WO2022081114A1

WO2022081114A1 - Method and apparatus for channel estimation in mimo-ofdm systems based on phase correction in pilot depatterning

Info

Publication number: WO2022081114A1
Application number: PCT/TR2021/050846
Authority: WO
Inventors: Çağri Göken
Original assignee: Aselsan Elektroni̇k Sanayi̇ Ve Ti̇caret Anoni̇m Şi̇rketi̇
Priority date: 2020-10-13
Filing date: 2021-08-21
Publication date: 2022-04-21
Also published as: TR202016333A1

Abstract

The present invention relates to a channel estimation method (100) and apparatus for MIMO-OFDM communications systems with an improved performance compared to conventional receivers. The method (100) is based on compensating the phase changes in the resource elements in a CDM group during pilot depatterning stage. The received signal is OFDM demodulated to obtain received symbols in the resource grid and an initial pilot depatterning is performed to the received symbols. The phase change rates in the resource grid is determined, phase correction terms are calculated and applied. The pilot depatterning is performed using phase corrected received signals and the channel estimates at pilot locations are updated using phase change correction terms. The estimated values in pilot locations are interpolated to obtain the channel estimation values for all resource elements.

Description

METHOD AND APPARATUS FOR CHANNEL ESTIMATION IN MIMO-OFDM SYSTEMS BASED ON PHASE CORRECTION IN PILOT DEPATTERNING

Technical Field

The invention relates to the field of communication and particularly to the channel estimation methods in a multiple-input multiple-output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) communication system.

State of the Art

In modern communication systems, achieving high data rates, low latency, high reliability and connectivity have become the main performance targets with the recent advances. Satisfying these requirements in a wide range of use-cases and deployment scenarios is a challenging task, and this makes the accurate channel knowledge at the receiver even more critical. Therefore, channel estimation is one of the most crucial blocks of a communication system and it can be performed based on pre-defined reference signals also known as pilots.

The global standardization bodies such as 3GPP conduct necessary studies to define modern communications standards such as Long Term Evolution (LTE), Long Term Evolution-Advanced (LTE-A) and lately 5G New Radio (NR). 5G waveform is based on Cyclic-Prefix OFDM (CP-OFDM) (as in LTE and LTE-A) for both sub-6 GHz and millimetre-wave frequencies and supports single and multi-user MIMO as a key enabler to achieve high data rates. Both LTE and NR define reference signals for wide range of physical channels to enable channel estimation at the receiver. The reference signals which are defined for coherent demodulation of the control and user data in both uplink and downlink are known as demodulation reference signals (DMRS). The design of DMRS to allow multiple layers of data transmission through a MIMO system needs to consider many aspects at once. Some of the factors, which have been considered in DMRS design of 5G, include the pilot density, the power variation in frequency, number of layers with orthogonal pilot symbols, configuration flexibility, the location of pilots to support low-latency demodulation, and allowing a common receiver structure for different configurations.

Both 5G and LTE utilize code-domain multiplexing (CDM) for orthogonal transmission of pilot signals in different MIMO layers. To support pilot transmission in more than one layer in the same resources, orthogonal cover codes (OCC) or cyclic shift (CS) operations are used to achieve orthogonality among pilots. For example, in 5G, every DMRS configuration (except single layer transmission) includes an OCC based pilot allocation in the code domain, and it is possible to define up to 12 orthogonal layers by combining CDM and frequency domain multiplexing (FDM). Usage of CDM based pilots also have an inherent advantage over other orthogonal methods such as time or frequency domain multiplexing due to the processing gain. Note that Channel State Information Reference Signal (CSI-RS) and Sounding Reference Signal (SRS) defined in NR also utilize CDM structure to extend single port operation to multiple ports. Therefore, CDM based pilot allocations are widely used in the state-of-the-art communications systems.

Even though CDM based designs have certain advantages and attractive properties, they rely on the assumption that the channel does not change over the resource elements where the CDM is defined. For example, when the CDM group location consists of resource elements in frequency, and the channel is frequency selective or there is time synchronization error between transmitter and receiver, then the orthogonality in frequency domain is lost at the receiver. Similarly, when the CDM group location consists of resource elements in time, and when the channel is fast-fading or there is a frequency synchronization error between transmitter and receiver, then the orthogonality in time domain is lost at the receiver. This is detrimental for channel estimation performance especially for high spectral efficiency scenarios, as the performance is limited by the channel estimation error as a result of inter-layer interference at pilot symbols. Therefore, it is important to evaluate and deal with such cases to achieve high data rate targets in wide range of channel scenarios.

It is important for the channel estimation algorithm in a MIMO-OFDM system to correctly recover CDM pilots at the receiver and interpolate estimated channel values at the pilot locations to cover all the resource grid. Recovering the channel at pilots of each MIMO layer belonging to the same CDM group from the received signal and obtaining an initial estimation at pilot locations is referred as pilot depatterning operation. Pilot depatterning and interpolation of the pilots can be performed jointly in an optimal MMSE estimator. However, such an optimal depatterning is not very practical due to excessively large complexity. In particular, it requires a large matrix inversion, which consists of the pilots values at every pilot occasion in real time. Another problem with optimal depatterning is that when the DMRS pilots are used for multiuser interference measurement, and the users utilize different resource grid sizes to be interpolated, the performance will be degraded due to mismatch among users. Hence, a lower complexity channel estimation algorithm is implemented in realistic receivers.

Conventional channel estimation procedure for MIMO systems involves two separate stages: a pilot depatterning stage and an MMSE estimation stage which interpolates the estimated channel values in pilot locations as a result of pilot depatterning. In this case, pilot depatterning is performed based on least-squares (LS) method with the assumption that radio channel stays flat in the pilot depatterning occasions, and it has linear complexity. The interpolation can be performed independently by using the pilot values after depatterning operation. This method has much lower complexity compared to optimal depatterning. The performance of the optimal depatterning method reduces to conventional method, if the channel is exactly same in the CDM resources. However, due to multipath and mobility, the wireless channel has a certain delay and Doppler spread, which might cause small or large changes in the channel in time and/or frequency. Also, when there are non-idealities such as time synchronization error or carrier frequency offset (CFO) error, the channel can not stay flat, which causes serious performance loss in channel estimation for conventional receivers. Especially, considering the fact that modern communication systems need to support the high date rate and spectral efficiency via MIMO technology, the channel estimation errors can limit the performance of such systems in previously said conditions.

In a MIMO-OFDM system, the received signal at the kth subcarrier of nth OFDM symbol at the mth receive antenna,

can be expressed as:

coefficient observed at the k th subcarrier of the nth OFDM symbol between ith layer and mth receive antenna, xfy is the complex pilot symbol carried at the Zcth subcarrier of the nth OFDM symbol at the ith layer and is the complex Gaussian noise component effective at Zcth subcarrier of the nth OFDM symbol at the mth receive antenna. Note that N_L is the number of data layers, N_R is the number of antennas at the receiver, N_siot is the total number of OFDM symbols in the channel estimation window/slot and K is the total number of OFDM subcarriers in the transmission band. Note that the N_siotK resource elements define a resource grid for a given layer. In the channel estimation problem, the aim is to obtain an estimate of the effective channel coefficient Hn^^l> given the pilot (reference signal) symbols and the received signal at the location of pilot symbols.

In practical systems, the pilots should be allocated orthogonally between N_L MIMO layers to avoid inter-layer interference, which considerably degrades the overall performance. There are different multiplexing options for pilot allocation to satisfy this goal. In Code Domain Multiplexing (CDM), the pilot symbols in different layers belonging to the same CDM group use the same resource elements, i.e. (n, k) in equation (1 ), while the separation is achieved via various codes such as orthogonal cover codes (OCC). In Time Domain Multiplexing (TDM), the pilot symbols in different layers are transmitted in different OFDM symbols in a slot. In Frequency Domain Multiplexing (FDM), the pilot symbols in different layers are transmitted in different subcarriers within an OFDM symbol. One or more of these schemes can be utilized to ensure orthogonality of pilot symbols.

In CDM, a certain list of MIMO layers shares the same resource elements in time and frequency and belong to the same CDM group. Therefore, they have to be orthogonalized in the code domain. Each CDM group can be characterized by its base location indices set in terms of subcarrier and OFDM symbol indices. For example, CDM group d can be specified with base sets which means that all layers in CDM

group d have pilot signals located in the subcarrier indices given in set K_d and in OFDM symbol indices given in set £_d. Also, each CDM group can support at most |C_d| = number of orthogonal layers, where C_d is the set of indices of layers belonging

to the CDM group d and 1. 1 denotes the cardinality of the set.

Different CDM groups should be transmitted in different resource grid locations, and they should not overlap. Therefore, no symbol should be transmitted in other layers in the resource elements corresponding to the those of given CDM group. For example, given layer i, which does not belong to CDM group d₂ with base sets

, then X

^

Different CDM groups can be multiplexed using FDM and/or TDM. Let F and T denote the number of CDM groups separated via FDM and TDM, respectively; then D = FT and, where D is the number of different CDM groups in the resource grid.

In practical systems, the pilot pattern specified with CDM base sets, i.e.

for CDM group d, needs to be repeated regularly in frequency and time to increase pilot density. This is because of the fact that sufficiently dense pilot allocation is required to capture the channel effects and changes in time and frequency domain in the given grid. This means that in the entire resource grid, the repeated pilots for a CDM group d are located not only in the pilot locations in the base sets

but also in the repeated occasions. One example of pilot allocation is given in Figure 1. In this example,

{1,2} and £_d = {3,4}, and the resource grid contains K = 120 subcarriers and

OFDM symbols. Also, for CDM group j, the pilots are repeated at every 6 subcarriers in frequency for entire grid and repeated once in the time domain such that there are 6 OFDM symbol between the repeated pilot symbols. Hence, the pilot symbols of CDM group d are located in subcarrier indices given as {1 , 2, 7, 8, ..., 109, 110, 115, 116} and at OFDM symbol indices given as {3, 4, 1 1 , 12}. However, while applying the orthogonal cover code (length-2 in frequency and length-2 in time), each repeated pilot occasion is treated separately, which implies that pilot depatterning is applied separately to the repeated pilot occasions. In the present disclosure, this is referred as pilot depatterning group. For example, the pilots located at the subcarriers {1 , 2} of the OFDM symbols {3, 4} form a pilot depatterning group, and the pilots located at the subcarriers {7,8} of the OFDM symbols {3, 4} form another pilot depatterning group.

First, the conventional channel estimation in MIMO-OFDM systems involving CDM pilot allocation is described. In all channel estimation algorithms, the common goal is to obtain the estimates using the

received symbols at pilot locations and pilot values according to the system model given in equation (1 ) in this embodiment. For a given layer i and receive antenna can

be estimated separately for each (m, i) pair using the same procedures in a practical communications system. The main operations in a conventional channel estimation procedure are illustrated in Figure 2. In the first step, the signal is received in a receive antenna, e.g. receive antenna m, and OFDM demodulation is performed to obtain received symbols

in the frequency domain in the resource grid of receive antenna m. In the second step, the conventional pilot depatterning is performed at all pilot depatterning groups using the corresponding OCC code and received symbols for the resource grids of all layers in a CDM group, e.g. CDM group d. As a result of this step, for a given specific pilot depatterning group, a single channel estimation value is assigned as the estimate for all locations in the group; however channel estimations at different pilot depatterning groups are most likely to be different. Pilot depatterning operation needs to be performed for all CDM groups, i.e. d = 1, ... , D to complete this step. In the third step, the estimated values in pilot locations are used to obtain the channel estimation for all resource elements in the resource grid using an interpolation method. Interpolation procedure can be performed using following different exemplary methods: MMSE estimation in both time and frequency (2D-MMSE), MMSE estimation first in time and then in frequency or first in frequency then in time (MMSE 1 D-1 D), linear interpolation, nearest point interpolation, sliding window averaging. In the final step, all operations given in the first three steps of the procedure are performed for all remaining receiver antenna signals,

In one example, the conventional pilot depatterning operation in the second step of conventional channel estimation procedure given in Figure 2 is based on Least-Squares (LS) estimation with the assumption that radio channel stays flat in the pilot depatterning occasions. An example of this procedure is provided. For this purpose, how OCC is applied in a pilot depatterning group at the transmitter, and how the estimation can be obtained for each layer by depatterning operation are explained. Suppose that a pilot depatterning group in the resource grid of a layer belonging to the CDM group 1 is located at the subcarriers {1 , 2} of the OFDM symbols {1 , 2}. This means there are four pilots in the pilot depatterning group. The layer indices in the CDM group 1 are given as Q = {1, 2, 3, 4}. This means that all layers in Q have the same locations for all pilots in their respective resource grids. When length-2 OCC in frequency and length-2 OCC in time are applied to achieve orthogonalization, the transmitted pilot symbols can be written as:

wherein with each is as defined in the equation (1 ) and

are complex-valued pilot symbols with unit amplitude. For mth receive antenna,

the received and OFDM demodulated signals at the considered pilot depatterning group are and Then, based on equations (1) and (2), the following

equations hold

The orthogonality of the pilots using CDM at the receiver is based on flat channel assumption, for any (m, i) pair. Under this assumption,

and based on equation (2) the following equation is obtained

= h_it for brevity)

the matrix form, this can be expressed as The least squares (LS) solution

for estimate is expressed as:

According to equation (4), the elements of h becomes:

Herein, h_t is the common channel estimate value assigned to the pilot locations in the considered pilot depatterning group between ith layer in the CDM group 1 and receiver antenna m, that is

This example procedure is repeated for all pilot depatterning groups in the resource grids of all layers in all CDM group for a given receive antenna signal. As a result of the operations of conventional pilot depatterning step, initial estimates at all pilot locations between ith layer and mth receive antenna are obtained and they can be stored in a vector,

In the conventional pilot depatterning procedure, the main assumption is that the radio channel stays flat in a pilot depatterning group. If this assumption fails, then the orthogonality of the pilots at the receiver is lost. Such an assumption also needs to hold for all layers due to cross terms as exemplified in the equation (5). For example,

can be expressed in terms of actual channel values based on equations (3) and (5) as:

which implies that even if the flatness assumption holds in the pilot depatterning group for layer 1 in the CDM group, the estimate will be affected by the difference of the channel values in layers {2,3,4}, introducing extra interference for the estimate.

In practical systems, the channel flatness assumption rarely holds perfectly due to effects such as large delay spread in the wireless environment, Doppler spread due to mobility, carrier frequency offset (CFO) and possible time synchronization errors. Such a loss in orthogonality can result in serious performance loss especially in transmission scenarios requiring high spectral efficiency to ensure high data rates and throughput (via large number of MIMO layers, higher modulation order and code rates).

The patent numbered US10116478B2 is related with scattered pilot pattern and channel estimation method for MIMO-OFDM systems in present art. The method and an apparatus are provided for reducing the number of pilot symbols within a MIMO-OFDM communication system, and for improving channel estimation within such a system. However, this document does not disclose a channel estimation method for MIMO- OFDM systems based on compensating the phase changes in the resource elements in a CDM group during pilot depatterning

Therefore, there is a need for methods and devices of channel estimation with practical computational complexity and better and more robust performance in MIMO-OFDM systems utilizing CDM in pilot allocations.

Brief Description of the Invention

The present invention relates to a channel estimation method and apparatus for MIMO- OFDM communications systems with an improved performance compared to conventional receivers. In accordance with a particular embodiment of the present invention, a method and an apparatus for channel estimation in MIMO-OFDM systems utilizing CDM in pilot allocations is provided, which is based on compensating the phase changes in the resource elements in a CDM group during pilot depatterning and has an improved performance compared to conventional receivers.

In accordance with an embodiment of the present invention, a channel estimation method is provided for MIMO-OFDM communications systems utilizing CDM in pilot allocations. The method is based on compensating the phase changes in the resource elements in a CDM group during pilot depatterning stage. The received signal is OFDM demodulated to obtain received symbols in the resource grid and an initial pilot depatterning is performed to the received symbols. The phase change rates in the resource grid is determined, phase correction terms are calculated and applied. The pilot depatterning is performed using phase corrected received signals and the channel estimates at pilot locations are updated using phase change correction terms. The estimated values in pilot locations are interpolated to obtain the channel estimation values for all resource elements.

In accordance with an embodiment of the present invention, an apparatus for channel estimation is provided for MIMO-OFDM communications systems utilizing CDM in pilot allocations. The apparatus includes a pre-processing module for receiving the transmitted signal and performing OFDM demodulation to obtain received symbols in the resource grid and performing an initial pilot depatterning to the received symbols; a phase correction based pilot depatterning performer module for determining the phase change rates in the resource grid, calculating and applying the phase correction terms, performing pilot depatterning using phase corrected received signals and updating the channel estimates at pilot locations using phase change correction terms; and a channel estimator module interpolating the estimated values in pilot locations to obtain the channel estimation values for all resource elements.

Brief Description of the Figures

Figure 1 illustrates an exemplary pilot allocation in a resource grid of a layer belonging to a CDM group.

Figure 2 illustrates the flowchart of operations for conventional channel estimation in a MIMO-OFDM system involving CDM pilot allocation.

Figure 3 illustrates the flowchart of operations for the disclosed invention for channel estimation in MIMO-OFDM systems based on phase correction in pilot depatterning.

Figure 4 illustrates the pilot allocations for two exemplary 5G Physical Downlink Shared Channel (PDSCH) DMRS configurations.

Figure 5 illustrates the mean-squared error (MSE) versus signal-to-noise ratio (SNR) performances for DMRS Type 1 on TDL-C channel with delay spread 1 μs for eight different implementations of the disclosed invention and conventional channel estimation.

Figure 6 illustrates the MSE versus SNR performances for DMRS Type 2 on TDL-C channel with delay spread 1 μs for eight different implementations of the disclosed invention and conventional channel estimation.

Figure 7 illustrates the MSE versus SNR performances for DMRS Type 1 on TDL-C channel with delay spread 2μs for eight different implementations of the disclosed invention and conventional channel estimation.

Figure 8 illustrates the MSE versus SNR performances for DMRS Type 2 on TDL-C channel with delay spread 2μs for eight different implementations of the disclosed invention and conventional channel estimation.

Figure 9 illustrates the MSE versus SNR performances for various Eo values with the disclosed invention, wherein ε₀ is an integer representing the single group phase change rate used to calculate phase correction terms.

Figure 10 illustrates the MSE versus SNR performances for DMRS Type 1 and Type 2 on TDL-C channel with delay spread 1 μs for two implementations of the disclosed invention and conventional channel estimation. Figure 11 illustrates the MSE versus SNR performances for DMRS Type 1 and Type 2 on TDL-C channel with delay spread 2μs for two different implementations of the disclosed invention and conventional channel estimation.

Figure 12 illustrates block error rate (BLER) versus SNR performances for 16-QAM with rate 3/4 for DMRS Type 1 and 2 on TDL-C channel with delay spread 2μs for the disclosed invention and conventional channel estimation.

Figure 13 illustrates the BLER versus SNR performances for 64-QAM with rate 3/4 for DMRS Type 1 and 2 on TDL-C channel with delay spread 1 μs for the disclosed invention and conventional channel estimation.

Figure 14 illustrates the BLER versus SNR performances for 256-QAM with rate 2/3 for DMRS Type 1 and 2 on TDL-C channel with delay spread 1 μs for the disclosed invention and conventional channel estimation.

Reference list

100. A computer implemented method

101. Signal reception and performing OFDM demodulation

102. Initial pilot depatterning

103. Determining phase change rates

104. Calculating phase correction terms

105. Applying phase correction terms

106. Performing pilot depatterning using phase corrected received signals

107. Updating the channel estimates at pilot locations using phase change correction terms

108. Interpolating the estimated values in pilot locations

Detailed Description

Hereinafter, the detailed descriptions of the embodiments of the present invention will be given with accompanying drawings. The present invention relates to a channel estimation method and device for MIMO-OFDM communications systems with an improved performance compared to conventional receivers and is based on compensating the phase changes in the resource elements in a CDM group during pilot depatterning stage. When there is a time synchronization error or the strongest channel tap occasionally occurs after a certain delay with respect to the start of receive window due to delay spread, there will be a dominant phase rotation in the frequency domain of the effective channel, which violates the assumption required for orthogonality. When, there is carrier frequency offset or there is Doppler spread due to mobility, again the phase of the channel will change in the time domain. The disclosed method improves the performance of the conventional channel estimation by estimating the phase change rate in the transmission band in the frequency domain and/or transmission slot in the time domain and compensating the phase change during pilot depatterning operation.

The disclosed method (100) for channel estimation in MIMO-OFDM wireless communication system utilizing CDM groups in pilot allocations, comprises the steps of:

• receiving signal and performing OFDM demodulation (101 ) to each receive antenna signal to obtain received symbols in the resource grid of each receive antenna,

• performing an initial pilot depatterning (102 in all pilot depatterning groups using the corresponding OCC code and received symbols in the resource grid of all layers at all CDM groups for each receive antenna signal,

• determining the phase change rates (103) in the resource grid for all layers at all CDM groups for each receive antenna signal,

• calculating the phase correction terms (104) to be used for each resource element in all pilot depatterning groups at all CDM groups for each receive antenna signal,

• applying the phase correction term (105) to each resource element in all pilot depatterning groups at all CDM groups for each receive antenna signal,

• performing pilot depatterning using the phase corrected received signal (106) in all pilot depatterning groups at all CDM groups and receive antenna signals (106),

• updating the channel estimates at pilot locations in all pilot depatterning groups using phase change correction terms (107) at all CDM groups and receive antenna signals,

• interpolating the estimated values in pilot locations (108) to obtain the channel estimation values for all resource elements at each layer for each receive antenna signal.

In Figure 3, the flowchart of operations for the disclosed invention for channel estimation in MIMO-OFDM systems based on phase correction in pilot depatterning is provided. In the present invention (100), as a first step, the signal is received and OFDM demodulation is performed to obtain received symbols

in the frequency domain in the resource grid of each receive antenna m, for m = 1, ... , N_R (101 ). In the present invention (100), as a second step,, an initial pilot depatterning (102) is performed at all pilot depatterning groups using the corresponding OCC code and received symbols for all CDM groups and each receive antenna signal, i.e. CDM group d, for d = 1, receive antenna m, for m = 1, ... , N_R. In one example, the initial pilot depatternin

operation is based on Least-Squares (LS) estimation method with the assumption that radio channel stays flat in the pilot depatterning occasions. The first two steps (101 ), (102) of the disclosed invention (100) can be viewed as the pre-processing steps before phase correction based pilot depatterning.

In the present invention (100), as a third step, for a given layer i, receive antenna m, OFDM pilot symbol n and pilot subcarrier index k, the phase change rates in the transmission resource grid in frequency (denoted by

and/or time domain (denoted by

is determined (103). Note that the phase change rate in frequency is a real, scalar value representing the channel phase changes over subcarriers at a given OFDM pilot symbol, and can differ in different pilot symbols n, therefore it is denoted as a function of n, i.e

Similarly, the phase change rate in time is a real, scalar value representing the channel phase changes over time symbols at a given pilot subcarrier and can differ in different subcarriers k, therefore it is denoted as a function of k, i.e E^i(k). In one example, ^(n) can be determined via calculations using the estimated channel values in the pilot locations as a result of initial pilot depatterning (102) operation. In another example,

can be determined via calculations using the estimated channel values in the pilot locations as a result of initial pilot depatterning (102). In another example, ^(n) can be determined by taking it as an input from a processing block in the receiver. In yet another example,

^i ^can be determined by taking it as an input from a processing block in the receiver. After determining phase change rates (103), phase change rate should be available in time domain or in frequency domain or in both domains. The phase change rates need to be obtained for all layers at all CDM groups for each receive antenna signal, i.e CDM group d, for d =

receive antenna m, for

In the present invention (100), as a fourth step, the phase correction term to be used in each resource element in the pilot depatterning group at all CDM groups and receive antenna signals is calculated (104). First, when the phase change rates in the transmission resource grid in frequency, are available after determining phase

change rates (103), the group phase change rates in frequency, denoted by , for

each CDM group d and receive antenna m are calculated using available values.

Based on the phase correction term in the frequency domain can be calculated

as follows. For a given pilot depatterning group at a CDM group d located at subcarriers with for v < u, the phase correction term in the frequency

domain for the considered pilot depatterning group at nth pilot symbol is A^'^n) = . . . . for nth subcarrier position wherein N_fft is the FFT size used in

OFDM modulation and j is the imaginary unit. When the phase change rates in the transmission resource grid in time, j(/c), are available after determining phase change rates (103), the group phase change rates in time, denoted for each CDM

group d and receive antenna m are calculated using available

values. For a given pilot depatterning group at a CDM group d located at OFDM symbols

with n_v < n_u, for v < u, the phase correction term in the time domain for the considered pilot depatterning group at Zcth pilot subcarrier is for uth

symbol position, wherein Ts is the number of samples at an OFDM symbol. Calculation of phase correction terms (104) needs to be performed for all pilot depatterning groups at all CDM groups, i.e CDM group d for d = 1,

and receive antenna signals, i.e.

In the present invention (100), as a fifth step, at least one of the calculated phase correction terms in the frequency domain and calculated phase correction terms in the time domain is applied to the received signal at all pilot depatterning groups (105). For a given pilot depatterning group at a CDM group d located at the subcarriers {/c₀, /ci, . . ., a^{ncl nth} OFDM symbol position, applying the phase correction term (105) in the frequency domain to the received signal at receive antenna m is expressed as

wherein

is the complex conjugate of For a given pilot depatterning group at a CDM group d located at

the OFDM symbols and k th subcarrier, applying the phase correction

term (105) in the time domain to the received signal at receive antenna m is expressed as wherein is the complex

conjugate of

Application of the phase correction terms (105) need to be performed for all CDM groups, i.e CDM group d for

and receive antenna signals,

In the present invention (100), as a sixth step, pilot depatterning is performed using the phase corrected received signal, that is Y^, at all pilot depatterning occasions (106). In one example, pilot depatterning (106) is least squares based solution similarly to conventional pilot depatterning. For a given CDM group d, the channel estimates are obtained for all pilot depatterning positions for all layers belonging to the CDM

group d. For example, if a pilot depatterning group is located at subcarriers

u, then pilot depatterning (106) operation produces a single channel estimate for all resource elements at the considered pilot depatterning group, and it is assigned as the channel estimate for pilot location , and denoted by Pilot depatterning using

the phase corrected received signal (106) needs to be performed for all CDM groups, i.e CDM group d for d = 1, ... , D and receive antenna signals, .

In the present invention (100), as a seventh step, the channel estimates at pilot locations in all pilot depatterning groups are updated using a phase term (107). For example, suppose that a pilot depatterning group is located at subcarriers

} and at the OFDM symbols with and for In

one

example, if the phase correction is applied in the frequency domain (105), then

wherein

. . . . .. and is a phase change rate value in the

frequency domain obtained at the step of determining the phase change rates (103) of present invention (100). In another example, for all

and is a group phase change rate in frequency domain obtained at the

step of calculating phase correction terms (104) of present invention (100). In one example, if the phase correction is applied in the time domain (105), then

and wherein

a_nd is a phase change rate value in the time

domain obtained at the step of determining the phase change rates (103) of present invention (100). In another example, _anc|

i^{s a} group phase change rate in frequency domain obtained at the step of calculating phase correction terms (104) of present invention (100). Update of the estimates at all pilot depatterning groups (107) needs to be performed for all CDM groups, i.e CDM group d for d = 1, ..., £), and receive antenna signals, i.e. m = 1, ... , N_R . After updating of the estimates at all pilot depatterning groups (107), the channel estimates at all pilot locations in all layers are obtained for all receive antenna signals. The channel estimates between ith layer and mth receive antenna can be stored in a vector,

In the present invention (100), as a eighth step, the estimated values after updating the channel estimates using a phase correction term (107) are used to obtain the channel estimation for all resource elements using an interpolation method in the resource grid at each layer for all receive antennas (108). Interpolation step (108) can be performed using at least of the following exemplary methods: MMSE estimation in both time and frequency (2D-MMSE), MMSE estimation first in time and then in frequency or first in frequency then in time (MMSE 1 D-1 D), linear interpolation, nearest point interpolation, sliding window averaging.

The phase change rate in frequency, for a layer i and receive antenna m at

OFDM pilot symbol n, obtained at the step of determining phase change rates (103) of the present invention (100) represents the average change in the phase of the estimated channels in the consecutive subcarriers of transmission band in the given OFDM pilot symbol. It is noted that the phase change rate does not need to be constant throughout the transmission band, however the correction is performed using a single value to have a low-complexity and robust algorithm. Ideally, ^(n) can be selected to minimize the average channel estimation error for a given channel model. However, as there is no closed-form expression to calculate such an error for statistical channel models, and they are obtained using performance simulations, which is not practical to perform in a realtime system for the selection

The phase change rate in frequency,

obtained at the step of determining phase change rates (103) of the present invention (100) can be determined using low- complexity methods via calculations based on the estimated channel values in the pilot locations obtained with initial pilot depatterning (102). In one example of this embodiment,

is determined using IFFT based approach. This method depends on the intuition that the phase rotation in the transmission band will be dominated by strong channel taps arriving with a delay. For this reason, the time domain channel response of the channel between layer i and receive antenna m at OFDM symbol n is estimated by taking the IFFT of the estimated channel values in pilot subcarriers at OFDM symbol n. Based on this is calculated as a function of the estimated power

of the channel taps. Some exemplary functions to be used for this purpose include choosing the index of the strongest channel tap, or the index of the latest tap whose power is above a pre-determined threshold in order not to miss any significant channel taps occurring later than the strongest tap. The chosen index needs to be normalized before assigned as The normalization factor equals to the difference between

the starting subcarrier indexes of the pilot depatterning groups. An example for this calculation method is provided to describe the procedure: For a layer i in a CDM group d, let

and , and the pilots given in the base sets

are repeated at every 4 subcarriers in frequency for entire grid of each layer belonging to the CDM group d. Therefore, the pilot symbols of layer i are located in subcarrier indices, i.e, {1 , 3, 5, 7, ..., 95, 97, 99} of the first OFDM symbol. According to this allocation, the first pilot depatterning group is located in the subcarriers indexed by {1,3}, the second pilot depatterning group is located in the subcarriers indexed by {5,7} and so on. Hence, the difference between the starting subcarrier indexes of the pilot depatterning groups for entire resource grid is 4. To calculate

first IFFT of the following vector is calculated: where

each term of is obtained in the initial pilot depatterning procedure (102) between

CDM group d and receive antenna m. Then, the index n_ind, which is the strongest channel tap (or, alternatively, the latest tap whose power is above a pre-determined threshold) is recorded. Finally,

In another example of this embodiment,

is determined using phase response approach as a direct approach to characterize the changes in the phase response of the channel between layer i and receive antenna m. This method starts with calculating the phase response of the estimated channel values with initial pilot depatterning (102) in the pilot symbols and recording the phase change between consecutive subcarriers. Based on this, an average value is assigned t

(with proper normalization by

In one example, this average is obtained using linear regression. In another example, it is obtained by taking mean of the recorded phase changes between consecutive subcarriers. In yet another example, it is obtained by taking median of the recorded phase changes between consecutive subcarriers.

In the step of calculating phase correction terms (104) of the present invention (100), the group phase change rate in frequency domain for each CDM group d and

receive antenna m at OFDM symbol n calculated in the fourth step of the present invention can be obtained using ) values using different methods. In one example,

is the mean °f the phase change rate,

values at OFDM symbol n for the layers belonging to CDM group d. In another example, is the median of the phase

change rate,

values at OFDM symbol n for the layers belonging to CDM group d. It is also possible to use a single group phase change rate value for all CDM groups, receive antennas and OFDM pilot symbols. In one example, for lal d =

and wherein Np is the total number of OFDM pilot

symbols in the slot, and £0 is the mean of the phase change rate, values of all

possible layer, receiver antenna and OFDM pilot symbol combinations. In another example, ε₀ is the median of the phase change rate, values of all possible layer,

receiver antenna and OFDM pilot symbol combinations. Using a single value for phase change rate is an attractive choice due to its simplicity and increased reliability due to increased number of phase change rate points to calculate a correction term especially in low signal-to-noise ratio scenarios. In one example, in the step of interpolating the estimated values in pilot locations (108) of present invention (100), 2D-MMSE interpolation is employed using the following expression:

wherein stores the final channel estimation values at entire resource grid between

ith layer and mth receive antenna and

is the channel estimation values after updating the channel estimates using phase change correction terms (107).

equation (6) is the correlation matrix for wireless channel between those at all resource grid positions and those at pilot locations for ith layer.

in equation (6) is the correlation matrix for wireless channel at pilot locations for ith layer, and a² is the noise variance. This operation given in equation (6) is repeated for all layers by

using corresponding correlations matrices for each layer.

In one example, the correlation matrices

and given in equations (6) can be

calculated using robust channel estimation method in two dimensions. The robust channel estimation method was introduced in Robust MMSE channel estimation in

OFDM systems with practical timing synchronization, by Vineet Srivastava et al., IEEE Wireless Commun. and Networking Conf., Atlanta, GA, USA, 2004, the entire contents of which are incorporated herein in its entirety. It is noted that the layer index i is dropped from the variables for the brevity of the description in the following. To calculate a correlation matrix, the correlations between resource elements in time and in frequency are calculated independently, and they are merged through a Kronocker product. The correlation matrices in the frequency domain are denoted by and and they

are denoted by and _P in the time domain. First, define the matrix which

can be calculated for a given number of channel taps L by

wherein the is the FFT size used in OFDM modulation and is the channel

correlation value between ath and bth subcarriers in the resource grid, and it is the element of the matrix at ath row and Mh column. can be obtained by taking

only the columns of corresponding to the pilot locations in the frequency domain,

and can be obtained by taking only the rows of R corresponding to the pilot

locations in the frequency domain. Similarly, define the matrix which utilizes the

Jakes model by

wherein D is the maximum Doppler spread, T is duration of the OFDM symbol,

is the channel correlation value between ath and bth OFDM symbols in the resource grid, and it is the element of the matrix

at rzth row and bth column. Also, /₀C) ^is zeroth order Bessel function of the first kind can be obtained by taking only the

columns of corresponding to the pilot locations in the time domain, and can

be obtained by taking only the rows of corresponding to the pilot locations in the

time domain. Then, the combined correlation matrices can be calculated as

wherein ® indicates the Kronocker product of the matrices. It is noted that, herein, the channel values in the resource grid h or in the pilot locations in

should be ordered as first in frequency then in time. This means that, for a given layer, where each vector (for

in h is a vector carrying the channel values at the subcarriers of the nth OFDM symbol in the slot, and each vector

(for

s in

is a vector carrying the channel values at the subcarriers of the sth pilot OFDM symbol in the slot, wherein denotes the total number of pilot OFDM symbols in the slot. It is also noted that matrix is common for each layer in the same CDM group.

The disclosed invention (100) does not bring a high computational cost over conventional method. In conventional method, conventional pilot depatterning can be performed in linear-time complexity with simple arithmetic operations and interpolation operation such as 2D-MMSE estimation can be performed by storing the required matrices for a set of channel and SNR parameters, that is, taking the inverse matrix offline, and using the corresponding matrix for given channel conditions to obtain MMSE estimation. This only requires matrix multiplication to perform estimation, which can be implemented very efficiently in the hardware. The disclosed invention (100) includes a low-cost preprocessing step to estimate the channel phase change rate (103) in the subcarriers, then it uses this value during depatterning process. For IFFT based method, IFFT block is already used in OFDM modulation and demodulation and can be implemented efficiently in the hardware with O(NlogN) complexity. For phase response based approach, the phase of estimated channel values can be obtained efficiently using CORDIC algorithm without using any multipliers. If the phase change rate is known, then the proposed algorithm only requires 2 extra complex multiplication per pilot depatterning occasion compared to conventional method. Therefore, the complexity of the disclosed method is only slightly higher than conventional method due to low-complexity pre-processing. Note that if the delay spread of the channel does not change very quickly, then the phase change rate can be calculated at certain update periods instead of calculating it at every demodulation instance. For this case, the complexity will be almost same as the conventional method between the updates. Another option could be to build a look-up table for phase change rate for different channel models and choose it from table instead of calculating it dynamically.

The performance of the disclosed invention (100) for channel estimation in MIMO-OFDM systems based on phase correction in pilot depatterning is provided using the pilot structure for 5G introduced by 3GPP in Release 15 standards. In 5G, the pilot symbols for data demodulation is called Demodulation Reference Signals (DMRS), downlink and uplink data channels are called as Physical Downlink Shared Channel (PDSCH) and Physical Uplink Shared Channel (PUSCH). The DMRS defined for both PDSCH and PUSCH have the same structure. Considering the wide range of scenarios that needs to be supported by 5G, the DMRS structure is very flexible and can be configured via relevant configuration parameters. PDSCH mapping type defines if the slot is conventional downlink slot (Type A) or a special slot structure defined in 5G called minislot (Type B), dmrs-TypeA-Position defines the starting symbol of first DMRS in the slot (3 or 4). dmrs-AdditionalPosition indicates if there are additional OFDM symbols in the slot which carries DMRS (0,1 ,2 or 3), dmrs-Type specifies the frequency domain pattern of DMRS in a given symbol (Type 1 or Type 2), and maxLength indicates if the CDM group is defined in 1 (single) or 2 (double) symbols, i.e.

In Figure 4, two examples for DMRS configurations are provided. The top two resource grids show the DMRS patterns for the first example. In that case, there are N_L = 4 orthogonal layers and 1 resource block (K = 12 subcarriers). The entire transmission band is collection of such resource blocks and N_siot = 14 OFDM symbols. For larger resource block sizes, the given pattern is repeated in the frequency domain. For this case, DMRS parameters are configured as PDSCH mapping type = Type A, dmrs- TypeA-Position= 3, dmrs-AdditionalPosition = 3, dmrs-Type = Type 1 and maxLength =1. Herein, there are two CDM groups with CDM group 1 having layers Q = {1,2} with group 2 having layers C₂ = {3,4} with K₂ = {2,4} and

The CDM groups are multiplexed in frequency domain with each other,

therefore F = 2, T = 1 and D = 2. For Type 1 , each pilot depatterning group is utilized three times in the frequency domain inside each resource block. For example, in Figure 4, the subcarrier starting positions of pilot depatterning groups for the first example are {1,5, 9} for each symbol. The regular extension of the pilot depatterning groups in frequency domain is automatically carried out throughout the transmission band of the data by taking

as the base reference. However, time domain allocation is configurable via dmrs-AdditionalPosition parameter. For example, there are pilots at 4 OFDM symbols (at locations {3,6,9,12} in the slot) in Figure 4. For Type 1 and maxLength =1 , there can be maximum 2 different CDM groups, and each CDM group can carry maximum two layers implying the maximum number of orthogonal layers that can be supported is 4 for that configuration.

In Figure 4, the bottom two resource grids show the DMRS patterns for the second example. In that case, the main difference is that dmrs-Type = Type 2 is used instead of Type 1 compared to the first example. For this case, there are again two CDM groups with CDM group 1 having layers

and CDM group 2 having layers

with For Type 2, each pilot

depatterning group is utilized two times in the frequency domain inside each resource block. For example, in Figure 4, the subcarrier starting positions of pilot depatterning groups for second example are {1,7} for each symbol. This shows that Type 2 has lower density in the frequency domain, however it can support larger number of layers in general. This is because of the fact that there can be maximum 3 different CDM groups multiplexed in the frequency domain for Type 2. Also, each CDM group can carry maximum two layers with maxLength =1 implying the maximum number of orthogonal layers that can be supported is 6 for that configuration. Only N_L = 4 of them is utilized in this example. If maxLength =2, the maximum number of layers with orthogonal pilots doubles compared to single symbol case and becomes 8 and 12 for Type 1 and Type 2, respectively. This is due to the fact that each CDM group can carry maximum four layers instead of two this time, and the maximum number of CDM groups do not change. Even though the examples are provided using 5G PDSCH DMRS pilot signals, the disclosed invention can be applied in any pilot allocation scheme involving CDM groups such as for channel estimation with multiport CSI-RS, SRS, PUSCH DMRS in 5G, or channel estimation with UE specific DMRS in LTE (Transmission Modes 8,9 and 10).

In simulations, the DMRS parameters are chosen as in the examples given in Figure 4 and the 5G waveform is utilized. In particular, Tapped Delay Line-C (TDL-C) channel model is used with delay spread 1 or 2 ps with no user mobility. The modulation type is 16, 64 or 256-QAM. The channel coding is NR LDPC with base graph 1 and the code rates are either 2/3 or 3/4. The channel decoder is min-sum algorithm with 20 iterations. The transmission band consists of K = 240 subcarriers, and the subcarrier spacing is 15 kHz. The number of orthogonal layers is N_L = 4 and number of receiver antennas is N_R = 8, no MIMO precoding is used, carrier frequency 3.5 GHz. The synchronization is assumed to be perfect and soft MMSE equalizer is used as MIMO detector. For channel estimation, 2D-MMSE is utilized at the interpolation stage for both conventional method and disclosed invention.

In Figures 5, 6, 7 and 8, eight different implementations of the disclosed invention and conventional channel estimation are compared. The conventional channel estimation follows the steps given in Figure 2. In considered implementations of the disclosed invention (100), a single group phase change rate, £₀ which is used at calculating phase correction terms (104), is utilized for all CDM groups, receive antennas and OFDM pilot symbols. Therefore,

^ for all feasible d, m, n combinations.

• The first implementation, called as Median phase based / Set avg. median, represents that while determining the phase change rates (103), the phase change rate in the frequency domain,

is obtained using the phase response approach by taking median of the recorded phase changes between consecutive subcarriers. Also, for this implementation, ε₀ is the median of the phase change rate values, of all possible layer, receiver antenna and OFDM pilot symbol combinations.

• The second implementation, called as Median phase based / Set avg. mean, represents that while determining the phase change rates (103), the phase change rate in the frequency domain,

is obtained using the phase response approach by taking the median of the recorded phase changes between consecutive subcarriers. Also, for this implementation, e₀ is the mean of the phase change rate values, ^{of al1} possible layer, receiver antenna and OFDM pilot symbol

combinations.

• The third implementation, called as Mean phase based / Set avg. median, represents that while determining the phase change rates (103), the phase change rate in the frequency domain, ^(n), is obtained using the phase response approach by taking the mean of the recorded phase changes between consecutive subcarriers. Also, for this implementation, e₀ is the median of the phase change rate values, s of all

possible layer, receiver antenna and OFDM pilot symbol combinations.

• The fourth implementation, called as Mean phase based / Set avg. mean, represents that while determining the phase change rates (103), the phase change rate in the frequency domain, ^(n), is obtained using the phase response approach by taking the mean of the recorded phase changes between consecutive subcarriers. Also, for this implementation, e₀ is the mean of the phase change rate values, of all

possible layer, receiver antenna and OFDM pilot symbol combinations.

• The fifth implementation, called as IFFT (max peak) based / Set avg. mean, represents that while determining the phase change rates (103), the phase change rate in the frequency domain, , is obtained using the IFFT based approach by

choosing and normalizing the index of the strongest channel tap. Also, for this implementation, e₀ is the mean of the phase change rate values, , of all

possible layer, receiver antenna and OFDM pilot symbol combinations.

• The sixth implementation, called as IFFT (max peak) based / Set avg. median, represents that while determining the phase change rates (103), the phase change rate in the frequency domain

^(n), is obtained using the IFFT based approach by choosing and normalizing the index of the strongest channel tap. Also, for this implementation, e₀ is the median of the phase change rate values, of all

possible layer, receiver antenna and OFDM pilot symbol combinations.

• The seventh implementation, called as IFFT (threshold) based / Set avg. median, represents that while determining the phase change rates (103), the phase change rate in the frequency domain,

^ ), is obtained using the IFFT based approach by taking and normalizing the index of the latest tap whose power is above a predetermined threshold. The threshold is set as the 3/4 of the power of the strongest channel tap. Also, for this implementation, e₀ is the median of the phase change rate values, of all possible layer, receiver antenna and OFDM pilot symbol

combinations.

• The eighth implementation, called as IFFT (threshold) based / Set avg. mean, represents that while determining the phase change rates (103), the phase change rate in the frequency domain, is obtained using the IFFT based approach,

wherein the is obtained by taking and normalizing the index of the latest tap

whose power is above a pre-determined threshold. The threshold is set as the 3/4 of the power of the strongest channel tap. Also, for this implementation, e₀ is the mean of the phase change rate values, , of all possible layer, receiver antenna and

OFDM pilot symbol combinations.

In Figures 5, 6, 7 and 8, the MSE versus SNR performances are provided for DMRS Type 1 and channel delay spread 1 μs, for DMRS Type 1 and channel delay spread 2μs, for DMRS Type 2 and channel delay spread 1 μs and for DMRS Type 2 and channel delay spread 2μs, respectively. In all of the figures, it is observed that all of the eight different implementations of the disclosed method improve the performance of the conventional method, especially as the SNR increases. The performances of the Median phase based, Mean phase based and IFFT (threshold) based implementations are very close to each other in large SNR values. IFFT (max peak) based implementation performs slightly worse than the others. Also, it is observed that Median phase based implementations are slightly better than Mean phase based implementations in all four figures. In terms of set averaging methods to obtain E₀, the performance of Set avg. mean and Set avg. median values are very close to each other, and one can be slightly better than the other one depending on the phase change rate calculation method and the simulation scenario. When the figures are further zoomed in, it is noticed that the general performance ranking of the methods change as the scenario changes. However, it is important to note that these differences will not likely create any observable performance difference in the practical SNR and code rate scenarios in terms of link level performance. Hence, any of the exemplary implementations of the disclosed invention can be used. For other simulations in Figures 10, 1 1 , 12, 13 and 14, Median phase based / Set avg. median implementation is used for the performance of the disclosed invention.

When a single group phase change rate E₀ is used to calculate phase correction terms at all possible phase correction instances, it is of interest to find out the best value for it in terms of channel estimation performance in the considered scenarios. This is important to check out the performance of the low-complexity phase rate calculation options disclosed in the invention. As £₀ is a positive scalar real value, hence its exact value needs to be searched over real numbers through Monte-Carlo simulations ideally, however this is not realistic and practical. Instead of this, its value can be restricted to the integer values, that is, £₀ is swept on different positive integers as a good approximation. In Figure 8, the MSE versus SNR performances are provided with various integer ε₀ values to be used at calculating phase correction terms (104) for DMRS Type 1 configuration when the delay spread is 1 ps. It is observed that the performance improves as ε₀ increases until ε₀ = 5, then it starts to decrease if it is further increased from ε₀ = 5. The performances of ε₀ = 5 and £₀ = 6 are very close to each other. This implies that even though optimal value for ε₀ does not need to be integer in general, the performance of the best integer value will tightly approximate that of the optimal (real) value. Similar observations can be obtained for Type 2 configuration for this channel model, and the best integer value is obtained as ε₀ = 5 again, even though it is not explicitly provided in Figure 9. When the delay spread is 2 ps, the best (integer) performances can be obtained when ε₀ = 8 and £₀ = 7 for Type 1 and Type 2, respectively, even though they are not explicitly provided in Figure 9 either.

In Figure 10, the MSE versus SNR performances for DMRS Type 1 and Type 2 on TDL- C channel with delay spread 1 μs are provided for two implementations of the disclosed invention and conventional channel estimation. Disclosed invention (s₀ = 5) implementation refers to a single group phase change rate ε₀ is used at all possible phase correction instances while calculating the phase correction terms (104) . By using Figure 8, the optimal integer value is used for ε.₀ Disclosed invention (phase response approach) refers to Median phase based / Set avg. median implementation, which is used in Figures 5, 6, 7 and 8. It is observed that there is a clear performance improvement compared to conventional method, when the disclosed method is applied as the SNR increases. It is also observed that the performance of the Disclosed invention (phase response approach) achieves the performance of the disclosed invention with optimal integer selection, i.e. Disclosed invention (ε₀ = 5), in both Type 1 and Type 2 configurations. This implies that disclosed low-complexity calculation methods for phase change rates actually achieve the optimal performance with single group phase change rate in practice. This is due to the observations that the performance gap to the optimal selection using a real number for ε₀ compared to the best integer selection will not be significant as argued for Figure 8. For all methods, Type 2 configuration provides better performance compared to Type 1 at medium and high SNR values. This is due to the fact that the main factor to limit the performance in that case is the loss of orthogonality in the pilot depatterning groups due to frequency selective channel. The subcarriers in a pilot depatterning group are consecutive in Type 2 as illustrated in Figure 4, therefore it is less effected compared to Type 1 configuration. On the other hand, Type 1 configuration is better than Type 2 at low SNR, because it has a more regular pattern and higher pilot density compared to Type 2, which enables to suppress noise more effectively using Type 1 configuration.

In Figure 11 , the MSE versus SNR performances for DMRS Type 1 and Type 2 on TDL- C channel with delay spread 1 μs are provided for two implementations of the disclosed invention (100) and conventional channel estimation. The implementations of the disclosed invention in this figure are the same type as in Figure 10, wherein the optimal integer selections are used for corresponding channel model, i.e. £₀ = 8 is used for Type 1 and £₀ = 7 is used for Type 2. The performance of the Disclosed invention (phase response approach) achieves the performance of optimal integer selection for ε₀ in both configurations. It is observed that for a given channel estimation method, Type 1 provides better performance compared to Type 2 at all SNR regions. This is because of the fact that the channel is highly frequency selective, and Type 2 does not have enough density to capture this due to the gap between pilot depatterning groups in the frequency domain. The disclosed method improves the performance of conventional method in both configurations. It is noted that the best performance is achieved when the disclosed method is used with Type 1 configuration among all options considered in the figure.

In Figure 12, BLER versus SNR performances for 16-QAM with rate 3/4 for DMRS Type 1 and 2 on TDL-C channel with delay spread 2μs for the disclosed invention (100) and conventional channel estimation. The performance result with perfect (ideal) channel knowledge is also provided for comparison purposes. For Type 1 , the BLER target 0.01 is achieved when SNR is 9.7 and 1 1.8 dB with disclosed method and conventional method, respectively. Therefore, the performance of conventional method is improved by 2.1 dB by applying the disclosed invention, and the performance gap between ideal channel information case and Type 1 with disclosed invention is 3.8 dB. For Type 2, BLER target is achieved, when SNR is 12.2 and 12.9 with disclosed method and conventional method, respectively, which indicates 0.7 dB performance improvement. It is important to note that the improvement via disclosed method is larger for Type 1 , as the distance between subcarriers in the same pilot depatterning group are larger in that case, therefore the phase rotation to be compensated is more significant as compared to that of Type 2.

In Figure 13, the BLER versus SNR performances for 64-QAM with rate 3/4 for DMRS Type 1 and 2 on TDL-C channel with delay spread 1 μs are provided for the disclosed invention (100) and conventional channel estimation. In this case, Type 2 gives better performance compared to Type 1 for both methods. The BLER target 0.01 is achieved when SNR is 14 and 14.3 dB with disclosed method and conventional method, respectively for Type 2, whereas it is achieved when SNR is 15.4 and 18.8 dB with disclosed and conventional method, respectively for Type 1. Therefore, the disclosed invention improves the performances of Type 1 and Type 2 configurations by 3.4 and 0.3 dB, respectively. The performance gap between disclosed method and the ideal channel information in Type 1 and Type 2 case is 2.8 and 4.2 dB respectively. It is important to note that performance improvement via disclosed method is more significant for Type 1. According to Figure 10, when SNR is 15 dB, the disclosed method provides 28% and 13% improvement in terms of MSE for Type 1 and Type 2, respectively. Note that regarding the improvement in BLER performance, it is also important which MSE values are actually achieved. For example, for Type 2, when the disclosed algorithm is applied, then MSE value becomes 0.0067 at 15 dB, whereas the noise variance is 0.0316. Therefore, the system performance is still limited by noise primarily. As a result, the significant improvements in BLER performance occurs, when there is a significant MSE improvement, and achieved MSE is around (or larger than) the noise variance values corresponding to the SNR region system operates for given spectral efficiency.

In Figure 14, the BLER versus SNR performances for 256-QAM with rate 2/3 for DMRS Type 1 and 2 on TDL-C channel with delay spread 1 μs are provided for the disclosed invention (100) and conventional channel estimation. It is observed that Type 1 configuration with conventional method can not operate for this modulation and code rate. The proposed algorithm is able to reach 0.1 BLER when SNR is 30 dB for Type 1 . On the other hand, the BLER target 0.01 is achieved, when SNR is 19.6 and 21.4 dB with disclosed method and conventional method, respectively for Type 2. Therefore, the disclosed method improves the performance of Type 2 configuration by 1 .8 dB. Also, the performance gap of the disclosed method to the ideal channel information case is 4.2 dB. According to Figure 10, the MSE values goes to error floor for Type 1 at 0.0089 and 0.0133 for the cases with disclosed method and conventional method, respectively. Therefore, the effective SINR can not be larger than 20.5 and 18.8 dB due to channel estimation error, which explains the large BLER values observed in Type 1. However, for Type 2, when SNR is 22 dB, the noise variance is 0.0063, as the MSE values with and without disclosed invention are 0.0041 and 0.0052. As these values are close to the noise variance value, the %21 improvement in MSE results in a considerable improvement in SNR values to reach target BLER. Therefore, it is possible to obtain significant performance improvements for both Type 1 and Type 2 by using the disclosed invention.

The results obtained from simulations regarding the performance of the disclosed invention (100) can be summarized as follows. First, the disclosed invention can use different low-complexity implementation approaches to calculate phase change rates (103) and phase correction terms (104) at the pilot depatterning occasions. It is observed that the performances of these approaches are very close to each other, and all of them improves the performance of the conventional method. The exact performance ranking of them depends on the channel and DMRS configuration type, however as the performance differences are not significant, the selection of the method can be left as an implementation choice. Second, different low-complexity implementation approaches achieve the average error performance of the optimal selection for single group phase change rate, which is used to calculate phase correction terms (104) at all occasions. This is because of the observation that restricting the phase change rate to integer values provide a tight approximation for general real-valued case, and the low-complexity implementation methods have almost identical performance with optimal integer selection. Third, the disclosed invention provides performance improvements for both Type 1 and Type 2 configurations. For example, it is observed that the disclosed method can provide up to 3.4 dB for Type 1 and 1 .8 dB for Type 2 improvements to reach target BLER in certain test cases. The performance improvement is larger for Type 1 configuration in general, as it is more prone to frequency selectivity. Also, larger performance improvements are observed in high spectral efficiency scenarios. This is particularly important, as one of the key reasons to utilize CDM groups in pilot design is to be able to support higher number of MIMO layers to achieve high data rates. The present invention (100) also relates to an apparatus for channel estimation in MIMO- OFDM wireless communication system utilizing CDM groups in pilot allocations. In accordance with an embodiment of the present invention, the apparatus comprises:

• a pre-processing module, which receives and performs OFDM demodulation to each receive antenna signal to obtain received symbols in the resource grid of each receive antenna, and performs an initial pilot depatterning in all pilot depatterning groups using the corresponding OCC code and received symbols in the resource grid of all layers at all CDM groups for each receive antenna signal,

• a phase correction based pilot depatterning performer module, which determines the phase change rates in the resource grid for all layers at all CDM groups for each receive antenna signal, calculates the phase correction terms to be used for each resource element in all pilot depatterning groups at all CDM groups for each receive antenna signal, applies the phase correction term to each resource element in all pilot depatterning groups at all CDM groups for each receive antenna signal and updates the channel estimates at pilot locations in all pilot depatterning groups using phase change correction terms at all CDM groups and receive antenna signals,

• a channel estimator module configured to interpolate the estimated values in pilot locations to obtain the channel estimation values for all resource elements at each layer for each receive antenna signal.

The various modificiations to the present invention (100) may be suggested to one skilled in the art, and the exemplary embodiments used in the description of the disclosed invention shall not limit the scope of the appended claims.

Claims

CLAIMS A method (100) for channel estimation in MIMO-OFDM wireless communication system utilizing CDM groups in pilot allocations, characterized by comprising; the following steps:

• signal reception and performing OFDM demodulation (101 ) to each receive antenna signal to obtain received symbols in the resource grid of each receive antenna,

• performing an initial pilot depatterning (102) in all pilot depatterning groups using the corresponding OCC code and received symbols in the resource grid of all layers at all CDM groups for each receive antenna signal,

• performing pilot depatterning using the phase corrected received signal (106) in all pilot depatterning groups at all CDM groups and receive antenna signals,

• interpolating the estimated values in pilot locations (108) to obtain the channel estimation values for all resource elements at each layer for each receive antenna signal. The method of claim 1 , wherein the initial pilot depatterning (102) at all pilot depatterning groups is based on Least-Squares (LS) estimation with the assumption that radio channel stays flat in the pilot depatterning occasions. The method of claim 1 , wherein the phase change rates in the resource grid are determined (103) at certain update periods. The method of claim 1 , wherein the phase change rates in the resource grid are determined (103) from a look-up table which contains phase change rate values for different channel models and statistics. The method of claim 1 , wherein the phase change rates in the resource grid are determined (103) only in the frequency domain or only in the time domain or in both frequency and time domains. The method of claim 5, wherein the phase change rates in the resource grid in the frequency domain are determined (103) by calculations using the estimated channel values in the pilot locations after initial pilot depatterning operation (102) applied to the received symbols. The method of claim 5, wherein the phase change rates in the resource grid in the frequency domain are determined (103) by taking it as an input from a different processing block in the receiver. The method of claim 5, wherein the phase change rates in the resource grid in the time domain are determined (103) by taking it as an input from a different processing block in the receiver. The method of claim 6, wherein the calculations using the estimated channel values in the pilot locations after initial pilot depatterning operation (102) while determining the phase change rates (103) in the frequency domain, further comprises:

• taking the IFFT of the estimated channel values in pilot subcarriers at an OFDM symbol to obtain a time domain channel response,

• calculating the phase change rate as a function of the power of the channel taps in the time domain channel response. The method of claim 9, wherein calculating the phase change rate as a function of the power of the channel taps, further comprises choosing an index of the strongest channel tap and normalizing the chosen index to assign it as phase change rate in the frequency domain. The method of claim 9, wherein calculating the phase change rate as a function of the power of the channel taps, further comprises choosing an index of the latest tap whose power is above a pre-determined threshold and normalizing the chosen index to assign it as phase change rate in the frequency domain. The method of claim 6, wherein the calculations using the estimated channel values in the pilot locations after pilot depatterning operation (102) while determining the phase change rates (103) in the frequency domain, further comprises:

• calculating the phase response of the estimated channel values in the pilot symbols,

• recording the phase changes between consecutive subcarriers,

• calculating an average value of the recorded phase changes,

• normalizing the average value to assign it as phase change rate in the frequency domain. The method of claim 12, wherein the average value of the recorded phase changes is obtained using linear regression method. The method of claim 12, wherein the average value is obtained by taking mean of the recorded phase changes. The method of claim 12, wherein the average value is obtained by taking median of the recorded phase changes. The method of claim 1 , wherein calculating the phase correction terms (104) to be used for each resource element in all pilot depatterning groups, further comprises calculating a group phase change rate for each CDM group based on available phase change rates. The method of claim 16, wherein a group phase change rate for a CDM group is the mean of the phase change rate values for the layers belonging to the said CDM group.

18. The method of claim 16, wherein a group phase change rate for a CDM group is the median of the phase change rate values for the layers belonging to the said CDM group.

19. The method of claim 16, wherein a group phase change rate is a single value to be used for all CDM groups, receive antennas and OFDM pilot symbols.

20. The method of claim 19, wherein the single value for group phase change rate be used for all CDM groups, receive antennas and OFDM pilot symbols is the mean of the phase change rate values obtained for all possible layer, receiver antenna and OFDM pilot symbol combinations.

21. The method of claim 19, wherein the single value for group phase change rate be used for all CDM groups, receive antennas and OFDM pilot symbols is the median of the phase change rate values obtained for all possible layer, receiver antenna and OFDM pilot symbol combinations.

22. The method of claim 1 , wherein performing pilot depatteming using the phase corrected received signal (106) in all pilot depatterning groups is based on Least- Squares estimation with the assumption that radio channel stays flat in the pilot depatterning occasions.

23. The method of claim 1 , wherein the interpolation of the estimated values in pilot locations (108) to obtain the channel estimation values for all resource elements is performed using 2D-MMSE estimation represented by the equation

where stores the final channel estimation values at entire resource grid between ith layer and mth receive antenna; is the outcome of pilot

depatterning using the phase corrected received signal (106) is the correlation

matrix for wireless channel between those at all resource grid positions and those at pilot locations for ith layer and

is the correlation matrix for wireless channel at pilot locations for ith layer, and is the noise variance. An apparatus for channel estimation in MIMO-OFDM wireless communication system utilizing CDM groups in pilot allocations, characterized by comprising: