Abstract:
One or more communications parameters associated with a multiple input, multiple output (MIMO) signal transmitted by a transmitter are identified. The one or more communications parameters include one or more of (i) a number of receive antennas via which the MIMO signal is received, (ii) a number of spatial streams in the MIMO signal, and (iii) a signal to noise ratio (SNR) corresponding to the MIMO signal. A particular data detection technique of a plurality of data detection techniques employed by a receiver is selected in accordance with at least one of the one or more communications parameters.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application is a continuation application of U.S. application Ser. No. 13/346,476, now U.S. Pat. No. 8,411,786, entitled “SYSTEM AND METHOD OF SELECTING A DATA DETECTION TECHNIQUE FOR A MIMO SYSTEM,” filed on Jan. 9, 2012, which is a continuation application of U.S. application Ser. No. 12/111,143, now U.S. Pat. No. 8,094,744, entitled “SYSTEM AND METHOD OF SELECTING A DATA DETECTION TECHNIQUE FOR A MULTIPLE-INPUT MULTIPLE-OUTPUT (MIMO) SYSTEM,” filed on Apr. 28, 2008, which claims the benefit of U.S. Provisional Application No. 60/914,500, filed Apr. 27, 2007, entitled “Performance Study of MIMO Equalization: Maximum Likelihood (ML) vs Zero Forcing (ZF).” The disclosures of the above-referenced applications are hereby incorporated by reference herein in their entireties. 
    
    
     BACKGROUND 
     1. Field of the Disclosure 
     Aspects of the present disclosure relate generally to wireless communication techniques, and more particularly to an equalization system and method alternately implementing different data detection techniques such as, for example, maximum likelihood and zero forcing. 
     2. Description of Related Art 
     Recently, the wireless communication industry has demonstrated an increasing interest in multiple input, multiple output (MIMO) systems. For example, MIMO beamforming systems have been considered in standards promulgated by the Institute of Electrical and Electronics Engineers (IEEE), most notably the IEEE 802.11n standard for wireless local area networks (WLANs). One advantage of beamforming methodologies is that they can increase the data rate between networked devices without the attendant increase in transmit power or bandwidth that would be necessary to achieve a similar data rate in MIMO systems without beamforming. Spatial multiplexing techniques for MIMO systems have also been considered in several standards such as the IEEE 802.11a/g standard. One advantage of spatial multiplexing is that it can increase channel capacity at high signal to noise ratios (SNRs). 
     One relatively popular MIMO equalization approach uses maximum likelihood (ML) data detection techniques. Generally, ML detection is considered optimal to the extent that it exhaustively searches all the distances between a received data vector and the universe of possible data vectors in the constellation to identify an optimal point or data stream, i.e., the data received on multiple dimensions are considered collectively. In many instances, an exhaustive search ML approach may be too complex to be practical, so many systems employ a limited ML approach constrained by hardware or processing bandwidth limitations. Zero forcing (ZF) techniques, on the other hand, forcibly decouple the data received on each dimension (e.g., by zeroing out inter-antenna interference), then detect or decode each received data stream individually. In that regard, ML techniques, even limited by system constraints, can exhibit significant performance advantages as compared to so-called “sub-optimal” ZF techniques, particularly for flat fading channels and lower order modulation schemes such as Quadrature Amplitude Modulation (QAM)-16 and Quaternary Phase Shift Keying (QPSK) operating at low SNRs. 
     In theory, under ideal conditions (i.e., no radio frequency (RF) or other system impairments), and where processing power is unlimited, it is expected that exhaustive search ML strategies will always provide superior results as compared to ZF strategies, particularly with respect to MIMO systems. However, in practical applications, RF impairments such as phase noise, flicker noise, power amplifier non-linearities, in-phase/quadrature-phase (I/Q) mismatches, and carrier frequency offsets, either individually or in combination, can affect performance of both ML and ZF techniques. 
     Therefore, it may be desirable in some instances to provide an equalization system and method that can advantageously employ different data detection techniques depending, for example, upon certain circumstances or certain operating conditions. 
     SUMMARY OF THE DISCLOSURE 
     In one embodiment, a method includes identifying, using one or more integrated circuit (IC) devices, one or more communications parameters associated with a multiple input, multiple output (MIMO) signal transmitted by a transmitter. The one or more communications parameters comprises one or more of (i) a number of receive antennas via which the MIMO signal is received, (ii) a number of spatial streams in the MIMO signal, and (iii) a signal to noise ratio (SNR) corresponding to the MIMO signal. The method also includes selecting, using one or more IC devices, a particular data detection technique of a plurality of data detection techniques employed by a receiver in accordance with at least one of the one or more communications parameters. 
     In another embodiment, a communication device comprises one or more integrated circuit (IC) devices configured to (i) implement a plurality of data detection techniques, (ii) identify one or more communications parameters associated with a multiple input, multiple output (MIMO) signal transmitted by a transmitter, wherein the one or more communications parameters comprises one or more of (i) a number of receive antennas via which the MIMO signal is received, (ii) a number of spatial streams in the MIMO signal, and (iii) a signal to noise ratio (SNR) corresponding to the MIMO signal, and (iii) select, in accordance with at least one of the one or more communications parameters, a particular data detection technique of the plurality of data detection techniques to process the MIMO signal. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWING FIGURES 
         FIGS. 1-4  are graphs illustrating results of performance studies comparing maximum likelihood techniques with zero forcing techniques and plotting frame error rates versus signal to noise ratios for several modulation and coding schemes. 
         FIGS. 5-6  are graphs illustrating results of performance studies comparing maximum likelihood techniques with zero forcing techniques and plotting frame error rates versus signal to noise ratios taking into account phase noise effects. 
         FIG. 7  is a simplified table illustrating certain modulation and coding schemes for wireless fidelity applications. 
         FIG. 8  is a simplified high level functional block diagram of a multiple input, multiple output system capable of alternately implementing different data detection techniques. 
         FIG. 9  is a simplified flow diagram illustrating operation of one embodiment of a method of selecting a data detection strategy for a multiple input, multiple output system. 
     
    
    
     DETAILED DESCRIPTION 
     Introduction 
     The equalization techniques described below may be utilized for flat fading channels as well as for frequency selective fading channels with orthogonal frequency division multiplexing (OFDM). For simplicity, the following discussion addresses the technology in the context of single carrier systems for flat fading channels; those of skill in the art will appreciate that the disclosed methodologies may readily be extended to be implemented in OFDM systems. 
     In order to evaluate the relative advantages of exhaustive search maximum likelihood (ML) techniques as compared to zero forcing (ZF) techniques, a performance study has been conducted for different multiple input, multiple output (MIMO) configurations under spatial multiplexing for two or three spatial streams. The study involved a wireless local area network (WLAN) as a system platform. It will be appreciated that the techniques described herein may have utility in connection with various communications standards including, but not limited to, the wireless fidelity (WiFi) (e.g., IEEE 802.11n and IEEE 802.11a/g) and the worldwide interoperability for microwave access (WiMAX) (e.g., IEEE 802.16) standards. 
     As noted above, exhaustive search ML techniques are generally expected to outperform ZF techniques under ideal conditions. However, one assumption that leads to this expectation is that only “white noise” (i.e., noise that is uncorrelated and of equal power among all dimensions) affects the MIMO system. In real applications, phase noise is a major contributor to errors caused by RF impairment; this phase noise may result from random phase changes in a local oscillator, for instance, when a received signal is down-converted to baseband. Phase noise can introduce inter-carrier interference ( 101 ), which nullifies the “white noise assumption.” As set forth in more detail below, when phase noise reaches a threshold value at certain signal to noise ratios (SNRs), ML techniques may experience an error floor. 
     The performance study included 2×2, 2×3, 3×3, and 3×4 (transmitter antennae×receiver antennae) configurations, for Quaternary Phase Shift Keying (QPSK), Quadrature Amplitude Modulation (QAM)-16, and QAM-64 modulation schemes. The study considered all of the following RF impairments: phase noise; flicker noise (1/f noise); power amplifier non-linearities; in-phase/quadrature-phase (I/Q) mismatches; and carrier frequency offsets. Phase noise was considered at 150 kHz bandwidth with up to 1 degree root-mean-square (rms) θ values. The flicker noise value was −101 dBm, and the carrier offset values ranged from 60 kHz to 120 kHz. 
       FIGS. 1-4  are graphs illustrating results of performance studies comparing maximum likelihood techniques with zero forcing techniques and plotting frame error rates versus signal to noise ratios for several modulation and coding schemes. 
     The results illustrated in  FIG. 1  were obtained using a 2×2 MIMO configuration and two spatial streams, while the results illustrated in  FIG. 2  were obtained using a 2×3 MIMO configuration and two spatial streams. As indicated at the left and center of  FIG. 1 , for QPSK and QAM-16 modulation schemes, respectively, the ML technique provides approximately a 5 dB gain over the ZF technique at a frame error rate (FER) of about 1%. However, with respect to the higher order modulation (QAM-64) at higher SNRs, the ML technique floors due to ICI as indicated at the right of  FIG. 1 . As indicated at the left and center of  FIG. 2 , for QPSK and QAM-16, respectively, the ML technique provides a relatively smaller performance gain (approximately 1.5 or 2.0 dB) over the ZF technique where the FER is about 1%. Also, with respect to QAM-64 at higher SNRs, the ML technique and the ZF technique provide similar performance as indicated at the right of  FIG. 2 . The improved performance of the ZF systems (in  FIG. 2  as compared to  FIG. 1 ) may be attributable to introduction of the additional receive antenna, which provides an additional array gain. The fact that the ML system did not exhibit an error floor in the configuration that provided the  FIG. 2  data may also be attributable to the additional receive antenna. 
       FIGS. 3 and 4  illustrate a similar trend, where the results illustrated in  FIG. 3  were obtained using a 3×3 MIMO configuration and three spatial streams, while the results illustrated in  FIG. 4  were obtained using a 3×4 MIMO configuration and three spatial streams. With three receive antennae and QPSK modulation, the ML system provides a performance gain of approximately 10 dB where the FER is about 1%; this advantage is reduced to about 5.5 dB where four receive antennae are employed. Similarly, a performance gain of about 7 dB is reduced to about 4 dB where an additional receive antenna is used with QAM-16 modulation. 
     As is clearly indicated by the data, the performance advantages of ML systems decrease when the number of receive antenna paths exceeds the number of spatial streams. The effects of additional array gain may substantially benefit systems employing various types of modulation schemes including, but not limited to, Binary Phase Shift Keying (BPSK), QPSK, QAM-16, and QAM-64. Also, the data show that for higher order modulations with high coding rates operating at high SNRs (e.g., QAM-64, rate ¾), ML systems may experience the error floor phenomenon due to practical impairments that result in ICI or that otherwise create deleterious RF interference. 
       FIGS. 5-6  are graphs illustrating results of performance studies comparing maximum likelihood techniques with zero forcing techniques and plotting frame error rates versus signal to noise ratios taking into account phase noise effects. The data for  FIG. 5  were obtained on a DNLOS channel, while the data in  FIG. 6  were obtained on a BNLOS channel, which is relatively less frequency selective. The results illustrated in  FIGS. 5 and 6  were obtained using a 2×2 MIMO configuration and two spatial streams; as indicated in the legends, the root mean square phase noise values were θ=0.0, θ=0.4, and θ=1.0. One goal of the study that resulted in the data of  FIGS. 5 and 6  was to determine a phase noise value at which an ML system might fail, i.e., exhibit an error floor and substantially underperform a ZF system. A QAM-64 modulation scheme was tested, as this scheme can support both WiFi (e.g., IEEE 802.11n) and WiMAX (e.g., IEEE 802.16) applications. 
     Ideally, the rms of the angle (θ) value for phase noise is 0; in this situation, an exhaustive search ML system exhibits about a 3.0 dB performance advantage over a ZF system when the FER is about 1%. Where the phase noise has a 0.4 degree rms θ value, however, this advantage decreases to about 2.2 dB for the same FER. Additionally, as can be seen at the two plots furthest to the right of  FIGS. 5 and 6 , the ML system floors and performs substantially worse than the ZF system at an operating SNR of about 33 dB or 34 dB; these data were obtained using a phase noise having a 1.0 degree rms θ value, which is typical for MIMO systems in WiFi or WiMAX applications. 
       FIGS. 5 and 6  indicate a marked decrease in performance of ML techniques for higher order modulation schemes with typical phase noise when the operating SNR is above about 33 dB. As the SNR increases (e.g., to the range above about 35 dB), ICI terms tend to dominate over noise power levels, causing the rate of performance degradation in increase. 
     Implementation 
     In accordance with some embodiments, an equalization system and method alternately implementing both ML and ZF data detection techniques may employ ML detection for lower order modulations (such as QPSK and QAM-16), on the one hand, and ZF detection for higher order modulations (such as QAM-64), on the other hand. In some instances, implementing ML for higher order modulation may become too complex to be practical, and the error floor problem makes such systems inferior to ZF strategies for certain schemes operating at high SNRs in any event. It is difficult to obtain both the benefits of ML and the benefits of ZF in systems with realistic impairments for all modulation codes. Accordingly, a strategy of selectively switching between different data detection techniques depending, for example, upon a given modulation and coding scheme (MCS), desired or required data throughput rates, and operating conditions (such as SNR, phase noise, and other communications parameters), represents a good compromise between overall system performance and implementation complexity. 
     In that regard,  FIG. 7  is a simplified table illustrating certain modulation and coding schemes for wireless fidelity applications. It will be appreciated that only a few MCS options are provided in  FIG. 7  by way of example, and that many more MCSs are generally available for a particular communication standard. 
     For rate adaptation and to maintain a stable or desired physical (PHY) data rate, adaptation techniques may be employed selectively to implement a particular MCS and a specific data detection technique (e.g., either ML or ZF) as a function of operating environment, system capabilities, user or administrator preferences, communications parameters, or a combination of these and other criteria. For example, to maintain a PHY data rate of 162.0 kilobits per second (kbps), either MCS 12 or MCS 19 may be selected. Whereas an ML technique has a 5.5 dB performance advantage over a ZF technique for MCS 19, it may nevertheless be desirable in some circumstances to employ a ZF technique with MCS 12, which only underperforms by 2.5 dB, for the same PHY data rate. This is especially true where phase noise is considered and the system is operating at a high SNR. 
     In a WiFi or WiMAX system, for example, ML detection may be used for a QPSK MCS, since the number of points in the constellation is generally small (so exhaustive searching may readily be conducted without requiring excessive processing loads) and since the performance gain is substantial over ZF techniques. With respect to a QAM-16 MCS, however, exhaustive search ML schemes may be very difficult to implement; in this case, it may be desirable to use another data detection scheme such as ZF. With respect to QAM-64, for instance, the data in  FIGS. 1-6  suggest that ZF strategies are most appropriate. It will be appreciated that other detection techniques may also be implemented. For example, a minimum mean square error (MMSE) approach may be used in lieu of ZF, or may be used in addition to ML or ZF approaches. 
     In that regard,  FIG. 8  is a simplified high level functional block diagram of a multiple input, multiple output system capable of alternately implementing different data detection techniques. As indicated in  FIG. 8 , a MIMO system  800  may generally comprise an antenna array  811  and RF module  812 , a processor  820 , a memory  824 , a selector module  830 , and data detectors  891 - 893 . 
     In some embodiments, the functional blocks illustrated in  FIG. 8  may be implemented as hardware components. For example, the various elements may be integrated into a single, monolithic integrated circuit (IC) or an application specific IC (ASIC). Additionally or alternatively, some or all of the functional blocks may be implemented independently (i.e., on multiple ICs or chips), for instance, using programmable logic hardware or more than one ASIC. 
     In some implementations, various elements may be programmable or otherwise configurable in accordance with system requirements, communications protocols, and other factors affecting desired or necessary operational characteristics of system  800 . In that regard, some of the functional blocks of system  800  may be implemented in software or other encoded instruction sets, for example, executed by processor  820 . The instruction sets executed by processor  820  may be stored in memory  824 . Accordingly, the term “module” in the context of the following description is intended to encompass both hardware and software embodiments of a particular functional block; those of skill in the art will appreciate that any number of software applications or instruction sets, and various circuitry or hardware components supported by firmware may readily be designed to support the functionality set forth herein. 
     Antenna array  811  and RF module  812  generally include hardware elements operative to enable bi-directional wireless communications as is generally known. As set forth above, array  811  and module  812  may enable system  800  to communicate in accordance with various standards such as WiFi and WiMAX. Processor  820  may be embodied in or comprise a general purpose microprocessor, microcomputer, microcontroller, or any other type of data processing engine suitably configured and operative to support the functionality of system  800 . It will be appreciated that various technologies may have utility in applications of array  811 , module  812 , and processor  820 ; the present disclosure is not intended to be limited by the structural nature or operational characteristics of the elements illustrated in  FIG. 8 . 
     Selector module  830  may be operative, e.g., under control of or otherwise in cooperation with processor  820 , to select a particular detector  891 - 893  in accordance with various criteria. In that regard, selector module  830  may determine the type of MCS employed by system  800  for particular applications. In some embodiments, a detector  891 - 893  may be selected in accordance with the detected MCS. For example, if the MCS is QAM-64, ZF detector  891  or MMSE detector  893  may be selected, whereas if the MCS is QPSK or QAM-16, ML detector  892  may be selected. 
     Additionally or alternatively, a detector  891 - 893  may be selected in accordance with an operating SNR, a desired PHY data rate, or a combination of these factors. As set forth above with reference to  FIGS. 1-6 , SNR can affect operation of numerous system components, and in particular, can adversely affect ML techniques when employed in conjunction with certain MCSs. In such embodiments, if the MCS is QAM-64, ZF detector  891  may be selected for a certain SNR or a certain range, whereas MMSE detector  893  may be selected for other SNR values or a different range. In the foregoing manner, even when the MCS does not change, the data detection technique may be tuned in accordance with real-time system operating conditions or other factors. 
     It will be appreciated that various types of detectors  891 - 893  are generally known and suitable for wireless communication applications. The present disclosure is not intended to be limited to any particular structural or architectural arrangement for detectors  891 - 893 . Further detectors  891 - 893  may be configured and operative to execute any of various algorithms or instruction sets generally known or developed in accordance with known principles. Any suitable hardware element, ASIC, or other circuitry adapted to perform zero forcing detection, exhaustive search ML detection, and MMSE detection is contemplated for detectors  891 - 893 , respectively. 
       FIG. 9  is a simplified flow diagram illustrating operation of one embodiment of a method of selecting a data detection strategy for a multiple input, multiple output system. It will be appreciated that the operations depicted in  FIG. 9  may readily be executed by, e.g., the system  800  illustrated in  FIG. 8  or by any similarly capable system. 
     As indicated at block  901 , a method of selecting a data detector may begin by determining an MCS employed by the MIMO system. In some instances, a detector may be selected (block  903 ) in accordance with the MCS. For example, where only two detectors (one of which is an ML detector) may be employed and the MCS is QAM-64, a detector (e.g., a ZF or MMSE detector) other than the ML detector may be selected based (solely, in this case) upon the MCS. 
     In some implementations, selection of a detector (at block  903 ) may additionally be facilitated by identifying communications parameters or other operating conditions as indicated at block  902 . For example, a PHY data rate or operating SNR may be identified at block  902 , and this identification may influence selection at block  903 . In these embodiments, an appropriate data detection technique may be implemented in accordance with operating conditions (such as varying SNR, real-time processing loads, and the like) even when the MCS is not changed. 
     As set forth above, a system and method as described herein may employ exhaustive search ML data detection at lower order modulations, while selecting ZF or MMSE data detection at higher order modulations, higher SNR values, or both. It will be appreciated that, in this context “lower order” modulation generally defines constellations having relatively fewer points than “higher order” modulation. While BPSK, QPSK, and QAM-16 are generally considered lower order and QAM-64 is generally considered higher order, the present disclosure is not intended to be limited to these MCSs. It will be appreciated that the relative distance between the constellation points is generally shorter for higher order modulations. In some embodiments, it may be desirable to select a data detection technique based upon the normalized Euclidian distance between the receive point and all the constellation points, where greater distances generally represent lower order modulation schemes. 
     Several features and aspects of the present invention have been illustrated and described in detail with reference to particular embodiments by way of example only, and not by way of limitation. Those of skill in the art will appreciate that alternative implementations and various modifications to the disclosed embodiments are within the scope and contemplation of the present disclosure. Therefore, it is intended that the invention be considered as limited only by the scope of the appended claims.