Precoded FIR and Redundant V-BLAST Systems for Frequency-Selective MIMO Channels

The vertical Bell labs layered space-time (V-BLAST) system is a multi-input multioutput (MIMO) system designed to achieve good multiplexing gain. In recent literature, a precoder, which exploits channel information, has been added in the V-BLAST transmitter. This precoder forces each symbol stream to have an identical mean square error (MSE). It can be viewed as an alternative to the bit-loading method. In this paper, this precoded V-BLAST system is extended to the case of frequency-selective MIMO channels. Both the FIR and redundant types of transceivers, which use cyclic-prefixing and zero-padding, are considered. A fast algorithm for computing a cyclic-prefixing-based precoded V-BLAST transceiver is developed. Experiments show that the proposed methods with redundancy have better performance than the SVD-based system with optimal powerloading and bit loading for frequency-selective MIMO channels. The gain comes from the fact that the MSE-equalizing precoder has better bit-error rate performance than the optimal bitloading method

Performance comparison of differential space-time signalling schemes for OFDM systems

Differential transmit diversity is an attractive alternative to its coherent counterpart, especially for multiple antenna systems where channel estimation is more difficult to attain compared to that of single antenna systems. In this paper we compare two different types of differential transmit diversity techniques for OFDM based transmissions. The first technique uses differential space-time block codes (DSTBC) from orthogonal designs and the second uses the differential cyclic delay diversity (DCDD). The results compare the bit error performance for several transmit antenna configurations. The results show that DCDD offers a very close performance to that of DSTBC, with the advantage of a simplified receiver structure

Low-Complexity Detection/Equalization in Large-Dimension MIMO-ISI Channels Using Graphical Models

In this paper, we deal with low-complexity near-optimal detection/equalization in large-dimension multiple-input multiple-output inter-symbol interference (MIMO-ISI) channels using message passing on graphical models. A key contribution in the paper is the demonstration that near-optimal performance in MIMO-ISI channels with large dimensions can be achieved at low complexities through simple yet effective simplifications/approximations, although the graphical models that represent MIMO-ISI channels are fully/densely connected (loopy graphs). These include 1) use of Markov Random Field (MRF) based graphical model with pairwise interaction, in conjunction with {\em message/belief damping}, and 2) use of Factor Graph (FG) based graphical model with {\em Gaussian approximation of interference} (GAI). The per-symbol complexities are $O(K^2n_t^2)$ and $O(Kn_t)$ for the MRF and the FG with GAI approaches, respectively, where $K$ and $n_t$ denote the number of channel uses per frame, and number of transmit antennas, respectively. These low-complexities are quite attractive for large dimensions, i.e., for large $Kn_t$. From a performance perspective, these algorithms are even more interesting in large-dimensions since they achieve increasingly closer to optimum detection performance for increasing $Kn_t$. Also, we show that these message passing algorithms can be used in an iterative manner with local neighborhood search algorithms to improve the reliability/performance of $M$-QAM symbol detection

Future transmitter/receiver diversity schemes in broadcast wireless networks

An open diversity architecture for a cooperating broadcast wireless network is presented that exploits the strengths of the existing digital broadcast standards. Different diversity techniques for broadcast networks that will minimize the complexity of broadcast systems and improve received SNR of broadcast signals are described. Resulting digital broadcast networks could require fewer transmitter sites and thus be more cost-effective with less environmental impact. Transmit diversity is particularly investigated since it obviates the major disadvantage of receive diversity being the difficulty of locating two receive antennas far enough apart in a small mobile device. The schemes examined here are compatible with existing broadcast and cellular telecom standards and can be incorporated into existing systems without change

Blind Receiver Design for OFDM Systems Over Doubly Selective Channels

We develop blind data detectors for orthogonal frequency-division multiplexing (OFDM) systems over doubly selective channels by exploiting both frequency-domain and time-domain correlations of the received signal. We thus derive two blind data detectors: a time-domain data detector and a frequency-domain data detector. We also contribute a reduced complexity, suboptimal version of a time-domain data detector that performs robustly when the normalized Doppler rate is less than 3%. Our frequency-domain data detector and suboptimal time-domain data detector both result in integer least-squares (LS) problems. We propose the use of the V-BLAST detector and the sphere decoder. The time-domain data detector is not limited to the Doppler rates less than 3%, but cannot be posed as an integer LS problem. Our solution is to develop an iterative algorithm that starts from the suboptimal time-domain data detector output. We also propose channel estimation and prediction algorithms using a polynomial expansion model, and these estimators work with data detectors (decision-directed mode) to reduce the complexity. The estimators for the channel statistics and the noise variance are derived using the likelihood function for the data. Our blind data detectors are fairly robust against the parameter mismatch

Optimal low-complexity detection for space division multiple access wireless systems

A symbol detector for wireless systems using space division multiple access (SDMA) and orthogonal frequency division multiplexing (OFDM) is derived. The detector uses a sphere decoder (SD) and has much less computational complexity than the naive maximum likelihood (ML) detector. We also show how to detect non-constant modulus signals with constrained least squares (CLS) receiver, which is designed for constant modulus (unitary) signals. The new detector outperforms existing suboptimal detectors for both uncoded and coded systems