Pulse or digital communications – Transmitters
Reexamination Certificate
2001-12-07
2004-07-06
Chin, Stephen (Department: 2631)
Pulse or digital communications
Transmitters
Reexamination Certificate
active
06760388
ABSTRACT:
BACKGROUND
1. Field
The present invention relates generally to data communication, and more specifically to techniques for time-domain transmit and receive processing with channel eigen-mode decomposition for multiple-input multiple-output (MIMO) communication systems.
2. Background
In a wireless communication system, an RF modulated signal from a transmitter may reach a receiver via a number of propagation paths. The characteristics of the propagation paths typically vary over time due to a number of factors such as fading and multipath. To provide diversity against deleterious path effects and improve performance, multiple transmit and receive antennas may be used. If the propagation paths between the transmit and receive antennas are linearly independent (i.e., a transmission on one path is not formed as a linear combination of the transmissions on other paths), which is generally true to at least an extent, then the likelihood of correctly receiving a data transmission increases as the number of antennas increases. Generally, diversity increases and performance improves as the number of transmit and receive antennas increases.
A multiple-input multiple-output (MIMO) communication system employs multiple (N
T
) transmit antennas and multiple (N
R
) receive antennas for data transmission. A MIMO channel formed by the N
T
transmit and N
R
receive antennas may be decomposed into N
C
independent channels, with N
C
≦min {N
T
, N
R
}. Each of the N
C
independent channels is also referred to as a spatial subchannel of the MIMO channel and corresponds to a dimension. The MIMO system can provide improved performance (e.g., increased transmission capacity) if the additional dimensionalities created by the multiple transmit and receive antennas are utilized.
The spatial subchannels of a wideband MIMO system may experience different channel conditions (e.g., different fading and multipath effects) across its bandwidth and may achieve different signal-to-noise-and-interference ratios (SNRs) at different frequencies (i.e., different frequency bins or subbands) of the overall system bandwidth. Consequently, the number of information bits per modulation symbol (i.e., the data rate) that may be transmitted at different frequency bins of each spatial subchannel for a particular level of performance may be different from bin to bin. Moreover, the channel conditions typically vary with time. As a result, the supported data rates for the bins of the spatial subchannels also vary with time.
To combat the frequency selective nature of the wideband channel (i.e., different channel gains for different bins), orthogonal frequency division multiplexing (OFDM) may be used to effectively partition the system bandwidth into a number of (N
F
) subbands (which may be referred to as frequency bins or subchannels). In OFDM, each frequency subchannel is associated with a respective subcarrier upon which data may be modulated, and thus may also be viewed as an independent transmission channel.
A key challenge in a coded communication system is the selection of the appropriate data rates and coding and modulation schemes to be used for a data transmission based on channel conditions. The goal of this selection process is to maximize throughput while meeting quality objectives, which may be quantified by a particular frame error rate (FER), certain latency criteria, and so on.
One straightforward technique for selecting data rates and coding and modulation schemes is to “bit load” each frequency bin of each spatial subchannel according to its transmission capability, which may be quantified by the bin's short-term average SNR. However, this technique has several major drawbacks. First, coding and modulating individually for each bin of each spatial subchannel can significantly increase the complexity of the processing at both the transmitter and receiver. Second, coding individually for each bin may greatly increase coding and decoding delay. And third, a high feedback rate may be needed to send channel state information (CSI) indicative of the channel conditions (e.g., the gain, phase and SNR) of each bin.
There is therefore a need in the art for achieving high throughput in a coded MIMO system without having to individually code different frequency bins of the spatial subchannels.
SUMMARY
Aspects of the invention provide techniques for processing a data transmission at the transmitter and receiver of a MIMO system such that high performance (i.e., high throughput) is achieved without the need to individually code/modulate for different frequency bins. In an aspect, a time-domain implementation is provided herein which uses frequency-domain singular value decomposition and “water-pouring” results to derive pulse-shaping and beam-steering solutions at the transmitter and receiver. The singular value decomposition is performed at the transmitter to determine the eigen-modes (i.e., the spatial subchannels) of the MIMO channel and to derive a first set of steering vectors that are used to “precondition” modulation symbols. The singular value decomposition is also performed at the receiver to derive a second set of steering vectors that are used to precondition the received signals such that orthogonal symbol streams are recovered at the receiver, which can simplify the receiver processing. Water-pouring analysis is used to more optimally allocate the total available transmit power for the MIMO system to the eigen-modes of the MIMO channel. The allocated transmit power may then determine the data rate and the coding and modulation scheme to be used for each eigen-mode.
At the transmitter, data is initially coded in accordance with one or more coding schemes to provide coded data, which is then modulated in accordance with one or more modulation schemes to provide a number of modulation symbol streams (e.g., one stream for each eigen-mode). An estimated channel response matrix for the MIMO channel is determined (e.g., at the receiver and sent to the transmitter) and decomposed (e.g., in the frequency domain, using singular value decomposition) to obtain a first sequence of matrices of (right) eigen-vectors and a second sequence of matrices of singular values. Water-pouring analysis may be performed based on the matrices of singular values to derive a third sequence of matrices of values indicative of the transmit power allocated to the eigen-modes of the MIMO channel. A pulse-shaping matrix for the transmitter is then derived based on the first and third sequences of matrices. The pulse-shaping matrix comprises the steering vectors that are used to precondition the modulation symbol streams to obtain a number of preconditioned signals, which are then transmitted over the MIMO channel to the receiver.
At the receiver, the estimated channel response matrix is also determined and decomposed to obtain a fourth sequence of matrices of (left) eigen-vectors, which are then used to derive a pulse-shaping matrix for the receiver. A number of signals is received at the receiver and preconditioned based on this pulse-shaping matrix to obtain a number of received symbol streams. Each received symbol stream may be equalized to obtain a corresponding recovered symbol stream, which is then demodulated and decoded to recover the transmitted data.
Various aspects and embodiments of the invention are described in further detail below. The invention further provides methods, digital signal processors, transmitter and receiver units, and other apparatuses and elements that implement various aspects, embodiments, and features of the invention, as described in further detail below.
REFERENCES:
patent: 6473467 (2002-10-01), Wallace et al.
patent: 9809381 (1998-03-01), None
Burr, A.G. “Adaptive Space-Time Signal Processing and Coding” IEEE 2000.*
Joonsuk Kim et al. “Transmission Optimization with a Space-Time Filter at Low SNR Wireless Environment,” Global Telecommunications Conference. Globecom '99, Rio De Janeiro, Brazil, vol.1B, Dec. 5-9, 1999, pp. 889-893.
A.G. Burr, “Adaptive Space-Time Signal Processing
Howard Steven J.
Ketchum John W.
Wallace Mark
Walton Jay Rod
Baker Kent D.
Chin Stephen
Nguyen Thien T.
Odom Curtis
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