We propose a modified layered structure for multiple-input multiple-output systems, where the layer detection order is fixed and the data rate for each layer is allocated based on the detection order and channel statistics. Using a Gaussian approximation of the layer capacities, we derive an asymptotic optimum data-rate-allocation approach. For optimum data-rate allocation, the amount of backoff from the mean layer capacity is proportional to the standard deviation of the layer capacity. With statistical data-rate allocation, only limited channel feedback is needed to update channel statistics at the transmitter. Simulation results show significant performance improvement with the proposed algorithm. We also find that the performance gap between the layered structure and the channel capacity diminishes with increasing ergodicity within each codeword. Numerical results show a singal-to-noise ratio improvement of 6.3 and 3.6 dB for TGn channels B and D, respectively, for 1% outage probability and 9 b/s/Hz spectral efficiency.

Multiple-input multiple-output (MIMO) techniques promise huge capacity increase in fading channels. Groupwise space–time coding is a tradeoff between complexity and performance. In this letter, the structure of particular component space–time code trellises is exploited, using partial information from a Viterbi decoder of the simultaneously decoded interfering component codes. We have shown that by calculating the a posteriori probabilities of different states of the encoder at each time, and predicting the conditional mean and covariance of interference for the next time index, the system performance can be improved by soft interference cancellation. The word-error rate performance improvement for a system with four transmit and four receive antennas is 1.7 and 1.5 dB for two space–time codes with 8 and 16 states, respectively. In addition, the parallel structure does not have a decoding delay, as in successive interference cancellation, and is highly modular for very-large-scale integration implementation.

We propose a modified layered structure for multiple-input multiple-output (MIMO) systems, where the layer detection order is fixed and the data rate for each layer is allocated based on the detection order and channel statistics. With Gaussian approximation of layer capacities, we derive the optimum data rate allocation and the amount of backoff from mean layer capacity is proportional to the standard deviation of the layer capacity. The minimum overall outage probability of a layered system is uniquely determined by the normalized capacity margin. We then investigate how to select the total information rate to maximize effective throughput. Simulation results show significant performance improvement with the proposed algorithm, and the performance gap between layered structure and the channel capacity diminishes with increasing ergodicity within each codeword.

In this letter, we investigate the issue of selecting the number of antennas at the base station and at the mobile to optimize ergodic capacity of a multiple-input/multiple-output system, when the costs of implementing antennas at the base station and at the mobile are unequal. Total system capacity, defined as a linear combination of the uplink and downlink ergodic capacity, is used as the objective function to be maximized. The asymptotic expression for the ergodic capacity is used as an approximation. The limiting case gives some insight on how the ratio of the number of antennas at the base station to the number of antennas at the mobile may change with signal-to-noise ratio and cost ratio when the total system capacity is maximized.

Multiple-input-multiple-output (MlMO) techniques promise huge capacity increase in fading channels. Group-wise space-time coding is a tradeoff between complexity and performance. In this paper, structure of particular component space-time code trellises is exploited using partial information from Viterbi decoder of the simultaneously decoded interfering component code. We have shown that calculating the a posteriori probabilities of different states of the encoder at each time, and predicting the conditional mean and covariance of interference for the next time index, the system performance is improved by soft interference cancellation. The word-error-rate (WER) performance improvement for a system with 4 transmit 4 receive antennas is 1.7 dB and 1.5 dB for 2-space-time codes with 8 and 16 states, respectively. In addition, the parallel structure does not have decoding delay as in successive interference cancellation and is highly modular for VLSI implementation.

In this paper, we investigate the issue of selecting the number of antennas at the base station and at the mobile to optimize asymptotic ergodic capacity of a multiple-input-multiple-output (MIMO) system when the costs of implementing antennas at the base station and at the mobile are unequal. Total system capacity, defined as a linear combination of the uplink and downlink ergodic capacity, is used as the objective function to be maximized. The limiting case gives some insight on how the ratio of the number of antennas at the base station to the number of antennas at the mobile may change with signal-to-noise ratio (SNR) and cost ratio when the total system capacity is maximized.

The Diagonal Bell Laboratories Layered Space-Time (D-BLAST) structure offers a low complexity solution to realize the attractive capacity of multiple-input and multiple-output (MIMO) systems. We apply D-BLAST in orthogonal frequency division multiplexing (OFDM) systems and address the issue of channel estimation. Different from other MIMO-OFDM, where symbols at all tones are always available for decision-directed channel estimation, in D-BLAST OFDM, we update estimated channel parameters each time a layer is detected with a least square (LS) approach, using a pieced combination of received signals at previous and current OFDM blocks. The initial estimate is further refined by a robust estimator to exploit the time correlation of channel parameters among OFDM blocks. Computer simulation results show the performance improvement over block-wise channel estimation. It is also shown that D-BLAST with proposed channel estimation is robust to fast fading of channel parameters.