Visual area detection method for super-large-scale intelligent metasurface auxiliary communication system
By employing a visible area detection method with uniform planar array deployment in a large-scale RIS-assisted communication system, and utilizing the DFS algorithm and binary search strategy, the problem of detecting the visible area of user equipment in complex environments is solved, achieving efficient and accurate visible area detection and low-complexity channel estimation.
Patent Information
- Application Number
- CN202511776419.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-28
- Publication Date
- 2026-03-03
AI Technical Summary
Existing technologies struggle to efficiently and accurately detect the visible area of user equipment in ultra-large-scale RIS-assisted communication systems, especially in complex environments, leading to high computational complexity and wasted system resources.
A large-scale RIS-assisted communication system based on uniform planar array deployment is adopted. The visible area detection method delineates the initial area by sequentially detecting whether RIS units are occluded, and then uses the DFS algorithm and binary search strategy to recover the visible area, thereby reducing computational complexity and measurement overhead.
It enables efficient and accurate detection of the visible area of user equipment in complex environments, reduces computational complexity and system resource consumption, and ensures the accuracy and low complexity of subsequent RIS-assisted channel estimation.
Smart Images
Figure CN121604009A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of wireless communication technology, specifically relating to a method for detecting the visible area of an ultra-large-scale intelligent metasurface-assisted communication system. Background Technology
[0002] Reconfigurable smart metasurfaces (RIS), a key technology in sixth-generation mobile communication systems, have received widespread attention from academia and industry in recent years. Typically composed of numerous passive reflective elements, RIS is deployed between base stations and user equipment to construct auxiliary communication links, thereby effectively improving system transmission performance and expanding network coverage. Compared to traditional relay equipment, RIS offers significant advantages such as flexible deployment, low energy consumption, and high cost-effectiveness.
[0003] To fully realize the system potential of the RIS (Radio Router Array), accurate estimation of its incident and outgoing channels is necessary to obtain reliable channel state information, which is crucial for achieving passive beamforming gain in the RIS. However, most existing research on RIS-assisted channel estimation is based on the assumption of an ideal propagation environment, failing to adequately consider the near-field effects caused by the RIS's massive structure and the shadowing effect between user equipment and the RIS caused by obstacles in the real environment. Under the influence of the shadowing effect, only a portion of the RIS units can receive and process electromagnetic wave signals from the user equipment; this region is called the user equipment's visible area. Accurately identifying the visible area corresponding to each user equipment is the foundation for subsequent high-precision channel estimation and user localization, and is also an important prerequisite for ensuring the full utilization of RIS performance.
[0004] Existing line-of-sight (LOS) detection methods typically begin with a coarse cascaded channel estimation, followed by LOS reconstruction based on the estimation results. However, this process consumes significant system resources and increases computational complexity, especially in ultra-large-scale RIS applications, leading to a significant decrease in overall system efficiency. Furthermore, to improve LOS detection accuracy, these methods often rely on large amounts of channel training data, further increasing the system burden. Moreover, the aforementioned LOS detection studies all assume that RIS cells are arranged in a uniform linear array, an assumption reasonable for smaller-scale RIS applications. However, as RIS scales up, uniform planar arrays are commonly used in practical deployments. It is important to note that obstacles in the environment often possess complex and random characteristics, especially in urban environments, where the shape and distribution of these obstacles significantly impact LOS detection in uniform planar array RIS systems. Therefore, achieving efficient and accurate LOS detection in real-world applications with complex environmental factors has become one of the key problems urgently needing to be solved in RIS-assisted communication systems.
[0005] In summary, the challenge of identifying and classifying whether the RIS cells to be detected are occluded with extremely low computational complexity, and on this basis, solving the problem of visible area detection in ultra-large-scale RIS-assisted wireless communication systems deployed in uniform planar arrays, thereby ensuring that subsequent RIS-assisted channel estimation has high accuracy and low complexity, has become a major challenge for RIS-assisted communication systems. Summary of the Invention
[0006] Technical Problem: To solve the above problems, this invention provides a visible area detection method for a large-scale RIS-assisted communication system based on a uniform planar array deployment. The aim is to achieve relatively accurate visible area detection with low complexity and measurement overhead, thereby ensuring that subsequent RIS-assisted channel estimation has high accuracy and low complexity.
[0007] Technical solution: The present invention provides a visible region detection method for a large-scale intelligent metasurface RIS-assisted communication system, applicable to uniform planar array RIS, comprising the following steps:
[0008] Step S1: Sequentially detect the first RIS array on the array surface. , , , and Five units, determine whether they are occluded, among which This represents the cell in row a and column b. Q is a preset positive integer;
[0009] Step S2: Based on the occlusion status of the five units in Step S1, define the initial area and determine whether the initial area is completely occluded.
[0010] Step S3: If the initial region defined in step S2 is completely occluded, use the DFS algorithm on all cells on the boundary of the initial region in sequence to detect all occluded cells, thereby restoring the final visible region.
[0011] Step S4: If the initial area defined in step S2 is not completely occluded, a binary search strategy is adopted. First, an occluded cell is detected, and then, starting from that cell, the DFS algorithm is used to detect all occluded cells, thereby restoring the final visible area.
[0012] Furthermore, step S1 specifically includes:
[0013] Step 1.1: Set the phase of all units to... The base station receives signals sent from the user equipment. ;
[0014] Step 1.2: Set the phase of the unit to be detected to... The phase of other units remains unchanged. The signal remains unchanged; the base station receives the signal sent from the user equipment. ;
[0015] Step 1.3: Measure the noise power of the current environment and set a threshold. The threshold can be set according to a multiple of the noise power. To better adapt to different transmission power ranges, especially when the transmission power of user equipment is typically between 10dBm and 30dBm, the threshold should be dynamically adjusted between 6 and 16 times the noise power. When the transmission power is low, the threshold can be appropriately set to 6 times the noise power to optimize performance and system stability. When the transmission power is high, an appropriately higher multiple can be selected to meet specific system requirements. This adjustment mechanism is designed to flexibly adjust the threshold according to changes in ambient noise and transmission power, thereby ensuring optimal system performance under various conditions.
[0016] Step 1.4: Obtain the difference signal power Compare it with the threshold set in step 1.3. If If the condition is met, the cell is determined to be occluded; otherwise, the cell is determined to be unoccluded.
[0017] Furthermore, step S2 specifically involves:
[0018] Step 2.1: Divide the initial area according to the occlusion of the five units in step S1; if the initial area cannot be set, proceed directly to step S4.
[0019] Step 2.2: Set the phase of all units to... The base station receives signals sent from the user equipment. ;
[0020] Step 2.3: Set the phase of all elements in the initial region to... The phase of other units remains unchanged. The signal remains unchanged; the base station receives the signal sent from the user equipment. ;
[0021] Step 2.4: Obtain the difference signal power Compare it with the threshold set in step 1.3. If If the initial region is completely occluded, it is determined that the initial region is completely occluded; otherwise, it is determined that the initial region is not completely occluded.
[0022] Furthermore, step S3 specifically includes:
[0023] If the initial region defined in step S2 is completely occluded, the DFS algorithm is executed sequentially on all cells on the boundary of the initial region. The execution process of the DFS algorithm is as follows: starting from a certain occluded cell, the cells in the four adjacent directions above, right, down, and left are detected in sequence, and the detected cells are marked as detected to avoid duplicate detection. If an adjacent cell is detected to be occluded in a certain direction, the adjacent cell is used as the new starting point, and the cells in the four adjacent directions above, right, down, and left are recursively detected until the cells in the detected direction are no longer occluded or have exceeded the boundary of the RIS plane. Then, the process returns to the previous level cell to continue detecting the remaining undetected directions. When all directions of the initially occluded cell have been detected, the DFS traversal ends.
[0024] Furthermore, step S4 specifically involves:
[0025] Step 4.1: First, define the i-th order bisection point as... ,in The number of rows and columns of the RIS is represented by Q, which is a preset positive integer. Then, according to the preset order and the detection method proposed in step S1, the bisection point units of each order are traversed and detected sequentially starting from the 3rd order until the first occluded unit is detected.
[0026] Step 4.2: Starting from the first occluded cell detected in Step 4.1, use the DFS algorithm to detect all occluded cells and then restore the final visible area.
[0027] The present invention has the following advantages:
[0028] 1. Most existing RIS-assisted communication system line-of-sight detection schemes are based on coarse channel estimation. The scheme proposed in this invention utilizes the continuity of the line-of-sight area, which can accurately detect the line-of-sight area of user equipment with low complexity and measurement overhead when the channel state information is unknown.
[0029] 2. Most existing RIS-assisted communication systems target RIS deployed in uniform linear arrays for visible area detection. This invention proposes a RIS visible area detection scheme suitable for uniform planar array deployments in ultra-large-scale scenarios, effectively solving the detection problem of more complex structures in actual deployments.
[0030] 3. The proposed solution of this invention improves the detection accuracy of the visible area under poor signal-to-noise ratio conditions by reversing the power accumulation effect of the components in the initial area at one time due to the strategy of defining the initial area. Attached Figure Description
[0031] Figure 1 Initial region partitioning of eight ultra-large-scale RIS-assisted communication systems provided in embodiments of the present invention. Figure 1 ;
[0032] Figure 2 Initial region partitioning of eight ultra-large-scale RIS-assisted communication systems provided in embodiments of the present invention. Figure 2 ;
[0033] Figure 3 Initial region partitioning of eight ultra-large-scale RIS-assisted communication systems provided in embodiments of the present invention. Figure 3 ;
[0034] Figure 4 Initial region partitioning of eight ultra-large-scale RIS-assisted communication systems provided in embodiments of the present invention. Figure 4 ;
[0035] Figure 5 Initial region partitioning of eight ultra-large-scale RIS-assisted communication systems provided in embodiments of the present invention. Figure 5 ;
[0036] Figure 6 Initial region partitioning of eight ultra-large-scale RIS-assisted communication systems provided in embodiments of the present invention. Figure 6 ;
[0037] Figure 7 Initial region partitioning of eight ultra-large-scale RIS-assisted communication systems provided in embodiments of the present invention. Figure 7 ;
[0038] Figure 8 Initial region partitioning of eight ultra-large-scale RIS-assisted communication systems provided in embodiments of the present invention. Figure 8 ;
[0039] Figure 9 A diagram showing the distribution of bipartite points of various orders in a large-scale RIS-assisted communication system provided in an embodiment of the present invention;
[0040] Figure 10 A flowchart of a visible region detection algorithm for a large-scale RIS-assisted communication system provided in an embodiment of the present invention. Detailed Implementation
[0041] In the following description, specific values for the number of RIS units are set forth for illustrative purposes and not for limitation, so as to provide a thorough understanding of the invention. However, those skilled in the art will understand that the invention may be practiced in other embodiments without these specific details.
[0042] like Figure 1 As shown, a visible area detection method for a large-scale RIS-assisted communication system according to the present invention includes the following steps:
[0043] Step S1: Sequentially detect the first RIS array surface... , , , and Five units, determine whether they are occluded, among which Represents the cell in row a and column b;
[0044] Step S2: Based on the occlusion status of the five units in Step S1, define the initial area and determine whether the initial area is completely occluded.
[0045] Step S3: If the initial region defined in step S2 is completely occluded, use the DFS algorithm on all cells on the boundary of the initial region in sequence to detect all occluded cells, thereby restoring the final visible region.
[0046] Step S4: If the initial area defined in step S2 is not completely occluded, a binary search strategy is adopted. First, an occluded cell is detected, and then, starting from that cell, the DFS algorithm is used to detect all occluded cells, thereby restoring the final visible area.
[0047] To make the objectives, technical solutions, and advantages of the present invention clearer, a detailed description will be provided below in conjunction with the accompanying drawings. The present invention provides a method for detecting the visible area in a large-scale RIS-assisted communication system, wherein the base station is equipped with… A uniform linear array of antennas, RIS is equipped with A uniform planar array of reflective elements, wherein The signal transmitted by a single-antenna user equipment is received by the base station after being reflected by the RIS.
[0048] This invention models the visible area of a user device as a continuous subset of RIS units, and assumes that the size of the visible area is not less than... ,in This indicates the minimum coverage rate.
[0049] Step S1: Sequentially detect the first RIS UPA plane. , , , and Five units, determine whether they are occluded, among which The cell representing row a and column b is defined as follows:
[0050] 1.1) Set all unit phases to The base station receives signals sent from the user equipment. ;
[0051] 1.2) Sequentially place the first RIS UPA plane on the RIS UPA plane , , , and The phase of the five units is set to The phase of other units remains unchanged. The system remains unchanged, with the base station receiving signals transmitted from the user equipment. , , , and ;
[0052] 1.3) Measure the noise power of the current environment, threshold. The threshold is set based on a multiple of the noise power. To better adapt to different transmission power ranges, especially when the user equipment transmission power is between 10dBm and 30dBm, the threshold is dynamically adjusted between 6 and 16 times the noise power. When the transmission power is low, the threshold is set to 6 times the noise power to optimize performance and system stability. When the transmission power is high, a higher multiple is selected to meet specific system requirements. This adjustment mechanism aims to flexibly adjust the threshold according to changes in ambient noise and transmission power, thereby ensuring optimal system performance under various conditions.
[0053] 1.4) Obtain the difference signal power Compare it with the threshold set in step 1.3. If If the i-th cell is occluded, then the i-th cell is determined to be occluded; otherwise, the cell is determined to be unoccluded.
[0054] Step S2: Based on the occlusion status of the five units in Step S1, define the initial region and determine whether the initial region is completely occluded. The specific steps are as follows:
[0055] 2.1) Based on the occlusion status of the five units in step S1, proceed as follows: Figure 1-8 The diagram illustrates the division of the initial region, where the gray covered area represents the actual occlusion, and the blue covered area represents the initial region defined based on the occlusion status of the five units in step S1. Specifically, Figure 5 This indicates that the initial area cannot be set, so proceed directly to step S4;
[0056] 2.2) Set the phase of all units to The base station receives signals sent from the user equipment. ;
[0057] 2.3) Set the phase of all elements in the initial region to... The phase of other units remains unchanged. The signal remains unchanged; the base station receives the signal sent from the user equipment. ;
[0058] 2.4) Obtain the difference signal power Compare it with the threshold set in step 1.3. If If the initial region is completely occluded, it is determined that the initial region is completely occluded; otherwise, it is determined that the initial region is not completely occluded.
[0059] Step S3: If the initial region defined in step S2 is completely occluded, apply the DFS algorithm to all cells on the boundary of the initial region in sequence to detect all occluded cells and restore the final visible region. The specific steps are as follows:
[0060] If the initial region defined in step S2 is completely occluded, then the DFS algorithm is executed sequentially on all cells on the boundary of the initial region, such as... Figure 1-8 The green-covered area is shown in the diagram. The execution process of the DFS algorithm is as follows: Starting from a certain occluded cell, the cells in its four adjacent directions (up, right, down, and left) are detected in sequence, and the detected cells are marked as detected to avoid duplicate detection. If an adjacent cell is detected to be occluded in a certain direction, the adjacent cell is used as the new starting point, and the cells in its four adjacent directions (up, right, down, and left) are recursively detected until the cells in the detected direction are no longer occluded or have exceeded the boundary of the RIS plane. Then, the algorithm returns to the previous level cell to continue detecting the remaining undetected directions. When all directions of the initially occluded cell have been detected, the DFS traversal ends.
[0061] Step S4: If the initial region defined in step S2 is not completely occluded, a binary search approach is adopted. First, an occluded cell is detected, and then the DFS algorithm is used starting from this occluded cell to detect all occluded cells, thus restoring the final visible region. The specific steps are as follows:
[0062] 4.1) First, define the i-th order bisection point as... ,in This represents the number of rows and columns of the RIS, where Q is a preset positive integer; subsequently, it follows the preset order of order (e.g., ...). Figure 9 As shown, the red, yellow and green units represent first-order, second-order and third-order bisection points, respectively. Following the detection method proposed in step S1, the bisection point units of each order are traversed and detected sequentially starting from the third order until the first occluded unit is detected.
[0063] 4.2) Starting from the first occluded cell detected in step 4.1, use the DFS algorithm to detect all occluded cells and then restore the final visible area.
[0064] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
Claims
1. A method for detecting the visible area of an ultra-large-scale intelligent metasurface-assisted communication system, characterized in that, The intelligent metasurface RIS suitable for uniform planar arrays includes the following steps: Step S1: Sequentially detect the first RIS array surface... , , , and Five units, determine whether they are occluded, among which This represents the cell in row a and column b. Q is a preset positive integer; Step S2: Based on the occlusion status of the five units in Step S1, define the initial area and determine whether the initial area is completely occluded. Step S3: If the initial region defined in step S2 is completely occluded, use the DFS algorithm on all cells on the boundary of the initial region in sequence to detect all occluded cells, thereby restoring the final visible region. Step S4: If the initial area defined in step S2 is not completely occluded, a binary search strategy is adopted. First, an occluded cell is detected, and then, starting from that cell, the DFS algorithm is used to detect all occluded cells, thereby restoring the final visible area.
2. The visible area detection method for an ultra-large-scale intelligent metasurface-assisted communication system according to claim 1, characterized in that, Step S1, determining whether the unit is obstructed, specifically includes: Step 1.1: Set the phase of all units to... The base station receives signals sent from the user equipment. ; Step 1.2: Set the phase of the unit to be detected to... The phase of other units remains unchanged. The signal remains unchanged; the base station receives the signal sent from the user equipment. ; Step 1.3: Measure the noise power of the current environment and set a threshold. The threshold is set based on a multiple of the noise power. To better adapt to different transmission power ranges, especially when the user equipment transmission power is between 10dBm and 30dBm, the threshold is dynamically adjusted between 6 and 16 times the noise power. When the transmission power is low, the threshold is set to 6 times the noise power to optimize performance and system stability. When the transmission power is high, a higher multiple is selected to meet specific system requirements. This adjustment mechanism aims to flexibly adjust the threshold according to changes in ambient noise and transmission power, thereby ensuring optimal system performance under various conditions. Step 1.4: Obtain the difference signal power Compare it with the threshold set in step 1.
3. If If the condition is met, the cell is determined to be occluded; otherwise, the cell is determined to be unoccluded.
3. The visible area detection method for an ultra-large-scale intelligent metasurface-assisted communication system according to claim 1, characterized in that, Step S2 specifically includes: Step 2.1: Divide the initial area according to the occlusion of the five units in step S1; if the initial area cannot be set, proceed directly to step S4. Step 2.2: Set the phase of all units to... The base station receives signals sent from the user equipment. ; Step 2.3: Set the phase of all elements in the initial region to... The phase of other units remains unchanged. The signal remains unchanged; the base station receives the signal sent from the user equipment. ; Step 2.4: Obtain the difference signal power Compare it with the threshold set in step 1.
3. If If the initial region is completely occluded, it is determined that the initial region is completely occluded; otherwise, it is determined that the initial region is not completely occluded.
4. The visible area detection method for an ultra-large-scale intelligent metasurface-assisted communication system according to claim 1, characterized in that, Step S3 specifically includes: If the initial region defined in step S2 is completely occluded, the DFS algorithm is executed sequentially on all cells on the boundary of the initial region. The execution process of the DFS algorithm is as follows: starting from a certain occluded cell, the cells in the four adjacent directions above, right, down, and left are detected in sequence, and the detected cells are marked as detected to avoid duplicate detection. If an adjacent cell is detected to be occluded in a certain direction, the adjacent cell is used as the new starting point, and the cells in the four adjacent directions above, right, down, and left are recursively detected until the cells in the detected directions are no longer occluded or have exceeded the boundary of the RIS plane. Then, the process returns to the previous level cell to continue detecting the remaining undetected directions. When all directions of the initially occluded cell have been detected, the DFS traversal ends.
5. The visible area detection method for an intelligent metasurface-assisted communication system according to claim 1, characterized in that, The binary search in step S4 specifically includes the following steps: First, define the i-th bisection point as... ,in Let Q represent the number of rows and columns of the RIS, and let Q be a preset positive integer. Then, according to the preset order and the detection method proposed in step S1, the bisection point units of each order are traversed and detected sequentially starting from the 3rd order until the first occluded unit is detected.