MIMO-OFDM modem for WLAN by mercy2beans117


									                                   MIMO-OFDM modem for WLAN

                       Authors: Lisa Meilhac, Alain Chiodini, Clement Boudesocque,
                                   Chrislin Lele, Anil Gercekci

                                    NewLogic Technologies S.A.R.L., France

                                                        OFDM signals are generated using the fast Fourier
                                                        transform (FFT).
                                                        On the receiver side, the equalization of the
The future 802.11n WLAN standard that will              received signals, which are generally distorted by a
emerge from the TGn group work should be based          frequency-selective channel (due to multipath), is
on a MIMO-OFDM physical layer. This paper               easily performed in the frequency domain: each
gives an introduction to this promising technology      subcarrier is indeed equalized by a single complex
and to 11n standardization process. Then, we            coefficient, which makes this technique very robust
highlight the changes required to extend an OFDM        against multipath fading.
WLAN modem to a MIMO-OFDM modem.
Finally, we focus our study on the MIMO-OFDM            The worldwide success of the OFDM based
decoding block, giving performance and                  802.11a/g WLAN standards [3-4] along with the
complexity results.                                     astonishingly high spectral efficiencies achieved by
                                                        systems implementing the MIMO technology have
                                                        naturally led engineers to envisioning high date
1. Introduction                                         rate, OFDM, MIMO-based, WLAN standards to
                                                        supersede existing ones.
Multiple Input Multiple Output (MIMO) is an
advanced physical layer technology that uses            First, this paper gives an overview of this MIMO
multiple antennas at both transmit and receive          based WLAN standardization process. Then, in
sides. It is well known that antenna diversity offers   section 3, we present the general architecture of a
robustness and gain over single antenna schemes.        WLAN MIMO modem. Main functionalities are
MIMO also improves the transmission’s spectral          described. In section 4, we focus our study on the
efficiency. Indeed, different signals can be            specific MIMO detection block. We describe and
transmitted simultaneously (i.e. time- and              compare existing decoding algorithms in terms of
frequency-wise) by the different transmit antennas,     performance and complexity.
and be correctly decoded by the multi-antenna
receiver. Even more, the capacity of MIMO
systems can linearly increase with the number of
                                                        2. The 11n standardization process
transmit antennas under a rich multipath
environment [1-2].                                      802.11 has mandated Task Group “n” [5] with
                                                        developing a high-throughput WLAN method
On the other hand, Orthogonal Frequency Division        allowing a throughput greater than 100 Mbps at the
Multiplexing (OFDM) is a spectrally efficient           MAC level. Now, given the inefficiency of the
modulation technique invented in the 1960’s and         CSMA/CA protocol, this requirement translates
now widely used in such popular communication           into a 130 Mbps throughput at the physical layer
systems as WLAN, DVB, etc. In a nutshell, this          (PHY) level.
technique allows squeezing a (usually) large            As of March 2005, two contending proposals have
number of complex sine waves (also called               emerged and are still being debated in the TGn.
subcarriers) in a limited bandwidth. Since these        They go under the names of TGnSync (led by Intel,
subcarriers are mutually orthogonal, they don’t         Agere and Qualcomm) and WWiSE, (led by TI,
interferer with one another. On the transmitter side,   ST). These two groups continue to progress in the
definition of their own proposals whilst looking at                         3. MIMO-OFDM modem
ways of harmonizing in order to eventually avoid a
standstill. Despite this attitude, the March 2005                           architecture
vote has revealed a persistent 54%-46 % split for
both proposals.                                                             A schematic representation of a two-antenna
To reach >130Mbps transmission rates without                                MIMO-OFDM receiver for WLAN is given in
using complex modulation schemes, MIMO                                      Figure 1.
technique has been proposed by both groups with                             The first operation consists in detecting the signal
20MHz channelization and up to 64 QAM                                       of interest and appropriately setting the analog gain.
modulation as mandatory features. The main                                  Then, the frequency offset must be estimated and
differences come from the ratio data/pilot                                  compensated for. Indeed, due to OFDM properties,
subcarriers and preamble definition. Main optional                          the frequency synchronization is one of the most
PHY modes are LDPC coding, and 40MHz                                        critical issues. In parallel, timing synchronization
channelization. Some of the features are shown in                           algorithms are run to correctly position the FFT
Table 1. Even though major differences remain at                            window within the OFDM symbol. Despite the
both the PHY and MAC levels, a significant                                  presence of a guard interval, the OFDM receivers
industry pressure will help both groups converge to                         are sensitive to timing inaccuracies. In the
a standard.                                                                 considered WLAN context, time and frequency
                                                                            synchronizations are initiated during the preamble.
    Features                TgnSync                   WWiSE                 Then, a pilot-based tracking mechanism takes over
   Bandwidth           (M) 20 MHz mode           (M) 20 MHz mode            during the rest of the packet.
   extension           (O) 40 MHz mode           (O) 40 MHz mode            All of these synchronization functionalities are
                       (M) 2 spatial             (M) 2 spatial
  MIMO OFDM;           streams                   streams
                                                                            already part of a classical WLAN OFDM receiver.
     SDM               @ 20MHz mode              @ 20MHz mode               The stake in designing a MIMO-OFDM modem is
                       (max 144 Mpbs)            (max 135 Mpbs)             to extend these algorithms to the multi-antenna
   Support for         (O) 3 or 4 spatial        (O) 3 or 4 spatial         context taking advantage of the diversity while
   higher rates        streams                   streams
                                                                            avoiding a huge increase of the complexity. Once
  Higher coding        (M) 1/2, 2/3, 3/4,        (M) 1/2, 2/3, 3/4,
       rate            5/6                       5/6                        the synchronization is acquired, channel estimation
                       (M) 800 ns                (M) 800 ns                 is performed in the frequency domain using a
  Guard Interval
                       (M) 400 ns                                           dedicated preamble section. In MIMO, each sub-
     Transmit          (O) Basic (SVD                                       carrier’s channel frequency response is a matrix
                                                 (O) Supported
   beamforming         beamforming)
                                                                            and the preamble should be defined to facilitate its
      STBC             (O) Spatial               (O) Nt=1 and Nt
                       Spreading + CS            =2 STBC                    estimation. This matrix is then used in the MIMO
                       (M) 52 (4 pilots)         (M) 54 (2 pilots)          detection block, which aims at compensating the
    Number of          @ 20 MHz                  @ 20 MHz                   channel-induced mix between transmitted antenna
    subcarriers        (O) 108 (6 pilots)        (O) 108 (4 pilots)         streams.
                       @ 40 MHz                  @ 40 MHz
                       (O) LDPC (max             (O) LDPC (max
                                                                            The decoded sequence of bits is finally provided to
 Advanced coding                                                            a Viterbi decoder after de-interleaving.
                       block: 1728 bits)         block: 1944 bits)
(M: Mandatory / O: Optional)

Table 1 – Main characteristics of IEEE’s MIMO-
OFDM standards

                                               GI           Freq off    FFT                                                Domain
             ADC             Rx Filter
                                             removal        Comp.      64 / 128

      RF                  Time                Time     Frequency
                                                                                                        decoding          Deinter-
    Analog                Domain              Sync        est                                            (spatial         leaving
                                                                                      using pilots

                                               GI           Freq off    FFT                                               Viterbi
             ADC             Rx Filter
                                             removal        Comp.      64 / 128

                                                                                     Channel matrix
                   AGC                                                                 Estimation
              Packet detection


                                         Figure 1 – A typical MIMO-OFDM modem architecture
4. MIMO-OFDM detection block                                       given transmitted hard bit b ij ( ± 1 ), the associated
                                                                   soft bit s ij is ideally a real number, which gives us
4.1. Context and notations                                         the following information:
                                                                   • Through the sign of s ij we can infer whether
Various detection algorithms have been developed
for implementation in flat fading MIMO systems,                             the transmitted bit b             ij   was a one or a zero.
assuming a constant channel matrix. They can be
                                                                   • The module of s ij , s ij , gives us a degree of
easily extended to the MIMO-OFDM context by
applying them on every subcarrier constituting an                           reliability concerning the correctness of our
OFDM symbol. Indeed, in the case of an OFDM                                 decision. The greater the module, the higher the
system, it is customary to assume that each received                        confidence about the decision made.
subcarrier is distorted through the application of a
single channel coefficient (i.e. flat fading is                    The relevance of the soft-bit is very important as a
assumed on the subcarrier scale). Thus, for the sake               convolutional code decoder, implemented in the
of both simplicity and clarity, the algorithm                      form of a Viterbi decoder, follows the MIMO
description shall just apply to a single subcarrier.               decoder. It is well known that a soft-input Viterbi
                                                                   algorithm performs more efficiently in terms of bit
Let us denote by N and M the respective numbers                    errors than a hard-input one. Thus, all the decoding
of transmit and receive antennas. In the 802.11n                   algorithms described here include a soft-bit
context, mapping is done through the use of K-                     generation part.
QAM constellations with K= 4, 16, 64. Let us
denote by p = log 2 (K ) the number of bits per                    4.2. Main algorithms
                                                             th    This section describes some reference MIMO
symbol and by ai, the symbol transmitted on the i
antenna. It corresponds to the set of p bits denoted               decoding algorithms.
by bi1, …, bip. This model is illustrated on Figure 2.
                                                                   4.2.1 Soft-output ML-Bit decoder
b11 … b1p             a1                               s11 … s1p
            Mapping                         MIMO
                           Channel         Decoder                 This algorithm consists in directly evaluating the
                              H            Soft bit                reliability of each decoded bit according to the Log-
bN1 … bNp   Mapping                       generation   sN1 … sNp
                                                                   Likelihood Ratio criterion. Each soft-bit value is
Figure 2 - Schematic representation of a MIMO                      given by the following formula
transmission                                                                                            P(bij = +1 / r )
                                                                                    s ij = ln
Let us denote by x and r the transmitted and                                                            P(bij = −1 / r )
received vectors respectively,                                     After doing some mathematical manipulations,
            x = [a1 a 2 ...a N ]
                                 T                                 using the Bayes rule and the max-log
                                                                   approximation, we get
            r = [r1 r2 ...rM ]
                                                                                                   r−Hx                             r−Hx
                                                                                                             2                             2
u designates the transpose of vector u.                            s i, j   ≈ min                                  - min
                                                                               { x / bij = −1}          σ2          { x / bij =1}    σ2
From the previously defined channel assumption,
we get                                                             The complexity of this algorithm exponentially
             r = Hx+n                                              grows with the number of transmit antennas and
                                                                   soon becomes prohibitive if several antennas are
where n is the noise vector and H is the M × N
channel matrix. We assume that the noise is white
and Gaussian with its covariance matrix given by
σ 2 * IM     where I M designates the M × M identity
                                                                   4.2.2 ML-Symbol decoder
matrix.                                                            The purpose of this algorithm is to reduce the
                                                                   complexity of the previous one by determining the
MIMO detection allows recovering the transmitted
sequence of bits. Soft-bits are generated for this                 most likely transmitted multi-antenna symbol x  ˆ
purpose. What we call soft bit is a metric                         rather than its binary elements.
representing the probability of a demodulated bit
                                                                                    x = min r − H x
being a one or a zero. It is obtained by weighting a                                ˆ
given decoded bit by a reliability factor obtained                                               { x}
from the MIMO decoding. This means that for a

Then, a soft-bit value is obtained for each bit        4.3. Performance and complexity study
composing the N-symbol through regular                 In this section we present some performance
demapping (and possibly weighting).                    simulation results in typical WLAN propagation
                                                       environments. We have applied the channel model
4.2.3 Linear decoders                                  studied and developed by the IEEE 802.11 TGn
                                                       Channel Model Special Committee whose principle
The idea consists in finding a matrix G such that      is described in [6]. This model introduces several
           x = Gr
           ˆ                                           reference channels, called A to F, corresponding to
                                                       different environments. We use the channel model
be an estimate of the transmitted symbol vector. G     B, which corresponds to a typical residential
can be the pseudo-inverse of the channel matrix H      environment with a delay spread of 15ns, and the
          G = ( H H H ) −1 H H                         channel model D, which simulates a typical office
This estimator is called Zero-Forcing. It produces     environment with a delay spread of 50ns.
an enhancement of noise. G can also be defined by
minimizing the Minimum Mean Square Error               The simulation has been performed following the
criterion. This leads to                               TgnSync features. However, as the main purpose is
          G = ( H H H + σ 2 I N ) −1 H H               to compare the decoding algorithm performance,
                                                       the same conclusions would have been obtained
                                                       using the WWISE scheme. 1000-byte packets have
4.2.3 Successive interference cancellation             been transmitted over 2 antennas and received with
(SIC) based decoders                                   2 antennas. Two modulation coding schemes
                                                       (MCS) have been tested: MCS = 10, which
The idea here is to successively estimate each         involves a QPSK and a coding rate ¾, and MCS =
component of the transmitted N-symbol vector           14, which features the same coding rate but a 64-
while treating the other ones as mere interference.    QAM. Figures 3 and 4 present the performance
Each detected component is then removed from the       results in channel B and D for MCS of 10. Figure 5
composite received signal, thereby decreasing the      presents the results in channel D for MCS 14.
overall level of interference before the estimation
of the following component takes place. This           One can see on the different figures that the ML-Bit
process improves the estimator performance on the      algorithm performs at least 3dB better than the ML-
next component compared to the previous one.           symbol one. This incidentally confirms a well-
However, it may suffer from an error propagation       known result regarding the performance of hard
effect. The order in which the components are          versus soft input decoding. On the other hand,
processed is indeed crucial to achieving high          linear and SIC decoders reach about the same
performance.     An optimal ordering procedure         performances. But, the most critical remark is the
(typically based on the SINR) is thus needed to        loss of the linear approach compared to ML based
enhance the performance of the algorithm.              one.
The algorithm steps are given below.
•     Determine a detection order ( i1 , i 2 … i N )
•    Perform initialization
     o k = 1;
     o y =r
•    Loop over index k, while k <=N
     o Define the estimator
           g k = hik (σ 2 I M + ∑ hil hil ) −1
                     H                     H

                                    l >k
     o Calculate the estimate of the k-th symbol
          aik = g k y k
     o Make a decision on the estimation
          aik = slicing (aik )
                                                       Figure 3 – MIMO-OFDM detection algorithms
     o      Remove the estimated symbol from
                                                       comparison in PER vs SNR for a 2x2 system with
         received signal
                                                       QPSK modulation and channel of type B
           y k +1 = y k − aik hik
     o Increment the symbol index
        k = k+1;

                                                        5. Conclusion
                                                        In this paper, a comparative study of several
                                                        popular MIMO detection algorithms has been
                                                        presented. Performance-wise, simulations results
                                                        have clearly established that the ML-bit one
                                                        outperforms its peers. However, its prohibitive
                                                        complexity makes its near future implementation
                                                        highly unlikely given the current state of the art.
                                                        On the other hand, since ZF and SIC perform rather
                                                        poorly, it is very likely that one will move towards
                                                        a suboptimal, hybrid detection scheme.

Figure 4 – MIMO-OFDM detection algorithms               References
comparison in PER vs SNR for a 2x2 system with          [1] G. J. Foschini and M. J. Gans, “On limits of
QPSK modulation and channel of type D.                  wireless communications in a fading environment
                                                        when using multiple antennas,” Wireless
                                                        Pers.Commun., vol. 6, no. 3, pp. 311–335, Mar.

                                                        [2] D. Gesbert, M. Shafi, D.-S. Shiu, P. J. Smith,
                                                        and A. Naguib, “From theory to practice: An
                                                        overview of MIMO space-time coded wireless
                                                        systems,” IEEE Jour. Select. Areas in Commun.,
                                                        vol. 21, no. 3, pp. 281– 302, April 2003.

                                                        [3] Wireless LAN Medium Access Control (MAC)
                                                        and Physical Layer (PHY) Specifications: High-
                                                        Speed Physical Layer in the 5 GHz Band, IEEE
                                                        Standard 802.11a-1999.
Figure 5 – MIMO-OFDM detection algorithms
comparison in PER vs SNR for a 2x2 system with          [4] Further Higher-Speed Physical Layer Extension
64QAM modulation and channel of type D.                 in the 2.4 GHz Band, Draft IEEE 802.11g Stand.

                                                        [5] “IEEE 802.11n,” http://grouper.ieee.org/groups/802/11.
Finally, Figure 7 presents a coarse comparison of
the different algorithms in terms of complexity. In     [6] J-P. Kermoal, L. Schumacher, K. I. Pedersen, P.
particular, it illustrates the exponential complexity   E. Mogensenand, F. Frederiksen, “A stochastic
of ML based algorithms.                                 MIMO radio channel model with experimental
                                                        validation” IEEE Jour. Select. Areas in Commun.,
                                                        vol. 20, no. 6, pp. 1211– 1226, August 2002.

Figure 7 – MIMO-OFDM detection algorithms
comparison in terms of complexity for 2x2 systems
and different constellation size.


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