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This paper proposes a novel approach to low complexity soft input decoding for lattice reduction-aided MIMO receivers. The proposed approach feeds a soft input decoder with soft signals made from hard decision signals generated by using a lattice reduction-aided linear detector. The soft signal is a weighted-sum of some candidate vectors that are near by the hard decision signal coming out from the lattice reduction-aided linear detector. This paper proposes a technique to adjust the weight adapt to the channel for the higher transmission performance. Furthermore, we propose to introduce a coefficient that is used for the weights in order to enhance the transmission performance. The transmission performance is evaluated in a 4×4 MIMO channel. When a linear MMSE filter or a serial interference canceller is used as the linear detector, the proposed technique achieves about 1.0dB better transmission performance at the BER of 10-5 than the decoder fed with the hard decision signals. In addition, the low computational complexity of the proposed technique is quantitatively evaluated.

The Lattice-reduction aided MIMO receiver with virtual channels has been proposed that can increase transmission speed for a user in downlinks. Although the proposed receiver achieves superior performance even when the number of receive antennas is much less than that of spatially multiplexed streams, however, the receiver has more computational complexity than conventional lattice reduction-aided MIMO receivers. Then, this paper proposes novel techniques to reduce computational complexity for lattice-reduction aided MIMO receivers with virtual channels. In fact, the proposed techniques are shown to reduce the computational complexity to about 40 % of the original configuration in 8 × 2 MIMO-OFDM systems. Even if the proposed techniques are applied, the MIMO receiver is also shown to achieve superior performance in 10×2 MIMO-OFDM systems.

Channel-factorization aided detector (CFAD) is one of the important low-complexity detectors used in multiple input, multiple output (MIMO) receivers. Through channel factorization, this method transforms the original MIMO system into an equivalent system with a better-conditioned channel where detection is performed with a low-complexity detector; the estimate is then transferred back to the original system to obtain the final decision. Traditionally, the channel factorization is done with the lattice reduction algorithms such as the Lenstra-Lenstra-Lovasz (LLL) and Seysen's algorithms with no consideration of the low-complexity detector used. In this paper, we propose a different approach: the channel factorization is designed specifically for the minimum mean-square-error (MMSE) detector that is a popular low-complexity detector in CFADs. Two new types of factorization algorithms are proposed. Type-I is LLL based, where the well-known DLLL-extended algorithm, the LLL algorithm working on the dual matrix of the extended channel matrix, is a member of this type but with a higher complexity. DLLL-extended is the best-performed factorization algorithm found in the literature, Type-II is greedy-search based where its members are differentiated with different algorithm's parameters. Type-II algorithms can provide around 0.5-1.0dB gain over Type-I algorithms and have a fixed computational complexity which is advantageous in hardware implementation.

A reduced complexity quantization error correction method for lattice reduction aided (LRA) vector precoding is proposed. For LRA vector precoding, Babai's approximation procedure can generate quantization errors leading to performance loss. Instead of making a list to correct all possible errors as is done in the existing scheme, we propose a novel method in which only a subset of all possible errors are corrected. The size of the subset is determined by the probability distribution of the number of actual errors. Thus, the computation complexity of our correction procedure is reduced with little performance loss compared with the existing correction scheme.

In this letter, a pre-processed lattice reduction (PLR) scheme is developed for the lattice reduction aided (LRA) detection of multiple input multiple-output (MIMO) systems in spatially correlated channel. The PLR computes the LLL-reduced matrix of the equivalent matrix, which is the product of the present channel matrix and unimodular transformation matrix for LR of spatial correlation matrix, rather than the present channel matrix itself. In conjunction with PLR followed by recursive lattice reduction (RLR) scheme [7], pre-processed RLR (PRLR) is shown to efficiently carry out the LR of the channel matrix, especially for the burst packet message in spatially and temporally correlated channel while matching the performance of conventional LRA detection.

In this letter, we propose two computationally efficient precoding algorithms that achieve near-ML performance for multiuser MIMO downlink. The proposed algorithms perform tree expansion after lattice reduction. The first full expansion is tried by selecting the first level node with a minimum metric, constituting a reference metric. To find an optimal sequence, they iteratively visit each node and terminate the expansion by comparing node metrics with the calculated reference metric. By doing this, they significantly reduce the number of undesirable node visit. Monte-Carlo simulations show that both proposed algorithms yield near-ML performance with considerable reduction in complexity compared with that of the conventional schemes such as sphere encoding.

Based on an analysis of the error patterns in lattice-reduction (LR) precoding in a multiple-antenna broadcast channel, this paper proposes a simple precoding technique that can reduce the quantization error. The proposed scheme establishes a lattice list to provide more can-didates for transmission power reduction based on the analysis of the patterns of the error in the LR precoding method [9]. Simulation results show that the proposed scheme matches the BER performance of more complex precedents (such as the vector perturbation using sphere encoding) with significant saving in complexity.

In this letter, a lattice-reduction-aided (LRA) minimum mean square error (MMSE) Tomlinson-Harashima precoding (THP) is proposed for multiple input multiple output (MIMO) systems. The extended channel is exploited to develop the LRA MMSE-THP based on the lattice reduction method. Simulation results show that the proposed scheme significantly outperforms the conventional MMSE THP and the LRA zero-forcing (ZF) THP and achieves full diversity order.