WO2022233250A1 - 一种针对硬件失真的irs辅助miso系统性能优化方法 - Google Patents
一种针对硬件失真的irs辅助miso系统性能优化方法 Download PDFInfo
- Publication number
- WO2022233250A1 WO2022233250A1 PCT/CN2022/089076 CN2022089076W WO2022233250A1 WO 2022233250 A1 WO2022233250 A1 WO 2022233250A1 CN 2022089076 W CN2022089076 W CN 2022089076W WO 2022233250 A1 WO2022233250 A1 WO 2022233250A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- base station
- signal
- information
- phase shift
- irs
- Prior art date
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 45
- 238000000034 method Methods 0.000 title claims abstract description 23
- 235000015429 Mirabilis expansa Nutrition 0.000 title claims abstract description 16
- 244000294411 Mirabilis expansa Species 0.000 title claims abstract description 16
- 235000013536 miso Nutrition 0.000 title claims abstract description 16
- 239000013598 vector Substances 0.000 claims abstract description 64
- 230000010363 phase shift Effects 0.000 claims abstract description 52
- 230000005540 biological transmission Effects 0.000 claims abstract description 27
- 238000011156 evaluation Methods 0.000 claims abstract description 4
- 239000011159 matrix material Substances 0.000 claims description 33
- 239000000654 additive Substances 0.000 claims description 16
- 230000000996 additive effect Effects 0.000 claims description 16
- 238000005562 fading Methods 0.000 claims description 8
- 230000003190 augmentative effect Effects 0.000 claims description 7
- 238000004422 calculation algorithm Methods 0.000 claims description 7
- 238000012545 processing Methods 0.000 abstract description 4
- 125000004122 cyclic group Chemical group 0.000 description 11
- 238000004891 communication Methods 0.000 description 6
- 238000013461 design Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 230000008054 signal transmission Effects 0.000 description 5
- 238000004088 simulation Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000001427 coherent effect Effects 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/01—Reducing phase shift
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/309—Measuring or estimating channel quality parameters
- H04B17/318—Received signal strength
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/309—Measuring or estimating channel quality parameters
- H04B17/345—Interference values
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
-
- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- 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
Definitions
- the invention belongs to the technical field of wireless communication, and in particular relates to an IRS-assisted MISO system performance optimization method for hardware distortion.
- a multi-antenna technology In a wireless communication system, in order to serve multiple users on the same frequency at the same time, a multi-antenna technology needs to be adopted at the base station.
- the multi-antenna technology significantly improves the degree of spatial freedom, which is conducive to eliminating interference between users, thereby increasing the reachable rate of information users.
- deploying an intelligent reflective surface (IRS) near the user by adjusting the phase shift at the IRS, gathers the radio frequency signal from the base station at the information user receiver, which can significantly improve the signal strength at the information user receiver. Further increase the reachable rate of information users.
- Hardware distortion includes amplitude error and phase error (I/Q imbalance) caused by mismatch of phase shifter and local oscillator, and the addition of nonlinearity caused by digital-to-analog conversion, band-pass filter, and high-power amplifier. Distortion noise. This results in a mismatch between the desired and actual transmitted signals, thereby reducing the reachable rate for information users.
- the additive noise caused by hardware distortion at the base station is modeled as cyclic symmetric complex Gaussian noise, and the distorted noise power at the base station is proportional to the signal power at the base station antenna.
- this model cannot accurately model the asymmetric nature of base station hardware distortion (I/Q imbalance).
- the technical problem to be solved by the present invention is to provide an IRS-assisted MISO system performance optimization method for hardware distortion in view of the above-mentioned deficiencies in the prior art.
- the base station beamforming vector and the IRS location are jointly optimized. Phase shift vector to improve the reachable rate of information users.
- the present invention adopts following technical scheme:
- An IRS-assisted MISO system performance optimization method for hardware distortion comprising the following steps:
- the multi-antenna base station performs wide linear precoding on the messages of the M information users to generate a baseband transmission signal.
- the baseband transmission signal is an asymmetric Gaussian signal.
- the asymmetric Gaussian signal is converted into an analog signal by a digital-to-analog converter, and then mixed by frequency.
- the frequency converter is up-converted to the carrier frequency, and finally the output signal is generated by the high-power amplifier; with the assistance of the intelligent reflective surface, the multi-antenna base station transmits the output signal generated by the high-power amplifier in a broadcast manner, and the controller on the intelligent reflective surface is used in real time. Control the phase shift of the reflector;
- M information users receive the signal transmitted by the multi-antenna base station in step S1, and decode to obtain the rate of the M information users;
- step S3 Taking the rate of the M information users obtained in step S2 as performance evaluation, under the condition of satisfying the total power constraint of the base station, optimize the beamforming vector of the base station and the phase shift vector at the smart reflection surface to maximize the minimum reachable rate of the information user , to complete the performance optimization.
- step S1 set is the message of information user dl , the multi-antenna base station pair Asymmetric Gaussian signal after wide linear precoding as follows:
- the base station baseband transmission signal x BS is:
- d l is the l-th information user
- ⁇ I is the set containing all information users.
- the multi-antenna base station is equipped with NT antennas.
- the I/Q imbalance of the mixer causes self-interference of the transmitted signal.
- the analog-to-digital converter, high-power amplifier, band-pass filter The nonlinearity of the device produces additive distortion noise d T ⁇ CN(0, C T ), where C T is the variance of the additive distortion noise, is the variance of the additive distortion noise at each antenna, is an identity matrix of NT ⁇ NT ; the actual transmitted signal of the multi-antenna base station is x' BS +d T .
- the equivalent baseband transmission signal x' BS is expressed as:
- ⁇ 1 , ⁇ 2 are respectively expressed as:
- step S2 the rate at which M information users are obtained by decoding is specifically:
- ⁇ is the phase shift vector at the smart reflective surface
- I 2 is a 2 ⁇ 2 identity matrix
- I 2 is a 2 ⁇ 2 identity matrix
- ⁇ is the augmented representation of the useful signal received for the jth information user
- I 2 is a 2 ⁇ 2 identity matrix
- I 2 is a 2 ⁇ 2 identity matrix
- ⁇ is the augmented representation of the useful signal received for the jth information user
- I 2 is a 2 ⁇ 2 identity matrix
- I 2 is a 2 ⁇ 2 identity matrix
- I 2 is a 2 ⁇ 2 identity matrix
- I 2 is a 2 ⁇ 2 identity matrix
- I 2 is a 2 ⁇ 2 identity matrix
- augmented representation of the useful signal received for the jth information user is the wide linear transform of the beamforming vector for the jth information user
- step S3 in each channel coherence time, the beamforming vector at the multi-antenna base station is obtained through an alternate optimization algorithm and the phase shift vector ⁇ at the smart reflective surface to maximize the minimum reachable rate of M information users, and then the multi-antenna base station sends the phase shift vector ⁇ to the smart reflective surface controller through the control link to control the smart reflective surface each reflector of ;
- the minimum achievable rate optimization problem of maximizing information users is formulated as:
- ⁇ is the phase shift vector at the IRS, st d j ⁇ I , ⁇ denotes the reachable rate of all information users the minimum value of ;
- n ⁇ 1,... N L ⁇ , n is the index value of the reflection element of the smart reflective surface, and NL is the number of reflection elements.
- ⁇ n is the initial value of the nth iteration ⁇
- ⁇ is the penalty factor
- ⁇ is the negative mean square penalty term, for the nth iteration
- Another technical solution of the present invention is an IRS-assisted MISO system performance optimization system for hardware distortion, comprising:
- the processing module the multi-antenna base station performs wide linear precoding on the message of the information user, generates a baseband transmission signal, processes the baseband transmission signal into an asymmetric Gaussian signal, and converts the asymmetric Gaussian signal into an analog signal through a digital-to-analog converter.
- the frequency converter obtains the carrier frequency by frequency conversion, and generates the output signal through the high-power amplifier; with the assistance of the intelligent reflective surface, the multi-antenna base station transmits the output signal generated by the high-power amplifier in a broadcast manner, and the controller on the intelligent reflective surface controls the reflection in real time. the phase shift of the element;
- a decoding module where M information users receive the signals transmitted by the multi-antenna base station from the processing module, and decode to obtain the rate of the M information users;
- the optimization module uses the rate at which the decoding module obtains M information users as the performance evaluation, and under the condition of satisfying the total power constraints of the base station, optimizes the base station beamforming vector and the phase shift vector at the smart reflection surface, and maximizes the minimum reachable rate of information users , to complete the performance optimization.
- the present invention at least has the following beneficial effects:
- the present invention is an IRS-assisted MISO system optimization design method aiming at hardware distortion.
- I/Q imbalance occurs, resulting in the amplitudes of the in-direction component and the quadrature component of the transmitted signal.
- the phase difference is not exactly ⁇ /2.
- the base station transmits a cyclic symmetric complex Gaussian signal
- the signal actually transmitted by the base station is an asymmetric Gaussian signal.
- the information entropy of cyclic symmetric complex Gaussian signal is the largest. Therefore, I/Q imbalance will reduce the reachable rate of information users.
- asymmetric Gaussian signal for transmission the statistical characteristics of the asymmetric Gaussian signal are optimized. The user's reachable rate.
- the message sent by the base station is a cyclic symmetric complex Gaussian signal, and an asymmetric Gaussian signal is generated after wide linear precoding.
- the wide linear precoding vector By optimizing the wide linear precoding vector, the statistical properties of asymmetric Gaussian signals can be adjusted.
- the asymmetric Gaussian signal generated after wide linear precoding is used to compensate the I/Q imbalance at the base station, and also helps to cancel the influence of the interference signal at the information user receiver.
- the complex signal processing of the asymmetric Gaussian signal is avoided, and the traditional signal processing method of the cyclic symmetric complex Gaussian signal is changed.
- the base station is equipped with NT antennas, and by using the space diversity technology, the base station can serve multiple users on the same frequency at the same time.
- the intelligent reflective surface IRS has NL reflection elements. By adjusting the phase shift at the IRS, the radio frequency signal from the base station is focused to the information receiver, thereby improving the received signal strength of the information user.
- the mismatch between the local oscillator and the phase shifter introduces phase error and amplitude error, and the I/Q mismatch model is used to accurately model the distortion produced by the local oscillator and the phase shifter.
- the nonlinearity of the high-power amplifier and band-pass filter produces additive distortion noise d T ⁇ CN(0,C T ),
- the cyclic symmetric complex Gaussian noise model verified by experiments is used to accurately describe the actual hardware distortion wireless communication system.
- an equivalent baseband distortion model is used, which is convenient for analysis and calculation.
- the channel in each channel coherence time, the channel basically remains unchanged, so a quasi-static flat fading channel model is used for modeling.
- the independent control circuit structure of the reflection coefficient amplitude and phase shift at the IRS is complex, which is not conducive to practical design and implementation. Therefore, considering that the reflection amplitude is always 1, only the phase shift of the reflection element at the IRS is adjusted.
- the information user receiver regards the interference as noise, and can obtain the user's achievable rate through coherent detection.
- it is necessary to optimize the beamforming vector of the base station and the phase shift vector at the IRS, so as to maximize the minimum achievable rate among all users as much as possible.
- an alternate optimization algorithm is used to decompose the optimization problem into a base station beamforming optimization sub-problem and an IRS phase-shift optimization sub-problem.
- a path tracking algorithm is proposed to solve the problem.
- the solution is solved. The maximum and minimum reachable rates of information users.
- the beam vector of the base station ie, the power distribution coefficient between different antennas
- the transmit power is allocated to the antenna with better instantaneous channel conditions, so as to improve the reachable rate of information users.
- the radio frequency signal from the base station is reflected to the user receiver as much as possible, and coherent interference is generated to improve the signal strength of the received signal.
- the present invention considers deploying the IRS near the information user, and by adjusting the phase shift at the IRS, the radio frequency signal from the base station is reflected to the information user receiver, and the signal strength at the information user is improved, thereby improving the information
- the user's reachable rate adopts an asymmetric Gaussian signal transmission scheme, which eliminates the influence of asymmetric hardware distortion at the base station transmitter on the performance of the IRS-assisted MISO system, and further improves the information user's reachable rate.
- Fig. 1 is an IRS-assisted MISO system model diagram of the present invention
- FIG. 2 is a hardware distortion diagram of a base station transmitter of the present invention
- Fig. 3 is the simulation setting diagram of the present invention.
- FIG. 4 is a diagram showing the influence of the hardware distortion of the present invention on the maximum and minimum achievable rate of the user.
- the invention provides an IRS-assisted MISO system optimization design method aiming at hardware distortion, aiming at accurately modeling the asymmetric hardware distortion at the base station, and proposes to adopt an asymmetric Gaussian signal transmission scheme to eliminate the non-uniformity in the IRS-assisted MISO system.
- the phase shift of the reflection element at the IRS is continuously adjustable, but this method is also applicable to the case where the phase shift of the reflection element at the IRS is a discrete value.
- Bold lowercase letters represent vectors
- bold uppercase letters represent matrices
- lowercase letters represent scalars.
- a cyclic symmetric complex Gaussian random variable with mean 0 and variance 1 is denoted as x ⁇ CN(0,1).
- x * represents the conjugate of x
- xT represents the transpose of x
- xH represents the conjugate transpose of x
- ⁇ (x) represents the expected value of x
- diag(x) represents the diagonal matrix
- the nth element of x is the nth diagonal element of the matrix.
- tr(X) represents the trace of the matrix X
- X-1 represents the inverse of the matrix X
- represents the determinant of the matrix X
- [X] 2 represents XX H
- X>0 (X ⁇ 0) means that X is a positive definite matrix (positive semi-definite matrix), and x S (set S as a subscript) means the set ⁇ x s , s ⁇ S ⁇ .
- a kind of IRS-assisted MISO system optimization design method for hardware distortion of the present invention comprises the following steps:
- the base station transmits the asymmetric Gaussian signal to the information user in a broadcast manner.
- the mismatch between the mixer and the phase shifter at the base station transmitter, digital-to-analog converter, band-pass filter, high-power causes distortion of the signal transmitted by the base station;
- a multi-antenna base station transmits information to M information users. Assuming that the base station is equipped with N T antennas, the set of It represents a single-antenna information user group, and the IRS is deployed near the information user to assist communication. It is assumed that the IRS has NL reflectors, and the phase shift of the reflectors is controlled in real time by the controller on the IRS.
- a base station performs wide linear precoding on a message of an information user to generate a baseband transmission signal
- the base station After wide linear precoding, the following asymmetric Gaussian signal is generated for transmission.
- the baseband transmission signal after wide linear precoding of the base station is:
- the baseband transmission signal is converted into an analog signal through a digital-to-analog converter, and then up-converted to a carrier frequency through a mixer, and finally an output signal is generated through a band-pass filter and a high-power amplifier.
- ⁇ 1 and ⁇ 2 are respectively expressed as:
- a diagonal matrix including the amplitude error generated by each radio frequency link of the base station; is a diagonal matrix containing the phase errors produced by each RF link of the base station.
- the additive distortion noise d T ⁇ CN(0, C T ) is generated due to the nonlinearity of the high power amplifier and the band-pass filter, where C T is the variance of the additive distortion noise, is the variance of the distortion noise at each antenna of the base station; the signal actually sent by the base station is x' BS +d T .
- the information user decodes the signal transmitted by the base station to itself, and obtains the corresponding message
- ⁇ is the phase shift vector at the IRS
- I 2 is a 2 ⁇ 2 identity matrix
- ⁇ is the augmented representation of the useful signal received at the jth information user
- I 2 is a 2 ⁇ 2 identity matrix
- I 2 is a 2 ⁇ 2 identity matrix
- ⁇ is the augmented representation of the useful signal received at the jth information user
- I 2 is a 2 ⁇ 2 identity matrix
- I 2 is a 2 ⁇ 2 identity matrix
- I 2 is a 2 ⁇ 2 identity matrix
- I 2 is a 2 ⁇ 2 identity matrix
- augmented representation of the useful signal received at the jth information user is the wide linear transform of the beamforming vector for the jth information user
- d l is the lth information user.
- the beamforming vector of the base station and the phase shift vector at the IRS are optimized to maximize the minimum reachable rate of the information user.
- the minimum achievable rate optimization problem of maximizing information users is formulated as:
- P is the total transmit power of the base station.
- Constraint (12) represents the unit modulus constraint of the phase-shift matrix at the IRS.
- Constraint (13) represents the total power constraint of the base station.
- Constraint (14) represents the transmit power constraint for each user.
- constraint (12) since the base station beamforming vector and the coupling between the reflection phase shift vector ⁇ at the IRS, and constraint (12) is a non-convex set, so the optimization problem (P1) is non-convex and difficult to solve.
- the present invention utilizes an alternate optimization algorithm to alternately optimize the beamforming loss of the base station and the reflection phase shift vector ⁇ at the IRS.
- Phase shift vector ⁇ at fixed IRS, with respect to base station beamforming vector The optimization is expressed as
- the optimization problem (P1.1) is non-convex. Therefore, the optimization problem (P1.1) is a non-convex optimization problem, which is solved by a path tracing algorithm. At each iteration, boost the objective function value. For non-convex constraints (17), an interior convex approximation is made, assuming Feasible points found for the k-1th time.
- the optimization problem (P1.3) is a non-convex optimization problem. Difficult to solve.
- the optimization problem P1.3 is first transformed into a more tractable form using the negative mean square penalty.
- ⁇ is the penalty coefficient
- P1.4 by adding a sufficiently large penalty term to the objective function, the problem (P1.4) is a tight scaling of the problem (P1.3).
- the constraint set n ⁇ 1 ,...,NL is a convex set.
- the objective function is still non-concave because The objective function (27) is approximated as:
- Equation (31) is a linear mapping of the IRS phase shift vector ⁇ , which is similar to the base station beamforming vector
- Equation (31) is a linear mapping of the IRS phase shift vector ⁇ , which is similar to the base station beamforming vector.
- the next feasible point ⁇ (n+1) of the optimization problem (P1.4) is generated by solving the optimization problem (P1.5), until the problem (P1.5 )convergence.
- the beamforming vector at the base station is obtained through an alternate optimization algorithm
- the phase shift vector ⁇ at the IRS and then the base station sends the phase shift vector ⁇ to the IRS controller through the control link, thereby controlling each reflection element at the IRS.
- the reference element coordinates of the IRS are (0, dy , 0).
- the distance between adjacent reflectors is half a wavelength, i.e.
- Set N L N y N z , where N y and N z are the numbers of y-axis and z-axis reflection elements, respectively.
- a plane wave model is used for BS-IRS and BS-user links. Due to the IRS small signal coverage, for the IRS-user link, a spherical wave model is used. This means that the distance between each reflective element of the IRS and the user is calculated individually based on 3D coordinates.
- BS-IRS, IRS-user, BS-user link Obey the Rayleigh fading channel model.
- the present invention provides an IRS-assisted MISO system optimization design method for hardware distortion, considering that in an actual wireless communication system, hardware distortion seriously reduces the performance of the system.
- Hardware distortions at the base station include amplitude and phase errors (I/Q imbalance) due to local oscillator and phase shifter mismatch in mixers, nonlinearities in digital-to-analog converters, bandpass filters, high power amplifiers The resulting additive hardware distortion noise.
- the signal actually sent by the base station is an asymmetric Gaussian signal.
- the information entropy is the largest (corresponding to the information rate) only when the signal sent by the base station is a cyclic symmetric complex Gaussian signal.
- the invention creatively uses the pre-compensation scheme—asymmetric Gaussian signal for transmission, optimizes the statistical characteristics of the asymmetric Gaussian signal, and after hardware distortion, the signal actually sent by the base station is a cyclic symmetric complex Gaussian signal, thereby improving the reachable rate of information users.
- the radio frequency signal from the base station is focused on the information user receiver to improve the strength of the received signal, thereby further improving the reachable rate of the information user.
- the base station beamforming vector and the phase shift vector at the IRS are optimized alternately, so as to improve the information transmission rate of poor users as much as possible.
- the optimization framework proposed in the present invention is also applicable to the case where the phase shift of the reflector at the IRS is a finite phase shift level.
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Quality & Reliability (AREA)
- Radio Transmission System (AREA)
- Mobile Radio Communication Systems (AREA)
- Amplifiers (AREA)
- Transmitters (AREA)
Abstract
本发明公开了一种针对硬件失真的IRS辅助MISO系统性能优化方法，多天线基站对M个信息用户的消息进行宽线性预编码，产生基带传输信号，将基带传输信号处理成非对称高斯信号，通过高功率放大器产生输出信号；在智能反射表面的辅助下，多天线基站以广播的方式传输高功率放大器产生的输出信号，并通过智能反射表面上的控制器实时控制反射元的相移；解码获得M个信息用户的速率；以M个信息用户的速率作为性能评估，在满足基站的总功率约束下，优化基站波束形成矢量和智能反射表面处的相移矢量，最大化信息用户的最小可达速率，完成性能优化。本发明采用IGS进行传输，进一步提升了信息用户的可达速率。
Description
本发明属于无线通信技术领域，具体涉及一种针对硬件失真的IRS辅助MISO系统性能优化方法。
在无线通信系统中，为了同时同频服务于多个用户，需要在基站处采用多天线技术。多天线技术显著的提升了空间自由度，有利于消除用户间干扰，从而提升信息用户的可达速率。同时，在用户的附近部署智能反射表面(IRS)，通过调整IRS处的相移，将来自于基站的射频信号聚集在信息用户接收机处，可以显著提升信息用户接收机处的信号强度，从而进一步提升信息用户的可达速率。
在通信系统中，硬件失真始终存在。硬件失真包括相移器和本地振荡器的失配所带来的幅度误差和相位误差(I/Q不平衡)，数模转换、带通滤波器、高功率放大器的非线性所带来的加性失真噪声。这导致想要的和实际发送信号之间的失配，从而降低了信息用户的可达速率。在现有的研究中，将基站处硬件失真导致的加性噪声建模为循环对称复高斯噪声，且基站处的失真噪声功率与基站天线处的信号功率成正比。然而，这一模型并不能准确的建模基站硬件失真(I/Q不平衡)的非对称特性。
发明内容
本发明所要解决的技术问题在于针对上述现有技术中的不足，提供一种针对硬件失真的IRS辅助MISO系统性能优化方法，通过采用非对称高斯信号传输方案，联合优化基站波束形成矢量和IRS处相移矢量，提升信息用户的可达速率。
本发明采用以下技术方案：
一种针对硬件失真的IRS辅助MISO系统性能优化方法，包括以下步骤：
S1、多天线基站对M个信息用户的消息进行宽线性预编码，产生基带传输信号，基带传输信号为非对称高斯信号，非对称高斯信号通过数模转换器转换为模拟信号，然后通过混频器上变频到载波频率，最后通过高功率放大器产生输出信号；在智能反射表面的辅助下，多天线基站以广播的方式传输高功率放大器产生的输出信号，并通过智能反射表面上的控制器实时控制反射元的相移；
S2、M个信息用户接收步骤S1多天线基站传输的信号，解码获得M个信息用户的速率；
S3、以步骤S2获得的M个信息用户的速率作为性能评估，在满足基站的总功率约束下，优化基站波束形成矢量和智能反射表面处的相移矢量，最大化信息用户的最小可达速率，完成性能优化。
具体的，步骤S1中，设
是信息用户d
l的消息，多天线基站对
进行宽线性预编码后产生的非对称高斯信号
如下：
其中，
为信息波束形成矢量；
宽线性预编码后，基站基带传输信号x
BS为：
其中，d
l为第l个信息用户，κ
I为包含所有信息用户的集合。
具体的，步骤S1中，多天线基站配备N
T个天线，在基站的发射机处，混频器I/Q不平衡导致发送信号产生自干扰，模数转换器、高功率放大器、带通滤波器的非线性产生加性失真噪声d
T～CN(0,C
T)，C
T为加性失真噪声的方差，
为每 个天线处加性失真噪声的方差，
为N
T×N
T的单位矩阵；多天线基站的实际发送信号为x'
BS+d
T。
进一步的，在I/Q不平衡之后，等效基带传输信号x'
BS表示为：
其中，
为对角矩阵，包含混频器失配所导致的幅度失真和旋转误差；Λ
1,Λ
2分别表示为：
其中，
为对角矩阵，包含基站的每个射频链路产生的幅度误差；
为对角矩阵，包含基站的每个射频链路产生的相位误差。
具体的，步骤S2中，解码获得M个信息用户的速率具体为：
在每一个信道相干时间内，多天线基站已知所有信道的信道状态信息，考虑智能反射表面一次反射的信号，所有信道为准静态平衰落信道模型；
表示基站到信息用户d
j的基带等效信道，
为智能反射表面到信息用户d
j的基带等效信道；考虑智能反射表面一次反射的信号，忽略两次和多次反射的信号，用
表示基站到智能反射表面的基带等效信道，智能反射表面处反射系数矩阵为
β
n∈(0,1]为第n个反射元的反射幅度，θ
n∈[0,2π)为第n个反射元的相移；设置β
n＝1,n∈1…N
L，最大化IRS处的信号反射，通过无线信道传播得到信息用户d
j处的接收信号
将干扰作为噪声，确定信息用户d
j的可达速率
进一步的，信息用户d
j的可达速率
表示为：
其中，
为所有信息用户波束形成矢量组成的集合，θ为智能反射表面处的相移矢量，I
2为2×2的单位矩阵，
为第j个信息用户接收到有用信号的增广表示形式，
为第j个信息用户波束形成矢量的宽线性变换，
为第j个信息用户接收到多天线基站发送给其他用户信号的增广表示形式，
为基站每个发射天线处加性硬件失真噪声的方差，
为基站到第j个信息用户的组合信道，
为
的共轭转置形式，σ为信息用户接收机热噪声的方差，d
l为第l个信息用户。
具体的，步骤S3中，在每一个信道相干时间内，通过交替优化算法获得多天线基站处的波束形成矢量
和智能反射表面处的相移矢量θ，使M个信息用户的最小可达速率最大化，然后多天线基站通过控制链路将相移矢量θ发送给智能反射表面控制器，控制智能反射表面处的每一个反射元；
最大化信息用户的最小可达速率优化问题表述为：
其中，
为所有信息用户波束形成矢量组成的集合，θ为IRS处的相移矢量，s.t.
d
j∈κ
I，γ表示所有信息用户可达速率
的最小值；
智能反射表面处相移矩阵的单位模约束
表示如下：
其中，n∈{1,…N
L}，n为智能反射表面的反射元索引值，N
L为反射元个数。
进一步的，优化多天线基站波束形成矢量
具体为：
其中，
为第k次迭代
的凹下界近似，d
j为第j个信息用户，κ
I为包含所有信息用户的集合，P为基站的总发送功率。
进一步的，优化智能反射表面处反射相移矢量θ具体为：
其中，θ
n为第n次迭代θ的初始值，η为惩罚因子，
为负均方惩罚项，
为第n次迭代
的凹下界近似。
本发明的另一技术方案是，一种针对硬件失真的IRS辅助MISO系统性能优化系统，包括：
处理模块，多天线基站对信息用户的消息进行宽线性预编码，产生基带传输信号，将基带传输信号处理成非对称高斯信号，通过数模转换器将非对称高斯信号转换为模拟信号，通过混频器变频得到载波频率，通过高功率放大器产生输出信号；在智能反射表面的辅助下，多天线基站以广播的方式传输高功率放大器产生的输出信号，通过智能反射表面上的控制器实时控制反射元的相移；
解码模块，M个信息用户接收处理模块多天线基站传输的信号，解码获得M个信息用户的速率；
优化模块，以解码模块获得M个信息用户的速率作为性能评估，在满足基站的总功率约束下，优化基站波束形成矢量和智能反射表面处的相移矢量，最大化 信息用户的最小可达速率，完成性能优化。
与现有技术相比，本发明至少具有以下有益效果：
本发明一种针对硬件失真的IRS辅助MISO系统优化设计方法，在IRS辅助的MISO系统中，由于基站混频器失真产生I/Q不平衡，导致发送信号的同向分量和正交分量的幅度不同，相位差也不是精确的π/2。假设基站传输循环对称复高斯信号，经过I/Q不平衡，基站实际传输的信号为非对称高斯信号。由信息论可知，循环对称复高斯信号的信息熵是最大的。因此，I/Q不平衡将降低信息用户的可达速率。在本发明中，通过引入预补偿方案—非对称高斯信号进行传输，优化非对称高斯信号的统计特性，在I/Q不平衡之后，使基站实际传输的信号为循环对称复高斯信号，从而提升用户的可达速率。
进一步的，基站发送的消息为循环对称复高斯信号，通过宽线性预编码后产生非对称高斯信号。通过优化宽线性预编码矢量，可以调整非对称高斯信号的统计特性。宽线性预编码后产生的非对称高斯信号用于补偿基站处的I/Q不平衡，同时也有利于抵消信息用户接收机处干扰信号的影响。通过增加预编码矢量的维度，避免了非对称高斯信号复杂的信号处理，转为传统的循环对称复高斯信号的信号处理方法。
进一步的，基站配备N
T个天线，利用空间分集技术，使基站可以同时同频服务于多个用户。智能反射表面IRS具有N
L个反射元，通过调整IRS处的相移，将来自基站的射频信号聚焦到信息接收机处，从而提升信息用户的接收信号强度。本地振荡器和相移器的失配引入相位误差和幅度误差，采用I/Q失配模型准确的建模本地振荡器和相移器产生的失真。在基站发射机处，高功率放大器、带通滤波器的非线性产生加性失真噪声d
T～CN(0,C
T)，
采用通过实验验证的 循环对称复高斯噪声建模，从而准确的描述实际的硬件失真无线通信系统。
进一步的，对于I/Q不平衡，采用等效基带失真模型，便于分析和计算。进一步的，根据无线信道的衰落特征，在每一个信道相干时间内，信道基本保持不变，因此采用准静态平衰落信道模型进行建模。对于IRS处的信号反射，由于大尺度衰落，两次和多次反射的信号可以忽略。同时，IRS处反射系数的幅度和相移单独控制电路结构复杂，不利于实际设计和实现，因此，考虑反射幅度始终为1，只调整IRS处反射元的相移。
进一步的，信息用户接收机将干扰看作为噪声，通过相干检测，从而可以获得用户的可达速率。同时，为了保证用户之间的公平性，需要优化基站的波束形成矢量和IRS处的相移矢量，使所有用户中最小可达速率尽可能最大化。
进一步的，采用交替优化算法将优化问题分解为基站波束形成优化子问题和IRS相移优化子问题，针对每一个优化子问题，提出采用路径跟踪算法进行求解，在满足基站的总功率约束，求解信息用户的最大最小可达速率。
进一步的，在每一个信道相干时间内，优化基站的波束矢量(即不同天线之间的功率分配系数)，将发送功率分配给瞬时信道条件较好的天线处，提升信息用户的可达速率。
进一步的，通过调整IRS处的相移矢量，将来自基站的射频信号尽可能的反射到用户接收机处，并产生相干干涉，提升接收信号的信号强度。
综上所述，本发明考虑在信息用户的附近部署IRS，通过调整IRS处的相移，将来自于基站的射频信号反射到信息用户接收机处，提升信息用户处的信号强度，从而提升信息用户的可达速率，采用非对称高斯信号传输方案，消除基站发射机处非对称硬件失真对IRS辅助的MISO系统性能的影响，进一步提升了信息用户 的可达速率。
下面通过附图和实施例，对本发明的技术方案做进一步的详细描述。
图1为本发明的IRS辅助的MISO系统模型图；
图2为本发明的基站发射机硬件失真图；
图3为本发明的仿真设置图；
图4为本发明的硬件失真对用户最大最小可达速率的影响图。
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
应当理解，当在本说明书和所附权利要求书中使用时，术语“包括”和“包含”指示所描述特征、整体、步骤、操作、元素和/或组件的存在，但并不排除一个或多个其它特征、整体、步骤、操作、元素、组件和/或其集合的存在或添加。
还应当理解，在本发明说明书中所使用的术语仅仅是出于描述特定实施例的目的而并不意在限制本发明。如在本发明说明书和所附权利要求书中所使用的那样，除非上下文清楚地指明其它情况，否则单数形式的“一”、“一个”及“该”意在包括复数形式。
还应当进一步理解，在本发明说明书和所附权利要求书中使用的术语“和/或”是指相关联列出的项中的一个或多个的任何组合以及所有可能组合，并且包括这些组合。
在附图中示出了根据本发明公开实施例的各种结构示意图。这些图并非是按比例绘制的，其中为了清楚表达的目的，放大了某些细节，并且可能省略了某些细节。图中所示出的各种区域、层的形状及它们之间的相对大小、位置关系仅是示例性的，实际中可能由于制造公差或技术限制而有所偏差，并且本领域技术人员根据实际所需可以另外设计具有不同形状、大小、相对位置的区域/层。
本发明提供了一种针对硬件失真的IRS辅助MISO系统优化设计方法，旨在准确的建模基站处的非对称硬件失真，并提出采用非对称高斯信号传输方案来消除IRS辅助的MISO系统中非对称硬件失真的影响，考虑IRS处反射元相移是连续可调的，但本方法也适用于IRS处反射元相移是离散值的情况。
定义如下：
粗体小写字母(比如：x)表示矢量，粗体大写字母(比如:X)表示矩阵，小写字母(比如：x)表示标量。
均值为0，方差为1的循环对称复高斯随机变量表示为x～CN(0,1)。
对于标量x，
表示x的实部。
对于矢量x，x
*表示x的共轭，x
T表示x的转置，x
H表示x的共轭转置。Ξ(x)表示x的期望值，diag(x)表示对角矩阵，x的第n个元素为矩阵的第n个对角元。
对于方阵X,tr(X)表示矩阵X的迹，X-1表示矩阵X的逆，|X|表示矩阵X的行列式，[X]
2表示XX
H,||X||
F为矩阵X的F范数，<X>＝tr(X),<X,Y>＝tr(X
HY)。
对于厄密特对称矩阵X,X＞0(X≥0)表示X是正定矩阵(正半定矩阵)，x
S(集合S作为下标)表示集合{x
s,s∈S}。
本发明一种针对硬件失真的IRS辅助MISO系统优化设计方法，包括以下步 骤：
S1、在IRS的辅助下，基站以广播的方式传输非对称高斯信号到信息用户，同时基站发射机处混频器和相移器的失配，数模转换器、带通滤波器、高功率放大器的非线性，导致基站传输的信号产生失真；
请参阅图1，在IRS的辅助下，多天线基站传输信息到M个信息用户，假设基站配备N
T个天线，集合
表示单天线信息用户组，通过在信息用户的附近部署IRS来辅助通信，假设IRS具有N
L个反射元，通过IRS上的控制器实时控制反射元的相移。
S101、基站对信息用户的消息进行宽线性预编码，产生基带传输信号；
假设
是信息用户d
l的消息，基站对
进行宽线性预编码后产生如下的非对称高斯信号进行传输。
式(1)中，
为信息波束形成矢量，基站宽线性预编码后的基带传输信号为：
S102、基带传输信号经过数模转换器将数字信号变为模拟信号，然后通过混频器上变频到载波频率，最后通过带通滤波器、高功率放大器产生输出信号。
请参阅图2，由于本地振荡器和相移器的失配引入相位误差和幅度误差(即I/Q不平衡)，从而导致了自干扰。根据I/Q失配模型，等效正交不平衡基带传输信号表示为：
其中，
为对角矩阵，包含了混频器失配所导致的幅度误差和旋转误差。
Λ
1,Λ
2分别表示为：
其中，
为对角矩阵，包含了基站的每个射频链路产生的幅度误差；
为对角矩阵，包含了基站的每个射频链路产生的相位误差。
同时，在基站发射机处，由于高功率放大器、带通滤波器的非线性产生加性失真噪声d
T～CN(0,C
T)，C
T为加性失真噪声的方差，
为基站每个天线处失真噪声的方差；基站实际发送的信号为x'
BS+d
T。
S2、信息用户解码基站传输给自己的信号，获得相应的消息；
在每一个信道相干时间内，假设基站完美已知所有信道的信道状态信息，由于显著的路径损耗，仅考虑IRS一次反射的信号，忽略两次或多次反射的信号；除此之外，对于所有信道，假设准静态平衰落信道模型。
假设
表示基站到信息用户d
j的基带等效信道，
为IRS到信息用户d
j的基带等效信道。
对于基站到IRS的基带等效信道，用
进行表示，假设
表示IRS处反射系数矩阵；其中：β
n∈(0,1]为第n个反射元的反射幅度，θ
n∈[0,2π)为第n个反射元的相移。
在本发明中，为了最大化IRS处的信号反射，设置β
n＝1,n∈1…N
L；因此，信息用户d
j处的接收信号表示为：
在式(6)中，
为智能反射表面处的相移矢量；
为信息用户接收机处的加性白高斯噪声。
假设
则
定义
式(6)的增广方程表示为：
式(7)中，
表示从
到
的线性映射，
信息用户d
j为了解码其想要的信息，将干扰看作为噪声，信息用户d
j的可达速率表示为：
其中，
为第j个信息用户的可达速率，
为符号表示，无实际意义，
为所有信息用户的波束形成矢量集合，θ为IRS处的相移矢量，I
2为2×2的单位矩阵，
为第j个信息用户处接收到的有用信号的增广表示形式，
为第j个信息用户波束形成矢量的宽线性变换，
为第j个信息用户处接收到的干扰信号的增广表示形式，
为基站每个天线处加性硬件失真噪声的方差，
为基站到第j个信息用户的组合信道，
为
的共轭转置形式，σ为信息用户接收机热噪声的方差，d
l为第l个信息用户。
S3、在满足基站的总功率约束下，优化基站波束形成矢量和IRS处的相移矢 量，最大化信息用户的最小可达速率。
最大化信息用户的最小可达速率优化问题表述为：
优化问题(P1)中，P为基站的总发送功率。
由式(11)可知，(10)式中的γ表示所有信息用户可达速率
的最小值，即
约束(12)表示IRS处相移矩阵的单位模约束。
约束(13)表示基站的总功率约束。
约束(14)表示每个用户的传输功率约束。
在约束(11)中，由于基站波束形成矢量
和IRS处反射相移矢量θ之间的耦合，且约束(12)为非凸集，因此优化问题(P1)是非凸的，很难进行求解。
本发明利用交替优化算法，交替优化基站波束形成失量
和IRS处反射相移矢量θ。
S301、优化基站波束形成矢量
固定IRS处相移矢量θ，关于基站波束形成矢量
的优化表述为
s.t.(13)(14) (16)
优化问题(P1.1)中，速率约束(17)是非凸的。因此，优化问题(P1.1)为非凸优化问题，采用路径跟踪算法进行求解。在每次迭代中，提升目标函数值。对于非凸约束(17)，对其进行内部凸近似，假设
为第k-1次找到的可行点。
对于维度为2×2的矩阵，根据不等式(18)：
非凸约束(17)式的凹上界近似为：
在第k次迭代，给定可行点
通过求解优化问题(P1.2)，生成问题(P1.1)的下一个可行点
直到问题收敛
S302、优化IRS处的相移矢量θ
固定基站波束形成矢量
IRS相移矢量θ的优化表示为：
优化问题(P1.3)中，速率约束(25)式是非凸的，且IRS相移约束(26)式为非凸约束集，因此，优化问题(P1.3)为非凸优化问题，很难进行求解。对于非凸约束集(26)，利用负均方惩罚法，首先将优化问题P1.3变换为更易处理的形式。
其中，η为惩罚系数，通过在目标函数中增加足够大的惩罚项，问题(P1.4)是问题(P1.3)的紧放缩。而且，在优化问题(P1.4)中，约束集
n∈1,…,N
L为凸集。
目标函数仍然是非凹的，因为
目标函数(27)近似 为：
对于非凸约束(28)式，将
代入(7)式中得：
式(31)为关于IRS相移矢量θ的线性映射，类似于基站波束形成矢量
的优化过程，假设(θ
n,γ
(n))为第(n-1)次找到的可行点，利用不等式(18)，速率约束(25)的凹上界近似为：
在第n次迭代，给定可行点θ
(n)，通过求解优化问题(P1.5)生成优化问题(P1.4)的下一个可行点θ
(n+1)，直到问题(P1.5)收敛。
在每一个信道相干时间内，通过交替优化算法，获得基站处的波束形成矢量
IRS处的相移矢量θ，然后基站通过控制链路将相移矢量θ发送给IRS控制器，从而控制IRS处的每一个反射元。
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。通常在此处附图中的描述和所示的本发明实施例的组件可以通过各种不同的配置来布置和设计。因此，以下对在附图中提供的本发明的实施例的详细描述并非旨在限制要求保护的本发明的范围，而是仅仅表示本发明的选定实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
仿真验证
为了评估基站处非对称硬件失真对IRS辅助的MISO系统性能的影响，我们采用如下的仿真设置。假设系统工作在750MHz的载波频率，系统带宽为1MHz,噪声功率谱密度为-150dBm/Hz。
请参阅图3中的仿真设置所示，基站位于x轴，坐标为(d
x,0,0)；IRS位于y-z 平面，以便于建立局部热点簇；簇中心为(d
x,d
y,0)，簇半径为r＝2.5m。IRS的参考元坐标为(0,d
y,0)。相邻反射元之间的距离为半个波长，即
设置N
L＝N
yN
z，N
y,N
z分别为y轴、z轴反射元的个数。
在本次实验中，N
y＝5,N
z＝10；路径损耗模型为
为参考距离D
0＝1m处的路径损耗，d表示链路距离，α为路径损耗指数。对于BS-IRS和BS-用户链路，采用平面波模型。由于IRS小信号覆盖范围，对于IRS-用户链路，采用球面波模型。这意味着IRS每个反射元和用户之间的距离根据三维坐标单独计算。
BS-IRS和IRS-用户链路的路径损耗指数为α＝2.2.BS-用户链路的路径损耗指数α＝3.8.对于小尺度衰落，假设BS-IRS,IRS-用户，BS-用户链路服从瑞利衰落信道模型。IRS处的相移矢量θ的初始值在-180°～180°随机选择，d
x＝3.5m,d
y＝8m，
假设信息用户在信息簇内随机分布。
请参阅图4，为四个信息用户的最大最小可达速率，随着发送功率的增加，非对称高斯信号传输方案(IGS)和对称高斯信号传输方案(PGS)下信息用户的最大最小可达速率增大。假设没有IRS的辅助，在非对称硬件失真的影响下，相比于PGS传输方案，采用IGS进行传输带来0.5比特的速率提升，这是因为IGS传输方案可以消除非对称硬件失真对系统性能的影响，从而提升信息用户的可达速率。在IRS的辅助下，PGS传输方案下信息用户的最大最小可达速率大约有1比特的速率提升，同时，IRS辅助的IGS传输方案明显优于IRS辅助的PGS传输方案，信息用户的最大最小可达速率显著的提升。
综上所述，本发明一种针对硬件失真的IRS辅助MISO系统优化设计方法，考虑实际的无线通信系统中，硬件失真严重降低系统的性能。基站处的硬件失真 包括混频器中本地振荡器和相移器失配产生的幅度误差和相位误差(I/Q不平衡)，数模转换器、带通滤波器、高功率放大器的非线性产生的加性硬件失真噪声。通过实验验证的I/Q失配模型和循环对称复高斯噪声分别建模I/Q不平衡和加性硬件失真噪声。假设基站传输循环对称复高斯信号，在上述硬件失真的影响下，基站实际发送的信号为非对称高斯信号。由信息论可知，只有当基站发送信号为循环对称复高斯信号时，信息熵是最大的(对应于信息速率)。本发明创造性的使用预补偿方案—非对称高斯信号进行传输，优化非对称高斯信号统计特性，使硬件失真之后，基站实际发送的信号为循环对称复高斯信号，从而提升信息用户的可达速率。通过在信息用户的附近部署IRS，将来自基站的射频信号聚焦在信息用户接收机处，提升接收信号的强度，从而进一步提升信息用户的可达速率。与此同时，考虑用户之间的公平性，在每一个信道相干时间内，根据信道状态信息，交替优化基站波束形成矢量和IRS处相移矢量，尽可能的提升差用户的信息传输速率。本发明提出的优化框架也适用于IRS处反射元相移为有限相移水平的情况。
以上内容仅为说明本发明的技术思想，不能以此限定本发明的保护范围，凡是按照本发明提出的技术思想，在技术方案基础上所做的任何改动，均落入本发明权利要求书的保护范围之内。
Claims (9)
- 一种针对硬件失真的IRS辅助MISO系统性能优化方法，其特征在于，包括以下步骤：S1、多天线基站对M个信息用户的消息进行宽线性预编码，产生基带传输信号，基带传输信号为非对称高斯信号，非对称高斯信号通过数模转换器转换为模拟信号，然后通过混频器上变频到载波频率，最后通过高功率放大器产生输出信号；在智能反射表面的辅助下，多天线基站以广播的方式传输高功率放大器产生的输出信号，并通过智能反射表面上的控制器实时控制反射元的相移；S2、M个信息用户接收步骤S1多天线基站传输的信号，解码获得M个信息用户的速率；S3、以步骤S2获得的M个信息用户的速率作为性能评估，在满足基站的总功率约束下，优化基站波束形成矢量和智能反射表面处的相移矢量，最大化信息用户的最小可达速率，完成性能优化。
- 根据权利要求1所述的方法，其特征在于，步骤S1中，设是信息用户d l的消息，多天线基站对
进行宽线性预编码后产生的非对称高斯信号
如下：
其中，为信息波束形成矢量；
宽线性预编码后，基站基带传输信号x BS为：
其中，d l为第l个信息用户，κ I为包含所有信息用户的集合。 - 根据权利要求1所述的方法，其特征在于，步骤S1中，多天线基站配备N T个天线，在基站的发射机处，混频器I/Q不平衡导致发送信号产生自干扰，模数 转换器、高功率放大器、带通滤波器的非线性产生加性失真噪声d T～CN(0,C T)，C T为加性失真噪声的方差，为每个天线处加性失真噪声的方差，
为N T×N T的单位矩阵；多天线基站的实际发送信号为x' BS+d T。
- 根据权利要求3所述的方法，其特征在于，在I/Q不平衡之后，等效基带传输信号x' BS表示为：
其中，为对角矩阵，包含混频器失配所导致的幅度失真和旋转误差；Λ 1,Λ 2分别表示为：
其中，为对角矩阵，包含基站的每个射频链路产生的幅度误差；
为对角矩阵，包含基站的每个射频链路产生的相位误差。
- 根据权利要求1所述的方法，其特征在于，步骤S2中，解码获得M个信息用户的速率具体为：在每一个信道相干时间内，多天线基站已知所有信道的信道状态信息，考虑智能反射表面一次反射的信号，所有信道为准静态平衰落信道模型；表示基站到信息用户d j的基带等效信道，
为智能反射表面到信息用户d j的基带等效信道；考虑智能反射表面一次反射的信号，忽略两次和多次反射的信号，用
表示基站到智能反射表面的基带等效信道，智能反射表面处反射系数矩阵为
β n∈(0,1]为第n个反射元的反射幅度，θ n∈[0,2π)为第n个反射元的相移；设置β n＝1,n∈1…N L，最大化IRS处的信号反射，通过无线 信道传播得到信息用户d j处的接收信号
将干扰作为噪声，确定信息用户d j的可达速率
- 根据权利要求5所述的方法，其特征在于，信息用户d j的可达速率表示为：
其中，为所有信息用户波束形成矢量组成的集合，θ为智能反射表面处的相移矢量，I 2为2×2的单位矩阵，
为第j个信息用户接收到有用信号的增广表示形式，
为第j个信息用户波束形成矢量的宽线性变换，
为第j个信息用户接收到多天线基站发送给其他用户信号的增广表示形式，
为基站每个发射天线处加性硬件失真噪声的方差，
为基站到第j个信息用户的组合信道，
为
的共轭转置形式，σ为信息用户接收机热噪声的方差，d l为第l个信息用户。
- 根据权利要求1所述的方法，其特征在于，步骤S3中，在每一个信道相干时间内，通过交替优化算法获得多天线基站处的波束形成矢量和智能反射表面处的相移矢量θ，使M个信息用户的最小可达速率最大化，然后多天线基站通过控制链路将相移矢量θ发送给智能反射表面控制器，控制智能反射表面处的每一个反射元；
最大化信息用户的最小可达速率优化问题表述为：
其中，为所有信息用户波束形成矢量组成的集合，θ为IRS处的相移矢量，
γ表示所有信息用户可达速率
的最小 值；
智能反射表面处相移矩阵的单位模约束表示如下：
其中，n∈{1,…N L}，n为智能反射表面的反射元索引值，N L为反射元个数。 - 根据权利要求7所述的方法，其特征在于，优化多天线基站波束形成矢量具体为：
其中，为第k次迭代
的凹下界近似，d j为第j个信息用户，κ I为包含所有信息用户的集合，P为基站的总发送功率。
- 根据权利要求7所述的方法，其特征在于，优化智能反射表面处反射相移矢量θ具体为：
其中，θ n为第n次迭代θ的初始值，η为惩罚因子，为负均方惩罚项，
为第n次迭代
的凹下界近似。
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110499408.8 | 2021-05-06 | ||
| CN202110499408.8A CN113364494B (zh) | 2021-05-06 | 2021-05-06 | 一种针对硬件失真的irs辅助miso系统性能优化方法 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| WO2022233250A1 true WO2022233250A1 (zh) | 2022-11-10 |
Family
ID=77525903
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/CN2022/089076 WO2022233250A1 (zh) | 2021-05-06 | 2022-04-25 | 一种针对硬件失真的irs辅助miso系统性能优化方法 |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN113364494B (zh) |
| WO (1) | WO2022233250A1 (zh) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113364494B (zh) * | 2021-05-06 | 2022-08-16 | 西安交通大学 | 一种针对硬件失真的irs辅助miso系统性能优化方法 |
| CN114040478A (zh) * | 2021-10-29 | 2022-02-11 | 清华大学 | 低功耗的智能超表面硬件结构、预编码方法及装置 |
| CN114039828B (zh) * | 2021-11-08 | 2024-01-19 | 上海电机学院 | 一种基于irs的空间反射调制的无线通信方法 |
| CN114222310B (zh) * | 2021-11-22 | 2023-06-20 | 西南交通大学 | 一种联合3d波束赋形和智能反射面反射优化方法 |
| CN114244469B (zh) * | 2021-11-22 | 2023-09-19 | 西安电子科技大学 | 一种安全传输方法、系统、介质、设备及数据处理终端 |
| CN114726414A (zh) * | 2022-03-04 | 2022-07-08 | 西安电子科技大学 | 一种联合传输波束优化的方法、系统、介质、设备及终端 |
| CN115278697B (zh) * | 2022-07-28 | 2024-05-07 | 重庆邮电大学 | 一种面向硬件损伤的工业物联网波束优化方法 |
| CN115834322B (zh) * | 2022-11-11 | 2024-04-12 | 西南交通大学 | 一种基于分离接收机和智能反射面辅助的通信系统 |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111447618A (zh) * | 2020-03-13 | 2020-07-24 | 重庆邮电大学 | 一种基于安全通信的智能反射面能效最大资源分配方法 |
| CN112054830A (zh) * | 2020-08-13 | 2020-12-08 | 西安交通大学 | 一种针对硬件损伤的Massive MIMO信能同传系统优化方法 |
| CN112383332A (zh) * | 2020-11-03 | 2021-02-19 | 电子科技大学 | 一种基于智能反射表面的蜂窝基站通信系统 |
| US20210126359A1 (en) * | 2019-10-28 | 2021-04-29 | Research & Business Foundation Sungkyunkwan University | Data processing method and apparatus with wireless communication system including intelligent reflecting surface |
| CN113364494A (zh) * | 2021-05-06 | 2021-09-07 | 西安交通大学 | 一种针对硬件失真的irs辅助miso系统性能优化方法 |
Family Cites Families (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7421015B2 (en) * | 2004-02-26 | 2008-09-02 | 2Wire, Inc. | Bit-loading in multicarrier communication systems in the presence of an asymmetric, correlated gaussian noise sources |
| WO2009088328A1 (en) * | 2008-01-08 | 2009-07-16 | Telefonaktiebolaget Lm Ericsson (Publ) | Zero-forcing linear beamforming for coordinated cellular networks with distributed antennas |
| JP5753022B2 (ja) * | 2011-08-15 | 2015-07-22 | 株式会社Ｎｔｔドコモ | 無線通信システム、無線基地局装置、ユーザ端末及び無線通信方法 |
| MY161041A (en) * | 2012-12-27 | 2017-04-14 | Huawei Tech Co Ltd | Method for feeding back channel state information, user equipment, and base station |
| KR101978771B1 (ko) * | 2013-01-31 | 2019-05-15 | 삼성전자주식회사 | 무선 통신 시스템에서 스트림 별 채널 이득 피드백을 통한 다중 스트림 mu-cqi 추정 방법 및 장치 |
| US9698887B2 (en) * | 2013-03-08 | 2017-07-04 | Qualcomm Incorporated | Systems and methods for enhanced MIMO operation |
| CN104168574B (zh) * | 2014-08-15 | 2017-05-10 | 西安电子科技大学 | 一种混合蜂窝系统中基于干扰适变选择的上行传输方法 |
| CN105991491A (zh) * | 2015-02-15 | 2016-10-05 | 中兴通讯股份有限公司 | 一种实现可见光无线通信的方法和装置 |
| KR102189161B1 (ko) * | 2017-03-07 | 2020-12-09 | 삼성전자주식회사 | 다중 안테나 시스템에서의 피드백 장치 및 방법 |
| CN110225538B (zh) * | 2019-06-21 | 2021-04-09 | 电子科技大学 | 反射面辅助的非正交多址接入通信系统设计方法 |
| CN111246491B (zh) * | 2020-03-10 | 2021-06-08 | 电子科技大学 | 一种智能反射表面辅助的太赫兹通信系统设计方法 |
| CN111355519B (zh) * | 2020-03-10 | 2021-10-26 | 电子科技大学 | 智能反射表面协助的室内太赫兹mimo通信系统设计方法 |
| CN112564758B (zh) * | 2020-11-25 | 2022-07-05 | 东南大学 | 一种采用分布式智能反射面辅助的宽带无线传输方法 |
| CN112672375B (zh) * | 2020-12-07 | 2023-09-05 | 大连理工大学 | 一种智能反射面辅助的非正交多址接入网络中安全通信方法 |
-
2021
- 2021-05-06 CN CN202110499408.8A patent/CN113364494B/zh active Active
-
2022
- 2022-04-25 WO PCT/CN2022/089076 patent/WO2022233250A1/zh active Application Filing
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20210126359A1 (en) * | 2019-10-28 | 2021-04-29 | Research & Business Foundation Sungkyunkwan University | Data processing method and apparatus with wireless communication system including intelligent reflecting surface |
| CN111447618A (zh) * | 2020-03-13 | 2020-07-24 | 重庆邮电大学 | 一种基于安全通信的智能反射面能效最大资源分配方法 |
| CN112054830A (zh) * | 2020-08-13 | 2020-12-08 | 西安交通大学 | 一种针对硬件损伤的Massive MIMO信能同传系统优化方法 |
| CN112383332A (zh) * | 2020-11-03 | 2021-02-19 | 电子科技大学 | 一种基于智能反射表面的蜂窝基站通信系统 |
| CN113364494A (zh) * | 2021-05-06 | 2021-09-07 | 西安交通大学 | 一种针对硬件失真的irs辅助miso系统性能优化方法 |
Non-Patent Citations (2)
| Title |
|---|
| KAI CAIHONG, DING WENQI, HUANG WEI: "Max-Min Fairness in IRS-Aided MISO Broadcast Channel via Joint Transmit and Reflective Beamforming", GLOBECOM 2020, 2020 IEEE GLOBAL COMMUNICATIONS CONFERENCE, IEEE, 1 December 2020 (2020-12-01) - 11 December 2020 (2020-12-11), pages 1 - 6, XP055983076, ISBN: 978-1-7281-8298-8, DOI: 10.1109/GLOBECOM42002.2020.9348016 * |
| ZENG YONG; ZHANG RUI; GUNAWAN ERRY; GUAN YONG LIANG: "Optimized Transmission with Improper Gaussian Signaling in the K-User MISO Interference Channel", IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, IEEE SERVICE CENTER, PISCATAWAY, NJ., US, vol. 12, no. 12, 1 December 2013 (2013-12-01), US , pages 6303 - 6313, XP011534586, ISSN: 1536-1276, DOI: 10.1109/TWC.2013.103013.130439 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113364494B (zh) | 2022-08-16 |
| CN113364494A (zh) | 2021-09-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2022233250A1 (zh) | 一种针对硬件失真的irs辅助miso系统性能优化方法 | |
| CN111447618B (zh) | 一种基于安全通信的智能反射面能效最大资源分配方法 | |
| CN111294096B (zh) | 一种智能反射面miso无线通信系统的信道容量优化方法 | |
| CN112672375B (zh) | 一种智能反射面辅助的非正交多址接入网络中安全通信方法 | |
| Feng et al. | Joint hybrid and passive RIS-assisted beamforming for mmWave MIMO systems relying on dynamically configured subarrays | |
| CN113225108A (zh) | 一种智能反射面辅助多小区协同多点传输的鲁棒波束赋形方法 | |
| CN112601242B (zh) | 一种智能反射面协助的两小区noma上行低功耗传输方法 | |
| CN113114317B (zh) | 一种irs辅助下行多用户通信系统的相移优化方法 | |
| CN114828253A (zh) | 一种ris辅助多无人机通信系统的资源分配方法 | |
| Li et al. | Beamforming design and power allocation for transmissive RMS-based transmitter architectures | |
| Zhang et al. | Near-optimal design for hybrid beamforming in mmWave massive multi-user MIMO systems | |
| CN113890578A (zh) | 基于ris和相关信道无蜂窝系统功率设计方法 | |
| CN110138427A (zh) | 基于部分连接的大规模多输入多输出混合波束赋形算法 | |
| Zhu et al. | Multi-UAV aided millimeter-wave networks: Positioning, clustering, and beamforming | |
| Zhang et al. | Blockage-aware beamforming design for active IRS-aided mmWave communication systems | |
| CN111431568A (zh) | 一种毫米波noma上行通信系统中联合功率分配和波束成形设计方法 | |
| CN109067446B (zh) | 一种多天线多用户大规模天线的混合预编码方法 | |
| CN112202476B (zh) | 基于线圈圆阵列的涡旋电磁波近场无线通信方法 | |
| CN112312569A (zh) | 一种基于透镜阵列的预编码和波束选择矩阵联合设计方法 | |
| CN116033461B (zh) | 一种基于star-ris辅助的共生无线电传输方法 | |
| CN112398513A (zh) | 一种massive MIMO系统的波束赋形方法 | |
| Li et al. | Weighted sum rate maximization for intelligent reflecting surface assisted multiuser system with low-resolution DACs | |
| CN114337902B (zh) | 一种irs辅助的毫米波多小区间干扰的抑制方法 | |
| CN114598363B (zh) | 一种面向3d空间无线信道的大规模mimo安全预编码方法 | |
| CN113890634B (zh) | 一种智能反射面辅助的干扰抵消波束设计方法 |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| 121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 22798591 Country of ref document: EP Kind code of ref document: A1 |
|
| NENP | Non-entry into the national phase |
Ref country code: DE |
|
| 122 | Ep: pct application non-entry in european phase |
Ref document number: 22798591 Country of ref document: EP Kind code of ref document: A1 |