How to cope with the complexities of sophisticated multiple-input multiple-output (MIMO) techniques is critical to realizing these MIMO techniques in real-life applications. In this paper, we propose a list-based soft-decision MIMO detection algorithm based on the memory constraint tree search algorithm. Our algorithm achieves a flexible tradeoff between the memory requirement and computational complexity by tuning the number of available memory units. In comparison with the list sphere decoding detector, our algorithm has reduced computational complexity; as opposed to the list sequential detector, our algorithm achieves similar computational complexity while requiring much less memory. Hence, our algorithm is suitable for hardware implementations.

We propose turbo-sum-product (TSP) and shuffled-sum-product (SSP) decoding algorithms for quasi-cyclic low-density parity-check codes, which not only achieve faster convergence and better error performance than the sum-product algorithm, but also require less memory in partly parallel decoder architectures. Compared with the turbo decoding algorithm, our TSP algorithm saves the same amount of memory and may achieve a higher decoding throughput. The convergence behaviors of our TSP and SSP algorithms are also compared with those of the SP, turbo, and shuffled algorithms by their extrinsic information transfer (EXIT) charts.

We propose a low-complexity soft detector for coded MIMO systems, which generates soft information by combining the hard-decision maximum likelihood (ML) estimate with the soft information generated by the minimum mean square error (MMSE) detector. The hard-decision ML estimate is produced by a modified MMSE based sphere decoder (MMSE-SD). The MMSE-SD is desired since it has lower average and worst-case computational complexity than the zero-forcing (ZF) based SD (ZF-SD) and can be used for arbitrary MIMO configurations. The proposed modification simplifies the implementation of the MMSE-SD, and it can be efficiently implemented by previously proposed complex ZF-SD architecture with minor modification. Our soft detector offers a low-complexity alternative when list based soft detectors are not affordable.

Efficient hardware implementation of low-density parity-check (LDPC) codes is of great interest since LDPC codes are being considered for a wide range of applications. Recently, overlapped message passing (OMP) decoding has been proposed to improve the throughput and hardware utilization efficiency (HUE) of decoder architectures for LDPC codes. In this paper, we first study the scheduling for the OMP decoding of LDPC codes, and show that maximizing the throughput gain amounts to minimizing the intra- and inter-iteration waiting times. We then focus on the OMP decoding of quasi-cyclic (QC) LDPC codes. We propose a partly parallel OMP decoder architecture and implement it using FPGA. For any QC LDPC code, our OMP decoder achieves the maximum throughput gain and HUE due to overlapping, hence has higher throughput and HUE than previously proposed OMP decoders while maintaining the same hardware requirements. We also show that the maximum throughput gain and HUE achieved by our OMP decoder are ultimately determined by the given code. Thus, we propose a coset-based construction method, which results in QC LDPC codes that allow our optimal OMP decoder to achieve higher throughput and HUE.

In this paper, we first propose a parallel turbo-sum-product (PTSP) decoding algorithm for quasi-cyclic (QC) low-density parity-check codes, and show that our proposed algorithm achieves better error performance and faster convergence than the sum-product (SP) or overlapped SP (OSP) algorithm. Based on our PTSP algorithm, we then propose a partly parallel decoder architecture, which requires smaller memory size, less memory access and power consumption, and simpler control than decoder architectures based on the OSP algorithm.

We propose a pseudo-inverse minimum mean squared error (MMSE) based QR decomposition (QRD) M-algorithm (QRD-M) for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. The proposed algorithm can effectively mitigate the detrimental effect of MIMO channel ill-conditions on the bit error rate (BER) performance. As a result, the number of retained branches (M value) can be reduced to half of the original QRD-M algorithm in [1] in order to achieve the same performance for both the uncoded and coded systems. The detection complexity is therefore greatly reduced.

In this paper, we first present a general scheduling model for the overlapped message passing (OMP) decoding of LDPC codes, and show that maximizing the throughput amounts to minimizing the intra- and inter-iteration waiting times. Then we focus on maximizing the throughput for OMP decoders of quasi-cyclic (QC) LDPC codes using a partly parallel hardware structure, which can be modelled as a data dependency and hardware constrained optimization problem. Finally, we propose a scheduling scheme, which gives the optimal solution to the optimization problem under certain conditions. In comparison to previously proposed scheduling schemes, our new scheduling scheme leads to both higher hardware utilization efficiency (HUE) and greater throughput gain.

We present a comparative study of two tree search based detection algorithms, namely, the M-algorithm combined with QR decomposition (QRD-M) and the sphere decoding (SD) algorithms, for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. First, we show that nodes ordering before and during the tree search is important for both algorithms. With appropriate ordering, QRDM can improve detection performance significantly and SD can reduce decoding complexity substantially. Then we compare the implementation complexity of the two algorithms, in terms of the number of nodes required to search or the required number of multiplications to achieve maximum likelihood detection performance. It is interesting to show that the average complexity of SD is lower than that of QRD-M, whereas the worst case complexity of SD is much higher than that of QRD-M.

A list MIMO detection scheme is proposed to achieve near optimal maximum likelihood detection performance. The scheme is especially efficient in high data rate packet transmission systems, such as wireless LAN based hotspots, fixed broadband wireless access systems, 4G cellular based hotspots etc. The complexity of the optimal performance achieving scheme is significantly lower than that of the ML detection and the state of art technologies.

A performance comparison between convolutional coded vertical Bell-lab layered space time (V-BLAST) and groupwise space time trellis coded (GSTTC) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is presented in this paper. Both systems have the same data rate and employ receivers with comparable complexity, which are zero-forcing, minimum mean square error (MMSE) and list sphere decoder (LSD) based turbo receivers. Simulation results show that V-BLAST significantly outperform the GSTTC systems when MMSE or LSD based turbo receiver is applied, while GSTTC perform similarly to or better than the V-BLAST systems when zero-forcing detection algorithm is applied.

In this paper, an iterative interference cancellation (IIC) scheme is proposed for the ordered zero-forcing (ZF) algorithm to regain the signal energies lost during the interference suppression (IS) process in groupwise space lime trellis coded (GSTTC) systems. Both hard decision and soft decision based IIC are studied. With hard decision based IIC, significant improvement over the ordered ZF algorithm is achieved and a performance comparable to that of the complex ordered maximum SNR (MaxSNR) algorithm is reached. With soft decision IIC, the proposed scheme can even outperform the ordered MaxSNR algorithm and provides a better performance-complexity tradeoff in systems with large number of antennas and simple space time trellis codes.