A highly reliable orthogonal multiple access communication method

Abstract

The present invention provides a highly reliable orthogonal multiple access communication method, which combines multiple-input multiple-output technology with NOMA technology. By using minimum mean square error detection, the influence of interference and noise between different antennas can be considered simultaneously, minimizing the total error. By designing the precoding matrix and power allocation coefficient, the potential of NOMA technology can be maximized under the condition of the same user channel conditions, and the QoS requirements of different terminal users can be better satisfied. The present invention can make full use of the spatial resources of the channel, effectively improve the utilization rate of spectrum resources, and meet the connection requirements of today's massive device interconnection. The present invention can show better effects under the condition of similar channel conditions of terminal users, can better meet the performance requirements of terminal devices with high QoS requirements in the long term, and improve the stability of system communication and the utilization rate of spectrum resources.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication networks, and particularly to a downlink communication method for non-orthogonal multiple access (NOMA) technology for Internet of Things (IoT) applications. Background Art

[0002] With the rapid development of the mobile Internet, more and more devices are connected to the mobile network, and new services and applications emerge in an endless stream. The explosion of mobile data traffic will pose severe challenges to the network. The fifth-generation mobile communication system (5G) stands out with its advantages such as stable transmission quality, high spectral efficiency, strong timeliness, and wide coverage. It can effectively solve problems such as network instability and transmission delay in mobile communication, and better improve the user experience, becoming the development trend of future mobile communication technology.

[0003] The IoT calls for 5G, and 5G promotes the IoT. Supporting IoT functions in a 5G network is a challenge because it is not an easy task to connect billions of intelligent IoT devices with different quality of service (QoS) requirements under the constraint of limited bandwidth. How to effectively allocate corresponding spatial resources according to the QoS requirements of different terminal devices, meet the performance of different terminals, and at the same time efficiently utilize the existing spectrum resources is an important issue that modern wireless communication technology needs to consider.

[0004] In addition, in many scenarios, some terminal devices are stacked together, the distances to the base station are almost the same, and the channel conditions are similar. However, most of the existing technologies are proposed under different channel conditions, and the performance of the system is not very ideal. Summary of the Invention

[0005] In order to overcome the deficiencies of the prior art, the present invention provides a high-reliability orthogonal multiple access communication method. Aiming at the defects in the prior art, the purpose of the present invention is to solve the deficiencies in the prior art, and proposes a NOMA downlink transmission method for IoT applications, which combines multiple-input multiple-output (MIMO) technology with NOMA technology. By using minimum mean square error (MMSE) detection, the influence of interference and noise between different antennas can be considered simultaneously, minimizing the total error. By designing the precoding matrix and power allocation coefficient, the potential of NOMA technology can be maximized under the condition of the same user channel conditions, and the QoS requirements of different terminal users can be better met.

[0006] The technical solution adopted by the present invention to solve its technical problems includes the following steps:

[0007] Step 1: Establish a Multiple Input and Multiple Output - Non-orthogonal Multiple Access (MIMO-NOMA) system. The MIMO-NOMA system includes a base station equipped with M antennas, and user 1 and user 2 are respectively equipped with N antennas. R 1,i represents the target data rate of user 1 at the i-th layer, and R 2,i represents the target data rate of user 2 at the i-th layer, and ρ represents the signal-to-noise ratio at the transmitter;

[0008] Step 2: Respectively construct the N×M-dimensional channel matrices H1 and H2 of user 1 and user 2; H1 and H2 are randomly generated with each element independent of each other and satisfy a complex Gaussian distribution with a mean of zero and a variance of 1;

[0009] Step 3: Conjugate transpose the H2 matrix to obtain Perform QR decomposition on to obtain:

[0010]

[0011] where the matrix Q2 is M×M-dimensional, and the matrix is M×N-dimensional;

[0012] Step 4: Take the first N columns from Q2 to obtain an M×N-type matrix V2, and let the precoding matrix be V2; Take an N×N-dimensional upper triangular matrix R2 from , and the transpose of R2 gives a lower triangular matrix R2 H ;

[0013] Step 5: Design the target outage probability P of user 1 1,i,target ;

[0014] Step 6: According to the target outage probability P in step 5 1,i,target , design the power allocation coefficients α i , β i ;

[0015] Step 7: From the channel matrices obtained in steps 2 and 4, denote:

[0016]

[0017] The signal-to-interference-plus-noise ratio for user 1 to decode its i-th layer data stream is:

[0018]

[0019] Step 8: User 2 processes the data stream s of User 1 at the i-th layer i The decoded signal-to-interference-plus-noise ratio is:

[0020]

[0021] The signal-to-noise ratio SNR for User 2 to decode its own message is:

[0022]

[0023] Step 9: From the channel matrix R2 obtained in Step 4, take the diagonal elements of the channel matrix R2, denoted as And substitute them into the signal-to-interference-plus-noise ratio and signal-to-noise ratio in Step 8;

[0024] Step 10: The outage probability for User 1 to decode the data stream at the i-th layer is:

[0025]

[0026] Step 11: Definition of the outage probability for User 2 to decode its data stream at the i-th layer:

[0027]

[0028] Where

[0029]

[0030] Step 12: Theoretical calculation of the outage probability for User 2 to decode its data stream at the i-th layer is:

[0031]

[0032] Where And γ(·) represents the incomplete gamma function;

[0033] Step 13: At high signal-to-noise ratio, i.e., ρ approaching infinity, the outage probability of User 2 is as follows:

[0034]

[0035] Where

[0036] Taking the outage probability As a measure of system performance, compare and analyze the system outage performance of MIMO-NOMA technology and MIMO-OMA technology, and verify the device outage performance of User 1 and User 2 under the proposed power allocation scheme, where the QoS requirements of User 1 are met at this time.

[0037] In the said Step 5, the outage probability P 1,i,target Is:

[0038]

[0039] For the sake of convenience of representation, denote

[0040] In step 6, according to the target outage probability P 1,i,target , the design is as follows:

[0041]

[0042] where α i , β i are power allocation coefficients respectively.

[0043] The beneficial effects of the present invention are that the NOMA technology can further improve the utilization rate of spectrum resources, the MIMO technology can make full use of the spatial resources of the channel, and combining the MIMO and NOMA technologies can effectively improve the utilization rate of spectrum resources and meet the connection requirements of today's massive device interconnection. At the same time, compared with the traditional technology, the proposed scheme of the present invention can show better effects when the channel conditions of the end users are similar, can better meet the performance requirements of the end devices with high QoS requirements in the long term, and improve the stability of system communication and the utilization rate of spectrum resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is the system model diagram of the present invention.

[0045] Figure 2 is the comparison diagram between the ZF detection algorithm and the MMSE detection algorithm.

[0046] Figure 3 is the outage performance of User 1.

[0047] Figure 4 is the outage performance of User 2.

[0048] Figure 5 is the performance comparison diagram between the technology of the present invention and the ZF-NOMA technology.

[0049] Figure 6 is the performance comparison diagram between the technology of the present invention and the MIMO-OMA technology. DETAILED DESCRIPTION OF THE INVENTION

[0050] The present invention will be further described below in conjunction with the drawings and embodiments.

[0051] According to a NOMA downlink transmission method for Internet of Things applications proposed by the present invention, taking the number of transmitting antennas and the number of receiving antennas both being 3 as an example, that is, M = N = 3, the target data rates of User 1 in the 1st, 2nd, and 3rd layers are respectively R 1,1 = 1 BPCU, R 1,2= 1.5 BPCU, R 2,i = 4 BPCU。

[0052] The target data rates of User 2 at the 1st and 2nd layers are R 2,1 = 1 BPCU, R 2,2 = 4 BPCU. The signal-to-noise ratio ρ at the transmitter is 10.

[0053] To achieve the above object, the present invention is implemented by the following technical solutions:

[0054] Step 1: Establish a Multiple Input and Multiple Output - Non-orthogonal Multiple Access (MIMO-NOMA) system, as Figure 1 shown, where the system includes a base station equipped with 3 antennas, User 1 and User 2 each equipped with 3 antennas.

[0055] Step 2: Respectively construct the N×M-dimensional channel matrices H1 and H2 for User 1 and User 2: Randomly generate matrices H1 and H2 where each element is independent of each other and satisfies a complex Gaussian distribution with a mean of zero and a variance of 1.

[0056] Step 3: Conjugate transpose the H2 matrix to obtain Perform QR decomposition on :

[0057]

[0058] The decomposition results in an M×M-dimensional matrix Q2 and an M×N-dimensional matrix

[0059] Step 4: Take the first N columns from Q2 to obtain an M×N-type matrix V2, and take an N×N-dimensional upper triangular matrix R2 from Take the lower triangular matrix obtained by conjugating and transposing R2

[0060] Step 5: The target outage probability of User 1 at the first layer:

[0061]

[0062] The target outage probability of User 1 at the second layer:

[0063]

[0064] The target outage probability of User 1 at the third layer:

[0065]

[0066] Step 6: Set the power allocation coefficient, from

[0067]

[0068] we can get:

[0069]

[0070]

[0071]

[0072] Step 7: Denote the channel matrices obtained in Steps 2 and 4 as:

[0073]

[0074] The signal-to-interference-plus-noise ratio (SINR) for User 1 to decode its data at the i-th layer is:

[0075]

[0076] Substitute α i 2 , β i 2 calculated in Step 6 and the channel matrices generated in Steps 2, 3, and 4 to obtain the SINR of User 1 at each layer 1,1 , SINR 1,2 , SINR 1,3 .

[0077] Step 8: The SINR for User 2 to decode the data of User 1 at the i-th layer is:

[0078]

[0079] The signal-to-noise ratio (SNR) for User 2 to decode its own message is:

[0080]

[0081] Substitute the diagonal elements of the channel matrix generated in Step 4 to obtain SINR 2,1 , SINR 2,2 , SNR 2,1 , SNR 2,2 .

[0082] Step 9: Calculate the outage probability for User 1 to decode its i-th data stream as:

[0083]

[0084] Substitute α i2 , β i 2 and Z in step 9 i are substituted, and the probability is obtained by statistical averaging.

[0085] Step 10: From Figure 3 It can be seen that the simulated value of the outage probability of User 1 almost coincides with the theoretical analysis value of the target outage probability, indicating that this scheme is feasible. Moreover, the higher the signal-to-noise ratio, the smaller the outage probability of User 1, indicating that the power allocation policy under this scheme can well meet the QoS requirements of Device 1.

[0086] Step 11: The outage event that User 2 decodes its own message at the i-th layer is expressed as:

[0087]

[0088] where indicates that User 2 cannot successfully decode the messages of s m and w m at the m-th layer, but can successfully decode all the messages of s n and w n (1 ≤ n < m) at the previous layers. When m ≠ n,

[0089] Therefore, the outage probability that User 2 decodes its own message at the i-th layer is:

[0090]

[0091] where,

[0092]

[0093]

[0094]

[0095]

[0096] Substitute the SINR and SNR values obtained in Steps 8 and 9, and the theoretical simulation value of User 2 can be obtained.

[0097] Step 12: Since the power allocation coefficient is not a function of the instantaneous channel gain, in Step 11 can be rewritten as:

[0098]

[0099] By simplification, and are further expressed as:

[0100]

[0101]

[0102] where and γ(·) represents the incomplete gamma function.

[0103] Therefore, the outage probability for User 2 to decode its own message at the i-th layer is:

[0104]

[0105] Substituting the data obtained in Steps 8 and 9 and the incomplete gamma function, the numerical analysis value of the outage probability of User 2 can be obtained.

[0106] Step 13: At high signal-to-noise ratio, i.e., when ρ approaches infinity, the outage probability of User 2 can be approximated as follows:

[0107]

[0108] where Substituting the numerical values, the approximate value of the outage probability of User 2 can be obtained.

[0109] Step 14: From Figure 4 it can be seen that at low signal-to-noise ratio, the simulated outage probability and the theoretical value of the target outage probability can coincide well. However, the simulated value and the approximate value do not coincide because the derivation of the approximate expression is obtained under the premise that the transmission signal-to-noise ratio ρ is very large.

[0110] From Figure 5 it can be seen that on different data stream layers, the outage probability of the MMO-NOMA scheme is less than that of the Zero Forcing-Non-orthogonal Multiple Access (ZF-NOMA) scheme with zero-forcing detection. Moreover, for MIMO-NOMA, on different data stream layers, the slope of the outage probability curve is changing, which means that the channel gains of different data stream layers are diverse. However, in the ZF-NOMA scheme, the slope of the outage probability curve is the same because there is a correlation between the effective channel gains in the ZF-NOMA technology.

[0111] As Figure 6 shown, the Multiple Input and Multiple Output-Orthogonal Multiple Access (MIMO-OMA) scheme is compared with the scheme of the present invention. From Figure 6It can be seen that, regardless of the layer, the outage performance of the MIMO-NOMA scheme is better than that of the MIMO-OMA scheme, which also shows that the present invention can effectively improve the system throughput by applying the NOMA technology when the QoS requirements of the two users are different.

Claims

1. A highly reliable orthogonal multiple access communication method, characterized in that Including the following steps: Step 1: Establish a multi-input multi-output non-orthogonal multiple access system. The MIMO-NOMA system includes a base station equipped with M antennas, and user 1 and user 2 are respectively equipped with N antennas. R 1,i represents the target data rate of user 1 at the i-th layer, and R 2,i represents the target data rate of user 2 at the i-th layer, and ρ represents the signal-to-noise ratio at the transmitter; Step 2: Construct the N×M dimensional channel matrices H1 and H2 of user 1 and user 2 respectively; H1 and H2 are matrices randomly generated with each element independent of each other and satisfying a complex Gaussian distribution with a mean of zero and a variance of 1; Step 3: Conjugate transpose the H2 matrix to obtain Perform QR decomposition to obtain: Among them, matrix Q2 is of dimension M×M, and matrix is of dimension M×N; Step 4: Take the first N columns from Q2 to obtain an M×N matrix V2, and let the precoding matrix be V2; from take an N×N upper triangular matrix R2, and obtain a lower triangular matrix after transposing R2 Step 5: Design the target outage probability \(P\) of User 1 1,i,target ; Step 6: Design the power distribution coefficients α 1,i,target , β i according to the target outage probability P in Step 5 i ; Step 7: From the channel matrices obtained in Step 2 and Step 4, denote: The signal-to-interference-plus-noise ratio for user 1 to decode its i-th layer data stream is: Step 8: User 2 processes the data stream s of User 1 at the i-th layer i The decoded signal-to-interference-plus-noise ratio is as follows: The signal-to-noise ratio SNR for user 2 to decode its own message is: Step 9: From the channel matrix R2 obtained in Step 4, take the diagonal elements of the channel matrix R2, denoted as and substitute them into the signal-to-interference-plus-noise ratio and signal-to-noise ratio in Step 8; Step 10: The outage probability for user 1 to decode the i-th layer data stream is: Step 11: The definition of the outage probability for user 2 to decode its i-th layer data stream is: Wherein, Step 12: The theoretically calculated outage probability for user 2 to decode its i-th layer data stream is: wherein and γ(·) represents the incomplete gamma function; Step 13: At high signal-to-noise ratio, that is, when ρ approaches infinity, the outage probability of user 2 is as follows: Among them Taking the outage probability as a measure of system performance, the system outage performances of MIMO-NOMA technology and MIMO-OMA technology are compared and analyzed, and the device outage performances of User 1 and User 2 under the proposed power allocation scheme are verified, where the QoS requirements of User 1 are satisfied.

2. The high-reliability orthogonal multiple access communication method according to claim 1, characterized in that: In the said step 5, the interruption probability P 1,i,target is as follows: For convenience of representation, denote 3. The highly reliable orthogonal multiple access communication method according to claim 1, characterized in that: In step 6, according to the target outage probability P 1,i,target , the design is as follows: where α i , β i are power distribution coefficients respectively.

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