CN108900461B - Wireless communication system broadband signal design method based on large-scale MIMO - Google Patents

Wireless communication system broadband signal design method based on large-scale MIMO Download PDF

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CN108900461B
CN108900461B CN201810685602.3A CN201810685602A CN108900461B CN 108900461 B CN108900461 B CN 108900461B CN 201810685602 A CN201810685602 A CN 201810685602A CN 108900461 B CN108900461 B CN 108900461B
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CN108900461A (en
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王捷
陈华宇
杨天棋
翟立君
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Southeast University
CETC 54 Research Institute
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CETC 54 Research Institute
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2602—Signal structure

Abstract

The invention discloses a method for designing broadband signals of a wireless communication system based on massive MIMO, which comprises the following steps of: each transmitting antenna in the large-scale MIMO system generates 32 data with 3MHz sub-band bandwidth, and each sub-band data is obtained by Orthogonal Frequency Division Multiplexing (OFDM), and the sub-carrier interval of the OFDM is 15 kHz; time domain data of 32 sub-band bandwidths and 3MHz are subjected to a rapid implementation algorithm of a generalized multi-carrier (GMC) synthesis filter bank to obtain synthesized transmission signals of 100MHz bandwidths; and the received signal of each receiving antenna passes through a corresponding GMC analysis filter bank and then is subjected to large-scale MIMO signal detection. The method combines the OFDM technology and the GMC filter bank to realize the algorithm quickly, generates the 100MHz bandwidth signal based on the large-scale MIMO wireless communication private network, and has the advantages of low algorithm complexity, easy hardware realization and the like.

Description

Wireless communication system broadband signal design method based on large-scale MIMO
Technical Field
The invention relates to downlink signal design of a wireless communication system, in particular to a design method based on broadband signals in a large-scale MIMO wireless communication private network system.
Background
With the increasingly mature 5G technology, the requirement of the application of the wireless communication private network based on massive MIMO is urgent. However, the coverage of the private network base station is much larger than that of a 5G cellular mobile communication system, so that the subcarrier spacing based on the direct Orthogonal Frequency Division Multiplexing (OFDM) technology is usually selected to be 15kHz to eliminate the multipath delay effect in the private network application environment, and 8192-point FFT conversion is required to generate a private network signal with a bandwidth of 100MHz, which is high in implementation complexity. Therefore, it is necessary to research a new, low-complexity design scheme of wideband signals for a massive MIMO-based wireless communication private network.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a low-complexity broadband signal design method which is fast to realize for a large-scale MIMO wireless communication system.
The technical scheme is as follows: the invention adopts the following technical scheme:
according to the signal bandwidth W required by data transmissionbAnd a preset sub-band bandwidth WsbObtaining the number M of sub-bands;
generating M sub-band bandwidths W on each transmit antenna in a massive MIMO systemsbAnd each subband data is obtained by OFDM modulation;
m subband bandwidths WsbThe OFDM data is synthesized into W through a rapid implementation algorithm of a generalized multi-carrier (GMC) synthesis filter bankbThe method for realizing the synthesis filter bank comprises the following steps of transmitting signals with bandwidth: initialization, subband modulation and pre-frequency shift, IDFT and post-frequency shift, cyclic extension, filtering, accumulation of signals and shift output.
The received signal of each receiving antenna is subjected to large-scale MIMO signal detection after passing through a corresponding GMC analysis filter bank, and the implementation of the analysis filter bank mainly comprises the following steps: initialization, sliding, filtering, summing, DFT and pre-frequency shifting, subband demodulation and post-frequency shifting, etc.
As a preferred embodiment, the bandwidth W is dependent on the signalbAnd a preset sub-band bandwidth WsbThe calculation method to obtain the number of subbands M is as follows: bandwidth W of signalbDivided by a predetermined subband bandwidth WsbAn even number smaller than and closest to the division result is taken as the subband number M.
As another preferred embodiment, according to the signal bandwidth WbAnd a preset sub-band bandwidth WsbThe calculation method to obtain the number of subbands M is as follows: will be provided withSignal bandwidth WbDivided by a predetermined subband bandwidth WsbAn odd number smaller than and closest to the division result is taken as the subband number M.
As a preferred embodiment, the prototype filters of the GMC synthesis filter bank and the GMC analysis filter bank are low pass filters.
As a further preferred embodiment, the low-pass filter is a root-mean-square raised cosine filter.
As a further preferred embodiment, the low-pass filter is formed by windowing a sinusoidal pulse signal, the filter coefficients of which are constructed as follows:
Figure BDA0001711585090000021
wherein w (k) is a time window function, a Hanning window is adopted,
Figure BDA0001711585090000022
Twk represents a discrete form of time, being the window duration;
pi(k) is a sinusoidal pulse signal of the filter and,
Figure BDA0001711585090000023
u is the number of filter taps, which is equal to the filter order +1, S is the FFT size, W is the number of allocated data subcarriers,
Figure BDA0001711585090000024
is the pitch offset.
Has the advantages that: the technical scheme of the application is suitable for the transmission design of large-bandwidth signals (for example, signals with bandwidth larger than 20 MHz), and can remarkably reduce the calculation amount for generating OFDM transmission signals. Taking 100M bandwidth OFDM transmission signal as an example, the existing technology is to obtain 100MHz bandwidth OFDM transmission signal directly by 8192 point FFT transformation, and the computational complexity is 213× 13, and in the present application, 32 OFDM sub-band signals obtained by 256-point FFT are obtained first, and the sub-band signals are further processed by generalized multi-carrierThe processing of the wave filter bank forms a 100MHz bandwidth signal, the computational complexity of the implementation in this application is 213× 8, the amount of computation is reduced by about half compared to the prior art.
Drawings
Fig. 1 is a transmission system block diagram of a wideband signal design scheme of a massive MIMO private network system according to an embodiment of the present invention;
fig. 2 is a block diagram of a filter bank implementation of GMC modem in accordance with an embodiment of the present invention;
fig. 3 is a flow chart of a fast implementation of a private network signal GMC filter bank according to an embodiment of the present invention;
FIG. 4 is a graph of a transmit signal spectrum obtained by a transmit-side synthesis filter bank in accordance with an embodiment of the present invention;
FIG. 5 is a schematic diagram of adjacent channel leakage ratios of a transmit signal according to an embodiment of the invention;
fig. 6 is a 32 subband signal spectrum diagram obtained by analyzing a filter bank at a receiving end according to an embodiment of the present invention;
FIG. 7 is a diagram of a spectrum of one of the subband signals obtained by analyzing a filter bank at a receiving end according to an embodiment of the present invention;
fig. 8 is a constellation diagram after PDSCH data equalization at the receiving end according to an embodiment of the present invention;
fig. 9 is an error vector magnitude diagram of PDSCH received data and transmitted data according to an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
In one embodiment, the sub-band takes 3MHz bandwidth, taking the transmission design of a wireless communication private network signal with 100M bandwidth as an example. Given the total signal bandwidth and subband bandwidth, the division result is about 33.33, and the odd number or even number that is smaller than the value and closest to the value can be used as the corresponding subband number, and the subband number is 32 in this embodiment based on symmetry. Since the interval between the sub-band data is slightly larger than 3MHz, 32 3MHz are equal to about 100 MHz. At a transmitting end, 32 sub-band OFDM data are generated through OFDM modulation, and then 100M broadband signals are obtained through a GMC synthesis filter. At the receiving end, the GMC analysis filter bank carries out operation demodulation to obtain time domain data.
The sub-band data of 3MHz bandwidth of a transmitting terminal of a large-scale MIMO wireless communication private network system obtained by OFDM modulation is generated according to the steps of a physical layer in a 3GPP standard: firstly, DLSCH processing is carried out on a binary sequence of original data to be transmitted, namely load data, wherein the DLSCH processing comprises transmission block addition CRC, code block segmentation and CRC addition, channel coding, code rate matching and code block connection to form PDSCH input code words. The obtained PDSCH code words are firstly subjected to scrambling and modulation, then modulated data are subjected to layer mapping, then beam forming precoding is carried out, and the coded data are mapped to a multi-antenna resource grid. And carrying out OFDM modulation on the frequency domain data in each antenna resource grid to obtain time domain data of each sub-band.
The 32 sub-band OFDM data are processed by a rapid realization algorithm of a GMC synthesis filter bank to obtain 100MHz broadband signals. The discrete-time form of the transmitted composite multi-carrier signal and the discrete-time form of the multi-carrier analysis are:
Figure BDA0001711585090000031
Figure BDA0001711585090000032
wherein: l is discrete representation form of time, M is number of sub-bands, N is decimation multiple, a is constant, j represents imaginary part of complex number, T is time interval of each sampling point of sending signal, r (l) is time domain discrete receiving signal of receiving end, hs,mShaping the pulse filter for the base band of the mth sub-band at the transmitting end, ha,mAnd the matched filter is the mth subband of the receiving end.
Figure BDA0001711585090000033
Figure BDA0001711585090000034
p (l) is the impulse response of the low-pass filter.
In order to verify the correctness of the signal design scheme, characteristics such as spectrograms of a transmitting end and a receiving end of a signal, adjacent channel leakage ratios, a constellation diagram of received data, error vector magnitude and the like need to be observed. The receiving end only performs reverse PDSCH processing. The time domain signal after passing through the noise channel is subjected to a rapid implementation algorithm of a GMC analysis filter bank to obtain a resource grid, and after data in the time frequency resource grid is subjected to demapping, precoding and layer demapping, the characteristics of representation signals such as a constellation diagram and an EVM can be obtained, so that broadband signal analysis of the large-scale MIMO wireless communication private network is completed.
The following describes the steps for fast implementation of the wideband signal in detail with reference to the accompanying drawings and specific examples.
Fig. 1 shows the structure of a wireless communication private network system based on massive MIMO, in which the following technical indicators are specified, which are correspondingly proposed to meet certain specific requirements of the private network:
(1) and (3) transmission frequency points: 3.4-3.6 GHz;
(2) transmission bandwidth: greater than or equal to 100 MHz;
(3) number of massive MIMO antennas supported: greater than or equal to 128;
(4) and supporting the number of parallel sub-streams of the user: greater than or equal to 8;
(5) supporting scenes: suburbs, islands, mountainous areas;
(6) system peak transmission rate: greater than or equal to 1 Gbps;
(7) the frequency spectrum utilization rate is as follows: greater than 8 bps/Hz;
(8) demodulation threshold: -1dB (QPSK 1/3 code rate bit error rate less than 1E-6)
11dB (16QAM 2/3 bit error rate is less than 1E-6)
17dB (64QAM 3/4 bit error rate is less than 1E-6)
A downlink transmitting end of each antenna in the system adopts a beam forming precoding technology to generate 32 sub-band time domain data with the bandwidth of 3MHz, each sub-band has 3584 sampling points within 1ms, each sub-band adopts an OFDM modulation technology, the sub-carrier interval of the OFDM data is 15kHz, and 64QAM modulation and demodulation data are adopted in the embodiment because the maximum technical indexes specified by the wireless communication private network system based on massive MIMO use 64QAM modulation.
1. Fast implementation of synthesis filter bank
First, the number M of subbands is 32, the decimation factor N is 36, the constant a is 0.0028 pi, and the sampling rate f is sets3.84MHz, sample time interval T M/fsN, the length L of the filter is 2KN +1, where K is a constant, the larger the K value, the longer L, the better the frequency characteristic of the shaped symbol, the smaller the intersymbol interference, but the higher the implementation complexity, and the length of the prototype filter is selected depending on the transmission characteristic of the system and the performance requirement of the system. In this embodiment, the system performance and the system implementation cost are considered comprehensively, and K is finally selected to be 6. The filter coefficients are denoted as p (k), k being 0,1, 2. n, k and L are all discrete-time representations, but the discrete-time sample lengths represented are different, for example, the length k is the number of filter coefficients k is 0,1,2,.. multidot.l-1, and the length n is the number of samples n within 1ms is 1,2,3,. multidot.3584. The method comprises the following steps:
s11, initialization: setting initial values of n and l: n is 0, l is 0; an L-point data sequence is set, which acts as a shift register: d (k) 0, k 0,1, 2.
S12, sub-band modulation: and modulating the M point sequence, wherein the M point sequence is sampling points of the M sub-bands at a certain time, and the sampling points of the M sub-bands at the same sampling moment are processed in parallel. Data x in the m-th subband time domainm(n) as modulated input data, the process of modulation is: each input data multiplied by
Figure BDA0001711585090000041
Then, M point sequence y is obtainedm(n)，0≤m≤M-1。
S13, pre-shift, IDFT, and post-shift: using IFFT to M point sequence ym(n) performing a transformation with a transformation matrix of T ═ V2WM *V1In which V is1Is a value on the diagonal of
Figure BDA0001711585090000051
Diagonal matrix of, V2Is a value e on the diagonal-jπk(M-1)/MDiagonal matrix of WM＝[e-j2πmn/M]M×MIs IDFT matrix of M × M to obtain
Figure BDA0001711585090000052
Thus, M baseband signal inputs are modulated on different carriers to form a multi-carrier.
S14, cyclic extension: sequence of M points
Figure BDA0001711585090000053
Extension to 2M Point sequences
Figure BDA0001711585090000054
Then the L point sequence is expanded to obtain
Figure BDA0001711585090000055
S15, filtering: computing
Figure BDA0001711585090000056
S16, accumulation: computing
d(k)＝d(k)+sn(k),k＝0,1,...,L-1
This is a one-step accumulation operation, where the product result obtained in the previous step is added to d (k), which is a continuously updated sequence.
S17, shift output: sending the first M data in the L-point sequence d (k) to a sending signal sequence, namely:
s(l+k)＝d(k),k＝0,1,2,...N-1
update L-point sequence d (k) is:
Figure BDA0001711585090000057
s18, updating: n +1, l + N, and go to S12.
S19, the resulting transmission signal:
Figure BDA0001711585090000058
wherein t isn1,2, …, len, len denotes s (t)n) Length of (d).
2. The fast implementation process of the analysis filter bank comprises the following steps:
s21, initialization: setting an initial value n of n to be 1;
s22, sliding: taking L-point sequences from received signals r (L)
Figure BDA0001711585090000059
In which the time t is discretenn＝N(n-1)+k，k＝1,2,...,L；
S23, filtering: calculating rn(k)＝p(k)rn(k),k＝0,1,...,L-1；
S24, summation: calculating the residue delta of L/(2M), pair
Figure BDA0001711585090000061
Make 2M-delta 0 at the end so that
Figure BDA0001711585090000062
Is exactly an integer multiple of 2M, and then
Figure BDA0001711585090000063
Changing into a matrix with 2M rows, summing each row of data to obtain a matrix of 2M × 1
Figure BDA0001711585090000064
Then, find out
Figure BDA0001711585090000065
S25, post-frequency shift, DFT and pre-frequency shift: using FFT to M point sequence
Figure BDA0001711585090000066
Transforming into matrix TH＝(V2WM *V1)HTo obtain a reconstructed signal
Figure BDA0001711585090000067
S26, sub-band demodulation:
Figure BDA0001711585090000068
and matrix
Figure BDA0001711585090000069
Dot product is obtained
Figure BDA00017115850900000610
Finally dividing the power coefficient to obtain
Figure BDA00017115850900000611
S27, updating: when n is equal to n +1, the process proceeds to S22.
Finally, time domain data of length 3584 is obtained. After operations such as demapping, precoding decoding and the like, large-scale MIMO signal detection or signal analysis can be performed.
3. Design of low-pass prototype filter in filter bank
Fig. 2 and 3 show the filter bank implementation structure and fast implementation flow of GMC modem, respectively. In the GMC filter combination and analysis system, the impulse response of each of the synthesis filter and the analysis filter is obtained by modulating the impulse response of a low-pass filter, which is called a modulation type filter bank, and a prototype filter. Each sub-band can be viewed as a sub-band through a low-pass filter, a band-pass filter and a high-pass filter after being modulated, and the whole broadband signal is divided.
The implementation of the low-pass prototype filter bank has two schemes, one of which uses a root-mean-square raised cosine filter and can be directly generated by using r cos fir function in MATLAB, and parameters are selected as M ═ 32, N ═ 36, K ═ 6, and L ═ 433. The corresponding MATLAB statement is:
P＝rcosfir(0.15,[-66],36，1,'sqrt')
wherein, 0.15 is raised cosine factor, 'sqrt' indicates that all the designed filters are root mean square raised cosine filters.
Another filter implementation is a soft truncation based filter design. In particular, the soft truncation of the prototype filter used is as follows: considering a filter with a rectangular frequency response as the prototype filter, i.e. a sinusoidal pulse signal p with a suitable bandwidthi(k) Such a filter is desirable because it produces no distortion in the passband while providing overall out-of-band rejection. To pi(k) Applies a time window w (k) and then shifts the frequency of the filter to the center of the desired frequency. Both ends of The window mask transition smoothly to zero, so that sudden jumps at The beginning and end of The truncation filter can be avoided, thereby avoiding frequency overflow in The truncation filter; and the window provides reasonable time positioning in the impulse response of the truncation filter such that the inter-symbol interference (ISI) in the resulting f-OFDM signal is kept within acceptable limits.
The soft truncation based filter is generated as follows:
s31: generating a window function: w (k), in the embodiment, the selected duration of the window function is TwIn the form of:
Figure BDA0001711585090000071
s32: generating a sinc function:
Figure BDA0001711585090000072
where U is the number of filter taps equal to the filter order +1, S is the FFT size, and W is the assigned data subcarrierOf a quantity of
Figure BDA0001711585090000075
Is the tone offset (tone offset).
S33: generating normalized low pass filter coefficients:
Figure BDA0001711585090000073
in one embodiment, according to a total of 15 resource blocks, each resource block has 12 subcarriers, and constitutes a system parameter with a bandwidth of 3MHz, and the subcarrier spacing Δ f is 15kHz, and the FFT size is 256, the f-OFDM filter parameters are obtained as follows: filter order 432, tone offset
Figure BDA0001711585090000076
2.5 subcarriers with a time window of
Figure BDA0001711585090000074
Wherein L is 432.
Finally, various characteristic measurement values of signals of the GMC-based private network system are shown in figures 4-9, and comparison is carried out according to technical indexes such as adjacent channel leakage power ratio ACLR and error vector magnitude EVM given by a 3GPP protocol. The 3GPP protocol mentions that when 64QAM modulation and demodulation is adopted for each subcarrier data of PDSCH, the EVM is required to be less than 9%, and according to fig. 9, the average EVM of 32 OFDM subband data is 2.6%; the 3GPP protocol specifies that the adjacent channel leakage ratios should all be less than-44.2 dB, and according to fig. 5, the ACLR is at most-73.7 dB, and the measured values all meet the criteria. Compared with the calculation complexity of realizing 100MHz bandwidth signals by 8192-point FFT in the 5G standard, the calculation complexity of the 8192-point FFT is 213× 13, the implementation in this application has a computational complexity of 213× 8, a reduction of about half the computational effort.

Claims (7)

1. A method for designing broadband signals of a wireless communication system based on massive MIMO is characterized by comprising the following steps:
according to data transmission requirementsSignal bandwidth WbAnd a preset sub-band bandwidth WsbObtaining the number M of sub-bands;
generating M sub-bands with bandwidth W on each transmitting antenna in large-scale MIMO systemsbAnd each subband data is obtained by OFDM modulation;
m subband bandwidths WsbThe OFDM data is subjected to a rapid realization algorithm of a GMC synthesis filter bank to obtain synthesized WbThe method comprises the following steps of transmitting signals with bandwidth, and realizing a synthesis filter bank: initialization, subband modulation and pre-frequency shift, IDFT and post-frequency shift, cyclic extension, filtering, signal accumulation and shift output, which are as follows:
s11, initialization: setting initial values of n and l: n-0, l-0, n and l being discrete time representations; an L-point data sequence is set, which acts as a shift register: d (k) 0, k 0,1,2, L-1;
s12, sub-band modulation: modulating M point sequence which is sampling points of M sub-bands at a certain time, processing the sampling points of the M sub-bands at the same sampling time in parallel, and obtaining data x on the M sub-band time domainm(n) as modulated input data, the process of modulation is: each input data multiplied by
Figure FDA0002644015060000011
Then, M point sequence y is obtainedm(N), M is more than or equal to 0 and less than or equal to M-1, N is an extraction multiple, T is a time interval of each sampling point, and a is a constant;
s13, pre-shift, IDFT, and post-shift: using IFFT to M point sequence ym(n) performing a transformation with a transformation matrix of Z ═ V2WM *V1In which V is1Is a value on the diagonal of
Figure FDA0002644015060000012
Diagonal matrix of, V2Is a value e on the diagonal-jπk(M-1)/MDiagonal matrix of WM＝[e-j2πmn/M]M×MIs IDFT matrix of M × M to obtain
Figure FDA0002644015060000013
S14, cyclic extension: sequence of M points
Figure FDA0002644015060000014
Extension to 2M Point sequences
Figure FDA0002644015060000015
Then the L point sequence is expanded to obtain
Figure FDA0002644015060000016
S15, filtering: calculating the filter coefficient as p (k)
Figure FDA0002644015060000017
S16, accumulation: computing
d(k)＝d(k)+sn(k),k＝0,1,...,L-1
S17, shift output: sending the first M data in the L-point sequence d (k) to a sending signal sequence, namely:
s(l+k)＝d(k),k＝0,1,2,...N-1
update L-point sequence d (k) is:
Figure FDA0002644015060000021
s18, updating: n +1, l + N, and go to S12;
s19, the resulting transmission signal:
Figure FDA0002644015060000022
wherein t isn1,2, …, len, len denotes s (t)n) Length of (d);
the received signal of each receiving antenna is subjected to large-scale MIMO signal detection after passing through a corresponding GMC analysis filter bank, and the analysis filter bank implementation steps comprise: initialization, sliding, filtering, summing, DFT and pre-frequency shifting, subband demodulation, and post-frequency shifting as follows:
s21, initialization: setting an initial value n of n to be 1;
s22, sliding: taking L-point sequences from received signals r (L)
Figure FDA0002644015060000023
In which the time t is discretenn＝N(n-1)+k，k＝1,2,...,L；
S23, filtering: computing
Figure FDA0002644015060000024
S24, summation: calculating the residue delta of L/(2M), pair
Figure FDA0002644015060000025
Make 2M-delta 0 at the end so that
Figure FDA0002644015060000026
Is exactly an integer multiple of 2M, and then
Figure FDA0002644015060000027
Changing into a matrix with 2M rows, summing each row of data to obtain a matrix of 2M × 1
Figure FDA0002644015060000028
Then, find out
Figure FDA0002644015060000029
S25, post-frequency shift, DFT and pre-frequency shift: using FFT to M point sequence
Figure FDA00026440150600000210
Transforming the matrix into ZH＝(V2WM *V1)HTo obtain a reconstructed signal
Figure FDA00026440150600000211
S26, sub-band demodulation:
Figure FDA00026440150600000212
and matrix
Figure FDA00026440150600000213
Dot product is obtained
Figure FDA00026440150600000214
Finally dividing the power coefficient to obtain
Figure FDA00026440150600000215
S27, updating: when n is equal to n +1, the process proceeds to S22.
2. The massive MIMO-based wireless communication system wideband signal design method as claimed in claim 1, wherein the W is the signal bandwidthbAnd a preset sub-band bandwidth WsbThe calculation method for obtaining the number M of the sub-bands comprises the following steps: bandwidth W of signalbDivided by a predetermined subband bandwidth WsbAn even number smaller than and closest to the division result is taken as the subband number M.
3. The massive MIMO-based wireless communication system wideband signal design method as claimed in claim 1, wherein the W is the signal bandwidthbAnd a preset sub-band bandwidth WsbThe calculation method for obtaining the number M of the sub-bands comprises the following steps: bandwidth W of signalbDivided by a predetermined subband bandwidth WsbAn odd number smaller than and closest to the division result is taken as the subband number M.
4. The massive MIMO based wireless communication system wideband signal design method according to any of claims 1-3, wherein the impulse response of the GMC synthesis filter or GMC analysis filter is obtained by modulating the impulse response of the low pass filter.
5. The massive MIMO based wireless communication system wideband signal design method as claimed in claim 4, wherein the low pass filter is a root mean square raised cosine filter.
6. The massive MIMO based wireless communication system wideband signal design method as claimed in claim 4, wherein the low pass filter is formed by sinusoidal pulse signal windowing.
7. The massive MIMO based wireless communication system wideband signal design method of claim 6, wherein the low pass filter coefficients are constructed as follows:
Figure FDA0002644015060000031
wherein w (k) is a time window function, a Hanning window is adopted,
Figure FDA0002644015060000032
Twk represents a discrete form of time, being the window duration;
pi(k) is a sinusoidal pulse signal of the filter and,
Figure FDA0002644015060000033
u is the number of filter taps, which is equal to the filter order +1, S is the FFT size, W is the number of allocated data subcarriers,
Figure FDA0002644015060000034
is the pitch offset.
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