Symbiotic radio network distributed multiple access system based on reconfigurable holographic device

Abstract

The invention discloses a symbiotic radio network distributed multiple access system based on reconfigurable holographic devices. The symbiotic radio network distributed multiple access system comprises an environment network (BS / UE) and an IoT network composed of K reconfigurable holographic devices (RHD) and an IoT receiver (IR). Each RHD integrated antenna, a load modulator and a reconfigurable holographic surface (RHS) convert a reference wave carrying IoT information into a target wave and radiate the target wave to a free space by activating a sub-wavelength radiation element, so that the size limitation of a traditional RIS / phased array is solved; an IoT receiver (IR) adopts a code division multiple access (CDMA) distributed decoding based on a random code, a matched filter (MF) or minimum mean square error (MMSE) receiver is combined with an asymptotic signal to interference plus noise ratio theory, and real-time feedback of the random code is avoided; a radiation pattern is optimized based on a weighted minimum mean square error (WMMSE) algorithm, and RHD and rate maximization are achieved. The device has the advantages that the size of the device supports integration of micro IoT (Internet of Things) equipment; the IoT signal to interference plus noise ratio is increased by more than 20dB; and massive IoT equipment access is realized.

Description

Technical Field

[0001] The present invention relates to the fields of wireless communication technology and Internet of Things technology, and in particular to a symbiotic radio network distributed multiple access system based on a reconfigurable holographic device. Background Art

[0002] Symbiotic radio technology is considered a key solution for realizing 6G IoT. Its core is the mutually beneficial sharing of spectrum and energy. The network consists of a primary transmitter, a primary receiver, secondary transmitters, and secondary receivers. The primary network is typically the existing cellular network infrastructure, while the secondary network consists of IoT devices. Backscatter devices modulate and transmit their IoT information using ambient RF signals, thereby sharing resources with the primary network. However, this architecture faces a serious problem: the backscatter link often experiences two-path channel fading, significantly degrading the IoT signal quality and, in turn, impacting the performance of the entire symbiotic radio system.

[0003] To improve channel conditions, existing technologies have introduced reconfigurable smart surfaces to enhance backscatter links. These artificial planar structures optimize wireless transmission paths by adjusting the phase characteristics of reflective elements. While this solution improves signal quality, its physical structure is fundamentally limited: the spacing between adjacent reflective elements cannot be less than half a wavelength. This physical constraint results in the large size of reconfigurable smart surfaces, typically requiring deployment in fixed locations such as building surfaces. This makes them incompatible with miniaturized IoT sensors or devices, severely limiting their application flexibility in mobile IoT scenarios.

[0004] Traditional phased array antennas face similar physical limitations, requiring adjacent elements to maintain a half-wavelength spacing. This constraint creates a dilemma for antenna design: increasing the number of elements improves spatial gain but results in a physically large antenna; reducing the size, on the other hand, requires fewer elements, significantly degrading antenna performance. This size-performance trade-off severely limits the applicability of traditional antennas in space-constrained IoT devices.

[0005] Existing multiple access technologies each have their own shortcomings when it comes to supporting large-scale IoT device access. Non-orthogonal multiple access (NMA) achieves multi-user access by allocating non-orthogonal waveforms on the same time-frequency resource block. At the receiver, continuous interference cancellation (CIC) separates the signals of different users. While this approach improves spectrum efficiency when applied in coexisting radio networks, the CIC process can lead to error propagation as the number of IoT devices increases. Errors in decoding a device's signal directly impact signal recovery for all subsequent devices, causing system performance to plummet as the number of devices increases. This characteristic makes this approach unsuitable for large-scale IoT device access scenarios. While code division multiple access (CDMA) enables large-scale access through spectrum sharing, in practice it requires the receiver to know the specific random code information for each device, increasing system complexity and signaling overhead.

[0006] Overall, existing symbiotic radio network technologies suffer from three key flaws: an inability to effectively address the dual channel attenuation problem of backscatter links; a lack of efficient solutions to support large-scale IoT device access; and a fundamental conflict between the physical size and performance requirements of antenna systems. These issues severely hinder the development of 6G IoT, necessitating an innovative solution that simultaneously addresses channel attenuation, large-scale IoT device access, and hardware integration. Summary of the Invention

[0007] The present invention aims to overcome the defects of the prior art and provides a symbiotic radio network distributed multiple access system based on a reconfigurable holographic device.

[0008] In order to achieve the above object of the invention, the technical solution adopted by the present invention is as follows:

[0009] A symbiotic radio network distributed multiple access system based on a reconfigurable holographic device, comprising:

[0010] An environment network consisting of a base station BS and a user equipment UE;

[0011] An IoT network consisting of K reconfigurable holographic devices (RHDs) and IoT receivers (IRs);

[0012] Each RHD includes an antenna, a load modulator, and a reconfigurable holographic surface (RHS);

[0013] The load modulator modulates the IoT signal onto the ambient RF signal by adjusting the load impedance to generate a reference wave;

[0014] The RHS receives a reference wave through feeding, and converts the reference wave into a target wave by activating M sub-wavelength metamaterial radiation elements and radiates it into free space;

[0015] The IoT receiver (IR) uses the random code-based code division multiple access (CDMA) technology to jointly decode the signals of K RHDs.

[0016] Furthermore, the radiation pattern of the RHD satisfies:

[0017] Radiation pattern parameter φ k,m Satisfy the constraint 0≤φ k,m ≤1, k=1,...,K is the RHD number, m=1,...,M is the radiating element number;

[0018] Holographic beamforming ψ k =Qφ k , where Q = diag{q1,…,q M},

[0019] q m : the inherent phase of the reference wave of the mth element;

[0020] α m : the amplitude attenuation of the reference wave from the feed to the mth element;

[0021] k s : propagation vector of the reference wave;

[0022] r m : Position vector of the mth element.

[0023] Furthermore, the decoding process of the Internet of Things receiver (IR) includes:

[0024] Base station BS signal s l Perform interference cancellation (SIC) to obtain the intermediate signal:

[0025]

[0026] g k : channel vector from the kth RHD to IR;

[0027] h k : channel coefficient from base station BS to the kth RHD;

[0028] S=diag(s1,…,s L ): Collect base station BS signals of L time slots;

[0029] The spread spectrum signal of the kth RHD, b k is the random code vector, c k It is the code element of the Internet of Things;

[0030] n r : noise vector;

[0031] p t : Base station BS power;

[0032] The intermediate signal is decoded using a matched filter (MF) or minimum mean square error (MMSE) receiver.

[0033] Furthermore, the filter vector of the MF receiver is:

[0034] The filter vector of the MMSE receiver is:

[0035] σ 2 : noise power;

[0036] I L : L-dimensional identity matrix.

[0037] Furthermore, the asymptotic signal-to-interference-and-noise ratio of the RHD satisfies:

[0038] Under MF receiver:

[0039]

[0040] Under MMSE receiver:

[0041]

[0042] L: Spread spectrum code length.

[0043] Furthermore, an optimization module is included to solve the RHD asymptotic and rate maximization problems:

[0044]

[0045] C3:0≤φ k,m ≤1,

[0046] in:

[0047] The asymptotic signal to interference and noise ratio (SIN) defined in claim 5 (MF receiver) Or x under MMSE receiver k );

[0048] γ r,0 : Signal-to-interference-noise ratio of IoT receiver (IR) to base station BS signal

[0049] The signal-to-interference-and-noise ratio threshold required for successful decoding of the base station BS signal;

[0050] R u : The achievable rate of the user equipment (UE);

[0051] The minimum rate threshold required by the environment network;

[0052] φ k,m : radiation pattern parameters of the m-th radiating element of the k-th RHD;

[0053] K: total number of RHDs;

[0054] m: radiating element number (m = 1, 2, ..., M);

[0055] k: RHD number (k = 1, 2, ..., K).

[0056] The present invention also discloses a reconfigurable holographic device (RHD), which is used in the above system and is characterized by comprising:

[0057] Antenna, receiving ambient RF signals;

[0058] Load modulator, which converts IoT signal into k Loaded with ambient RF signals;

[0059] Reconfigurable holographic surface (RHS) converts reference waves carrying IoT information into target waves and radiates them into free space;

[0060] The radiation pattern of the RHS φ k,m Optimized by weighted minimum mean square error (WMMSE) algorithm.

[0061] Furthermore, the WMMSE algorithm includes:

[0062] For the MF receiver system, iteratively update the auxiliary variables ζ=[ζ1,…,ζ K ] T and β=[β1,…,β K ] T and the radiation pattern {φ k}. Maintain the radiation pattern {φ k}Fixed, auxiliary variable ζ k and β k The optimal expression of is:

[0063]

[0064]

[0065] Keeping the auxiliary variables ζ and β fixed, the optimal radiation pattern {φ k} can be obtained by using CVX to solve the following convex optimization problem.

[0066]

[0067] (C3),

[0068] in

[0069] For MMSE receiver systems, iteratively update the asymptotic signal-to-interference-and-noise ratio Auxiliary variable w=[w1,…,w K ] T ,u=[u1,…,u K ] T and radiation pattern {φ k}. Maintain auxiliary variables w,u and radiation pattern {φ k Fixed, asymptotic signal-to-interference-and-noise ratio It can be calculated by the following expression.

[0070]

[0071] Maintain asymptotic signal-to-interference-and-noise ratio and radiation pattern {φ k}Fixed, auxiliary variable w k ,u k The optimal expression for

[0072]

[0073] Maintain asymptotic signal-to-interference-and-noise ratio and auxiliary variables w,u are fixed, the optimal radiation pattern {φ k} can be obtained by using CVX to solve the following convex optimization problem.

[0074]

[0075] (C3),

[0076] in

[0077] The present invention also discloses an Internet of Things receiver (IR) decoding method, which is used in the above system and is characterized by comprising:

[0078] Receive signal:

[0079]

[0080] Where, h0: direct channel from base station BS to IR;

[0081] n r,l : additive white Gaussian noise of the lth time slot IR;

[0082] Decode base station BS signal s l and perform interference cancellation;

[0083] The intermediate signal obtained after interference elimination Use CDMA despreading and MF or MMSE receiver to recover IoT signal c k .

[0084] The present invention also discloses a computer-readable storage medium storing a computer program, characterized in that when the computer program is executed by a processor, an Internet of Things receiver (IR) decoding method is implemented.

[0085] Compared with the prior art, the advantages of the present invention are:

[0086] 1. Breaking through traditional limitations: The RHS based on subwavelength metamaterial structure breaks the half-wavelength spacing limitation, reducing the size of traditional RIS / phased array devices by more than 60% with the same number of elements / antennas, and can be directly integrated into micro IoT terminals.

[0087] 2. Overcoming channel attenuation: This invention compensates for the double channel attenuation experienced by IoT links in symbiotic radio networks by optimizing the holographic beamforming radiation pattern on the IoT device side, thereby improving the signal-to-interference-and-noise ratio of the IoT link by 20-25dB (compared to the solution without RHS).

[0088] 3. Massive device access: The random code-based CDMA distributed architecture reduces the complexity of IoT receiver design and supports concurrent communication of large-scale IoT devices.

[0089] 4. Theoretical innovation and energy saving: The closed-form solution of the asymptotic signal-to-interference-and-noise ratio eliminates the need for real-time random code feedback, reducing system complexity and signaling overhead. When the random code length is large, the MF receiver performance approaches that of the MMSE. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 This is a model diagram of a multi-RHD SRN system according to an embodiment of the present invention;

[0091] Figure 2 This is a block diagram of the RHD structure of an embodiment of the present invention;

[0092] Figure 3 This is a block diagram of the IR structure of an embodiment of the present invention;

[0093] Figure 4 Schematic diagram of the relationship between the RHD achievable signal-to-interference-and-noise ratio and the random code length L according to an embodiment of the present invention;

[0094] Figure 5 Schematic diagram of the relationship between the RHD achievable signal-to-interference-plus-noise ratio and the number of RHDs K when an MMSE receiver is used in an embodiment of the present invention;

[0095] Figure 6FIG. 1 is a schematic diagram showing the relationship between the achievable signal-to-interference-and-noise ratio (SIR) of RHD and the number K of RHDs when an MF receiver is used in an embodiment of the present invention. DETAILED DESCRIPTION

[0096] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and examples.

[0097] Figure 1 The proposed multi-RHD SRN system model is presented, where a BS equipped with a single antenna sends information to a single-antenna UE, and at the same time, K RHDs use the RF signal from the BS in the environment to send their own IoT information to a single-antenna IR. Figure 2 As shown in the figure, the RHD proposed in this invention primarily consists of an antenna, a load modulator, and a RHS. The load modulator modulates the device's IoT signal onto the ambient signal absorbed by the antenna by adjusting the load impedance. The IoT signal, acting as a reference wave, is then injected into the RHS waveguide via a feed, activating the RHS metamaterial element. The target wave is then radiated into free space as a target wave.

[0098] The channels from BS to UE, BS to IR, and BS to RHD k are denoted as f0, h0, and h k The channels from RHD k to UE and RHD k to IR are expressed as The signal transmitted by the BS to the UE can be expressed as s~CN(0,1), and the BS transmission power is p t The IoT symbol of RHD k is represented by c k ∈{-1,+1}. The random code used for RHD k spread spectrum is represented by b k =[b k,1 ,...,b k,L ] T , and there are The spread spectrum signal at RHD k can be expressed as The IoT symbol period of RHDk is L times the BS symbol period. In the lth time slot, the signal transmitted by RHDk can be expressed as where ψ k is the holographic beamforming of RHD k. k The mth element of is where φ k,m is the radiation pattern, α m is the amplitude attenuation of the reference wave during propagation from the feed to the mth element, k s is the reference wave propagation vector.

[0099] The received signal of the lth time slot at the UE can be expressed as

[0100]

[0101] where n u,l ~CN(0,σ 2 ) represents the additive white Gaussian noise at the UE. The signal to interference noise ratio and achievable rate of the UE can be expressed as

[0102]

[0103] R u =log2(1+γ u ), (3)

[0104] The received signal at IR can be expressed as

[0105]

[0106] where n r,l ~CN(0,σ 2 ) represents the additive white Gaussian noise at the IR. In the lth time slot, the IR first demodulates the signal from the BS, and its signal-to-interference-noise ratio expression is

[0107]

[0108] In decoding l After that, IR performs SIC to obtain the following intermediate signal

[0109]

[0110] After L time slots, the received signal at IR is

[0111]

[0112] Where S = diag(s1,…,s L ) collected the BS primary symbols of L time slots, n r =[n r,1 ,…,n r,L ] T is the IR noise of L time slots.

[0113] In order to detect all IoT code elements c k , the IR deploys a receive filter vector for each RHD k Then the output signal of RHD k at IR can be expressed as

[0114]

[0115] Corresponding IoT code element c k The signal-to-interference-and-noise ratio can be written as

[0116]

[0117] The joint decoding block diagram at IR is composed of Figure 3 The IR filter can use two receivers: MF and MMSE. Next, we give the performance analysis results of these two receivers respectively.

[0118] Based on formula (7), we can get the detection IoT code element c k The MF receiver expression is

[0119]

[0120] Then the output signal expression of the MF receiver is

[0121]

[0122] Using an MF receiver, the signal-to-interference-and-noise ratio of RHD k can be written as

[0123]

[0124] Correspondingly, as the spreading code length L and the number of RHDs K grow large enough and their ratio converges to a constant, we can obtain the asymptotic signal-to-interference-noise ratio of the RHD k based on the MF receiver as

[0125]

[0126] Based on formula (7), we can get the detection IoT code element c k The MMSE receiver expression is

[0127]

[0128] Using an MMSE receiver, the signal-to-interference-and-noise ratio of RHD k can be written as

[0129]

[0130] Correspondingly, as the spreading code length L and the number of RHDs K grow large enough and their ratio converges to a constant, we can obtain the asymptotic signal-to-interference-and-noise ratio of the RHD k based on the MMSE receiver as

[0131]

[0132] Using the obtained theoretical results, we formulate the RHD asymptotic sum rate maximization problem for the proposed system as follows, where the optimization variable is the radiation pattern of RHD k, while satisfying the constraints of perfect SIC at the IR and quality of service requirements at the UE. The overall optimization problem can be formulated as

[0133]

[0134] in represents the asymptotic signal-to-interference-and-noise ratio of RHD k, Is the IR successfully decoded s l The signal-to-interference-noise ratio threshold, is the minimum transmission rate requirement of the UE, and constraint C3 limits the feasible domain of the RHS radiation pattern. Next, we give the optimization problem expressions for the MF and MMSE receivers respectively.

[0135] For the MF receiver, the theoretical analysis result (13) is substituted into the asymptotic signal-to-interference-and-noise ratio of RHD k Problem (17) can be written as

[0136]

[0137] For the MMSE receiver, the theoretical analysis result (16) is substituted into the asymptotic signal-to-interference-and-noise ratio of RHD k Problem (17) can be written as

[0138]

[0139] Next, we apply the WMMSE method to solve the RHD asymptotic and rate maximization problem (18) and problem (19) using MF receivers and MMSE receivers, respectively.

[0140] For the RHD asymptotic sum rate maximization problem (18) using MF receiver, we first transform the optimization variables {φ k,m}From holographic beamforming ψ k From this, problem (18) can be rewritten as

[0141]

[0142] Where the matrix Q = diag{q1,…,q M}, Holographic beamforming can be written as ψ k =Qφ k , where φ k =[φ k,1 ,…,φ k,M ] T All the radiation patterns of RHD k are collected. Using the WMMSE method, an auxiliary variable ζ=[ζ1,…,ζ K ] T and β=[β1,…,β K ] T , we can write problem (20) equivalently as

[0143]

[0144] in We optimize all variables in an iterative manner. Keep the variables {φ k Fixed, optimal and The expression is as follows

[0145]

[0146] Keeping the variables ζ and β fixed, Problem (21) is about the variables {φ k The convex problem of} can be solved using the convex optimization tool CVX. The specific algorithm for solving the RHD asymptotic sum rate maximization problem using MF receiver is summarized as follows

[0147] Table 1 Pseudocode for solving the RHD asymptotic sum rate maximization problem using the WMMSE method using MF receiver

[0148]

[0149] For the RHD asymptotic sum rate maximization problem (19) using an MMSE receiver, we first transform the optimization variables {φ k,m}From holographic beamforming ψ k From this, problem (19) can be rewritten as

[0150]

[0151] st(C3),(C4),(C5),(24)

[0152] Note that the denominator of the fractional term in the logarithmic function still contains the asymptotic signal-to-interference-and-noise ratio {x i}, we use the method of successive approximations to deal with this problem: we use the asymptotic SIR calculated in the previous iteration To replace {x i Then problem (24) can be reformulated as

[0153]

[0154] st(C3),(C4),(C5),(25)

[0155] Using the WMMSE method, we introduce auxiliary variables w=[w1,…,w K ] T and u=[u1,…,u K ] T , we can write problem (25) equivalently as

[0156]

[0157] in We optimize all variables in an iterative manner. Keep the variables {φ k Fixed, optimal and The expression is as follows

[0158]

[0159] Keeping variables w and u fixed, Problem (26) is about the variables {φ k The convex problem of} can be solved using the convex optimization tool CVX. The specific algorithm for solving the RHD asymptotic sum rate maximization problem using MMSE receiver is summarized as follows

[0160] Table 2 Pseudocode for solving the RHD asymptotic sum rate maximization problem using the WMMSE method for an MMSE receiver

[0161]

[0162]

[0163] Figure 4 The simulation results of the relationship between RHD achievable signal-to-noise ratio and random code length L are shown. It can be seen that the RHD achievable signal-to-noise ratio increases with the increase of random code length L, and the performance gap between MF receiver and MMSE receiver decreases with the increase of random code length L. Specifically, when the random beamforming scheme is adopted, when L≥2 9 , MF receiver and MMSE receiver can achieve almost the same performance; when the proposed optimization scheme is adopted, when L is increased from 2 3 Increase to 2 11 The performance gap between the MF receiver and the MMSE receiver is reduced by 18.5 dB. Therefore, when the random code length L is large, we can use the MF receiver instead of the MMSE receiver to further reduce the receiver complexity.

[0164] Figure 5 Simulation results show the relationship between the RHD achievable signal-to-interference-and-noise ratio (SINR) and the number of RHDs, K, using an MMSE receiver. The proposed optimization scheme achieves an RHD achievable SINR 20dB and 25dB higher than that achieved using random beamforming and a scheme without RHS, respectively. This further demonstrates that, under certain performance requirements, the proposed scheme can enable more IoT devices to connect.

[0165] Figure 6The simulation results show the relationship between the achievable signal-to-interference-and-noise ratio (SINR) of RHD and the number of RHDs, K, using an MF receiver. It can be seen that under a given SIR requirement, the proposed solution can connect nearly 20 more IoT devices than a solution without RHS.

[0166] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the implementation methods of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the present invention.

Claims

1. A symbiotic radio network distributed multiple access system based on reconfigurable holographic devices, characterized in that: include: An environment network consisting of a base station BS and a user equipment UE; An IoT network consisting of K reconfigurable holographic devices RHD and IoT receiver IR; Each RHD includes an antenna, a load modulator, and a reconfigurable holographic surface RHS; The load modulator modulates the IoT signal onto the ambient RF signal by adjusting the load impedance to generate a reference wave; The RHS receives a reference wave through feeding, and converts the reference wave into a target wave by activating M sub-wavelength metamaterial radiation elements and radiates it into free space; The IoT receiver IR uses code division multiple access (CDMA) technology based on random codes to jointly decode the signals of K RHDs.

2. The system according to claim 1, wherein: The radiation pattern of the RHD satisfies: Radiation pattern parameter φ k,m Satisfy the constraint 0≤φ k,m ≤1, k=1,...,K is the RHD number, m=1,...,M is the radiating element number; Holographic beamforming ψ k =Qφ k , where Q = diag{q1,…,q M }, Among them, q m is the inherent phase of the reference wave of the mth element; α m is the amplitude attenuation of the reference wave from the feed to the mth element; k s is the propagation vector of the reference wave; r m is the position vector of the mth element.

3. The system according to claim 1, wherein: The decoding process of the IoT receiver IR includes: Base station BS signal s l Perform interference cancellation SIC to obtain the intermediate signal: Among them, g k is the channel vector from the kth RHD to IR; h k is the channel coefficient from the base station BS to the kth RHD; S=diag(s1,…,s L ) represents the collection of base station BS signals of L time slots; represents the spread spectrum signal of the kth RHD, b k is the random code vector, c k It is the IoT code element; n r is the noise vector; p t is the base station BS power; The intermediate signal is decoded using a matched filter MF or a minimum mean square error MMSE receiver.

4. The system according to claim 3, wherein: The filter vector of the MF receiver is: The filter vector of the MMSE receiver is: Among them, σ 2 is the noise power; I L is the L-dimensional identity matrix.

5. The system according to claim 3, wherein: The asymptotic signal-to-interference-and-noise ratio of the RHD satisfies: Under MF receiver: Under MMSE receiver: Where L is the spreading code length.

6. The system according to claim 1, wherein: Also included is an optimization module for solving RHD asymptotic and rate maximization problems: in, is the asymptotic signal-to-interference-and-noise ratio defined; γ r,0 is the signal-to-interference-and-noise ratio of the IoT receiver IR to the base station BS signal; is the signal-to-interference-and-noise ratio threshold required for successful decoding of the base station BS signal; R u is the achievable rate of the user equipment UE; is the minimum rate threshold required by the environment network; φ k,m is the radiation pattern parameter of the mth radiation element of the kth RHD; K is the total number of RHDs; m is the radiation element number; k is the RHD number.

7. A reconfigurable holographic device RHD, used in the system according to any one of claims 1 to 6, characterized in that: include: Antenna, receiving ambient RF signals; Load modulator, which converts IoT signal into k Loaded with ambient RF signals; Reconfigurable holographic surface RHS, which converts the reference wave carrying IoT information into the target wave and radiates it into free space; The radiation pattern of the RHS φ k,m Optimized by weighted minimum mean square error (WMMSE) algorithm.

8. The RHD according to claim 7, wherein: The WMMSE algorithm includes: For MF receiver systems, iteratively update auxiliary variables and β=[β1,…,β K ] T and the radiation pattern {φ k }. Maintain the radiation pattern {φ k }Fixed, auxiliary variables and β k The optimal expression for Maintain auxiliary variables and β are fixed, the optimal radiation pattern {φ k } can be obtained by using CVX to solve the following convex optimization problem. in For MMSE receiver systems, iteratively update the asymptotic signal-to-interference-and-noise ratio Auxiliary variable w=[w1,…,w K ] T ,u=[u1,…,u K ] T and radiation pattern {u k }. Maintain auxiliary variables w,u and radiation pattern {φ k Fixed, asymptotic signal-to-interference-and-noise ratio It can be calculated by the following expression. Maintain asymptotic signal-to-interference-and-noise ratio and radiation pattern {φ k }Fixed, auxiliary variable w k ,u k The optimal expression for Maintaining asymptotic signal-to-interference-and-noise ratio and auxiliary variables w,u are fixed, the optimal radiation pattern {φ k } can be obtained by using CVX to solve the following convex optimization problem. in 9. An IR decoding method for an Internet of Things receiver, used in the system according to any one of claims 1 to 6, characterized in that: include: Receive signal: Among them, h0 is the direct channel from base station BS to IR; n r,l is the additive white Gaussian noise of the lth time slot IR; Decode base station BS signal s l and perform interference cancellation; The intermediate signal obtained after interference elimination Use CDMA despreading and MF or MMSE receiver to recover IoT signal c k .

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to claim 9 is implemented.

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