A method and system for minimizing noise and interference in a communication network

Abstract

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. In accordance with an aspect of the disclosure, a method for minimizing noise and interference in a communication network is provided. The method comprising: estimating channel based on reference signals transmitted by user equipment (UEs) and plurality of received signals, determining noise and interference sample based on estimated channel, generating noise and interference co-variance matrix based on noise and interference sample, generating an extended channel matrix including estimated channel and at least part of noise and interference covariance matrix based on the number of UEs, generating resultant signal based on extended channel matrix and plurality of received signals, and performing, based on resultant signal, equalization by applying equalization matrix across extended channel matrix. The present disclosure eliminates intra-cell and inter-cell interferences, reduces matrix elements, fronthaul load, computational complexity.

Description

A METHOD AND SYSTEM FOR MINIMIZING NOISE AND INTERFERENCE IN A COMMUNICATION NETWORK

[0001] The present disclosure relates to the field of wireless communication and advanced antenna technologies. Specifically, it relates to method and system for minimizing noise and interference in a communication network, with applications in wireless networks including future wireless networks like 6G.

[0002] At the beginning of the development of 5G mobile communication technologies, in order to support services and to satisfy performance requirements in connection with enhanced Mobile BroadBand (eMBB), Ultra Reliable Low Latency Communications (URLLC), and massive Machine-Type Communications (mMTC), there has been ongoing standardization regarding beamforming and massive MIMO for mitigating radio-wave path loss and increasing radio-wave transmission distances in mmWave, supporting numerologies (for example, operating multiple subcarrier spacings) for efficiently utilizing mmWave resources and dynamic operation of slot formats, initial access technologies for supporting multi-beam transmission and broadbands, definition and operation of BWP (BandWidth Part), new channel coding methods such as a LDPC (Low Density Parity Check) code for large amount of data transmission and a polar code for highly reliable transmission of control information, L2 pre-processing, and network slicing for providing a dedicated network specialized to a specific service.

[0003] Currently, there are ongoing discussions regarding improvement and performance enhancement of initial 5G mobile communication technologies in view of services to be supported by 5G mobile communication technologies, and there has been physical layer standardization regarding technologies such as V2X (Vehicle-to-everything) for aiding driving determination by autonomous vehicles based on information regarding positions and states of vehicles transmitted by the vehicles and for enhancing user convenience, NR-U (New Radio Unlicensed) aimed at system operations conforming to various regulation-related requirements in unlicensed bands, NR UE Power Saving, Non-Terrestrial Network (NTN) which is UE-satellite direct communication for providing coverage in an area in which communication with terrestrial networks is unavailable, and positioning.

[0004] Moreover, there has been ongoing standardization in air interface architecture / protocol regarding technologies such as Industrial Internet of Things (IIoT) for supporting new services through interworking and convergence with other industries, IAB (Integrated Access and Backhaul) for providing a node for network service area expansion by supporting a wireless backhaul link and an access link in an integrated manner, mobility enhancement including conditional handover and DAPS (Dual Active Protocol Stack) handover, and two-step random access for simplifying random access procedures (2-step RACH for NR). There also has been ongoing standardization in system architecture / service regarding a 5G baseline architecture (for example, service based architecture or service based interface) for combining Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) technologies, and Mobile Edge Computing (MEC) for receiving services based on UE positions.

[0005] As 5G mobile communication systems are commercialized, connected devices that have been exponentially increasing will be connected to communication networks, and it is accordingly expected that enhanced functions and performances of 5G mobile communication systems and integrated operations of connected devices will be necessary. To this end, new research is scheduled in connection with eXtended Reality (XR) for efficiently supporting AR (Augmented Reality), VR (Virtual Reality), MR (Mixed Reality) and the like, 5G performance improvement and complexity reduction by utilizing Artificial Intelligence (AI) and Machine Learning (ML), AI service support, metaverse service support, and drone communication.

[0006] Furthermore, such development of 5G mobile communication systems will serve as a basis for developing not only new waveforms for providing coverage in terahertz bands of 6G mobile communication technologies, multi-antenna transmission technologies such as Full Dimensional MIMO (FD-MIMO), array antennas and large-scale antennas, metamaterial-based lenses and antennas for improving coverage of terahertz band signals, high-dimensional space multiplexing technology using OAM (Orbital Angular Momentum), and RIS (Reconfigurable Intelligent Surface), but also full-duplex technology for increasing frequency efficiency of 6G mobile communication technologies and improving system networks, AI-based communication technology for implementing system optimization by utilizing satellites and AI (Artificial Intelligence) from the design stage and internalizing end-to-end AI support functions, and next-generation distributed computing technology for implementing services at levels of complexity exceeding the limit of UE operation capability by utilizing ultra-high-performance communication and computing resources.

[0007] Multiple input multiple output (MIMO) is the fundamental technology that has been used since the third generation (3G) wireless networks to enhance the performance of the wireless systems. The idea of MIMO is to use multiple antennas at the transmitter and the receiver to increase the spectral efficiency. As the number of mobile users and data traffic has been increasing exponentially every year, there is a need for high data rates, better mobility, high spectral efficiency and larger network capacity.

[0008] Massive MIMO (mMIMO) is one of the key enablers for fifth generation (5G) networks to achieve high spectral and energy efficiency, high data rates, better mobility, and larger network capacity. Massive MIMO is the scaled version of the small-scale MIMO to support multiuser communications by employing a large (hundreds) number of antennas at the base station (BS). Generally, the cell capacity is improved by using more spectrum whereas spectral efficiency can be improved by using larger antenna arrays.

[0009] Extreme Massive MIMO (extreme mMIMO) extends the capabilities of traditional massive MIMO technology by incorporating an even larger number of antennas at the base station. Extreme mMIMO plays a key role in the development and advancement of sixth-generation (6G) wireless communication networks, aiming to achieve unprecedented levels of spectral and energy efficiency, data rates, and network capacity. Extreme mMIMO leverages advanced signal processing techniques and massive parallelism to facilitate ultra-reliable and low-latency communications, supporting a vast number of simultaneous users and devices.

[0010] Upcoming spectrum blocks for 6G are mid spectrum (7-20GHz), low spectrum (470- 694 MHz), and THz spectrum (above 90Ghz). Mid-spectrum blocks improve the urban capacity whereas low-spectrum blocks provide extreme coverage and THz spectrum blocks provide the peak data rates. It has been observed 7-20 GHz spectrum provides the balance trade-off between the extreme coverage and extreme capacity which makes it ideal for 6G deployment in future. Accordingly, it has been observed that, 6G with 512 transceivers can provide five times more spectral efficiency over 5G with 64 transceivers.

[0011] Since multiple interfering messages are transmitted from different antennas, the message signals get corrupted from the inter-cell interferences and noise. Thus, there is a need for a receiver design to cancel the inter-cell interferences and noise to get a noise and interference-free message signal.

[0012] As per the 3GPP standards, the minimum mean square error- interference rejection combining (MMSE-IRC) receiver is the commonly used receiver by different telecommunication operators. MMSE-IRC receiver suppresses the co-channel interference efficiently. However, it requires the matrix inversion. As the mMIMO and extreme mMIMO have several advantages, their benefits come with increasing computational complexity as they consist of large number of antenna elements possibly hundreds or even thousands.

[0013] Since the number of antennas in mMIMO and extreme mMIMO is extremely large, it is very difficult to inverse a matrix of such a large dimension matrix. It requires large computational complexity. Thus, there is a need to develop a low complexity receiver for mMIMO and extreme mMIMO for increasing efficiency of the existing 5G networks and future 6G deployments and other generation networks.

[0014] The well-known signal detector method and system known in the art are maximum-likelihood (ML), zero-forcing (ZF) and minimum mean square error (MMSE) receiver. The ZF receiver cancels all inter-symbol interference, but it suffers from considerable noise enhancement. The ML detectors avoid noise enhancement and provide optimal performance but it is not feasible for large MIMO systems due to high complexity. ML detector complexity increases exponentially with the number of user terminals and modulation order. The MMSE detector is the most widely used MIMO detector as it provides near-optimal performance with low complexity over ML. However, MMSE detection involves matrix inversion which is computationally costly. In order to reduce the computational complexity of MMSE detectors there are various iterative and approximation methods that have been developed. Further, there are various receivers that have been developed in the recent past based on approximate matrix inversion approaches to reduce the computational complexity. Such approximate matrix inversion approaches include the Neumann series, Newton iteration, Gauss-Seidel method, successive over-relaxation (SOR), Jacobi method, conjugate gradients methods, coordinate descent method and their variants. The performance and complexity of such methods highly depends on the number of iterations, initial values, and the relaxation parameter. However, the MMSE-based receiver does not cancel the co-channel interference efficiently. To cancel the co-channel interference MMSE-IRC receiver has been widely used by telecommunication operators. In the recent past, some distributed approach of MMSE-IRC has been developed where MMSE-IRC has been functionally split between the remote radio unit (RRU) and baseband unit (BBU) to implement in the centralized / cloud access network (C-RAN).

[0015] For Example, let, is the estimation of the channel matrixH, the MMSE estimator is given as,

[0016]

[0017] Where is the background noise power.

[0018] However, the performance of the MMSE receiver degrades in the presence of inter-and-intra-cell interferences.

[0019] In order to supress the inter-and-intra-cell interferences, MMSE-IRC receiver has been recommended by the 3GPP.

[0020] The MMSE-IRC estimator is expressed as,

[0021]

[0022] Where, is the estimation of the noise and interference covariance matrix and is noise and interference sample matrix.

[0023] Since is estimated from the pilot signals (T) and usuallyT<N("N" is the BS antenna) in massive MIMO, this leads to the non-singular and non-invertible. To overcome the non-invertible issue, a typical solution is to use diagonal loading, where a scaled valued of the identity matrix ( ( ) ) is added to , the resultant noise and interference covariance.

[0024] The MMSE-IRC receiver is said to cancel the co-channel interference efficiently. However, the MMSE-IRC receiver requires the complexity of order , where N is the BS antenna. Since mMIMO and extreme mMIMO system, consist of a large number of antenna elements, possibly hundreds or even thousands, the MMSE-IRC receiver is impractical in such systems due to huge computational complexity and increased overhead cost of C-RAN architecture.

[0025] To reduce the computational complexity without degrading the performance of the mMIMO and extreme mMIMO system, there is a need to develop a low-complexity interference rejection combining receiver to cancel the interference efficiently with low computational complexity.

[0026] The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the disclosure and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.

[0027] The present disclosure relates to wireless communication systems and, more specifically, the present disclosure relates to a method and system for minimizing noise and interference in a communication network.

[0028] In accordance with an aspect of the disclosure, a method for minimizing noise and interference in a communication network is provided. The method comprising: estimating a channel based on a plurality of reference signals transmitted by one or more user equipment to the base station and a plurality of received signals corresponding to the plurality of reference signals; determining a noise and interference sample based on the estimated channel; generating a noise and interference co-variance matrix for each resource block of a plurality of resource blocks of the estimated channel based on the noise and interference sample; generating an extended channel matrix including the estimated channel and at least a part of the noise and interference covariance matrix comprising a first number of column elements identified from first column of the noise and interference covariance matrix, wherein the first number indicates a number of UEs communicating with the base station at a given instant; generating a resultant signal based on the extended channel matrix and the plurality of received signals; performing based on resultant signal, equalization by applying an equalization matrix across the extended channel matrix in the plurality of received signals.

[0029] In accordance with an aspect of the disclosure, a base station for minimizing interference in a communication network is provided. The base station comprising: a processor and a memory, communicatively coupled to the processor. The memory stores instructions, which, on execution, causes the processor to: estimate a channel based on a plurality of reference signals transmitted by one or more user equipment (UEs) to the base station, and a plurality of received signals corresponding to the plurality of reference signals; determine a noise and interference sample based on the estimated channel; generate a noise and interference co-variance matrix for each resource block of a plurality of resource blocks of the estimated channel based on the noise and interference sample; generate an extended channel matrix including the estimated channel and at least a part of the noise and interference co-variance matrix comprising a first number of column elements identified from first column of the noise and interference co-variance matrix, wherein the first number indicates a number of UEs communicating with the base station at a given instant; generate a resultant signal based on the extended channel matrix and the plurality of received signals; perform based on the resultant signal, equalization by applying an equalization matrix across the extended channel matrix in the plurality of received signals.

[0030] In accordance with an aspect of the disclosure, a method performed by a remote radio unit (RRU) of a base station for minimizing noise and interference in a communication network is provided. The method comprising: estimating a channel based on a plurality of reference signals transmitted by one or more user equipment (UEs) to the base station and a plurality of received signals corresponding to the plurality of reference signals; determining a noise and interference sample based on the estimated channel; generating a noise and interference co-variance matrix for each resource block of a plurality of resource blocks of the estimated channel based on the noise and interference sample; generating an extended channel matrix including the estimated channel and at least a part of the noise and interference co-variance matrix comprising a first number of column elements identified from first column of the noise and interference co-variance matrix, wherein the first number indicates a number of UEs communicating with the base station at a given instant; generating a resultant signal based on the extended channel matrix and the plurality of received signals; transmitting the resultant signal through a fronthaul link to a base band unit (BBU) of the base station.

[0031] In accordance with an aspect of the disclosure, a method performed by a base band unit (BBU) of a base station for minimizing noise and interference in a communication network is provided. The method comprising: receiving a resultant signal transmitted through a fronthaul link by a RRU of the base station; performing, based on the resultant signal, equalization by applying an equalization matrix across an extended channel matrix in a plurality of received signals corresponding to a plurality of reference signals transmitted by one or more UEs to the RRU.

[0032] In accordance with an aspect of the disclosure, a remote radio unit (RRU) for minimizing noise and interference in a communication network is provide. The RRU comprising: a processor; and a memory, communicatively coupled to the processor. The memory stores instructions, which, on execution, causes the processor to: estimate a channel based on a plurality of reference signals transmitted by one or more user equipment (UEs) to the RRU, and a plurality of received signals corresponding to the plurality of reference signals; determine a noise and interference sample based on the estimated channel; generate a noise and interference co-variance matrix for each resource block of a plurality of resource blocks of the estimated channel based on the noise and interference sample; generate an extended channel matrix including the estimated channel and at least a part of the noise and interference co-variance matrix comprising a first number of column elements identified from first column of the noise and interference co-variance matrix, wherein the first number indicates a number of UEs communicating with the RRU at a given instant; generate a resultant signal based on the extended channel matrix and the plurality of received signals; and transmit the resultant signal through a fronthaul link to a base band unit (BBU).

[0033] In accordance with an aspect of the disclosure, a base band unit (BBU) for minimizing noise and interference in a communication network is provided. The BBU comprising: a processor; and a memory, communicatively coupled to the processor, said the memory stores instructions, which, on execution, causes the processor to: receive a resultant signal transmitted through a fronthaul link by a RRU of the base station; perform, based on the resultant signal, equalization by applying an equalization matrix across an extended channel matrix in a plurality of received signals corresponding to a plurality of reference signals transmitted by one or more UEs to the RRU.

[0034] To further clarify the advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which are illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail in the accompanying drawings.

[0035] The present disclosure provides extended channel matrix which is the combination of the estimated channel and the noise and interference covariance matrix across the UEs, eliminates unnecessary matrix elements and reduces the number of matrix elements, ultimately, it decreases the overall size of the extended channel matrix and consequently decreases computational complexity.

[0036] The present disclosure further provides the equalization matrix which applied across an extended channel matrix, thereby eliminates the intra-cell interferences and the inter-cell interference without the background noise.

[0037] The present disclosure enables reducing the fronthaul load of the C-RAN network, as a split techniques in which split between the remote radio units (RRU) and centralized baseband unit (BBU) in which only needs fronthaul links to transfer the data from RRU to BBU.

[0038] The present disclosure reduces the computational complexity without degrading the performance of the mMIMO and extreme mMIMO system. The present disclosure provides the Z-IRC receiver reduces the computational complexity from the order to , where is the number of pilots and is the Rx antennas.

[0039] The present disclosure enables to reducing the fronthaul load and operational cost of the C-RAN architecture by incorporating the Z-IRC.

[0040] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles. Some embodiments of system and / or methods in accordance with embodiments of the present subject matter are now described, by way of example only, and regarding the accompanying figures, in which:

[0041] Fig. 1Ashows an exemplary architecture of an extreme massive multiple input multiple output (extreme m-MIMO) architecture, in accordance with some embodiments of the present disclosure;

[0042] Fig. 1Bshows an exemplary architecture of a centralized / cloud radio access network (C-RAN), in accordance with some embodiments of the present disclosure;

[0043] Fig. 2shows a detailed block diagram of a base station, in accordance with some embodiments of the present disclosure;

[0044] Fig. 3shows a detailed block diagram of a remote radio unit (RRU) for minimizing noise and interference in a communication network, in accordance with some embodiments of the present disclosure;

[0045] Fig. 4shows a detailed block diagram of a base band unit (BBU), in accordance with some embodiments of the present disclosure;

[0046] Fig. 5shows a flowchart for a method performed by a base station for minimizing noise and interference in a communication network, in accordance with some embodiments of the present disclosure;

[0047] Fig. 6shows a flowchart for a method performed by RRU for minimizing noise and interference in a communication network, in accordance with some embodiments of the present disclosure;

[0048] Fig. 7shows a flowchart for a method performed by BBU for minimizing noise and interference in a communication network, in accordance with some embodiments of the present disclosure; and

[0049] Fig. 8shows a general computer system architecture, in accordance with some embodiments of the present disclosure.

[0050] The figures depict embodiments of the disclosure for purposes of illustration only. One skilled in the art will readily recognize from the following description that alternative embodiments of the fixture illustrated herein may be employed without departing from the principles of the disclosure described herein.

[0051] In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

[0052] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the spirit and the scope of the disclosure.

[0053] The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device, or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by "comprises… a" does not, without more constraints, preclude the existence of other elements or additional elements in the system or method.

[0054] The terms "includes", "including", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device, or method that includes a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by "includes… a" does not, without more constraints, preclude the existence of other elements or additional elements in the system or method.

[0055] As discussed in the background section, there are multiple methods known in the art, such as Maximum-Likelihood (ML), Zero-Forcing (ZF), and Minimum Mean Square Error (MMSE) receivers to eliminate the noise and interference in the communication system. However, these methods have limitation such as high computational complexity, infeasible for large MIMO systems, suffer from noise enhancement, and involve costly matrix inversion. Furthermore, the existing MMSE-IRC receiver eliminates the noise and interference in the communication system. However, the MMSE-IRC receiver has huge computational complexity and increased overhead cost in C-RAN architecture. Hence, to reduce the computational complexity without degrading the performance of mMIMO and extreme mMIMO systems, there is a need to develop a low-complexity interference rejection combining receiver to eliminate the interference efficiently with low computational complexity. The present disclosure provides a method and system for minimizing noise and interference in a communication network. In the present disclosure, initially, the base station estimates a channel based on a plurality of reference signals transmitted by User Equipment and a plurality of received signals corresponding to that reference signals. Based on the estimated channel, the base station determines the noise and interference sample and generates the noise and interference co-variance matrix. Subsequently, the base station generates an extended channel matrix which comprises the estimated channel and at least a part of the noise and interference covariance matrix. The part of the noise and interference covariance matrix is based on the number of UEs communicating with the base station at a particular instant. Thereafter, the base station generates a resultant signal based on the extended channel matrix and the plurality of received signals. Said resultant signal reduces the fronthaul load of the C-RAN network, which only needs fronthaul links to transfer the data from RRU to BBU. Based on said resultant signal, the base station equalizes the extended channel matrix by applying an equalization matrix across the extended channel matrix. The above method and system eliminate intra-cell and inter-cell interferences, reduce the fronthaul load, reduce the matrix elements and thereby reduce computational complexity of the C-RAN network, and operational cost.

[0056] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[0057] Fig.1Ashows an exemplary architecture100of an extreme massive multiple input multiple output (extreme-mMIMO) architecture100,in accordance with some embodiments of the present disclosure.In an embodiment, the architecture100may include a user equipment (UE)1021to user equipment (UE)102n(collectively referred as one or more user equipments (UEs)102or at least one of user equipments (UEs)102), which may include any mobile or non-mobile computing device designed to communicate with a base station104via an over-the air connection. In the embodiment, the UE102may include, but not limited to, a smartphone, tablet computer, wearable computer device, desktop computer, laptop computer, head-up display device, dashtop mobile equipment, mobile data terminal, electronic / engine control unit, and the like. In an embodiment, the base station104may be a centralized / cloud radio access network (C-RAN).

[0058] As shown inFig.1A, a basic uplink (UL) system employing extreme-mMIMO system with base station (BS)104consisting of "N" number of antennas that serve "K" number of signal antennas corresponding to the "K" number of UEs102. For the m-MIMO and extreme-mMIMO systems, the value of "N" is considerably larger than the value of "K", i.e.,N≫K.

[0059] Fig. 1Bshows an exemplary architecture of a centralized / cloud radio access network (C-RAN), in accordance with some embodiments of the present disclosure.Fig.1Billustrates an overview of the C-RAN architecture where centralized base band unit (BBU)110connects to a core network114via backhaul links112.Fronthaul (FH) link108connects the remote radio unit (RRU)106with a BBU110to facilitate signal transmission. The RRU106togetherwith the centralized BBU110is referred as the base station104.The base station104connects to "K" number of UEs102and consist "N" number of antennas.As shown inFig. 1A and 1B, For the m-MIMO and extreme-mMIMO systems, the value of "N" is considerable larger than the value of "K", i.e.,N≫K.

[0060] The present disclosure discloses a zero-forcing interference rejection combining (Z-IRC) receiver implemented in centralized / cloud radio access networks (C-RAN)with functional split between the RRU106and centralized BBU110of the base station104to reduce computational complexity without degrading the performance of the mMIMO and extreme mMIMO system, minimize noise and interference in a communication network and reduce fronthaul load.

[0061] In some embodiments, the method disclosed in the present disclosure is performed by the base station104. In an embodiment, for minimizing interference in a communication network, the base station104may receive a plurality of received signals corresponding to the plurality of reference signals ( ) transmitted by one or more UEs102. Said received signals may be received by RRU106, BBU110and one or more other associated entities of the base station104. The base station104may estimate a channel ( ) based on the plurality of reference signals ( ) transmitted by the one or more user equipment (UEs)102to the base station104and the plurality of received signals corresponding to the plurality of reference signals ( ). Thereafter, the base station104may determine a noise and interference sample ( ) based on the estimated channel ( ). In some embodiments, the base station104may determine the noise and interference sample ( ) based on a difference between a plurality of received signals received in response to transmission of the plurality of reference signals ( ) and a product of the plurality of reference signals ( ) with the estimated channel ( ). In some embodiments, upon determining the noise and interference sample ( ), the base station104may generate a noise and interference co-variance matrix ( )for each resource block of a plurality of resource blocks of the estimated channel ( ) based on the noise and interference sample ( ). Further, the base station104may generate an extended channel matrix ( ) comprising the estimated channel ( ) and at least a part of the noise and interference covariance matrix ( ) comprising first "K" number of column elements identified from the noise and interference covariance matrix ( ), where "K" indicates a number of UEs102communicating with the base station104at a given instant.

[0062] The base station104may thereafter generate a resultant signal based on the extended channel matrix ( ) and the plurality of received signals , and perform, based on resultant signal , equalization by applying an equalization matrix across the extended channel matrix ( ) for minimizing the noise and interference in the plurality of received signals .

[0063] The equalization matrix is generated by the base station104. The base station104may generate a beamformer matrix based on an application of a zero forcing beamformer on the extended channel matrix ( ). Thereafter, the base station104may identify row elements of a predetermined row of the beamformer matrix and generate the equalization matrix based on an identified row elements.

[0064] In some other embodiments, the method disclosed in the present disclosure may be performed in a decentralized manner by different components of the base station104such as RRU106and BBU110, wherein part of the method may be performed at the RRU106and another part of the method may be performed at the BBU110. However, this should not be construed as a limitation of the present disclosure, since other components of the base station104capable of being configured to perform the method disclosed in the present disclosure may also be used other than RRU106and BBU110.The present disclosure explains this embodiment by considering RRU106and BBU110as the components of the base station104. For instance, the RRU106may beconfigured to estimate the channel based on the plurality of reference signals ( ) transmitted by one or more UEs102to the RRU106, and the plurality of received signals corresponding to the plurality of reference signals ( ).The RRU106may be further configured to determine a noise and interference sample ( ) based on the estimated channel ( ). In some embodiments, upon determining the noise and interference sample ( ), the RRU106may generate a noise and interference co-variance matrix ( ) for each resource block of a plurality of resource blocks of the estimated channel ( ) based on the noise and interference sample ( ). Thereafter, the RRU106may generate an extended channel matrix ( ) comprising the estimated channel ( ) and at least a part of the noise and interference co-variance matrix ( ) comprising first "K" number of column elements identified from the noise and interference co-variance matrix ( ), where "K" indicates a number of UEs communicating with the RRU106at a given instant. The RRU106may then generate a resultant signal ( ) based on the extended channel matrix ( ) and the plurality of received signals and transmit the resultant signal ( ) through a fronthaul link108to a BBU110for equalization.

[0065] In an embodiment, the BBU110may receive the resultant signal transmitted through the fronthaul link108from the RRU106and perform based on the resultant signal , equalization by applying the equalization matrix across an extended channel matrix ( ) for minimizing the noise and interference in the plurality of received signals corresponding to the plurality of reference signals ( ) transmitted by one or more UEs102to the RRU106. The equalization matrix is generated by the BBU110. The BBU110may generate a beamformer matrix based on an application of a zero forcing beamformer on the extended channel matrix ( ). Thereafter, the BBU110may identify row elements of a predetermined row of the beamformer matrix and generate the equalization matrix based on an identified row elements.

[0066] Fig. 2shows a detailed block diagram of a base station104,in accordance with some embodiments of the present disclosure.

[0067] In some implementations, the base station104may include an Input / Output (I / O) interface201, a processor203 and amemory205. In an embodiment, the memory205may be communicatively coupled to the processor203. The processor203may be configured to perform one or more functions of the base station104, using data207and the one or more modules208of the base station104. In an embodiment, the memory205may store the data207.

[0068] In some embodiments, the data207stored in the memory205may include, without limitation, signal data209, noise and interference data211, computational data213, beamformer data215and other data217. In some implementations, the data207may be stored within the memory205in the form of various data structures. Additionally, the data207may be organized using data models, such as relational or hierarchical data models. The other data217may include various temporary data and files generated by the one or more modules208.

[0069] In some embodiment, the signal data209may store reference signals transmitted from the one or more UEs102and received signals received by the base station104corresponding to the reference signals. The reference signals may include, but not be limited to, demodulation reference signals (DMRS), cell-specific reference signals (CRS), sounding reference signals (SRS) and the like. For instance, the demodulation reference signals (DMRS) are utilized as reference signals and transmitted on the 1stand 11thorthogonal frequency-division multiplexing (OFDM) symbols within the slot of the resource block of the base station104. The remaining OFDM sub-carriers are reserved for the transmission of quadrature amplitude modulation (QAM) data. Said reference signals or pilot signals can be employed to accurately estimate the characteristics of the channels through which a plurality of signals will be transmitted. The signal data209may further store the resultant signals generated by the base station104.

[0070] In some embodiments, the noise and interference data211may store the data including, but not limited to, additive noise in the signals received by the base station104, a noise and interference sample estimated by the base station104, noise and interference covariance matrices per resource block generated by the base station104, channel estimation errors, and interference power profiles, noise power levels and other data related to noise and interference.

[0071] In some embodiment, the computational data213may store the data related to the estimated channel which determines the characteristics of a wireless channel, extended channel matrices, beamformer matrices, equalization matrices and other related matrices.

[0072] In some embodiment, the beamformer data215may store the data related to the beamforming technique. The beamforming techniques may include, but not limited to, a zero forcing beamforming technique, minimum mean square error (MMSE) technique, hybrid beamforming and like. Said techniques enhance desired signals and mitigate interference and noise.

[0073] In an embodiment, the other data217may store the data which may be used to perform various miscellaneous functionalitiesfor minimizing noise and interference in a communication network.

[0074] In an embodiment, the data207may be processed by the one or more modules208of the base station104. In some implementations, the one or more modules208may be communicatively coupled to the processor203for performing one or more functions of the base station104. In an implementation, the one or more modules208may include, but not limited to, a transceiver module219, an estimation module221, a determination module223, a generation module225, an equalization module227and other modules229.

[0075] As used herein, the term "module" may refer to an application specific integrated circuit (ASIC), an electronic circuit, a hardware processor (shared, dedicated, or group) and memory that execute one or more software or firmware programs, a combinational logic circuit, and / or other suitable components that provide the described functionality. In an implementation, each of the one or more modules208may be configured as stand-alone hardware computing units. In an embodiment, the other modules229may be used to perform various miscellaneous functionalities on the base station104. It will be appreciated that such one or more modules208may be represented as a single module or a combination of different modules.

[0076] In some embodiments, the transceiver module219may receive the plurality of reference signals ( ) transmitted from the one or more UEs102. For example, the demodulation reference signals (DMRS) are utilized as a reference signal ( ) and are transmitted on the 1stand 11thorthogonal frequency-division multiplexing (OFDM) symbols within the slot in one configuration. The remaining OFDM sub-carriers are reserved for the transmission of quadrature amplitude modulation (QAM) data. The transceiver module219may receive the plurality of reference signals or pilot signals transmitted from the "K" number of UEs. The transceiver module219may be configured to identify received signals based on the channel matrix between the target users and BS antennas, signal transmitted from the target users and interference users, interference channel and additive noise. For instance, the transceiver module219may receive these pilot signals transmitted from the "K" UEs represented as,

[0077]

[0078] Where, " " denotes the channel matrix between the target users and BS antennas, "and denote the signals transmitted from the "K"targetusers and "J" interference users respectively, is the interference channel, and " " is the additive noise.

[0079] It may be further represented as,

[0080]

[0081] Where, z = is the received interference and noise vector.

[0082] In some embodiments, the estimation module221may estimate the channel based on a plurality of reference signals transmitted by one or more UEs102to the base station104and a plurality of received signals corresponding to the plurality of reference signals received by the transceiver module219of the base station104. For example, a channel ( ) may be estimated based on a plurality of reference signals ( ) transmitted by one or more UEs102to the base station104and a plurality of received signals corresponding to the plurality of reference signals ( ) received by the transceiver module219of the base station104.

[0083] In some embodiments, the determination module223may determine a noise and interference sample ( ) based on the estimated channel ( ). In some embodiments, the determination module223may determine said noise and interference sample ( ) based on the difference between the receiving signals and the multiplication of reference signals ( ) with estimated channel ( ) as shown in the belowEquation 5.

[0084]

[0085] In some embodiment, the generation module225may generate the noise and interference co-variance matrix, an extended channel matrix, a resultant signal etc. The noise and interference co-variance matrix may be generated based on the noise and interference sample determined by the determination module223. For example, the noise and interference co-variance matrix ( ) for each resource block of a plurality of resource blocks of the estimated channel ( ) may be determined based on the noise and interference sample ( ) determined by the determination module223of the base station104. The noise and interference co-variance matrix ( ) can be represented as

[0086]

[0087] In an embodiment, the generation module225may further generate the extended channel matrix. For example, an extended channel matrix ( ) may be generated by the generation module225which may comprise the estimated channel ( ) and at least a part of the noise and interference covariance matrix ( ). Said noise and interference covariance matrix ( ) may include first "K" number of column elements and said "K" indicates a number of UEs communicating with the base station104at a given instant. Said extended channel matrix may be represented as

[0088]

[0089] Where, extended channel matrix

[0090] = extended channel matrix

[0091] =noise and interference covariance matrix having first "K" number of column elements. Where, "K" = number of UEs communicating with the base station104at a given instant.

[0092] The extended channel matrix represents the estimated channel and at least a part of the noise and interference covariance matrix As the number of the elements in the noise and interference covariance matrix is according to "K" number of column elements, where "K" indicates the number of UEs communicating with the base station, the noise and interference covariance matrix reduces the elements of the extended channel matrix and thereby reduces the complexity in the computation.

[0093] In an embodiment, the generation module225may further generate the resultant signal. The resultant signal may be generated based on the extended channel matrix and the plurality of received signals. For example, the resultant signal may be generated by product of the conjugate-transpose of extended channel matrix ( ) and the plurality of received signals . The resultant signal may be represented as

[0094]

[0095] The number of the resultant signal matrix elements are reduced to 2K, thereby reducing the fronthaul load and operational cost of the C-RAN architecture by incorporating the Z-IRC.

[0096] In an embodiment, the generation module225may further generate the equalization matrix. Said equalizing matrix may be generated by initially generating a beamformer matrix based on an application of a zero forcing beamformer on the extended channel matrix and identifying row elements of a predetermined row of the beamformer matrix. For instance, to generate the equalization matrix , the generation module225may generate the beamformer matrix represented as,

[0097]

[0098] Said beamformer matrix may be generated based on an application of zero forcing beamformer on the extended channel matrix .

[0099] The generation module225generates the beamformer matrix ( ) based on the at least one intermediate pilot signal ( ) received by the base station104from the UEs102. The base station104generates the output of received intermediate pilot signal ( ) based on said intermediate pilot signals ( ). The output of the received intermediate pilot signals ( ) can be represented as follows:

[0100]

[0101] Where, = output of received intermediate pilot signal

[0102] received intermediate pilot signal

[0103] = extended channel matrix

[0104] The equation (9.1) can be further derived as follows:

[0105]

[0106] Where,

[0107] = output reference intermediate pilot signal

[0108] received intermediate pilot signal

[0109] = estimated channel

[0110] H = channel matrix between the UEs and base station

[0111] = reference signals

[0112] additive noise

[0113] = noise and interference covariance matrix

[0114] ;

[0115] The above equation can be written as,

[0116]

[0117] Where,

[0118] = the effective channel at the base station and

[0119] E= noise and errors

[0120] Based on the received intermediate pilot signals ( ), the base station104estimates the effective channel and based on said effective channel, the generation module225generates the beamformer matrix ( ) by applying the zero forcing beamformer.

[0121] The generation module225may identify row elements of a first K row of the beamformer matrix and generate the equalization matrix based on an identified row elements. Hence, the equalization matrix can be represented as

[0122]

[0123] Where, the firstKrows of the identity matrix.

[0124] Said can be further simplified by following the block-wise inversion,

[0125]

[0126] Where,

[0127]

[0128]

[0129] Block-wise inversion:

[0130]

[0131] In some embodiment, the equalization module227may perform equalization across the extended channel matrix for minimizing the noise and interference in the plurality of received signals. Said equalization may be performed based on resultant signal and by applying an equalization matrix may be generated by the generation module225. For instance, consider is the equalization matrix of the Z-IRC receiver. Based on resultant signal ( ), performing the equalization byequalization matrix across extended channel matrix represented as,

[0132]

[0133]

[0134] Where, = extended matrix of dimension ;

[0135] = identity matrix having dimension of ;

[0136] = zero matrix of dimension .

[0137] Here, corresponds to that mitigates the intra-cell interferences and corresponds to 0 that mitigates the inter-cell interference without the background noise.

[0138] It should be understood that the one or more modules208are not restricted to performing only the specified functions. Additionally, individual functions may be carried out by other distinct modules.

[0139] In some embodiments, as discussed in the above sections, the method disclosed in the present disclosure may be performed in a decentralized manner by different components of the base station104such as RRU106and BBU110, wherein part of the method may be performed at the RRU106and another part of the method may be performed at the BBU110. However, this should not be construed as a limitation of the present disclosure, since other components of the base station104capable of being configured to perform the method disclosed in the present disclosure may also be used other than RRU106and BBU110.

[0140] Fig. 3shows a detailed block diagram of a remote radio unit (RRU)106for minimizing noise and interference in a communication network, in accordance with some embodiments of the present disclosure.

[0141] In some implementations, the RRU106may include an input / output (I / O) interface301, a processor303and amemory305. In an embodiment, the memory305may be communicatively coupled to the processor303. The processor303may be configured to perform one or more functions of the RRU106, using the data307and the one or more modules309of the RRU106. In an embodiment, the memory305may store data307.

[0142] In some embodiments, the data307stored in the memory305may include, without limitation, signal data309, noise and interference data311, computational data313and other data317. In some implementations, the data307may be stored within the memory305in the form of various data structures. Additionally, the data307may be organized using data models, such as relational or hierarchical data models. The other data317may include various temporary data and files generated by the one or more modules308.

[0143] In some embodiments, the signal data309of the RRU106may be equivalent to and store similar information as signal data209of the base station104discussed in theFIG.2of the present disclosure. Similarly, the noise and interference data311of the RRU106may be equivalent to and store similar information the noise and interference data211of the base station104, the computational data313of the RRU106may be equivalent to and store similar information as computational data213of the base station104, beamformer data315of the RRU106may be equivalent to and store similar information as beamformer data215of the base station104, and other data317of the RRU106may be equivalent to and store similar information as other data217of the base station104, asdiscussed in theFIG.2of the present disclosure. The content fromFIG.2is thereby referred here in its entirety.

[0144] In an embodiment, the data307may be processed by the one or more modules309of the RRU106. In some implementations, the one or more modules309may be communicatively coupled to the processor303for performing one or more functions of the RRU106. In an implementation, the one or more modules309may include, without limiting to, a transceiver module319, an estimation module321, a determination module323, a generation module325, and other modules329.

[0145] As used herein, the term module may refer to an application specific integrated circuit (ASIC), an electronic circuit, a hardware processor (shared, dedicated, or group) and memory that execute one or more software or firmware programs, a combinational logic circuit, and / or other suitable components that provide the described functionality. In an implementation, each of the one or more modules309may be configured as stand-alone hardware computing units. In an embodiment, the other modules329may be used to perform various miscellaneous functionalities on the RRU106. It will be appreciated that such one or more modules309may be represented as a single module or a combination of different modules.

[0146] In some embodiments, the transceiver module319of the RRU106may be equivalent to and perform the function similar to the transceiver module219of the base station104discussed in theFIG.2of the present disclosure. Similarly, the estimation module321of the RRU106may be equivalent to and perform the function similar to the estimation module221of the base station104,the determination module323of the RRU106may be equivalent to and perform the function similar to the determination module223of the base station104,the generation module325of the RRU106may be equivalent to and perform the function similar to the generation module225of the base station104,the other modules329of the RRU106may be equivalent to and perform the function similar to the other modules229of base station104.The content fromFIG.2is thereby referred here in its entirety.

[0147] Furthermore, the transceiver module319may be further configured to transmit the resultant signal generated by the generation module325of the RRU106, through a fronthaul link108to a BBU110for equalization process.

[0148] It is to be construed that the one or more modules is not limited to perform only mentioned functions. Further, it is to be construed that the individual functions may performed by other individual modules.

[0149] Fig. 4showsa block diagram of a Base Band Unit (BBU)110, in accordance with some embodiments of the present disclosure;

[0150] In some implementations, the BBU110may include an input / output (I / O) interface401, a processor403and a memory405. In an embodiment, the memory405may be communicatively coupled to the processor403. The processor403may be configured to perform one or more functions of the BBU110, using the data407and the one or more modules408of the BBU403. In an embodiment, the memory405may store data407.

[0151] In some embodiments, the data407stored in the memory405may include, without limitation, computational data413, beamformer data415, and other data417. In some implementations, the data407may be stored within the memory405in the form of various data structures. Additionally, the data407may be organized using data models, such as relational or hierarchical data models. The other data417may include various temporary data and files generated by the one or more modules408.

[0152] In some embodiments, the signal data409of the BBU110may be equivalent to and store similar information as signal data209of the base station104discussed in theFIG.2of the present disclosure. Similarly, the computational data413of the BBU110may be equivalent to and store similar information as computational data213of the base station104, beamformer data415of the BBU110may be equivalent to and store similar information as beamformer data415of the base station104, and other data417of the BBU110may be equivalent to and store similar information as other data217of the base station104, asdiscussed in theFIG.2of the present disclosure. The content fromFIG.2is thereby referred here in its entirety.

[0153] In an embodiment, the data407may be processed by the one or more modules408of the BBU110. In some implementations, the one or more modules408may be communicatively coupled to the processor403for performing one or more functions of the BBU110. In an implementation, the one or more modules408may include, without limiting to, a transceiver module419, a generation module425, an equalization module427and other modules429.

[0154] As used herein, the term module may refer to an application specific integrated circuit (ASIC), an electronic circuit, a hardware processor (shared, dedicated, or group) and memory that execute one or more software or firmware programs, a combinational logic circuit, and / or other suitable components that provide the described functionality. In an implementation, each of the one or more modules408may be configured as stand-alone hardware computing units. In an embodiment, the other modules429may be used to perform various miscellaneous functionalities on the BBU110. It will be appreciated that such one or more modules408may be represented as a single module or a combination of different modules.

[0155] In some embodiments, the transceiver module419of the BBU110may be equivalent to and perform the function similar to the transceiver module219of the base station104discussed in theFIG.2of the present disclosure. The transceiver module419may be configured to receive the resultant signal generated by the generation module425of the RRU106, through a fronthaul link108for equalization. For instance, the transceiver module419may receive the signal from the RRU106is resultant signal ( ). The resultant signal ( ) may be represented as

[0156]

[0157] In an embodiment, the generation module425of the BBU110may be equivalent to and perform the function similar to the generation module225of the base station104discussed in theFIG.2of the present disclosure. Similarly, the equalization module427of the BBU110may be equivalent to and perform the function similar to the equalization module227of the base station104,the other modules429of the BBU110may be equivalent to and perform the function similar to the other modules229of the base station104.The content fromFIG.2is thereby referred here in its entirety.

[0158] It is to be construed that the one or more module is not limited to perform only mentioned functions. Further, it is to be construed that the individual functions may performed by other individual modules.

[0159] Henceforth, the method of the present disclosure is explained with the help of an exemplary scenario considering the base station elements RRU and BBU. However, this is only for the sake of easy understanding and should not be construed as a limitation of the present disclosure.

[0160] Consider an exemplary scenario, in which an uplink extreme massive MIMO system where a base station104(BS) with 64 antennas receives signals transmitted by 8 user equipments (UE). As described in theFig. 1Aand1B, the base station104connects to "K" number of UEs102and consists of N number of antennas. Hence, for the m-MIMO and extreme-mMIMO systems, the valueof N is considerably larger than the value of "K", i.e.,N≫K.

[0161] The reference signals, denoted as ( ) transmitted by the eight user equipments to at least one antenna of RRU106can be represented as follows:

[0162]

[0163] In similar manner, received signals, denoted as received by 64 antennas of RRU106of the base station104. Said received signals are corresponding to the reference signals ( ) transmitted by at least one of the UEs102. Hence, the received signals received by the antenna of RRU106of the base station104can be represented as follows:

[0164]

[0165] The channel, denoted by ( ) estimated based on a reference signals ( ) transmitted by 8 UEs to the at least one antenna of RRU106of the base station104and received signals ( ) received by the 64 antennas of the RRU106of the base station104transmitted by at least one UE102. The estimated channel ( ) can be represented as:

[0166]

[0167] The RRU106of the base station104determines the noise and interference sample ( ) based on the estimated channel ( ). Said noise and interference sample ( ) may be determined by the difference between the receiving signal and the product of reference signals ( ) with estimated channel ( ). It can be represented as follows:

[0168]

[0169] Hence, by incorporatingequations (18), (19)and(20)intoequation (21),

[0170]

[0171]

[0172] Based on the noise and interference sample ( ), the RRU106generates the noise and interference co-variance matrix ( ) for each resource block of a plurality of resource blocks of the estimated channel ( ). Said noise and interference co-variance matrix ( ) can be represented as follows:

[0173]

[0174] Where, the size of is 64 × 64 i.e. (N x N).

[0175] The RRU106generates an extended channel matrix ( ) comprising the estimated channel ( ) and at least a part of the noise and interference covariance matrix ( ). Said noise and interference covariance matrix ( ) comprising first "8" column elements identified from the noise and interference covariance matrix ( ). "8" indicates the number of UEs102communicating with the RRU106at a given instant.

[0176] The extended channel matrix ( ) can be represented as:

[0177]

[0178] Where, the size of extended channel matrix is 64×16 i.e., (N×2K).

[0179] The RRU106generates, a resultant signal ( ) based on the extended channel matrix ( ) and the plurality of received signals ( ). Said resultant signal ( ) can be represented as follows:

[0180]

[0181] Where, the size of resultant signal ( ) is 16×1 i.e., (2K×1).Said resultant signal ( ) is transmitted from the RRU106of the base station104to a BBU110of the base station104through a fronthaul link108for equalization.

[0182] As the derived matrix reduces the fronthaul load of the C-RAN network as Z-IRC receiver needs the fronthaul links108to transmit data streams from RRU106to BBU110, wherein "K" represents the number of the UEs.

[0183] Said resultant signal ( )may be received by the BBU110for the equalization. Said equalization is performed by deriving the beamformer matrix.

[0184] The beamformer matrix ( ) may be generated based on an application of zero forcing beamformer on the extended channel matrix .

[0185] The beamformer matrix ( ) can be represented as

[0186]

[0187] Where, the size of beamformer matrix ( ) is 16×64 i.e., (2K×N).The BBU110identify row elements of a first 8 row of the beamformer matrix ( ) and generate the equalization matrix based on an identified row elements. Hence, the equalization matrix can be represented as follows:

[0188]

[0189] Where, the size of equalization matrix is 8×64 i.e., (K×N).

[0190] The equalization is performed by BBU110of the base station104by applying an equalization matrix across an extended channel matrix for minimizing the noise and interference in a plurality of received signals corresponding to a plurality of reference signals transmitted by one or more User equipment102to the RRU106. It can be represented as follows:

[0191]

[0192] It can be further represented in the following manner:

[0193] and,

[0194]

[0195] Here, corresponds to i.e. mitigates the intra-cell interferences and corresponds to that mitigates the inter-cell interference without the background noise.

[0196] Below given table represents experimental data regarding computational complexity of the method disclosed in the present disclosure compared to the computational complexity of existing MMSE-IRC technique for one user equipment (K) and 16 antennas at the base station.

[0197]

[0198] As demonstrated in the table above, the method and system presented in present disclosure achieve a 96.55% reduction in computational complexity compared to existing MMSE-IRC methods. This significant improvement is accomplished by eliminating unnecessary matrix elements and reducing the total number of matrix elements, thereby decreasing the overall size of the extended channel matrix.

[0199] Fig. 5shows aflowchart for a method performed by a base station104for minimizing noise and interference in a communication network,in accordance with some embodiments of the present disclosure.

[0200] As illustrated inFig. 5, the method500may include one or more blocks illustrating a method for minimizing noise and interference in a communication network using the processor203configured in base station104illustrated inFIG. 1B and FIG. 2. The method500may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform specific functions or implement specific abstract data types.

[0201] The order in which the method500is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof.

[0202] At block501,the method500includes estimating, by a base station104, a channel based on a plurality of reference signals transmitted by one or more user equipment (UEs) or at least one of the UEs to the base station104and a plurality of received signals corresponding to the plurality of reference signals.

[0203] At block502,the method500includes determining, by the base station104, a noise and interference sample based on the estimated channel. The noise and interference sample is determined based on a difference between a plurality of received signals received in response to transmission of the plurality of reference signals and a product of the plurality of reference signals with the estimated channel.

[0204] At block503,the method500includes generating, by the base station104, a noise and interference co-variance matrix for each resource block of a plurality of resource blocks of the estimated channel based on the noise and interference sample.

[0205] At block504,the method500includes generating, by the base station104, an extended channel matrix including the estimated channel and at least a part of the noise and interference covariance matrix comprising first "K" number of column elements identified from the noise and interference covariance matrix. The term "K" indicates a number of UEs communicating with the base station104at a given instant.

[0206] At block505,the method500includes generating, by the base station104, a resultant signal based on the extended channel matrix and the plurality of received signals. The resultant signal includes a matrix generated by multiplication of the extended channel matrix and the plurality of received signals.

[0207] At block506,the method500includes performing, by the base station104, based on resultant signal, equalization by applying an equalization matrix across the extended channel matrix for minimizing the noise and interference in the plurality of received signals. In some embodiments, the base station104may generate the equalization matrix by initially generating a beamformer matrix based on an application of a beamformer on the extended channel matrix, identifying row elements of a predetermined row of the beamformer matrix, and using the identified row elements to generate the equalization matrix (or generating the equalization matrix based on the identified row elements). In some embodiments, the beamformer applied on the extended channel matrix includes a zero forcing beamformer.

[0208] Fig. 6shows aflowchart for a method performed by RRU106for minimizing noise and interference in a communication network, in accordance with some embodiments of the present disclosure.

[0209] As illustrated inFig. 6, the method600may include one or more blocks illustrating a method for minimizing noise and interference in a communication network using the processor303configured in RRU106as illustrated inFig. 1B and Fig. 3. The method600may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform specific functions or implement specific abstract data types.

[0210] The order in which the method600is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof.

[0211] At block601,the method600includes estimating, by a RRU106of a base station104, a channel based on a plurality of reference signals transmitted by one or more user equipment (UEs) or at least one of the UEs to the base station104and a plurality of received signals corresponding to the plurality of reference signals.

[0212] At block602,the method600includes determining, by the RRU106, a noise and interference sample based on the estimated channel.

[0213] The noise and interference sample is determined based on a difference between the plurality of received signals received in response to transmission of the plurality of reference signals and a product of the plurality of reference signals with the estimated channel.

[0214] At block603,the method600includes generating, by the RRU106, a noise and interference co-variance matrix for each resource block of a plurality of resource blocks of the estimated channel based on the noise and interference sample.

[0215] At block604,the method600includes generating, by the RRU106, an extended channel matrix including the estimated channel and at least a part of the noise and interference co-variance matrix comprising first "k" number of column elements identified from the noise and interference co-variance matrix, wherein "k" indicates a number of UEs communicating with the base station104at a given instant.

[0216] At block605,the method600includes generating, by the RRU106, a resultant signal based on the extended channel matrix and the plurality of received signals.

[0217] The resultant signal includes a matrix generated by multiplication of the extended channel matrix and the plurality of received signals.

[0218] At block606,the method600includes transmitting, by the RRU106, the resultant signal through a fronthaul link108to a base band unit (BBU)110of the base station104for equalization.

[0219] Fig. 7shows aflowchart for a method performed by BBU110for minimizing noise and interference in a communication network,in accordance with some embodiments of the present disclosure.

[0220] As illustrated inFig. 7, the method700may include one or more blocks illustrating a method for minimizing noise and interference in a communication network using the processor403configured in BBU110as illustrated inFig. 1B and Fig. 4. The method600may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform specific functions or implement specific abstract data types.

[0221] The order in which the method700is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof.

[0222] At block701,the method700includes receiving, by a BBU110of a base station104, a resultant signal transmitted through a fronthaul link108by a RRU106of the base station104.

[0223] At block702,the method700includes performing,by the BBU110, based on the resultant signal,equalization by applying an equalization matrix across an extended channel matrix for minimizing the noise and interference in a plurality of received signals corresponding to a plurality of reference signals transmitted by one or more user equipment (UEs) or at least one of the UEs to the RRU106. In some embodiments, the equalization matrix may be generated by initially generating a beamformer matrix based on an application of a beamformer on the extended channel matrix, identifying row elements of a predetermined row of the beamformer matrix,and thereafter generating the equalization matrix based on an identified row elements.

[0224] The beamformer applied on the extended channel matrix includes a zero forcing beamformer.

[0225] Computer System

[0226] Fig. 8shows a general computer system800architecture, in accordance with some embodiments of the present disclosure. In an embodiment, the computer system800may be a base station104illustrated inFIG. 1A and 1B. Alternatively, the computer system800may be a RRU106or BBU110. The computer system800may include a central processing unit ("CPU" or "processor" or "memory controller")802. The processor802may comprise at least one data processor for executing program components for executing user- or system-generated business processes. A user may include a network manager, an application developer, a programmer, an organization, or any system / sub-system being operated parallelly to the computer system800. The processor802may include specialized processing units such as integrated system (bus) controllers, memory controllers / memory management control units, floating point units, graphics processing units, digital signal processing units, etc.

[0227] The processor802may be disposed in communication with one or more Input / Output (I / O) devices (811and812) via I / O interface801. The I / O interface801may employ communication protocols / methods such as, without limitation, audio, analog, digital, stereo, IEEE®-1394, serial bus, Universal Serial Bus (USB), infrared, PS / 2, BNC, coaxial, component, composite, Digital Visual Interface (DVI), high-definition multimedia interface (HDMI), Radio Frequency (RF) antennas, S-Video, Video Graphics Array (VGA), IEEE®802.n / b / g / n / x, Bluetooth, cellular (e.g., Code-Division Multiple Access (CDMA), High-Speed Packet Access (HSPA+), Global System For Mobile Communications (GSM), Long-Term Evolution (LTE) or the like), etc. Using the I / O interface801, the computer system 800may communicate with one or more I / O devices811and812.

[0228] In some embodiments, the processor802may be disposed in communication with a network809via a network interface803. The network interface803may communicate with the network809. The network interface803may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10 / 100 / 1000 Base T), Transmission Control Protocol / Internet Protocol (TCP / IP), token ring, IEEE®802.11a / b / g / n / x, etc.

[0229] In an implementation, the preferred network809may be implemented as one of the several types of networks, such as intranet or Local Area Network (LAN) and such within the organization. The preferred network 809may either be a dedicated network or a shared network, which represents an association of several types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol / Internet Protocol (TCP / IP), Wireless Application Protocol (WAP) etc., to communicate with each other. Further, the network809may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc. Using the network interface803and the network809, the computer system800may communicate with One or more UEs102.

[0230] In some embodiments, the processor802may be disposed in communication with a memory805via a storage interface804. The storage interface804may connect to memory805including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as Serial Advanced Technology Attachment (SATA), Integrated Drive Electronics (IDE), IEEE-1394, Universal Serial Bus (USB), fiber channel, Small Computer Systems Interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc.

[0231] The memory805may store a collection of program or database components, including, without limitation, user / application interface806, an operating system807, a web browser808, and the like. In some embodiments, computer system800may store user / application data806, such as the data, variables, records, etc. as described in this invention. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle®or Sybase®.

[0232] The operating system807may facilitate resource management and operation of the computer system800. Examples of operating systems include, without limitation, APPLE®MACINTOSH®OS X®, UNIX®, UNIX-like system distributions (E.G., BERKELEY SOFTWARE DISTRIBUTION®(BSD), FREEBSD®, NETBSD®, OPENBSD, etc.), LINUX®DISTRIBUTIONS (E.G., RED HAT®, UBUNTU®, KUBUNTU®, etc.), IBM®OS / 2®, MICROSOFT®WINDOWS®(XP®, VISTA® / 7 / 8, 10 etc.), APPLE®IOS®, GOOGLETMANDROIDTM, BLACKBERRY®OS, or the like.

[0233] The user interface806may facilitate display, execution, interaction, manipulation, or operation of program components through textual or graphical facilities. For example, the user interface806may provide computer interaction interface elements on a display system operatively connected to the computer system800, such as cursors, icons, check boxes, menus, scrollers, windows, widgets, and the like. Further, Graphical User Interfaces (GUIs) may be employed, including, without limitation, APPLE®MACINTOSH®operating systems' Aqua®, IBM®OS / 2®, MICROSOFT®WINDOWS®(e.g., Aero, Metro, etc.), web interface libraries (e.g., ActiveX®, JAVA®, JAVASCRIPT®, AJAX, HTML, ADOBE®FLASH®, etc.), or the like.

[0234] The web browser808may be a hypertext viewing application. Secure web browsing may be provided using Secure Hypertext Transport Protocol (HTTPS), Secure Sockets Layer (SSL), Transport Layer Security (TLS), and the like. The web browsers808may utilize facilities such as AJAX, DHTML, ADOBE®FLASH®, JAVASCRIPT®, JAVA®, Application Programming Interfaces (APIs), and the like. Further, the computer system800may implement a mail server stored program component. The mail server may utilize facilities such as ASP, ACTIVEX®, ANSI®C++ / C#, MICROSOFT®, .NET, CGI SCRIPTS, JAVA®, JAVASCRIPT®, PERL®, PHP, PYTHON®, WEBOBJECTS®, etc. The mail server may utilize communication protocols such as Internet Message Access Protocol (IMAP), Messaging Application Programming Interface (MAPI), MICROSOFT®exchange, Post Office Protocol (POP), Simple Mail Transfer Protocol (SMTP), or the like. In some embodiments, the computer system800may implement a mail client stored program component. The mail client may be a mail viewing application, such as APPLE®MAIL, MICROSOFT®ENTOURAGE®, MICROSOFT®OUTLOOK®, MOZILLA®THUNDERBIRD®, and the like.

[0235] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present invention. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term "computer-readable medium" should be understood to include tangible items and exclude carrier waves and transient signals, i.e., non-transitory. Examples include Random Access Memory (RAM), Read-Only Memory (ROM), volatile memory, non-volatile memory, hard drives, Compact Disc (CD) ROMs, Digital Video Disc (DVDs), flash drives, disks, and any other known physical storage media.

[0236] The terms "an embodiment", "embodiment", "embodiments", "the embodiment", "the embodiments", "one or more embodiments", "some embodiments", and "one embodiment" mean "one or more (but not all) embodiments of the invention(s)" unless expressly specified otherwise.

[0237] The terms "including", "comprising", "having" and variations thereof mean "including but not limited to", unless expressly specified otherwise.

[0238] The enumerated listing of items does not imply that any or all of the items are mutually exclusive, unless expressly specified otherwise.

[0239] The terms "a", "an" and "the" mean "one or more", unless expressly specified otherwise.

[0240] A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary a variety of optional components are described to illustrate the wide variety of possible embodiments of the disclosure.

[0241] When a single device or article is described herein, it will be readily apparent that more than one device / article (whether or not they cooperate) may be used in place of a single device / article. Similarly, where more than one device or article is described herein (whether or not they cooperate), it will be readily apparent that a single device / article may be used in place of the more than one device or article, or a different number of devices / articles may be used instead of the shown number of devices or programs. The functionality and / or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality / features. Thus, other embodiments of the disclosure need not include the device itself.

[0242] The illustrated operations of the figures show certain events occurring in a certain order. In alternative embodiments, certain operations may be performed in a different order, modified, or removed. Moreover, steps may be added to the above-described logic and still conform to the described embodiments. Further, operations described herein may occur sequentially or certain operations may be processed in parallel. Yet further, operations may be performed by a single processing unit or by distributed processing units.

[0243] Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the disclosure be limited not by this description. Accordingly, the disclosure of the embodiments of the disclosure is intended to be illustrative, but not limiting, of the scope of the disclosure.

[0244] While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, to the true scope of the disclosure.

[0245]

[0246]

Claims

1.A method performed by a base station for minimizing noise and interference in a communication network, the method comprising:estimating a channel based on a plurality of reference signals transmitted by one or more user equipment (UEs) to the base station and a plurality of received signals corresponding to the plurality of reference signals;determining a noise and interference sample based on the estimated channel;generating a noise and interference co-variance matrix for each resource block of a plurality of resource blocks of the estimated channel based on the noise and interference sample;generating an extended channel matrix including the estimated channel and at least a part of the noise and interference covariance matrix comprising a first number of column elements identified from first column of the noise and interference covariance matrix, wherein the first number indicates a number of UEs communicating with the base station at a given instant;generating a resultant signal based on the extended channel matrix and the plurality of received signals; andperforming based on resultant signal,equalization by applying an equalization matrix across the extended channel matrix in the plurality of received signals.2.The method of claim 1, wherein the noise and interference sample is determined based on a difference between a plurality of received signals received in response to transmission of the plurality of reference signals and a product of the plurality of reference signals with the estimated channel.3.The method of claim 1, wherein the resultant signal includes a matrix generated by multiplication of the extended channel matrix and the plurality of received signals.4.The method of claim 1,wherein the equalization matrix is generated based on identified row elements of a predetermined row of a beamformer matrix,wherein the beamformer matrix is generated based on an application of a beamformer on the extended channel matrix, andwherein the beamformer includes a zero forcing beamformer.5.A base station for minimizing interference in a communication network, the base station comprising:a processor; anda memory, communicatively coupled to the processor, wherein the memory stores instructions, which, on execution, causes the processor to:estimate a channel based on a plurality of reference signals transmitted by one or more user equipment (UEs) to the base station, and a plurality of received signals corresponding to the plurality of reference signals;determine a noise and interference sample based on the estimated channel;generate a noise and interference co-variance matrix for each resource block of a plurality of resource blocks of the estimated channel based on the noise and interference sample;generate an extended channel matrix including the estimated channel and at least a part of the noise and interference co-variance matrix comprising a first number of column elements identified from first column of the noise and interference co-variance matrix, wherein the first number indicates a number of UEs communicating with the base station at a given instant;generate a resultant signal based on the extended channel matrix and the plurality of received signals; andperform based on the resultant signal,equalization by applying an equalization matrix across the extended channel matrix in the plurality of received signals.6.The base station of claim 5, wherein the noise and interference sample is determined based on a difference between a plurality of signals received in response to transmission of the plurality of reference signals and a product of the plurality of reference signals with the estimated channel.7.The base station of claim 5, wherein the resultant signal includes a matrix generated by multiplication of the extended channel matrix and the plurality of received signals.8.The base station of claim 5, wherein the equalization matrix is generated based on identified row elements of a predetermined row of a beamformer matrix,wherein the beamformer matrix is generated based on an application of a beamformer on the extended channel matrix, andwherein the beamformer includes a zero forcing beamformer.9.A method performed by a remote radio unit (RRU) of a base station for minimizing noise and interference in a communication network, the method comprising:estimating a channel based on a plurality of reference signals transmitted by one or more user equipment (UEs) to the base station and a plurality of received signals corresponding to the plurality of reference signals;determining a noise and interference sample based on the estimated channel;generating a noise and interference co-variance matrix for each resource block of a plurality of resource blocks of the estimated channel based on the noise and interference sample;generating an extended channel matrix including the estimated channel and at least a part of the noise and interference co-variance matrix comprising a first number of column elements identified from first column of the noise and interference co-variance matrix, wherein the first number indicates a number of UEs communicating with the base station at a given instant;generating a resultant signal based on the extended channel matrix and the plurality of received signals; andtransmitting the resultant signalthrough a fronthaul link to a base band unit (BBU) of the base station.10.The method of claim 9, wherein the noise and interference sample is determined based on a difference between the plurality of received signals received in response to transmission of the plurality of reference signals and a product of the plurality of reference signals with the estimated channel, andwherein the resultant signal comprises a matrix generated by multiplication of the extended channel matrix and the plurality of received signals.11.A method performed by a base band unit (BBU) of a base station for minimizing noise and interference in a communication network, the method comprising:receiving a resultant signal transmitted through a fronthaul link by a remote radio unit (RRU) of the base station; andperforming, based on the resultant signal, equalization by applying an equalization matrix across an extended channel matrix in a plurality of received signals corresponding to a plurality of reference signals transmitted by one or more user equipment (UEs) to the RRU.12.The method of claim 11, wherein the equalization matrix is generated based on identified row elements of a predetermined row of a beamformer matrix,wherein the beamformer matrix is generated based on an application of a beamformer on the extended channel matrix, andwherein the beamformer includes a zero forcing beamformer.13.A remote radio unit (RRU) for minimizing noise and interference in a communication network, the RRU comprising:a processor; anda memory, communicatively coupled to the processor, wherein the memory stores instructions, which, on execution, causes the processor to:estimate a channel based on a plurality of reference signals transmitted by one or more user equipment (UEs) to the RRU, and a plurality of received signals corresponding to the plurality of reference signals;determine a noise and interference sample based on the estimated channel;generate a noise and interference co-variance matrix for each resource block of a plurality of resource blocks of the estimated channel based on the noise and interference sample;generate an extended channel matrix including the estimated channel and at least a part of the noise and interference co-variance matrix comprising a first number of column elements identified from first column of the noise and interference co-variance matrix, wherein the first number indicates a number of UEs communicating with the RRU at a given instant;generate a resultant signal based on the extended channel matrix and the plurality of received signals; andtransmit the resultant signalthrough a fronthaul link to a base band unit (BBU).14.The RRU of claim 13, wherein the noise and interference sample is determined based on a difference between the plurality of received signals received in response to transmission of the plurality of reference signals and a product of the plurality of reference signals with the estimated channel, andwherein the resultant signal includes a matrix generated by multiplication of the extended channel matrix and the plurality of received signals.15.A base band unit (BBU) for minimizing noise and interference in a communication network, the BBU comprising:a processor; anda memory, communicatively coupled to the processor, wherein the memory stores instructions, which, on execution, causes the processor to:receive a resultant signal transmitted through a fronthaul link by a remote radio unit (RRU) of the base station; andperform equalization by applying an equalization matrix across an extended channel matrix in a plurality of received signals corresponding to a plurality of reference signals transmitted by one or more user equipment to the RRU,wherein the equalization matrix is generated based on identified row elements of a predetermined row of a beamformer matrix,wherein the beamformer matrix is generated based on an application of a beamformer on the extended channel matrix, andwherein the beamformer includes a zero forcing beamformer.

Images