The present invention relates to a simplified decoder for a coded orthogonal frequency division multiplexing-multiple input multiple output (COFDM-MIMO) system. More particularly, the present invention relates to a bit interleaved system with maximum (ML) likelihood decoding. Most particularly the present invention relates to a 2 by 2 MIMO system with Zero Forcing (ZF) guided maximum likelihood (ML) decoding that doubles the transmission data rate of a single input single output (SISO) IEEE 802.11a system based on orthogonal frequency division multiplexing (OFDM) technique.
MIMO systems have been studied as a promising candidate for the next generation of high data rate wireless communication system. Currently, for a single antenna system (SISO), IEEE 802.11a employing the OFDM modulation technique has a maximum data transmission rate of 54 Mbps. There is only one transmission antenna and one receiving antenna, i.e., it is a SISO system, and the signal constellation for 802.11a is 64 quadrature amplitude modulation (QAM). Transmission data rates in excess of 100 Mbps is a goal for the next generation wireless communication system.
Given the physical channel characteristics of wireless communication systems, it is almost impossible to increase the data rate with a single antenna system by increasing the order of the constellation of the signal.
One possible approach to achieving a greater than 100 Mbps data rate is a 2 by 2 MIMO system based on an IEEE 802.11a SISO system in which the two transmission antennae transmit different data streams that are coded in the same way as an 802.11a system at each antenna. This system can achieve a transmission data rate of 108 Mbps with approximately the same signal-to-noise ratio (SNR) as the prior art 54 Mbps IEEE 802.11a SISO system based on OFDM modulation that is illustrated in
Suppose the system of
where hij 20 represents the channel from transmitter antenna i to receiver antenna j, i.e., Txi to Rxj. Without losing generality, assume the four channels are Rayleigh fading channels that are independent of one another. Then the received signal in frequency domain on subcarrier k can be expressed as
Since each subcarrier is decoded separately, the subscript ks in equation (1) is omitted. In optimal maximum likelihood (ML) detection, for each received signal pair, r1 and r2, to determine whether a transmitted bit in these symbols is ‘1’ or ‘0’, it is necessary to find the largest probability
max(p(r|b)) (2)
where
are the bits in symbol s1 and s2 for which a decision needs to be made. In an add white gaussian noise (AWGN) environment, this is equivalent to finding
It is also equivalent to finding
In order to determine the bit metrics for a bit in symbol s1, the following equation must be evaluated. For bit i in symbol s1 to be ‘0’, it is necessary to evaluate
Where m1i0 represents the bit metrics for bit i in received symbol s1 to be ‘0’. S represents for the whole constellation point set, while S0 represents the subset of the constellation point set such that bit bi=0. For bit i in symbol s1 to be ‘1’, it is necessary to evaluate
where S1 represents the subset of the constellation point set such that bit bi=1.
Using the same method, it is possible to determine the bit metrics for transmitted symbol s2. For bit i in symbol s2 to be ‘0’, it is necessary to evaluate
For bit i in symbol s2 to be ‘1’, it is necessary to evaluate
Then, the bit metrics pairs (m1i0, m1i1) (m2i0, m2i1) are sent to corresponding deinterleavers and Viterbi decoders for FEC decoding of each of the data streams.
Simulation results show that using optimal decoding, the proposed 108 Mbps MIMO system actually performs 4 dB better than the SISO 54 Mbps system at a BER of 10−4. However, the computation cost for the optimal decoding is very high. To obtain bit metrics for a bit in signal s1 to be 0 and 1, it is necessary to evaluate 64*64 permutations of the s1 and s2 constellation, which cannot be accomplished cost effectively with existing computational capabilities. The computation cost for this 2 by 2 MIMO system decoding is too high to be practical.
Thus, there is a need for an alternative coding method to reduce the high computation cost when a 2 by 2 MIMO system based on and 54 Mbps IEEE 802.11a SISO system is employed for increasing the data transmission rate above 100 Mbps.
The present invention is a 108 Mbps 2 by 2 MIMO system based on a 54 Mbps SISO system, as illustrated in
The present invention employs a ZF guided maximum likelihood (ML) decoding method. For a SISO single carrier system, since a time-dispersed channel (frequency selective fading channel) brings the channel memory into the system, joint maximum likelihood (ML) equalization and decoding is not realistic because of the high computation cost. The general practice is to first use minimum-mean-square-error/zero forcing (MMSE/ZF) as the criteria to equalize the channel. Then the equalized signal is sent to a maximum likelihood (ML) detector for further decoding. However, this is a sub-optimal system.
In a SISO OFDM system, since the system is designed to let each sub-carrier experience flat fading channel, the real maximum likelihood (ML) equalization and decoding can be implemented with affordable computational cost. Yet in a MIMO OFDM system, because of the large number of permutation evaluations of the constellation set required in the metrics calculation, the computation cost for real maximum likelihood (ML) equalization and decoding is too high to be practical.
One way to avoid the large number of permutation computations is to first find the approximate value of the transmitted symbols s1 and s2 and then use the maximum likelihood (ML) detection method to find the bit metrics for s1 while taking s2 as the value calculated by the ZF method. It is reasonable to make the assumption that when the SNR is high enough, the ZF decision is very close to the optimal maximum likelihood decision. Thus, the present invention incurs approximately the same computation cost in a MIMO system to get the bit metrics for the transmitted symbols s1 and s2 as the SISO system incurs for transmitted symbols s.
The preferred embodiments of the present invention employ a simplified decoding method. The details of the simplified decoding method are described below with reference to the drawings.
The received signal can be written as
According to the ZF criteria, the transmitted signal can be estimated by the demapping and signal separation module 34 as
Using the minimum Euclidean distance calculated for the ZF calculated symbol and constellation point, the demapping and signal separation module 34 obtains the estimated transmitted symbol by hard decision. The symbols after the hard decision operation can be represented as
The bit metrics for transmitted symbol s1 are then calculated by the demapping and signal separation module 34 as
and bit metrics for transmitted symbol s2 can then be calculated as
where Sp represents the subset of the constellation points such that bit bi is p where p=0 or 1. Then, the bit metrics pairs (m1i0, m1i1) (m2i0, m2i1) are sent to corresponding first and second deinterleavers 30 and 31 and different Viterbi decoders 33 and 34, respectively, for forward error correction (FEC) decoding of each data stream.
In a second preferred embodiment, a further simplified decoding method is provided based on the first preferred embodiment. Unlike the first preferred embodiment in which the demapping and signal separation module 34 uses the MIMO ML criteria to calculate the bit metrics for each bit in the two transmitted symbols after the ZF operation, the SISO ML is used by the demapping and signal separation module 34 to find the constellation points for each bit that satisfy
where q=1,2 and pε{0,1}. Two constellation points are defined by the demapping and signal separation module 34 that correspond to the bit metrics calculation of (12) for bit i of the transmitted symbol sq to be sqip. In SISO decoding, bit metrics calculated from (12) are sent to a Viterbi decoder for decoding. In MIMO decoding, equation (12) is only used by the demapping and signal separation module 34 to determine the constellation points that satisfy (12) and use these constellation points in MIMO ML criteria to calculate the bit metrics for each bit that are sent to a Viterbi decoder for decoding. That is, the bit metrics are calculated by the demapping and signal separation module 34 as
m1ip=(∥r1−h11s1ip−h21ŝ2∥2+∥r2−h12s1ip−h22ŝ2∥2)
m2ip=(∥r1−h11ŝ1−h21s2ip∥2+∥r2−h12ŝ1−h22s2ip∥2) (13)
Then, the bit metrics pairs (m1i0, m1i1) (m2i0, m2i1) are sent to corresponding first and second deinterleavers 30 and 31 and different Viterbi decoders 33 and 34, respectively, for forward error correction (FEC) decoding of each data stream.
In a hardware implementation, the 12 constellation points for the 6 bits in one transmitted symbol can be obtained by a slice-compare-select operation. An example of quadrature-phase shift keying (QPSK) is illustrated in
Simulation results, shown in
Simulation results show that although the performance of the first embodiment of the simplified decoding method of the present invention is about 4 dB worse than the optimal decoding method at a BER level of 10−4, it is almost the same as the optimal decoding for the SISO system at 54 Mbps 43. This result shows that the first embodiment of the present invention comprising a 2 by 2 MIMO system 41 can double the transmission data rate of the SISO system 43 for the same SNR at reasonable computation cost. The second embodiment provides the same improvement for a further reduced computation cost. Therefore, the simulation show that both embodiments of the present invention have about the same BER vs SNR performance, which is almost the same as SISO 54 Mbps system 43 and 4 dB less than MIMO optimal decoding system 42 at BER level of 10−4. And, the increase in transmission rate by double is obtained for no increase in computation cost in the first embodiment and a reduced computation cost in the second embodiment
Referring to
This application claims the benefit of U.S. provisional application Ser. No. 60/430,424 filed Dec. 3, 2002, which is incorporated herein by reference.
Filing Document | Filing Date | Country | Kind | 371c Date |
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PCT/IB03/05277 | 11/18/2003 | WO | 00 | 6/3/2005 |
Publishing Document | Publishing Date | Country | Kind |
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WO2004/051914 | 6/17/2004 | WO | A |
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