Method for channel estimation in irs-enabled wireless communication systems based on angular domain suppression in angular domain

Abstract

This invention introduces an advanced framework for channel estimation in IRS-enabled wireless communication systems, leveraging a novel concept of "angular domain suppression" in the angular domain By strategically segmenting large antenna arrays and classifying scattering clusters into ordinary and controlled types, the method allows efficient decomposition of the channel into Type-S, Type-L, and Type-M channels. Passive beamforming at the IRS and active beamforming at the base station enable precise manipulation of signal reflections to control support sets. The process incorporates compressive sensing, compressive sensing, MMSE, LS, or machine learning techniques and metric-driven estimation, minimizing pilot overhead and computational complexity. The framework ensures resource-efficient estimation by terminating when predefined performance metrics are achieved. Furthermore, the system optimizes channel modeling through a hybrid approach, integrating ordinary scatterers and IRS elements for improved signal accuracy. This innovation provides a robust solution for enhancing communication reliability and efficiency in modern IRS-enabled networks.

Description

[0001] SPECIFICATION

[0002] METHOD FOR CHANNEL ESTIMATION IN IRS-ENABLED WIRELESS COMMUNICATION SYSTEMS BASED ON ANGULAR DOMAIN SUPPRESSION IN ANGULAR DOMAIN

[0003] Technical Field:

[0004] In this invention, it was presented a novel method for channel estimation in Intelligent Reflecting Surface (IRS)-enabled wireless communication systems that leverages angular domain suppression in the angular domain.

[0005] State of The Art:

[0006] The significant overhead in channel estimation poses a major challenge in IRS-enabled wireless communication systems, particularly when the IRS comprises a large number of antenna elements. This overhead arises from the need to estimate the complex channel gains between each antenna element at the transmitter, the IRS, and the receiver. The problem is further compounded by the possibility of non-stationarity over the IRS surface, where different segments of its elements experience different scattering clusters.

[0007] The problem of channel estimation overhead in IRS-enabled systems was primarily addressed by exploiting the sparsity of the channel in the angular domain and employing compressive sensing (CS) techniques. These methods leverage the fact that the channel can be represented by a small number of paths, each characterized by its angle of arrival (AoA), angle of departure (AoD), and path gain. By formulating the channel estimation problem as a sparse recovery problem, CS techniques can significantly reduce the number of pilot signals needed for accurate channel estimation. However, existing CS-based methods often assume far-field operation, where the IRS is located far away from the transmitter and receiver. This assumption simplifies the channel model but may not hold in practice, especially for large IRS deployments. Also, they do not fully address the non-stationarity issue over the IRS surface, where different segments of the IRS mayexperience different scattering environments. This can lead to inaccurate channel estimation if not properly accounted for.

[0008] There were also other approaches that aimed to improve channel estimation efficiency without relying on compressive sensing. These include:

[0009] Discrete phase shift and progressive refinement: This technique utilizes a progressive refinement approach, where the IRS phase shifts are adjusted in a step-wise manner to refine the channel estimation accuracy.

[0010] Deep denoising neural network assisted compressive channel estimation: This method employs a deep denoising neural network to aid in CS-based channel estimation, aiming to improve the accuracy and efficiency of the estimation process.

[0011] Cascaded channel estimation for large intelligent metasurface assisted massive MIMO: This approach proposes a cascaded channel estimation scheme for large IRS-assisted massive MIMO systems, breaking down the estimation problem into smaller, more manageable sub-problems.

[0012] Despite these advancements, existing solutions often treat the BS-IRS (F), IRS-UE (G), and BS-UE (H) channels separately. This can lead to increased overhead and complexity, especially when dealing with large numbers of antenna elements.

[0013] Previous documents proposed solutions for channel estimation in IRS-enabled systems with a focus on exploiting channel sparsity and utilizing compressive sensing (CS) methods. However, these methods often assume far-field operation and do not fully address the non-stationarity issue over the IRS surface, which can result in significant overhead.

[0014] Some specific solutions proposed in previous documents include:

[0015] Discrete phase shift and progressive refinement, which progressively adjusts IRS phase shifts to refine channel estimation accuracy.

[0016] Deep denoising neural network assisted compressive channel estimation, which uses a neural network to improve accuracy and efficiency.Cascaded channel estimation for large intelligent metasurface assisted massive MIMO, which breaks down the estimation problem into smaller sub-problems.

[0017] Existing solutions for channel estimation in IRS-enabled systems suffer from several drawbacks. Many methods require a large number of pilot signals for accurate estimation, leading to high training overhead, which becomes especially problematic for large IRS deployments. Furthermore, the reliance on the far-field assumption in many techniques can introduce inaccuracies, as this assumption may not hold in practical scenarios. Another key challenge is the non-stationarity of the channel over the IRS surface, where different segments experience different scattering environments. Existing methods often fail to adequately address this issue. Additionally, the common practice of treating the BS-IRS, IRS-UE, and BS-UE channels separately increases both complexity and overhead. Finally, some CS-based methods suffer from high computational complexity due to the use of large dictionaries or complex optimization algorithms, while geometricbased methods can be sensitive to model mismatches. These limitations underscore the need for a more efficient and robust approach to channel estimation in IRS-enabled systems, a need that this invention directly addresses.

[0018] This invention takes a different approach by treating the IRS as an integral part of the channel, rather than a separate entity. This allows for holistic modeling of the entire channel and leveraging the IRS's properties to manipulate the channel's support set in the angular domain. The result is a more efficient and accurate channel estimation process, even in the presence of near-field effects and non-stationarity.

[0019] Description of The Invention:

[0020] This invention offers several advantages over existing channel estimation methods in IRS-enabled systems:

[0021] Reduced Overhead: By considering the IRS as part of the channel and leveraging angular domain suppression, the proposed method significantly reduces the training overhead required for accurate channel estimation.Enhanced Robustness: The method is robust to near-field effects and variations in scattering environments, making it suitable for practical deployment scenarios.

[0022] Lower Complexity: By decomposing the channel based on scattering clusters and exploiting the IRS's properties, the proposed method can achieve lower computational complexity compared to some existing CS-based methods.

[0023] Adaptability: The method is adaptable to different IRS deployments and channel conditions, making it versatile for various applications.

[0024] In summary, this invention provides a more efficient and robust solution for channel estimation in IRS-enabled wireless communication systems, overcoming limitations of prior art and enabling enhanced performance in various deployment scenarios.

[0025] This invention pertains to wireless communication systems, specifically enhancing the process of channel estimation in the presence of Intelligent Reflecting Surfaces. IRS technology introduces unique challenges to channel estimation due to its ability to manipulate signal reflections. This invention addresses these challenges by introducing a novel method that leverages the concept of "angular domain suppression" within the angular domain of the channel. This technique allows for more efficient channel estimation by strategically controlling the IRS elements to selectively suppress or enhance certain signal paths. This leads to a significant reduction in the overhead required for channel estimation, making the process faster and less resource-intensive.

[0026] The invention is particularly beneficial for massive MIMO systems and loT networks where efficient channel estimation is crucial. By enabling more precise and efficient channel estimation, this invention contributes to the advancement of wireless communication technology, paving the way for faster, more reliable, and more efficient networks.

[0027] The purpose of this invention is to provide a novel and efficient method for channel estimation in IRS-enabled wireless communication systems. It aims to overcome the limitations of existing methods by treating the IRS as an integral part of the channel and leveraging its unique properties to manipulate the channel's support set in the angulardomain. This leads to several advantages, including reduced training overhead, enhanced robustness, lower complexity, and adaptability to different IRS deployments and channel conditions.

[0028] The invention also introduces a metric-driven approach to channel estimation, where the process is terminated once a desired performance metric is achieved, further reducing overhead. This innovative approach enables more efficient utilization of resources and enhances the performance of wireless communication systems, particularly in scenarios with large IRS deployments, massive MIMO systems, and loT networks.

[0029] This invention addresses critical technical challenges in channel estimation for Intelligent Reflecting Surface (IRS)-enabled wireless communication systems. Specifically, it tackles the limitations of conventional methods that struggle with high overhead, particularly in scenarios involving large IRS deployments and complex scattering environments.

[0030] The invention offers the following solutions:

[0031] IRS as an Integrated Channel Component: It proposes treating the IRS as an intrinsic part of the channel, rather than a separate entity. This allows for a holistic channel model that leverages the IRS's properties to manipulate signal reflections strategically.

[0032] Angular Domain Suppression: It introduces a novel technique called "angular domain suppression" to control the channel's support set in the angular domain. By selectively suppressing or enhancing certain signal paths, the method reduces overhead and improves estimation accuracy. In the method of the invention, angular domain suppression is used in three distinct angular domains associated with each channel type (Type-S, Type-L, Type-M).

[0033] Metric-Driven Estimation: It proposes a unique metric-driven approach, where the estimation process is guided by a specific performance metric. This allows for early termination of the estimation process if the desired metric is achieved, further reducing overhead.The structural and characteristic features and all advantages of the method subject to the invention will be understood more clearly thanks to the figures given below and the detailed explanation written by referring to these figures, and therefore the evaluation should be made by taking these figures and detailed explanation into consideration.

[0034] Description of the Figures:

[0035] The invention will be described with reference to the accompanying figures, so that the features of the invention will be more clearly understood and appreciated, but the purpose of this is not to limit the invention to these certain regulations. On the contrary, it is intended to cover all alternatives, changes and equivalences that can be included in the area of the invention defined by the accompanying claims. The details shown should be understood that they are shown only for the purpose of describing the preferred embodiments of the present invention and are presented in order to provide the most convenient and easily understandable description of both the shaping of methods and the rules and conceptual features of the invention. In these drawings;

[0036] Figure- 1 A view of channel decomposition and channel types,

[0037] Figure-2 A view of support sets for different channel types. It shows an example of the angular domain of each subchannel. The squares are the active angles, which means over those angles there is a strong received signal. This is just an illustrative example. Angles in the type S angular domain can be suppressed or selected by means of beamforming at the base station. The same for type-L sub-channel, but also in this sub channel it could be control those active angles and their gain by means of beamforming at IRS. Figure-3 A view of support sets manipulation during estimation. For figure 3, the steps in the estimation process were shown. In the first subfigure, it was shown how beamforming at IRS can kill all angles as if we switched off the entire type L sub-channel. The second subfigure shows how by means of beamforming at base station it was also suppressed all types of channels except type L to estimate it.The figures to help understand the present invention are numbered as indicated in the attached image and are given below along with their names.

[0038] Disclosure of References:

[0039] 1. Controlled Scattering Cluster

[0040] 2. Ordinary Scattering Cluster

[0041] 3. Heterogeneous Reflection (Diff. Scatterers)

[0042] 4. Controlled Power Gain (Quasi-LoS Link)

[0043] 5. Random or Deterministic Power Gain (Non-LoS Link)

[0044] 6. Overlapping

[0045] Ti. Type-M (HM n e)

[0046] T2. Type-L (HL n e)

[0047] T3. Type-M (HS n e)

[0048] Lx. Intelligent Reflecting Surface (IRS)

[0049] Tx„ Transmitter Which is The User In Uplink Pilot Transmission

[0050] Rx„ The Receiver Which is Base Station In Uplink Pilot Transmission

[0051] H. Support Set

[0052] Hs. Ordinary Support Set

[0053] HL. Focused IRS Support Set

[0054] HM. Mixed Support Set

[0055] Detail Description of The Invention:

[0056] This invention presents a novel framework for channel estimation in IRS-enabled wireless communication systems. The method is based on the innovative concept of "angular domain suppression" in the angular domain, which allows for efficient and accurate estimation of the channel by strategically manipulating the IRS elements to control signal reflections.

[0057] Key Components and Steps:

[0058] 1. Segmentation of Large Antenna Arrays: The large antenna array at IRS is divided into smaller segments to simplify analysis and modeling. Each segment is treatedas an independent source of radiation, giving rise to its own multipath component (MPC). The number of segments depends on factors like the distance to the scatterer, element spacing within the antenna, and the operating frequency.

[0059] Classification of Scattering Clusters: Scatterers in the environment are classified into two types: Ordinary Clusters: Random objects causing uncontrolled reflections. Controlled Clusters: Intelligent reflecting surfaces (IRSs) with segments acting as controlled scatterers.

[0060] Channel Decomposition: Based on the types of scattering clusters involved, the channel is decomposed into three types: Type-S: Reflections occur only from ordinary scatterers (uncontrolled); Type-L: Reflections occur only from IRS segments (controlled); and Type-M: Reflections involve both ordinary scatterers and IRS segments (partially controlled).

[0061] Angular domain suppression for Channel Estimation: Each channel type has support sets (H) in the angular domain, which are supposed to determine the overall channel support set (H). The support set (H) of Type-L channels can be controlled by passive beamforming at the IRS, and partially that of Type-M channels by the IRS as well. Beamforming and Suppression: Beamforming at both the IRS and BS enables suppression of the Type-L channel's angular domain (IRS reflections). Beamforming at the BS alone can suppress the Type-S channel's angular domain (ordinary scatterer reflections). Combined BS and IRS beamforming allows for suppression of the Type-M channel.

[0062] Channel Estimation Procedure: The invention proposes a three-step channel estimation procedure:

[0063] 5, L Type-L Channel Estimation:

[0064] LoS Detection: The BS focuses beams towards the IRS to suppress Type-S and Type-M channels, enabling detection of the LoS link between the IRS and UE. Each IRS segment scans the angular domain to determine the user angle and link condition (LoS or non-LoS).

[0065] Pilot Transmission and Tier-2 Beamforming: The UE transmits pilot signals. Each IRS segment focuses towards a specific angle on the IRS-UE side and reflects the signal towards the BS. Tier-2 beamforming at the IRS (e.g., using orthogonal FFT or Hadamard vectors) separates the reflections from different segments.Signal Processing: The BS performs the inverse operation (e.g., inverse FFT) to extract individual segment signals, measuring power and phase.

[0066] Angular Domain Spanning: This process is repeated for different angles in the angular domain of each segment. Exploit available information (e.g., LoS correlation, prior LoS knowledge) to reduce overhead.

[0067] User Positioning: Utilize the collected phase information to estimate the user's position.

[0068] 5.2, Type-S Channel Estimation:

[0069] IRS Phase Design: Design IRS phases to scatter signals in all directions except towards the BS, effectively suppressing Type-L and Type-M channels.

[0070] Estimation Technique: Apply any suitable estimation technique (e.g., compressive sensing, MMSE, LS, machine learning) to estimate the Type-S channel in its angular domain.

[0071] 5.3, Type-M Channel Estimation:

[0072] BS Beamforming: Focus the BS beams on the IRS, similar to the Type-L estimation process, to suppress other channel contributions.

[0073] Angular Domain Spanning and Orthogonal Pilot Transmission: Each IRS segment spans the angular domain. For each angle, use the orthogonal -based pilot transmission approach (as in Type-L estimation) to distinguish between segments.

[0074] Exploiting Available Information: Leverage any available information, such as correlation between segments, spatial consistency, or prior knowledge about the environment, to reduce estimation overhead.

[0075] Estimation Technique: Apply any suitable estimation technique (e.g., compressive sensing, MMSE, LS, machine learning) to estimate the Type-M channel for each segment.

[0076] Metric-Driven Estimation: The estimation procedure is guided by a predefined performance metric. If the desired metric is achieved in an earlier step, the process can be terminated, avoiding unnecessary overhead from further steps.Unique Elements:

[0077] - The invention uniquely defines IRS as a controllable scattering cluster, enabling manipulation of signal reflections.

[0078] - It introduces a new channel model that decomposes the channel based on the traveling paths of multipath components, classifying them into Type-S, Type-L, and Type-M channels.

[0079] - The concept of "angular domain suppression" allows for strategic manipulation of the channel's support set (H) in the angular domain, leading to efficient and accurate channel estimation.

[0080] - The metric-driven approach to estimation optimizes resource utilization by terminating the process once the desired performance metric is achieved.

[0081] The foregoing descriptions of specific embodiments of the present technology have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present technology to the precise forms disclosed. Obviously, many modifications and variations are possible considering the above teachings. The embodiments were chosen and described to best explain the principles of the present technology and its practical applications, enabling others skilled in the art to utilize the present technology and its various embodiments with appropriate modifications as suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such changes are intended to cover the software or implementation without departing from the spirit or scope of the claims of the present technology.

[0082] In cases where no conflict occurs, the embodiments in the present disclosure and their features may be combined. The foregoing descriptions are merely specific implementations of the present disclosure and are not intended to limit its protection scope. Any variation or replacement readily determined by a person skilled in the art within the technical scope of the present disclosure shall fall within its protection scope. Therefore, the protection scope of the present disclosure shall be determined by the claims.

Claims

CLAIMS1- A method for channel estimation in IRS-enabled wireless communication systems based on angular domain suppression in angular domain, comprising;performing beamforming at the IRS to suppress reflections from ordinary scatterers (Type-S channels),performing beamforming at the BS to suppress reflections from controlled scatterers (Type-L channels), andapplying combined IRS and BS beamforming to suppress partially controlled reflections (Type-M channels) in the angular domain.2- The method according to claim 1, wherein the method of decomposing the channel into three types based on scattering clusters, comprises;Type-S channel, reflections from ordinary scatterers,Type-L channel, reflections from IRS segments,Type-M channel, reflections involving both ordinary scatterers and IRS segments.3- The method according to claim 1, wherein the beamforming at the IRS is achieved by passive beamforming using orthogonal FFT or Hadamard vectors or any transformation that does the job (i.e., with orthogonal vectors based on phase changes only and constant magnitude) to separate reflections from different IRS segments.4- The method according to claim 1, wherein the Type-L channel estimation comprises; scanning the angular domain for each IRS segment to determine user position using phase information.5- The method according to claim 1, further comprising; designing IRS phases to scatter signals in all directions except towards the BS, thereby suppressing Type-L and Type-M channels during Type-S channel estimation.6- The method according to claim 5, wherein the Type-S channel estimation is performed using compressive sensing, MMSE, LS, or machine learning techniques.7- The method according to claim 1, further comprising; estimating the Type-M channel by combining orthogonal pilot transmission and angular domain spanning for each IRS segment, while leveraging correlation and spatial consistency information to reduce overhead.8- The method according to claim 1, further comprising; guiding the channel estimation process with a predefined performance metric, terminating the process when the metric is achieved to minimize unnecessary overhead.9- The method according to claim 1, further comprising a configuration for performing angular domain suppression for Type-S, Type-L, and Type-M channels through beamforming at the IRS and BS, estimating channels using a three-step procedure for Type-L, Type-S, and Type-M channels, and optimizing the estimation process using predefined performance metrics to reduce complexity and improve efficiency.10- The method according to claim 1, wherein the angular domain suppression comprises;suppressing the support set (H) of Type-L channels during the estimation of Type- S channels by applying a "scattering" phase profile at the IRS,suppressing the support set (H) of Type-S channels during the estimation of Type- L channels by focusing on known IRS angles and applying tier-2 beamforming using a Fast Fourier Transform (FFT) column at the IRS,partially suppressing the support set (H) of Type-M channels by sweeping beams at IRS segments and leveraging compressive sensing, MMSE, LS, machine learning to estimate multipath components.11- The method according to claim 1, further comprising a metric-driven estimation process, wherein;the estimation procedure is guided by a predefined performance metric, the process terminates early if the desired performance metric is achieved in an intermediate estimation step, reducing resource consumption and overhead.12- The method according to claim 1, wherein the segmentation of the large antenna array is based on factors comprises;the distance between the IRS and scatterers,the spacing between antenna elements,the operating frequency of the communication system.13- The method according to claim 1, wherein the classification of scatterers comprises;identifying ordinary clusters as random objects causing uncontrolled reflections, defining controlled clusters as IRS segments that are manipulated to control reflections through passive beamforming.14- The method according to claim 1, wherein the channel decomposition is performed to enable efficient modeling and estimation of the channel by separately addressing Type-S, Type-L, and Type-M channels.15- The method according to claim 10, wherein the compressive sensing, MMSE, LS, machine learning estimation of Type-S and Type-M channels leverages correlation between multipath components to reduce pilot overhead and estimation complexity.16- A system for channel estimation in IRS-enabled wireless communication systems, comprising;n IRS with a large antenna array segmented into smaller independently controlled segments,a base station equipped with active beamforming capabilities,a controller configured to classify scatterers into ordinary and controlled clusters, decompose the channel into Type-S, Type-L, and Type-M channels, apply angular domain suppression and passive beamforming at the IRS for manipulating signal reflections and perform metric-driven estimation to optimize resource utilization by terminating the process once the desired performance metric is achieved.17- The system of claim 16, wherein the IRS segments are controlled to generate a "scattering" phase profile for suppressing Type-L channel contributions during Type-S channel estimation.18- The system of claim 16, wherein the base station applies tier-2 beamforming with FFT columns to separate multipath components associated with Type-L channels.

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