Uplink transmission phase optimization method for RIS-aided Internet of Things

Abstract

The invention discloses an RIS-aided Internet of Things-oriented uplink transmission phase optimization method. The method specifically comprises the following steps: 1) dividing all devices into clusters with non-overlapped angle domains; 2) adopting a pilot frequency multiplexing strategy to distribute pilot frequencies for the devices in the cluster; 3) the base station obtains an uplink channel estimation value according to the received pilot signal; 4) the base station decomposes channel amplitudes from different devices to the RIS and from the devices to the BS through a least square method; and 5) the base station optimizes RIS phase configuration by using the estimated channel. According to the method, the channel estimation efficiency and accuracy are improved while the pilot frequency overhead is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of mobile communication, and particularly relates to a phase optimization method for RIS-assisted Internet of Things (IoT) uplink transmission. Background Art

[0002] The goal of next-generation mobile wireless communication is to achieve high-quality and high-rate broadband multimedia communication. Deploying high-frequency bandwidths such as millimeter waves and terahertz can effectively increase system capacity. However, in traditional methods, high-frequency communication between the base station and devices is vulnerable to the influence of obstacles. The intelligent reflecting surface can reasonably adjust the reflection coefficient, and the communication between the device and the base station can be carried out through the reflection link formed by the RIS. However, in the prior art, the training overhead required for channel estimation has a linear growth relationship with the scale of the RIS and the number of devices. When the RIS consists of a large number of reflecting elements, the pilot overhead for channel estimation is huge. Therefore, the current methods cannot reduce the required pilot overhead while ensuring the estimation accuracy. Summary of the Invention

[0003] Object of the Invention: To solve the above-mentioned drawbacks of the prior art, the present invention discloses a phase optimization method for RIS-assisted IoT uplink transmission.

[0004] Technical Solution: The present invention discloses a phase optimization method for RIS-assisted IoT uplink transmission, which specifically includes the following steps:

[0005] Step 1: Divide all K IoT devices into Z clusters with non-overlapping angular domains; there are Q IoT devices in each of the clusters from the 1st to the (Z - b)th, and there are Q - 1 IoT devices in each of the clusters from the (Z - b + 1)th to the Zth; b = ZQ - K; Q is the number of available orthogonal pilots, represents rounding up; combine the K IoT devices into a set

[0006] Step 2: Adopt a pilot reuse strategy to allocate pilots to the IoT devices, configure Q orthogonal pilot sequences for each cluster, the IoT devices within the same cluster are allocated different pilots, and the IoT devices with the same index in different clusters use the same pilot; the IoT devices send pilot signals.

[0007] Step 3: Based on the channel matrix received by the base station when the qth device in all clusters sends the lth pilot obtain the amplitude estimation value of the direct link channel from the qth IoT device in all clusters to the base station and the signal Y of the reflection link part from the IoT device to the base station l ′, q = 1, 2,..., Q; when q = Q, all clusters are the first (Z - b) clusters before, and use the least squares method to solve for Y lObtain the estimated channel from the IoT device to the RIS Decompose the direct - link channel amplitude from the IoT device to the BS using the same pilot based on the least - squares principle Obtain the estimated value of the direct - link signal from the q - th IoT device in cluster z to the base station Based on Obtain the estimated value of the signal from the q - th IoT device in cluster z to the RIS where z = 1, 2, …, Z;

[0008] Step 4: Let k′=(z - 1)*Q+q, and the k′ - th IoT device resends the pilot to the base station. Based on the estimated value of the direct - link signal from the k′ - th IoT device to the base station and the estimated value of the signal from the k′ - th IoT device to the RIS Iteratively optimize the RIS phase matrix Ψ

[0009] Furthermore, Step 1 is specifically as follows:

[0010] Step 1.1: Initialize the z - th cluster set Denote it as an empty set; calculate the angle φ from the k - th IoT device to the base station k and calculate the distance d from the k - th IoT device to the base station k and calculate the angle difference Δφ between any two IoT devices ik and the distance difference Δd ik and the weight index ε ik where the subscript i represents the i - th device, i = 1, 2, …, K, k = 1, 2, …, K; ε ik The expression of ε is as follows:

[0011]

[0012] where, φ max represents the maximum value among all φ k and d max is the maximum value among all d k and α is a weight, α∈(0, 1);

[0013] Step 1.2: When z = 1, randomly select an IoT device in the set as the central device and put it into the cluster - head set ; and delete this device from the cluster - head set ; z + 1, and go to Step 1.3;

[0014] Step 1.3: Calculate the remaining IoT devices in and the cluster - head set The weight indicators of all central devices in the set Select the IoT device corresponding to the maximum weight indicator as the new central device and put it into the cluster head set In, update the cluster head set And in the set Delete this device, determine whether z is greater than or equal to Z. If so, go to step 1.4; otherwise, z + 1, and continue to execute step 1.3;

[0015] Step 1.4: Allocate the central devices in To different cluster sets, that is Indicates The z-th central device in;

[0016] Step 1.5: When z ∈ [1, Z - b], for the z-th cluster set Calculate the set The weight indicators of the IoT devices in and Arrange the calculated weight indicators in ascending order, select the first Q - 1 weight indicator corresponding IoT devices, and put them into the cluster set In; and in the set Delete these IoT devices;

[0017] When z ∈ [Z - b + 1, Z], calculate the set The weight indicators of the IoT devices in and Arrange the calculated weight indicators in ascending order, select the first Q - 2 weight indicator corresponding IoT devices, and put them into the cluster set In; and in the set Delete these IoT devices;

[0018] Step 1.6: For any cluster Allocate the central device with an index value of 1, and the remaining IoT devices are sorted in ascending order of the angle difference from the central device, and the index values are allocated in sequence.

[0019] Furthermore, step three is specifically as follows:

[0020] Step 3.1: The expression of the channel matrix Received by the base station when the q-th IoT device in all clusters sends the l-th pilot is as follows:

[0021]

[0022] Among them, D h Is the diagonal channel from the BS to each element of the RIS, D h = diag(h1, h2,... h N), diag(·) represents taking the diagonal matrix, θ l represents the l-th phase shift of the RIS when the IoT device sends the l-th pilot; θ l = [θ1,..., θ N T θ N represents the phase shift of the N-th reflecting element in the RIS, and T represents the transpose; is the channel from the q-th IoT device in the z-th cluster to the RIS; is the sum of the signal amplitudes from the IoT devices numbered q in all clusters to the base station; is the steering vector of the base station antenna, j represents the imaginary unit, λ is the wavelength, is the angle of arrival when the IoT device transmits a direct-link signal to the base station, n l is the additive white Gaussian noise received by the base station, represents a matrix of all 1s with dimension ; for the first 1 to Z - b clusters, for the last Z - b + 1 to Z clusters,

[0023] Step 3.2: Add L to get Y d , and calculate d according to Y

[0024]

[0025] where H represents the conjugate transpose operation;

[0026] Step 3.3: Subtract the component from to get the signal Y l ′ of the reflected link part from the IoT device to the base station:

[0027]

[0028] The estimated channel from the IoT device to the RIS is expressed as follows:

[0029]

[0030] where ||·|| F represents the Frobenius norm operation, M is the number of base station antennas,

[0031] Step 3.4: The estimated value of the direct-link signal from the q-th IoT device in cluster z to the base station​ The expression of

[0032] where is the angle of arrival from the central device in cluster z to the base station and the steering vector of is the amplitude of the signal transmitted by the q-th Internet of Things device in cluster z received by the base station;

[0033] The estimated value of the signal from the q-th Internet of Things device in cluster z to the RIS is expressed as:

[0034]

[0035] where is the z-th element in the matrix , α0 is the channel amplitude from the RIS to the base station, h is the channel from the base station to the RIS, a B (θ B ) is the antenna steering vector of the base station, θ B is the angle of arrival of the signal reflected by the RIS to the base station, d is the antenna spacing distance of the base station, a R (θ R ) is the direction vector of the RIS, θ R is the angle of arrival of the signal transmitted to the RIS, is the antenna steering vector from the central device in cluster Z to the RIS, is the angle of arrival of the signal transmitted by the device in cluster Z received by the RIS.

[0036] Furthermore, in step four, the convex optimization method is used to solve the following objective function to obtain the optimal value of the RIS phase Ψ:

[0037]

[0038] where max represents the maximum value, ω k' is the weight of the k'-th Internet of Things device, σ 2 is the noise power, h is the channel from the base station to the RIS, Ψ = [ψ1, ψ2, …, ψ N T , ψ N represents the phase shift of the N-th reflecting element in the RIS, T represents the transpose, P k' represents the transmission power of the k'-th Internet of Things device, P max represents the maximum value of the transmission power, γ k' is the iterative optimization variable, and the expression of γ k' is: ​

[0039]

[0040] A computer device includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, the steps of an RIS-assisted Internet of Things uplink transmission phase optimization method are implemented.

[0041] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of an RIS-assisted Internet of Things uplink transmission phase optimization method are implemented.

[0042] Beneficial effects: The present invention uses a pilot reuse strategy to allocate the same orthogonal pilot to different clusters, sequentially estimates the direct channel and the reflection link channel using the least squares method, decomposes the amplitudes of the device signals using the same pilot in different clusters according to the known angles of arrival of the transmitted signals from the Internet of Things devices to the RIS and the base station respectively, and finally optimizes the RIS phase using Lagrangian dual transformation, further improving the accuracy of channel estimation and pilot overhead. Description of the Drawings

[0043] Figure 1 It is a system model diagram of an RIS-assisted MISO network of the present invention;

[0044] Figure 2 It is a flowchart of the present invention. Detailed Embodiments

[0045] The accompanying drawings that form a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention.

[0046] A pilot reuse and phase optimization method for RIS-assisted Internet of Things uplink transmission provided by an embodiment of the present invention uses pilot reuse and parametric channel estimation knowledge to effectively reduce pilot overhead and computational complexity.

[0047] As Figure 1 shown, in an RIS-assisted Internet of Things uplink transmission scenario, there is a base station BS equipped with M antennas, an intelligent reflecting surface RIS equipped with N reflecting elements, and K single-antenna devices, and the number of available orthogonal pilots is Q.

[0048] In one embodiment, as Figure 2 shown, a pilot reuse and phase optimization method for RIS-assisted Internet of Things uplink transmission provided by the present invention includes the following steps:

[0049] Step 1: Divide all K devices into Z clusters with non-overlapping angular domains. Each cluster contains Q or Q - 1 devices. When the channels of different Internet of Things devices can be strictly separated in the angular domain, the pilot interference emitted by devices between clusters will no longer have an impact;

[0050] Step 2: Adopt a pilot reuse strategy to allocate pilots to devices, that is, devices within the same cluster are assigned different orthogonal pilots, and devices with the same serial number in different clusters use the same pilot sequence;

[0051] Step 3: The base station eliminates the channel at the RIS end based on the pilot signals received multiple times, estimates the direct link channel, estimates the channel from the device to the RIS end based on the estimated direct link channel, and finally uses the least squares method to decompose the channel amplitudes between different devices to the RIS and from the device to the base station;

[0052] Step 4: Optimize the RIS phase configuration based on the estimated channel.

[0053] In one embodiment, Step 1 divides all Internet of Things devices into Z clusters with non-overlapping angular domains, and the specific steps are as follows:

[0054] Step a: Initialize Z clusters as z = 1, 2,..., Z, where represents rounding up; initialize the set of K Internet of Things devices as U = {u1, u2,..., u K}; let φ k be the angle from device k to the base station, d k be the distance from device k to the base station, φ max represents the maximum value among all φ k , and d max is the maximum value among all d k . Define the angular difference Δφ between any devices i, k in the set as ik = |φ i - φ k |, the distance difference Δd ik = |d i - d k |, and the weight index is where α ∈ (0, 1) is the weight value. Randomly select 1 device from the set , and its index value is put into the cluster head set , that is and remove this device from the original set, that is Let z = 2, then go to Step b.

[0055] Step b: Calculate the set The remaining devices within and the cluster head set For all the devices in the weight metrics, select the set The device corresponding to the maximum weight metric in; as the new cluster head, update And in the set Delete this central device, z = z + 1. When z < Z, repeat step b; otherwise, go to step c.

[0056] Step c: Assign the central devices in To different clusters, that is Calculate the set The weight metric within the central device .

[0057] Step d: For each central device When z ∈ [1, Z - b], select from the set The Q - 1 devices with the smallest weight difference from And assign them to the cluster And exclude the selected Q devices from the set ;

[0058] When z ∈ [Z - b + 1, Z], select from the set The Q - 2 devices with the smallest weight difference from And assign them to the cluster ; And exclude the selected Q - 2 devices from the set .

[0059] Step e: For each cluster First, assign the index value 1 to the central device Then, the other devices within this cluster are sorted in ascending order of the angle difference from the central device And assign index values.

[0060] In step two, the length of the available orthogonal pilot is Q. The pilot reuse strategy is used to send pilots to all devices within the cluster. Devices within the same cluster are assigned different pilots, and devices with the same index in different clusters use the same pilot sequence. The pilot signal is defined as:

[0061]

[0062] Where x i Is the pilot signal assigned to the i - th device within the cluster, P represents the transmission power of each device. δ(·) is the Kronecker function, defined as

[0063] The pilot reuse strategy is adopted to send pilots to all devices within a cluster. Specifically, the base station uniformly configures Q orthogonal pilot sequences for each cluster. In specific implementation, first, the Q pilots are sequentially assigned to each device within the cluster according to the index value of the devices within the cluster, ensuring that different devices within the same cluster always use non-overlapping pilot resources. For devices with the same index value in different clusters, the base station assigns the same pilot sequence, that is, devices within one cluster are assigned different pilots, and devices with the same index in each cluster are assigned the same pilot sequence. Since the pilot sequences are reused between clusters and the angular domains between clusters do not overlap, no pilot interference will be generated, and the pilot overhead required for channel estimation can be reduced by Z times.

[0064] In one embodiment, step three is divided into the following sub-steps:

[0065] Step a: The device sends pilots to the base station L times, and the RIS adjusts the phase L times accordingly. The l-th phase shift configuration is expressed as Using the principle of pilot orthogonality, when the q-th device in all clusters sends the l-th pilot, the channel matrix received by the BS is expressed as:

[0066]

[0067] Among them, D h is the diagonal channel from the BS to each element of the RIS, D h = diag(h1, h2,... h N ), diag(·) represents taking the diagonal matrix, θ l represents the l-th phase shift of the RIS when the IoT device sends the l-th pilot; θ l = [θ1,..., θ N T , θ N represents the phase shift of the N-th reflecting element in the RIS, and T represents the transpose; is the channel from the q-th IoT device in the z-th cluster to the RIS; is the sum of the signal amplitudes from the IoT devices numbered q in all clusters to the base station; is the steering vector of the base station antenna, j represents the imaginary unit, λ is the wavelength, is the angle of arrival when the IoT device transmits the direct link signal to the base station, and n l is the additive white Gaussian noise received by the base station, represents the all-ones matrix of dimension . For the first 1 to Z - b clusters, For the last Z - b + 1 to Z clusters,

[0068] ​Step b: Add L to approximately eliminate the impact of the RIS reflection link, that is

[0069]

[0070] where

[0071] Step c: According to the above steps, the amplitude estimate of the direct link channel from the q-th IoT device to the BS in all clusters is

[0072] Step d: By subtracting the direct link part estimated in step c from the channel matrix the signal of the reflection link part from the IoT device to the base station is obtained:

[0073]

[0074] Using the least squares algorithm, the estimated channel from the IoT device to the RIS is finally obtained as:

[0075]

[0076]

[0076] where The operator (·) H represents the conjugate transpose operation, and the operator ||·|| F represents the Frobenius norm operation.

[0077] Next, use the least squares algorithm to decompose the amplitude of the direct link channel from the IoT device using the same pilot to the BS Let the sum of the direct link channels from the q-th IoT device in all clusters to the BS be where is the angle of arrival from the central device in cluster z to the BS of the steering vector, is the amplitude of the signal transmitted by the q-th IoT device in cluster z received by the base station.

[0078] According to the least squares principle, the amplitude of the signal transmitted by the q-th IoT device in cluster z received by the base station is Finally, the direct link signal from the q-th IoT device in cluster z to the BS is obtained as

[0079] Similarly, the amplitude of the transmitted signal from the q-th device in different clusters arriving at the BS through the RIS reflection link can be obtained where α0 is the channel amplitude from the RIS to the BS, h is the channel from the base station to the RIS, where is the antenna steering vector of the BS, d is the distance between the base station antennas, and θ B is the angle of arrival of the signal reflected by the RIS to the BS, and a R (θ R ) is the steering vector of the RIS, and θ R is the angle of arrival of the signal transmitted to the RIS, Ψ is the RIS phase, is the antenna steering vector from the central device in cluster Z to the RIS, is the angle of arrival of the signal transmitted by the device in cluster Z received by the RIS.

[0080] Finally, the signal from the q-th device in cluster z to the RIS can be obtained as

[0081] In step four, the IoT device re-transmits the pilot to the base station, and uses the estimated channel and to iteratively optimize the RIS phase matrix Ψ. The Lagrangian dual transformation is used to simplify the optimization problem, which includes the following steps:

[0082] 1) Let k′ = (j - 1)*Q + q, so that k′ can specifically correspond to a device in a certain cluster; then set the initial phase of the RIS Ψ = Ψ0;

[0083] 2) Update the iterative optimization variable

[0084] 3) Use the convex optimization method to solve the following problem

[0085]

[0086] where represents the transmission power of each device, ω k' is the weight of the k'-th IoT device, σ 2 is the noise power, Assume that the optimized solution is Ψ * , update Ψ = Ψ * , execute steps 2) and 3) until convergence, and output Ψ as the final solution.

[0087] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection of the present invention.

Claims

1. A method for optimizing uplink transmission phase for RIS-assisted Internet of Things, characterized in that: The specific steps include: Step 1: Divide all K IoT devices into Z clusters with non-overlapping angular domains; there are Q IoT devices in clusters 1 to Zb, and Q-1 IoT devices in clusters Z-b+1 to Z; b = ZQ-K; Q is the number of available orthogonal pilots, represents rounding up; Combine K IoT devices into a set Step 2: Use the pilot reuse strategy to allocate pilots to IoT devices. Configure Q orthogonal pilot sequences for each cluster. Different pilots are allocated to IoT devices in the same cluster, and IoT devices with the same index in different clusters use the same pilot. The IoT device sends a pilot signal; Step 3: Based on the channel matrix received by the base station when the qth device in all clusters sends the lth pilot Get the amplitude estimate of the direct channel from the qth IoT device to the base station in all clusters and the signal Y of the reflected link from the IoT device to the base station l ′, q=1,2,…,Q; when q=Q, all clusters are the clusters before 1 to Zb, and the least squares method is used to solve Y l 'Get the estimated channel from IoT device to RIS Decomposition of the direct channel amplitude from IoT devices to BS using the same pilot based on the least squares principle Get the estimated value of the direct connection signal from the qth IoT device in cluster z to the base station based on Get the estimated value of the signal from the qth IoT device in cluster z to the RIS Where z=1,2,…,Z; Step 4: Let k′=(z-1)*Q+q, the k′th IoT device resends the pilot to the base station, based on the estimated value of the direct connection signal from the k′th IoT device to the base station and the estimated value of the signal from the k′th IoT device to the RIS Iteratively optimize the RIS phase matrix Ψ.

2. According to claim 1, a method for optimizing uplink transmission phase for RIS-assisted Internet of Things, characterized in that: Step 1 is as follows: Step 1.1: Initialize the zth cluster set represents an empty set; calculate the angle φ from the kth IoT device to the base station k , calculate the distance d from the kth IoT device to the base station k , calculate the angle difference Δφ between any two IoT devices ik , distance difference Δd ik And the weight index ε ik , subscript i represents the i-th device, i=1,2,...,K, k=1,2,...,K; ε ik The expression is as follows: Among them, φ max Represents all φ k The maximum value in d max For all d k The maximum value in , α is the weight, α∈(0,1); Step 1.2: When z = 1, in the set Randomly select an IoT device as the central device and put it into the cluster head set and gather at the cluster head Delete the device; z+1, and go to step 1.3; Step 1.3: Calculation The remaining IoT devices and cluster heads in the The weight index of all central devices in the selection set The IoT device corresponding to the maximum weight index is put into the cluster head set as the new central device In the cluster head set, update And in the collection Delete the device and determine whether z is greater than or equal to Z. If so, go to step 1.

4. Otherwise, z+1 and continue to step 1.

3. Step 1.4: The central devices in are assigned to different cluster sets, namely express The zth central device in; Step 1.5: When z∈[1,Zb], for the zth cluster set Calculating Collections IoT devices and The weight index is calculated, and the calculated weight index is arranged from small to large. The IoT devices corresponding to the first Q-1 weight indexes are selected and put into the cluster set. in; and in the collection Delete these IoT devices from your device list; When z∈[Z-b+1,Z], the calculation set IoT devices and The weight index is calculated, and the calculated weight index is arranged from small to large. The IoT devices corresponding to the first Q-2 weight indexes are selected and put into the cluster set. in; and in the collection Delete these IoT devices from your device list; Step 1.6: For any cluster The central device is assigned an index value of 1, and the remaining IoT devices are sorted in ascending order according to the angle difference with the central device, and the index values ​​are assigned in sequence.

3. The method for optimizing uplink transmission phase for RIS-assisted Internet of Things according to claim 1, characterized in that: Step three is as follows: Step 3.1: The channel matrix received by the base station when the qth IoT device in all clusters sends the lth pilot The expression is as follows: Among them, D h is the diagonal channel from BS to each element of RIS, D h =diag(h1,h2,...h N ), diag(·) represents taking the diagonal matrix, θ l represents the lth phase shift of RIS when the IoT device sends the lth pilot; θ l =[θ1,...,θ N ] T ,θ N represents the phase shift of the Nth reflection element in RIS, and T represents transposition; is the channel from the qth IoT device in the zth cluster to the RIS; is the sum of the signal amplitudes from the IoT device numbered q in all clusters to the base station; is the steering vector of the base station antenna, j represents the imaginary unit, λ is the wavelength, is the arrival angle of the direct link signal transmitted by the IoT device to the base station, n l is the additive white Gaussian noise received by the base station, The representative dimension is All 1 matrices; for the first 1 to Zb clusters, For the clusters after Z-b+1~Z, Step 3.2: L Add together to get Y d , according to Y d calculate Where H represents the conjugate transpose operation; Step 3.3: Subtract weight Get the signal Y of the reflection link from the IoT device to the base station l ′: Estimated channel from IoT device to RIS The expression is as follows: Among them, ||·|| F represents the F-norm operation, M is the number of base station antennas, Step 3.4: Estimation of the direct signal from the qth IoT device in cluster z to the base station The expression is: in, is the arrival angle from the central device in cluster z to the base station The guiding vector, is the amplitude of the signal transmitted by the qth IoT device in cluster z received by the base station; Estimated value of the signal from the qth IoT device in cluster z to the RIS The expression is: in, For the matrix The zth element in α0 is the channel amplitude from RIS to the base station, h is the channel from the base station to the RIS, a B (θ B ) is the antenna steering vector of the base station, θ B is the arrival angle of the signal reflected from the RIS to the base station, d is the distance between the base station antennas, and a R (θ R ) is the direction vector of RIS, θ R is the arrival angle of the signal transmitted to RIS, is the antenna steering vector from the central device in cluster Z to the RIS, is the arrival angle of the signal transmitted by the device in cluster Z received by RIS.

4. The method for optimizing uplink transmission phase for RIS-assisted Internet of Things according to claim 1, characterized in that: The fourth step uses the convex optimization method to solve the following objective function to obtain the optimal value of the RIS phase Ψ: Among them, max represents the maximum value, ω k' is the weight of the k'th IoT device, σ 2 is the noise power, h is the channel from the base station to RIS, Ψ=[ψ1,ψ2,…,ψ N ] T , ψ N represents the phase shift of the Nth reflection element in RIS, T represents transposition, and P k' represents the transmission power of the k'th IoT device, P max Indicates the maximum value of the transmission power, γ k' is the iterative optimization variable, γ k' The expression is:

5. A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the computer program, the steps of the method for optimizing uplink transmission phase of RIS-assisted Internet of Things as described in any one of claims 1 to 4 are implemented.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of a method for optimizing uplink transmission phase of a RIS-assisted Internet of Things as described in any one of claims 1 to 4 are implemented.

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