Technical advantages are generally achieved, by embodiments of this disclosure which describe system and method for a scale-invariant symbol demodulator.
In accordance with an embodiment, a method for communicating over a multiple input multiple output (MIMO) channel is provided. In this example, the method includes transmitting a downlink MIMO signal from a base station to a user equipment. The downlink MIMO signal comprises multiple layers communicated directly to the user equipment in accordance with a phase parameter and a magnitude parameter. The method further comprises signaling the phase parameter to the user equipment without signaling the magnitude parameter to the user equipment. An apparatus for performing this method is also provided.
In accordance with another embodiment, yet another method for communicating over a MIMO channel is provided. In this example, the method includes receiving a downlink MIMO signal from a base station at a user equipment. The downlink MIMO signal comprises multiple layers communicated directly to the user equipment. The method further comprises receiving a phase parameter associated with the downlink MIMO signal at the UE, estimating a magnitude parameter associated with the downlink MIMO signal, and demodulating the downlink MIMO signal in accordance with the phase parameter and the magnitude parameter. An apparatus for performing this method is also provided.
For a more complete understanding of the present invention, and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawing.
Corresponding numerals and symbols in the different figures generally refer to corresponding parts unless otherwise indicated. The figures are drawn to clearly illustrate the relevant aspects of the embodiments and are not necessarily drawn to scale.
The making and using of embodiments of this disclosure are discussed in detail below. It should be appreciated, however, that the present invention provides many applicable inventive concepts that can be embodied in a wide variety of specific contexts. The specific embodiments discussed are merely illustrative of specific ways to make and use the invention, and do not limit the scope of the invention.
Techniques for estimating magnitude parameters for single-layer MIMO signals are described in U.S. Patent Application No. 2009/0247175 entitled “System and Method for Downlink Control Signal Structure for Multi-User MIMO,” which is hereby incorporated by reference herein as if reproduced in its entirety. Aspects of this disclosure extend the concept of estimating magnitude parameters to multi-layer MIMO signals.
With spatial multiplexing, a base station may send multiple data streams to UEs in a downlink transmission over the same frequency. The downlink transmission may have one or more layers for each data stream generated by spatial multiplexing. The layers in spatial multiplexing are associated with the number of codewords (e.g., codeword 1, codeword 2, etc.). Each layer may be identified by a size of the corresponding precoding vector, which may be equal to the number of transmit antenna ports. The number of layers (or streams) may also correspond to the rank of the transmission. A codeword is an independently encoded data block corresponding to a single transport block (TB) through higher-layer signaling, e.g., medium access control (MAC). The number of codewords in spatial multiplexing directly affects the control overhead and receiver complexity since spatial multiplexing with multiple codewords may apply adaptive modulation and coding (AMC) and error control on a per-codeword basis.
An embodiment LTE UE receiver is insensitive to an arbitrary and unknown scaling of a QAM symbol constellation. Typically this arbitrary scaling can occur due to the enhanced Node B (eNB) changes the physical downlink shared channel (PDSCH) to cell-specific reference signal (C-RS) power ratio (also known as Pa) without informing the UE. An embodiment UE estimates this arbitrary scaling factor for decoding any M-QAM constellation with M>4. Embodiments may be used for both single-input single output (SISO) and multiple-input multiple-output (MIMO) channels, and there have not been observed any noticeable degradation in block error rate (BLER) performance when the UE estimates the arbitrary scale factor versus knowing it perfectly. An embodiment uses blind scaling estimates based on prior knowledge of scheduler constraints and provides a simplified standard by removing the Pa parameter. In further embodiments the estimation generally may be performed in any receiver and the scaling is especially useful when the receiver does the symbol to bit slicing. Note that the noise power estimation (σ2) is typically performed based on the variance of the received signal relative to the C-RS. Given the presence of an arbitrary scaling, this scaling may be applied to the noise estimate as well.
With respect to a scaling estimator, consider the following signal model y(t)=αhx(t)+w(t) where y(t) is the received symbol, x(t) is the transmitted symbol, h is the channel, w(t) is the additive white gaussian noise (AWGN), and α is the arbitrary and unknown scaling. Without loss of generality, and to simplify the notation, the time (t) dependence may be dropped and ignored. According to prior knowledge of the expected values of E{xx*}=1, E{ww*}=σ2, and h, an unknown scale factor α may be estimated. Because both the symbols and the noise are zero mean, independent and identically distributed (i.i.d.) and independent of each other E{yy*}=α2(hh*)+σ2.
Given N channel uses, a vector of the received symbols [y1, . . . , yN]T and a vector of the channels [h1, . . . , hN]T may be defined. Based on ykyk* to estimate E{yy*}, therefore the minimum least square estimator of α2 is
The estimator in (3) is specific to scalar variable signal model for the MIMO configuration with nt transmit antennas and nr receive antennas, the signal model y(t)=αHx(t)+w(t) (4) may be defined, where y(t) is the received symbol vector, x(t) is the transmitted symbol vector, H is the channel matrix, and w(t) is the AWGN noise vector. From assumption,
the equation (5) may be derived.
For N channel use with received symbol vector [y1, . . . , yN]T and channel matrix vector [H1, . . . , HN]T, the least square estimator is
Embodiment C++ code for the estimator in (6) is provided in Table 1.
In a link simulation, a SISO channel with a rate (1/3) convolution (and turbo) encoding followed by a soft input soft output (SISO) decoder may be evaluated. The simulation parameters listed in Table 2 are used to obtain a block error rate (BLER), particularly in a 1% to 10% range, as the only performance metric. In each of
For the AWGN Channel, h=1 is constant. The BLER curves are shown in
For the Block Fading Raleigh Channel, h is complex Gaussian and changes every 500 symbols i.i.d. The BLER curves are shown in
For the TU Channel with convolution encoding, h is generated according to a typical urban 3 km/h channel model with 3 resource blocks (RBs) per codeblock. The BLER curves are shown in
For the TU Channel with turbo encoding, h is generated according to a typical urban 3 km/h channel model with 3 RBs per codeblock. The BLER curves are shown in
In another embodiment, a link level simulation of a 2×2 MIMO channel with a rate (1/3) convolution (& turbo) encoding is evaluated. The same simulation parameters in Table 2 are used.
For the AWGN Channel, H is a fixed unit matrix. The BLER curves are shown in
For the TU channel with convolution encoding, H is generated according to a typical urban 3 km/h channel model with 3 RBs per codeblock. The BLER curves are shown in
For the TU channel with turbo encoding, H is generated according to a typical urban 3 km/h channel model with 3 RBs per codeblock. The BLER curves are shown in
The solution described above provides a receiver demodulating a higher order modulation symbol without an explicit magnitude reference received from a base station. It has not been noticed any performance degradation for the cases where the UE has to estimate the arbitrary scaling compared to knowing it.
Another estimator which is not optimal is derived for the MIMO channel as in (4), an estimator for α2 is
For the scalar signal model, the above estimator reduces to
However, this estimator may not always have good performance. For example, as shown in the simulation below, for the typical urban Rayleigh fading channel, performance degrades due to an inaccurate estimation of α.
Embodiment C++ code for the estimator in (8) is provided in Table 3.
For these cases the simulation takes the same parameter as given in Table 2. The channel h is generated according to a typical urban 3 km/h channel model with 3 RBs per codeblock for both convolutional and turbo code with 1/3 rate.
The BLER curves for convolutional code (1/3) are shown in
The BLER curves for Turbo code (1/3) are shown in
The bus may be one or more of any type of several bus architectures including a memory bus or memory controller, a peripheral bus, video bus, or the like. The CPU may comprise any type of electronic data processor. The memory may comprise any type of system memory such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), read-only memory (ROM), a combination thereof, or the like. In an embodiment, the memory may include ROM for use at boot-up, and DRAM for program and data storage for use while executing programs.
The mass storage device may comprise any type of storage device configured to store data, programs, and other information and to make the data, programs, and other information accessible via the bus. The mass storage device may comprise, for example, one or more of a solid state drive, hard disk drive, a magnetic disk drive, an optical disk drive, or the like.
The video adapter and the I/O interface provide interfaces to couple external input and output devices to the processing unit. As illustrated, examples of input and output devices include the display coupled to the video adapter and the mouse/keyboard/printer coupled to the I/O interface. Other devices may be coupled to the processing unit and additional or fewer interface cards may be utilized. For example, a serial interface such as Universal Serial Bus (USB) (not shown) may be used to provide an interface for a printer.
The processing unit also includes one or more network interfaces, which may comprise wired links, such as an Ethernet cable or the like, and/or wireless links to access nodes or different networks. The network interface allows the processing unit to communicate with remote units via the networks. For example, the network interface may provide wireless communication via one or more transmitters/transmit antennas and one or more receivers/receive antennas. In an embodiment, the processing unit is coupled to a local-area network or a wide-area network for data processing and communications with remote devices, such as other processing units, the Internet, remote storage facilities, or the like.
An embodiment estimator has been described specifically as an implementation of a UE receiver for receiving downlink transmissions. However, it can equally be implemented in an eNB receiver when it receives uplink transmissions from a UE. Furthermore, embodiments are specifically described for an LTE receiver, but the principles can be applied to any wireless (such as WiFi, etc.) or wire line (such as digital subscriber line (DSL), etc.) receiver employing higher order modulation.
The current embodiment describes only an SU-MIMO scenario. This invention can be equally applied to an MU-MIMO scenario. Reduced signaling overhead since there is no need to signal reference power. It makes dynamic Power control possible. Both lead to higher capacity. Wireless and wireline receiver products (LTE, DSL, WiMaX, WiFi).
The following references are related to subject matter of the present application. Each of these references is incorporated herein by reference in its entirety:
While this invention has been described with reference to illustrative embodiments, this description is not intended to be construed in a limiting sense. Various modifications and combinations of the illustrative embodiments, as well as other embodiments of the invention, will be apparent to persons skilled in the art upon reference to the description. It is therefore intended that the appended claims encompass any such modifications or embodiments.
This patent application claims priority to U.S. Provisional Application No. 61/909,250, filed on Nov. 26, 2013 and entitled “System and Method for a Scale-Invariant Symbol Demodulator,” which is hereby incorporated by reference herein as if reproduced in its entirety. The present invention relates to communications, and, in particular embodiments, to a system and method for a scale-invariant symbol demodulator. When a receiver demodulates a higher order modulation symbol such as quadrature amplitude modulation (QAM)-16 or QAM-64, it typically needs a magnitude reference parameter in order to determine the regions for slicing. Typically, this magnitude reference parameter is provided by a pilot signal as well as some additional messages when there is a known offset between the powers of the pilot symbol and the data symbol. In Long Term Evolution (LTE) networks, the magnitude parameter is signaled to each user equipment (UE) via a Layer 3 radio resource control (RRC) message referred to as the Pa parameter. This message consumes valuable bandwidth, and may cause a delay.
Number | Date | Country | |
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61909250 | Nov 2013 | US |