CN117917870A - Wireless station device and wireless communication method executed by wireless station device - Google Patents

Abstract

The present disclosure relates to a wireless station apparatus and a wireless communication method performed by the wireless station apparatus. In an exemplary embodiment, a system and method of calculating a near Maximum Likelihood Detection (MLD) performance capability signal to interference plus noise ratio (SINR) from instructions stored in a non-transitory computer readable memory are disclosed that when executed by a processor of a multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) wireless communication receiver device cause the processor to: a processor obtains a channel estimate Hi and a noise variance for each subcarrier in a set of subcarriers between a MIMO-OFDM wireless communication transmitter device and the wireless communication receiver deviceThe average received bit inter-information rate (RBIR) over all subcarriers is calculated, the average RBIR is converted into an effective SINR, and a Modulation Coding Scheme (MCS) is selected.

Description

Wireless station device and wireless communication method executed by wireless station device

Technical Field

The present application relates to the field of communications, and in particular, to a wireless station apparatus and a wireless communication method performed by the wireless station apparatus. Embodiments disclosed herein include the ability to analyze channel orthogonality of SISO (Single-input Single-output) or MIMO (Multi-input Multi-output) wireless Local Area Network (LAN) systems using link adaptation of an adaptive modulation coding (Adaptive Modulation and Coding, AMC) scheme, and to provide near maximum likelihood detection (Maximum Likelihood Detection, MLD) performance by upper and lower limits using an MMSE detector (detector) and an ideal (gene-air) Interference Free (IF) detector. The effective SINR (Signal to Interference plus Noise Ratio ) may be calculated by a relationship between the upper and lower limits and may be used to estimate channel quality. The selection of the MCS (Modulation Coding Scheme ) may be determined using the estimated channel quality.

Background

Orthogonal frequency division multiplexing (Orthogonal Frequency Division Multiplexing, OFDM), which is one of many wireless communication technologies supporting high-speed mobile communication, is used in many fields, such as 3GPP (third generation partnership project) LTE (long term evolution), 5G NR (fifth generation mobile communication technology new air interface), WLAN (wireless local area network), etc., as a multi-carrier modulation method. OFDM is operable to convert a serial input symbol vector into parallel vectors, and the converted parallel vectors are modulated into subcarriers having mutual orthogonality. In addition, OFDM is designed for high-speed packet data transmission, which can be achieved by effectively using a transmission bandwidth or canceling intersymbol interference caused by a frequency selective fading channel. Packet-by-packet transmission may be performed over a frequency selective channel without using an equalizer.

MIMO systems can achieve considerable throughput gains compared to SISO systems because they are capable of transmitting multiple independent data streams. Thus, the most common form of recent wireless communication systems is MIMO-OFDM, i.e. a combination of MIMO and OFDM techniques.

MIMO-OFDM technology is incorporated and used in many communication standards, and is particularly widely implemented in WLANs. WLAN standards promulgated by the Institute of Electrical and Electronics Engineers (IEEE) are commonly known under the 802.11 designation and are specifically a set of media access Control (MEDIA ACCESS Control, MAC) and physical layer (PHYSICAL LAYER, PHY) protocols. OFDM was first adopted in 802.11a published around 1999, while MIMO-OFDM was adopted in 802.11n published around 2008. High order modulation (e.g., up to 256-QAM (Quadrature Amplitude Modulation, quadrature amplitude modulation)) and Multi-User (MU) MIMO techniques are incorporated into the 802.11ac standard promulgated by around 2014, and more recent wireless communication techniques, such as 1024-QAM, orthogonal frequency division multiple access (Orthogonal Frequency Division Multiple Access, OFDMA), and Uplink (UL) MU techniques, are applied in the 802.11ax promulgated by around 2020. 802.11be and other future WLAN standards have standardized many of the latest communication technologies including using 320MHz bandwidth, 16 spatial streams, multi-Access Point (AP) coordination, etc.

For time-varying channels, adaptive modulation coding is a wireless communication technique that improves link performance by adjusting a number of variables, including transmission power level, channel coding rate, and modulation order, using current Channel State Information (CSI). By adjusting the data transmission rate according to the channel state, data can be efficiently transmitted on the channel, and throughput can be increased. Thus, accurate preemptive estimates of performance are needed before wireless transmission occurs, and feedback from and about the receiver side in a given channel environment is needed.

For techniques that combine and integrate AMC schemes in a MIMO system, the overall performance will depend on the demodulation architecture at the receiver side. As considered in some embodiments herein, when a linear equalizer (e.g., minimum mean square error (Minimum Mean Square Error, MMSE), zero-Forcing (ZF), spatially multiplexed multi-stream signal) is employed, the multi-stream signal may be transmitted to some single-user MIMO (SU-MIMO). The equation for a linear equalizer may be used to convert multi-user MIMO (MU-MIMO) to multiple SISO antenna signals. Even if multiple signals are transmitted simultaneously, each of the multiple signals may be recovered using separate estimates. In other words, if a linear equalizer is used to mitigate inter-symbol interference, an estimation method of a SISO antenna system may be employed because a MIMO antenna system is considered to be equivalent to a multiple SISO antenna system. In the case of a MIMO system with linear receivers (e.g., MMSE and/or ZF receivers), post-processed SINR can be easily provided by outputting SINR. However, for Maximum-Likelihood (ML) receivers, it is not straightforward to calculate post-processed SINR, as ML-based demodulation is a nonlinear process. This is true, although using ML receivers for MIMO systems provides the best performance. For optimal data estimation for MIMO systems, it is useful to employ MLD as a nonlinear equalizer. MLD provides better performance than linear equalizer by its simultaneous estimation and transmission of data. Unfortunately, since the MLD performs estimation and data transmission at the same time, a problem occurs in the case where AMC cannot be applied to a single antenna system. Since MLD is nonlinear, another problem arises in that the complexity of MLD increases exponentially with increasing modulation order.

Accordingly, there is a need for a system and method for effectively applying AMC in a MIMO system requiring the use of MLD.

The descriptions made in the background section should not be deemed prior art merely because of the statements in the background section. The background section may describe various aspects, features, advantages or embodiments of the disclosure.

Disclosure of Invention

It is some of the objects of the present disclosure to provide systems, methods and apparatus that enable the so-called superior decision rules of AMC configurations to be practically applied in real-world construction that is affected by time-varying channels and unavoidable impairments caused by MIMO system implementations. In practice, both SISO and MIMO systems suffer from performance loss due to channel estimation errors caused by implementation problems, channel interference, radio Frequency (RF) problems, and the like. Due to inherent channel estimation errors, the expected data loss that may occur at or in the receiver unit should be predicted to the highest possible extent and considered in the design of the receiver unit to minimize such loss in practice. Embodiments herein provide calibration methods that strive to address the loss of channel integrity caused by estimation errors. These embodiments also contemplate employing a receiver algorithm with near-MLD (near-MLD) performance in a MIMO system so that effective AMC and accurate channel quality estimation can be effectively applied over the MIMO system.

The objects of the present disclosure are not limited to the above objects, other objects and advantages not described in the present disclosure may be understood by the following description, and other objects and advantages not described in the present disclosure will be more clearly understood by the embodiments of the present disclosure. Furthermore, it is apparent that the objects and advantages of the present disclosure can be realized by means of the instrumentalities and combinations pointed out in the claims.

According to some aspects of the present disclosure, a mathematical algorithm with near-MLD performance is implemented on a MIMO receiver by calibrating the channel estimation error, post-processing Signal-to-Noise Ratio (SNR), and effective SINR calculation process. Methods of calculating a calibration factor are provided as compensation methods for channel estimation loss on SISO systems. Further, by analysis of the channel orthogonality ratio, the upper and lower limits of use define a method of calculating the effective SINR with near MLD performance. For the AMC scheme using the estimated channel quality information, a decision rule of a Modulation Coding Scheme (MCS) level is provided as a proposed method of calculating an effective SINR.

Among various ways of estimating channel quality of the AMC scheme, predicting link performance based on a packet error rate (Packet Error Rate, PER) is a well-known way and is adapted to reduce complexity and improve accuracy of estimating actual link performance. Link performance prediction refers to PER performance information for an Additive White Gaussian Noise (AWGN) channel. The link performance predictions compare the channel quality between the AWGN performance and the estimated actual channel state performance. For this approach, a calculation method of converting each subcarrier (subcarrier) -SNR into effective SNR at an Orthogonal Frequency Division Multiplexing (OFDM) link layer is required. Two representative ways of link performance prediction are 1) an effective index SINR map (EFFECTIVE EXPONENTIAL SINR METRIC, EESM) and 2) a received bit inter-information rate (Received Bit mutual Information Rate, RBIR).

One of the drawbacks of employing the EESM algorithm is that scalar normalization parameter (e.g., β) calculations are required for each MCS level and for the various channel states. The parameter (β) of EESM is different at each MCS level, which also depends on the characteristics of each channel. Thus, the EESM algorithm requires various parameter (β) values for all scenarios. On the other hand, the RBIR algorithm is used in a method of mapping mutual information of transmitted symbols to effective SINR. Since the mutual information at the symbol level is less dependent on the channel characteristics than EESM, accurate information rate estimation can be achieved by the mutual information at the symbol level. Thus, the innovations disclosed herein employ RBIR algorithms to provide as accurate a channel quality estimate as possible.

According to some aspects of the present disclosure, there is provided a wireless station apparatus comprising: a transceiver configured to receive a signal from another station device; and a processor operatively coupled to the transceiver, the processor configured to: obtaining a channel estimate from the received signal; calculating a first signal to interference plus noise ratio, SINR, based on the channel estimate; calculating a second SINR based on the channel estimate; calculating a third SINR based on the first SINR and the second SINR; calculating a received bit inter-information rate RBIR based on the third SINR; and selecting a modulation and coding scheme, MCS, based on the RBIR, and wherein the transceiver is further configured to transmit the selected MCS to the other station apparatus.

According to some aspects of the present disclosure, there is provided a method of wireless communication performed by a wireless station apparatus, the method comprising: receiving a signal from another site device; obtaining a channel estimate from the received signal; calculating a first signal to interference plus noise ratio, SINR, based on the channel estimate; calculating a second SINR based on the channel estimate; calculating a third SINR based on the first SINR and the second SINR; calculating a received bit inter-information rate RBIR based on the third SINR; and selecting a Modulation and Coding Scheme (MCS) based on the RBIR, and transmitting the selected MCS to the another station apparatus.

The technical solutions of the present disclosure are not limited to the above-described solutions, and the non-mentioned solutions will be clearly understood by those skilled in the art to which the present disclosure pertains from the present specification and the accompanying drawings.

Drawings

Various aspects, features and advantages of specific embodiments of the disclosure will become more apparent from the following description taken in conjunction with the accompanying drawings in which:

FIG. 1 is a block diagram illustrating a1 by 1SISO channel model according to some embodiments of the present disclosure;

fig. 2 is a block diagram illustrating a2 by 2MIMO channel model according to some embodiments of the present disclosure;

Fig. 3 is a block diagram illustrating a wireless communication device according to some embodiments of the present disclosure;

Fig. 4 is a schematic diagram illustrating RBIR ESM curves of RBIR values versus input SINR values according to some embodiments of the present disclosure;

FIG. 5 is a schematic diagram showing simulation results of RBIR ESM fitting performance versus AWGN PER curve for various channel models of a1 by 1SISO system, in accordance with some embodiments of the present disclosure;

FIG. 6 is a schematic diagram illustrating simulation results of RBIR ESM fitting performance versus AWGN PER curve for various channel models of a2 by 2MIMO system, according to some embodiments of the present disclosure;

Fig. 7 is a flow chart illustrating a SINR calculation method in accordance with some embodiments of the present disclosure;

Fig. 8 is a flowchart illustrating a near MLD SINR calculation method based on RBIR ESM in a MIMO-OFDM system according to some embodiments of the present disclosure;

FIG. 9 is a block diagram illustrating processor functions according to some embodiments of the present disclosure; and

Fig. 10 is a table showing RBIR values of various modulation schemes at different SNRs in dB.

Detailed Description

The terminology used in the present specification will be briefly described, and the present disclosure will be described in detail.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It is noted that the use of any and all examples, or example terms, provided herein is intended to be better illustrate the invention and is not to be taken as limiting the scope of the invention unless otherwise specified. General terms currently widely used are selected as terms used in the embodiments of the present disclosure in view of functions in the present disclosure, but may be changed according to the intention or judicial precedents of those skilled in the art, the appearance of new technologies, and the like. Furthermore, in certain circumstances, terms arbitrarily chosen by the applicant may be used. In this case, the meaning of these terms will be mentioned in detail in the corresponding description section of the present disclosure. Accordingly, the terms used in the present disclosure should be defined based on meanings of the terms and simple names of the terms not throughout the present disclosure.

The present disclosure will now be described in further detail with reference to the accompanying drawings, in which preferred embodiments of the invention are shown. The present invention may, however, be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. In the specification, like reference numerals denote like components. In the drawings, the thickness of layers and regions may be exaggerated for clarity. The elements in the drawings are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the embodiments. Moreover, all illustrations are intended to convey concepts, where relative sizes, shapes, and other specific attributes may be illustrated schematically, rather than literally or precisely.

It will be understood that when an element or layer is referred to as being "connected" or "coupled" to another element or layer, it can be directly connected or coupled to the other element or layer or intervening elements or layers may be present. In contrast, when an element is referred to as being "directly connected to" or "directly coupled to" another element or layer, there are no intervening elements or layers present. Like numbers refer to like elements throughout. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

It will also be understood that when an element or layer is referred to as being "on" another element or layer or substrate, it can be directly on the other element or layer or substrate, or intervening elements or layers may also be present. In contrast, when an element or layer is referred to as being "directly on" another element or layer, there are no intervening elements or layers present.

It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. Thus, for example, a first element, component, or section discussed below could be termed a second element, component, or section without departing from the teachings of the present disclosure.

The use of the terms "a" and "an" and "the" and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. Unless otherwise indicated, the terms "comprising," "having," "including," and "containing" are to be construed as open-ended terms (i.e., meaning "including, but not limited to").

The term "module" or "unit" herein refers to a software or hardware component, and the "module" or "unit" performs some function. The meaning of "module" or "unit" is strictly not limited to hardware or software. The "module" or "unit" may be configured in an addressable non-transitory storage medium or configured to reproduce one or more processors. Thus, a "module" or "unit" may include components such as software components, object-oriented software components, class components, task components, and at least one process, function, attribute, flow, subroutine, program code segment, driver, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, variables, and the like. The functionality provided by the components and "modules" or "units" may be divided or combined into smaller or larger "modules" or "units.

In some embodiments, a "module" or "unit" may be implemented as a processor and memory. "processor" may be construed broadly to include a general purpose processor, a Central Processing Unit (CPU), a microprocessor, a Digital Signal Processor (DSP), a controller, a microcontroller, a state machine, and the like. In some embodiments, a "processor" may refer to an Application Specific Integrated Circuit (ASIC), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), or the like. A "processor" may include a combination of processing devices in various configurations. The "memory" may include electronic components capable of storing electronic information, such as a processor-readable medium. The "memory" may be Random Access Memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically Erasable PROM (EEPROM), flash memory, magnetic data memory, optical data memory, registers, and so forth. The memory is in electronic communication with the processor and the processor can read information and/or data from, and/or write information and/or data to, the memory. A memory integrated with the processor is in electronic communication with the processor.

According to some embodiments of the present disclosure, calibrating channel estimation errors and calculating effective SINR to provide better data transmission characteristics will be described in connection with fig. 1-9.

Fig. 1 is a block diagram 100 illustrating a1 by 1SISO channel model in accordance with some embodiments of the present disclosure. As shown in the exemplary embodiment, the transmitter unit Tx 102 may include at least one antenna 104. Likewise, the receiver unit Rx 106 may comprise at least one antenna 108. Signals including data may be wirelessly transmitted from antenna 104 via a wireless transmission channel and received at antenna 108. The wireless transmission channel may have a channel transfer coefficient h.

Fig. 2 is a block diagram 200 illustrating a 2 by 2MIMO channel model according to some embodiments of the present disclosure. As shown in the exemplary embodiment, the transmitter unit Tx 202 may include at least one antenna (e.g., two transmit antennas are shown, namely a first antenna 204a and a second antenna 204 b). Likewise, the receiver unit Rx 206 may comprise at least one antenna (e.g. two receive antennas are shown, namely a first antenna 208a and a second antenna 208 b). Signals including data may be wirelessly transmitted from antennas 204a, 204b via at least one wireless transmission channel and received at antennas 208a, 208 b. Each of the at least one wireless transmission channel may have a channel transfer coefficient h _ij. As shown, the first channel between antenna 204a and antenna 208a may have a channel transfer coefficient h ₁₁. The second channel between antenna 204a and antenna 208b may have a channel transfer coefficient h ₂₁. The third channel between antenna 204b and antenna 208a may have a channel transfer coefficient h ₁₂. The fourth channel between antenna 204b and antenna 208b may have a channel transfer coefficient h ₂₂.

Fig. 3 is a block diagram 300 illustrating a wireless communication device 302 according to some embodiments of the present disclosure. As shown in an exemplary embodiment, the wireless communication device 302 may include at least one processor 304, which processor 304 may operate to perform operations that may be stored in at least one non-transitory memory, such as memory 314. Accordingly, the processor 304 and the memory 314 are coupled, and the processor 304 can also store information and/or data in the memory 314. The processor 304 may control or use the at least one transceiver 306 to transmit and/or receive signals including data via the antenna unit 312, which antenna unit 312 may include at least one antenna. Transceiver 306 may include at least one transmitter 308 and at least one receiver 310. In some embodiments, transceiver 306 is directly coupled to memory 314. As shown, the processor 304 may send data to the transmitter 308, which is then sent to the antenna element 312 for transmission via a wireless channel. In various embodiments, antenna element 312 may receive data via a wireless channel, which is transmitted to receiver 310 and then transmitted to processor 304 or accessed by processor 304. In various embodiments, data to be transmitted and/or received from at least one wireless channel may be stored in short-term memory and/or cache while awaiting processing.

In various embodiments, processor 304 may be a processor that performs operations implemented by hardware (e.g., gates), or a general purpose or special purpose processor to perform operations according to instructions stored in a memory.

Although not shown in fig. 3, one skilled in the art will appreciate that at least one power source will provide power to the wireless communication device 302 and that in various embodiments, backup and/or redundant power sources may be provided. Further, those skilled in the art will appreciate that various interfaces may be included in the wireless communication device 302 and/or coupled to the wireless communication device 302, the wireless communication device 302 may include status indicators such as Light Emitting Diodes (LEDs), user interface screens that may be touch screens, buttons, and/or other components that allow a person to interact with the wireless communication device 302. Also, any number of machine interfaces that allow data transfer and/or control may be included, such as plugs, jacks, and the like.

Calibration factor for RBIR mapping of SISO/MIMO system

In a MIMO-OFDM system, after a fast fourier transform (Fast Fourier Transform, FFT) demodulation operation by N _t by N _r, the received signal on subcarrier k is defined as:

y_k＝H_ks_k+w_k (1)

Wherein:

y _k is the received vector of N _r x 1;

Is an N _r×N_t -dimensional rayleigh fading MIMO channel matrix, wherein each element in the matrix has an independent co-distribution (INDEPENDENT AND IDENTICALLY distributed, "i.i.d.)"), Complex gaussian distribution of (a); /(I)It satisfies/>, as a transmission symbol vectorAnd has evenly distributed power across the transmit antennas; and

W _k represents an Additive White Gaussian Noise (AWGN) vector with zero mean and covariance matrix E

Assuming that the channel is an imperfect channel, an estimation method, e.g., least squares estimation or MMSE, etc., may allow the channel estimation error to be represented as an additive noise term:

Wherein:

is an estimated channel matrix; and

Is a noise vector with the noise variance of the channel estimation error plus the AWGN noise variance, so that when/>Is the noise variance of the estimation error, and/>Is Gaussian noise variance,/>

Then, assuming a SISO system, the calculation of SINR, λ _k, for the kth subcarrier is given by:

Wherein:

Is the noise variance of the estimation error;

is gaussian noise variance; and

Is the channel value of subcarrier k, e.g., the voltage measured at a certain frequency (e.g., at pilot/subcarrier frequency).

In order to accurately predict the Packet Error Rate (PER) performance with the calculated SINR value lambda _k, the effective SINR value may be calculated using the received bit inter-information rate (Received Bit mutual Information Rate, RBIR). For all M-ary modulation symbols on each subcarrier, the mutual information at the symbol level may be calculated and then the effective SINR may be achieved by demapping the average mutual information into the effective SINR. The Effective SINR Mapping (ESM) function of the RBIR algorithm is given by:

Wherein:

m is the number of constellation points of the MCS;

u is a complex gaussian random variable with mean zero and variance 1; and

S _k is a constellation point with normalized energy.

Fig. 4 shows simulated RBIR ESM results for various modulation orders based on equation (4). For purposes of detailed description, fig. 4 is a schematic diagram 400 illustrating RBIR ESM curves of RBIR values versus input SINR values, according to some embodiments of the present disclosure. As shown in the exemplary embodiment, the graph 400 may include RBIR in bits shown on the y-axis and SNR in decibels (dB) on the x-axis. Each modulation scheme may start at 0 bit, approximately-20 dB, and follow a similar curve up to approximately-5 dB. Binary phase shift keying (Binary PHASE SHIFT KEYING, BPSK), which is represented by a curve with circular data points in fig. 4, levels off at 1 bit, approximately 5dB. Quadrature phase shift keying (Quadrature PHASE SHIFT KEYING, QPSK), which is represented by a curve with square data points in fig. 4, levels off at 2 bits, approximately 10 dB. 16QAM (Quadrature Amplitude Modulation ), which is represented in fig. 4 by a curve with diamond shaped data points, levels off at 4 bits, approximately 15 dB. 64QAM (which is represented in fig. 4 by a curve with triangle data points pointing upward) levels off at 6 bits, greater than 20 dB. 256QAM (which is represented in fig. 4 by a curve with triangle data points pointing downward) levels off at 8 bits, approximately 30 dB. 1024QAM (which is represented in fig. 4 by a curve with triangle data points pointing to the left) levels off at 10 bits, approximately 35 dB. 4096QAM (which is represented in fig. 4 by a curve with triangle data points pointing to the right) levels off at 12 bits, approximately 40 dB. See fig. 10 and its associated description for more details.

In various embodiments, the processor may calculate the average RBIR by mapping each subcarrier SINR value to equation (4):

Wherein:

N is the number of subcarriers;

t is the number of OFDM symbols; and

N _ss is the number of spatial streams.

Once the average RBIR value is determined, the effective SINR can be obtained by reverse mapping each M-QAM according to the following equation:

λ_eff＝α·Φ^―1(RBIR；M) (6)

Wherein: alpha represents a calibration factor taking into account channel estimation errors.

By comparing PER performance on AWGN channels, the calibration factor α can be calculated. This calculation of α can be modeled as:

Wherein:

PER (lambda _awgn) is PER performance on AWGN channel, lambda _awgn is SNR value; and PER (lambda _eff (alpha)) is the PER performance of lambda _eff (alpha) with the effective SINR of the calibration factor.

In various embodiments employing the innovations described herein in accordance with the 802.11 standard, table 1 below shows a list of numerical optima for α at 10 different MCS levels (e.g., 0, 1, 2,3, 4,5,6, 7, 8, 9) that are representative in a binary convolutional code (Binary Convolutional Code, BCC).

TABLE 1 optimal value of alpha for different MCS levels

RBIR mapping-channel orthogonality ratio for MIMO systems

In order to efficiently calculate the SINR of a MIMO-OFDM system with multiple spatial streams, two optimization parameters for channel realization, namely a calibration factor α and a channel orthogonality ratio R _co, are employed. To design a MIMO detector with near MLD performance, MIMO-MLD performance boundaries can be predicted by computation of MMSE and an ideal interference-free (IF) receiver. The use of MMSE and ZF equalizers as linear equalizers has the advantage that the SNR or SINR values can be easily calculated. This is achieved by treating the output signal of the receiver as an approximation system with multiple single antennas. However, linear equalizers also have drawbacks because they have a relatively high error detection rate compared to MLDs. Since MMSE detectors provide relatively better performance in terms of error detection than ZF detectors, MMSE detectors can help define a lower bound on MLD performance. In view of these considerations, D _k can represent the MMSE filter equation for the kth subcarrier as:

Wherein:

D _k,m is the column vector of D _k; and

[. Cndot. ] ^H is the transposed conjugate of the matrix.

By combiningThe received signal vector y _k applied to equation (1) is used to calculate the output signal of the jth stream at the kth subcarrier of the MMSE filter. The corresponding SINR may be expressed as follows:

to define the upper limit of MLD performance, it is assumed that the interference between data symbols is completely eliminated at the receiver. This is known as a non-Interfering (IF) receiver. The SINR upper limit of post MLD (post-MLD) can be set by such an ideal IF receiver. Thus, the corresponding SINR for the j-th stream at the k-th subcarrier can be expressed as:

Where H _k,m is the mth column vector of H _k. For all j+.m, when satisfied Equation (10) is calculated at this time.

Using equations (9) and (10), the lower and upper limits of the near MLD SINR can be set as follows:

In addition, function F may be employed as a different metric to use SINR, e.g., channel capacity (e.g., By means of a function/>The channel capacity formula of (2) is such that relation (11) is replaced with:

The rearrangement formula (12) allows it to be converted into Thus, the SINR/>, of the near MLD can be approximatedExpressed as:

The MIMO channel characteristics are used to define the parameter β. A2 by 2MIMO-OFDM system may be used herein to simplify modeling.

Fig. 2 is a concept of a MIMO system. When a system such as that shown in fig. 2 is employed, the transmit signal and the interfering signal may be received simultaneously at antennas 208a and 208b of receiver unit 206. Due to interference, channel imbalance may occur, whereby eigenvalues of the channel matrix may become very unbalanced and result in throughput loss. Assuming an ideal spatially multiplexed MIMO channel, a 2 by 2MIMO channel, it is possible to approximate a combination of multiple 1 by 2SIMO channels, since the eigenvalues or singular values of each channel are close to 1. Each signal received at the receiver antennas 208a, 208b may be represented as a separate signal based on the channel matrix power (power)And/>The ratio of channel orthogonality may be defined according to a channel relation as follows:

Wherein: trace () represents the trace of the matrix and det () represents the determinant. _F is the Frobenius matrix norm of French Luo Beini. Indirectly shows how much interference signals have on the channel when separated into 1 by 2SIMO channels. When the determinant is normalized by the fjor Luo Beini us norm, R _co can be expressed as a ratio of channel orthogonality, which is limited to between 0 and 1. The assumption when R _co is 1 is that the MIMO channel can be ideally divided into multiple orthogonal SISO channels. Then, due to/>Thus SINR value of MLD/>SINR value close to IF receiver/>

On the other hand, the assumption when R _co ≡0 is that the channel is completely interrupted and unbalanced. Due to Thus MLD SINR/>Near MMSE SINR/>By applying the parameter R _co as β in equation (13) above, the SINR value of the post-MLD can be calculated as follows:

The MLD SINR of equation (15) may be applied according to equations (5) and (6), so that the effective SINR on the MIMO channel may be calculated. Thus, the calibration factor α of the MIMO channel may be approximately equal to α of the SISO channel.

Finally, the calculated effective SINR lambda _eff may be mapped to PER performance on AWGN. This allows PER performance prediction for AWGN by applying the effective SINR value for each MCS level. The PER of the PL equation is predicted as follows:

Wherein:

the packet length on the AWGN PER curve as a reference is denoted PL _ref; and

The packet length in transmission is denoted PL.

In the context of a variety of embodiments of the present invention,Depending on the MCS. The/> can be obtained by using a look-up table of MCS and SINR(See, e.g., FIGS. 5-6 and related descriptions herein).

To estimate the PER of the transmission, the following expression may be employed:

Coding scheme, reference packet length)/>

Reference packet length

Wherein:

PER performance for a particular reference packet length PL _ref on AWGN;

the RBIR ESM value versus input SINR; and

The calibration factors are stored in a look-up table.

Further, in order to apply the AMC scheme, the expected throughput R _thr,i of the i-th MCS level may be calculated as follows using the PER _PL obtained from equation (16):

R_thr,i＝(1―PER_PL,i)·N_ss·R_c,i·log₂M_i bps/Hz (17)

Wherein:

PER _PL,i is the expected PER performance;

R _c,i is the channel coding rate; and

M _i is the modulation order of the i-th MCS level.

A processor employing the receiver of the innovations herein may select the MCS level with the maximum data rate from equation (17) and may recommend and/or otherwise implement the MCS level at the upper layer stack for link adaptation.

The selection of MCS may be made according to the following equation:

Wherein:

R (MCS) =per ^* rate (MCS); and

rate(MCS)＝R_thr,i。

In various embodiments, table 2 shows the SNR values required for each MCS for the case where PER is 10% and PL _ref is 4096 bytes for various channel models. Tables 1 and 2 are examples of optimization parameters for a MIMO-OFDM system, and different values may be set and/or used depending on the configuration of the particular system.

Table 2 SINR values and number of spatial streams required for MCS level

Fig. 5-6 illustrate the performance of the proposed effective SINR mapping algorithm based on AWGN reference PER exact allocation, although PER performance for each channel model may be different. By applying the parameters presented herein to MIMO-MLD calculations, the proposed ESM scheme provides new prediction accuracy.

For purposes of detailed description, fig. 5 is a schematic diagram 500 showing simulation results of RBIR ESM fitting performance versus AWGN PER curve for a1 by 1SISO system of various channel models, in accordance with some embodiments of the present disclosure.

Fig. 6 is a diagram 600 illustrating simulation results of RBIR ESM fitting performance versus AWGN PER curve for various channel models of a 2 by 2MIMO system, according to some embodiments of the present disclosure. In fig. 6, the calibration factor α of the channel estimation error may be used regardless of the number of streams, which may have different values according to the system configuration. However, the proposed MIMO-MLD SINR calculation method may have an error range within 0.3dB, which suggests that the proposed method is applicable and valid for all system configurations.

Fig. 7 is a flow chart 700 illustrating a SINR calculation method in accordance with some embodiments of the present disclosure. In some embodiments, the method may include a plurality of operations that may be performed by the at least one processor 304. In some embodiments, the method may include a plurality of operations according to executable instructions stored in memory and/or otherwise performed by the at least one processor 304.

As shown in an exemplary embodiment, the method may include an operation 702, the operation 702 including obtaining or acquiring a channel estimate H _i and a noise variance on each subcarrier in a set of subcarriers. In some embodiments, this may be achieved by measuring a value. Next, in operation 704, the processor 304 may calculate SINR _MMSE for the subcarrier (e.g., this may be calculated according to equation (3) herein). In operation 706, the processor 304 may calculate RBIR _i using SINR _MMSE of the subcarriers (e.g., this may be calculated according to equation (4) herein). In operation 708, the processor 304 may determine whether RBIR for all subcarriers in the set of subcarriers has been calculated. If all RBIRs for all subcarriers have not been calculated, processor 304 may return to and perform operations 704 and 706 for the next subcarrier in the set of subcarriers before repeating operation 708. Conversely, if all RBIRs for all subcarriers have been calculated, processor 304 may continue to perform operation 710, which includes calculating an average RBIR for all RBIRs (e.g., which may be calculated according to equation (5) herein). Next, in operation 712, the processor 304 may convert the average RBIR to an effective SINR (e.g., this may be calculated according to equation (6) herein). In some embodiments, this is performed in accordance with a SINR-to-RBIR (SINR-to-RBIR) table. In operation 714, the processor 304 may look up through an AWGN PER table (e.g., IEEE 802.11 ax) to predict PER. For example, fig. 5 and 6 show the relationship between effective SINR and PER, which depends on the MCS of the AWGN PER table. In operation 716, the processor 304 may select an MCS. Finally, in operation 718, the processor may transmit a physical layer protocol data unit (PHYSICAL LAYER Protocol Data Unit, PPDU) including the selected MCS to the transmitter.

Fig. 8 is a flowchart 800 illustrating a near MLD SINR calculation method based on RBIR ESM in a MIMO-OFDM system. In some embodiments, such a method may include a plurality of operations that may be performed by the at least one processor 304. In some embodiments, such a method may include a plurality of operations according to executable instructions stored in memory and/or otherwise performed by at least one processor.

As shown in an exemplary embodiment, such a method may include an operation 802, the operation 802 including obtaining or acquiring a channel estimate H _i and a noise variance σ _n on each subcarrier in a set of subcarriers. Next, in operation 804, the processor 304 may calculate post-processed SINRs _MMSE and _IF for the subcarriers (e.g., this may be calculated according to equations (9) and (10) herein). In operation 806, the processor 304 may calculate det (H _i)、trace(H_i) and R _co,i for the subcarriers (e.g., this may be calculated according to equation (14) herein). In operation 808, the processor 304 may calculate SINR _MLD for the subcarrier (e.g., this may be calculated according to equation (15) herein). Then, in operation 810, the processor 304 may calculate RBIR _i using SINR _MLD of the subcarrier. This may include using a look-up table between SINR values and RBIR values as shown in fig. 4 to obtain RBIR corresponding to SINR. In operation 812, the processor 304 may determine whether RBIR for all subcarriers in the set of subcarriers has been calculated. If all RBIRs for all subcarriers have not been calculated, processor 304 may return and perform operations 804, 806, 808, and 810 for the next subcarrier in the set of subcarriers before repeating operation 812. Conversely, if all RBIRs for all subcarriers have been calculated, processor 304 may continue to perform operation 814, operation 814 including calculating an average RBIR for all RBIRs (e.g., this may be calculated according to equation (5) herein). Next, in operation 816, the processor 304 may convert the average RBIR to an effective SINR (e.g., this may be calculated according to equation (6) herein). In some embodiments, this is performed according to a SINR to RBIR table. In operation 818, the processor 304 may look up via an AWGN PER table (e.g., IEEE 802.11 ax) to predict PER (e.g., this may be calculated according to equation (16) herein). In operation 820, the processor 304 may select an MCS (e.g., this may be performed according to equation (18) herein). Finally, in operation 822, the processor 304 may transmit a PPDU including the selected MCS to the transmitter.

Fig. 9 is a block diagram 900 illustrating processor functions according to some embodiments of the present disclosure. As shown in the exemplary embodiment, processor 304 may include an equalizer SINR calculation module 902, RBIR ESM module 904, and PER module 906 for MRC (Maximal Ratio Combining, maximum ratio combining)/MMSE. Initially, at least one H (n, t) may be an input to module 902. In various embodiments, equations 9, 10, 14 and/or 15 may be performed by module 902. Acquisition of RBIR using SINR to RBIR lookup table may also be performed by block 902. At least one SINR (i _ss, n, t) may be an output from block 902 and received as an input to block 904. In various embodiments, equations 5 and/or 6 may be performed by module 904 based on the modulation input. The output SNR _eff of block 904 may be received as an input to block 906. MCS, BCC/LDPC (Low-DENSITY PARITY-Check), packet length, etc. may be additional inputs to PER module 906, and PER module 906 may perform at least equation 16. Module 906 may then output the PER.

Fig. 10 is a table 1000 showing RBIR values of various modulation schemes at different SNRs in dB. As shown, the various modulation schemes shown include BPSK, QPSK, 16QAM, 64QAM, 256QAM, 1024QAM, and 4096QAM. In the case where the range of SNR values is-20 dB to 46dB, the RBIR values for each scheme are shown in table 1000 and correspond to the various curves shown and discussed with respect to fig. 4.

It is contemplated that any optional feature of the inventive variations described may be set forth and claimed separately or in combination with any one or more features described herein. It should also be noted that the claims may be written to exclude any optional elements of the embodiments. Accordingly, this statement is intended to serve as antecedent basis for use of such exclusive terminology as "solely," "only" and the like in connection with the recitation of claim elements, or use of a "negative" limitation. Unless defined otherwise herein, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The breadth of the present invention is not to be limited by the subject specification, but only by the explicit meaning of the claim terms employed.

Furthermore, in the detailed description of the embodiments of the present disclosure, effects that may be obtained or predicted by the embodiments of the present disclosure have been disclosed, either directly or implicitly. For example, various effects predicted according to embodiments of the present disclosure have been disclosed in the above detailed description.

The embodiments described herein and the claims thereto are directed to patentable subject matter. For countless reasons, these embodiments do not constitute an abstract idea. One such reason is that any claim provides the ability to calculate a near maximum likelihood detection performance capability signal to interference plus noise ratio. These apparatus and computer-implemented methods allow for improved communication in MIMO-OFDM and other wireless systems, thus making improvements to the functionality of the computer itself, which may not otherwise operate at peak efficiency itself, and thus are "significantly more" qualified than the abstract idea.

Other aspects, advantages and salient features of the disclosure will become apparent to those skilled in the art from the foregoing detailed description, which, taken in conjunction with the annexed drawings, discloses various embodiments of the disclosure.

The above description illustrates only the technical idea of the present embodiment, and those skilled in the art to which the embodiments belong can make various modifications and changes without departing from the essential features of the embodiment. Accordingly, the present embodiment is intended to describe, but not limit, the technical idea of the present embodiment, and the scope of the technical idea of the present embodiment is not limited thereto. The scope of the present embodiment should be construed according to the following claims, and all technical ideas within the scope equivalent thereto should be construed to be included in the scope of the present embodiment.

Claims (20)

1. A wireless station apparatus, comprising:

a transceiver configured to receive a signal from another station device; and

A processor operably coupled to the transceiver, the processor configured to:

obtaining a channel estimate from the received signal;

Calculating a first signal to interference plus noise ratio, SINR, based on the channel estimate;

calculating a second SINR based on the channel estimate;

Calculating a third SINR based on the first SINR and the second SINR;

Calculating a received bit inter-information rate RBIR based on the third SINR; and

Selecting a Modulation Coding Scheme (MCS) based on the RBIR, and

Wherein the transceiver is further configured to transmit the selected MCS to the other station apparatus.

2. The wireless station apparatus of claim 1, wherein the first SINR is a lower bound for the third SINR and the second SINR is an upper bound for the third SINR.

3. The wireless station apparatus of claim 1, wherein the processor is further configured to:

calculating a ratio of channel orthogonality based on said channel estimates; and

The third SINR is calculated based on a ratio of the first SINR, the second SINR, and the channel orthogonality.

4. The wireless station apparatus of claim 3, wherein the ratio of channel orthogonality is less than or equal to 1.

5. The wireless station apparatus of claim 3, wherein the ratio of channel orthogonality represents a degree of influence of an interference signal on a channel.

6. The wireless station apparatus of claim 1, wherein the processor is further configured to calculate an average RBIR over a set of subcarriers and to select the MCS based on the average RBIR.

7. The wireless station apparatus of claim 1, wherein the first SINR is an SINR of a minimum mean square error MMSE receiver and the second SINR is an SINR of a non-interfering IF receiver.

8. The wireless station apparatus of claim 1, wherein the processor is further configured to calculate the RBIR based on the third SINR by using a look-up table.

9. The wireless station apparatus of claim 6, wherein the processor is further configured to convert the average RBIR to a fourth SINR using a SINR-to-RBIR table and select an MCS based on the fourth SINR.

10. The wireless station apparatus of claim 9, wherein the processor is further configured to:

An additive white gaussian noise, AWGN, packet error rate, PER, table is looked up based on the fourth SINR to predict a PER, and the MCS is selected based on the predicted PER.

11. A method of wireless communication performed by a wireless station apparatus, the method comprising:

Receiving a signal from another site device;

obtaining a channel estimate from the received signal;

Calculating a first signal to interference plus noise ratio, SINR, based on the channel estimate;

calculating a second SINR based on the channel estimate;

Calculating a third SINR based on the first SINR and the second SINR;

Calculating a received bit inter-information rate RBIR based on the third SINR; and

Selecting a Modulation Coding Scheme (MCS) based on the RBIR, and

And transmitting the selected MCS to the other station equipment.

12. The method of claim 11, wherein the first SINR is a lower bound for the third SINR and the second SINR is an upper bound for the third SINR.

13. The method as recited in claim 11, further comprising:

a ratio of channel orthogonality is calculated based on the channel estimates,

Wherein calculating the third SINR includes:

the third SINR is calculated based on a ratio of the first SINR, the second SINR, and the channel orthogonality.

14. The method of claim 13, wherein the ratio of channel orthogonality is less than or equal to 1.

15. The method of claim 13, wherein the ratio of channel orthogonality represents a degree of influence of an interfering signal on a channel.

16. The method as recited in claim 11, further comprising:

An average RBIR over a set of subcarriers is calculated and the MCS is selected based on the average RBIR.

17. The method of claim 11, wherein the first SINR is an SINR of a minimum mean square error MMSE receiver and the second SINR is an SINR of a non-interfering IF receiver.

18. The method as recited in claim 11, further comprising:

The RBIR is calculated based on the third SINR by using a lookup table.

19. The method as recited in claim 16, further comprising:

Converting the average RBIR to a fourth SINR using a SINR-to-RBIR table; and

The MCS is selected based on the fourth SINR.

20. The method as recited in claim 19, further comprising:

An additive white gaussian noise, AWGN, packet error rate, PER, table is looked up based on the fourth SINR to predict a PER, and the MCS is selected based on the predicted PER.