Description:TECHNICAL FIELD
The present disclosure relates generally to the field of millimeters (mm) wave communication. More particularly, the present disclosure relates to a system for channel estimation and a method thereof.
BACKGROUND
Millimetre wave (mmWave) communications is a promising technology for both current and the next-generation wireless cellular networks due to its huge potential for addressing spectrum scarcity. Due to the limited scattering effects at mmWave frequencies, the mmWave communication is susceptible to impediments. Recent advancements in wireless communication systems include the use of intelligent reflecting surfaces (IRSs), mainly due to their ability to provide favorable wireless propagation environment. For the mmWave communications, the IRSs can reflect the incident signals to create an efficient virtual line of sight (LoS) link when the direct LoS links between the transceivers are obstructed. Despite the significant advantages of the aforementioned IRS assisted mmWave MIMO system, accurate channel estimation is difficult due to the lack of signal processing capability in passive reflecting elements. Recently, several studies have explored channel prediction in IRS-assisted mmWave MIMO system. Since the mmWave channel is sparse in nature, it is possible to directly apply common compressive sensing (CS) techniques like the generalized approximate message passing (GAMP), orthogonal matching pursuit (OMP), and sparse Bayesian learning (SBL) algorithms to the mmWave MIMO systems with IRS assisted channel estimation.
There are already some channel estimation schemes for IRS-assisted wireless communications on sub-6GHz band. However, these schemes are not applicable to the IRS-assisted mmWave multiple-input-multiple-output (MIMO) communications. The reasons are listed as follows: 1) the channel matrix has a large size due to the large-scale antenna arrays deployed at both transceivers and IRS for mmWave communications, which makes the entry wise channel estimation schemes suffer from prohibitive computation complexity and pilot overhead, 2) the random phase of IRS is not reasonable for high-directional mmWave links since beam misalignment can significantly degrade the channel estimation performance. Despite these difficulties, the research on channel estimation for IRS assisted mmWave communications is still very limited. Specifically, by exploiting the sparsity of mmWave channels, there are studies on compressive sensing (CS) based cascaded channel estimation for IRS-assisted multiple-input-single-output (MISO) systems with single-antenna transmitter/receiver. Moreover, studies also indicate to divide the cascaded channel estimation into two stages, i.e., estimating the BS-IRS channel and UE-IRS channel by equipping IRS with active RF chains, which makes channel estimation a high energy consuming operation. Prior arts also suggest a beam training-based channel estimation scheme. However, due to the imperfect hardware manufacturing technique, it is impractical to fabricate IRS with infinite phase resolution, which severely degrades the accuracy of channel estimation. Furthermore, prior arts also indicate high-resolution cascaded channel estimation for IRS-assisted mmWave MIMO communications, while it is imperative for further system optimization and data transmission. The major work in the literature focus on the offline greedy algorithms e.g., orthogonal matching pursuit (OMP), sparse Bayesian learning (SBL), however, However, these algorithms have a substantial high computation and storage cost due to multiple matrix inversion in each iteration, hence, they provide lower estimation speed (i.e. higher latency) which is not suitable for real time applications. These problems are addressed in the proposed adaptive filtering framework.
Therefore, there exists a need for an improved technique that can solve the aforementioned problems of conventional channel estimation techniques.
SUMMARY
In view of the foregoing, a system for channel estimation for millimeter-wave (mmWave) Multiple-Input Multiple-Output (MIMO) with Intelligent Reflecting Surface (IRS) is disclosed. The system includes a receiver, an adaptive filter, and processing circuitry. The receiver is configured to receive one or more signals transmitted through a communication channel. The processing circuitry is coupled to the receiver and the adaptive filter. The processing circuitry is configured to generate a reference signal associated with the one or more transmitted signals. The processing circuitry is further configured to initialize one or more filter coefficients for the adaptive filter. The processing circuitry is further configured to iteratively calculate, for each signal of the one or more signals, an error signal based on the received signal and an estimated signal. The processing circuitry is further configured to iteratively adjust, for each signal of the one or more signals, the one or more filter coefficients for the adaptive filter employing a Variable Step Size Least Mean Square (VSS-LMS) technique to adjust a step size of the one or more filter coefficients. The processing circuitry is further configured to iteratively apply, for each signal of the one or more signals, an exponential forgetting window to weight one or more historical observations. The processing circuitry is further configured to exponentially assign decreasing weights for one or more older observations. Specifically, the processing circuitry is configured to exponentially assign decreasing weights relative to one or more recent observations, to adaptively update the channel estimation based on changing channel conditions.
In some embodiments of the present disclosure, the system further includes a transmitter. The transmitter is configured to transmit the one or more signals to the receiver through the communication channel.
In some embodiments of the present disclosure, to balance between fast convergence and stability, the step size is dynamically adjusted based on one of, a convergence behavior and a set of pre-defined criteria.
In some embodiments of the present disclosure, to generate the estimated signal, the processing circuitry is configured to filter the reference signal with the current filter coefficients.
In some aspects of the present disclosure, a method for channel estimation for millimeter-wave (mmWave) Multiple-Input Multiple-Output (MIMO) with Intelligent Reflecting Surface (IRS) is disclosed. The method includes a step of receiving, by way of a receiver, one or more signals transmitted through a communication channel. The method further includes a step of generating, by way of processing circuitry coupled to the receiver, a reference signal associated with the one or more transmitted signals. The method further includes a step of initializing, by way of the processing circuitry, one or more filter coefficients for an adaptive filter that is coupled to the processing circuitry. The method further includes a step of iteratively calculating, for each signal of the one or more signals, by way of the processing circuitry, an error signal based on the received signal and an estimated signal. The method further includes a step of iteratively, adjusting, for each signal of the one or more signals, by way of the processing circuitry, one or more filter coefficients for the adaptive filter employing a Variable Step Size Least Mean Square (VSS-LMS) technique to adjust a step size of the one or more filter coefficients. The method further includes a step of iteratively, applying, for each signal of the one or more signals, by way of the processing circuitry, an exponential forgetting window to weight one or more historical observations such that one or more older observations are assigned exponentially decreasing weights relative to one or more recent observations, to adaptively update the channel estimation based on changing channel conditions.
In some embodiments of the present disclosure, the method further comprises transmitting, by way of a transmitter coupled to the receiver, the one or more signals to the receiver through the communication channel.
In some embodiments of the present disclosure, to balance between fast convergence and stability, the step size is dynamically adjusted based on one of, a convergence behavior and a set of pre-defined criteria.
In some embodiments of the present disclosure, to generate the estimated signal, the processing circuitry is configured to filter the reference signal with the current filter coefficients.
BRIEF DESCRIPTION OF DRAWINGS
The above and still further features and advantages of aspects of the present disclosure becomes apparent upon consideration of the following detailed description of aspects thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
FIG. 1 illustrates a block diagram of a system 100 for channel estimation for millimeter-wave (mmWave) Multiple-Input Multiple-Output (MIMO) with Intelligent Reflecting Surfaces (IRS), in accordance with an embodiment of the present disclosure; and
FIG. 2 illustrates a flowchart for a method 200 for channel estimation for millimeter-wave (mmWave) Multiple-Input Multiple-Output (MIMO) with Intelligent Reflecting Surface (IRS), in accordance with an embodiment of the present disclosure.
To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures.
DETAILED DESCRIPTION
Various aspects of the present disclosure provide a system for channel estimation and a method thereof. The following description provides specific details of certain aspects of the disclosure illustrated in the drawings to provide a thorough understanding of those aspects. It should be recognized, however, that the present disclosure can be reflected in additional aspects and the disclosure may be practiced without some of the details in the following description.
The various aspects including the example aspects are now described more fully with reference to the accompanying drawings, in which the various aspects of the disclosure are shown. The disclosure may, however, be embodied in different forms and should not be construed as limited to the aspects set forth herein. Rather, these aspects are provided so that this disclosure is thorough and complete, and fully conveys the scope of the disclosure to those skilled in the art. In the drawings, the sizes of components may be exaggerated for clarity.
It is understood that when an element or layer is referred to as being “on,” “connected to,” or “coupled to” another element or layer, it can be directly on, connected to, or coupled to the other element or layer or intervening elements or layers that may be present. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.
The subject matter of example aspects, as disclosed herein, is described specifically to meet statutory requirements. However, the description itself is not intended to limit the scope of this disclosure. Rather, the inventor/inventors have contemplated that the claimed subject matter might also be embodied in other ways, to include different features or combinations of features similar to the ones described in this document, in conjunction with other technologies. Generally, the various aspects including the example aspects relate to a system for channel estimation and a method thereof.
As mentioned there remains a need to provide an improved technique that can solve the problems of conventional channel estimation techniques. Accordingly, the present disclosure provides a system and a method for channel estimation for millimeter-wave (mmWave) Multiple-Input Multiple-Output (MIMO) with Intelligent Reflecting Surfaces (IRS). The system of the present disclosure facilitates adaptive filtering to automatically modify filter's parameters or coefficients to minimize a specific objective function which is often related to an error or difference between the filter's output and a desired or target signal. The adaptation process allows the filter to continually adjust to changes in the input signal or to compensate for varying environmental conditions) based algorithms to solve the compressive sensing problem.
FIG. 1 illustrates a block diagram of a system 100 for channel estimation for millimeter-wave (mmWave) Multiple-Input Multiple-Output (MIMO) with Intelligent Reflecting Surfaces (IRS), in accordance with an embodiment of the present disclosure. The system 100 may employ adaptive filtering algorithms for downlink IRS assisted mmWave MIMO channel estimation that provide improved accuracy and tracking at lower computational and storage cost that is desired for an estimator. The system 100 may facilitate adaptive filtering to automatically modify filter's parameters or coefficients to minimize a specific objective function which is often related to an error or difference between the filter's output and a desired or target signal. The adaptation process may allow the filter to continually adjust to changes in the input signal or to compensate for varying environmental conditions) based algorithms to solve the compressive sensing problem. The system 100 employs a variable step size exponential forgetting window least mean square (VSS EFWLMS) algorithms-based channel estimator for IRS-aided mmWave MIMO channel estimation. This substantially simplifies the channel estimation procedure and the subsequent data detection process. The system 100 only relies on instantaneous estimates of the second order statistics of the signal, namely the cross-covariance vector and auto covariance matrix, but do not require prior knowledge of these quantities for the IRS-aided mmWave MIMO channel. Therefore, the system 100 may be eminently suited for both stationary and non-stationary environments, and thereby strengthening practical importance/implementation of the system 100. The VSSEFWLMS (SVSSEFWLMS) framework that may be employed in the system 100 may facilitate use of a regularized cost function by incorporating a sparsity including penalty term along with the standard mean squared observation error (MSOE). The system 100 may advantageously exhibit lower computational complexity and memory storage, which may result in higher estimation speed (i.e., lower latency) since no matrix inversion is involved in the composite channel estimation.
The system 100 may include a transmitter 102, a receiver 104, an adaptive filter 106, and processing circuitry 108. The transmitter 102, the receiver 104, the adaptive filter 106, and the processing circuitry 108 may be communicatively coupled to each other. Specifically, the transmitter 102, the receiver 104, the adaptive filter 106, and the processing circuitry 108 may be communicatively coupled to each other by way of a communication channel 110.
The communication channel 110 may be configured to enable the transmitter 102, the receiver 104, the adaptive filter 106, and the processing circuitry 108 to communicate with each other. Examples of the communication channel 110 may include, but not limited to, a modem, a network interface such as an Ethernet card, a communication port, and/or a Personal Computer Memory Card International Association (PCMCIA) slot and card, an antenna, a radio frequency (RF) transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a coder-decoder (CODEC) chipset, a subscriber identity module (SIM) card, and a local buffer circuit. It will be apparent to a person of ordinary skill in the art that the communication channel 110 may include any device and/or apparatus capable of providing wireless or wired communications between the transmitter 102, the receiver 104, the adaptive filter 106, and the processing circuitry 108.
The communication channel 110 may include suitable logic, circuitry, and interfaces that may be configured to provide a number of network ports and a number of communication channels for transmission and reception of data related to operations of various entities (such as the transmitter 102, the receiver 104, the adaptive filter 106, and the processing circuitry 108) of the system 100. Each network port may correspond to a virtual address (or a physical machine address) for transmission and reception of the communication data. For example, the virtual address may be an Internet Protocol Version 4 (IPV4) (or an IPV6 address) and the physical address may be a Media Access Control (MAC) address. The communication channel 110 may be associated with an application layer for implementation of communication protocols based on one or more communication requests from the transmitter 102, the receiver 104, the adaptive filter 106, and the processing circuitry 108. The communication data may be transmitted or received, via the communication protocols. Examples of the communication protocols may include, but not limited to, Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), Simple Mail Transfer Protocol (SMTP), Domain Network System (DNS) protocol, Common Management Interface Protocol (CMIP), Transmission Control Protocol and Internet Protocol (TCP/IP), User Datagram Protocol (UDP), Long Term Evolution (LTE) communication protocols, or any combination thereof.
In some aspects of the present disclosure, the communication data may be transmitted or received via at least one communication channel 110 of a number of communication channels in a communication network. The communication channel 110 may include, but is not limited to, a wireless channel, a wired channel, a combination of wireless and wired channel thereof. The wireless or wired channel may be associated with a data standard which may be defined by one of a Local Area Network (LAN), a Personal Area Network (PAN), a Wireless Local Area Network (WLAN), a Wireless Sensor Network (WSN), Wireless Area Network (WAN), Wireless Wide Area Network (WWAN), a metropolitan area network (MAN), a satellite network, the Internet, an optical fiber network, a coaxial cable network, an infrared (IR) network, a radio frequency (RF) network, and a combination thereof. Aspects of the present disclosure are intended to include and/or otherwise cover any type of communication channel, including known, related art, and/or later developed technologies.
The transmitter 102 may be configured to transmit one or more signals. Specifically, the transmitter 102 may be configured to transmit the one or more signals to the receiver 104 through the communication channel 110.
The receiver 104 may be configured to receive the one or more signals. Specifically, the receiver 104 may be configured to receive the one or more signals through the communication channel 110.
The processing circuitry 108 may include suitable logic, instructions, circuitry, interfaces, and/or codes for executing various operations. In some aspects of the present disclosure, the processing circuitry 108 may utilize one or more processors such as Arduino or raspberry pi or the like. Examples of the processing circuitry 108 may include, but not limited to, an application-specific integrated circuit (ASIC) processor, a reduced instruction set computing (RISC) 10 processor, a complex instruction set computing (CISC) processor, a field-programmable gate array (FPGA), a Programmable Logic Control unit (PLC), and the like. Aspects of the present disclosure are intended to include or otherwise cover any type of processing circuitry 108 including known, related art, and/or later developed processing units.
The processing circuitry 108 may be configured to the receiver 104 and the adaptive filter 106. The processing circuitry 108 may be configured to generate a reference signal that may be associated with the one or more transmitted signals. The processing circuitry 108 may be further configured to initialize one or more filter coefficients for the adaptive filter 106. The processing circuitry 108 may be further configured to iteratively calculate, for each signal of the one or more signals, an error signal based on the received signal and an estimated signal. The processing circuitry 108 may be further configured to iteratively adjust, for each signal of the one or more signals, adjust the one or more filter coefficients for the adaptive filter 106 employing a Variable Step Size Least Means Square (VSS-LMS) technique to adjust a step size of the one or more filter coefficients. The processing circuitry 108 may be further configured to, for each signal of the one or more signals, apply an exponential forgetting window to weight one or more historical observations. Specifically, the processing circuitry 108, for one or more older observations, may be configured to assign exponentially decreasing weights relative to one or more recent observations. The processing circuitry 108 may thus be configured to adaptively update the channel estimation based on changing channel conditions.
The processing circuitry 108 may employ the variable step size exponential forgetting window least mean square (VSS EFWLMS) framework for IRS-aided mmWave MIMO channel estimation. The VSS EFWLMS framework facilitates adaptive learning for the system 100. The VSS EFWLMS framework facilitates dynamically adjusting the step size, allowing for adaptive learning based on the convergence behavior of the channel. The VSS EFWLMS framework further facilitates fast convergence by allowing larger steps during initial stages when significant adaptation is needed and smaller steps during steady-state conditions. The VSS EFWLMS framework facilitates fast convergence and adaptive learning that reduces delay in channel estimation and thus making the system 100 suitable for real time applications. The VSS EFWLMS framework facilitates efficient use of the channel state information, and the adaptive nature of the framework improves efficiency in channel estimation. The system 100, by virtue of the VSS EFWLMS framework, exhibits low computational complexity, and the variable step size adaptation adds efficiency without significantly increasing computational demands. The system 100 facilitates sparse channel adaptation. The VSS EFWLMS framework may exploit sparsity in mmWave channel that further enhances efficiency of the system 100. The system 100, by virtue of the VSS EFWLMS, may be adaptable due to the online nature and thus the system 100 exhibits robustness to changes in the channel conditions, such as fading or interference.
In some embodiments of the present disclosure, to balance between fast convergence and stability, the processing circuitry 108 may be configured to dynamically adjust the step size. Preferably, the processing circuitry 108 may be configured to dynamically adjust the step size based on one of, a convergence behavior and a set of pre-defined criteria.
In some embodiments of the present disclosure, to generate the estimated signal, the processing circuitry 108 may be configured to filter the reference signal with the current filter coefficients.
FIG. 2 illustrates a flowchart for a method 200 for channel estimation for millimeter-wave (mmWave) Multiple-Input Multiple-Output (MIMO) with Intelligent Reflecting Surface (IRS). The method 200 includes the following steps for channel estimation for millimeter-wave (mmWave) Multiple-Input Multiple-Output (MIMO) with Intelligent Reflecting Surface (IRS).
At step 202, the system 100 may be configured to transmit the one or more signals. Specifically, the system 100, by way of the transmitter 102, may be configured to transmit the one or more signals through the communication channel 110.
At step 204, the system 100 may be configured to receive the one or more signals. Specifically, the system 100, by way of the receiver 104, may be configured to receive the one or more signals through the communication channel 110.
At step 206, the system 100 may be configured to generate the reference signal that may be associated with the one or more transmitted signals. Specifically, the system 100, by way of the processing circuitry 108, may be configured to generate the reference signals that may be associated with the one or more transmitted signals.
At step 208, the system 100 may be configured to initialize the one or more filter coefficients for the adaptive filter 106. Specifically, the system 100, by way of the processing circuitry 108, may be configured to initialize the one or more filter coefficients for the adaptive filter 106 that may be coupled to the processing circuitry 108.
At step 210, the system 100 may be configured to iteratively calculate, for each signal of the one or more signals, the error signal based on the received signal and the estimated signal. Specifically, the system 100, by way of the processing circuitry 108, may be configured to iteratively calculate, for each signal of the one or more signals, the error signal based on the received signal and the estimated signal.
At step 212, the system 100 may be configured to iteratively adjust, for each signal of the one or more signals, the one or more filter coefficients for the adaptive filter 106 employing a Variable Step Size Least Mean Square (VSS-LMS) technique to adjust a step size of the one or more filter coefficients. Specifically, the system 100 may be configured to, by way of the processing circuitry 108, iteratively adjust, for each signal of the one or more signals, the one or more filter coefficients for the adaptive filter 106 employing a Variable Step Size Least Mean Square (VSS-LMS) technique to adjust a step size of the one or more filter coefficients.
At step 214, the system 100 may be configured to apply, for each signal of the one or more signals, the exponential forgetting window to weight one or more historical observations. Specifically, the system 100, by way of the processing circuitry 108, may be configured to, apply, for each signal of the one or more signals, the exponential forgetting window to weight one or more historical observations such that one or more older observations are assigned exponentially decreasing weights relative to one or more recent observations, to adaptively update the channel estimation based on changing channel conditions.
In some embodiments of the present disclosure, to balance between fast convergence and stability, the processing circuitry 108 may be configured to dynamically adjust the step size. Preferably, the processing circuitry 108 may be configured to dynamically adjust the step size based on one of, a convergence behavior and a set of pre-defined criteria.
In some embodiments of the present disclosure, to generate the estimated signal, the processing circuitry 108 may be configured to filter the reference signal with the current filter coefficients.
The foregoing discussion of the present disclosure has been presented for purposes of illustration and description. It is not intended to limit the present disclosure to the form or forms disclosed herein. In the foregoing Detailed Description, for example, various features of the present disclosure are grouped together in one or more aspects, configurations, or aspects for the purpose of streamlining the disclosure. The features of the aspects, configurations, or aspects may be combined in alternate aspects, configurations, or aspects other than those discussed above. This method of disclosure is not to be interpreted as reflecting an intention the present disclosure requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed aspect, configuration, or aspect. Thus, the following claims are hereby incorporated into this Detailed Description, with each claim standing on its own as a separate aspect of the present disclosure.
Moreover, though the description of the present disclosure has included description of one or more aspects, configurations, or aspects and certain variations and modifications, other variations, combinations, and modifications are within the scope of the present disclosure, e.g., as may be within the skill and knowledge of those in the art, after understanding the present disclosure. It is intended to obtain rights which include alternative aspects, configurations, or aspects to the extent permitted, including alternate, interchangeable and/or equivalent structures, functions, ranges or steps to those claimed, whether or not such alternate, interchangeable and/or equivalent structures, functions, ranges or steps are disclosed herein, and without intending to publicly dedicate any patentable subject matter. ,
Claims:1. A system (100) for channel estimation for millimeter-wave (mmWave) Multiple-Input Multiple-Output (MIMO) with Intelligent Reflecting Surface (IRS), the system (100) comprising:
a receiver (104) configured to receive one or more signals transmitted through a communication channel (110);
an adaptive filter (106);
processing circuitry (108) that is coupled to the receiver (104) and the adaptive filter (106), and configured to:
generate a reference signal associated with the one or more transmitted signals;
initialize one or more filter coefficients for the adaptive filter (106);
iteratively perform for each signal of the one or more signals:
calculate an error signal based on the received signal and an estimated signal;
adjust the one or more filter coefficients for the adaptive filter (106) employing a Variable Step Size Least Mean Square (VSS-LMS) technique to adjust a step size of the one or more filter coefficients; and
apply an exponential forgetting window to weight one or more historical observations, wherein for one or more older observations, exponentially decreasing weights are assigned relative to one or more recent observations, to adaptively update the channel estimation based on changing channel conditions.

2. The system (100) as claimed in claim 1, further comprising a transmitter (102) configured to transmit the one or more signals to the receiver (104) through the communication channel (110).

3. The system (100) as claimed in claim 1, wherein, to balance between fast convergence and stability, the step size is dynamically adjusted based on one of, a convergence behavior and a set of pre-defined criteria.

4. The system (100) as claimed in claim 1, wherein to generate the estimated signal, the processing circuitry (108) is configured to filter the reference signal with the current filter coefficients.

5. A method (200) for channel estimation for millimeter-wave (mmWave) Multiple-Input Multiple-Output (MIMO) with Intelligent Reflecting Surface (IRS), the method (200) comprising:
receiving (204), by way of a receiver (104), one or more signals transmitted through a communication channel (110);
generating (206), by way of processing circuitry (108) coupled to the receiver (104), a reference signal associated with the one or more transmitted signals;
initializing (208), by way of the processing circuitry (108), one or more filter coefficients for an adaptive filter (106) that is coupled to the processing circuitry (108);
iteratively performing for each signal of the one or more signals;
calculating (210), by way of the processing circuitry (108), an error signal based on the received signal and an estimated signal;
adjusting (212), by way of the processing circuitry (108), one or more filter coefficients for the adaptive filter (106) employing a Variable Step Size Least Mean Square (VSS-LMS) technique to adjust a step size of the one or more filter coefficients; and
applying (214), by way of the processing circuitry (108), an exponential forgetting window to weight one or more historical observations such that one or more older observations are assigned exponentially decreasing weights relative to one or more recent observations, to adaptively update the channel estimation based on changing channel conditions.

6. The method (200) as claimed in claim 5, further comprising transmitting (202), by way of a transmitter (102) coupled to the receiver (104), the one or more signals to the receiver (104) through the communication channel (110).

7. The method (200) as claimed in claim 5, wherein, to balance between fast convergence and stability, the step size is dynamically adjusted based on one of, a convergence behavior and a set of pre-defined criteria.

8. The method (200) as claimed in claim 5, wherein to generate the estimated signal, the processing circuitry (108) is configured to filter the reference signal with the current filter coefficients.