CN111294098B - Large-scale multi-antenna system signal detection method for low-precision ADC - Google Patents

Large-scale multi-antenna system signal detection method for low-precision ADC Download PDF

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CN111294098B
CN111294098B CN202010087576.1A CN202010087576A CN111294098B CN 111294098 B CN111294098 B CN 111294098B CN 202010087576 A CN202010087576 A CN 202010087576A CN 111294098 B CN111294098 B CN 111294098B
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CN111294098A (en
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陈翔
宋威
邱继云
龚杰
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Sun Yat Sen University
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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
    • H04B7/0452—Multi-user MIMO systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/10—Monitoring; Testing of transmitters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/0242—Channel estimation channel estimation algorithms using matrix methods
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The invention discloses a large-scale multi-antenna system signal detection method for a low-precision ADC (analog to digital converter), and belongs to the technical field of wireless communication. The signal detection method comprises the following steps: firstly, the base station carries out channel estimation and calculates orthogonal triangular decomposition (QR decomposition) of an augmented channel matrix, Maximum Ratio Transmission (MRT) vector and quantization thereof, then the base station carries out iteration through a dynamic programming method to obtain an approximately optimal base station transmitting signal and a detection coefficient, and finally, a receiving end carries out signal detection according to the detection coefficient and the received signal. Compared with a large-scale multi-antenna system of a traditional high-precision ADC, the method can effectively reduce hardware cost and power consumption loss caused by the high-precision ADC.

Description

Large-scale multi-antenna system signal detection method for low-precision ADC
Technical Field
The invention relates to the technical field of wireless communication, in particular to a large-scale multi-antenna system signal detection method for a low-precision Analog Digital Converter (ADC).
Background
In a multi-user multi-antenna (MU-MIMO) technology, a conventional signal detection method relies on a high-precision ADC, for example, a conventional base station generally uses a 12-16 bit high-precision ADC for signal detection. On one hand, the hardware structure of such a high-precision ADC is complex, and an automatic gain control unit is required, which increases the hardware cost, and on the other hand, the power consumption of an ADC (Analog digital converter) increases with the exponential increase of the quantization bit number, and the high-precision ADC increases the power consumption and reduces the energy efficiency. For example, the power consumption of a single ADC with 16-bit quantization precision is generally in the order of 1W, and in a large-scale multi-antenna system with 100 antennas, the power consumption of only the ADC portion can reach about 100W. Therefore, in a large-scale multi-antenna system, the traditional signal detection method relying on a high-precision ADC faces the technical problems of high hardware cost, large power loss and low energy efficiency. And the low-precision ADC with the quantization precision of 1 bit does not need an automatic gain control unit, has the advantages of simple hardware structure, low cost and low power consumption, and is suitable for a large-scale multi-antenna system. But the reduction of quantization accuracy brings nonlinear distortion, resulting in the performance degradation of the conventional signal detection technology. Therefore, a large-scale multi-antenna system signal detection method suitable for low-precision ADCs is needed.
Disclosure of Invention
The present invention is directed to solve the above-mentioned drawbacks of the prior art, and to provide a large-scale multi-antenna system signal detection method for low-precision ADCs.
The purpose of the invention can be achieved by adopting the following technical scheme:
a large-scale multi-antenna system signal detection method for low-precision ADC comprises the following steps:
s1, initializing base station antenna number B, user terminal antenna number U, noise power N0The base station transmits the signal
Figure GDA0003292044020000021
Wherein
Figure GDA0003292044020000022
Represents a QPSK modulation symbol set;
s2, the base station obtains the down link channel matrix by the channel estimation method
Figure GDA0003292044020000023
Figure GDA0003292044020000024
Representing the complex field, and the base station then calculates an augmented channel matrix
Figure GDA0003292044020000025
Figure GDA0003292044020000026
The upper right hand corner mark T represents the transpose of the matrix, IBRepresenting B-dimensional identity matrices, base station pair augmented channel matrices
Figure GDA0003292044020000027
QR decomposition is carried out to obtain an upper triangular matrix
Figure GDA0003292044020000028
S3, base station calculates MRT vector z ═ HHs, the upper right corner mark H represents the conjugate transpose of the matrix, the base station quantizes the real part and the imaginary part of the MRT vector z respectively by using a low-precision ADC with the quantization precision of 1 bit to obtain the quantized MRT vector
Figure GDA0003292044020000029
Figure GDA00032920440200000210
Wherein MRT is English abbreviation of maximum ratio transmission, sign () is a sign function, representing that the value is 1 when the number in the bracket is positive, otherwise, the value is-1,
Figure GDA00032920440200000211
the representation takes its real part for each element in the vector,
Figure GDA00032920440200000212
representing taking the imaginary part of each element in the vector;
s4, the base station iterates through a dynamic programming method to obtain an approximately optimal base station transmitting signal and detection coefficient, and the specific process is as follows:
s41 shows that the state index B is B, and the state vector y when the state index B is calculatedBExpected cost J in sum state index BBA mixture of J andBwritten as JB(yB) Denotes JBIs yBFunction of (2)；
S42, if b > 1, updating the state index b to b-1, and calculating the state vector y of the state index bbCost function g at state index bbExpected cost J at state index bb(yb) G is mixingbWritten as gb(yb) Denotes gbIs ybA function of (a);
s43, when b is 1, from y14 of (2)BSelecting from among the possible states so that J1(y1) Minimum state vector
Figure GDA0003292044020000031
Obtaining near-optimal base station transmission signal
Figure GDA0003292044020000032
S44, calculating detection coefficient
Figure GDA0003292044020000033
The square of matrix two norm is solved, if beta is less than 0, x is-x and beta is-beta;
s5, the base station sends a base station sending signal x with the approximate optimal value as a sending signal, and sends a detection coefficient beta;
s6, the user terminal receives the signal transmitted from the base station,
Figure GDA0003292044020000034
where y is Hx + n, and n is the receiver thermal noise of the user terminal. User terminal performing signal detection
Figure GDA0003292044020000035
Obtaining an estimate of a base station transmitted signal s
Figure GDA0003292044020000036
Further, the step S41 is as follows:
s411, constructing quantized symbol space with order state index B ═ B
Figure GDA0003292044020000037
Figure GDA0003292044020000038
S412, calculating the state vector of the state index B
Figure GDA00032920440200000318
yBIs a quantized symbol space
Figure GDA00032920440200000319
A 1-dimensional vector of (1), each element in the vector being a quantized symbol space
Figure GDA00032920440200000320
The element of (2), wherein the symbol space is quantized
Figure GDA00032920440200000321
A total of 4 elements, the state vector y at state index BBThere are 4 possible states in total;
s413 for yBWill vector yBEach element of (2) is represented by a symbol yBIs represented by, i.e. yB＝[yB]. Calculating the expected cost in State B
Figure GDA0003292044020000039
Indicating the absolute value of RB，BRepresenting the elements of the B-th row and B-th column of the upper triangular matrix R,
Figure GDA00032920440200000310
and
Figure GDA00032920440200000311
respectively representing the conjugate transposes of the kth and the B-th elements of the MRT vector z,
Figure GDA00032920440200000312
representing quantized MRT vectors
Figure GDA00032920440200000313
The (k) th element of (a),
Figure GDA00032920440200000314
and
Figure GDA00032920440200000315
respectively represent pair
Figure GDA00032920440200000316
And
Figure GDA00032920440200000317
the calculation result of (2) is taken as a real part. Will JBWritten as JB(yB) Denotes JBIs yBAs a function of (c).
Further, the step S42 is as follows:
s421, when b is larger than 1, updating the state index b to be b-1;
s422, calculating the state vector y of the state index bb∈xB-b+14 of (2)B-b+1A possible state;
s423 for the state vector ybWill vector ybEach element of (2) is represented by a symbol yb，yb+1，...，yBIs represented by, i.e. yb＝[yb yb+1 ... yB]Cost function when calculating the state index b
Figure GDA0003292044020000041
Wherein R isk，kAnd Rb，kRespectively representing the kth row and kth column elements of the upper triangular matrix R and the kth row and kth column elements of the upper triangular matrix R;
s424, aiming at the state vector ybFor each possible state of (a), the expected cost J in computing the state index bb(yb)＝gb(yb)+Jb+1(yb(2: end)), wherein y isb(2: end) represents ybThe 2 nd bit to last bit element in the selected state.
Compared with the prior art, the invention has the following advantages and effects:
1. the large-scale multi-antenna system signal detection method for the low-precision ADC reduces the quantization precision of the ADC module to 1 bit, thereby simplifying the hardware structure of the ADC module, avoiding the need of an automatic gain control unit and reducing the hardware cost.
2. The large-scale multi-antenna system signal detection method for the low-precision ADC, disclosed by the invention, has the advantages that the reduction of the quantization precision of the ADC is benefited, the power consumption of an ADC module can be effectively reduced, and therefore the energy efficiency of a large-scale multi-antenna system is improved.
3. The large-scale multi-antenna system signal detection method for the low-precision ADC disclosed by the invention utilizes a dynamic programming method to relieve the problem of nonlinear distortion caused by the low-precision ADC, and obtains approximately optimal signal detection performance. The signal detection performance of the method is superior to that of the traditional linear signal detection method of a large-scale multi-antenna system for low-precision ADC.
4. The invention discloses a large-scale multi-antenna system signal detection method for a low-precision ADC (analog to digital converter). the characteristic that the signal state space of the low-precision ADC is limited is utilized, the large-scale multi-antenna system signal detection problem of the low-precision ADC is reconstructed into a dynamic programming problem, a cost function is defined firstly, then a base station transmitting signal after low-precision quantization is used as a state vector in the dynamic programming problem, an expression among an expected cost, the state vector and the cost function is obtained by combining a general form of the dynamic programming method, and then an approximately optimal base station transmitting signal is obtained by iterative solution.
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FIG. 1 is a flow chart of a method for large scale multi-antenna system signal detection for low precision ADC according to the present invention;
FIG. 2 is a flow chart of parameter initialization, base station channel estimation, QR decomposition, calculation of MRT vectors and their quantization in the present invention;
FIG. 3 is a flowchart of the dynamic programming method iteration of the present invention;
FIG. 4 is a flowchart of the steps of the receiver signal detection in the present invention;
fig. 5 is a schematic diagram of a bit error rate curve of signal detection performance under QPSK modulation, where the number of base station antennas is 8, the number of single-antenna users is 4 in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
The embodiment discloses a large-scale multi-antenna system signal detection method for low-precision ADC. The method for detecting a large-scale multi-antenna system signal for a low-precision ADC according to the present invention is described in detail with reference to fig. 1 to 5 and a specific example of detecting a Massive MIMO system signal based on a low-precision ADC.
Consider the system model as follows: in large-scale multi-antenna system, the number of base station antennas B is 8, the number of user terminal antennas U is 4, and noise power N is0Base station transmitting signal
Figure GDA0003292044020000061
Wherein
Figure GDA0003292044020000062
Represents a Quadrature Phase Shift Keying (QPSK) modulation symbol set.
Base station obtains downlink channel matrix by channel estimation method
Figure GDA0003292044020000063
The base station then calculates an augmented channel matrix
Figure GDA0003292044020000064
The upper right hand corner mark T represents the transpose of the matrix, IBRepresenting a B-dimensional identity matrix. Base station pair augmented channel matrix
Figure GDA0003292044020000065
Performing orthogonal triangular and rectangular matrix decomposition (QR decomposition) to obtain an upper triangular matrix
Figure GDA0003292044020000066
The base station calculates the Maximum Ratio Transmission (MRT) vector z ═ HHs, the upper right hand corner mark H represents the conjugate transpose of the matrix. The base station quantizes the real part and the imaginary part of the MRT vector z by using a low-precision ADC with the quantization precision of 1 bit respectively to obtain a quantized MRT vector, wherein sign () is a sign function and represents that the value is 1 when the parenthesis is positive, otherwise the value is-1;
Figure GDA0003292044020000067
the representation takes its real part for each element in the vector,
Figure GDA0003292044020000068
the representation takes its imaginary part for each element in the vector.
Method for base station to execute dynamic programming, order state index B ═ B, construct quantization symbol space
Figure GDA0003292044020000069
State vector y in computing state index BB∈x。yBThere are 4 possible states. For yBWill vector yBEach element of (2) is represented by a symbol yBIs represented by, i.e. yB＝[yB]. Calculating the expected cost in State B
Figure GDA00032920440200000610
Figure GDA00032920440200000611
I represents solving the absolute value, RB，BRepresenting the elements of the B-th row and B-th column of the upper triangular matrix R,
Figure GDA00032920440200000612
and
Figure GDA00032920440200000613
respectively representing the conjugate transposes of the kth and the B-th elements of the MRT vector z,
Figure GDA00032920440200000614
representing quantized MRT vectors
Figure GDA00032920440200000615
The (k) th element of (a),
Figure GDA00032920440200000616
and
Figure GDA0003292044020000071
respectively represent pair
Figure GDA0003292044020000072
And
Figure GDA0003292044020000073
the calculation result of (2) is taken as a real part. Will JBWritten as JB(yB) Denotes JBIs yBAs a function of (c).
When b > 1, the state index b is updated to b-1.
State vector y in calculating state index bb∈xB-b+14 of (2)B-b+1And a possible state.
For ybWill vector ybEach element of (2) is represented by a symbol yb，yb+1，...，yBIs represented by, i.e. yb＝[ybyb+1 ... yB]. Cost function in computing state index b
Figure GDA0003292044020000074
Wherein R isk，kAnd Rb，kRespectively representing the kth row and kth column elements of the upper triangular matrix R and the kth row and kth column elements of R. For ybFor each possible state of (a), the expected cost J in computing the state index bb(yb)＝gb(yb)+Jb+1(yb(2: end)), wherein y isb(2: end) represents ybThe 2 nd bit to last bit element in the selected state.
When b is 1, from y14 of (2)BSelecting from among the possible states so that J1(y1) Minimum state vector
Figure GDA0003292044020000075
Obtaining near-optimal base station transmission signal
Figure GDA0003292044020000076
Calculating the detection coefficient
Figure GDA0003292044020000077
Representing squaring the two norms of the matrix. If β < 0, let x be-x and β be- β.
And the base station sends the approximately optimal base station sending signal x as a sending signal and simultaneously sends a detection coefficient beta.
The user terminal receives the signal transmitted from the base station,
Figure GDA0003292044020000078
where y is Hx + n, and n is the receiver thermal noise of the user terminal. User terminal performing signal detection
Figure GDA0003292044020000079
Obtaining an estimate of a base station transmitted signal s
Figure GDA00032920440200000710
In summary, the key of the signal detection method disclosed in the above embodiment is to use a dynamic programming method to solve the problem of non-linear distortion caused by introducing a low-precision ADC. In a large-scale multi-antenna system, a low-precision ADC is adopted to reduce the hardware cost and the power consumption loss of the ADC, but the low-precision ADC brings nonlinear distortion, so that the performance of a signal detection method under the traditional high-precision ADC is obviously reduced. After the low-precision ADC is used for quantization, the state space of the quantized signal is limited, and by combining the characteristic, the approximately optimal base station transmitting signal and detection coefficient can be iteratively solved by adopting a dynamic programming method, so that the signal detection is realized at the receiving end.
Fig. 5 is a schematic diagram of a bit error rate curve of signal detection performance under the conditions of 8 antennas of a base station, 4 users of single antennas, and QPSK modulation in this embodiment, where two curves in the diagram indicate that DP represents a large-scale multi-antenna system signal detection method for low-precision ADC according to the present invention; MRT as a comparison represents the maximum ratio transmission signal detection method in the conventional MU-MIMO technique. From the bit error rate BER simulation comparison result in fig. 5, it is proved that, in the low-precision ADC environment, the BER performance of the large-scale multi-antenna system signal detection method for the low-precision ADC proposed in this embodiment is significantly better than that of the signal detection method MRT used in the conventional MU-MIMO technology.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.

Claims (3)

1. A large-scale multi-antenna system signal detection method for low-precision ADC is characterized by comprising the following steps:
s1, initializing base station antenna number B, user terminal antenna number U, noise power N0The base station transmits the signal
Figure FDA0003292044010000011
Wherein
Figure FDA0003292044010000012
Represents a QPSK modulation symbol set;
s2, the base station obtains the down link channel matrix by the channel estimation method
Figure FDA0003292044010000013
Figure FDA0003292044010000014
Representing the complex field, and the base station then calculates an augmented channel matrix
Figure FDA0003292044010000015
Figure FDA0003292044010000016
The upper right hand corner mark T represents the transpose of the matrix, IBRepresenting B-dimensional identity matrices, base station pair augmented channel matrices
Figure FDA0003292044010000017
QR decomposition is carried out to obtain an upper triangular matrix
Figure FDA0003292044010000018
S3, base station calculates MRT vector z ═ HHs, the upper right corner mark H represents the conjugate transpose of the matrix, the base station quantizes the real part and the imaginary part of the MRT vector z respectively by using a low-precision ADC with the quantization precision of 1 bit to obtain the quantized MRT vector
Figure FDA0003292044010000019
Figure FDA00032920440100000110
Wherein MRT is English abbreviation of maximum ratio transmission, sign () is a sign function, representing that the value is 1 when the number in the bracket is positive, otherwise, the value is-1,
Figure FDA00032920440100000111
the representation takes its real part for each element in the vector,
Figure FDA00032920440100000112
representing taking the imaginary part of each element in the vector;
s4, the base station iterates by using a dynamic programming method to obtain an approximately optimal base station transmitting signal and detection coefficient, and the specific process is as follows:
s41 shows that the state index B is B, and the state vector y when the state index B is calculatedBExpected cost J in sum state index BBA mixture of J andBwritten as JB(yB) Denotes JBIs yBA function of (a);
s42, if b > 1, updating the state index b to b-1, and calculating the state vector y of the state index bbCost function g at state index bbExpected cost J at state index bb(yb) G is mixingbWritten as gb(yb) Denotes gbIs ybA function of (a);
s43, when b is 1, from y14 of (2)BSelecting from among the possible states so that J1(y1) Minimum state vector
Figure FDA00032920440100000113
Obtaining near-optimal base station transmission signal
Figure FDA00032920440100000114
S44, calculating detection coefficient
Figure FDA0003292044010000021
Figure FDA0003292044010000022
Means squaring the two norms of the matrix, if beta < 0, let x be-x andβ＝-β；
s5, the base station sends a base station sending signal x with the approximate optimal value as a sending signal, and sends a detection coefficient beta;
s6, the user terminal receives the signal transmitted from the base station,
Figure FDA0003292044010000023
where y is Hx + n, n is the receiver thermal noise of the user terminal, which performs signal detection
Figure FDA0003292044010000024
Obtaining an estimate of a base station transmitted signal s
Figure FDA0003292044010000025
2. The method as claimed in claim 1, wherein the step S41 is performed by:
s411, constructing quantized symbol space with order state index B ═ B
Figure FDA0003292044010000026
Figure FDA0003292044010000027
S412, calculating the state vector of the state index B
Figure FDA0003292044010000028
yBIs a quantized symbol space
Figure FDA0003292044010000029
A 1-dimensional vector of (1), each element in the vector being a quantized symbol space
Figure FDA00032920440100000210
Wherein, the amountTo change the symbol space
Figure FDA00032920440100000211
A total of 4 elements, the state vector y at state index BBThere are 4 possible states in total;
s413 for yBWill vector yBEach element of (2) is represented by a symbol yBIs represented by, i.e. yB＝[yB]Calculating the expected cost in state B
Figure FDA00032920440100000212
I represents solving the absolute value, RB，BRepresenting the elements of the B-th row and B-th column of the upper triangular matrix R,
Figure FDA00032920440100000213
and
Figure FDA00032920440100000214
respectively representing the conjugate transposes of the kth and the B-th elements of the MRT vector z,
Figure FDA00032920440100000215
representing quantized MRT vectors
Figure FDA00032920440100000216
The (k) th element of (a),
Figure FDA00032920440100000217
and
Figure FDA00032920440100000218
respectively represent pair
Figure FDA00032920440100000219
And
Figure FDA00032920440100000220
the calculation result obtaining partA mixture of J andBwritten as JB(yB) Denotes JBIs yBAs a function of (c).
3. The method as claimed in claim 2, wherein the step S42 is performed by:
s421, when b is larger than 1, updating the state index b to be b-1;
s422, calculating the state vector of the state index b
Figure FDA0003292044010000032
4 of (2)B-b+1A possible state;
s423 for the state vector ybWill vector ybEach element of (2) is represented by a symbol yb，yb+1，...，yBIs represented by, i.e. yb＝[yb yb+1…yB]Cost function when calculating the state index b
Figure FDA0003292044010000031
Wherein R isk，kAnd Rb，kRespectively representing the kth row and kth column elements of the upper triangular matrix R and the kth row and kth column elements of the upper triangular matrix R;
s424, aiming at the state vector ybFor each possible state of (a), the expected cost J in computing the state index bb(yb)＝gb(yb)+Jb+1(yb(2: end)), wherein y isb(2: end) represents ybThe 2 nd bit to last bit element in the selected state.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2017044335A1 (en) * 2015-09-09 2017-03-16 Microchip Technology Incorporated Mixed signal automatic gain control for increased resolution
CN108512795A (en) * 2018-03-19 2018-09-07 东南大学 A kind of OFDM receiver baseband processing method and system based on low Precision A/D C
CN110190879A (en) * 2019-04-30 2019-08-30 杭州电子科技大学 Efficiency optimization method based on the low extensive mimo system of Precision A/D C

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8320510B2 (en) * 2008-09-17 2012-11-27 Qualcomm Incorporated MMSE MIMO decoder using QR decomposition
US9712218B2 (en) * 2014-07-28 2017-07-18 Samsung Electronics Co., Ltd. Method and apparatus for facilitating high data rate transmission in the wireless communication

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2017044335A1 (en) * 2015-09-09 2017-03-16 Microchip Technology Incorporated Mixed signal automatic gain control for increased resolution
CN108512795A (en) * 2018-03-19 2018-09-07 东南大学 A kind of OFDM receiver baseband processing method and system based on low Precision A/D C
CN110190879A (en) * 2019-04-30 2019-08-30 杭州电子科技大学 Efficiency optimization method based on the low extensive mimo system of Precision A/D C

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
An efficient channel estimation method for OFDM systems based on comb pilots;Yang Guangxi;《 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference》;20160510;全文 *
基于低精度量化的大规模MIMO-OFDM系统可达速率的研究;陶成等;《电子与信息学报》;20180716(第10期);全文 *
基于低精度量化的无线传输理论最新研究进展;乔丹等;《电信科学》;20170620(第06期);全文 *

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