Downlink channel estimation method of cellular-removed MIMO system based on OTFS modulation

Abstract

The invention discloses a downlink channel estimation method for a cellular-removed MIMO (Multiple Input Multiple Output) system based on orthogonal time-frequency-space OTFS (Over-The-Flight Spectrum) modulation, which comprises the following steps that: the system passes through a time-frequency dual-selective fading channel, an access point (AP) sends a downlink signal by adopting conjugate beam forming, and user equipment performs data decoding by utilizing a downlink channel estimation value; the method comprises the following steps: analyzing a system downlink input and output relational expression, approximately simplifying a downlink channel by using diagonal elements of a system downlink channel matrix, and providing minimum mean square error estimation of the downlink channel based on the approximately simplified downlink channel; through simulation verification, compared with the method that the user equipment decodes data by using the statistical information of the channel, the downlink channel method provided by the invention can effectively improve the downlink reachable rate of the system.

Description

Technical field:

[0001] The invention relates to a channel estimation method for a mobile communication system, and in particular to a downlink channel estimation method for a decellularized MIMO system based on orthogonal time-frequency air modulation, and belongs to the field of mobile communications. Background technology:

[0002] Cell-free Multiple Input Multiple Output (Cell-free MIMO) is a research branch of Distributed Antenna System (DAS), which combines the advantages of both DAS and MIMO systems [1]. Compared with the MIMO system under the cellular network architecture, Cell-free MIMO eliminates the concept of cells and uses a large number of distributed wireless access points (APs) to shorten the distance between users and access points, reduce path loss, and ensure the balance of service quality for all users in the entire area [2].

[0003] One of the goals of future wireless communications (emerging 5G) is to support a large number of users in high-mobility scenarios (such as high-speed rail, vehicle-to-vehicle communication, and drone communication) with low-latency, high-data-rate services. The current mainstream modulation technology is orthogonal frequency division multiplexing (OFDM). However, it is still an open problem for cell-free massive MIMO-OFDM systems to provide high-quality services for highly mobile users [3]. This is because the Doppler spread of time-varying channels (also known as time-frequency dual-selective channels) will lead to severe inter-carrier interference (ICI) in OFDM, which significantly degrades system performance. Orthogonal time-frequency space (OTFS) modulation, compared to OFDM information symbols modulating information in the one-dimensional frequency domain, OTFS modulates information in the delay-Doppler (DD) domain represented by the delay and Doppler. It is a two-dimensional modulation scheme. OTFS has shown significant effectiveness in solving the Doppler problem and is considered to be a promising candidate for high-mobility communications [4].

[0004] Existing studies have not proposed a downlink channel estimation method for a Cell-free MIMO system under OTFS modulation, but the present invention solves this problem and verifies the effectiveness of the scheme through simulation.

[0005] [1] Zhang Jiayi. Research progress and development trend of decellularized massive MIMO system[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2019, 031(003): 285-292.

[0006] [2] Marzetta, Thomas, L, et al. Cell-Free Massive MIMO Versus Small Cells[J]. IEEE transactions on wireless communications, 2017, 16(5): 1834-1850.

[0007] [3] Zheng J, Zhang J, E, et al. Cell-free massive MIMO-OFDM for high-speed train communications. IEEE Journal on Selected Areas in Communications, 2022, 40(10): 2823-2839.

[0008] [4] R.Hadani et al.Orthogonal time frequency space modulation[C], Proc.IEEE Wireless Communication Network Conference(WCNC), Mar.2017, pp.1-6. Summary of the invention:

[0009] The invention provides a downlink channel estimation method of a decellularized MIMO system based on OTFS modulation.

[0010] Here are the steps:

[0011] Step 1: Establish a decellularized MIMO system model under OTFS modulation;

[0012] Step 2: Give the input-output relationship between the user and the AP during downlink transmission;

[0013] Step 3: Based on the input-output relationship given in step 2, a minimum mean square error (MMSE) estimation method for the downlink equivalent channel is proposed;

[0014] Step 4: Using the downlink channel estimation value obtained in step 3, a method for calculating the downlink achievable rate of each UE in the system is given.

[0015] The present invention has the following effects: a downlink channel estimation scheme for a decellularized MIMO system with OTFS modulation is provided; in practical applications, a user can obtain a downlink channel estimation value through the provided channel estimation scheme, and then effectively decode downlink data, thereby improving the transmission rate of the communication system. Description of the drawings:

[0016] Figure 1 It is a schematic diagram of a downlink channel estimation method of a de-cellularized MIMO system based on OTFS modulation according to the present invention.

[0017] Figure 2 The normalized mean square error curve of the downlink channel estimation value when the downlink pilot symbol power is changed in the present invention.

[0018] Figure 3 The present invention is a curve diagram of system performance theory and simulation when the simulation parameters are fixed and the user uses the downlink channel estimation value to perform data decoding.

[0019] Figure 4 The present invention is a theoretical curve diagram of system performance when the number of APs is fixed and the number of users changes and the users respectively use downlink channel estimation values ​​and downlink channel statistical information to perform data decoding. Specific implementation method:

[0020] The present invention will be further described below in conjunction with the accompanying drawings.

[0021] 1. Analytical Method Process

[0022] The process of the channel estimation method in the decellularized MIMO system based on OTFS modulation of the present invention is as follows: Figure 1 As shown:

[0023] 2. System Model

[0024] The present invention is based on OTFS modulation to remove the cellular MIMO system by K u Single-antenna user equipment UE, M a The channel between AP and UE is a dual-selective fading channel with time and frequency. The number of multipaths between the pth AP and the qth UE is expressed as L pq , p=1…M a , q=1…K u , represents a complex Gaussian variable with mean a and variance b, AP p With UE q The complex channel gain and its estimated value, delay index, and Doppler frequency shift index of the i-th multipath between are expressed as l pq,i , k pq，i , i=1…L pq, ignoring the effect of fractional delay exponent, so l pq,i is a positive integer. The maximum Doppler index and the maximum delay index between the AP and the UE in the system are respectively expressed as k max , l max ; The system works in time division duplex TDD mode, and divides a TDD coherent interval into two stages: uplink channel estimation and uplink data transmission, and downlink channel estimation and downlink data transmission. In the uplink channel estimation process, it is known that the AP has obtained the estimated value of the complex channel gain, and the multipath number, delay index, and Doppler frequency shift index are also known. The AP uses conjugate beamforming to transmit downlink signal frames, and the UE uses the received signal frames to perform channel estimation and detect data locally;

[0025] During downlink transmission, the DD domain symbol matrix sent by the AP to the qth UE is composed of Indicates that Represents complex numbers and natural numbers respectively, S q The k-th row and l-th column element is denoted as S q [k, l], k=0...N-1, l=0...M-1, S q Embedded pilot EP is used, which is composed of data symbols, pilot symbols, and zero protection symbols. Each pilot symbol is surrounded by a large rectangular zero symbol protection area, which is expressed as:

[0026]

[0027] in s dsta，q [k,l] represent pilot symbols and data symbols respectively, and there are Respectively represent the downlink pilot symbol power and the downlink data symbol power, represents the expectation, k q , l q Respectively represent the ordinate and abscissa of the qth UE pilot symbol, and the protection area The horizontal axis range Vertical axis range in Indicates rounding down. Indicates the size of the additional protection area, which can be in the range The pilot symbol positions of different users are regularly and closely arranged, and the pilot position of the first user on the horizontal and vertical axes is set to 2l respectively. max , The user interval is 2l max , The maximum number of users that can be accommodated is expressed as The maximum number of users that the system can accommodate is K max =M u N u , the pilot overhead of each user is The AP uses conjugate beamforming to simultaneously transmit K u The signal sent by the pth UE is expressed as:

[0028]

[0029] Where: (·) H 、(·) T denote conjugate transpose and transpose respectively, vec(·) denotes matrix vectorization, η pq Indicates AP p and UE q The downlink power control coefficient between Indicates AP p and UE q The DD domain channel matrix estimate between In the uplink channel estimation stage, the matrix Contains information such as delay and Doppler, among which represents the Kronecker product, represents the N-point discrete Fourier transform DFT matrix, a, b = 0...N-1, identity matrix Diagonal Matrix Permutation Matrix

[0030] The received signal matrix of the qth UE It is expressed as:

[0031]

[0032] in Indicates AP p and UE q The DD domain channel matrix between Indicates UE q The additive white Gaussian noise matrix at , is the noise power, and is defined

[0033]

[0034] is the downlink effective channel gain, UE q Need to get H q To reliably decode S q , according to the existing channel hardening conditions, we can obtain And use it to decode the data Considering that channel hardening is not very obvious in cell-free MIMO, the present invention uses H q The estimated value of the decoded data; since the delay index and Doppler index remain unchanged in multiple frames, it is considered It is known that since the matrix It has the mathematical properties of an approximately diagonal matrix, so H q The approximate estimate is For decoding, express estimates;

[0035] 3. Minimum Mean Square Error (MMSE) Estimation of Downlink Equivalent Channel

[0036] Rewrite equation (1) as:

[0037]

[0038] It is not difficult to obtain the first term α pqq，ii (k q )=1, in the second term α pqq，ii (n′)=0, and since the third term involves the zero protection zone, equation (5) can be rewritten as:

[0039]

[0040]

[0041] Using formula (7) to calculate h q Perform MMSE estimation, h q can be estimated as Because h q , I 1，q , I 2，q and W q [k q , l q ] are independent of each other, so c q Given by:

[0042]

[0043] For item Exploiting the independence between channels p≠p′ and the relation ε pq,i represents the estimation error, and We can get:

[0044]

[0045] use and the independence of the transmitted symbols, and Can be expressed as

[0046]

[0047] The last UE q Using the downlink channel estimate To decode the data, rearrange equation (6) to make it suitable for detecting the rth symbol of the transmitted signal.

[0048]

[0049]

[0050] Where: (r) represents the rth symbol of the vector, r = 0…MN-1, and the downlink achievable rate can be obtained from equations (12) and (13):

[0051]

[0052] where ω dl =(MN-N guard，dl ) / MN reflects the pilot overhead, and further calculation expectation is:

[0053]

[0054] in As a comparison benchmark, UE q Using statistical CSI for decoding, we get:

[0055]

[0056] and

[0057] Similarly, the downlink achievable rate is:

[0058]

[0059] 5. Simulation Verification

[0060] Now consider the carrier frequency to be f c =4GHz, subcarrier spacing Δf=15kHz OTFS modulation cellular system, the maximum mobile speed in this scenario is 300 km / h, and the corresponding maximum Doppler delay is v max =1.11kHz. Using the Extended Vehicle A (EVA) model in the 3GPP vehicle model, the maximum multipath number L max =9,τ max =2.5us; for AP p and UE qFor each channel path between , we randomly select the delay index and Doppler index based on a uniform distribution so that -k max ≤k pq，i ≤k max , 0≤l pq,i ≤l max , We assume that in 1×1km 2 In the square area, K u UEs are randomly distributed, and M a The AP locations are evenly distributed. We use the 3GPP urban micro-cell model to calculate the large-scale fading coefficient. Modeling, (dB) represents the shadow effect, represents the path loss, PL pq =-30.5-36.7log 10 (d pq / 1m)(dB),d pq Indicates AP p and UE q In addition, AP p and UE q The shadow term correlation between Setting the noise power Downlink transmission power During downlink transmission, the AP transmits at full power and performs equal power allocation.

[0061] Figure 2 The normalized mean square error NMSE of the downlink channel estimation value when changing the ratio of downlink pilot symbol power to downlink data symbol is given, where NMSE is defined as Simulation parameter M a =100K u =2, number of subcarriers M = 512, frame length N = 128. The numerical results show that the higher the downlink pilot symbol power, the higher the downlink channel estimation quality, which is in line with expectations.

[0062] Figure 3 The cumulative probability distribution diagram of the achievable rate derived from equation (15) is given, and the simulation parameter M a =16, K u =2, the number of subcarriers M=32, and the frame length N=16. It can be seen from the figure that the given theoretical value of the achievable rate is very close to the Monte Carlo simulation curve, which shows that the given downlink achievable rate formula is effective.

[0063] Figure 4 The average downlink rate performance of users under different numbers of users with a fixed number of APs is given, and the simulation parameter M a =100, Ku =2, the number of subcarriers M=512, and the frame length N=128. The results show that the UE uses the downlink channel estimation value of the present invention to perform data decoding, and obtains a higher achievable rate than when using statistical channel information decoding.

[0064] The above description is only an embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements without departing from the principle of the present invention. These improvements should also be regarded as within the protection scope of the present invention.

Claims

1. A method for estimating downlink channels of a decellularized MIMO system based on orthogonal time-frequency-space (OTFS) modulation, characterized in that: Here are the steps: Step 1: Establish a decellularized MIMO system model based on OTFS modulation. The system adopts the time division duplex (TDD) mode and consists of K u Single-antenna user equipment UE, M a The AP is composed of a single-antenna access point AP. The AP and the UE are transmitted through a delay Doppler DD domain signal frame of size N×M, where N is the number of symbols carried by each carrier and M is the number of subcarriers. The AP uses the uplink channel estimation value and adopts conjugate beamforming to transmit the downlink signal frame. The UE uses the received signal frame to perform channel estimation and detect data locally. Step 2: Give the input-output relationship between UE and AP during downlink transmission, expressed as: and Where: and represents the received signal matrix and Gaussian white noise matrix of the qth UE, [k q , l q ] indicates that the symbol used by the qth UE for channel estimation is in the matrix Y q The position in represents the symbol matrix sent by AP to the qth UE, η pq With L pq represents the downlink power control coefficient and multipath number between the pth AP and the qth UE, h pq，i , l pq，i , k pq，i Respectively represent the complex channel gain and its estimated value, delay index, and Doppler shift index of the i-th multipath between the p-th AP and the q-th UE, where p = 1…M a , q=1…K u , i=1…L pq ; Step 3: Based on the downlink effective channel between the qth UE and the AP Using formula (1), we can get the channel h q MMSE estimation of the minimum mean square error for: Where: β pq，i , γ pq,i Respectively represent h pq,i , The second moment of Indicates rounding down, k max represents the maximum possible value of the Doppler frequency shift index in the system, Indicates the additional protection distance, They represent the downlink data symbol transmission power and the downlink pilot symbol transmission power respectively. represents the noise power; Step 4: Using the downlink channel estimation value obtained in step 3, the downlink achievable rate of the qth UE in the system is given as: Where: Reflects that in each OTFS frame, among the MN symbols, N guard，dl symbols are used for pilot and guard interval; Contains information such as delay and Doppler, diagonal matrix Permutation Matrix I M is the M-order unit matrix, c q Depend on And (3) to obtain, F N Represents the N-point discrete Fourier transform DFT matrix.

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