Method and apparatus for signal detection in wireless communication system

Abstract

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. A method for detecting a transmitted signal in a wireless communication system is disclosed. The method includes receiving, from a plurality of user equipments (UEs), a plurality of signals including a plurality of transmitted layers, modelling symbol detection as a tree search based on a number of MIMO layers and a number of the transmitted layers, applying an M-algorithm on a tree based on the tree search, and detecting the transmitted signal based on the tree.

Description

METHOD AND APPARATUS FOR SIGNAL DETECTION IN WIRELESS COMMUNICATION SYSTEM

[0001] The present invention generally relates to wireless communications, and more particularly relates to a system and a method for low-complexity, near optimal signal detection in Multiple Input Multiple Output (MIMO) communication systems.

[0002] 5G mobile communication technologies define broad frequency bands such that high transmission rates and new services are possible, and can be implemented not only in “Sub 6GHz” bands such as 3.5GHz, but also in “Above 6GHz” bands referred to as mmWave including 28GHz and 39GHz. In addition, it has been considered to implement 6G mobile communication technologies (referred to as Beyond 5G systems) in terahertz bands (for example, 95GHz to 3THz bands) in order to accomplish transmission rates fifty times faster than 5G mobile communication technologies and ultra-low latencies one-tenth of 5G mobile communication technologies.

[0003] 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.

[0004] 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.

[0005] 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.

[0006] 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.

[0007] 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.

[0008] Signal detection and equalisation are fundamental operations in wireless communication systems that allows receivers to recover transmitted information from signals sent through wireless communication channels. In modern communication systems, Multiple Input Multiple Output (MIMO) technology is essential for achieving higher data rates through spatial multiplexing and better reliability through spatial diversity. MIMO technology requires effective signal detection mechanisms at both the base station and user equipment to separate and recover multiple data streams transmitted simultaneously.

[0009] MIMO detection techniques have advanced significantly over the past decades. Maximum likelihood detection (MLD) is the optimal detection method for MIMO systems. However, MLD requires computational resources that increase exponentially with the number of transmit layers and modulation order, making practical implementation impossible beyond basic configurations. Linear detection methods, such as minimum mean squared error (MMSE) detection, and iterative approaches like MMSE with successive interference cancellation (MMSE-SIC), are commonly used in practical systems because of their lower computational complexity.

[0010] These conventional detection approaches work well in scenarios where the number of receive antennas is more than the number of transmit layers. However, these methods show poor performance in practical scenarios where the number of receive antennas is equal to the number of transmit layers. The performance limitations of conventional detection methods in such scenarios cause increased power consumption and reduced energy efficiency, creating significant challenges for wireless communication systems.

[0011] Several tree-search based detection algorithms have been disclosed to address the limitations of conventional approaches. The sphere decoder (SD) is a tree-search based approach that achieves optimal performance with lower complexity compared to MLD. However, sphere decoders are difficult to implement due to high computational requirements, especially in low signal-to-noise ratio conditions. The QR decomposition-based M algorithm (QRDM) is another tree-search based approach that offers lower complexity through controlled performance trade-offs. Despite these improvements, QRDM has much higher complexity compared to linear detectors while showing performance gaps compared to optimal detection.

[0012] Mixed modulation schemes in practical systems create additional complexity for MIMO detection. Different users typically use different modulation schemes based on their channel conditions, requiring detection algorithms to adapt accordingly. While linear detectors can handle mixed modulation scenarios with minimal changes, tree-search based detectors need significant algorithmic adjustments to remain effective.

[0013] Therefore, there exists a need for a system and method for low-complexity, near optimal signal detection in MIMO communication systems. Furthermore, there exists a need for a MIMO detection system and a method that can efficiently handle mixed modulation scenarios without reducing performance or significantly increasing complexity.

[0014] This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention. This summary is neither intended to identify key or essential inventive concepts of the invention nor is it intended for determining the scope of the invention.

[0015] According to one embodiment of the present disclosure, a method for detecting a transmitted signal in a wireless communication system is disclosed. The method includes receiving, from a plurality of user equipments (UEs), a plurality of signals including a plurality of transmitted layers, modelling symbol detection as a tree search based on a number of MIMO layers and a number of the transmitted layers, applying an M-algorithm on a tree based on the tree search, and detecting the transmitted signal based on the tree.

[0016] According to one embodiment of the present disclosure, an apparatus for detecting a transmitted signal in a wireless communication system is disclosed. The apparatus includes a transceiver, and at least one processor coupled to the transceiver and configured to receive, from a plurality of user equipments (UEs), a plurality of signals including a plurality of transmitted layers, model symbol detection as a tree search based on a number of MIMO layers and a number of the transmitted layers, apply an M-algorithm on a tree based on the tree search; and detect the transmitted signal based on the tree.

[0017] According to one embodiment of the present disclosure, a method for detecting a transmitted signal in Multiple Input Multiple Output communication systems with a set of pre-defined parameters is disclosed. The method includes receiving, at a MIMO receiving station, a plurality of signals comprising a plurality of transmitted layers from a plurality of user equipment (UEs). Further, the method includes pre-processing the plurality of received signals based on a pre-estimated channel matrix to obtain a pre-processed signal. Further, the method includes, applying an initial linear detection technique on the pre-processed signal to obtain an initial estimate of each of the plurality of transmitted layers from each of the plurality of UEs. Furthermore, the method includes obtaining a neighbourhood of each of the initial estimate. Furthermore, the method includes determining a threshold distance for each transmitted layer based on the corresponding initial estimate. Furthermore, the method includes modelling symbol detection as a tree search wherein, a number of layers in a tree search is equal to a number of MIMO layers, and a number of branches emanating from each node in the layer is equal to size of a modulation alphabet in the corresponding transmitted layer. Furthermore, the method includes applying an M-algorithm on the tree by computing a cost for each branch in the neighbourhood of the initial estimate, and pruning nodes corresponding to the branches having the cost more than the threshold distance. Furthermore, the method includes determining log-likelihood ratio for information bits associated with the final surviving leaf nodes. Furthermore, the method includes detecting the transmitted signal by recovering the information bits from the determined log-likelihood ratio using a channel decoder.

[0018] According to one embodiment of the present disclosure, a system for detecting a transmitted signal in Multiple Input Multiple Output (MIMO) communication systems with a set of pre-defined parameters is disclosed. The system includes a memory and at least one processor in communication with the memory. The at least one processor is configured to receive, at a MIMO receiving station, a plurality of signals comprising a plurality of transmitted layers from a plurality of user equipment. Further, the at least one processor is configured to pre-process the plurality of received signals based on a pre-estimated channel matrix to obtain a pre-processed signal. Further, the at least one processor is configured to apply an initial linear detection technique on the pre-processed signal to obtain an initial estimate of each of the plurality of transmitted layers from each of the plurality of UEs. Further, the at least one processor is configured to obtain a neighbourhood of each of the initial estimate;. Further, the at least one processor is configured to determine a threshold distance for each transmitted layer based on the corresponding initial estimate. Furthermore, the at least one processor is configured to model symbol detection as a tree search wherein, a number of layers in a tree search is equal to a number of MIMO layers, and a number of branches emanating from each node in the layer is equal to size of a modulation alphabet in the corresponding transmitted layer. Furthermore, the at least one processor is configured to apply an M-algorithm on the tree by computing a cost for each branch in the neighbourhood of the initial estimate, and pruning nodes corresponding to the branches having the cost more than the threshold distance. Furthermore, the at least one processor is configured to determine log-likelihood ratio for information bits associated with the final surviving leaf nodes. Furthermore, the at least one processor is configured to detect the transmitted signal by recovering the information bits from the determined log-likelihood ratio using a channel decoder.

[0019] 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.

[0020] These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:

[0021] Figure 1 illustrates a schematic block diagram depicting an environment for implementation of a system for signal detection in Multiple Input Multiple Output (MIMO) communication systems, according to an embodiment of the present disclosure;

[0022] Figure 2 illustrates a schematic block diagram of the system for signal detection in MIMO communication systems, according to an embodiment of the present disclosure;

[0023] Figure 3 illustrates a process flow associated with a pre-processing module of the system, according to an embodiment of the present disclosure;

[0024] Figure 4a illustrates a process flow associated with a neighbourhood obtaining module of the system, according to an embodiment of the present disclosure;

[0025] Figure 4b illustrates an exemplary constellation diagram depicting retention of closest neighborhood points for a transmission layer by the neighborhood obtaining module of the system, according to an embodiment of the present disclosure;

[0026] Figure 5a illustrates a process flow associated with an M-algorithm application module of the system, according to an embodiment of the present disclosure;

[0027] Figure 5b illustrates an exemplary tree structure depicting a pruning of nodes by the M-algorithm application module of the system, according to an embodiment of the present disclosure;

[0028] Figure 5c illustrates an exemplary tree structure depicting implementation of M-algorithm by the M-algorithm application module, according to an embodiment of the present disclosure;

[0029] Figure 6a illustrates an exemplary hardware architecture for implementation of the system, according to an embodiment of the present disclosure;

[0030] Figure 6b illustrates an exemplary candidate search module of the system, according to an embodiment of the present disclosure;

[0031] Figure 7a illustrates an exemplary Block Error Rate (BLER) performance graph of the system implementing a 3-neighborhood search under mixed modulation scenario, according to an embodiment of the present disclosure;

[0032] Figure 7b illustrates an exemplary complexity graph the system implementing a 3-neighborhood search under mixed modulation scenario, according to an embodiment of the present disclosure;

[0033] Figure 7c illustrates an exemplary BLER performance graph of the system implementing a 2-neighborhood search under mixed modulation scenario, according to an embodiment of the present disclosure;

[0034] Figure 7d illustrates an exemplary complexity graph of the system implementing a 2-neighborhood search under mixed modulation scenario, according to an embodiment of the present disclosure; and

[0035] Figure 8 illustrates a flowchart of a method for detecting a transmitted signal in a wireless communication system, according to an embodiment of the present disclosure.

[0036] Figure 9 illustrates a flowchart of a method for detecting a transmitted signal in MIMO communication systems with a set of pre-defined parameters, according to an embodiment of the present disclosure.

[0037] Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present invention. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

[0038] For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the various embodiments and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.

[0039] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are explanatory of the invention and are not intended to be restrictive thereof.

[0040] Reference throughout this specification to “an aspect,” “another aspect” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrase “in an embodiment,” “in another embodiment” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0041] The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by “comprises... a” does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.

[0042] Any of the functions or operations described herein can be processed by one processor or a combination of processors. The one processor or the combination of processors is circuitry performing processing and includes circuitry like an application processor (AP, e.g. a central processing unit (CPU)), a communication processor (CP, e.g., a modem), a graphics processing unit (GPU), a neural processing unit (NPU) (e.g., an artificial intelligence (AI) chip), a Wi-Fi chip, a Bluetooth® chip, a global positioning system (GPS) chip, a near field communication (NFC) chip, connectivity chips, a sensor controller, a touch controller, a finger-print sensor controller, a display driver integrated circuit (IC), an audio CODEC chip, a universal serial bus (USB) controller, a camera controller, an image processing IC, a microprocessor unit (MPU), a system on chip (SoC), an IC, or the like.

[0043] Embodiments of the present disclosure may be described below in detail with reference to the accompanying drawings.

[0044] Figure 1 illustrates a schematic block diagram depicting an environment for implementation of a system for signal detection in Multiple Input Multiple Output (MIMO) communication systems, according to an embodiment of the present disclosure.

[0045] In an embodiment, the system 102 may be implemented at a MIMO receiving station 104. The MIMO receiving station 104 may be a base station associated with the MIMO communication system. Although, the Figure 1 illustrates an up-link scenario, the present disclosure is not limited to the up-link scenario. The environment 100 may support implementation of the system 102 in both uplink and downlink scenarios. In an uplink scenario, the MIMO receiving station 104 may be a base station. In a downlink scenario, the MIMO receiving station 104 may include a plurality of user equipment.

[0046] In the uplink scenario, the plurality of user equipment 106A, 106B may transmit signals to the MIMO receiving station 104. The transmitted signals may be associated with a plurality of transmission layers 108A, 108B, 108C, 108D. Each transmission layer from the plurality of transmission layers 108A, 108B, 108C, 108D may be configured with different modulation schemes, thereby enabling mixed modulation transmission.

[0047] In the downlink scenario, the MIMO receiving station 104 may include the plurality of user equipment configured to receive signals from the base station. The base station may transmit signals through multiple transmission layers, wherein each transmission layer may be configured with different modulation schemes.

[0048] For the purpose of description, embodiments of the present disclosure may be described hereinafter with reference to signals transmitted from the plurality of user equipment 106A, 106B to the base station (also referred to as MIMO receiving station 104) in the uplink scenario. However, embodiments of the present disclosure may be equally applicable to, and may be implemented in scenarios where signals may be transmitted from the base station to the plurality of user equipment in the downlink scenario. In the downlink scenario, the MIMO receiving station 104 may include a user equipment, and techniques described hereinafter may be implemented at the user equipment for detection of signals transmitted from the base station.

[0049] In an embodiment, a set of pre-defined parameters may be associated with the MIMO communication system. The set of parameters may include a number of transmit antennas, a number of receive antennas, a subcarrier index (k), an Orthogonal Frequency Division Multiplexing (OFDM) symbol index (ℓ), a concatenated signal from the plurality of user equipment, modulation alphabets associated with the plurality of user equipment, a concatenated channel matrix (also referred as a pre-estimated channel matrix) associated with the plurality of user equipment and the MIMO communication system, received signal across the MIMO receiving station antennas, a modulation order vector, a neighbor count vector, a surviving nodes vector, a constellation table, and a neighborhood table.

[0050] The number of transmit antennas may represent a total count of antennas available at each user equipment among the plurality of user equipment 106A, 106B for transmitting signals to the MIMO receiving station 104. The number of receive antennas may represent a total count of antennas available at the MIMO receiving station 104 for receiving signals from each of the user equipment among the plurality of user equipment 106A, 106B. The subcarrier index (k) may represent a specific frequency component within an OFDM symbol for transmitting signals. The OFDM symbol index (l) may represent a specific time instance at which signals may be transmitted using an OFDM modulation scheme.

[0051] The modulation order vector (a) may represent different modulation schemes assigned to different transmission layers. Each element in the modulation order vector (a) may specify a number of bits per symbol for the corresponding transmission layer. For example, in mixed modulation scenarios, some transmission layers may use Quadrature Phase Shift Keying (QPSK) modulation (2 bits per symbol) while other transmission layers may use 16-QAM (Quadrature Amplitude Modulation) modulation (4 bits per symbol) or 64-QAM modulation (6 bits per symbol).

[0052] The neighbour count vector (k) may be a vector of pre-defined constants representing a number of nearest constellation points to be considered for each transmission layer during signal detection. The number of neighbours may vary across transmission layers based on the corresponding modulation scheme. For instance, transmission layers with higher modulation orders may require consideration of more neighboring points for accurate signal detection.

[0053] The surviving nodes vector (M)may be a vector of pre-defined constants representing a maximum number of candidate solutions to be retained at each transmission layer during a tree search operation. The transmission layers with higher order modulation schemes may retain more candidate solutions to maintain detection accuracy.

[0054] The constellation table (A) may represent a lookup table containing modulation symbols for each transmission layer based on the corresponding modulation scheme. In mixed modulation scenarios, different rows of the constellation table may contain different numbers of modulation symbols corresponding to different modulation schemes used across corresponding transmission layers.

[0055] The neighbourhood table ( ) may represent nearest neighbours for each constellation point in each transmission layer. The dimensions of neighbourhood tables may vary across transmission layers based on the corresponding modulation schemes. For instance, transmission layers using higher order modulation schemes may have larger neighbourhood tables.

[0056] In an exemplary mathematical representation, a concatenated signal fromKuser equipment at subcarrierkand OFDM symbollmay be represented as shown in equation 1.

[0057] [Equation 1]

[0058]

[0059] wherein, may represent a signal from user equipment m, and may take values from the modulation alphabet , wheremmay range from 1 toK.In scenarios where all user equipment among the plurality of user equipment employ a same modulation alphabet, the modulation alphabets may be represented as .

[0060] A concatenated channel matrix forKuser equipment at subcarrierkand OFDM symbollmay be represented as shown in equation 2.

[0061] [Equation 2]

[0062]

[0063] A received signal acrossMbase station (MIMO receiving station 104) antennas at subcarrierkand OFDM symbollmay be represented as shown in equation 3.

[0064] [Equation 3]

[0065]

[0066] In an exemplary mathematical representation, mthrow in the constellation table (A) may contain modulation symbols corresponding to the modulation order in the modulation order vector (a) at indexm.

[0067] In an exemplary mathematical representation, the neighbourhood table ( ) may be of dimensions | | × | |, wherein | | may represent a size of constellation corresponding to transmission layerm. An ithrow in the neighbourhood table ( ) may contain symbols from the constellation arranged in ascending order of distance from an ithsymbol of constellation .

[0068] Further, signal detection in the MIMO communication systems with the set of pre-defined parameters may be achieved using modules of the system 102 as explained in forthcoming paragraphs of Figures 2-5C.

[0069] Figure 2 illustrates a schematic block diagram of the system for signal detection in MIMO communication systems, according to an embodiment of the present disclosure.

[0070] The system 102 may include but is not limited to, a processor 202, a memory 204, modules 206, and data 208. The modules 206 and the memory 204 may be coupled to the processor 202.

[0071] The processor 202 may be a single processing unit or several units, all of which could include multiple computing units. The processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and / or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor 202 is adapted to fetch and execute computer-readable instructions and data stored in the memory 204.

[0072] The memory 204 may include any non-transitory computer-readable medium known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic random-access memory (DRAM), and / or non-volatile memory, such as read-only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The memory 204 may alternatively be referred to as a database 204 in the present disclosure, within the scope of the invention.

[0073] The modules 206, amongst other things, include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement data types. The modules 206 may also be implemented as, signal processor(s), state machine(s), logic circuitries, and / or any other device or component that manipulates signals based on operational instructions.

[0074] Further, the modules 206 may be implemented in hardware, instructions executed by a processing unit, or by a combination thereof. The processor 202 can comprise a computer, a processor, a state machine, a logic array, or any other suitable devices capable of processing instructions. The processing unit can be a general-purpose processor (e.g., processor 202) which executes instructions to cause the general-purpose processor to perform the required tasks, or, the processing unit can be dedicated to performing the required functions. In another embodiment of the present disclosure, the modules 206 may be machine-readable instructions (software) which, when executed by the processor 202 / processing unit, perform any of the described functionalities / methods, as discussed throughout the present disclosure.

[0075] Furthermore, the modules 206 may be implemented through an artificial intelligence (AI) model. A function associated with AI may be performed through the non-volatile memory, the volatile memory, and the processor.

[0076] The processor 202 may include one or a plurality of processors. At this time, one or a plurality of processors may be a general purpose processor, such as a central processing unit (CPU), an application processor (AP), or the like, a graphics-only processing unit such as a graphics processing unit (GPU), a visual processing unit (VPU), and / or an AI-dedicated processor such as a neural processing unit (NPU).

[0077] The one or the plurality of processors control the processing of the input data in accordance with a predefined operating rule or artificial intelligence (AI) model stored in the non-volatile memory and the volatile memory. The predefined operating rule or artificial intelligence model is provided through training or learning.

[0078] In an embodiment, the modules 206 may include a receiving module 210, a pre-processing module 212, a linear detection technique application module 214, a neighbourhood obtaining module 216, a threshold determination module 218, a symbol detection modelling module 220, an m-algorithm application module 222, a log-likelihood ratio determination module 224, and a transmitted signal detection module 226. The receiving module 210, the pre-processing module 212, the linear detection technique application module 214, the neighbourhood obtaining module 216, the threshold determination module 218, the symbol detection modelling module 220, the M-algorithm application module 222, the log-likelihood ratio determination module 224, and the transmitted signal detection module 226 may be in communication with each other. The data 208 serves, amongst other things, as a repository for storing data processed, received, and generated by one or more of the modules 206.

[0079] In an embodiment, the receiving module 210 may be configured to receive a plurality of signals 110 at the MIMO receiving station 104. The plurality of signals 110 may be transmitted from the plurality of user equipment 106A, 106B. The plurality of signals 110 may carry data in a plurality of layers associated with the plurality of transmission layers 108A, 108B, 108C, 108D. In an embodiment, the modules 206 may include a transceiver including the receiving module 210.

[0080] In an exemplary mathematical representation, the received signal at the MIMO receiving station 104 at subcarrierkand OFDM symbollmay be represented as shown in equation 4.

[0081] [Equation 4]

[0082]

[0083] i.e.

[0084] wherein, may represent the received signal vector; may represent the channel matrix; may represent the transmitted signal vector, and may represent an additive white Gaussian noise vector.

[0085] The additive white Gaussian noise vector may represent random noise components added to the transmitted signal during propagation through the wireless communication channel. The noise components may be modelled as complex Gaussian random variables with zero mean and variance σ², wherein σ² may represent a noise power.

[0086] In an embodiment, the pre-processing module 212 may be configured to pre-process the plurality of received signals based on the pre-estimated channel matrix.

[0087] Figure 3 illustrates a process flow 300 associated with the pre-processing module of the system 102, according to an embodiment of the present disclosure.

[0088] At block 302, the process flow 300 may include sorting a plurality of columns of the pre-estimated channel matrix in a decreasing order of energy to obtain a sorted channel matrix ( ). The energy associated with each column of the pre-estimated channel matrix may be computed, and the columns may be re-arranged in decreasing order based on the computed energy values.

[0089] In an embodiment, the re-arrangement order applied to the plurality of columns of the pre-estimated channel matrix may be stored for associated parameters. The modulation order vector (a), the neighbor count vector (k), the surviving nodes vector (M), and the rows of the constellation table (A) may be reordered according to the re-arrangement order applied to the columns of the pre-estimated channel matrix. For an exemplary mathematical representation, the re-ordered transmit vector, modulation order vector, the re-ordered neighbor count vector, the re-ordered surviving nodes vector, and the constellation table with re-ordered rows may be denoted as (x') (a′), (k′), (M′), (A′) respectively.

[0090] At block 304, the process flow 300 includes decomposing the sorted channel matrix to obtain an orthogonal matrix and an upper triangular matrix. The sorted channel matrix may be decomposed using QR decomposition technique to transform the sorted channel matrix into a product of an orthogonal matrix and an upper triangular matrix. In an exemplary mathematical representation, the QR Decomposition of the channel matrix may be represented as shown in equation 5.

[0091] [Equation 5]

[0092] whereQis the orthonormal matrix and given by equation 6.

[0093] [Equation 6]

[0094]

[0095] Where,Ris upper triangular matrix as shown in equation 7.

[0096] [Equation 7]

[0097]

[0098] At block 306, the process flow 300 includes obtaining a pre-processed received signal using a Hermitian transpose of the orthogonal matrix. The Hermitian transpose of the orthogonal matrix may be multiplied with the received signal vector to obtain the pre-processed received signal.

[0099] In an exemplary mathematical representation, the pre-processed received signal may be represented as shown in equation 8.

[0100] [Equation 8]

[0101]

[0102] where may be the pre-processed signal.

[0103] Referring again to Figure 2, in an embodiment, the linear detection technique application module (214) may be configured to apply an initial linear detection technique to obtain an initial estimate of each of the plurality of transmitted layers from each of the plurality of user equipment. The initial linear detection technique may be implemented using a Minimum Mean Square Error (MMSE) detection method.

[0104] In an exemplary mathematical representation, an initial linear solution may be obtained as shown in equation 9.

[0105] [Equation 9]

[0106]

[0107] wherein, may represent the initial linear solution vector; may represent the sorted channel matrix; may represent the Hermitian transpose of the sorted channel matrix; ‘σ²’ may represent the noise variance; ‘I’ may represent an identity matrix, and ‘y’ may represent the received signal vector.

[0108] In an embodiment, each element of the initial linear solution vector may be mapped to a nearest modulation symbol in the corresponding transmission layer. The mthelement of may be mapped to a nearest symbol from a modulation alphabet specified in the mthrow of the constellation table with re-ordered rows (A').

[0109] In an embodiment, the neighbourhood obtaining module 216 may be configured to obtain a neighbourhood of each of the initial estimate.

[0110] Figure 4a illustrates a process flow 400 associated with the neighbourhood obtaining module 216 of the system 102, according to an embodiment of the present disclosure.

[0111] At block 402, the process flow 400 may include calculating a cost between the initial estimate and transmitted symbols in the modulation alphabet. In an exemplary mathematical representation, an accumulated cost of the initial estimate for each transmission layer (m), may be computed as shown in equation 10.

[0112] [Equation 10]

[0113] may represent elementsKthroughmof the pre-processed received signal vector may represent a submatrix of R formed by extracting elements K through m along both rows and columns, and may represent elementsKthroughmof the initial estimate vector .

[0114] At block 404, the process flow 400 may include retaining, in each of the plurality of transmitted layers, a set of closest points to each of the initial estimate based on the pre-configured look-up table. The pre-configured look-up table may refer to the constellation tableA'mdefined for each transmission layerm.

[0115] Figure 4B illustrates an exemplary constellation diagram depicting retention of closest neighborhood points for a transmission layer by the neighborhood obtaining module of the system, according to an embodiment of the present disclosure. The initial estimate 406 may be represented by a cross marker. A set of neighboring constellation points in closest proximity 408 to the initial estimate 406 may be represented by circle markers, and in next-closest 410 proximity may be represented as square mark. A subset of closest neighbors may be retained based on a valuek'(m) specified in the neighbor count vectork'.

[0116] In an embodiment, for each transmission layerm, each row in N'mmay contain constellation points fromA'marranged in ascending order of distance from a reference constellation point (initial estimate).k'(m) closest points may be retained from the row corresponding to the initial estimate. The retained points may form a neighbourhood setBmcorresponding to transmission layerm. In an embodiment, the neighbourhood may be obtained sequentially for all transmission layers from layerKto layer 1, resulting in neighbourhood sets

[0117] Referring again to Figure 2, in an embodiment, the threshold determination module 218 may be configured to determine a threshold distance for each transmitted layer based on the corresponding initial estimate. The threshold distance in each layer may be determined based on a noise variance and the cost associated with the initial estimate and corresponding to the corresponding layer.

[0118] In an exemplary mathematical representation, at transmission layer m, the threshold distance may be determined according to equation 11.

[0119] [Equation 11]

[0120]

[0121] wherein Δmmay represent the threshold distance corresponding to transmission layerm, Em,MMSEmay represent an accumulated cost corresponding to initial estimate up to transmission layerm. The accumulated cost may be obtained by traversing a path on the tree search corresponding to symbols in initial estimate from transmission layer ‘K’ to transmission layer m. ‘X’ may represent a pre-determined positive constant, andσ² may represent the noise variance.

[0122] In an embodiment, the symbol detection modelling module 220 may be configured to model symbol detection as the tree search. The tree search model may comprise multiple layers, wherein each layer may correspond to a transmission layer in the MIMO communication system. A number of layers in the tree search may be equal to a number of MIMO transmission layers.

[0123] In an embodiment, each node in the tree search model may represent a candidate symbol for detection in a corresponding transmission layer. Each node may be connected to nodes in an adjacent layer through branches. A number of branches emanating from each node in a layer may be equal to a size of modulation alphabet used in the corresponding transmission layer. In mixed modulation scenarios, different layers may have different numbers of branches based on respective modulation schemes.

[0124] In an embodiment, nodes at a topmost layer of the tree may be referred to as root nodes, and nodes at a bottommost layer may be referred to as leaf nodes. A path from a root node to a leaf node through intermediate nodes may represent a complete candidate solution for symbol detection across each of the plurality of transmission layers.

[0125] In an embodiment, the M-algorithm application module 222 may be configured to apply an M-algorithm on the tree.

[0126] Figure 5 illustrates a process flow 500 associated with the M-algorithm application module 222 of the system 102, according to an embodiment of the present disclosure.

[0127] At block 502, the process flow 500 may include computing a cost for each branch in the neighbourhood of the initial estimate. The cost computation may begin at a root layerKof the tree search. For each symbol in the neighbourhood set the cost may be computed and stored in a set according to equation 12.

[0128] [Equation 12]

[0129]

[0130] where is the (K,K)th element of the upper triangular matrixR.

[0131] In an embodiment, a set of surviving symbols may be set equal to . The set may represent surviving nodes corresponding to the neighbourhood of initial estimate at layerK.

[0132] In an embodiment, the cost may be computed by traversing through the tree for layersmranging from K-1 to 1. At each layerm,partial symbol vectors may be formed according to equation 13.

[0133] [Equation 13]

[0134]

[0135] wherein may represent a Kronecker product of sets and . The Kronecker product may generate vectors by combining elements from with .

[0136] In an embodiment, for each vector in , an accumulated cost may be computed and stored in a set . For an ithvector formed by combining lthelement of with an element from , the cost may be computed as shown in equation 14.

[0137] [Equation 14]

[0138]

[0139] wherein may represent the (m,k)th element ofR, and may represent the kthelement of vector .

[0140] At block 504, the process flow 500 may include pruning nodes corresponding to the branches having the cost more than the threshold distance.

[0141] Figure 5b illustrates an exemplary tree structure depicting a pruning of nodes by the M-algorithm application module of the system, according to an embodiment of the present disclosure. The root node 505A may be connected to multiple branches representing possible symbol combinations. Branches corresponding to nodes having accumulated cost exceeding the threshold distance may be pruned, as indicated by dotted lines 505B in Figure 5B.

[0142] In an embodiment, for non-leaf nodes (i.e. m ≠ 1), accumulated costs in the set may be compared with the threshold distance Δm. Symbol vectors from set having corresponding accumulated cost less than Δmmay be retained and stored in set .

[0143] In an embodiment, when a number of vectors in the set exceeds a maximum allowed number of surviving nodes M'(m) at layer m, only M'(m) vectors having smallest accumulated cost may be retained in set . Remaining vectors may be removed from set . Accumulated costs corresponding to retained partial symbol vectors in set may be stored in set .

[0144] In an embodiment, for leaf nodes (i.e. m = 1), sets and may be set equal to and respectively. The sets and may contain final shortlisted vectors at the leaf node and corresponding accumulated costs respectively.

[0145] Figure 5c illustrates an exemplary tree structure depicting implementation of M-algorithm by the M-algorithm application module, according to an embodiment of the present disclosure. A root node 506 may represent a starting point of tree search. First layer nodes 508 may represent possible symbol combinations at layerK, wherein second layer nodes 510 may represent partial symbol vectors formed at layerK-1. Subsequent layer nodes may represent partial symbol vectors at respective layers. The branches with dotted outline may represent pruned paths 512 based on threshold distance and maximum surviving nodes criteria, while nodes with solid outline may represent retained paths having accumulated costs below threshold distance. The leaf nodes 514 at bottom layer may represent final surviving nodes.

[0146] Referring again to Figure 2, the log-likelihood ratio determination module 224 may be configured to determine log-likelihood ratio for information bits associated with the final surviving leaf nodes.

[0147] In an exemplary mathematical representation, log-likelihood ratio for each bit may be computed according to equation 15.

[0148] [Equation 15]

[0149]

[0150] wherein may represent a subset of containing vectors ‘x’ for which bthbit of jthsymbol may be mapped to 0; may represent a subset of containing vectors ‘x’ for which bthbit of jthsymbol may be mapped to 1, and may represent an accumulated cost for vector ‘x’ in set .

[0151] In an embodiment, the transmitted signal detection module 226 may be configured to detect the transmitted signal by recovering the information bits from the determined log-likelihood ratio using a channel decoder.

[0152] In an embodiment, the log-likelihood ratios may be rearranged to reverse the energy-based reordering performed during pre-processing operation. The rearrangement operation may restore original ordering of transmission layers, wherein the rearranged log-likelihood ratios may correspond to information bits associated with original transmission layer sequence. The rearranged log-likelihood ratios may be provided as input to the channel decoder for recovery of transmitted information bits.

[0153] Figure 6a illustrates an exemplary hardware architecture for implementation of the system, according to an embodiment of the present disclosure. Figure 6b illustrates an exemplary candidate search module of the system, according to an embodiment of the present disclosure.

[0154] In an embodiment, the system 102 may receive the set of pre-defined parameters including but not limited to the received vectory, the channel matrixH, the modulation order vectorafor each transmission layer, the neighbor count vectork, the maximum surviving nodes vectorM, and the noise varianceσ².

[0155] In an embodiment, the pre-processing module 212 may be configured to pre-process the plurality of received signals based on the pre-estimated channel matrix. The pre-processing module 212 may be configured to sort columns of the pre-estimated channel matrix in a decreasing order of energy to obtain a sorted channel matrix. The pre-processing module 212 may be configured to decompose the sorted channel matrix to obtain an orthogonal matrix and an upper triangular matrix. The pre-processing module 212 may be configured to obtain a pre-processed received signal using a Hermitian transpose of the orthogonal matrix.

[0156] In an embodiment, the memory 204 may be configured to store neighbourhood tables for all transmission layers, wherein for transmission layerm, the neighbourhood table may be of dimensions | | × | |, wherein may represent constellation used in transmission layerm. Each row ‘i’ in the neighbourhood table may contain constellation points from arranged in ascending order of distance from an ithconstellation point of . The memory unit 204 may further store the sorted channel matrix and sorted vectorsa',k′,M′.

[0157] In an embodiment, the candidate search module 602 may be configured to perform neighborhood search and apply the M-algorithm. The candidate search module 602 may include a plurality of arithmetic units 604 arranged in parallel. Each arithmetic unit among the plurality of arithmetic unit 604 may be configured to compute accumulated cost for corresponding symbol combinations. The candidate search module 602 may further include a plurality of comparator units 606 coupled to respective arithmetic units 604, wherein each comparator unit among the plurality of comparator unit 606 may be configured to compare computed cost with the threshold distance. A bit counter 608 may be coupled to outputs of the plurality of comparator units 606. The bit counter 608 may be configured to count number of costs below threshold distance.

[0158] The system 102 may further include a demultiplexer 610 configured to receive symbol-cost pairs from the plurality of comparator units 606, and a sorter unit 612 coupled to the demultiplexer 610. The sorter unit 612 may be configured to arrange received symbol-cost pairs in ascending order of cost. A control logic module 614 may be coupled to the sorter unit 612, wherein the control logic module 614 may be configured to select a predetermined number of symbol-cost pairs based on maximum surviving nodes vector.

[0159] For root layer (m=K), when the initial estimate corresponds to symbollof constellation , the system 102 may be configured to fetch symbols 1 to (k'(m)+1) from row-lof the neighbourhood table into the neighbourhood set . The cost for each element in the neighbourhood set may be computed using arithmetic units 604 and stored in . Each cost in may be compared against the threshold distance . The bit counter 608 may be configured to count number of costs less than threshold distance . When count may be less thanM'(K), each symbol-cost pair having cost less than threshold distance may be stored in a register bank 618 as . When count may exceedM'(K), symbol-cost pairs may be passed through a demultiplexer 610 to a sorter unit 612. The sorter unit 612 may be configured to sort costs in ascending order and pass sorted symbol-cost pairs to a control logic module 614. The control logic module 614 may be configured to select firstM'(K) pairs for storage in the register bank 618.

[0160] For subsequent layers (m=K-1 to 1), when the initial estimate corresponds to symbollof constellation , the system 102 may be configured to fetch symbols 1 to from row-lof the neighbourhood table into the neighbourhood set . Partial vector-cost pairs from previous layer may be fetched from the register bank 618 by candidate search modules. Each candidate search module may be configured to form partial symbol vectors by combining with elements in neighbourhood set . Accumulated costs may be calculated using arithmetic units 604. Costs below threshold distance may be output from each candidate search module. Total costs below threshold distance may be counted using an adder. When count may be less than M'(m), outputs may be stored in register bank 618 as . When count may exceedM'(m), symbol-cost pairs may be sorted by sorter unit 612, and control logic module 614 may be configured to select firstM'(m) pairs for storage.

[0161] In an embodiment, the log-likelihood ratio determination module 224 may be configured to track layer index m using a down-counter 620. Upon reaching leaf layer (m=1), final symbol vectors and associated costs may be processed to compute log-likelihood ratios for each bit based on final surviving symbol vectors and costs.

[0162] Figure 7a illustrates an exemplary Block Error Rate (BLER) performance graph 702 of the system implementing a 3-neighborhood search under mixed modulation scenario, according to an embodiment of the present disclosure. The mixed modulation scenario may include two transmission layers using 16-Quadrature Amplitude Modulation (16-QAM) and two transmission layers using 64-QAM modulation schemes. The graph 702 indicates that the system 102 may achieve Signal-to-Noise Ratio (SNR) gain of 4.5 dB compared to conventional Minimum Mean Square Error (MMSE) detection at 10% BLER.

[0163] Figure 7b illustrates an exemplary complexity graph 704 the system implementing a 3-neighborhood search under mixed modulation scenario, according to an embodiment of the present disclosure. The graph 704 indicates that computational complexity of the system 102 may be as low as 12 times the MMSE detection method, whereas the conventional QML detection method may require 150 times complexity compared to the MMSE detection method.

[0164] Figure 7c illustrates an exemplary BLER performance graph 706 of the system implementing a 2-neighborhood search under mixed modulation scenario, according to an embodiment of the present disclosure. The graph 706 indicates that the system 102 may achieve SNR gain of 2.5 dB compared to MMSE detection at 10% BLER.

[0165] Figure 7d illustrates an exemplary complexity graph 708 of the system implementing a 2-neighborhood search under mixed modulation scenario, according to an embodiment of the present disclosure. The graph 708 may indicate that computational complexity of the system 102 may be as low as 8 times the complexity of MMSE detection method, whereas the conventional QML detection method may work at complexity of 150 times the complexity of MMSE detection method.

[0166] Figure 8 illustrates a flowchart of a method for detecting a transmitted signal in a wireless communication system, according to an embodiment of the present disclosure.

[0167] The method 800 may be a computer-implemented method executed, for example, by the processor 202 and the modules 206 of the system 102. For the sake of brevity, constructional and operational features of the system 102 that are already explained in the description of Figure 1, Figure 2, Figure 3, Figures 4A-4B, Figures 5A-5C, Figures 6A-6B, and Figures 7A-7D are not explained in detail in the description of Figure 8.

[0168] At step 802, the method 800 may include receiving, from a plurality of user equipments (UEs), a plurality of signals including a plurality of transmitted layers.

[0169] At step 804, the method 800 may include modelling symbol detection as a tree search based on a number of MIMO layers and a number of the transmitted layers.

[0170] At step 806, the method may include applying an M-algorithm on a tree based on the tree search.

[0171] At step 808, the method may include detecting the transmitted signal based on the tree.

[0172] In an embodiment, the method may include pre-processing the plurality of signals based on a pre-estimated channel matrix to obtain a pre-processed signal.

[0173] In an embodiment, the method may include applying an initial linear detection technique on the pre-processed signal to obtain an initial estimate of each of the plurality of transmitted layers from each of the plurality of UEs.

[0174] In an embodiment, the method may include obtaining a neighborhood of each of the initial estimate.

[0175] In an embodiment, the method may include determining a threshold distance for each transmitted layer based on the corresponding initial estimate.

[0176] In an embodiment, a number of layers of the tree search is equal to a number of the MIMO layers.

[0177] In an embodiment, a number of branches emanating from each node in the layers of the tree search is equal to size of a modulation alphabet in the corresponding transmitted layer.

[0178] In an embodiment, the method of applying the M-algorithm on the tree based on the tree search may include computing each cost for each branch in the neighborhood of each of the initial estimate. In an embodiment, the method of applying the M-algorithm on the tree based on the tree search may include pruning nodes corresponding to each branch having a cost more than the threshold distance.

[0179] In an embodiment, the method may include determining log-likelihood ratio for information bits associated with final surviving nodes after the pruning nodes.

[0180] In an embodiment, the method of detecting the transmitted signal based on the tree may include detecting the transmitted signal by recovering the information bits from the determined log-likelihood ratio using a channel decoder.

[0181] Figure 9 illustrates a flowchart of a method for detecting the transmitted signal in MIMO communication systems with the set of pre-defined parameters, according to an embodiment of the present disclosure.

[0182] The method 900 may be a computer-implemented method executed, for example, by the processor 202 and the modules 206 of the system 102. For the sake of brevity, constructional and operational features of the system 102 that are already explained in the description of Figure 1, Figure 2, Figure 3, Figures 4A-4B, Figures 5A-5C, Figures 6A-6B, and Figures 7A-7D are not explained in detail in the description of Figure 9.

[0183] At step 902, the method 900 may include receiving, at the MIMO receiving station, the plurality of signals comprising the plurality of transmitted layers from the plurality of UEs.

[0184] At step 904, the method 900 may include pre-processing the plurality of received signals based on the pre-estimated channel matrix to obtain the pre-processed signal.

[0185] At step 906, the method may include applying an initial linear detection technique on the pre-processed signal to obtain the initial estimate of each of the plurality of transmitted layers from each of the plurality of UEs.

[0186] At step 908, the method may include obtaining a neighbourhood of each of the initial estimate.

[0187] At step 910, the method 900 may include determining the threshold distance for each transmitted layer based on the corresponding initial estimate.

[0188] At step 912, the method 900 may include modelling symbol detection as a tree search wherein the number of layers in the tree search is equal to the number of MIMO layers, and the number of branches emanating from each node in the layer is equal to size of the modulation alphabet in the corresponding transmitted layer.

[0189] At step 914, the method 900 may include applying the M-algorithm on the tree by: computing the cost for each branch in the neighbourhood of the initial estimate, and pruning nodes corresponding to the branches having the cost more than the threshold distance.

[0190] At step 916, the method 900 may include determining log-likelihood ratio for information bits associated with the final surviving leaf nodes.

[0191] At step 918, the method 900 may include detecting the transmitted signal by recovering the information bits from the determined log-likelihood ratio using the channel decoder.

[0192] At least by virtue of aforesaid, the present subject matter may provide following advantages:

[0193] The system disclosed in the present disclosure achieves reduced computational complexity through neighborhood search technique by computing tree branches only for selected neighbors of initial linear solution in each transmission layer rather than all modulation symbols.

[0194] The system disclosed in the present disclosure achieves reduced computational complexity by dynamically adjusting number of surviving nodes in each layer based on initial linear solution.

[0195] The system disclosed in the present disclosure achieves near-optimal detection performance while maintaining low complexity by retaining linear solution and corresponding neighbors throughout the tree search process.

[0196] The system disclosed in the present disclosure achieves significant Signal-to-Noise Ratio gain compared to Minimum Mean Square Error detection while maintaining comparable computational complexity.

[0197] The system disclosed in the present disclosure achieves enhanced coverage for cell edge users is enabled through reliable signal detection at low received energy levels.

[0198] The system disclosed in the present disclosure achieves high throughput by supporting higher modulation orders while maintaining required error performance.

[0199] The method disclosed in the present disclosure improves battery life of user equipment through reduced transmission energy requirements for achieving desired throughput.

[0200] The system disclosed in the present disclosure achieves enhanced coverage for cell edge users through reliable signal detection at low received energy levels.

[0201] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.

[0202] The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein.

Claims

1.A method for detecting a transmitted signal in a wireless communication system, the method comprising:receiving, from a plurality of user equipments (UEs), a plurality of signals including a plurality of transmitted layers;modelling symbol detection as a tree search based on a number of MIMO layers and a number of the transmitted layers;applying an M-algorithm on a tree based on the tree search; anddetecting the transmitted signal based on the tree.2.The method of claim 1, further comprising:pre-processing the plurality of signals based on a pre-estimated channel matrix to obtain a pre-processed signal;applying an initial linear detection technique on the pre-processed signal to obtain an initial estimate of each of the plurality of transmitted layers from each of the plurality of UEs;obtaining a neighborhood of each of the initial estimate; anddetermining a threshold distance for each transmitted layer based on the corresponding initial estimate.3.The method of claim 1,wherein a number of layers of the tree search is equal to a number of the MIMO layers, andwherein a number of branches emanating from each node in the layers of the tree search is equal to size of a modulation alphabet in the corresponding transmitted layer.4.The method of claim 2, wherein applying the M-algorithm on the tree based on the tree search comprises:computing each cost for each branch in the neighborhood of each of the initial estimate, andpruning nodes corresponding to each branch having a cost more than the threshold distance.5.The method of claim 4, further comprising:determining log-likelihood ratio for information bits associated with final surviving nodes after the pruning nodes.6.The method of claim 5, wherein detecting the transmitted signal based on the tree comprises:detecting the transmitted signal by recovering the information bits from the determined log-likelihood ratio using a channel decoder.7.The method of claim 2, wherein pre-processing the plurality of signals comprises:sorting a plurality of columns of the pre-estimated channel matrix in a decreasing order of energy to obtain a sorted channel matrix;decomposing the sorted channel matrix to obtain an orthogonal matrix and an upper triangular matrix; andobtaining a pre-processed received signal using a Hermitian transpose of the orthogonal matrix.8.The method of claim 2, wherein obtaining the neighborhood of the each of the initial estimates comprises:calculating, in each of the plurality of transmitted layers, a cost between the initial estimate and transmitted symbols in a modulation alphabet; andretaining, in each of the plurality of transmitted layers, a set of closest points to each of the initial estimate based on a pre-configured look-up table.9.The method of claim 2, wherein the threshold distance in each layer is determined based on a noise variance and the cost associated with the initial estimate and corresponding to the corresponding layer.10.An apparatus for detecting a transmitted signal in a wireless communication system, the apparatus comprising:a transceiver; andat least one processor coupled to the transceiver and configured to:receive, from a plurality of user equipments (UEs), a plurality of signals including a plurality of transmitted layers,model symbol detection as a tree search based on a number of MIMO layers and a number of the transmitted layers,apply an M-algorithm on a tree based on the tree search; anddetect the transmitted signal based on the tree.11.The apparatus of claim 10, wherein the at least one processor is further configured to:pre-process the plurality of signals based on a pre-estimated channel matrix to obtain a pre-processed signal,apply an initial linear detection technique on the pre-processed signal to obtain an initial estimate of each of the plurality of transmitted layers from each of the plurality of UEs,obtain a neighborhood of each of the initial estimate, anddetermine a threshold distance for each transmitted layer based on the corresponding initial estimate.12.The apparatus of claim 10,wherein a number of layers of the tree search is equal to a number of the MIMO layers, andwherein a number of branches emanating from each node in the layers of the tree search is equal to size of a modulation alphabet in the corresponding transmitted layer.13.The apparatus of claim 11, wherein the at least one processor is further configured to:compute each cost for each branch in the neighborhood of each of the initial estimate, andprune nodes corresponding to each branch having a cost more than the threshold distance.14.The apparatus of claim 13, wherein the at least one processor is further configured to:determine log-likelihood ratio for information bits associated with final surviving nodes after the pruning nodes.15.The apparatus of claim 14, wherein the at least one processor is further configured to:detect the transmitted signal by recovering the information bits from the determined log-likelihood ratio using a channel decoder.

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