Terminal identity identification method based on Hadamard code and beamforming

Abstract

This invention relates to satellite communication technology. Specifically, it is a terminal identity identification method based on Hadamard codes and beamforming. The method includes two parts: beamforming design and terminal identity detection. The beamforming design uses Hadamard codes as the terminal identity sequence in an asynchronous environment. The terminal sends a signal to a cluster head, which then transmits the signal to the satellite through beamforming. Terminal identity detection involves detecting the active terminal identity sequence from the received sequence, estimating the channel coefficient, and then subtracting the detected active terminal from the received sequence. This iterative detection process continues, and finally, energy detection is used to set a decision threshold to terminate the iteration. This invention addresses the difficulty of correctly detecting terminal identity sequences in satellites with massive IoT terminals and in asynchronous scenarios.

Description

Technical Field

[0001] The invention belongs to satellite communication technology, in particular to a terminal identity recognition method based on Hadamard code and beamforming. Background Art

[0002] The Internet of Things (IoT) has become a key driver of 5G and the upcoming 6G communication systems, as IoT networks can realize the vision of the interconnected world. Furthermore, by 2025, the number of IoT endpoints is expected to exceed 27 billion, demonstrating tremendous potential for development. However, this massive influx of IoT endpoints poses significant challenges to IoT networks, especially in isolated areas such as oceans, mountains, and deserts. These challenges stem from the fact that deploying IoT base stations (BSs) in remote areas can be challenging and uneconomical. Compared to primarily residential areas, these remote areas are highly dependent on IoT networks for applications such as environmental monitoring, smart agriculture, and ocean monitoring. Furthermore, terrestrial IoT BSs can be disrupted by natural disasters such as floods, earthquakes, and tsunamis. In contrast, deploying IoT BSs on satellites can be considered a complement and extension to terrestrial IoT networks, as satellites can overcome the aforementioned lack of IoT BSs in isolated areas. Furthermore, compared to geostationary satellites, low-Earth orbit (LEO) satellites may be more suitable for IoT deployment due to the significantly shorter distance between LEO satellites and IoT endpoints.

[0003] The rapid development of satellite IoT technology has also posed significant challenges to satellite communications. Ensuring user service quality and meeting the demands of large-scale user access are pressing challenges. Large-scale machine-type communications serve multiple sectors, including transportation, automation, healthcare, industry, and agriculture, driving changes in social production methods. The random access process is crucial for achieving large-scale connectivity. In such scenarios, frequent collisions and sudden retransmissions lead to network congestion, increased latency, and wasted resources. To reduce congestion and achieve low latency, this process must be performed with high accuracy and low complexity. Generally speaking, detecting active users from a sequence space of dozens or hundreds of users can be achieved through careful sequence design and exhaustive sequence searches. However, in large-scale access scenarios, where the user sequence space may reach hundreds of thousands or even millions, achieving this goal is extremely challenging, like searching for a needle in a haystack. Because different active users transmit signals at different times, the user sequences received by the access point are not synchronized, further increasing the difficulty of correctly detecting multiple users in the received signal. Therefore, the key challenge is to maintain a relatively large sequence space while achieving reliable detection.

[0004] In satellite IoT, IoT terminals can access the network through direct connection or random access. In direct connection mode, due to insufficient available frequency and transmission time resources, massive IoT terminals using orthogonal multiple access (such as frequency division multiple access and time division multiple access) may not be able to successfully communicate with the satellite. Therefore, non-orthogonal multiple access (NOMA) has become a viable design solution. However, random access of a large number of terminals will cause severe co-channel interference (CFI), limiting access capacity. This also increases the difficulty of detecting active terminals.

[0005] Based on the above insights, this paper studies an asynchronous satellite IoT terminal identification method based on Hadamard and beamforming. Based on a clustering model, IoT terminals first transmit data to relay nodes, which then forward the received data to the satellite. This model can improve transmission capacity by reducing the cross-talk interference (CFI) caused by the simultaneous transmission of a large number of IoT terminals. Furthermore, by leveraging spatial resources to generate beams, terminals in different beams can share the same frequency resources without incurring significant CFI. Active user detection is then performed on the satellite side, using Hadamard as the user sequence. Hadamard codes have good orthogonality and low detection complexity, resulting in good detection performance. Furthermore, user sequences can be uniquely and unambiguously mapped to user identities. Least squares methods are then used for channel estimation, followed by iterative interference cancellation. Summary of the Invention

[0006] To overcome the shortcomings of the existing technology, the present invention provides a terminal identity identification method based on Hadamard code and beamforming. The beamforming design is used to suppress interference between beams, improve detection performance, and increase system capacity. By adopting Hadamard as the user identity sequence and taking advantage of its good orthogonality, active terminal detection in large-scale scenarios is achieved. Finally, each active user identity sequence can be separated in asynchronous multi-user scenarios.

[0007] To achieve the above object, the technical solution adopted by the present invention is:

[0008] A terminal identity identification method based on Hadamard code and beamforming includes two parts: beamforming design and terminal identity detection. The specific steps are as follows:

[0009] Step 1: Beamforming design, including two parts: IoT terminal power allocation and beamforming vector optimization. For IoT terminal power allocation, during the transmission process from the IoT terminal to the cluster head, the objective function is to maximize the total rate. To meet transmission requirements, a signal-to-interference-and-noise ratio constraint is set. Assuming that the total power of each cluster head is fixed, less power is allocated to terminals with good channel conditions. The optimization function is then solved to obtain the power allocation coefficient for each IoT terminal. For beamforming vector optimization, during the transmission process from the cluster head to the satellite, the objective function is to maximize the total rate. The beamforming vector modulus is set to 1, and the optimization function is then solved to obtain the beamforming vector.

[0010] Step 2: Terminal identity detection: For the received signal of a certain beam, first estimate the delay, then perform autocorrelation detection at the corresponding delay, then estimate the channel coefficient, then subtract the estimated user sequence from the received sequence, and finally compare it with the decision threshold to see if all users are detected.

[0011] Specifically, step 1 includes two parts: IoT terminal power allocation and beamforming vector optimization:

[0012] (1.1) Without loss of generality, for N in the mth cluster m Terminal -1 sends its data to the cluster, so in the asynchronous case, the signal received by the cluster head can be written as:

[0013]

[0014] Where: a is the order of the Hadamard code sequence, (·) j represents the jth symbol of the sequence, δ max is the maximum delay, N m is the number of IoT terminals in the mth beam, α m,n is the power of the nth terminal in the mth cluster, s m,n is the signal of the nth terminal in the mth cluster, using the Hadamard sequence, n ch,m is zero in mean and has a variance of Additive Gaussian noise, h m,n Represents the instantaneous channel state information of the link between the nth terminal in the mth cluster and the cluster head.

[0015] Then, the cluster head of the mth cluster sends the received data and its own data to the satellite. The signal received by the mth cluster on the satellite can be expressed as:

[0016]

[0017] in, is the signal transmitted by the cluster head, αm,1 is the transmit power allocated to the cluster head, assuming s m,1 is the signal of the cluster head itself, w m is the beamforming vector of the mth beam, h m is the channel model between the satellite and the mth cluster head.

[0018] In order to improve the transmission capacity of the terminal-to-cluster head link, an optimized power allocation method is adopted to maximize the sum rate of the IoT terminal-to-cluster head link in each cluster. In addition, the optimization problem is formulated as:

[0019]

[0020] in, represents the signal-to-interference-noise ratio threshold, Denotes the sum of the terminal powers in the mth beam. By solving this optimization problem, the power allocation coefficient is obtained and used in the next step of beamforming.

[0021] (1.2) Based on power allocation, beamforming is used to maximize the sum rate. Therefore, the design optimization problem can be expressed as:

[0022]

[0023] By solving this optimization problem, we can obtain the beamforming vector and finally the satellite's received signal.

[0024] Specifically, in step 2, in the current scenario, no information about the delay is known. Assuming that the delay is not large, each delay is traversed to find the best matching delay. Assuming that the delay is t, the detection formula is:

[0025]

[0026] in: It is the Hadamard codebook sequence. Through Hadamard autocorrelation detection, at each delay, we can obtain the peak value peak(t) and the peak position index(t) generated by multiplying the received signal sequence by the codebook. Then, we find the maximum value in the peak. At this time, t is the best matching delay. At this time, index is the terminal's identity sequence number. The terminal's identity sequence can be found in the codebook through the sequence number.

[0027] Then, the channel coefficient is estimated by the least squares method, as follows:

[0028]

[0029] Where y is the received sequence at the optimal matching delay, and x is the detected terminal identity sequence. Solving this optimization problem yields the channel coefficient. This coefficient can then be subtracted from the received sequence to reduce interference to undetected users. The formula is as follows:

[0030]

[0031] Where: y is the received sequence, δ i Indicates the delay of the currently detected i-th user, Indicates the channel estimation coefficient corresponding to the currently detected i-th user, Represents the currently detected i-th user sequence;

[0032] Finally, the energy of the received sequence after offset is calculated. If the energy value is less than the decision threshold, it means that all user identities have been detected. If the energy value is greater than the decision threshold, the user identity sequence will continue to be detected until the energy value is less than the decision threshold. The energy value calculation formula is as follows:

[0033]

[0034] Beneficial effects of the present invention: The terminal identification method based on Hadamard codes and beamforming, provided by the present invention, adopts a full-frequency reuse scheme to address limited frequency resources. To suppress inter-beam interference and improve user detection performance, it utilizes spatial domain resources and performs beamforming design, effectively suppressing co-channel interference. For asynchronous multi-user scenarios, Hadamard is used as the user sequence, leveraging its good autocorrelation to detect active users among a large number of terminals. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is an implementation flow chart of the present invention.

[0036] Figure 2 is the relationship curve between the total power of a single beam and the probability of successful detection. DETAILED DESCRIPTION

[0037] In order to deepen the understanding of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. The embodiments are only used to explain the present invention and do not limit the scope of protection of the present invention.

[0038] Example: Figure 1 The figure shows a terminal identity identification method based on Hadamard code and beamforming, which includes two parts: beamforming design and terminal identity detection.

[0039] Part 1: Beamforming Design

[0040] It includes two parts: IoT terminal power allocation and beamforming vector optimization:

[0041] (1.1) Without loss of generality, for N in the mth cluster m Terminal -1 sends its data to the cluster, so in the asynchronous case, the signal received by the cluster head can be written as:

[0042]

[0043] Where: a is the order of the Hadamard code sequence, (·) j represents the jth symbol of the sequence, δ max is the maximum delay, N m is the number of IoT terminals in the mth beam, α m,n is the power of the nth terminal in the mth cluster, s m,n is the signal of the nth terminal in the mth cluster, using the Hadamard sequence, n ch,m is zero in mean and has a variance of Additive Gaussian noise, h m,n Represents the instantaneous channel state information of the link between the nth terminal in the mth cluster and the cluster head.

[0044] Then, the cluster head of the mth cluster sends the received data and its own data to the satellite. The signal received by the mth cluster on the satellite can be expressed as:

[0045]

[0046] in, is the signal transmitted by the cluster head, α m,1 is the transmit power allocated to the cluster head, assuming s m,1 is the signal of the cluster head itself, w m is the beamforming vector of the mth beam, h m is the channel model between the satellite and the mth cluster head.

[0047] In order to improve the transmission capacity of the terminal-to-cluster head link, an optimized power allocation method is adopted to maximize the sum rate of the IoT terminal-to-cluster head link in each cluster. In addition, the optimization problem is formulated as:

[0048]

[0049] in, represents the signal-to-interference-noise ratio threshold, Denotes the sum of the terminal powers in the mth beam. By solving this optimization problem, the power allocation coefficient is obtained and used in the next step of beamforming.

[0050] (1.2) Based on power allocation, beamforming is used to maximize the sum rate. Therefore, the design optimization problem can be expressed as:

[0051]

[0052] By solving this optimization problem, we can obtain the beamforming vector and finally the satellite's received signal.

[0053] Part 2: Terminal Identity Detection

[0054] It includes two parts: delay matching and user identity detection:

[0055] In the current scenario, we have no information about the delay. We assume that the delay is not large and traverse each delay to find the best matching delay. Assuming the delay is t, the detection formula is:

[0056]

[0057] in: It is the codebook sequence of Hadamard. Through the autocorrelation detection of Hadamard, at each delay, we can obtain the peak value peak(t) and the peak position index(t) generated by multiplying the received signal sequence by the codebook. Then we find the maximum value in the peak. At this time, t is the best matching delay. At this time, index is the identity sequence number of the terminal. The identity sequence of the terminal can be found in the codebook through the sequence number.

[0058] Then the channel coefficient is estimated by the least squares method, the formula is as follows:

[0059]

[0060] Where y is the received sequence at the optimal matching delay, and x is the detected terminal identity sequence. Solving this optimization problem yields the channel coefficient. This coefficient can then be subtracted from the received sequence to reduce interference to undetected users. The formula is as follows:

[0061]

[0062] Where: y is the received sequence, δ i Indicates the delay of the currently detected i-th user, Indicates the channel estimation coefficient corresponding to the currently detected i-th user, Represents the currently detected i-th user sequence;

[0063] Finally, the energy of the received sequence after offset is calculated. If the energy value is less than the decision threshold, it means that all user identities have been detected. If the energy value is greater than the decision threshold, the user identity sequence will continue to be detected until the energy value is less than the decision threshold. The energy value calculation formula is as follows:

[0064]

[0065] Figure 2 The following is a simulation based on the method of the present invention, showing the comparison curve between our proposed method and the traditional four-color multiplexing scheme when the power of a single beam ranges from -10 to 20 dB.

[0066] In summary, the terminal identification method based on Hadamard codes and beamforming, provided by this invention, addresses limited frequency resources by adopting a full-frequency reuse scheme. To suppress inter-beam interference and improve user detection performance, it leverages spatial resources and performs beamforming design, effectively suppressing co-channel interference. For asynchronous multi-user scenarios, Hadamard codes are used as the user sequence, leveraging its good autocorrelation to detect active users among a large number of terminals.

[0067] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A terminal identity identification method based on Hadamard code and beamforming, characterized in that: It includes two parts: beamforming design and terminal identity detection. The specific steps are as follows: Step 1: Beamforming design, which includes two parts: IoT terminal power allocation and beamforming vector optimization. For IoT terminal power allocation, assume that the total power of each cluster head is fixed, allocate less power to terminals with good channel conditions, and then solve the optimization function to obtain the power allocation coefficient for each IoT terminal. For beamforming vector optimization, during the transmission process from the cluster head to the satellite, maximize the total rate as the objective function, set the beamforming vector modulus to 1, and then solve the optimization function to obtain the beamforming vector. Step 2: Terminal identity detection: For the received signal of a certain beam, first estimate the delay, then perform autocorrelation detection at the corresponding delay, estimate the channel coefficient, select the Hadamard code as the user sequence, and then subtract the estimated user sequence from the received sequence.

2. The terminal identity identification method based on Hadamard code and beamforming according to claim 1, characterized in that: The power allocation of the IoT terminal in step 1 specifically includes the following process: First, for N in the mth cluster m -1 terminal sends its data to the cluster. In the asynchronous case, the signal received by the cluster head is written as: Where a is the order of the Hadamard code sequence, (·) j represents the jth symbol of the sequence, δ max is the maximum delay, N m is the number of IoT terminals in the mth beam, α m,n is the power of the nth terminal in the mth cluster, s m,n is the signal of the nth terminal in the mth cluster, using the Hadamard sequence, n ch,m is zero in mean and has a variance of Additive Gaussian noise, h m,n Represents the instantaneous channel state information of the link between the nth terminal in the mth cluster and the cluster head; Then, the cluster head of the mth cluster sends the received data and its own data to the satellite. The signal received by the mth cluster on the satellite is expressed as: in, is the signal transmitted by the cluster head, α m,1 is the transmit power allocated to the cluster head, assuming s m,1 is the signal of the cluster head itself, w m is the beamforming vector of the mth beam, h m is the channel model between the satellite and the mth cluster head; Furthermore, the optimization problem is formulated as: in, represents the signal-to-interference-noise ratio threshold, represents the sum of the terminal powers in the mth beam. By solving this optimization problem, the power allocation coefficient is obtained and used in the next step of beamforming.

3. The terminal identity identification method based on Hadamard code and beamforming according to claim 2, characterized in that: In step 1, beamforming is used to maximize the sum rate based on power allocation. The optimization problem is expressed as: By solving this optimization problem, we can obtain the beamforming vector and finally the satellite's received signal.

4. The terminal identity identification method based on Hadamard code and beamforming according to claim 3, characterized in that: The specific process in step 2 is as follows: first, the delay is set, then the channel coefficient is estimated using the least squares method, and finally, the energy of the received sequence after offset is calculated. If the energy value is less than the decision threshold, it means that all user identities have been detected. If the energy value is greater than the decision threshold, the user identity sequence is continued to be detected until the energy value is less than the decision threshold.

5. The terminal identity identification method based on Hadamard code and beamforming according to claim 4, characterized in that: In step 2, the time delay is set to t, and the detection formula is: in: It is the Hadamard codebook sequence. Through Hadamard autocorrelation detection, at each delay, the peak value peak(t) and the peak position index(t) generated by multiplying the received signal sequence by the codebook are obtained. Then, the maximum value in the peak is found. At this time, t is the best matching delay. At this time, index is the terminal's identity sequence number. The terminal's identity sequence is found in the codebook through the sequence number.

6. The terminal identity identification method based on Hadamard code and beamforming according to claim 5, characterized in that: In the second step, the channel coefficient is estimated by the least square method, and the formula is as follows: Where y is the received sequence under the optimal matching delay, and x is the detected terminal identity sequence. By solving this optimization problem, we can obtain the channel coefficient, which is then subtracted from the received sequence to reduce interference to undetected users. The formula is as follows: Where: y is the received sequence, δ i Indicates the delay of the currently detected i-th user, Indicates the channel estimation coefficient corresponding to the currently detected i-th user, Represents the i-th user sequence currently detected.

7. The terminal identity identification method based on Hadamard code and beamforming according to claim 6, characterized in that: In the second step, the energy of the received sequence after cancellation is calculated. The energy value calculation formula is as follows:

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