A Fast Decodable Full-Rate STBC with High Coding Gain for 4x2 MIMO Systems

In this work, a new fast-decodable space-time block code (STBC) is proposed. The code is full-rate and full-diversity for 4x2 multiple-input multiple-output (MIMO) transmission. Due to the unique structure of the codeword, the proposed code requires a much lower computational complexity to provide maximum-likelihood (ML) decoding performance. It is shown that the ML decoding complexity is only O(M^{4.5}) when M-ary square QAM constellation is used. Finally, the proposed code has highest minimum determinant among the fast-decodable STBCs known in the literature. Simulation results prove that the proposed code provides the best bit error rate (BER) performance among the state-of-the-art STBCs.Comment: 2013 IEEE 24th International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), London : United Kingdom (2013

Distributed MIMO coding scheme with low decoding complexity for future mobile TV broadcasting

A novel distributed space-time block code (STBC) for the next generation mobile TV broadcasting is proposed. The new code provides efficient performance within a wide range of power imbalance showing strong adaptivity to the single frequency network (SFN) broadcasting deployments. The new code outperforms existing STBCs with equivalent decoding complexity and approaches those with much higher complexities

Reduced-complexity maximum-likelihood decoding for 3D MIMO code

The 3D MIMO code is a robust and efficient space-time coding scheme for the distributed MIMO broadcasting. However, it suffers from the high computational complexity if the optimal maximum-likelihood (ML) decoding is used. In this paper we first investigate the unique properties of the 3D MIMO code and consequently propose a simplified decoding algorithm without sacrificing the ML optimality. Analysis shows that the decoding complexity is reduced from O(M^8) to O(M^{4.5}) in quasi-static channels when M-ary square QAM constellation is used. Moreover, we propose an efficient implementation of the simplified ML decoder which achieves a much lower decoding time delay compared to the classical sphere decoder with Schnorr-Euchner enumeration.Comment: IEEE Wireless Communications and Networking Conference (WCNC 2013), Shanghai : China (2013

Achieving Low-Complexity Maximum-Likelihood Detection for the 3D MIMO Code

The 3D MIMO code is a robust and efficient space-time block code (STBC) for the distributed MIMO broadcasting but suffers from high maximum-likelihood (ML) decoding complexity. In this paper, we first analyze some properties of the 3D MIMO code to show that the 3D MIMO code is fast-decodable. It is proved that the ML decoding performance can be achieved with a complexity of O(M^{4.5}) instead of O(M^8) in quasi static channel with M-ary square QAM modulations. Consequently, we propose a simplified ML decoder exploiting the unique properties of 3D MIMO code. Simulation results show that the proposed simplified ML decoder can achieve much lower processing time latency compared to the classical sphere decoder with Schnorr-Euchner enumeration

Cross-layer Resource Allocation Scheme for Multi-band High Rate UWB Systems

In this paper, we investigate the use of a cross-layer allocation mechanism for the high-rate ultra-wideband (UWB) systems. The aim of this paper is twofold. First, through the cross-layer approach that provides a new service differentiation approach to the fully distributed UWB systems, we support traffic with quality of service (QoS) guarantee in a multi-user context. Second, we exploit the effective SINR method that represents the characteristics of multiple sub-carrier SINRs in the multi-band WiMedia solution proposed for UWB systems, in order to provide the channel state information needed for the multi-user sub-band allocation. This new approach improves the system performance and optimizes the spectrum utilization with a low cost data exchange between the different users while guaranteeing the required QoS. In addition, this new approach solves the problem of the cohabitation of more than three users in the same WiMedia channel

Robustness maximization of parallel multichannel systems

Bit error rate (BER) minimization and SNR-gap maximization, two robustness optimization problems, are solved, under average power and bit-rate constraints, according to the waterfilling policy. Under peak-power constraint the solutions differ and this paper gives bit-loading solutions of both robustness optimization problems over independent parallel channels. The study is based on analytical approach with generalized Lagrangian relaxation tool and on greedy-type algorithm approach. Tight BER expressions are used for square and rectangular quadrature amplitude modulations. Integer bit solution of analytical continuous bit-rates is performed with a new generalized secant method. The asymptotic convergence of both robustness optimizations is proved for both analytical and algorithmic approaches. We also prove that, in conventional margin maximization problem, the equivalence between SNR-gap maximization and power minimization does not hold with peak-power limitation. Based on a defined dissimilarity measure, bit-loading solutions are compared over power line communication channel for multicarrier systems. Simulation results confirm the asymptotic convergence of both allocation policies. In non asymptotic regime the allocation policies can be interchanged depending on the robustness measure and the operating point of the communication system. The low computational effort of the suboptimal solution based on analytical approach leads to a good trade-off between performance and complexity.Comment: 27 pages, 8 figures, submitted to IEEE Trans. Inform. Theor

A Novel Data-Aided Channel Estimation with Reduced Complexity for TDS-OFDM Systems

In contrast to the classical cyclic prefix (CP)-OFDM, the time domain synchronous (TDS)-OFDM employs a known pseudo noise (PN) sequence as guard interval (GI). Conventional channel estimation methods for TDS-OFDM are based on the exploitation of the PN sequence and consequently suffer from intersymbol interference (ISI). This paper proposes a novel dataaided channel estimation method which combines the channel estimates obtained from the PN sequence and, most importantly, additional channel estimates extracted from OFDM data symbols. Data-aided channel estimation is carried out using the rebuilt OFDM data symbols as virtual training sequences. In contrast to the classical turbo channel estimation, interleaving and decoding functions are not included in the feedback loop when rebuilding OFDM data symbols thereby reducing the complexity. Several improved techniques are proposed to refine the data-aided channel estimates, namely one-dimensional (1-D)/two-dimensional (2-D) moving average and Wiener filtering. Finally, the MMSE criteria is used to obtain the best combination results and an iterative process is proposed to progressively refine the estimation. Both MSE and BER simulations using specifications of the DTMB system are carried out to prove the effectiveness of the proposed algorithm even in very harsh channel conditions such as in the single frequency network (SFN) case