation parameters α and β, thus continuing pilot-less PUSCH transmissions.

In step <NUM>, the UE uses the previously agreed transformed constellation corresponding with the previous constellation parameter values to transmit one or more PUSCH transmissions without any pilots.

In case the UE should start to use legacy PUSCH with pilots (DMRS and PTRS), the gNB may schedule the PUSCH using the DCI Format 0_1. This may implicitly instruct the UE to set α = <NUM> and β = <NUM>, and to insert pilots to the upcoming PUSCH transmissions.

<FIG> illustrates a flow chart according to an exemplary embodiment to describe the UE behaviour on a high level. In other words, the steps illustrated in <FIG> may be performed by an apparatus such as, or comprised in, a UE. The UE may also be referred to as a user device, user equipment, or terminal device herein.

Referring to <FIG>, in step <NUM>, the UE performs an initial access procedure (i.e., random-access procedure) to obtain initial access to a gNB. During the initial access procedure, the UE obtains initial constellation parameter values from the gNB.

In step <NUM>, the UE stores the initial constellation parameter values from the initial access to its memory.

In step <NUM>, the UE determines whether or not to use a transformed constellation corresponding to the initial constellation parameter values.

In step <NUM>-<NUM>, if the UE determines to use the transformed constellation (<NUM>: yes), then the UE uses the constellation parameter values to transform the constellation.

In step <NUM>-<NUM>, following step <NUM>-<NUM>, the UE generates a PUSCH signal based on the transformed constellation.

Alternatively, in step <NUM>-<NUM>, if the UE determines to not use the transformed constellation (<NUM>: no), then the UE generates a PUSCH signal without using the transformed constellation.

In step <NUM>-<NUM>, following step <NUM>-<NUM>, the UE inserts DMRS and PTRS to the PUSCH signal.

In step <NUM>, following step <NUM>-<NUM> or step <NUM>-<NUM>, the UE transmits the generated PUSCH signal.

In step <NUM>, the UE receives DCI from the gNB.

In step <NUM>, the UE determines the format of the DCI.

In step <NUM>-<NUM>, if the DCI format is DCI Format 4_1 (<NUM>: 4_1), then the UE stores the new constellation parameter values provided in the DCI Format 4_1 to its memory, and the process returns to step <NUM>-<NUM>, wherein the UE uses the most recently provided values of the constellation parameters provided in the DCI Format 4_1.

Alternatively, if the DCI format is DCI Format 4_0 (<NUM>: 4_0) indicating that no change in the constellation parameters is needed, then the process returns to step <NUM>-<NUM> following step <NUM>. In other words, if the PUSCH is scheduled with DCI Format 4_0, then the UE utilizes the constellation parameter values it has in its memory from either the initial access or from the most recent DCI Format 4_1.

In step <NUM>-<NUM>, if the DCI format is DCI Format 0_1 (<NUM>: 0_1), i.e., no transformation is instructed, then the UE sets the values of the constellation parameters as α = <NUM> and β = <NUM>, and the process returns to step <NUM>-<NUM>. In other words, the UE resorts to legacy PUSCH transmission with DMRS and PTRS upon receiving DCI Format 0_1.

As for the gNB behavior, the selection of constellation transformation parameters may depend on the receiver capabilities and amount of knowledge regarding the UE channel conditions. If the gNB has an ML receiver capable of pilot-less detection, it may use the transformed constellation by scheduling PUSCH with DCI Format 4_0 or 4_1, as this may increase the UL throughput. The optimal constellation parameters for the current scenario can be then deduced based on UL channel estimates and any other relevant information, such as UE velocity estimates. On the other hand, a gNB with no ML receiver may specify α = <NUM> and β = <NUM> at initial access and schedule PUSCH with DCI Formats 0_0 and 0_1.

<FIG> illustrates a flow chart according to an exemplary embodiment. The steps illustrated in <FIG> may be performed by an apparatus such as, or comprised in, a UE. The UE may also be referred to as a user device, user equipment, or terminal device herein.

Referring to <FIG>, in step <NUM>, a signal constellation is transformed based on one or more parameter values, wherein the one or more parameter values indicate a shape of the transformed signal constellation.

The shape of the transformed signal constellation refers to a distribution of the corresponding individual constellation points in the utilized space. For example, the utilized space may be the complex plane, where the complex-valued constellation points represent the phases and amplitudes of individual subcarriers. However, it should be noted that this is just one example, and it is possible to encode the information in other ways as well.

For example, the signal constellation may be transformed by adjusting a scaling of an imaginary axis of the signal constellation based on a first parameter value of the one or more parameter values, and controlling a level of asymmetry of the signal constellation based on a second parameter value of the one or more parameter values. Herein the transformation may refer to a mathematical transformation.

In step <NUM>, one or more signals are transmitted based at least partly on the transformed signal constellation. The one or more signals may be transmitted without any pilot symbols. The one or more signals may be transmitted to a receiver capable of machine learning, such as DeepRx.

<FIG> illustrates a flow chart according to an exemplary embodiment. The steps illustrated in <FIG> may be performed by an apparatus such as, or comprised in, a network element of a wireless communication network.

Referring to <FIG>, in step <NUM>, one or more parameter values are indicated to a UE, wherein the one or more parameter values indicate a shape of a transformed signal constellation. For example, a first parameter value of the one or more parameter values may indicate a scaling of an imaginary axis of the transformed signal constellation, and a second parameter value of the one or more parameter values may indicate a level of asymmetry of the transformed signal constellation. The UE may also be referred to as a user device, user equipment, or terminal device herein.

In step <NUM>, one or more signals are received from the UE based at least partly on the transformed signal constellation. The one or more signals may be received without any pilot symbols. The one or more signals may be received by using a receiver capable of machine learning, such as DeepRx.

The steps and/or blocks described above by means of <FIG> are in no absolute chronological order, and some of them may be performed simultaneously or in an order differing from the described one. Other steps and/or blocks may also be executed between them or within them.

A technical advantage provided by some exemplary embodiments is that they reduce signaling overhead for communicating the used constellation shape, thus facilitating efficient pilot-less transmissions. Furthermore, some exemplary embodiments may enable using an optimal constellation shape for a given scenario, thanks to the low overhead of communicating the constellation shape. Moreover, some exemplary embodiments facilitate the use of constellation shaping in cellular networks.

<FIG> illustrates an example of a learned constellation shape <NUM> according to an exemplary embodiment, wherein the values for α and β have been learned jointly with a DeepRx-type receiver under a UE velocity range of <NUM>-<NUM>/s. In <FIG>, the constellation shape <NUM> represents a distribution of the constellation points of a transformed constellation on the complex plane, where the horizontal axis (x-axis) is the real part, and the vertical axis (y-axis) is the imaginary part. The curves <NUM>, <NUM>, <NUM> represent the bit error rate (BER), i.e., proportion of erroneously detected bits, with respect to the signal-to-noise ratio (SNR) of the received signal.

In <FIG>, the BER of the transformed constellation and DeepRx receiver is represented by the curve <NUM>, together with the BER of a legacy scheme represented by the curve <NUM>, both with DMRS-based channel estimate, and the BER of a known channel response represented by the curve <NUM> (the latter represents the upper bound of the achievable performance). The DMRS configuration used by the legacy baseline comprises two DMRS OFDM symbols per slot, wherein a given slot comprises <NUM> OFDM symbols. The benefit of utilizing the transformed constellation and DeepRx receiver is evident, as it achieves similar or better BER than the legacy receiver, despite not having the additional overhead of DMRS. Thus, the transformed constellation achieves higher spectral efficiency than the legacy receiver which utilizes pilots. In this example, the learned parameter values are α ≈ <NUM> and β ≈ <NUM>.

<FIG> illustrates an example of a constellation shape <NUM> that has been learned under a higher UE mobility of <NUM>-<NUM>/s according to an exemplary embodiment, in order to more clearly illustrate the effect of velocity on the constellation shape. In <FIG>, the constellation shape <NUM> represents a distribution of the constellation points of a transformed constellation on the complex plane, where the horizontal axis (x-axis) is the real part, and the vertical axis (y-axis) is the imaginary part. The curves <NUM>, <NUM>, <NUM> represent the BER with respect to the SNR of the received signal.

In <FIG>, the curve <NUM> represents the BER of the transformed constellation and DeepRx receiver, the curve <NUM> represents the BER of a legacy receiver, and the curve <NUM> represents the BER of a known channel response (i.e., upper bound of the achievable performance). As can be observed by comparing <FIG> and <FIG>, the higher velocity needs a steeper angle for the slope, to facilitate accurate detection of the quickly changing channel. Therefore, the gNB may increase β and reduce α, if the gNB estimates an increase in the UE velocity. Even though this reduces the separation of the constellation points, this is a favorable trade-off to make due to the challenging channel conditions. Indeed, when investigating the BER of the transformed constellation and DeepRx receiver according to some exemplary embodiments, it can be observed that they outperform the legacy DMRS-based system with a considerable margin. In this example, the learned parameter values are α ≈ <NUM> and β ≈ <NUM>.

<FIG> illustrates an apparatus <NUM>, which may be an apparatus such as, or comprised in, a terminal device, according to an exemplary embodiment. The terminal device may also be referred to as a UE or user equipment herein. The apparatus <NUM> comprises a processor <NUM>. The processor <NUM> interprets computer program instructions and processes data. The processor <NUM> may comprise one or more programmable processors. The processor <NUM> may comprise programmable hardware with embedded firmware and may, alternatively or additionally, comprise one or more application-specific integrated circuits (ASICs).

The processor <NUM> is coupled to a memory <NUM>. The processor is configured to read and write data to and from the memory <NUM>. The memory <NUM> may comprise one or more memory units. The memory units may be volatile or non-volatile. It is to be noted that in some exemplary embodiments there may be one or more units of non-volatile memory and one or more units of volatile memory or, alternatively, one or more units of non-volatile memory, or, alternatively, one or more units of volatile memory. Volatile memory may be for example random-access memory (RAM), dynamic random-access memory (DRAM) or synchronous dynamic random-access memory (SDRAM). Non-volatile memory may be for example read-only memory (ROM), programmable read-only memory (PROM), electronically erasable programmable read-only memory (EEPROM), flash memory, optical storage or magnetic storage. In general, memories may be referred to as non-transitory computer readable media. The memory <NUM> stores computer readable instructions that are executed by the processor <NUM>. For example, non-volatile memory stores the computer readable instructions and the processor <NUM> executes the instructions using volatile memory for temporary storage of data and/or instructions.

The computer readable instructions may have been pre-stored to the memory <NUM> or, alternatively or additionally, they may be received, by the apparatus, via an electromagnetic carrier signal and/or may be copied from a physical entity such as a computer program product. Execution of the computer readable instructions causes the apparatus <NUM> to perform one or more of the functionalities described above.

In the context of this document, a "memory" or "computer-readable media" or "computer-readable medium" may be any non-transitory media or medium or means that can contain, store, communicate, propagate or transport the instructions for use by or in connection with an instruction execution system, apparatus, or device, such as a computer.

The apparatus <NUM> may further comprise, or be connected to, an input unit <NUM>. The input unit <NUM> may comprise one or more interfaces for receiving input. The one or more interfaces may comprise for example one or more temperature, motion and/or orientation sensors, one or more cameras, one or more accelerometers, one or more microphones, one or more buttons and/or one or more touch detection units. Further, the input unit <NUM> may comprise an interface to which external devices may connect to.

The apparatus <NUM> may also comprise an output unit <NUM>. The output unit may comprise or be connected to one or more displays capable of rendering visual content, such as a light emitting diode (LED) display, a liquid crystal display (LCD) and/or a liquid crystal on silicon (LCoS) display. The output unit <NUM> may further comprise one or more audio outputs. The one or more audio outputs may be for example loudspeakers.

The apparatus <NUM> further comprises a connectivity unit <NUM>. The connectivity unit <NUM> enables wireless connectivity to one or more external devices. The connectivity unit <NUM> comprises at least one transmitter and at least one receiver that may be integrated to the apparatus <NUM> or that the apparatus <NUM> may be connected to. The at least one transmitter comprises at least one transmission antenna, and the at least one receiver comprises at least one receiving antenna. The connectivity unit <NUM> may comprise an integrated circuit or a set of integrated circuits that provide the wireless communication capability for the apparatus <NUM>. Alternatively, the wireless connectivity may be a hardwired application-specific integrated circuit (ASIC). The connectivity unit <NUM> may comprise one or more components such as a power amplifier, digital front end (DFE), analog-to-digital converter (ADC), digital-to-analog converter (DAC), frequency converter, (de)modulator, and/or encoder/decoder circuitries, controlled by the corresponding controlling units.

It is to be noted that the apparatus <NUM> may further comprise various components not illustrated in <FIG>. The various components may be hardware components and/or software components.

The apparatus <NUM> of <FIG> illustrates an exemplary embodiment of an apparatus such as, or comprised in, a network element of a wireless communication network. The network element may also be referred to, for example, as a network node, a RAN node, a NodeB, an LTE evolved NodeB (eNB), a gNB, a base station, an NR base station, a <NUM> base station, an access node, an access point (AP), a relay node, a repeater, a smart repeater, an integrated access and backhaul (IAB) node, an IAB donor node, a distributed unit (DU), a central unit (CU), a baseband unit (BBU), a radio unit (RU), a radio head, a remote radio head (RRH), or a transmission and reception point (TRP).

The apparatus <NUM> may comprise, for example, a circuitry or a chipset applicable for realizing some of the described exemplary embodiments. The apparatus <NUM> may be an electronic device comprising one or more electronic circuitries. The apparatus <NUM> may comprise a communication control circuitry <NUM> such as at least one processor, and at least one memory <NUM> including a computer program code (software) <NUM> wherein the at least one memory and the computer program code (software) <NUM> are configured, with the at least one processor, to cause the apparatus <NUM> to carry out some of the exemplary embodiments described above. Herein computer program code may refer to instructions that cause the apparatus <NUM> to perform some of the exemplary embodiments described above. That is, the at least one processor and the at least one memory <NUM> storing the instructions may cause said performance of the apparatus.

The processor is coupled to the memory <NUM>. The processor is configured to read and write data to and from the memory <NUM>. The memory <NUM> may comprise one or more memory units. The memory units may be volatile or non-volatile. It is to be noted that in some exemplary embodiments there may be one or more units of non-volatile memory and one or more units of volatile memory or, alternatively, one or more units of non-volatile memory, or, alternatively, one or more units of volatile memory. Volatile memory may be for example random-access memory (RAM), dynamic random-access memory (DRAM) or synchronous dynamic random-access memory (SDRAM). Non-volatile memory may be for example read-only memory (ROM), programmable read-only memory (PROM), electronically erasable programmable read-only memory (EEPROM), flash memory, optical storage or magnetic storage. In general, memories may be referred to as non-transitory computer readable media. The memory <NUM> stores computer readable instructions that are executed by the processor. For example, non-volatile memory stores the computer readable instructions and the processor executes the instructions using volatile memory for temporary storage of data and/or instructions.

The memory <NUM> may be implemented using any suitable data storage technology, such as semiconductor-based memory devices, flash memory, magnetic memory devices and systems, optical memory devices and systems, fixed memory and/or removable memory. The memory may comprise a configuration database for storing configuration data. For example, the configuration database may store a current neighbour cell list, and, in some exemplary embodiments, structures of the frames used in the detected neighbour cells.

The apparatus <NUM> may further comprise a communication interface <NUM> comprising hardware and/or software for realizing communication connectivity according to one or more communication protocols. The communication interface <NUM> comprises at least one transmitter (Tx) and at least one receiver (Rx) that may be integrated to the apparatus <NUM> or that the apparatus <NUM> may be connected to. The communication interface <NUM> provides the apparatus with radio communication capabilities to communicate in the cellular communication system. The communication interface may, for example, provide a radio interface to terminal devices. The apparatus <NUM> may further comprise another interface towards a core network such as the network coordinator apparatus and/or to the access nodes of the cellular communication system. The apparatus <NUM> may further comprise a scheduler <NUM> that is configured to allocate resources. The scheduler <NUM> may be configured along with the communication control circuitry <NUM> or it may be separately configured.

As used in this application, the term "circuitry" may refer to one or more or all of the following: a) hardware-only circuit implementations (such as implementations in only analog and/or digital circuitry); and b) combinations of hardware circuits and software, such as (as applicable): i) a combination of analog and/or digital hardware circuit(s) with software/firmware and ii) any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone, to perform various functions); and c) hardware circuit(s) and/or processor(s), such as a microprocessor(s) or a portion of a microprocessor(s), that requires software (for example firmware) for operation, but the software may not be present when it is not needed for operation.

The techniques and methods described herein may be implemented by various means. For example, these techniques may be implemented in hardware (one or more devices), firmware (one or more devices), software (one or more modules), or combinations thereof. For a hardware implementation, the apparatus(es) of exemplary embodiments may be implemented within one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), graphics processing units (GPUs), processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described herein, or a combination thereof. For firmware or software, the implementation can be carried out through modules of at least one chipset (for example procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in a memory unit and executed by processors. The memory unit may be implemented within the processor or externally to the processor. In the latter case, it can be communicatively coupled to the processor via various means, as is known in the art. Additionally, the components of the systems described herein may be rearranged and/or complemented by additional components in order to facilitate the achievements of the various aspects, etc., described with regard thereto, and they are not limited to the precise configurations set forth in the given figures, as will be appreciated by one skilled in the art.

Claim 1:
An apparatus comprising at least one processor, and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured, with the at least one processor, to cause the apparatus to:
transform a signal constellation based on one or more parameter values, wherein the one or more parameter values indicate a shape of the transformed signal constellation; and
transmit one or more signals based at least partly on the transformed signal constellation, wherein the signal constellation is transformed by adjusting a scaling of an imaginary axis of the signal constellation based on a first parameter value of the one or more parameter values, and controlling a level of asymmetry of the signal constellation based on a second parameter value of the one or more parameter values.