MULTI-MODE RECEIVER DESIGN FOR MULTI-BWP PRS-BASED SENSING

Abstract

The invention is related to a multi-mode receiver architecture for Integrated Sensing and Communications (ISAC) systems that exploits standardized Positioning Reference Signals (PRS) transmitted across parallel, non-overlapping Bandwidth Parts (BWPs) with heterogeneous pilot densities.

Description

1 TARIFF MULTI-MODE RECEIVER DESIGN FOR MULTI-BWP PRS-BASED DETECTION Technical Area 5 The invention relates to parallel and non-overlapping Bandwidth Segments with heterogeneous pilot densities. Standardized Positioning Reference Signals (PRS) transmitted via (BWPs) with a multimode receiver architecture for Integrated Sensing and Communication (ISAC) systems It is related. 10 Previous Technique Conventional PRS uses a fixed pilot density KPRS with comb-type subcarrier allocation. It uses a sparse and periodic frequency structure, ∆𝑅𝑎𝑚𝑏 = c 2𝐾𝑃𝑅𝑆∆𝑓 at regular intervals 15 Appearing as ghost targets, radar exhibits both delay (range) and Doppler (velocity) capabilities. It creates periodic peaks in the uncertainty function. These phantom targets are multi-target. In urban scenarios, they are indistinguishable from real scatterers, leading to collisions with tracking. This reduces prevention performance. The limitations mentioned above make multi-BWPs and This has led to the use of heterogeneous pilot densities. 20 Many techniques have been developed to address both range-Doppler uncertainties, and These can be divided into two categories: post-processing techniques and waveform level. solutions In [1], the authors argue that symbol-based optimization, which is incompatible with the 3GPP framework, requires The study in [2] examines symbol-level precoding (SLP) for side lobe suppression. Pulse shaping for random ISAC signals that alter waveform design. It is a new method that uses optimization. The authors of [3], PDSCH availability in the operating mode that requires and therefore only the control plane is active (PDSCH 30 (in the absence of) the superposition of the PRS and demodulation reference signal which fails It uses [4], which provides partial uncertainty suppression but frequency in bands below 6 GHz. Multi-slot averaging that exhibits distortion under selective damping is discussed in [5]. 2 The study utilizes frequency diversity at the BWP level only, without taking advantage of it at the post-processing level. The implemented CRT-based Doppler addresses uncertainty resolution. Similarly, [6], improved ghost target elimination but achieved through an unparalleled comb factor fusion An optimal Irregular PRS pilot lacking the capability for precise range extension. It presents a pattern. 5 Regarding existing patents covering the problem mentioned above, [7], flexibly A flexible OFDM for shared perception and communication based on configured OFDM symbols. On the other hand [8], to eliminate the time delay uncertainty, a waveform is proposed. 10 for OFDM that includes specifying the shift offset format for the OFDM reference signal symbol. The authors of [9] propose a common method of communication and perception, zero power reference signal It presents OFDM reference signal symbols configured with. In conclusion, all the problems mentioned above necessitate an innovation in the relevant field. It has made it necessary. 15 Purposes of the Invention The main objective of the present invention is to address the issues caused by periodic positioning reference signal structures. 20 for target detection in an integrated sensing and communication system that reduces the effects of uncertainty. The goal is to offer an improved method. Another aim of the invention is to create multiple bandwidths with different pilot densities. By using the differences in the uncertainty ranges associated with the section, uncertain range The goal is to suppress ghost targets that correspond to their replicas. 25 Another purpose of the invention is to combine information obtained from multiple bandwidth segments. by combining, effectively without increasing the bandwidth allocated to individual bandwidth segments. and to broaden the precise detection range. Another aim of the invention is to perform bandwidth-to-band processing only when uncertainty is detected. By offering an adaptive processing mechanism that selectively enables computation. The goal is to reduce its complexity. 3 Description of the Forms of the Invention The figures and relevant explanations necessary for a better understanding of the subject matter of the invention are given below. is provided. Figure 1. Multiple-BWP PRS transmission in multiple regions of uncertainty. Figure 2. A flowchart of an adaptive multi-BWP fusion algorithm for ghost suppression. diagram. Detailed Description of the Invention 10 The invention relates to parallel and non-overlapping Bandwidth Segments with heterogeneous pilot densities. Standardized Positioning Reference Signals (PRS) transmitted via (BWPs) with a multi-mode receiver architecture for Integrated Sensing and Communication (ISAC) systems It is related. 15 Referring to Figures 1 and 1a; In an integrated sensing and communication system, at least one the first bandwidth section is configured with a first pilot density 𝐾1 and a the second bandwidth section having a higher density than the first pilot density. Pilot density configured with 𝐾2, positioning reference signals, multiple 20 It is transmitted over a non-overlapping bandwidth section. 𝐾2 are sparse pilots. Positioning reference signals are discrete subcarriers corresponding to bandwidth segments. They are transmitted in parallel through clusters, thus preventing interference between bandwidths. is being prevented. Due to the periodic nature of the positioning reference signals, 𝑛 is an integer. n. ∆Rab With additional detections to be made in 2, a target located within the range of Ri will be precisely identified. It performs repeated detections within the range. These repeated detections are ghost detections. It corresponds to the objectives. Referring to Figure 2; Positioning reference signals, different pilot densities 𝐾1 and When transmitted over multiple bandwidth segments with C2, the corresponding precise range intervals Δ𝑅𝑎𝑚𝑏 1 and ΔR𝑅𝑎𝑚𝑏 The two are different from each other, and this means that each bandwidth... 4 This results in different ghost patterns for the section. With the first bandwidth section. An associated phantom target, 𝑅𝑖 + 𝑛, where 𝑚 and 𝑛 are integers. ∆𝑅𝑎𝑚𝑏 Appearing in position 1 Upon exiting, a ghost target associated with the second bandwidth section is 𝑅𝑖 + 𝑚. ∆𝑅𝑎𝑚𝑏 2 They appear in their positions. Ghost targets across bandwidth sections. The collision occurs only when the condition 𝑛. 𝐾2 = 𝑚. 𝐾1 is satisfied, which is initially 5 This occurs at a distance corresponding to the least common multiple of the uncertainty intervals. Consequently, ghost targets generated in different bandwidth segments are generally They do not overlap. Bandwidth-to-bandwidth processing uses this feature to process only bandwidth. It confirms the findings that are consistent across the sections, thus meeting the consistency requirements. It suppresses unmet phantom targets. 10 The precise range interval associated with each bandwidth segment corresponds to the corresponding pilot density. and depends on the subcarrier range. Specifically, the precise range associated with the second bandwidth segment. The range is defined as follows: ∆𝑅𝑎𝑚𝑏 2 = c 2𝐾2∆𝑓 Here, 𝐾2 is the pilot intensity of the second BWP, 𝑐 is the speed of light, and ∆𝑓 is the subcarrier gap. At the receiver, the signals corresponding to each bandwidth segment are processed independently. A matching filter is applied to each bandwidth segment, and an inverse discrete Fourier 20 A discrete Fourier transform with windowing such as Hamming windowing and windowing with a transformation. A two-dimensional range-Doppler map is calculated using this method. This process is performed in the first band. a first target estimate set from the width section and a second target estimate set from the bandwidth section The second objective is to create a set of predictions. The receiver verifies the following with the second bandwidth section of the second target forecast set: It determines whether it falls within the related precise range. 𝑅𝑖 ^2 < ∆𝑅𝑎𝑚𝑏 2 , ∀ 𝑖′ ∈ {1, … … … ., 𝑁2} If the condition is met for all detections, the receiver will receive the data from the second bandwidth segment. It operates in a first mode where the predictions are used directly as final target estimates. If the condition is not met, a second mode is used where inter-band processing is performed. It is working. In this mode, for each detection obtained from the first bandwidth section, the second According to the findings from the bandwidth section, it is based on a modular residual control. An association set is calculated as follows. The range consistency condition is as follows: is defined; 5 |R ^1 − 𝑅𝑖′ ^2| mod ∆𝑅𝑎𝑚𝑏 2 < ε𝑅 here 𝑅𝑖 ^1 is a range estimate derived from the first BWP, 𝑅𝑖′ ^2, obtained from the second BWP This is a range estimate, ∆𝑅𝑎𝑚𝑏 2 is the precise range interval associated with the second BWP, and εR is the previously 10 It is a defined range threshold (association tolerance). The speed consistency requirement is defined as follows: |w ^1 − 𝑣𝑖′ ^2 | < εv, 15 here 𝑣𝑖 ^1 is a speed estimate derived from the first BWP, 𝑣𝑖′ ^2, one obtained from the second BWP εv is a speed estimate, and εv is a predefined speed threshold (association tolerance). A detection fails if the association set is not empty or if a detection amplitude is less than a predefined 20. If the threshold value exceeds ŋhigh, it is classified as a real target. Otherwise, the detection is a It is classified as a phantom target and is ignored. For each verified target, a level of uncertainty is determined according to the following: 𝑚𝑖 ^ = ⌊ 𝑅𝑖 1− 𝑹 𝑖′ 2 ∆𝑅𝑎𝑚𝑏 2 ⌉ . Then, a precise range estimate is reconstructed as follows: 𝑅𝑖 ^𝑓𝑢𝑠𝑒𝑑 = R ^2 + 𝑚𝑖 ^. ∆𝑅𝑎𝑚𝑏 2, 30 6 here 𝑅𝑖 ^𝑓𝑢𝑠𝑒𝑑 , is a reconstructed precise range estimate, 𝑅𝑖 ^2, obtained from the second BWP It is a range estimate, 𝑚𝑖 ^ is a degree of uncertainty and . ∆𝑅𝑎𝑚𝑏 2, associated with the second BWP. This is the precise range. Estimates derived from bandwidth segments using reconstructed range estimation, 5 A final target estimate is created by combining the following elements: 𝑅𝑖 ^ = 𝑤1𝑅𝑖 ^𝑓𝑢𝑠𝑒𝑑 + 𝑤2𝑅𝑖 ^2 𝑤1+ 𝑤2 , 𝑣𝑖 ^ = 𝑤1𝑣𝑖 ^1+ 𝑤2𝑣𝑖 ^2 𝑤1+ 𝑤2 here 𝑅𝑖 ^ is an estimate of the final range, 𝑤1 is an estimate of the final speed, 𝑅𝑖 ^𝑓𝑢𝑠𝑒𝑑 , again 10 This is a generated range estimate, 𝑅𝑖 ^2 is a range estimate derived from the second BWP, 𝑣𝑖 ^1 and 𝑣𝑖 ^2, speed estimates obtained from the first and second bandwidth sections, respectively. This shows that 𝑤1 and 𝑤2 are the signal-to-noise ratios associated with the respective bandwidth segments. It shows the proportional weighting coefficients. Pilot densities 𝐾1 and 𝐾2 are determined such that their greatest common divisor (gcd)(𝐾1, 𝐾2) is minimized. is selected. As a result, an extended and precise range is obtained, which is proportional to the following: is being done; 𝑙𝑐𝑚 (𝐾1, 𝐾2). 𝐶 2. 𝐾1. 𝐾2. ∆𝑓 Here l𝑐𝑚 (𝐾1, 𝐾2) is the least common multiple of the pilot densities, 𝐶 is the speed of light, and ∆𝑓 is the sub-density. It is the carrier range. The receiver distinguishes between the uncertainty ranges associated with multiple bandwidth segments by 25. By using it, it suppresses ghost targets while maintaining detection accuracy and provides an effective, precise range. It expands. The method described here involves receiving positioning reference signals and estimating targets. the creation of, whether the target predictions fall within a precise range 30 Identification and matching, verification, determination of the level of uncertainty, reconstruction. and the steps involved in performing cross-bandwidth processing, including fusion 7 by a data processing device that includes a processor configured to perform this task This is possible. The processor is configured to execute instructions stored in memory. This can be done using a general-purpose processor, a digital signal processor, or specialized hardware. It can be implemented as a circuit. The functionality described here, when performed by a data processing device, enables data processing. a command that enables the device to perform the steps described here It can also be implemented in the form of a computer program. A computer program can be stored on a memory device, a magnetic storage medium, or an optical storage 10 including but not limited to non-volatile storage media such as medium, It can be stored on a computer-readable data carrier. The method described shows that multipath propagation and closely spaced targets increase the uncertainty effects. Integrated sensing and communication in sub-6 GHz environments, including urban scenarios. 15 This can be applied to deployments. In such scenarios, multiple units are needed in close proximity. The presence of a target can lead to overlapping, ambiguous replicas, thus creating phantom targets. It can increase the frequency of occurrence. The method requires accurate detection of multiple closely spaced targets under dynamic conditions. especially for vehicle and automotive sensing applications that utilize the base station infrastructure. It is suitable. Interband processing enables the suppression of ghost targets and range. This improves the reliability of speed predictions. The method provides the necessary 25 multi-target range-Doppler estimation with a reduced probability of false alarms. It can also be applied in smart city and industrial automation environments. Multiple The use of bandwidth partitioning and coherence-based matching enables frequency-selective channeling. It improves the robustness of the system under these conditions.

Claims

9 REQUESTS 1. A computer-based method for target detection in an integrated sensing and communication system. It is characterized by the following: • at least one first bandwidth section with a first pilot density of 5 having a second pilot density higher than the first pilot density Multiple non-overlapping bandwidth sections, including a second bandwidth section. Receiving positioning reference signals transmitted via, • a first target estimate set from the first bandwidth section and the second bandwidth Creating a second target forecast set from the width section and the second 10 the predefined target prediction set associated with the second bandwidth segment determining whether it falls within a precise range, • It was determined that at least one of the target predictions was not within the precise range. In response, cross-bandwidth processing including the following implementation 15 based on a range consistency condition and a speed consistency condition Matching target estimates from the first and second sets, By maintaining the predictions that meet those consistency conditions and an amplitude threshold, and By rejecting detections identified as ghost targets, the matched target confirmation of predictions, 20 For each verified target, a difference between the associated target estimates. Determining a level of uncertainty based on that level of uncertainty and the precise relation to the second bandwidth section a precise range estimate based on the range interval creation, 25 using weighting coefficients that depend on the signal-to-noise ratio, First and second targets with reconstructed range estimate a final target estimate by combining at least one of the estimates creation.

2. A method according to claim 1, where the precise range interval associated with the second BWP is given. It is defined as follows: ∆𝑅𝑎𝑚𝑏 2 = c 2𝐾2∆𝑓 Here, 𝐾2 is the pilot intensity of the second BWP, 𝑐 is the speed of light, and ∆𝑓 is the subcarrier interval.

3. A method according to claim 1, where the second set of target estimates is the second band located within a predetermined, precise range interval associated with the width section. Determining whether or not you received it involves verifying the following: 5 𝑅𝑖 ^2 < ∆𝑅𝑎𝑚𝑏 2 , here 𝑅𝑖 ^2 is a range estimate obtained from the second BWP and ∆𝑅𝑎𝑚𝑏 2, with the second BWP It is the related precise range interval.

4. A method according to Claim 1, where the range consistency condition is one of the following 10 includes verification, |R ^1 − 𝑅𝑖′ ^2| mod ∆𝑅𝑎𝑚𝑏 2 < ε𝑅 here 𝑅𝑖 ^1 is a range estimate derived from the first BWP, 𝑅𝑖′ ^2 obtained from the second BWP This is a range estimate, ∆𝑅𝑎𝑚𝑏 2 is the precise range interval associated with the second BWP and εR It is a predefined range threshold. 15 5. A method according to Claim 1, where the speed consistency condition is the following: includes verification, |w ^1 − 𝑣𝑖′ ^2 | < ε𝑣, here v𝑣𝑖 ^1 is a speed estimate derived from the first BWP, 𝑣𝑖′ ^2 obtained from the second BWP 20 εv is a given speed estimate, and εv is a predefined speed threshold.

6. This is a method according to claim 1, where the level of uncertainty is as follows: is determined, 𝑚𝑖 ^ = ⌊ 𝑅𝑖 1− 𝑹 𝑖′ 2 ∆𝑅𝑎𝑚𝑏 2 ⌉ 25 here 𝑚𝑖 ^ is a degree of uncertainty associated with a goal, 𝑅𝑖 1 obtained from the first BWP This is a range estimate, 𝑹𝑖′ 2 is a range estimate obtained from the second BWP. ∆𝑅𝑎𝑚𝑏 2 is the precise range associated with the second BWP.

7. This is a method according to claim 1, whereby the reconstruction is as follows: It includes the calculation. 11 𝑅𝑖 ^𝑓𝑢𝑠𝑒𝑑 = R ^2 + 𝑚𝑖 ^. ∆𝑅𝑎𝑚𝑏 2 , here 𝑅𝑖 ^𝑓𝑢𝑠𝑒𝑑 , is a reconstructed precise range estimate, 𝑅𝑖 ^2 from the second BWP This is a range estimate obtained, 𝑚𝑖 ^ is a degree of uncertainty and . ∆𝑅𝑎𝑚𝑏 2 second BWP This is the precise range associated with it.

8. A method according to Claim 1, whereby the final target estimate is generated as follows: includes, 𝑅𝑖 ^ = 𝑤1𝑅𝑖 ^𝑓𝑢𝑠𝑒𝑑 + 𝑤2𝑅𝑖 ^2 𝑤1+ 𝑤2 , 𝑣𝑖 ^ = 𝑤1𝑣𝑖 ^1+ 𝑤2𝑣𝑖 ^2 𝑤1+ 𝑤2 here 𝑅𝑖 ^ is an estimate of the final range, 𝑤1 is an estimate of the final speed, 𝑅𝑖 ^𝑓𝑢𝑠𝑒𝑑 again This is a generated range estimate, 𝑅𝑖 ^2 is a range obtained from the second BWP 10 It is an estimate, 𝑣𝑖 ^1 and 𝑣𝑖 ^2 obtained from the first and second bandwidth sections respectively It shows speed estimates and is associated with the respective bandwidth segments 𝑤1 and 𝑤2. It shows the weighting coefficients proportional to the signal-to-noise ratios.

9. A method according to Claim 1, where the first and second bandwidth sections are piloted. 15 The densities 𝐾1 and 𝐾2 are such that the greatest common divisor gcd(𝐾1, 𝐾2) is minimized. are selected, where 𝐾1 and 𝐾2 are the first and second bandwidth sections, respectively. This refers to pilot densities.

10. A method according to claim 1, where an extended precise range is given below with 20 is proportional, 𝑙𝑐𝑚 (𝐾1, 𝐾2). 𝐶 2. 𝐾1. 𝐾2. ∆𝑓 Here, l𝑐𝑚 (𝐾1, 𝐾2) is the least common multiple of the pilot densities, 𝐶 is the speed of light, and ∆𝑓 It is the sub-carrier spacing.

11. Perform the steps of the method that corresponds to any of claims 1 to 10. A data processing device containing a configured processor.

12. When executed by a data processing device, the data processing device must comply with Claim 1 to 30 that enables the user to perform the steps of the method specified in any of the 10. A computer program that contains commands. 12 13. The computer on which the computer program product described in Claim 12 is stored. a data carrier that can be read by the robot.

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