FIELD

[0001] The present disclosure relates, in general, to methods, systems, and apparatuses for multiuser multiple-input multiple-output (MIMO) technology.

BACKGROUND

[0002] Multiuser MIMO technology is widely deployed in 4G and 5G networks where the base station can simultaneously transmit signals to multiple users through orthogonal pre-coding / beamforming. With multiuser (MU) MIMO, user equipment (UE) often suffers from interference from signals intended for other UEs due to imperfect channel measurements at the transmitter side. Conventional techniques for interference mitigation, however, either rely on knowledge of the modulation format of the interference signals, or utilize a blind estimation, which may be inefficient and degrade performance.

[0003] Thus, a framework for MU-MIMO, without the need to know the modulation formats of the interference layers, is provided.

SUMMARY

[0004] Tools and techniques for MU-MIMO are provided.

[0005] A method includes receiving, via a user equipment, a reference signal from a base station, the reference signal comprising a first signal associated with the user equipment and an interference signal, and generating, via the user equipment, a first channel estimation of a channel between the user equipment and the base station, based on the reference signal. The method further includes generating, via the user equipment, a second channel estimation of the interference signal at the user equipment, wherein the second channel estimation is generated based, at least in part, on an approximated modulation constellation of the interference signal. The method continues by mitigating, via the user equipment, interference at the user equipment based, at least in part, on the second channel estimation.

[0006] An apparatus includes a non-transitory computer readable medium in communication with the processor, the non-transitory computer readable medium having encoded thereon a set of instructions executable by the processor to perform various functions. The set of instructions may be execu table by the processor to receive, via a user equipment, a reference signal from a base station, the reference signal comprising a first signal associated with the user equipment and an interference signal, and generate, via the user equipment, a first channel estimation of a channel between the user equipment and the base station, based on the reference signal. The set of instructions is further executable by the processor to generate, via the user equipment, a second channel estimation of the interference signal at the user equipment, wherein the second channel estimation is generated based, at least in part, on an approximated modulation constellation of the interference signal, and perform, via the user equipment, noise mitigation based, at least in part, on the second channel estimation.

[0007] A system includes a base station, and a user equipment in communication with the base station. The user equipment further includes a processor, and a non-transitory? computer readable medium in communication with the processor, the non-transitory' computer readable medium having encoded thereon a set of instructions executable by the processor to perform various processes. The set of instructions is executable by the processor to receive a reference signal from the base station, the reference signal comprising a first signal associated with the user equipment and an interference signal, and generate a first channel estimation of a channel between the user equipment and the base station, based on the reference signal. The set of instructions is further executable by the processor to determine a scaling factor of an approximated modulation constellation of the interference signal, wherein the scaling factor is determined based, at least in part, on an error between the approximated modulation constellation and one or more unsealed modulation constellations, and generate a second channel estimation of the interference signal at the user equipment, wherein the second channel estimation is generated based, at least in part, on the approximated modulation constellation of the interference signal. The set of instractions may further be executable by the processor to mitigate interference based, at least in part, on the second channel estimation.

[0008] These illustrative embodiments are mentioned not to limit or define the disclosure, but to provide examples to aid understanding thereof. Additional embodiments are discussed in the Detailed Description, and further description is provided therein.

BRIEF DESCRIPTION OF THE DRAWINGS

[0009] A further und erstanding of the nature and advantages of particular embodiments may be realized by reference to the remaining portions of the specification and the drawings, in which like reference numerals are used to refer to similar components. In some instances, a sub-label is associated with a reference numeral to denote one of multiple similar components. When reference is made to a reference numeral without specification to an existing sub-label, it is intended to refer to all such multiple similar components.

[0010] Fig. 1 is a schematic block diagram of a system for MU-MIMO, in accordance with various embodiments;

[0011] Fig. 2 is a schematic block diagram of a system for joint detection in MU-MIMO using a first noise injection scheme, in accordance with various embodiments;

[0012] Fig. 3 is a schematic block diagram of a system for joint detection in MU-MIMO using a second noise injection scheme, in accordance with various embodiments;

[0013] Fig. 4 is a schematic block diagram of a system for joint detection in MU-MIMO with coarse modulation classification, in accordance with various embodiments;

[0014] Fig. 5 is a flow diagram of one implementation of a method for joint detection in MU-MIMO, in accordance with various embodiments, [0015] Fig. 6 is a schematic block diagram of a computer system joint detection in MU-MIMO, in accordance with various embodiments.

DETAILED DESCRIPTION OF EMBODIMENTS

[0016] Various embodiments provide tools and techniques for joint detection in MU-MIMO.

[0017] In some embodiments, a method for joint detection in MU-MIMO is provided. A method includes receiving, via a user equipment, a reference signal from a base station, the reference signal comprising a first signal associated with the user equipment and an interference signal, and generating, via the user equipment, a first channel estimation of a channel between the user equipment and the base station, based on the reference signal. The method further includes generating, via the user equipment, a second channel estimation of the interference signal at the user equipment, wherein the second channel estimation is generated based, at least in part., on an approximated modulation constellation of the interference signal. The method continues by mitigating, via the user equipment, interference at the user equipment based, at least in part, on the second channel estimation.

[0018] In some examples, the method may further include determining a scaling factor of the approximated modulation constellation of the interference signal, wherein the scaling factor is determined based, at least in part, on an error between the approximated modulation constellation and one or more unsealed modulation constellations. In some examples, the approximated modulation constellation is a quadrature amplitude modulated (QAM) constellation that is scaled by the scaling factor. In further examples, mitigating interference at the user equipment, further includes estimating a noise covariance based, at least in part, on the second channel estimation and the error between the approximated modulation constellation and the one or more unsealed modulation constellations. In further examples, the noise covariance is estimated based on a mean squared error between the approximated modulation constellation and the one or more unsealed modulation constellations, wherein the one or more unsealed modulation constellations includes at least one of quadrature phase-shift key, 16QAM, 64QAM and 25(?QAM constellations. [0019] In further examples, the method further includes selecting, via the user equipment, the scaling factor the approximated modulation constellation from two or more pre-determined scaling factors based on a classification of a modulation constellation of the interference signals. In some examples, the classification is determined based on a maximum likelihood detection (MU)) algorithm. In further examples, classification of the modulation constellation of the interference signals includes classifying the modulation constellation of the interference signals as being part of one of a first set of QAM modulation constellations or a second set of QAM m odul ation constellation s .

[0020] In some embodiments, an apparatus for joint detection in MU-MIMO i s provided. An apparatus includes a non-transitory computer readable medium in communication with the processor, the non-transitory computer readable medium having encoded thereon a set of instructi ons executable by the processor to perform various functions. The set of instructions may be executable by the processor to receive, via a user equipment, a reference signal from a base station, the reference signal comprising a first signal associated with the user equipment and an interference signal, and generate, via the user equipment, a first channel estimation of a channel between the user equipment and the base station, based on the reference signal. The set of instructions is further executable by the processor to generate, via the user equipment, a second channel estimation of the interference signal at the user equipment, wherein the second channel estimation is generated based, at least in part, on an approximated modulation constellation of the interference signal, and perform, via the user equipment, noise mitigation based, at least in part, on the second channel estimation.

[0021] In some examples, the set of instructions may further be executable by the processor to determine a scaling factor of the approximated modulation constellation of the interference signal, wherein the scaling factor is determined based, at least in part, on an error between the approximated modulation constellation and one or more unsealed modulation constellations, wherein the approximated modulation constellation is a quadrature amplitude modulated (QAM) constellation that is scaled by the scaling factor. In some examples, performing noise mitigation further includes estimating a noise covariance based, at least in part, on the second channel estimation and the error between the approximated modulation constellation and the one or more unsealed modulation constellations. In further examples, the noise covariance is estimated based on a mean squared error between the approximated modulation constellation and the one or more unsealed modulation constellations. In some further examples, the noise covariance is estimated based on a mean squared error between the approximated modulation constellation and the one or more unsealed modulation constellations.

[0022] In some examples, the set of instructions is further executable by the processor to select, via the user equipment, the scaling factor the approximated modulation constellation from two or more pre-determined scaling factors based on a classification of a modulation constellation of the interference signals. In some examples, the classification is determined based on a maximum likelihood detection (MLD) algorithm. In further examples, classification of the modulation constellation of the interference signals includes classifying the modulation constellation of the interference signals as being part of a first set of QAM modulation constellations of a plurality of sets of QAM modulation constellations. In some examples, the one or more unsealed modulation constellations includes at least one of quadrature phaseshift key, 16QAM, 64QAM and 256QAM constellations.

[0023] In in further embodiments, a system for joint detection in MU-MIMO is provided. A system includes a base station, and a user equipment in communication with the base station. The user equipment further includes a processor, and a non- transitory computer readable medium in communication with the processor, the non- transitory computer readable medium having encoded thereon a set of instructions executable by the processor to perform various processes. The set of instructions is executable by the processor to receive a reference signal from the base station, the reference signal comprising a first signal associated with the user equipment and an interference signal, and generate a first channel estimation of a channel between the user equipment and the base station, based on the reference signal. The set. of instructions is further executable by the processor to determine a scaling factor of an approximated modulation constellation of the interference signal, wherein the scaling factor is determined based, at least in part, on an error between the approximated modulation constellation and one or more unsealed modulation constellations, and generate a second channel estimation of the interference signal at the user equipment, wherein the second channel estimation is generated based, at least in part, on the approximated modulation constellation of the interference signal. The set of instructions may further be executable by the processor to mitigate interference based, at least in part, on the second channel estimation.

[0024] In some examples, mitigating interference at the user equipment further includes estimating a noise covariance based, at least in part, on the second channel estimation and the error between the approximated modulation constellation and the one or more unsealed modulation constellations. In some examples, the set of instructions is further executable by the processor to select, via the user equipment, the scaling factor the approximated modulation constellation from two or more predetermined scaling factors based on a predicted modulation constellation of the interference signals. In yet further examples, the approximated modulation constellation is a quadrature amplitude modulated (QAM) constellation that is scaled by the scaling factor.

[0025] In the following description, for the purposes of explanation, numerous details are set forth to provide a thorough understanding of the described embodiments. It will be apparent to one skilled in the art, however, that other embodiments may be practiced without some of these details. Several embodiments are described herein, and while various features are ascribed to different embodiments, it should be appreciated that the features described with respect to one embodiment may be incorporated with other embodiments as well. By the same token, however, no single feature or features of any described embodiment should be considered essential to every embodiment of the invention, as other embodiments of the invention may omit such features.

[0026] Similarly, when an element is referred to herein as being "connected" or "coupled" to another element, it is to be understood that the elements can be directly connected to the other element, or have intervening elements present between the elements. In contrast, when an element is referred to as being "directly connected" or "directly coupled" to another element, it should be understood that no intervening elements are present in the "direct" connection between the elements. However, the existence of a direct connection does not exclude other connections, in which intervening elements may be present. [0027] Furthermore, the methods and processes described herein may be described in a particular order for ease of description. However, it should be understood that, unless the context dictates otherwise, intervening processes may take place before and/or after any portion of the described process, and further various procedures may be reordered, added, and/or omitted in accordance with various embodiments.

[0028] Unless otherwise indicated, all numbers used herein to express quantities, dimensions, and so forth should be understood as being modified in ah instances by the term "about." In this application, the use of the singular includes the plural unless specifically stated otherwise, and use of the terms "and" and "or" means "and/or" unless otherwise indicated. Moreover, the use of the term "including," as well as other forms, such as "includes" and "included," should be considered nonexclusive. Also, terms such as "element" or "component" encompass both elements and components comprising one unit and elements and components that comprise more than one unit, unless specifically stated otherwise.

[0029] In one conventional approach to joint detection in MU-MIMO (referred to interchangeably as "joint MIMO detection"), a modulation format of the interference layers must be known (e.g., a modulation order). A blind estimation of the interference layer modulation formats may be utilized for joint detection in MU- MIMO. As used herein, interference layers, interference UEs, and interference ports are used interchangeably to describe interference caused by signals sent to one or more UEs. Modulation formats may include, for example, quadrature phase shift keying (QPSK), and quadrature amplitude modulation (QAM), such as 16QAM, 64QAM, 256QAM, etc. In other conventional approaches, the received signals are projected to a linear space that is orthogonal to the interference, e.g., by using QR decomposition. Single user detection on the projected received signal is then performed.. These approaches, however, lead to increased power consumption, as well as increased computational complexity.

[0030] Accordingly, the embodiments below set forth a more efficient, less computationally intensive approach to joint detection in MU-MIMO. Specifically, approximated modulation constellations are used for joint MIMO detection, where an approximated modulation constellation is selected to minimize an average approximation error.

[0031] Fig. 1 is a schematic block diagram of a system 100 for MU-MIMO, in accordance with various embodiments. The system 100 includes a base station (gNodeB/eNodeB) 105, which further includes layer mapping logic 110, precoding logic 115, one or more antennas 120, first UE (UE1) 125, second UE (UE2) 130, and third UE (UE3) 135. It should be noted that the various components of the system 100 are schematically illustrated in Fig. 1, and that modifications to the various components and other arrangements of system 100 may be possible and in accordance with the various embodiments.

[0032] In various embodiments, the base station 105 may include one or more gNodeB, eNodeB, or other base stations. The base station 105 may, in some examples, include base stations for transmitting and/or receiving communications in a long term evolution (LTE) or fifth-generation new radio (5G NR) network. In various examples, at the basestation 105, data associated with multiple users may be multiplexed through joint layer mapping (via layer mapping logic 110), precoded (via precoding logic 115), and beamformed (via one or more antennas 120). The one or more antennas 120 may include the antennas of an antenna array (physical and/or virtual).

[0033] Under ideal operation, the transmitted signals to each individual UE (UEl 125 - UE3 135) are orthogonal at each UE, such that any individual UE does not observe interference from signals intended for the other respective UEs. However, in practice, multiuser interference is inevitable due to imperfect channel measurements at the eNodeB/gNodeB side, and/or due to movement of the UEs 125- 135. Thus, some interference may be observed by the UEs. For example, UEl 125 may encounter interference from signals as illustrated by the dashed arrows. Thus, UEl 125 may encounter MU interference from signals for UEs, such as UE2 130 and UE3 135.

[0034] At higher frequencies, such as frequency range 2 (FR2) in 5G NR systems, the performance degradation due to MU interference can be quite significant. Therefore, MU-MIMO interference mitigation, such as through joint MIMO detection, on the UE-side can significantly improve its throughput performance. [0035] In various examples, to describe MU-MIMO with a mathematical model, we assume that a UE has N number of Rx antennas and is assigned L _u layers. In addition, assume that there are Lj number of interference layers, with N > L _u 4- L _} . Let x _u (column vector of L _u X 1) be the transmitted signal for the UE from the base station, (column vector of X 1) be the transmitted signal for the interfering UE; H _u (matrix of IV X L _u ) be the channel matrix (also commonly referred to as a "channel state matrix") from the transmitter to the UE and similarly, (matrix of N X Lj) the channel matrix of the interference layer(s). Thus, the system may be described by: where Y (vector of At X 1) is the received signal and N is additive white Gaussian noise (AWGN).

[ 0036] Alternatively, the system can be described in matrix form as: where is the joint channel matrix and x ~ [x _M X/]' is the joint transmitted signal .

[0037 ] Therefore, to mitigate the interference, the UE can perform joint MIMO detection on x treating interference signals as its designated signals with the joint channel matrix Ji. In practice, the UE does not have perfect channel information and thus uses an estimated channel from the pilot symbols. The system equation may thus be written as: and where are used to denote respective estimated channel matrices.

[0038] Fig. 2 i s a schematic block diagram of a system 200 for joint detection in MU-MIMO using a first noise injection scheme, in accordance with various embodiments. System 200 includes one or more antennas 205, receive (Rx) I/Q samples 210, UE RS demodulation logic 215, interference reference signal (RS) demodulation logic 220, UE channel estimation logic 225, interference channel estimation logic 230, first scaling logic 235, second scaling logic 240, noise covariance estimation logic 245, joint MIMO detection logic 250, and UE channel decoding logic 255. It should be noted that the various components of system 200 are schematically illustrated in Fig. 2, and that modifications to the various components and other arrangements of system 200 may be possible and in accordance with the various embodiments.

[ 0039] According to various examples, each of the UE RS demodulation logic 215, interference RS demodulation logic 220, UE channel estimation logic 225, interference channel estimation logic 230, first scaling logic 235, second scaling logic 240, noise covariance estimation logic 245, joint MIMO detection logic 250, and LIE channel decoding logic 255 may be implemented as hardware, software, or a combination of both hardware and software. For example, logic may include, without limitation, software logic, implemented as software and/or firmware code, hardware in the form of circuits, or both.

[0040] In various examples, system 200 may include a receiver and/or a receiver chain of a device, such as UEs 125*135 of Fig. 1, in a MU-MIMO network. The one or more antennas 205 of the system 200 may be configured to receive signals from a base station, which includes UE signals and interference from other UE signals. Rx 17Q samples 210 may refer to in-phase (I) and quadrature (Q) sampling (e.g., 1/Q sampling) of the received signals at the one or more antennas 205, which may include the UE signal (e.g., the signal intended for the UE) and interference signals (e.g., signals intended for other UEs). Accordingly, in various embodiments, Rx L'Q samples 210 include samples of a received Rx signal. The received Rx signal may include, for example, a wireless signal transmitted by a base station, such as an eNodeB, gNodeB, wireless access point, or other suitable base station, to the UE, such as system 200. The Rx I/Q samples 210 may, in some examples, may include a signal of the UE, such as system 200, and interference (e.g., signals for other UEs).

[0041] MU-MIMO joint detection includes the following conditions. First, the number of Rx antennas is greater than the number of UE layers. For example, if a UE has 4 receiver antennas and 2 layers, then it can perform joint MIMO detection to mitigate up to 2 interference layers. Additionally, the UE is able to extract interference layers' pilot symbols in order to subsequently perform channel estimation on the interference layers. Note that, this is possible for fourth generation (4G) LTE transmission modes with UE specific RS, and in 5GNR. Furthermore, with conventional joint MIMO detection, the UE uses knowledge of the modulation formats (e.g., QPSK, 16QAM, 64QAM etc.) of the interference layers to apply detection algorithms, such as maximum likelihood detection (MLD).

[0042] In contrast, various embodiments set forth an approach enabling blind joint MIMO detection to mitigate multi-user interference without the knowledge of the modulation formats of the interference layers. Accordingly, instead of using the exact modulation format information for the interference layers during joint MIMO detection, a hypothetical modulation for all interference layers may be assumed. In other words, an approximated modulation constellation is used for the interference layers during MLD (or any type of near-MLD).

[0043] In various embodiments, the I/Q sampled reference signals may then be demodulated, for example, by UE RS demodulation logic 215 and interference RS demodulation logic 220, and channel estimation performed on the respectively demodulated signals via UE channel estimation logic 325 and interference channel estimation logic 230. Accordingly, in various embodiments, interference channel estimation logic 230 may generate an estimated channel matrix of the interference layer(s), and UE channel estimation logic 225 may generate an estimated channel matrix from the transmitter (e.g., base station) to the UE,

[0044] The interference channel estimation, and specifically the estimated channel matrix of the interference layer, may be scaled by first scaling logic 235 by a factor of c and provided to the joint MIMO detection logic 250, and scaled by second scaling logic 240 by a scaling factor of 1 — e and provided to noise covariance estimation logic 245. Joint MIMO detection logic 250 may thus be configured to perform joint detection of MU-MIMO based on the estimated channel matrices.

[0045] The generation of the approximated modulation constellation is set forth in greater detail below, with reference to Figs. 2 & 3.

[0046] In various examples, denotes the set of regular

QAM constellation symbols for QPSK, 16QAM, 64QAM and 256QAM, respectively. In some further examples, 1024 QAM may further be included. Thus, denotes the set of constellation points for the approximated QAM, and x ^a E M _a denotes a constellation point in that set. Let p _m (m G {2,4, 6, 8}) be the probability for each modulation format being the actual modulation format used for an interference layer by the base station. Thus, a modulation format may be found to best approximate M ₂ , M _4/ M ₆ , M ₈ on average. Accordingly, in some examples, the mean square error (MSE) may be minimized relative to the regular QAM constellations used by the base station. The MSE may be defined as follows:

[0047] In some examples, the probabilities p _m can be calculated offline based on empirical data from a UE's assigned modulation orders. If an assigned modulation order is not available, the assumption of equal probability, i.e., p _m = 1/4 in this case, can be made for all m (e.g., all modulation orders).

[0048] Moreover, because the approximated QAM does not match any constellation exactly, additional noise may effectively be introduced into the system and may be accounted for in a joint demodulation process. The additional noise can be included, for example, in the noise covariance estimation. Specifically, in some examples, let xf denote an approximate transmitted symbols using the approximated QAM. Eq. 1 may then be written alternatively as follows where is the effective noise vector including the additional noise introduced by the approximate QAM and Thus, the new noise covariance may be given as: where £ ² is the minimized mean squared error in Eq. 5. As used here, H _s is used to denote that the channel matrix is an estimated channel matrix of the interference layer, where superscript H denotes the Hermitian transpose operation. Assuming p _m = 1/

4, the minimum of Eq. 5 can be empirically calculated and E = 0.096386772951479.

[0049] Often, the noise covariance may be estimated from pilot symbols where x _u and x _s - are known QPSK symbols. Thus, in some embodiments, a statistically equivalent way of estimating the new noise covariance may be:

Where £ has the same value as in Eq. 7.

As above, H _u is used to denote that the channel matrix is an estimated channel matrix of the UE.

[0050] Fig. 3 is a schematic block diagram of a system 300 for joint detection in MU-MIMO, using a second noise injection scheme, in accordance with various embodiments. System 300 includes one or more antennas 305, Rx I/Q samples 310, UE RS demodulation logic 315, interference RS demodulation logic 320, UE channel estimation logic 325, interference channel estimation logic 330, first scaling logic 335, second scaling logic 340, noise covariance estimation logic 345, joint MIMO detection logic 350, and UE channel decoding logic 355. System 300 further includes an addition block 360. It should be noted that the various components of system 300 are schematically illustrated in Fig. 3, and that modifications to the various components and other arrangements of system 300 may be possible and in accordance with the various embodiments.

[0051] As previously described, Eq. 7 & Eq. 8 are statistically equivalent ways of estimating the noise covariance. Accordingly, whereas Fig. 2 illustrates an implementation of a first noise injection scheme based on Eq. 8, Fig. 3 illustrates a second noise injection scheme based on Eq. 7.

[0052] According to various examples, like Fig. 2, each of the UE RS demodulation logic 315, interference RS demodulation logic 320, UE channel estimation logic 325, interference channel estimation logic 330, first scaling logic 335, second scaling logic 340, addition block 360, noise covariance estimation logic 345, joint MIMO detection logic 350, and UE channel decoding logic 355 may be implemented as hardware, software, or a combination of both hardware and software. For example, logic may include, without limitation, software logic, implemented as software and/or firmware code, hardware in the form of circuits, or both.

[0053] As previously described with respect to system 200 of Fig. 2, system 300 may include a receiver and/or a receiver chain of a device, such as UEs 125-135 of Fig. 1, in a MU-MIMO network. In various embodiments, the T/Q sampled signals may be demodulated, for example, by UE RS demodulation logic 315 and interference RS demodulation logic 320, and channel estimation performed on the respectively demodulated signals via UE channel estimation logic 325 and interference channel estimation logic 330. Accordingly, in various embodiments, interference channel estimation logic 330 may generate an estimated channel matrix of the interference layer, and UE channel estimation logic 330 may generate an estimated channel matrix from the transmitter (e.g., base station) to the UE.

[0054] The interference channel estimation logic 325, and specifically the estimated channel matrix of the interference layer, may be scaled by first scaling logic 335, which scales by a factor of c, and provided to the joint MIMO detection logic 350, and further scaled by second scaling logic 340, which scales by a factor of and provided to noise covariance estimation logic 345 and addition lock 360. Accordingly, the output of the noise covariance estimation logic 345 may be added with the scaled estimation of the interference channel to produce the noise covariance estimation, as described with respect to Eq. 7. Joint MIMO detection logic 350 may thus be configured to perform joint detection of MU-M1M0 based on the estimated channel matrices to generate an approximated QAM constellation (e.g., an approximated modulation constellation) for interference mitigation, for example, by UE channel decoding logic 355. In various examples, joint MIMO logic 350 includes MLD and/or near MLD algorithms. UE channel decoding logic includes interference- aware decoding based on MLD.

[0055] Accordingly, Figs. 2 & 3 provide alternative ways to inject noise into the system to estimate error (e.g., introduced by the approximation error of the approximate QAM). With reference to Figs. 2 & 3, various approximated modulation constellations may be determined based on a minimization of MSE, as described above. [0056] For example, in some embodiments, the set M _a , representing the symbols of an approximated modulation constellation, may be a scaled version of a regular 64QA.M constellation. The scaling is applied to ensure that, the approximation error is minimized relative to all possible modulation formats on average.

Specifically, the constellation points of a regular 64QAM are:

[0057] Thus, in various embodiments, the approximated QAM constellation may be defined as: where c is a scaling factor.

[0058] In various examples, the scaling factor, c, may be determined based on a minimization of MSE, as previously described. In some examples, with the assumption of p _m ~ 1/4, the MSE minimization may be written as:

[0059] The minimization may be solved numerically. In some examples, a scaling factor of c ~ 0.9311 may be utilized, which results in an MSE of -20.32dB. Accordingly, in various examples, the joint MIMO detection logic 250 and/or LIE channel decoding logic 255 may be configured to utilize an approximated QAM constellation, in this example, a scaled 64QAM constellation, for interference mitigation of the interference l ayers.

[0060] In some further examples, may be a scaled version of the regular 256QAM constellation. The scaling is applied to ensure that the approximation error is minimized relative to all possible modulation formats on average. Specifically, the constellation points of a regular 256Q AM are:

[0061] Thus, in various embodiments, the approximated QAM constellation may be defined as:

[0062] With the assumption of p _m = 1/4, the MSE minimization may be written as:

[0063] In various examples, the minimization may be solved numerically, resulting in a scaling factor of c = 0.965 and an MSE of -24.93dB. Relative to embodiments utilizing an approximated 64QAM constellation, the MSE of the approximated 256QAM is smaller.

[0064] In some further embodiments, the set may be a lattice constellation other than regular QAM (e.g., QPSK, 16QAM, 64QAM or 256QAM). For example, an irregular QAM constellation may include 25QAM, 36QAM, etc. Given an order of the QAM, the minimization problem in Eq. 5 may be solved numerically. In some examples, the minimization of MSE may be solved offline via an exhaustive search.

[0065] In further embodiments, multiple sets of approximated modulation constellations may be utilized. In some examples, two sets of approximate QAM may be utilized: and M«. IM!/ may be utilized to approximate QPSK and 16QAM, while M/ is used to approximate 64QAM and 256QAM. Thus, for and M„, respective MSE minimization problems (MSE1 and MSE2) may be solved: and

[0066] In some examples, M/ may be a customized lattice modulation constellation to approximate QPSK and 16QAM, or alternatively, may be a scaled 16QAM constellation. Similarly, in some examples, Ma may be a customized lattice modulation constellation to approximate 64QAM and 256QAM, or alternatively, maybe a scaled 64QAM constellation. The constellation points for M/ and IMI ² . may be calculated numerically by solving the minimization problems in Eq, 15 & Eq. 16. [0067] Fig. 4 is a schematic block diagram of a system 400 for joint detection in MU-MIMO with coarse modulation classification, in accordance with various embodiments. System 400 includes one or more antennas 405, Rx I/Q samples 410, UE RS demodulation logic 415, interference RS demodulation logic 420, UE channel estimation logic 425, interference channel estimation logic 430, first scaling logic 435, second scaling logic 440, noise covariance estimation logic 445, joint MEMO detection logic 450, and UE channel decoding logic 455. System 400 further includes a coarse modulation classification logic 360. It should be noted that the various components of system 300 are schematically illustrated in Fig. 3, and that modifications to the various components and other arrangements of system 300 may be possible and in accordance with the various embodiments.

[0068] According to various examples, like Figs. 2 & 3, each of the UE RS demodulation logic 415, interference RS demodulation logic 420, LIE channel estimation logic 425, interference channel estimation logic 430, first scaling logic 435, second scaling logic 440, noise covariance estimation logic 445, joint MEMO detection logic 450, UE channel decoding logic 455 and coarse modulation classification logic 460, may be implemented as hardware, software, or a combination of both hardware and software. For example, logic may include, without limitation, software logic, implemented as software and/or firmware code, hardware in the form of circuits, or both.

[0069] As previously described with respect to system 200 of Fig. 2, system 400 may utilize a first scaling logic 435, which scales by a factor of c., and second scaling logic 440, which scales by a factor of where i corresponds to a selected set of predicted modulation formats. Thus, in contrast with Fig. 2, different scaling factors may be utilized based on a predicted modulation format (or set of formats) output by the coarse modulation classification logic 460. In some examples, scaling factors may be selected from a plurality of pre-determined scaling factors, based on the predicted modulation format (e.g., the output of the coarse modulation classification logic 460).

[0070] For example, in some embodiments, the coarse modulation classification logic 460 may be configured to receive I/Q samples (e.g., Rx I/Q samples 410), as well as the channel estimates for UE and the interference layers, output respectively by UE channel estimation logic 425 and interference channel estimation logic 430, and determine if an interference layer modulation format is in one of a number of sets. In some examples, the coarse modulation classification logic 460 may be configured to determine whether an interference layer modulation format is in one of the sets of {QPSK, 16QAM} or {64QAM, 256QAM}. It is to be understood in other embodiments, different numbers of sets may be utilized, and each set may further comprise a different number of modulation formats. In some examples, algorithms for modulation format classification may include, without limitation, maximum likelihood-based or higher order statistics-based algorithms.

[0071] The classification result may then be used to select an approximated QAM. For example, for a modulation format predicted to be in the set {QPSK, 16QAM}, Me may be used for joint MEMO detection, where the approximated QAM or other approximated modulation constellation is determined as previously described. Corresponding scaling factors may be used for channel and noise estimates.

[0072] For the set may be used for joint MIMO detection, wherein the approximated QAM or other approximated modulation constellation is determined as previously described. Corresponding scaling factors c ₂ and e ₂ niay be used for channel and noise estimates.

[0073] In some further embodiments, two sets of approximated QAM constellations, may be utilized may be used to approximate QPSK only, while is used to approximate 16QAM, 64QAM and 256QAM. The corresponding scaling factors can be calculated similarly as in the previous embodiments. In this example, the coarse modulation classification logic 460 may be determine whether the modulation format of an interference layer is QPSK.

[0074] Fig. 5 is a flow diagram of one implementation of a method 500 for joint detection in MU-MIMO, in accordance with various embodiments. The method 500 includes, at block 505, receiving, via a UE, a reference signal from a base station. In various examples, the reference signal may include both a signal intended for the UE, as well as an interference signal. As previously described, in some examples, the reference signal may be a simulated signal, in which known pilot symbols are transmitted by the base station to the UE, for both a UE channel and the interference layer.

[0075] The method 500 continues, at block 510, by generating a UE channel estimation based on the reference signal. As previously described, generating the UE channel estimation may include extracting known pilot symbols from the reference signal to generate the UE channel estimation. In various examples, generating the UE channel estimation may include generating a channel matrix characterizing the channel between the UE and the base station.

[0076] At block 515, the method 500 includes generating an interference channel estimation. As previously described, in some examples, generating the interference channel estimation may include generating an approximated modulation constellation. The approximated modulation constellation may, in some examples, be a scaled QAM constellation. As previously described, generating the interference channel estimation may include extracting known pilot symbols of the interference signal from the reference signal to generate the interference channel estimation. In various examples, generating the interference channel estimation may include generating a channel matrix characterizing the interference layer at the UE.

[0077] The method 500 includes, at block 520, classifying a modulation format of the interference layers. As previously described, with respect to Fig. 4, in some examples, the UE may be configured to predict a modulation format of the interference signal. In some examples, a coarse classification of the interference layer may be performed to determine whether the modulation format of the interference layer belongs to a set of two or more modulation formats. For example, in some embodiments, a first set includes QPSK and 16QAM, while a second set includes 64QAM and 256QAM modulation formats. Thus, the UE may classify and/or predict the modulation format of the interference layer as belonging to the first or second set of modulation formats. Depending on the set predicted, a different scaling factor (e.g., scaling factor of an approximated modulation constellation) may be selected.

Accordingly, each set may be associated with a respective scaling factor. In some examples, the scaling factors may be pre-determined [0078] The method 500 continues, at block 525, by determining the scaling factor for the approximated modulation constellation. As previously described, the scaling factor of the approximated modulation constellation may be determined based on an error between the approximated modulation constellation and one or more modulation formats (e.g., QAM modulation formats). In some examples, a scaling factor may be determined empirically, based on minimization of MSE between the approximated modulation constellation (as scaled by the scaling factor), and a set of one or more unsealed modulation constellations. In yet further examples, the scaling factor may be selected from a set of two or more pre-determined scaling factors based on the classification of the modulation format and/or predicted modulation format of the interference signal. Accordingly, in some further examples, the two or more predetermined scaling factors may be determined based on minimization of error (such as MSE) between the modulation formats included in the respective sets of modulation formats, and the approximated modulation constellation.

[0079] The method 500 further includes, at block 530, mitigating interference at the UE. In some examples, mitigating interference at the UE may include performing noise mitigation at the UE, based on an estimated noise covariance, as previously described. In further examples, the noise covariance may be estimated based, at least in part, on the interference channel estimation. As previously described, in some examples, the noise covariance may be estimated utilizing techniques known to those skilled in the art. Furthermore, additional noise introduced by the approximated modulation constellation (e.g., the minimized MSE) may be added to the noise covariance estimate. Accordingly, interference may be accounted for in the joint demodulation and/or MU-MIMO joint detection process.

[0080] The techniques and processes described above with respect to various embodiments may be performed by one or more computer systems. Fig. 6 is a schematic block diagram of a computer system 600 for joint detection in MU-MIMO, in accordance with various embodiments. Fig. 6 provides a schematic illustration of one embodiment of a computer system 600, such as the systems 100, 200, 300, 400, or subsystems thereof which may perform the methods provided by various other embodiments, as described herein. It should be noted that Fig. 6 only provides a generalized illustration of various components, of which one or more of each may be utilized as appropriate. Fig. 6, therefore, broadly illustrates how individual system elements may be implemented in a separated or integrated manner.

[0081] The computer system 600 includes multiple hardware elements that may be electrically coupled via a bus 605 (or may otherwise be in communication, as appropriate). The hardware elements may include one or more processors 610, including, without limitation, one or more general -purpose processors and/or one or more special-purpose processors (such as microprocessors, digital signal processing chips, graphics acceleration processors, and microcontrollers); one or more input devices 615, which include, without limitation, a mouse, a keyboard, one or more sensors, and/or the like; and one or more output devices 620, which can include, without limitation, a display device, and/or the like.

[0082] The computer system 600 may further include (and/or be in communication with) one or more storage devices 625, which can comprise, without limitation, local and/or network accessible storage, and/or can include, without limitation, a disk drive, a drive array, an optical storage device, solid-state storage device such as a random-access memory ("RAM") and/or a read-only memory' ("ROM"), which can be programmable, flash-updateable, and/or the like. Such storage devices may be configured to implement any appropriate data stores, including, without limitation, various file systems, database structures, and/or the like.

[0083] The computer system 600 might also include a communications subsystem 630, which may include, without limitation, a modem, a network card (wireless or wired), an IR communication device, a wireless communication device and/or chipset (such as a Bluetooth™ device, an 802.11 device, a WiFi device, a WiMax device, a WWAN device, a Z-Wave device, a ZigBee device, cellular communication facilities, etc.), and/or a low-power wireless device. The communications subsystem 630 may permit data to be exchanged with a network (such as the network described below, to name one example), with other computer or hardware systems, between data centers or different cloud platforms, and/or with any other devices described herein. In many embodiments, the computer system 600 further comprises a working memory 635, which can include a RAM or ROM device, as described above.

77 [0084] The computer system 600 also may comprise software elements, shown as being currently located within the working memory 635, including an operating system 640, device drivers, executable libraries, and/or other code, such as one or more application programs 645, which may comprise computer programs provided by various embodiments, and/or may be designed to implement methods, and/or configure systems, provided by other embodiments, as described herein. Merely by way of example, one or more procedures described with respect to the method(s) discussed above might be implemented as code and/or instructions executable by a computer (and/or a processor within a computer); in an aspect, then, such code and/or instructions can be used to configure and/or adapt a general purpose computer (or other device) to perform one or more operations in accordance with the described methods.

[0085] A set of these instructions and/or code might be encoded and/or stored on a non-transitory computer readable storage medium, such as the storage device(s) 625 described above. In some cases, the storage medium might be incorporated within a computer system, such as the system 600. In other embodiments, the storage medium might be separate from a computer system (i.e., a removable medium, such as a compact disc, etc.), and/or provided in an installation package, such that the storage medium can be used to program, configure, and/or adapt a general purpose computer with the instructions/code stored thereon. These instructions might take the form of executable code, which is executable by the computer system 600 and/or might take the form of source and/or installable code, which, upon compilation and/or installation on the computer system 600 (e.g., using any of a variety of generally available compilers, installation programs, compression/decompression utilities, etc.) then takes the form of executable code.

[0086] It will be apparent to those skilled in the art that substantial variations may be made in accordance with specific requirements. For example, customized hardware (such as programmable logic controllers, single board computers, FPGAs, ASICs, and SoCs) might also be used, and/or particular elements might be implemented in hardware, software (including portable software, such as applets, etc.), or both. Further, connection to other computing devices such as network input/output devices may be employed. [0087] As mentioned above, in one aspect, some embodiments may employ a computer or hardware system (such as the computer system 600) to perform methods in accordance with various embodiments of the invention. According to a set of embodiments, some or all of the procedures of such methods are performed by the computer system 600 in response to processor 610 executing one or more sequences of one or more instructions (which may be incorporated into the operating system 640 and/or other code, such as an application program 645) contained in the working memory' 635. Such instructions may be read into the working memory/ 635 from another computer readable medium, such as one or more of the storage device(s) 625. Merely by way of example, execution of the sequences of instructions contained in the working memory 635 might cause the processor/ s) 610 to perform one or more procedures of the methods described herein.

[0088] The terms "machine readable medium" and "computer readable medium," as used herein, refer to any medium that participates in providing data that causes a machine to operate in a specific fashion. In an embodiment implemented using the computer system 600, various computer readable media might be involved in providing instructions/code to processor/ s) 610 for execution and/or might be used to store and/or carry' such instructions/code (e.g., as signals). In many implementations, a computer readable medium is a non -transitory', physical, and/or tangible storage medium. In some embodiments, a computer readable medium may take many forms, including, but not limited to, non-volatile media, volatile media, or the like. Non-volatile media includes, for example, optical and/or magnetic disks, such as the storage device(s) 625. Volatile media includes, without limitation, dynamic memory, such as the working memory 635. In some alternative embodiments, a computer readable medium may take the form of transmission media, which includes, without limitation, coaxial cables, copper wire and fiber optics, including the wires that comprise the bus 605, as well as the various components of the communication subsystem 630 (and/or the media by which the communications subsystem 630 provides communication with other devices). In an alternative set of embodiments, transmission media can also take the form of waves (including, without limitation, radio, acoustic, and/or light waves, such as those generated during radiowave and infra-red data communications). [0089] Common forms of physical and/or tangible computer readable media include, for example, a floppy disk, a flexible disk, a hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read instructions and/or code,

[0090] Various forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to the processor(s) 610 for execution. Merely by way of example, the instructions may initially be carried on a magnetic disk and/or optical disc of a remote computer. A remote computer might load the instructions into its dynamic memory/ and send the instructions as signals over a transmission medium to be received and/or executed by the computer system 600. These signals, which might be in the form of electromagnetic signals, acoustic signals, optical signals, and/or the like, are all examples of carrier waves on which instructions can be encoded, in accordance with vari ous embodiments of the invention.

[0091] The communications subsystem 630 (and/or components thereof) generally receives the signals, and the bus 605 then might carry the signals (and/or the data, instructions, etc. carried by the signals) to the working memory 635, from which the processor(s) 610 retrieves and executes the instructions. The instructions received by the working memory 635 may optionally be stored on a storage device 625 either before or after execution by the processor/ s) 610.

[0092] While some features and aspects have been described with respect to the embodiments, one skilled in the art will recognize that numerous modifications are possible. For example, the methods and processes described herein may be implemented using hardware components, software components, and/or any combination thereof. Further, while various methods and processes described herein may be described with respect to particular structural and/or functional components for ease of description, methods provided by various embodiments are not limited to any particular structural and/or functional architecture but instead can be implemented on any suitable hardware, firmware and/or software configuration. Similarly, while some functionality is ascribed to one or more system components, unless the context dictates otherwise, this functionality can be distributed among various other system components in accordance with the several embodiments.

[0093] Moreover, while the procedures of the methods and processes described herein are described in a particular order for ease of description, unless the context dictates otherwise, various procedures may be reordered, added, and/or omitted in accordance with various embodiments. Moreover, the procedures described with respect to one method or process may be incorporated within other described methods or processes; likewise, system components described according to a particular structural architecture and/or with respect to one system may be organized in alternative structural architectures and/or incorporated within other described systems. Hence, while various embodiments are described with or without some features for ease of description and to illustrate aspects of those embodiments, the various components and/or features described herein with respect to a particular embodiment can be substituted, added and/or subtracted from among other described embodiments, unless the context dictates otherwise. Consequently, although several embodiments are described above, it will be appreciated that the invention is intended to cover all modifications and equivalents within the scope of the following claims.