WO2021068007A2 - Apparatus and method of multiple-input multiple-output detection with successive transmission layer detection and soft interference cancellation - Google Patents

Abstract

An apparatus of MIMO detection is disclosed. The apparatus may perform a first tree search based at least in part on a first triangular R matrix and a first received signal to obtain a first set of candidate symbol paths associated with a first transmission layer. The apparatus may generate a first set of LLRs associated with the first transmission layer. The apparatus may generate a first reconstructed symbol associated with the first transmission layer based at least in part on the first set of LLRs. The apparatus may remove a last row and a last column from the first triangular R matrix to obtain a second triangular R matrix associated with a second transmission layer. The apparatus may subtract the first reconstructed symbol from the first received signal to obtain a second estimated signal associated with the second transmission layer.

Description

APPARATUS AND METHOD OF MULTIPLE-INPUT MULTIPLE- OUTPUT DETECTION WITH SUCCESSIVE TRANSMISSION LAYER DETECTION AND SOFT INTERFERENCE CANCELLATION

CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims the benefit of priority to U. S. Provisional Patent Application

No. 62/991,360, filed March 18, 2020, entitled “MIMO DETECTION WITH SUCCESSIVE LAYER DETECTION AND SOFT CANCELATION,” which is hereby incorporated by reference in its entirety.

BACKGROUND

[0002] Embodiments of the present disclosure relate to apparatus and method for wireless communication.

[0003] Wireless communication systems are widely deployed to provide various telecommunication services such as telephony, video, data, messaging, and broadcasts. The development of wireless communication, especially the cellular communication systems, such as the 4th-generation (4G) Long Term Evolution (LTE) and the 5th-generation (5G) New Radio (NR), makes the higher speed data service critical. Multiple-input and multiple-output (MIMO) communication has been spotlighted since MIMO communication uses a spatial multiplexing method in which multipath propagation can be performed by transmitting multiple signal streams (also referred to as “transmission layers”) using multiple transmission and receiving antennas to satisfy high-speed data requirement.

SUMMARY

[0004] Embodiments of apparatus and method for MIMO detection with successive transmission layer detection and soft interference cancellation are disclosed herein.

[0005] According to one aspect of the present disclosure, an apparatus for wireless communication is disclosed that can include a memory and at least one processor coupled to the memory and configured to perform operations associated with MIMO detection. For example, in certain embodiments, the at least one processor may be configured to perform a first tree search based at least in part on a first triangular R matrix and a first received signal to obtain a first set of candidate symbol paths associated with a first transmission layer. In certain other embodiments, the at least one processor may be further configured to generate a first set of log-likelihood ratios (LLRs) associated with the first transmission layer based at least in part on the first set of candidate symbol paths. In certain other embodiments, the at least one processor may be further configured to generate a first reconstructed symbol associated with the first transmission layer based at least in part on the first set of LLRs. In certain other embodiments, the at least one processor may be further configured to remove a last row and a last column from the first triangular R matrix to obtain a second triangular R matrix associated with a second transmission layer. In certain other embodiments, the at least one processor may be further configured to subtract the first reconstructed symbol from the first received signal to obtain a second signal associated with the second transmission layer.

[0006] According to another aspect of the present disclosure, a method for wireless communication is disclosed. In some embodiments, the method may include performing a first tree search based at least in part on a first triangular R matrix and a first received signal to obtain a first set of candidate symbol paths associated with a first transmission layer. In some embodiments, the method may include generating a first set of LLRs associated with the first transmission layer based at least in part on the first set of candidate symbol paths. In some embodiments, the method may include generating a first reconstructed symbol associated with the first transmission layer based at least in part on the first set of LLRs. In some embodiments, the method may include removing a last row and a last column from the first triangular R matrix to obtain a second triangular R matrix associated with a second transmission layer. In some embodiments, the method may include subtracting the first reconstructed symbol from the first received signal to obtain a second signal associated with the second transmission layer.

[0007] According to still another aspect of the present disclosure, a baseband chip for wireless communication is disclosed. The baseband chip may include a MIMO detection circuit. In certain embodiments, the MIMO detection circuit may be configured to perform a first tree search based at least in part on a first triangular R matrix and a first received signal to obtain a first set of candidate symbol paths associated with a first transmission layer. In certain other embodiments, the MIMO detection circuit may be configured to generate a first set of LLRs associated with the first transmission layer based at least in part on the first set of candidate symbol paths. In certain other embodiments, the MIMO detection circuit may be configured to generate a first reconstructed symbol associated with the first transmission layer based at least in part on the first set of LLRs. In certain other embodiments, the MIMO detection circuit may be configured to a last row and a last column from the first triangular R matrix to obtain a second triangular R matrix associated with a second transmission layer. In certain other embodiments, the MIMO detection circuit may be configured to subtract the first reconstructed symbol from the first received signal to obtain a second signal associated with the second transmission layer. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate embodiments of the present disclosure and, together with the description, further serve to explain the principles of the present disclosure and to enable a person skilled in the pertinent art to make and use the present disclosure. [0009] FIG. 1 illustrates an exemplary wireless network, according to some embodiments of the present disclosure.

[0010] FIG. 2 illustrates an exemplary MIMO communication system, according to some embodiments of the present disclosure.

[0011] FIG. 3 illustrates a detailed block diagram of the MIMO communication system in FIG. 2, according to some embodiments of the present disclosure.

[0012] FIG. 4 illustrates a schematic diagram of an exemplary MIMO channel, according to some embodiments of the present disclosure.

[0013] FIG. 5 illustrates a block diagram of an exemplary MIMO detection system, according to some embodiments of the present disclosure. [0014] FIGs. 6A and 6B illustrate a flow chart of an exemplary method for MIMO detection, according to some embodiments of the present disclosure.

[0015] FIGs. 7A and 7B illustrate block diagrams of an exemplary apparatus including a host chip, a radio frequency (RF) chip, and a baseband chip implementing the MIMO detection system in FIG. 5 in software and hardware, respectively, according to some embodiments of the present disclosure.

[0016] FIG. 8 illustrates a block diagram of an exemplary receiving device, according to some embodiments of the present disclosure.

[0017] Embodiments of the present disclosure will be described with reference to the accompanying drawings. DETAILED DESCRIPTION

[0018] Although specific configurations and arrangements are discussed, it should be understood that this is done for illustrative purposes only. A person skilled in the pertinent art will recognize that other configurations and arrangements can be used without departing from the spirit and scope of the present disclosure. It will be apparent to a person skilled in the pertinent art that the present disclosure can also be employed in a variety of other applications.

[0019] It is noted that references in the specification to “one embodiment,” “an embodiment,” “an example embodiment,” “some embodiments,” “certain embodiments,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases do not necessarily refer to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it would be within the knowledge of a person skilled in the pertinent art to effect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.

[0020] In general, terminology may be understood at least in part from usage in context.

For example, the term “one or more” as used herein, depending at least in part upon context, may be used to describe any feature, structure, or characteristic in a singular sense or may be used to describe combinations of features, structures or characteristics in a plural sense. Similarly, terms, such as “a,” “an,” or “the,” again, may be understood to convey a singular usage or to convey a plural usage, depending at least in part upon context. In addition, the term “based on” may be understood as not necessarily intended to convey an exclusive set of factors and may, instead, allow for existence of additional factors not necessarily expressly described, again, depending at least in part on context.

[0021] Various aspects of wireless communication systems will now be described with reference to various apparatus and methods. These apparatus and methods will be described in the following detailed description and illustrated in the accompanying drawings by various blocks, modules, units, components, circuits, steps, operations, processes, algorithms, etc. (collectively referred to as “elements”). These elements may be implemented using electronic hardware, firmware, computer software, or any combination thereof. Whether such elements are implemented as hardware, firmware, or software depends upon the particular application and design constraints imposed on the overall system. [0022] The techniques described herein may be used for various wireless communication networks, such as code division multiple access (CDMA) system, time division multiple access (TDMA) system, frequency division multiple access (FDMA) system, orthogonal frequency division multiple access (OFDMA) system, single-carrier frequency division multiple access (SC- FDMA) system, and other networks, including but not limited to 4G, LTE, and 5G cellular networks. The terms “network” and “system” are often used interchangeably. The techniques described herein may be used for the wireless networks mentioned above, as well as other wireless networks.

[0023] Multiple-input multiple-output (MIMO) technology constitutes the basis for various wireless communication systems, e.g., such as 3G, LTE, and NR, just to name a few. In a MIMO system, both the transmitter and the receiver are equipped with multiple antennas. Multiple data streams can be delivered simultaneously to the receiver with spatial multiplexing. Each data stream may be associated with a transmission layer. Spatial multiplexing provides high spectral efficiency, but comes at the cost of increased signal processing complexity, most prominently in the MIMO detector of the baseband chip, which may be used to recover the data stream from the channel and noise/interference.

[0024] For a MIMO system with N transmission layers and M receiver antennas, the mathematical system model can be described using Equation (1): y = Hx + n (1), where y E ⁽C^M is the received signal vector; x e M^w is the transmitted quadrature amplitude modulation (QAM) symbol vector where M is the set of possible QAM symbols for a particular modulation order m (e.g., m= 2 for quadrature phase shift keying (QPSK), m=4 for 16QAM, m= 6 for 64QAM, m= 8 for 256QAM, m=10 for 1024QAM, etc.); H e (C^MxW is the complex channel matrix; and n e (C^M is the white Gaussian noise vector. In certain implementation, each of the transmission layers may have the same modulation order. However, in certain other implementations, different transmission layers may have different modulation orders. For example, layer 0 and layer 1 may use 16QAM while layer 2 and layer 3 may use 64QAM. The operations described herein may be performed for either implementation.

[0025] After performing noise whitening on the received signal, n may be a complex

Gaussian noise vector with units of variance and may be considered uncorrelated across vector elements, e.g., noise covariance matrix F_h = I_M, where I_M denotes an M dimensional identity matrix. The baseband chip may be configured to perform MIMO detection to estimate x given the estimated channel matrix H² and received signal vector y.

[0026] Among the various types of MIMO detection techniques, maximum likelihood detection (MLD) provides the best theoretical error performance. With the above system model, given the assumption of white noise F_h = I_N, the maximum likelihood (ML) solution is equivalent to solving the least squares problem of Equation (2):

where x_ML is the ML of the estimated transmitted QAM symbol vector.

[0027] In certain implementations, the least squares problem may be solved by means of

QR decomposition techniques, e.g., by performing QR decomposition on H = QR , where Q _e ^^MXM j_{s an or} hogonal matrix and R e (C^MxW is an upper triangular matrix. It may be assumed that, without loss of generality, the number of receiver antennas is greater than or equal to the number of transmission layers, e.g., M ³ N. Under this assumption, the lower M — N rows of upper triangular matrix R are always zeros, and thus only the upper N X N square and upper rows of the triangular matrix are meaningful. By processing received vector y with Q^H , e.g., y = Q^H y, which is a sufficient statistic of y, the system model can be transformed as seen below in Equation

(3): y = Q^H y = Rx + n (3), where n is still an uncorrelated white Gaussian noise vector with F_h = I_N. Thus, the problem of finding the ML solution can be equivalent to solving Equation (4):

X_ML = argminlly - Rx\\² (4).

XEM^N

[0028] By way of example and not limitation, given a system with four transmission layers and four receiver antennas (e.g., 4x4 system), Equation (3) may be expressed as:

-

-

[0029] For a hard-output detection scheme, such as Vertical-Bell Laboratories Layered

Space-Time (V-BLAST), the detection starts from layer 3 by solving Equation (5):

[0030] Then, layer-2 can be detected by canceling the detection result for layer-3 as seen below in Equation (6): [0031] The successive interference cancellation/back-substitution process performed on layer-2 may be iteratively performed for layers 1 and 0. This class of techniques suffers from error propagation, however. Namely, the detection error for layer k can negatively impact the detection of layers k-1,..., 0. Therefore, the transmission layers can be ordered such that stronger layers are detected first to minimize the error propagation issue.

[0032] On the other hand, a soft-output detection scheme based on ML detection essentially searches over the vector space of all x E M^w for solutions to Equation (4). It can be shown that the problem in Equation (4) can then be transformed to an equivalent QAM symbol-tree search problem where a root-to-leaf tree path represents a transmitted QAM symbol vector based at least in part on the upper-triangular matrix R. Such a QAM symbol vector is of length N and may include QAM symbols from those JV transmission layers. The search result for one iteration of the tree-search may be a path metric representative of one or more QAM symbols.

[0033] Soft-output (e.g., log-likelihood ratio(s) (LLR)) of the QAM symbol vector may be calculated based on one or more path metrics resulting in the tree-search. The increased performance for the soft-output system is achieved at the cost of higher computational complexity associated with the tree-search. As the modulation order m and/or the number of transmission layers N increases, the size of the tree, and thus the computational complexity of the ML detection algorithm increases exponentially. Thus, even for conventional near-MLD schemes that reduce computational complexity by limiting the number of paths in the tree search, the computational resources used to perform such calculations are still undesirably high, especially for higher order QAM, e.g., 16QAM, 64QAM, 256QAM, 1024QAM, etc.

[0034] Thus, there exists an unmet need for a MIMO detection technique that provides the performance improvement associated with the tree-search without introducing significant computational complexity required by conventional near-MLD schemes techniques.

[0035] The exemplary MIMO detection scheme of the present disclosure combines a soft- output tree-search and successive interference cancellation (SIC) to achieve increased performance with reduced complexity as compared to conventional MIMO detection schemes. For example, the exemplary baseband chip of the present disclosure uses soft output LLRs as the detection output for each transmission layer. Then the soft output is also used to reconstruct a soft transmitted QAM symbol for the detected transmission layer in a probabilistic way. Namely, the reconstructed symbol may not be part of the transmitted constellation M, but rather a statistical mean of the possibly transmitted QAM symbols based on the received signal vector y. The reconstructed and/or remapped symbol is then subtracted from the received signals for the rest of the yet to be detected transmission layers.

[0036] FIG. 1 illustrates an exemplary wireless network 100, in which certain aspects of the present disclosure may be implemented, according to some embodiments of the present disclosure. As shown in FIG. 1, wireless network 100 may include a network of nodes, such as a user equipment (UE) 102, an access node 104, and a core network element 106. User equipment 102 may be any terminal device, such as a mobile phone, a desktop computer, a laptop computer, a tablet, a vehicle computer, a gaming console, a printer, a positioning device, a wearable electronic device, a smart sensor, or any other device capable of receiving, processing, and transmitting information, such as any member of a vehicle to everything (V2X) network, a cluster network, a smart grid node, or an Intemet-of-Things (IoT) node. It is understood that user equipment 102 is illustrated as a mobile phone simply by way of illustration and not by way of limitation.

[0037] Access node 104 may be a device that communicates with user equipment 102, such as a wireless access point, a base station (BS), a Node B, an enhanced Node B (eNodeB or eNB), a next-generation NodeB (gNodeB or gNB), a cluster master node, or the like. Access node 104 may have a wired connection to user equipment 102, a wireless connection to user equipment 102, or any combination thereof. Access node 104 may be connected to user equipment 102 by multiple connections, and user equipment 102 may be connected to other access nodes in addition to access node 104. Access node 104 may also be connected to other user equipments. It is understood that access node 104 is illustrated by a radio tower by way of illustration and not by way of limitation. [0038] Core network element 106 may serve access node 104 and user equipment 102 to provide core network services. Examples of core network element 106 may include a home subscriber server (HSS), a mobility management entity (MME), a serving gateway (SGW), or a packet data network gateway (PGW). These are examples of core network elements of an evolved packet core (EPC) system, which is a core network for the LTE system. Other core network elements may be used in LTE and in other communication systems. In some embodiments, core network element 106 includes an access and mobility management function (AMF) device, a session management function (SMF) device, or a user plane function (UPF) device, of a core network for the NR system. It is understood that core network element 106 is shown as a set of rack-mounted servers by way of illustration and not by way of limitation. [0039] Core network element 106 may connect with a large network, such as the Internet

108, or another Internet Protocol (IP) network, to communicate packet data over any distance. In this way, data from user equipment 102 may be communicated to other user equipments connected to other access points, including, for example, a computer 110 connected to Internet 108, for example, using a wired connection or a wireless connection, or to a tablet 112 wirelessly connected to Internet 108 via a router 114. Thus, computer 110 and tablet 112 provide additional examples of possible user equipment, and router 114 provides an example of another possible access node. [0040] A generic example of a rack-mounted server is provided as an illustration of core network element 106. However, there may be multiple elements in the core network including database servers, such as a database 116, and security and authentication servers, such as an authentication server 118. Database 116 may, for example, manage data related to user subscription to network services. A home location register (HLR) is an example of a standardized database of subscriber information for a cellular network. Likewise, authentication server 118 may handle authentication of users, sessions, and so on. In the NR system, an authentication server function (AUSF) device may be the specific entity to perform user equipment authentication. In some embodiments, a single server rack may handle multiple such functions, such that the connections between core network element 106, authentication server 118, and database 116, may be local connections within a single rack.

[0041] As described below in detail, in some embodiments, MIMO communication can be established between any suitable nodes in wireless network 100, such as between user equipment 102 and access node 104, for sending and receiving data through a MIMO channel. A transmitting node may establish the MIMO channel with a receiving node (e.g., establishing a multipath communication link between multiple transmitting antennas and multiple receiving antennas) and transmit encoded symbols in multiple signal streams through the MIMO channel. The receiving node may receive the multiple transmitted signal streams through the MIMO channel and may detect the symbol vector using a baseband chip implementing the MIMO detection scheme disclosed herein based on successive transmission layer detection and soft interference cancellation.

[0042] Each node of wireless network 100 in FIG. 1 that is suitable for receiving data may be considered a receiving device in MIMO communication. More detail regarding the possible implementation of a receiving device is provided by way of example in the description of a receiving device 800 in FIG. 8. Receiving device 800 may be configured as user equipment 102, access node 104, or core network element 106 in FIG. 1. Similarly, receiving device 800 may also be configured as computer 110, router 114, tablet 112, database 116, or authentication server 118 in FIG. 1. As shown in FIG. 8, receiving device 800 may include a processor 802, a memory 804, and a transceiver 806. These components are shown as connected to one another by a bus, but other connection types are also permitted. When receiving device 800 is user equipment 102, additional components may also be included, such as a user interface (UI), sensors, and the like. Similarly, receiving device 800 may be implemented as a blade in a server system when receiving device 800 is configured as core network element 106. Other implementations are also possible. [0043] Transceiver 806 may include any suitable device for sending and/or receiving data.

Receiving device 800 may include one or more transceivers, although only one transceiver 806 is shown for simplicity of illustration. An antenna 808 is shown as a possible communication mechanism for receiving device 800. Multiple antennas and/or arrays of antennas may be utilized for MIMO communication. Additionally, examples of receiving device 800 may communicate using wired techniques rather than (or in addition to) wireless techniques. For example, access node 104 may communicate wirelessly to user equipment 102 and may communicate by a wired connection (for example, by optical or coaxial cable) to core network element 106. Other communication hardware, such as a network interface card (NIC), may be included as well.

[0044] As shown in FIG. 8, receiving device 800 may include processor 802. Although only one processor is shown, it is understood that multiple processors can be included. Processor 802 may include microprocessors, microcontrollers, digital signal processors (DSPs), application- specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), programmable logic devices (PLDs), state machines, gated logic, discrete hardware circuits, and other suitable hardware configured to perform the various functions described throughout the present disclosure. Processor 802 may be a hardware device having one or more processing cores. Processor 802 may execute software. Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executables, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise. Software can include computer instructions written in an interpreted language, a compiled language, or machine code. Other techniques for instructing hardware are also permitted under the broad category of software. [0045] As shown in FIG. 8, receiving device 800 may also include memory 804. Although only one memory is shown, it is understood that multiple memories can be included. Memory 804 can broadly include both memory and storage. For example, memory 804 may include random- access memory (RAM), read-only memory (ROM), static RAM (SRAM), dynamic RAM (DRAM), ferro-electric RAM (FRAM), electrically erasable programmable ROM (EEPROM), CD-ROM or other optical disk storage, hard disk drive (HDD), such as magnetic disk storage or other magnetic storage devices, Flash drive, solid-state drive (SSD), or any other medium that can be used to carry or store desired program code in the form of instructions that can be accessed and executed by processor 802. Broadly, memory 804 may be embodied by any computer-readable medium, such as a non-transitory computer-readable medium.

[0046] Processor 802, memory 804, and transceiver 806 may be implemented in various forms in receiving device 800 for performing MIMO communication functions. In some embodiments, processor 802, memory 804, and transceiver 806 of receiving device 800 are implemented (e.g., integrated) on one or more system-on-chips (SoCs). In one example, processor 802 and memory 804 may be integrated on an application processor (AP) SoC (sometimes known as a “host,” referred to herein as a “host chip”) that handles application processing in an operating system environment, including generating raw data to be transmitted. In another example, processor 802 and memory 804 may be integrated on a baseband processor (BP) SoC (sometimes known as a modem, referred to herein as a “baseband chip”) that converts the raw data, e.g., from the host chip, to signals that can be used to modulate the carrier frequency for transmission, and vice versa, which can run a real-time operating system (RTOS). In still another example, processor 802 and transceiver 806 (and memory 804 in some cases) may be integrated on an radio frequency (RF) SoC (sometimes known as a transceiver, referred to herein as an “RF chip”) that transmits and receives RF signals with antenna 808. It is understood that in some examples, some or all of the host chip, baseband chip, and RF chip may be integrated as a single SoC. For example, a baseband chip and an RF chip may be integrated into a single SoC that manages all the radio functions for cellular communication.

[0047] Various aspects of the present disclosure related to MIMO detection may be implemented as software and/or firmware elements executed by a generic processor in a baseband chip (e.g., a baseband processor). It is understood that in some examples, one or more of the software and/or firmware elements may be replaced by dedicated hardware components in the baseband chip, including integrated circuits (ICs), such as ASICs. Mapping to the wireless communication (e.g., 4G, LTE, 5G, etc.) layer architecture, the implementation of the present disclosure may be at Layer 1, e.g., the physical (PHY) layer.

[0048] FIG. 2 illustrates an exemplary MIMO communication system 200, according to some embodiments of the present disclosure. MIMO communication system 200 may be used between suitable nodes in wireless network 100. As shown in FIG. 2, MIMO communication system 200 may include a transmitting device 210, a receiving device 220, and a MIMO channel 230 (e.g., multipath communication links between the transmitting antennas and the receiving antennas). For example, transmitting device 210 and receiving device 220 each may be an example of user equipment 102, access node 104, or core network element 106 of wireless network 100 in FIG. 1. MIMO communication system 200 may be used for increasing the data transmission rate between transmitting device 210 and receiving terminal device. Both transmitting device 210 and receiving device 220 may include a processor, a memory, and a transceiver, which may be examples of processor 802, memory 804, and transceiver 806 described above in detail, respectively, with respect to FIG. 8.

[0049] As shown in FIG. 2, transmitting device 210 may process the original data (e.g., process the input bits through various function stages of coding and interleaving, modulation, symbol mapping, and layer mapping and precoding) and may transmit the processed data (e.g., the encoded symbols) in multiple signal streams to receiving device 220 through MIMO channel 230. Receiving device 220 may receive the multiple transmitted signal streams and detect the original data (e.g., the decoded bits) through reverse processes, such as de-precoding, MIMO detection, de mapping, and channel decoding.

[0050] As an example of a MIMO communication system that implements the MIMO detection scheme described above, FIG. 3 illustrates a detailed block diagram of MIMO communication system 200, according to some embodiments of the present disclosure. As shown in FIG. 3, transmitting device 210 may include a channel coding and interleaving module 312, a modulation module 314, a symbol mapping module 316, and a layer mapping and precoding module 318 for processing the original data to be transmitted.

[0051] For example, channel coding and interleaving module 312 may be configured to add extra bits (i.e., redundancy bits) to the original data (e.g., the input bits) for error detection purposes. Modulation module 314 (e.g., a QAM modulation module) may be configured to modulate and combine different signals (e.g., different bitstreams) by modulating two carrier waves (e.g., out of phase with each other by 90^°) using amplitude-shift keying (ASK) digital modulation scheme or amplitude modulation (AM) analog modulation scheme, and to add the two carrier waves together. Symbol mapping module 316 may be configured to map the combined signals to encoded symbols. Layer mapping and precoding module 318 may be configured to map the encoded symbols onto different signal streams/layers. For example, layer mapping and precoding module 318 may perform a time-space and/or a spatial multiplexing precoding where the encoded symbols on each signal stream/layer are pre-coded to a symbol vector (e.g., a QAM symbol vector) and transmitted via all transmitting antennas.

[0052] The QAM symbol vector may be transmitted to receiving antennas through MIMO channel 230 according to Equation (1) above.

[0053] As an example of a MIMO channel, FIG. 4 illustrates a schematic diagram of an exemplary MIMO channel 230, according to some embodiments of the present disclosure. As illustrated in FIG. 4, transmitting device 210 may include n transmitting antennas (e.g., labeled Txl, Tx2, ..., Txn, respectively) for transmitting n transmitted signal streams, and receiving device 220 may include m receiving antennas (e.g., labeled Rxl, Rx2, ..., Rxm respectively) for receiving the transmitted signal streams. The complex channel matrix H may include n columns (e.g., corresponding to conditions of the n transmitted signal streams) and m rows (e.g., corresponding to conditions of the m receiving antennas).

[0054] Referring back to FIG. 3, receiving device 220 may include a de-precoding module

322, a de-mapping module 326, and a channel decoding module 328 for reversing the transmitter processing operations (e.g., space-time-de-precoding, de-mapping, demodulation, decoding etc.) and may determine the original data transmitted by transmitting device 210 to generate the decoded bits. Receiving device 220 may also include a MIMO detection module 324 for detecting the transmitted QAM symbol vector v based on an estimated matrix H of channel matrix H and the received symbol vector y.

[0055] In some embodiments, MIMO detection module 324 may permutate the transmitted signal streams based on metric(s) for the signal streams and perform the MIMO detection based on the permutated signal streams. After the detection, MIMO detection module 324 may further de-permutate the signal streams back to the original order for further processing. For example, channel decoding module 328 may include a log-likelihood ratio (LLR) calculation unit for calculating an LLR based on the detected signal streams with the original order (e.g., the restored original order) and may feed the LLR calculation result as an input to the channel decoders such as Turbo decoder or low density parity check (LDPC) decoder, etc. for decoding. [0056] As an example of MIMO detection module 324 in FIG. 3, FIG. 5 illustrates a schematic diagram of an exemplary MIMO detection system 500, according to some embodiments of the present disclosure. As illustrated in FIG. 5, MIMO detection system 500 may be configured to perform successive layer detection and interference cancellation. For example, MIMO detection system 500 may include a QR decomposition module 502, one or more R matrix update/interference cancellation modules 504a, 504b, 504c, one or more tree search and LLR generation modules 506a, 506b, 506c, one or more of QAM symbol reconstruction modules 508a, 508b, and an LLR buffer 510. Moreover, the MIMO detection system 500 may include modules dedicated to each of the N transmission layers. The N transmission layers may be ordered in ascending order such that layer 0 has the lowest received signal strength and layer N - 1 has the highest received signal strength. Although not illustrated, there may be implementations in which MIMO detection system 500 includes a single R matrix update/interference cancellation module, a single tree search and LLR generation module, and a QAM symbol reconstruction module. In such an implementation, the modules may be time-multiplexed to process different transmission layers.

[0057] For a M X N, M ³ N system, with modulation order m, the operations set forth below in connection with FIG. 5 outline the procedure of an exemplary MIMO detection technique. There are N main steps to process N layers. Within each main step, there are generally three sub step with the exception of main step 1, which omits sub-step 1, and main step N , which omits sub step 3. The first sub-step of step 2 and step N performs interference cancelation of the already detected layers and prepares the input to the tree search algorithm in sub-step 2. In sub-step 2, a reduced complexity tree search algorithm generates LLRs which are sent to output and used for soft interference reconstruction in sub-step 3.

[0058] More specifically, to begin, MIMO detection system 500 may receive a data stream associated with a channel. The data stream may include, e.g., layers N - 1 , N - 2,...0. The data stream (e.g., received signal vector ) may be associated with a complex channel matrix H. MIMO detection system 500 may generate an estimated channel matrix H based at least in part on, e.g., dedicated pilot signals transmitted by the transmitter. The estimated channel matrix H and the received signal vector y may be input into the QR decomposition module 502. QR decomposition module 502 may perform a first QR decomposition of the estimated channel matrix H to generate a triangular R matrix, seen below in Equation (7): [0059] Moreover, QR decomposition module 502 may perform a second QR decomposition of the received signal vector y to generate an estimated signal y, seen below in Equation (8). In certain implementations, the first and second QR decomposition may be part of the same QR decomposition operation. For example, QR decomposition module 502 may perform a QR decomposition that generates the triangular R matrix (e.g., first QR decomposition) and generates the estimated signal y (e.g., second QR decomposition).

[0060] Transmission layer N - l has the highest received signal strength and thus is the initial layer detected by MIMO detection system 500. As mentioned above, first R matrix update/interference cancellation module 504a may not perform matrix subtraction and/or interference cancellation. Rather, first R matrix update/interference cancellation module 504a sets R =Ri and y = yi. Both R\ and yi may be input into first tree search and LLR generation module

506a.

[0061] First tree search and LLR generation module 506a may perform a first tree search based at least in part on Ri (first triangular R matrix) and yi (first estimated signal) to obtain a first set of candidate symbol paths associated with layer N- 1 (first transmission layer). The tree search for layer N- 1 is performed for all N layers. The tree search will result in K number of candidate symbol paths which are used to generate LLRs l (soft-output values) for layer N - 1: l$- / ^-1, ... , 1 \. At most, there may be 2"' number of candidate symbol paths, where m is the modulation order.

[0062] The reduced complexity tree search operation used in sub-step 2 of each main step performs a limited search on the reduced QAM symbol tree. For step k , layer N — k is being processed. The reduced QAM symbol tree includes layers N — k to 0, corresponding to the reduced input R_k matrix. The operation produces a set of candidate paths which are then used for LLRs calculation for the layer N — k. The operations used here can be any operations with fixed/deterministic complexity. A desirable common property for the operations is that the search scope, in terms of the explored number of QAM constellation points, shall be sufficiently large for the layer N — k (which is the first layer under tree root in the reduced QAM symbol tree). This is important to generate LLRs with at least a threshold accuracy. Additional details associated with generating LLRs are set forth below.

[0063] For example, the a-posteriori LLR for bit b_{i n} i = 0,1,

— 1 of layer n e

(0,1, ... , N — 1} , given the received vector y, is defined as Equation (9): h,n = log (9),

where Pr (b_in = +l|y) is the conditional probability that the transmitted bit b_in is +1 given the received vector y.

[0064] The entire symbol path of all N layers can be defined as Equation (10):

is the set of candidate paths (a subset of M^w) with bit i of layer n b_{i n} = +1 regardless of the bit values of other bits in other transmission layers, and Pr( |y) is the probability for vector x e M^w given that the received vector is y.

[0065] Using the Bayesian rule, Pr( |y) can be defined according to Equation (11):

where n is an uncorrelated white Gaussian noise vector, as mentioned above. Assuming Pr (x) is constant, e.g., all symbols are transmitted with equal probability and considering the ratio in Equation (9) may not depend on Pr (y), Equation (9) can be rewritten as Equation (12):

[0066] For a reduced complexity tree search, it is possible that

= 0 due to the limited search scope. Thus, the computation of LLR according to (9) is not possible. Therefore, the tree search algorithm used here must search over a sufficiently large subset of M such that there will be enough vectors in

to generate LLRs with an accuracy that meets a minimum threshold. For smaller modulation order, e.g., m < 64, the tree search algorithm can simply cover all constellation points for layer N k , where for larger modulation orders, e.g., 256QAM, 1024QAM, etc., the search scope can be reduced to a large subset of the constellation to control overall complexity.

[0067] Moreover, to limit the complexity of the overall tree search algorithm, it may be desirable to reduce the complexity of the search for the layers other than the one for which LLRs are being generated. For example, the number of searched symbols can be as few as one for the other layers. Also note that Equation (12) may be approximated by the max-log approximation, which uses the property that log ^zQ » ma xz_t for z_t > 0 . The approximated expression may i be written as Equation (13):

however, the problem with empty

remains. The reduced complexity tree search performed by each the tree search and LLR generation modules 506a, 506b, 506c may apply to both Equations (12) and (13) or any other approximations to Equation (9).

[0068] Although there is a tree search performed in each of the main steps (e.g., main step

1, main step 2, main step 3, etc.), the depth of the tree decreases with each main step, and thus the overall computational complexity is reduced as compared to conventional techniques.

[0069] Still referring to main step 1, first tree search and LLR generation module 506a may output the first set of LLRs to the first QAM symbol reconstruction module 508a. Moreover, first tree search and LLR generation module 506a may output the first set of LLRs to the LLR buffer 510.

[0070] First QAM symbol reconstruction module 508a may generate a first reconstructed symbol associated with the layer A - 1 (first transmission layer) based at least in part on the first set of LLRs. For example, the reconstructed symbol may be defined according to Equation (14):

denotes the probability for symbol x to have been transmitted given that the LLR values are I_Q ¹, Z ^_1, ... , I_m-i-

[0071] For example, the LLR values represent the probability of a bit being +1 or -1 given the received vector. From Equation (9), given the value of l_{b n} and considering Pr (b_in = + l|y) + Pr(b_{i n} = — l|y) = 1 , the probability of the LLR values of a bit being +1 or -1 may be defined according to Equations (15) and (16), respectively:

[0072] Thus, first QAM symbol reconstruction module 508a may calculate the a-posteriori probability of the QAM symbol for layer A- 1 given those LLRs according to how the transmitted bits are mapped to the QAM constellation symbol points at the transmitter. The rule of mapping bits to QAM symbols may be known to both the transmitter and the receiver. Suppose bits

where bit B_t = ±1 for i = 0,1 , ..., m — 1 maps to a QAM symbol bit x Then conversely, first QAM reconstruction module 508a may denote the bits corresponding to symbol x as B_Q, B -f, Then given the LLR values, the a-posteriori probability of x may be defined according to Equation (17):

PrOly) = PE RG (ii_,A) (17), and the soft reconstruction of the transmitted symbol can be generated according to Equation (18):

[0073] Turning to the layer N - 2, information associated with the soft reconstruction of the transmitted symbol generated according to Equation (18) may be input into the second R matrix update and interference cancellation module 504b.

[0074] Second R matrix update and interference cancellation module 504b may remove last row and last column of R to generate Ri (second triangular R matrix), which is illustrated below in Equation (19):

[0075] Moreover, second R matrix update and interference cancellation module 504b may subtract the soft reconstruction of w-ifrom the received values from the other layers to obtain y_2, as seen below in Equation (20):

[0076] The second tree search and LLR generation module 506b may perform a reduced complexity tree search using R₂ and y_2. Second tree search and LLR generation module 506b may perform a second tree search using the same or similar operations as described above in connection with the first tree search and LLR generation module 506a. In the interest of conciseness, those operations will not be repeated here. The second tree search will result in K number of candidate symbol path, which are used to generate soft values, e.g., LLRs for layer N- 2: I_Q ^~2, li ^~2,

The second set of LLRs generated by second tree search and LLR generation module 506b may be output to LLR buffer 510 and to the second QAM symbol reconstruction module 508b. Information associated with the second set of candidate symbol paths may be used to obtain a second set of LLRs by second tree search, and information associated with the second set of LLRs may be input into the second QAM symbol reconstruction module 508b. [0077] Using the second set of LLRs, second QAM symbol reconstruction module 508b, may generate a second reconstructed symbol for layer N-2 according to Equation (21):

[0078] The operations described above with respect to main step 1 and main step 2 may be is repeated for the rest of layers through layer 0. The operation terminates when the set of LLRs for layer 0 is calculated. Because layer 0 is the final layer, the soft reconstruction step may be omitted since reconstructed symbol is used in interference cancellation for the subsequent layer. [0079] Note that for a general step k-1, R_k can be obtained by removing the last column and row from R_k-±. However, the updated received vector is re-calculated from y according to Equation (22):

[0080] Using the exemplary MIMO detection scheme described above in connection with

FIG. 5, which combines a soft-output tree-search and SIC, increased performance can be achieved with reduced complexity as compared to conventional MIMO detection schemes.

[0081] FIGs. 6A and 6B illustrate a flow chart of an exemplary method 600 of MIMO detection, according to some embodiments of the present disclosure. Examples of the apparatus that can perform operations of method 600 include, for example, MIMO detection system 500 depicted in FIG. 5, baseband chip 704A depicted in FIG. 7A, baseband chip 704B depicted in FIG. 7B, or any other suitable apparatus disclosed herein. It is understood that the operations shown in method 600 are not exhaustive and that other operations can be performed as well before, after, or between any of the illustrated operations. Further, some of the operations may be performed simultaneously, or in a different order than shown in FIGs. 6 A and 6B.

[0082] Referring to FIG. 6A, at 602, the baseband chip may receive a data stream associated with a channel. In certain aspects, the channel may include a plurality of transmission layers. In certain other aspects, the plurality of transmission layers may include the first transmission layer associated with first constellation points and a second transmission layer associated with second constellation points. For example, referring to FIG. 5, to begin, MIMO detection system 500 may receive a data stream associated with a channel. The data stream may include, e.g., layers A - 1, A - 2,...0. The data stream (e.g., received signal vector ) may be associated with a complex channel matrix H.

[0083] At 604, the baseband chip may generate an estimated channel matrix based at least in part on the data stream associated with the channel. For example, referring to FIG. 5, MIMO detection system 500 may generate an estimated channel matrix H based at least in part on the received data stream. The estimated channel matrix H and the received signal vector y may be input into the QR decomposition module 502.

[0084] At 606, the baseband chip may perform a first QR decomposition on the estimated channel matrix to obtain a first triangular R matrix. For example, referring to FIG. 5, QR decomposition module 502 may perform a first QR decomposition of the estimated channel matrix H to generate a triangular R matrix, seen above in Equation (7). First R matrix update/interference cancellation module 504a may not perform matrix subtraction and/or interference cancellation. Rather, first R matrix update/interference cancellation 504a sets R = Ri and y = yi. Both R\ and yi may be input into first tree search and LLR generation module 506a.

[0085] At 608, the baseband chip may perform a second QR decomposition on the data stream to obtain a first estimated signal. For example, referring to FIG. 5, QR decomposition module 502 may perform a second QR decomposition of the received signal vector y to generate an estimated signal y , seen above in Equation (8). In certain implementations, the first and second QR decomposition may be part of the same QR decomposition operation. For example, QR decomposition module 502 may perform a QR decomposition that generates the triangular R matrix (e.g., first QR decomposition) and generates the estimated signal y (e.g., second QR decomposition).

[0086] At 610, the baseband chip may perform a first tree search based at least in part on a first triangular R matrix and a first estimated signal to obtain a first set of candidate symbol paths associated with a first transmission layer. For example, referring to FIG. 5, first tree search and LLR generation module 506a may perform a first tree search based at least in part on Ri (first triangular R matrix) and yi (first estimated signal) to obtain a first set of candidate symbol paths associated with layer A- 1 (first transmission layer). The tree search for layer A- 1 is performed for all N layers. The tree search will result in K number of candidate symbol paths which are used to generate LLRs l (soft-output values) for layer N- 1 : /_Q ^_1, / ^-1, , l^i\ .

[0087] At 612, the baseband chip may generate a first set of LLRs associated with the first transmission layer based at least in part on the first set of candidate symbol paths. For example, referring to FIG. 5, The tree search will result in m number of candidate symbol paths which are used to generate LLRs l (soft-output values) for layer A- 1 : I_Q ^_1, / ^-1, ... , 1 \, additional details of which are set forth above.

[0088] At 614, the baseband chip may generate a first reconstructed symbol associated with the first transmission layer based at least in part on the first set of LLRs. For example, referring to FIG. 5, First QAM symbol reconstruction module 508a may generate a first reconstructed symbol associated with the layer N - 1 (first transmission layer) based at least in part on the first set of LLRs. For example, the reconstructed symbol may be generated according to Equations (14) - (18).

[0089] At 616, the baseband chip may remove a last row and a last column from the first triangular R matrix to obtain a second triangular R matrix associated with a second transmission layer. For example, referring to FIG. 5, second R matrix update and interference cancellation module 504b may remove the last row and last column of R to generate Ri (second triangular R matrix), which is illustrated above in Equation (19).

[0090] At 618, the baseband chip may subtract the first reconstructed symbol from the first estimated signal to obtain a second estimated signal associated with the second transmission layer. For example, referring to FIG. 5, second R matrix update and interference cancellation module 504b may subtract the soft reconstruction of w-ifrom the received values from the other layers to obtain y₂ , as seen above in Equation (20).

[0091] Referring to FIG. 6B, at 620, the baseband chip may perform a second tree search based at least in part on the second triangular R matrix and the second estimated signal to obtain a second set of candidate symbol paths. For example, referring to FIG. 5, the second tree search and LLR generation module 506b may perform a reduced complexity tree search using R₂ and y_2. Second tree search and LLR generation module 506b may perform a second tree search using the same or similar operations as described above in connection with the first tree search and LLR generation module 506a.

[0092] At 622, the baseband chip may generate a second set of LLRs associated with the second transmission layer based at least in part on the second set of candidate symbol paths. For example, referring to FIG. 5, the second tree search will result in m number of candidate symbol paths, which are used to generate soft values, e.g., the second set of LLRs for layer N - 2: iN-2 f _iN—2 iN—2 Iggi

[0093] At 624, the baseband chip may maintain the first set of LLRs. For example, referring to FIG. 5, LLR buffer 510 may maintain the first set of LLRs until all transmission layers have been detected.

[0094] At 626, the baseband chip may maintain the second set of LLRs. For example, referring to FIG. 5, LLR buffer 510 may maintain the second set of LLRs until all transmission layers have been detected.

[0095] At 628, the baseband chip may perform channel decoding based at least in part on the first set of LLRs and the second set of LLRs. For example, referring to FIGs. 3 and 5, the LLR buffer 510 may output the first and second sets of LLRs to the channel decoding module 328. Channel decoding module 328 may perform channel decoding based at least in part on the first and second sets of LLRs. In other words, the LLRs generated for each transmission layer may be used in channel decoding.

[0096] For simplicity, the exemplary MIMO detection operations are described above in connection with FIGs. 6 A and 6B. However, one of ordinary skill in the art would readily understand that the MIMO detection operations of FIGs. 6A and 6B is not limited to two iterations performed for two transmission layers. Rather, the MIMO detection operations of FIGs. 6A and 6B may include an iteration for each transmission layer. Hence, when there are more than two transmission layers more than two iterations may be performed, e.g., as described above in connection with FIGs. 5 and 6.

[0097] It is contemplated that MIMO detection system 500 described above for MIMO communication may be implemented either in software or hardware. For example, FIGs. 7A and 7B illustrate block diagrams of an exemplary apparatus 700 including a host chip, an RF chip, and a baseband chip implementing the MIMO detection system in FIG. 5 in software and hardware, respectively, according to some embodiments of the present disclosure. Apparatus 700 may be an example of any suitable node of wireless network 100 in FIG. 1, such as user equipment 102 or access node 104. As shown in FIG. 7, apparatus 700 may include an RF chip 702, a baseband chip 704 (baseband chip 704A in FIG. 7A or baseband chip 704B in FIG. 7B), a host chip 706, and multiple antennas 710. In some embodiments, baseband chip 704 is implemented by processor 802 and memory 804, and RF chip 702 is implemented by processor 802, memory 804, and transceiver 806, as described with respect to FIG. 8. Besides on-chip memory 712 (also known as “internal memory,” e.g., as registers, buffers, or caches) on each chip 702, 704, or 706, apparatus 700 may further include a system memory 708 (also known as the main memory) that can be shared by each chip 702, 704, or 706 through the main bus. Although baseband chip 704 is illustrated as a standalone SoC in FIGs. 7A and 7B, it is understood that in one example, baseband chip 704 and RF chip 702 may be integrated as one SoC; in another example, baseband chip 704 and host chip 706 may be integrated as one SoC; in still another example, baseband chip 704, RF chip 702, and host chip 706 may be integrated as one SoC, as described above.

[0098] In the uplink, host chip 706 may generate original data and send it to baseband chip

704 for encoding, modulation, and mapping. Baseband chip 704 may access the original data from host chip 706 directly using an interface 714 or through system memory 708 and then perform the functions of channel coding and interleaving module 312, modulation module 314, symbol mapping module 316, and layer mapping and precoding module 318, as described above in detail with respect to FIG. 3. Baseband chip 704 then may pass the modulated signals to RF chip 702 through interface 714. A transmitter (Tx) 716 of RF chip 702 may convert the modulated signals in the digital form from baseband chip 704 into analog signals, i.e., RF signals, and transmit the RF signals in multiple signal streams through multiple antennas 710, respectively, into the MIMO channel.

[0099] In the downlink, multiple antennas 710 may receive the RF signals in the multiple transmitted signal streams through the MIMO channel and pass the RF signals to a receiver (Rx) 718 of RF chip 702. RF chip 702 may perform any suitable front-end RF functions, such as filtering, down-conversion, or sample-rate conversion, and convert the RF signals into low- frequency digital signals (baseband signals) that can be processed by baseband chip 704. In the downlink, interface 714 of baseband chip 704 may receive the baseband signals, for example, the multiple transmitted signals streams. Baseband chip 704 then may perform the functions of de precoding module 322, MIMO detection module 324, de-mapping module 326, and channel decoding module 328, as described above in detail with respect to FIG. 3. The original data may be extracted by baseband chip 704 from the baseband signals and passed to host chip 706 through interface 714 or stored into system memory 708.

[0100] In some embodiments, the MIMO detection schemes disclosed herein (e.g., by

MIMO detection module 324 or MIMO detection system 500) may be implemented in software by baseband chip 704A in FIG. 7A having a baseband processor 720 executing the stored instructions, as illustrated in FIG. 7A. Baseband processor 720 may be a generic processor, such as a central processing unit or a DSP, not dedicated to MIMO detection. That is, baseband processor 720 is also responsible for any other functions of baseband chip 704A and can be interrupted when performing MIMO detection due to other processes with higher priorities. Each element in MIMO detection system 500 may be implemented as a software module executed by baseband processor 720 to perform the respective functions described above in detail.

[0101] In some other embodiments, the MIMO detection schemes disclosed herein, for example, by MIMO detection module 324 or MIMO detection system 500, may be implemented in hardware by baseband chip 704B in FIG. 7B having a dedicated MIMO detection circuit 722, as illustrated in FIG. 7A. MIMO detection circuit 722 may include one or more ICs, such as ASICs, dedicated to implementing the MIMO detection schemes disclosed herein. Each element in MIMO detection system 500 may be implemented as a circuit to perform the respective functions described above in detail. One or more microcontrollers (not shown) in baseband chip 704B may be used to program and/or control the operations of MIMO detection circuit 722. It is understood that in some examples, the MIMO detection schemes disclosed herein may be implemented in a hybrid manner, e.g., in both hardware and software. For example, some elements in MIMO detection system 500 may be implemented as a software module executed by baseband processor 720, while some elements in MIMO detection system 500 may be implemented as circuits.

[0102] In various aspects of the present disclosure, the functions described herein may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or encoded as instructions or code on a non-transitory computer-readable medium. Computer-readable media includes computer storage media. Storage media may be any available media that can be accessed by a receiving device, such as receiving device 800 in FIG. 8. By way of example, and not limitation, such computer-readable media can include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, HDD, such as magnetic disk storage or other magnetic storage devices, Flash drive, SSD, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a processing system, such as a mobile device or a computer. Disk and disc, as used herein, includes CD, laser disc, optical disc, DVD, and floppy disk where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media. [0103] According to one aspect of the present disclosure, an apparatus for wireless communication is disclosed that can include a memory and at least one processor coupled to the memory and configured to perform operations associated with MIMO detection. For example, in certain embodiments, the at least one processor may be configured to perform a first tree search based at least in part on a first triangular R matrix and a first estimated signal to obtain a first set of candidate symbol paths associated with a first transmission layer. In certain other embodiments, the at least one processor may be further configured to generate a first set of log-likelihood ratios (LLRs) associated with the first transmission layer based at least in part on the first set of candidate symbol paths. In certain other embodiments, the at least one processor may be further configured to generate a first reconstructed symbol associated with the first transmission layer based at least in part on the first set of LLRs. In certain other embodiments, the at least one processor may be further configured to remove a last row and a last column from the first triangular R matrix to obtain a second triangular R matrix associated with a second transmission layer. In certain other embodiments, the at least one processor may be further configured to subtract the first reconstructed symbol from the first estimated signal to obtain a second estimated signal associated with the second transmission layer.

[0104] In certain other embodiments, the at least one processor may be further configured to receive a data stream associated with a channel. In certain aspects, the channel may include a plurality of transmission layers. In certain other aspects, the plurality of transmission layers may include the first transmission layer associated with first constellation points and a second transmission layer associated with second constellation points. In certain other embodiments, the at least one processor may be configured to generate an estimated channel matrix based at least in part on the data stream associated with the channel.

[0105] In certain other embodiments, the at least one processor may be further configured to perform a first QR decomposition on the estimated channel matrix to obtain the first triangular R matrix. In certain other embodiments, the at least one processor may be further configured to perform a second QR decomposition on the data stream to obtain the first estimated signal.

[0106] In certain other embodiments, the at least one processor may be further configured to perform a second tree search based at least in part on the second triangular R matrix and the second estimated signal to obtain a second set of candidate symbol paths. In certain other embodiments, the at least one processor may be further configured to generate a second set of LLRs associated with the second transmission layer based at least in part on the second set of candidate symbol paths.

[0107] In certain embodiments, the memory may be configured to maintain the first set of

LLRs. In certain embodiments, the memory may be configured to maintain the second set of LLRs. [0108] In certain other embodiments, the at least one processor may be further configured to perform channel decoding based at least in part on the first set of LLRs and the second set of LLRs.

[0109] In certain aspects, the first reconstructed symbol may be a statistical mean of possible symbols transmitted in the first transmission layer.

[0110] According to another aspect of the present disclosure, a method for wireless communication is disclosed. In some embodiments, the method may include performing a first tree search based at least in part on a first triangular R matrix and a first estimated signal to obtain a first set of candidate symbol paths associated with a first transmission layer. In some embodiments, the method may include generating a first set of LLRs associated with the first transmission layer based at least in part on the first set of candidate symbol paths. In some embodiments, the method may include generating a first reconstructed symbol associated with the first transmission layer based at least in part on the first set of LLRs. In some embodiments, the method may include removing a last row and a last column from the first triangular R matrix to obtain a second triangular R matrix associated with a second transmission layer. In some embodiments, the method may include subtracting the first reconstructed symbol from the first estimated signal to obtain a second estimated signal associated with the second transmission layer. [0111] In certain other embodiments, the method may further include receiving a data stream associated with a channel. In certain aspects, the channel may include a plurality of transmission layers. In certain other aspects, the plurality of transmission layers may include the first transmission layer associated with first constellation points and a second transmission layer associated with second constellation points. In certain other embodiments, the method may further include generating an estimated channel matrix based at least in part on the data stream associated with the channel.

[0112] In certain other embodiments, the method may further include performing a first

QR decomposition on the estimated channel matrix to obtain the first triangular R matrix. In certain other embodiments, the method may further include performing a second QR decomposition on the data stream to obtain the first estimated signal. [0113] In certain other embodiments, the method may further include performing a second tree search based at least in part on the second triangular R matrix and the second estimated signal to obtain a second set of candidate symbol paths. In certain other embodiments, the method may further include generating a second set of LLRs associated with the second transmission layer based at least in part on the second set of candidate symbol paths.

[0114] In certain other embodiments, the method may further include maintaining the first set of LLRs. In certain other embodiments, the method may further include maintaining the second set of LLRs to the buffer.

[0115] In certain other embodiments, the method may further include performing channel decoding based at least in part on the first set of LLRs and the second set of LLRs.

[0116] In certain aspects, the first reconstructed symbol may be a statistical mean of possible symbols transmitted in the first transmission layer.

[0117] According to still another aspect of the present disclosure, a baseband chip for wireless communication is disclosed. The baseband chip may include a MIMO detection circuit. In certain embodiments, the MIMO detection circuit may be configured to perform a first tree search based at least in part on a first triangular R matrix and a first estimated signal to obtain a first set of candidate symbol paths associated with a first transmission layer. In certain other embodiments, the MIMO detection circuit may be configured to generate a first set of LLRs associated with the first transmission layer based at least in part on the first set of candidate symbol paths. In certain other embodiments, the MIMO detection circuit may be configured to generate a first reconstructed symbol associated with the first transmission layer based at least in part on the first set of LLRs. In certain other embodiments, the MIMO detection circuit may be configured to a last row and a last column from the first triangular R matrix to obtain a second triangular R matrix associated with a second transmission layer. In certain other embodiments, the MIMO detection circuit may be configured to subtract the first reconstructed symbol from the first estimated signal to obtain a second estimated signal associated with the second transmission layer.

[0118] In certain other embodiments, the baseband chip may include an interface operatively coupled to the MIMO detection circuit and configured to receive a data stream associated with a channel. In certain aspects, the channel may include a plurality of transmission layers. In certain other aspects, the plurality of transmission layers may include the first transmission layer associated with first constellation points and a second transmission layer associated with second constellation points. In certain other embodiments, the MIMO detection circuit may be further configured to generate an estimated channel matrix based at least in part on the data stream associated with the channel.

[0119] In certain other embodiments, the MIMO detection circuit is further configured to perform a first QR decomposition on the estimated channel matrix to obtain the first triangular R matrix. In certain other embodiments, the MIMO detection circuit may be configured to perform a second QR decomposition on the data stream to obtain the first estimated signal.

[0120] In certain other embodiments, the MIMO detection circuit may be configured to perform a second tree search based at least in part on the second triangular R matrix and the second estimated signal to obtain a second set of candidate symbol paths. In certain other embodiments, the MIMO detection circuit may be configured to generate a second set of LLRs associated with the second transmission layer based at least in part on the second set of candidate symbol paths. [0121] In certain other embodiments, the baseband chip may further include a memory configured to maintain the first set of LLRs. In certain other aspects, the memory may be further configured to maintain the second set of LLRs.

[0122] In certain other aspects, the baseband chip may include a channel decoder circuit configured to perform channel decoding based at least in part on the first set of LLRs and the second set of LLRs.

[0123] The foregoing description of the specific embodiments will so reveal the general nature of the present disclosure that others can, by applying knowledge within the skill of the art, readily modify and/or adapt for various applications such specific embodiments, without undue experimentation, without departing from the general concept of the present disclosure. Therefore, such adaptations and modifications are intended to be within the meaning and range of equivalents of the disclosed embodiments, based on the teaching and guidance presented herein. It is to be understood that the phraseology or terminology herein is for the purpose of description and not of limitation, such that the terminology or phraseology of the present specification is to be interpreted by the skilled artisan in light of the teachings and guidance.

[0124] Embodiments of the present disclosure have been described above with the aid of functional building blocks illustrating the implementation of specified functions and relationships thereof. The boundaries of these functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternate boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. [0125] The Summary and Abstract sections may set forth one or more but not all exemplary embodiments of the present disclosure as contemplated by the inventor(s), and thus, are not intended to limit the present disclosure and the appended claims in any way.

[0126] Various functional blocks, modules, and steps are disclosed above. The particular arrangements provided are illustrative and without limitation. Accordingly, the functional blocks, modules, and steps may be re-ordered or combined in different ways than in the examples provided above. Likewise, certain embodiments include only a subset of the functional blocks, modules, and steps, and any such subset is permitted.

[0127] The breadth and scope of the present disclosure should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.

Claims

WHAT IS CLAIMED IS:

1. An apparatus for wireless communication, comprising: a memory; and at least one processor coupled to the memory and configured to: perform a first tree search based at least in part on a first triangular R matrix and a first received signal to obtain a first set of candidate symbol paths associated with a first transmission layer; generate a first set of log-likelihood ratios (LLRs) associated with the first transmission layer based at least in part on the first set of candidate symbol paths; generate a first reconstructed symbol associated with the first transmission layer based at least in part on the first set of LLRs; remove a last row and a last column from the first triangular R matrix to obtain a second triangular R matrix associated with a second transmission layer; and subtract the first reconstructed symbol from the first received signal to obtain a second signal associated with the second transmission layer.

2. The apparatus of claim 1, wherein the at least one processor is further configured to: receive a data stream associated with a channel, the channel including a plurality of transmission layers, and the plurality of transmission layers including the first transmission layer associated with first constellation points and a second transmission layer associated with second constellation points; and generate an estimated channel matrix based at least in part on the data stream associated with the channel.

3. The apparatus of claim 2, wherein the at least one processor is further configured to: perform a first QR decomposition on the estimated channel matrix to obtain the first triangular R matrix; and perform a second QR decomposition on the data stream to obtain the first received signal.

4. The apparatus of claim 1, wherein the at least one processor is further configured to: perform a second tree search based at least in part on the second triangular R matrix and the second signal to obtain a second set of candidate symbol paths; and generate a second set of LLRs associated with the second transmission layer based at least in part on the second set of candidate symbol paths.

5. The apparatus of claim 4, wherein the memory is configured to: buffer the first set of LLRs; and buffer the second set of LLRs.

6. The apparatus of claim 5, wherein the at least one processor is further configured to: perform channel decoding based at least in part on the first set of LLRs and the second set of LLRs.

7. The apparatus of claim 1, wherein the first reconstructed symbol is a statistical mean of possible symbols transmitted in the first transmission layer.

8. A method for wireless communication implemented by a baseband chip, comprising: performing a first tree search based at least in part on a first triangular R matrix and a first received signal to obtain a first set of candidate symbol paths associated with a first transmission layer; generating a first set of log-likelihood ratios (LLRs) associated with the first transmission layer based at least in part on the first set of candidate symbol paths; generating a first reconstructed symbol associated with the first transmission layer based at least in part on the first set of LLRs; removing a last row and a last column from the first triangular R matrix to obtain a second triangular R matrix associated with a second transmission layer; and subtracting the first reconstructed symbol from the first received signal to obtain a second signal associated with the second transmission layer.

9. The method of claim 8, further comprising: receiving a data stream associated with a channel, the channel including a plurality of transmission layers, and the plurality of transmission layers including the first transmission layer associated with first constellation points and a second transmission layer associated with second constellation points; and generating an estimated channel matrix based at least in part on the data stream associated with the channel.

10. The method of claim 9, further comprising: performing a first QR decomposition on the estimated channel matrix to obtain the first triangular R matrix; and performing a second QR decomposition on the data stream to obtain the first received signal.

11. The method of claim 8, further comprising: performing a second tree search based at least in part on the second triangular R matrix and the second signal to obtain a second set of candidate symbol paths; and generating a second set of LLRs associated with the second transmission layer based at least in part on the second set of candidate symbol paths.

12. The method of claim 11, further comprising: buffering the first set of LLRs; and buffering the second set of LLRs.

13. The method of claim 12, further comprising: performing channel decoding based at least in part on the first set of LLRs and the second set of LLRs.

14. The method of claim 8, wherein the first reconstructed symbol is a statistical mean of possible symbols transmitted in the first transmission layer.

15. A baseband chip for wireless communication, comprising: an interface; and a multiple-input and multiple-output (MIMO) detection circuit operatively coupled to the interface and configured to: perform a first tree search based at least in part on a first triangular R matrix and a first received signal to obtain a first set of candidate symbol paths associated with a first transmission layer; generate a first set of log-likelihood ratios (LLRs) associated with the first transmission layer based at least in part on the first set of candidate symbol paths; generate a first reconstructed symbol associated with the first transmission layer based at least in part on the first set of LLRs; remove a last row and a last column from the first triangular R matrix to obtain a second triangular R matrix associated with a second transmission layer; and subtract the first reconstructed symbol from the first received signal to obtain a second signal associated with the second transmission layer.

16. The baseband chip of claim 15, further comprising: the interface configured to receive a data stream associated with a channel, the channel including a plurality of transmission layers, and the plurality of transmission layers including the first transmission layer associated with first constellation points and a second transmission layer associated with second constellation points, the MIMO detection circuit being further configured to: generate an estimated channel matrix based at least in part on the data stream associated with the channel.

17. The baseband chip of claim 16, wherein the MIMO detection circuit is further configured to: perform a first QR decomposition on the estimated channel matrix to obtain the first triangular R matrix; and perform a second QR decomposition on the data stream to obtain the first received signal.

18. The baseband chip of claim 15, wherein the MIMO detection circuit is further configured to: perform a second tree search based at least in part on the second triangular R matrix and the second signal to obtain a second set of candidate symbol paths; and generate a second set of LLRs associated with the second transmission layer based at least in part on the second set of candidate symbol paths.

19. The baseband chip of claim 18, further comprising a memory configured to: buffer the first set of LLRs; and buffer the second set of LLRs.

20. The baseband chip of claim 19, further comprising a channel decoder circuit configured to: perform channel decoding based at least in part on the first set of LLRs and the second set of LLRs.