Uplink unlicensed system receiving method based on spatial modulation

Abstract

The present invention relates to an uplink unlicensed system receiving method based on spatial modulation. The purpose of the present invention is to solve the problems of excessive resource overhead of existing multiple access methods in large-scale access scenarios, excessive access delay in existing authorization mechanisms, and increased receiver complexity caused by the increase in the number of users. The process is: step one, establishing an uplink unlicensed system based on spatial modulation; step two, based on the uplink unlicensed system based on spatial modulation established in step one, correcting the minimum mean square error MMSE to obtain an estimated value of the channel matrix; step three, performing joint channel estimation and active user detection based on step one; step four, performing ZF‑ML signal detection based on step two and step three, and outputting the result of signal detection. The present invention is used in the field of communication signal detection technology.

Description

Technical Field

[0001] The present invention belongs to the technical field of communication signal detection, and specifically relates to a design scheme of an uplink unlicensed system receiver based on spatial modulation, which is suitable for channel estimation in a multi-user system, low-complexity detection of sparse signals, and user detection in multiple access. Background Art

[0002] In the era of the Internet of Everything, the total number of users is huge, but only a small number of users are active in each time slot. Therefore, if resource pre-allocation is used to achieve multiple access, it will introduce a lot of overhead. In addition, the authorization-based random access method will introduce a confirmation process between the user and the base station, increasing the access delay. Unauthorized random multiple access is an effective solution that can be applied to this scenario. It takes advantage of the small number of active users, avoids redundant overhead to a certain extent, and reduces access delay.

[0003] As a new modulation technology, spatial modulation uses the channel state information contained in different antenna resources to transmit information. Compared with traditional modulation technology, it increases the channel capacity of the system and has relatively low complexity.

[0004] At present, the research on random multiple access mainly focuses on the time domain, frequency domain and code domain, and less attention is paid to spatial resources; and there is less research on the design of uplink unlicensed system receivers using spatial modulation. Summary of the invention

[0005] The purpose of the present invention is to solve the problems of excessive resource overhead of existing multiple access methods in large-scale access scenarios, excessive access delay in existing authorization mechanisms, and increased receiver complexity caused by the increase in the number of users, and to propose an uplink unlicensed system reception method based on spatial modulation.

[0006] The specific process of the uplink unlicensed system receiving method based on spatial modulation is as follows:

[0007] Step 1: Establish an uplink unlicensed system based on spatial modulation;

[0008] Step 2: Based on the uplink unlicensed system based on spatial modulation established in step 1, the minimum mean square error estimation algorithm is modified to obtain an estimated value of the channel matrix;

[0009] Step 3: Perform joint channel estimation and active user detection based on step 1;

[0010] Step 4: Perform ZF-ML signal detection based on step 2 and step 3, and output the result of signal detection.

[0011] The beneficial effects of the present invention are:

[0012] The present invention proposes a receiver design scheme for an uplink unlicensed system based on spatial modulation. By separately designing channel estimation, active user detection and signal detection algorithms, the complexity of the receiver is reduced while ensuring the detection performance, providing a favorable support scheme for large-scale multiple access scenarios.

[0013] The uplink unlicensed system receiver-related algorithm based on spatial modulation proposed in the present invention adopts a modified minimum mean square error channel estimation algorithm to improve the performance of multi-user channel estimation. At the same time, the receiver adopts a joint channel estimation and active user detection algorithm and a ZF-ML signal detection algorithm to improve the signal detection performance while reducing the complexity of the receiver, thereby solving the problems of excessive resource overhead of existing multiple access methods in large-scale access scenarios, excessive access delay in existing authorization mechanisms, and increased receiver complexity caused by an increase in the number of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The figure is an overall block diagram of an uplink unlicensed system based on spatial modulation according to the present invention;

[0015] Figure 2 Design a block diagram of the system frame structure of the present invention;

[0016] Figure 3 It is a comparison chart of the performance curves of the modified MMSE estimation and the traditional MMSE estimation;

[0017] Figure 4 Performance curve of joint channel estimation and active user number detection algorithm;

[0018] Figure 5 This is a comparison chart of the error performance curves of the ZF-ML signal detection algorithm and the ML detection algorithm. DETAILED DESCRIPTION

[0019] Specific implementation method 1: The specific process of the uplink unlicensed system receiving method based on spatial modulation in this implementation method is as follows:

[0020] The present invention establishes an uplink random access system model using a spatial modulation transmitter, studies channel estimation, active user detection and signal detection schemes, and provides an overall design of a system receiver.

[0021] Step 1: Establish an uplink unlicensed system based on spatial modulation;

[0022] Step 2: Based on the uplink unlicensed system based on spatial modulation established in step 1, the minimum mean square error (MMSE) estimation algorithm is modified to obtain an estimated value of the channel matrix;

[0023] Step 3: Perform joint channel estimation and active user detection based on step 1;

[0024] Step 4: Perform ZF-ML signal detection based on step 2 and step 3, and output the result of signal detection.

[0025] Specific implementation method 2: This implementation method is different from the specific implementation method 1 in that an uplink unlicensed system based on spatial modulation is established in step 1; the specific process is as follows:

[0026] Step 1: If Figure 1 As shown in the figure, K in the uplink unlicensed system based on spatial modulation a Each active user uses a spatial modulation transmitter to transmit a signal x k , each user is equipped with N T The base station receiver is equipped with N transmitting antennas. R Root receiving antenna;

[0027] Step 1 and 2: The transmitted signal of each user reaches the base station receiver after passing through the Rayleigh flat fading channel H, and is processed by three modules of channel estimation, active user detection and signal detection in the base station receiver.

[0028] The other steps and parameters are the same as those in the first embodiment.

[0029] Specific implementation method three: This implementation method is different from specific implementation method one or two in that, in step two, based on the uplink unlicensed system based on spatial modulation established in step one, the minimum mean square error MMSE estimation algorithm is corrected to obtain an estimated value of the channel matrix; the specific process is:

[0030] This system uses a modified minimum mean square error (MMSE) estimation algorithm to estimate channel parameters. In a multi-user scenario, the undesirable cross-correlation of the ZC sequence will introduce multi-user interference to the channel estimation, causing the performance to be flat. The estimation algorithm used in this system corrects the traditional MMSE estimation method, eliminates multi-user interference, and improves the performance of channel estimation. This is achieved through the following steps:

[0031] Since the transmitter adopts a spatial modulation structure of a single radio frequency link, it can only transmit on one antenna at the same time. Therefore, before performing channel estimation, it is necessary to send pilot signals on each antenna at the transmitting end in turn.

[0032] The pilots sent on different antennas reach the receiver through different Rayleigh flat fading channels;

[0033] The receiver uses the modified MMSE channel estimation algorithm to obtain the estimated value of the channel matrix.

[0034] Step 21: Follow Figure 2 The frame structure given adopts Zadoff-Chu (ZC) sequence as the pilot sequence of the uplink unlicensed system based on spatial modulation;

[0035] Since the user transmitter has only one RF link, all transmitting antennas need to take turns sending pilot signals in different time slots (since the transmitter uses a spatial modulation structure of a single RF link, it can only send signals on one antenna at the same time, so it is necessary to send pilot signals on each antenna at the transmitting end in turn before performing channel estimation).

[0036] Step 22: The pilots sent on different antennas reach the receiver through different Rayleigh flat fading channels;

[0037] Step 23: The received signal is expressed as y=Hx p +n;

[0038] Where H is the Rayleigh flat fading channel, x p is the pilot sequence. In channel estimation, the transmitted signal is a known signal, namely the pilot sequence, and n is Gaussian white noise. Η (k) is the Rayleigh flat fading channel between the kth user and the base station, x p2 represents the pilot signal transmitted by user 2;

[0039] In the traditional MMSE estimation scheme, the estimation matrix is ​​expressed as

[0040] A=(x p H R H x p +σ 2 I) -1 x p H R H

[0041] in,(·) H is the conjugate transpose symbol, R H represents the channel autocorrelation matrix, σ 2 represents the variance of the noise, and I represents the unit matrix;

[0042] In a multi-user scenario, the traditional MMSE estimation scheme cannot eliminate multi-user interference, and the channel estimation performance is flat. Therefore, this scheme derives a modified MMSE estimation scheme. To simplify the derivation, it is assumed that K a = 2, the channel estimation is performed for user 1, y = H1x p1 +H2x p2 +n; Assuming that the estimation matrix that minimizes the channel estimation MSE value in this case is A1, then

[0043]

[0044] Where E is the mean; tr[(·)] represents the matrix trace operation, (·) H is the conjugate transposed symbol, y represents the received signal, H1 represents the channel matrix of user 1, H2 represents the channel matrix of user 2, and x p1 represents the pilot signal transmitted by user 1, x p2 represents the pilot signal transmitted by user 2, represents the estimated value of the channel matrix of user 1;

[0045] definition represents the channel autocorrelation matrix of user k, and is defined as is the channel correlation matrix between user i and user k. Since the channels between users are independent, we have The above formula can be rearranged into

[0046]

[0047] in, represents the channel autocorrelation matrix of user 1, A1 represents the estimation matrix of user 1, represents the channel autocorrelation matrix of user 2; I represents the unit matrix; σ 2 represents the variance of the noise;

[0048] make The estimated matrix of user 1 is

[0049]

[0050] Extending the above conclusion to K a For the scenario with active users, we get the modified MMSE estimation matrix A corresponding to user k. k ;

[0051]

[0052] Among them, K a Indicates the number of active users at a certain moment. represents the channel autocorrelation matrix of user i, represents the channel autocorrelation matrix of user k, x pi represents the pilot signal transmitted by user i, x pk represents the pilot signal transmitted by user k, A k represents the estimation matrix of user k;

[0053] The channel estimation result of user k is expressed as

[0054] Where y is the received signal.

[0055] The other steps and parameters are the same as those in the first or second embodiment.

[0056] Specific implementation method 4: This implementation method is different from the specific implementation methods 1 to 3 in that the pilot sequence in step 21 is generate;

[0057] where x p (l) is the value of the lth element in the pilot sequence, i is the imaginary unit, i 2 =-1, K t The maximum number of conflicting users that can be resolved in each time slot can be set as needed. In this scheme, K is selected t =5 can meet the needs of most practical scenarios; L p is the pilot length, 1≤l≤L p It is a quantity related to the pilot length.

[0058] The other steps and parameters are the same as those in Specific Embodiments 1 to 3.

[0059] Specific implementation mode 5: This implementation mode is different from the specific implementation modes 1 to 4 in that the pilot signals sent from different antennas in step 22 reach the receiver through different Rayleigh flat fading channels; the specific process is as follows:

[0060] Assume that the channel between user k and the base station is a Rayleigh flat fading channel (including every transmission to every reception), and the channel matrix is

[0061]

[0062] in, is the nth t The signal transmitted by the root antenna experiences a fading channel, H (k) is the Rayleigh flat fading channel between the kth user and the base station.

[0063] The other steps and parameters are the same as those in Specific Embodiments 1 to 4.

[0064] Specific implementation method 6: This implementation method is different from any one of specific implementation methods 1 to 5 in that, in step 3, joint channel estimation and active user detection are performed based on step 1; the specific process is as follows:

[0065] This system uses a joint channel estimation and active user detection algorithm to detect the number of active users in each time slot, in preparation for the next signal detection. The core idea of ​​the algorithm is to list possible pilot combinations for all possible user combinations; then, for a possible pilot combination, use the received signal to perform channel estimation, and multiply the estimated channel with the corresponding pilot combination as the constellation point, calculate the Euclidean distance between it and the received signal, and make a judgment based on the threshold. The specific process is:

[0066] According to the number of antennas equipped by the transmitting end user, the selectable signal constellation points and the number of decodable users, all possible transmission combinations are listed, and pilot selection and channel estimation are performed accordingly;

[0067] Corresponding to a certain number of active users, the Euclidean distance between all possible transmission combinations and received signals under this number is solved;

[0068] The minimum Euclidean distance corresponding to a certain number of active users is compared with the threshold to determine whether to perform iteration.

[0069] Step 3.1: Based on the maximum number of conflicting users K that can be resolved in each time slot t , initialize the possible user combinations Φ and pilot pool X′ (all x p );

[0070] Step 32: For a possible user combination Φ with a possible number of active users j j ,make Complete pilot selection and channel estimation;

[0071] Where X′ j (k) is the pilot selected according to the active user combination, Φ j (k) is a user combination with j active users, X′ is the pilot pool, The channel matrix is ​​obtained by using ZF channel estimation. The modified MMSE estimation algorithm with better performance is not used here because the complexity of the ZF channel estimation algorithm is relatively low and it can meet the actual needs in the detection of the number of active users.

[0072] Step 3. Multiply the obtained channel matrix with the pilot As a constellation point, solve the received signal y and The Euclidean distance between

[0073] Step 34: compare the obtained Euclidean distance with the optimal threshold. When the obtained Euclidean distance is less than the set optimal threshold, the iteration is terminated and the number of active users corresponding to this iteration is output; otherwise, the number of active users is updated to j+1, and steps 32 to 34 are iterated.

[0074] The other steps and parameters are the same as those in Specific Implementation Methods 1 to 5.

[0075] Specific implementation method 7: This implementation method is different from any one of specific implementation methods 1 to 6 in that the optimal threshold acquisition method is:

[0076] Threshold δ th The selection basis is to minimize the misjudgment rate. By analyzing this criterion, we can get the formula

[0077]

[0078] Among them, P(K a =g) represents the probability that the number of active users is g, where p a is the access probability, K is the total number of users in the uplink unlicensed system based on spatial modulation; is the probability density function corresponding to the Euclidean distance between the received signal and the constellation point in the case of missed alarm; is the probability density function corresponding to the Euclidean distance between the received signal and the constellation point in the case of false alarm;

[0079] The optimal threshold is obtained by solving the above equation using numerical methods.

[0080] The other steps and parameters are the same as those in Specific Embodiments 1 to 6.

[0081] Specific implementation eight: This implementation differs from any one of specific implementations one to seven in that in step four, ZF-ML signal detection is performed based on step two and step three, and the result of the signal detection is output;

[0082] This system uses the ZF-ML signal detection algorithm to detect the number of active users in each time slot. The core idea of ​​the algorithm is to first perform a screening process before the received signal is input into the maximum likelihood detector to reduce the number of combinations that the maximum likelihood detector needs to judge, thereby reducing the complexity of signal detection while obtaining better detection performance. The specific process is:

[0083] Initialize the set of signals to be detected, fix a certain user to be detected, subtract all possible interference combinations from the received signal to obtain the residual signal, and perform ZF processing on the residual signal;

[0084] Since the transmitting end adopts a spatial modulation structure and uses BPSK modulation, the possible signal to be sent can be determined by taking the real part and calculating the posterior probability corresponding to all possible situations;

[0085] Find the combination corresponding to the maximum a posteriori probability as the detection result, input it into the set to be detected, update the number of active users, and repeat steps 1, 2, and 3 until the maximum possible emission combination corresponding to all possible numbers of active users is found. These emission combinations constitute the set to be detected;

[0086] For the transmission combinations in the set to be detected, the maximum likelihood detector is used to make a decision, and the transmission combination with the minimum Euclidean distance to the received signal is output as the result of signal detection.

[0087] Step 41: Initialize the signal set to be detected (empty set);

[0088] Step 42: For a certain active user k, list the possible interference Ω k (Other users transmit signals), subtract all possible interference combinations from the received signal to obtain the residual signal y k =y-Ω k , and the residual signal y k Do ZF processing to get y k,inv ;

[0089] Step 4.3: In the uplink unlicensed system based on spatial modulation, the BPSK signal is sent. k,inv The real part of each position element in is used to narrow the range to be determined. When the real part is greater than zero, the BPSK signal that may be sent at this position is judged to be +1; when the real part is less than zero, the BPSK signal that may be sent at this position is judged to be -1.

[0090] pass Calculate the posterior probability;

[0091] Where real(·) means taking the real part, sgn(·) is the sign function, (m k ,n k ) indicates the location where the posterior probability is calculated, and prob(·) indicates the posterior probability;

[0092] Step 4. Find the combination corresponding to the maximum posterior probability As the detection result, it is input into the set of signals to be detected;

[0093] Step 45: Repeat step 42 to step 44 until the detection results corresponding to all active users are calculated and input into the set of signals to be detected;

[0094] Step 46: For the signal set to be detected, use the maximum likelihood detector to make a decision and output the signal detection result. Since this scheme screens the detection combination before the maximum likelihood detector, the time complexity is Reduced to

[0095]

[0096] Where T is the time complexity of matrix inversion.

[0097] The other steps and parameters are the same as those in Specific Embodiments 1 to 7.

[0098] Specific implementation method 9: This implementation method is different from the specific implementation methods 1 to 8 in that the step 42 (Other users transmit signals);

[0099] Where, H (i) is the Rayleigh flat fading channel between the ith user and the base station; x i Transmit a signal for the i-th user.

[0100] The other steps and parameters are the same as those in Specific Embodiments 1 to 8.

[0101] Specific implementation method 10: This implementation method is different from the specific implementation methods 1 to 9 in that

[0102] In the formula, is the channel matrix H k The generalized inverse of .

[0103] The other steps and parameters are the same as those in Specific Embodiments 1 to 9.

[0104] The following examples are used to verify the beneficial effects of the present invention:

[0105] The conditions are set as follows:

[0106] 1) The channel is a Rayleigh flat fading channel;

[0107] 2) Total number of users in the system K = 10 4 , access probability p a ≈2.7×10 -4 , the number of conflicting users that can be resolved in a single time slot of the system is 5;

[0108] 3) Pilot length L p =11, signal length L sym =300;

[0109] 4) Number of transmitting antennas N equipped for each user T =8, the number of receiving antennas equipped by the base station receiver is N R =8, the user adopts BPSK modulation;

[0110] The uplink unlicensed system receiver solution based on spatial modulation described in this embodiment is performed according to the following steps:

[0111] Step 1: Active users select pilots from the pilot pool, as follows: Figure 2 In the frame structure shown, pilot signals are transmitted through different antennas in different time slots for channel estimation.

[0112] Step 2: The base station receiver uses a modified MMSE channel estimation algorithm to perform channel estimation for each user, and uses the estimated channel matrix to detect the number of users using a joint channel estimation active user number detection algorithm.

[0113] Step 3: Each active user spatially modulates the input bit stream and sends it to the channel. Since the channel is modeled as a Rayleigh flat fading channel, the channel parameters can be considered unchanged during the same correlation time, so the estimated channel can be used for signal detection. The base station receiver uses the ZF-ML signal detection algorithm to demodulate the received signal and output the result.

[0114] The present invention may also have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may make various corresponding changes and modifications based on the present invention, but these corresponding changes and modifications should all fall within the scope of protection of the claims attached to the present invention.

Claims

1. An uplink unlicensed system receiving method based on spatial modulation, characterized in that: The specific process of the method is: Step 1: Establish an uplink unlicensed system based on spatial modulation; Step 2: Based on the uplink unlicensed system based on spatial modulation established in step 1, the minimum mean square error estimation algorithm is modified to obtain an estimated value of the channel matrix; Step 3: Perform joint channel estimation and active user detection based on step 1; Step 4: Perform ZF-ML signal detection based on step 2 and step 3, and output the result of signal detection; In the step 2, based on the uplink unlicensed system based on spatial modulation established in the step 1, the minimum mean square error estimation algorithm is corrected to obtain an estimated value of the channel matrix; the specific process is: Step 21: Using a Zadoff-Chu sequence as a pilot sequence for an uplink unlicensed system based on spatial modulation; Since the user transmitter has only one RF link, all transmitting antennas need to take turns to send pilot signals in different time slots; Step 22: The pilots sent on different antennas reach the receiver through different Rayleigh flat fading channels; Step 23: The received signal is expressed as y=Hx p +n; Where H is the Rayleigh flat fading channel, x p is the pilot sequence, n is Gaussian white noise, Η (k) is the Rayleigh flat fading channel between the kth user and the base station, x p2 represents the pilot signal transmitted by user 2; Assume that in K a = 2, the channel estimation is performed for user 1, y = H1x p1 +H2x p2 +n; Assuming that the estimation matrix that minimizes the channel estimation MSE value in this case is A1, then Where E is the mean; tr[(·)] represents the matrix trace operation, (·) H is the conjugate transposed symbol, y represents the received signal, H1 represents the channel matrix of user 1, H2 represents the channel matrix of user 2, and x p1 represents the pilot signal transmitted by user 1, x p2 represents the pilot signal transmitted by user 2, represents the estimated value of the channel matrix of user 1; definition represents the channel autocorrelation matrix of user k, and is defined as is the channel correlation matrix between user i and user k. Since the channels between users are independent, we have The above formula can be rearranged into in, represents the channel autocorrelation matrix of user 1, A1 represents the estimation matrix of user 1, represents the channel autocorrelation matrix of user 2; I represents the unit matrix; σ 2 represents the variance of the noise; make The estimated matrix of user 1 is Extending the above conclusion to K a For the scenario with active users, we get the modified MMSE estimation matrix A corresponding to user k. k ; Among them, K a Indicates the number of active users at a certain moment. represents the channel autocorrelation matrix of user i, represents the channel autocorrelation matrix of user k, x pi represents the pilot signal transmitted by user i, x pk represents the pilot signal transmitted by user k, A k represents the estimation matrix of user k; The channel estimation result of user k is expressed as Where y is the received signal; In step 3, joint channel estimation and active user detection are performed based on step 1; the specific process is as follows: Step 3.1: Based on the maximum number of conflicting users K that can be resolved in each time slot t , initialize the possible user combinations Φ and pilot pool X′; Step 32: For a possible user combination Φ with a possible number of active users j j , let X′ j (k) = X′Φ j (k), Complete pilot selection and channel estimation; Where X′ j (k) is the pilot selected according to the active user combination, Φ j (k) is a user combination with j active users, X′ is the pilot pool, is the channel matrix obtained by ZF channel estimation; Step 3. Multiply the obtained channel matrix with the pilot Solve for the received signal y and The Euclidean distance between Step 34: compare the obtained Euclidean distance with the optimal threshold. When the obtained Euclidean distance is less than the set optimal threshold, the iteration is terminated and the number of active users corresponding to the iteration is output; otherwise, the number of active users is updated to j+1, and steps 32 to 34 are iterated; In step 4, ZF-ML signal detection is performed based on step 2 and step 3, and the result of signal detection is output; the specific process is: Step 41: Initialize the set of signals to be detected; Step 42: For a certain active user k, list the possible interference Ω k , subtract all possible interference combinations from the received signal to obtain the residual signal y k =y-Ω k , and the residual signal y k Do ZF processing to get y k,inv ; Step 4.3: In the uplink unlicensed system based on spatial modulation, the BPSK signal is sent. k,inv The real part of each position element in is used to narrow the range to be determined. When the real part is greater than zero, the BPSK signal that may be sent at this position is judged to be +1; when the real part is less than zero, the BPSK signal that may be sent at this position is judged to be -1. pass Calculate the posterior probability; Where real(·) means taking the real part, sgn(·) is the sign function, (m k ,n k ) indicates the location where the posterior probability is calculated, and prob(·) indicates the posterior probability; Step 4. Find the combination corresponding to the maximum posterior probability As the detection result, it is input into the set of signals to be detected; Step 45: Repeat step 42 to step 44 until the detection results corresponding to all active users are calculated and input into the set of signals to be detected; Step 46: For the set of signals to be detected, use the maximum likelihood detector to make a decision and output the signal detection result.

2. The uplink unlicensed system receiving method based on spatial modulation according to claim 1, characterized in that: In the step 1, an uplink unlicensed system based on spatial modulation is established; the specific process is as follows: Step 1: K in the uplink unlicensed system based on spatial modulation a Each active user uses a spatial modulation transmitter to transmit a signal x k , each user is equipped with N T The base station receiver is equipped with N transmitting antennas. R Root receiving antenna; Step 1 and 2: The transmitted signal of each user reaches the base station receiver after passing through the Rayleigh flat fading channel H.

3. The uplink unlicensed system receiving method based on spatial modulation according to claim 2, characterized in that: In step 21, the pilot sequence is generate; where x p (l) is the value of the lth element in the pilot sequence, i is the imaginary unit, i 2 =-1, K t L is the maximum number of conflicting users that can be resolved in each time slot; p is the pilot length, 1≤l≤L p .

4. The uplink unlicensed system receiving method based on spatial modulation according to claim 3, characterized in that: In the step 22, the pilot signals sent from different antennas reach the receiver through different Rayleigh flat fading channels; the specific process is: Assume that the channel between user k and the base station is a Rayleigh flat fading channel, and the channel matrix is in, is the nth t The signal transmitted by the root antenna experiences a fading channel, H (k) is the Rayleigh flat fading channel between the kth user and the base station.

5. The uplink unlicensed system receiving method based on spatial modulation according to claim 4, characterized in that: The optimal threshold acquisition method is: Threshold δ th The selection basis is Among them, P(K a =g) represents the probability that the number of active users is g; is the probability density function corresponding to the Euclidean distance between the received signal and the constellation point in the case of missed alarm; is the probability density function corresponding to the Euclidean distance between the received signal and the constellation point in the case of false alarm; The optimal threshold is obtained by solving the above equation using numerical methods.

6. The uplink unlicensed system receiving method based on spatial modulation according to claim 5, characterized in that: In step 42 Where, H (i) is the Rayleigh flat fading channel between the ith user and the base station; x i Transmit a signal for the i-th user.

7. The uplink unlicensed system receiving method based on spatial modulation according to claim 6, characterized in that: Said In the formula, is the channel matrix H k The generalized inverse of .

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