Distributed Multiple Access System Based on Reconfigurable Holographic Devices and Symbiotic Radio Networks

Abstract

This invention discloses a distributed multiple access system for a symbiotic radio network based on reconfigurable holographic devices, comprising: a background network (BS / UE) and an IoT network consisting of K reconfigurable holographic devices (RHDs) and IoT receivers (IRs); each RHD integrates an antenna, a load modulator, and a reconfigurable holographic surface (RHS), which converts a reference wave carrying IoT information into a target wave and radiates it into free space by activating subwavelength radiating elements, overcoming the size limitations of traditional RIS / phased arrays; the IoT receiver (IR) employs distributed decoding based on random code-based code division multiple access (CDMA), utilizing matched filter (MF) or minimum mean square error (MMSE) receivers combined with asymptotic signal-to-interference-plus-noise ratio (SINNR) theory to eliminate the need for real-time random code feedback; the radiation pattern is optimized based on the weighted minimum mean square error (WMMSE) algorithm to maximize RHD and data rate. The advantages of this invention are: device size supports integration of micro-IoT devices; IoT SINNR is improved by more than 20dB; and it enables massive IoT device access.

Description

Technical Field

[0001] This invention relates to the fields of wireless communication technology and Internet of Things (IoT) technology, and in particular to a distributed multiple access system for a symbiotic radio network based on reconfigurable holographic devices. Background Technology

[0002] Symbiotic radio technology is considered a key solution for realizing 6G IoT, with its core being the reciprocal sharing of spectrum and energy. The network consists of a main transmitter, a main receiver, secondary transmitters, and secondary receivers. The main network is typically existing cellular network infrastructure, while the secondary network comprises IoT devices. Backscattering devices utilize ambient radio frequency signals to modulate and transmit their own IoT information, thereby achieving resource sharing with the main network. However, this architecture faces a serious problem: backscattering links typically experience dual-path channel attenuation, leading to a significant deterioration in IoT signal quality and consequently impacting the performance of the entire symbiotic radio system.

[0003] To improve channel conditions, existing technologies introduce reconfigurable smart surfaces to enhance backscatter links. This artificial planar structure optimizes the wireless transmission path by adjusting the phase characteristics of reflective elements. While this approach improves signal quality, its physical structure has a fundamental limitation: the spacing between adjacent reflective elements cannot be less than half the wavelength. This physical constraint results in reconfigurable smart surfaces being relatively large, typically requiring deployment in fixed locations such as building surfaces, and making integration with miniaturized IoT sensors or devices impossible, severely limiting their application flexibility in mobile IoT scenarios.

[0004] Traditional phased array antennas face similar physical constraints, requiring the spacing between adjacent elements to meet a half-wavelength requirement. This constraint creates a dilemma in antenna design: increasing the number of elements improves spatial gain but leads to an excessively large physical antenna size; conversely, reducing the size means reducing the number of elements, resulting in a significant degradation in antenna performance. This conflict between size and performance severely limits the application value of traditional antennas in space-constrained IoT devices.

[0005] Existing multiple access technologies each have their shortcomings in supporting large-scale IoT device access. Non-orthogonal multiple access (NOA) technology achieves multi-user access by allocating non-orthogonal waveforms on the same time-frequency resource block, relying on continuous interference cancellation (CICC) at the receiver to separate signals from different users. While this approach can improve spectrum efficiency in coexisting radio networks, error propagation occurs during CICC as the number of IoT devices increases. When an error occurs in decoding a device's signal, it directly affects the signal recovery of all subsequent devices, causing a sharp decline in system performance with the increasing number of devices. This characteristic makes this approach unsuitable for large-scale IoT device access scenarios. Code division multiple access (CDMA), while enabling large-scale spectrum sharing, requires the receiver to know the specific random code information of each device in practical applications, increasing system complexity and signaling overhead.

[0006] In summary, existing co-existing radio network technologies suffer from three key shortcomings: they cannot effectively address the dual channel attenuation problem of backscatter links; they lack efficient solutions to support large-scale IoT device access; and there is a fundamental contradiction between the physical size and performance requirements of the antenna system. These problems severely restrict the development of 6G IoT, urgently requiring an innovative solution that can simultaneously address channel attenuation, large-scale IoT device access, and hardware integration issues. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a distributed multiple access system for symbiotic radio networks based on reconfigurable holographic devices.

[0008] To achieve the above-mentioned objectives, the technical solution adopted by the present invention is as follows:

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

[0010] The environmental network consisting of base stations (BS) and user equipment (UE);

[0011] An Internet of Things (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 radio frequency signal by adjusting the load impedance, thereby generating a reference wave.

[0014] The RHS receives a reference wave by being fed and converts the reference wave into a target wave and radiates it into free space by activating M subwavelength metamaterial radiating elements.

[0015] The Internet of Things (IoT) receiver (IR) uses Code Division Multiple Access (CDMA) technology based on random codes 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 are RHD serial numbers, m=1,...,M are radiating element serial numbers;

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

[0019] q m The intrinsic phase of the reference wave of the m-th element;

[0020] α m The amplitude attenuation of the reference wave from the feed to the m-th element;

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

[0022] r m : The position vector of the m-th element.

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

[0024] For base station BS signal s l Interference cancellation (SIC) is performed to obtain the intermediate signal:

[0025]

[0026] g k : The channel vector from the k-th RHD to the IR;

[0027] h k Channel coefficients from base station BS to the k-th RHD;

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

[0029] The spread spectrum signal of the kth RHD, b k Let c be a random code vector. k For Internet of Things (IoT) code elements;

[0030] n r : Noise vector;

[0031] p t Base station (BS) power;

[0032] The intermediate signal is decoded using a matched filter (MF) or a 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-plus-noise ratio of the RHD satisfies:

[0038] MF receiver:

[0039]

[0040] MMSE receiver (under):

[0041]

[0042] L: Spread code length.

[0043] Furthermore, it also includes an optimization module for solving the RHD asymptotic and rate maximization problem:

[0044]

[0045] C3:0≤φ k,m ≤1,

[0046] in:

[0047] The asymptotic signal-to-interference-plus-noise ratio (SINOR) defined in claim 5 (for MF receivers) or x under MMSE receiver k );

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

[0049] The signal-to-interference-plus-noise ratio (SIR) threshold required for successful decoding of a base station (BS) signal;

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

[0051] The minimum rate threshold required by the environmental 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: Radiation element number (m = 1, 2, ..., M);

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

[0056] The present invention also discloses a reconfigurable holographic device (RHD) for the above-mentioned system, characterized in that it comprises:

[0057] Antenna, used to receive ambient radio frequency signals;

[0058] Load modulator, through impedance modulation, converts IoT signals c k Loaded onto the ambient radio frequency signal;

[0059] Reconfigurable holographic surfaces (RHS) convert reference waves carrying IoT information into target waves that radiate into free space;

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

[0061] Furthermore, the WMMSE algorithm includes:

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

[0063]

[0064]

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

[0066]

[0067] (C3),

[0068] in

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

[0070]

[0071] Maintaining asymptotic signal-to-interference-plus-noise ratio and the radiating pattern {φ k} Fixed, auxiliary variable w k ,u k The optimal expression is

[0072]

[0073] Maintaining asymptotic signal-to-interference-plus-noise ratio With auxiliary variables w and u fixed, the optimal radiation pattern {φ} k The following convex optimization problem can be solved using CVX.

[0074]

[0075] (C3),

[0076] in

[0077] The present invention also discloses an Internet of Things (IoT) receiver (IR) decoding method for the above-mentioned system, characterized in that it includes:

[0078] Received signal:

[0079]

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

[0081] n r,l Additive white Gaussian noise in the l-th time slot IR;

[0082] Decoding base station BS signal l And perform interference cancellation;

[0083] Intermediate signal obtained after interference cancellation CDMA despreading is employed, and the IoT signal is recovered using an MF or MMSE receiver. k .

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

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

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

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

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

[0089] 4. Theoretical innovation to reduce power consumption: The asymptotic signal-to-interference-plus-noise ratio closed-loop solution eliminates real-time feedback of random codes, reducing system complexity and signaling overhead. When the random code length is large, the performance of the MF receiver approaches that of the MMSE. Attached Figure Description

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

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

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

[0093] Figure 4 This is a schematic diagram illustrating the relationship between the reachability signal-to-interference-plus-noise ratio (RII) and the random code length L in an embodiment of the present invention.

[0094] Figure 5 This is a schematic diagram illustrating the relationship between the reachable signal-to-interference-plus-noise ratio (SINR) of RHDs and the number of RHDs (K) under the MMSE receiver in an embodiment of the present invention.

[0095] Figure 6This is a schematic diagram illustrating the relationship between the reachable signal-to-interference-plus-noise ratio (SINNR) of RHDs and the number of RHDs (K) under the MF receiver in an embodiment of the present invention. Detailed Implementation

[0096] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and examples.

[0097] Figure 1 This invention demonstrates a multi-RHD SRN system model, in which a BS equipped with a single antenna transmits information to a UE with a single antenna, while simultaneously, K RHDs transmit their own IoT information to a single-antenna IR using radio frequency signals from the BS in the environment. Figure 2 As shown, the main components of the RHD proposed in this invention include an antenna, a load modulator, and an RHS. The load modulator modulates the device's own IoT signal onto the ambient signal absorbed by the antenna by adjusting the load impedance. Then, the signal carrying IoT information is used as a reference wave and injected into the waveguide of the RHS via a feed, thereby activating the metamaterial element of the RHS, and finally radiated into free space in the form of a target wave.

[0098] The channels from BS to UE, BS to IR, and BS to RHD k are represented as f0, h0, and h, respectively. k The channels from RHD k to UE and from RHD k to IR are represented as follows: The signal transmitted from the BS to the UE can be represented as s~CN(0,1), and the BS transmission power is p. t The IoT symbol representation of RHD k is c. k ∈{-1,+1}. The random code used for RHD k spreading is represented as 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 symbol period of BS. In the l-th time slot, the signal transmitted by RHDk can be represented as... Where ψ k It is holographic beamforming of RHD k. ψ k The m-th element is Where φ k,m It is a radial pattern, α m k is the amplitude attenuation of the reference wave as it propagates from the feed to the m-th element. s It is the reference wave propagation vector.

[0099] The received signal in the l-th time slot at the UE can be expressed as:

[0100]

[0101] Where n u,l ~CN(0,σ 2 () represents additive white Gaussian noise at the UE. The signal-to-interference-plus-noise ratio and reachable data 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 represented as

[0105]

[0106] Where n r,l ~CN(0,σ 2 () represents additive white Gaussian noise at the IR. In the l-th time slot, the IR first demodulates the signal from the BS, and its signal-to-interference-plus-noise ratio (SINR) is expressed as:

[0107]

[0108] In decoding l Then, the IR instrument 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 The main symbols of the BS in L time slots were collected, n r =[n r,1 ,…,n r,L ] T It is the IR noise of L time slots.

[0113] In order to detect all IoT code elements c k At IR, a receive filter vector is deployed for each RHD k. The output signal of RHD k at IR can be expressed as

[0114]

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

[0116]

[0117] The joint decoding block diagram at the IR is composed of Figure 3 The IR filter can be achieved using either an MF or MMSE receiver. We will then present the performance analysis results for each of these two receivers.

[0118] Based on formula (7), we can obtain the detected IoT code c. k The expression for the MF receiver is:

[0119]

[0120] The output signal expression of the MF receiver is:

[0121]

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

[0123]

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

[0125]

[0126] Based on formula (7), we can obtain the detected IoT code c. k The expression for the MMSE receiver is:

[0127]

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

[0129]

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

[0131]

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

[0133]

[0134] in The asymptotic signal-to-interference-plus-noise ratio (SIR) represents RHD k. Successful decoding at IR. l The signal-to-interference-plus-noise ratio threshold, This represents the minimum transmission rate requirement for the UE, and constraint C3 restricts the feasible region of the RHS radiation pattern. Next, we present the optimization problem expressions for both MF and MMSE receivers.

[0135] For the MF receiver, the theoretical analysis result (13) is substituted into the asymptotic signal-to-interference-plus-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-plus-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 problems (18) and (19) using MF receivers and MMSE receivers, respectively.

[0140] For the RHD asymptotic and rate maximization problem using an MF receiver (18), we first optimize the variable {φ} k,m From holographic beamforming ψ k By deconstructing the problem, problem (18) can be rewritten as follows:

[0141]

[0142] Where 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 radiation patterns of RHD k were collected. Using the WMMSE method, auxiliary variables ζ = [ζ1,…,ζ] were introduced. K ] T and β=[β1,…,β K ] T We can write problem (20) as equivalent to

[0143]

[0144] in We use an iterative approach to optimize all variables, keeping variable {φ} constant. k Fixed, optimal and The expression is as follows:

[0145]

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

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

[0148]

[0149] For the RHD asymptotic and rate maximization problem using an MMSE receiver (19), we first optimize the variable {φ} k,m From holographic beamforming ψ k By deconstructing the problem, problem (19) can be rewritten as follows:

[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-plus-noise ratio {x}. i We address this problem using a continuous approximation method: we use the asymptotic signal-to-interference-plus-noise ratio calculated in the previous iteration. To replace {x} in the objective function i Then problem (24) can be restructured as follows:

[0153]

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

[0155] Using the WMMSE method, an auxiliary variable w = [w1, ..., w K ] T and u = [u1,…,u K ] T We can write problem (25) as equivalent to

[0156]

[0157] in We use an iterative approach to optimize all variables, keeping variable {φ} constant. k Fixed, optimal and The expression is as follows:

[0158]

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

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

[0161]

[0162]

[0163] Figure 4 Simulation results show the relationship between RHD reachability signal-to-noise ratio (SNR) and random code length L. It can be seen that the RHD reachability SNR increases with increasing random code length L, and the performance gap between the MF receiver and the MMSE receiver decreases with increasing random code length L. Specifically, when using a random beamforming scheme, when L≥2... 9 The MF receiver and MMSE receiver can achieve almost the same performance; when using the proposed optimization scheme, when L is from 2 3 Increased to 2 11 The performance gap between the MF receiver and the MMSE receiver has been reduced by 18.5dB. 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 reachable signal-to-noise ratio (RHD) and the number of RHDs (K) using an MMSE receiver. It can be seen that the RHD reachable signal-to-noise ratio obtained using the proposed optimized scheme is 20 dB and 25 dB higher than that obtained using a random beamforming scheme and without RHDs, respectively. This further demonstrates that, under certain performance requirements, the proposed scheme can enable more IoT devices to connect.

[0165] Figure 6Simulation results show the relationship between the reachable signal-to-interference-plus-noise ratio (SNR) of RHDs and the number of RHDs, K, using an MF receiver. It can be seen that, given the RHD SNR requirement, the proposed scheme can connect nearly 20 more IoT devices compared to the scheme without RHDs.

[0166] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the implementation methods of the present invention, and should be understood that the scope of protection of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of the present invention.

Claims

1. A distributed multiple access system for symbiotic radio networks based on reconfigurable holographic devices, characterized in that, include: The environmental network consisting of base stations (BS) and user equipment (UE); An Internet of Things (IoT) network consisting of K reconfigurable holographic devices (RHDs) and IoT receivers (IRs); Each RHD includes an antenna, a load modulator, and a reconfigurable holographic surface RHS; The load modulator modulates the IoT signal onto the ambient radio frequency signal by adjusting the load impedance, thereby generating a reference wave. The RHS receives a reference wave by being fed and converts the reference wave into a target wave and radiates it into free space by activating M subwavelength metamaterial radiating elements. The IoT receiver IR uses Code Division Multiple Access (CDMA) technology based on random codes to jointly decode the signals of K RHDs; The asymptotic signal-to-interference-plus-noise ratio of the RHD satisfies: MF receiver: ， MMSE receiver (under): ， Where L is the spreading code length; , The first The and the first One RHD to IR channel vector; , From base station BS to the The and the first Channel coefficients of each RHD; For base station (BS) power; , The first The and the first A holographic beamforming vector for RHD; Noise power; The system also includes an optimization module for solving the RHD asymptotic and rate maximization problem: ， in, Defined asymptotic signal-to-interference-plus-noise ratio; The signal-to-interference-plus-noise ratio (SINR) of the receiver (IR) to the base station (BS) signal The signal-to-interference-plus-noise ratio (SINORR) threshold required for successful signal decoding; The achievable rate of the UE; The minimum rate threshold required by the network; No. The first RHD Radiation pattern parameters of each radiating element; Total number of RHDs; Radiation element serial number; RHD serial number.

2. The system according to claim 1, characterized in that: The radiation pattern of the RHD satisfies: Radiation pattern parameters Satisfying constraints , =1,..., For RHD serial number, =1,..., For the serial number of the radiating element; Holographic beamforming ,in , ; in, No. The intrinsic phase of the reference wave of each element; Reference wave from feed to the first The amplitude attenuation of each component; The propagation vector of the reference wave; No. The position vector of each element.

3. The system according to claim 1, characterized in that, The decoding process of the IoT receiver IR includes: For base station BS signals Interference cancellation (SIC) is performed to obtain the intermediate signal: ， in, For the first One RHD to IR channel vector; For base station BS to the Channel coefficients of each RHD; This indicates the collection of base station (BS) signals across L time slots; =b k c k This represents the spread spectrum signal of the k-th RHD. It is a random code vector. For Internet of Things (IoT) code elements; This is the noise vector; For 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, characterized in that: The filter vector of the MF receiver is: ; The filter vector of the MMSE receiver is: ; in, Noise power; It is an L-dimensional identity matrix.

5. A reconfigurable holographic device (RHD) for use in the system according to any one of claims 1-4, characterized in that, include: Antenna, used to receive ambient radio frequency signals; Load modulator, which modulates IoT signals through impedance modulation Loaded onto the ambient radio frequency signal; The reconfigurable holographic surface RHS converts a reference wave carrying IoT information into a target wave and radiates it into free space. The radiation pattern of the RHS Optimized using the weighted minimum mean square error (WMMSE) algorithm.

6. The RHD according to claim 5, characterized in that, The WMMSE algorithm includes: For the MF receiver system, iteratively update the auxiliary variables. and and radiating patterns Maintain the radiation pattern Fixed, auxiliary variable and The optimal expression is , , Maintain auxiliary variables and Fixed, optimal radiation pattern The following convex optimization problem can be obtained by using CVX; ， in ; For the MMSE receiver system, iteratively update the asymptotic signal-to-interference-plus-noise ratio. Auxiliary variables , and radiating patterns { }; Maintain auxiliary variables w, u and radiation pattern Fixed, asymptotic signal-to-interference-plus-noise ratio It can be calculated using the following expression; ， Maintain asymptotic signal-to-interference-plus-noise ratio and radiating patterns Fixed, auxiliary variable w k ,u k The optimal expression is ， ； Maintain asymptotic signal-to-interference-plus-noise ratio With auxiliary variables w and u fixed, the optimal radiation pattern The following convex optimization problem can be obtained by using CVX; ， in .

7. An IR decoding method for an Internet of Things receiver, implemented based on the system described in any one of claims 1-4, characterized in that, include: Received signal: ， in, This is the direct channel from the base station (BS) to the IR. The additive white Gaussian noise in the l-th time slot IR; Decoding base station (BS) signals And perform interference cancellation; Intermediate signal obtained after interference cancellation CDMA despreading is employed, and MF or MMSE receivers are used to recover IoT signals. .

8. A computer-readable storage medium for storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of claim 7.

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