Jing Shiun Lin

Subspace-Based Blind Channel Estimation by Separating Real and Imaginary Symbols for Cyclic-Prefixed Single-Carrier Systems

Blind channel estimation based on a subspace-based algorithm for single-input single-output cyclic-prefixed single-carrier systems is proposed in this brief. The proposed method is different from conventional subspace-based approaches, which exploit the original complex structure of the received data symbols. In contrast, we separate the real part and the imaginary part of the received complex symbols and then exploit these two kinds of symbols to construct the signal model. The noise subspace can then be established to estimate the channel impulse response (CIR) using a subspace algorithm when real symbols, such as binary phase shift keying or pulse amplitude modulation symbols, are applied. The real part and the imaginary part of the CIR can be estimated individually and simultaneously with only a sign ambiguity. With the aid of repetition index, the proposed method is workable even if few data blocks are available. Simulation results demonstrate that the proposed approach outperforms conventional methods in normalized mean-squared error under static channel environments.

Design of high-throughput MIMO detectors using sort-free and early-pruned techniques

This paper presents a high-throughput multiple-input multiple-output (MIMO) signal detector based on the sort-free concept and the proposed mean-aided early-pruned scheme. The derived MIMO detector can reduce the number of node computation while maintaining the bit error rate (BER) performance of the conventional sort-free MIMO detection algorithm. Experimental results show that the proposed detector design with 4×4 antenna array and 16-QAM modulation has better normalized throughput rate compared to those of existing detectors under the same system configurations. Moreover, a significant reduction on node extensions can be achieved using the presented detection scheme compared with the conventional sort-free algorithm and the K-best algorithm.

Blind channel estimation for MIMO-OFDM systems with repeated time-domain symbols

Cyclic prefix (CP), virtual carriers (VCs), and multiple receive antennas are conventionally used to construct the required signal and noise subspaces for subspace-based blind channel estimation approaches in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Different to the conventional approaches, this paper proposes a subspace-based blind channel estimation approach with repeated time-domain symbols. A new signal model can then be developed to generate the required noise subspace for channel estimation. Simulation results demonstrate that the proposed subspace-based blind channel estimation method outperforms the approach with VCs in terms of normalized mean-squared error (NMSE) under the same transmission rate.

Blind Channel Estimation for CP/CP-Free OFDM Systems Using Subspace Approach

The existing subspace-based channel estimation approach for orthogonal frequency division multiplexing (OFDM) systems with an expanding factor, called the repetition index, suffers from the problem of low probability of full row rank of the signal matrix with few OFDM blocks, which may fail to estimate the channel impulse response (CIR). In this paper, a subspace-based channel estimation algorithm with a two-step signal matrix construction method is proposed. The probability of full row rank of the proposed signal matrix is very close to one even if few OFDM blocks are available. The proposed approach can also be applied to both cyclic prefix (CP)-based and non-CP-based OFDM systems whereas the conventional approach is only suitable for CP-based systems. Simulation results show that the proposed method outperforms related blind channel estimation approaches in terms of normalized mean square error (NMSE).

An efficient QR decomposition design for MIMO systems

Multiple-input multiple-output (MIMO) techniques have been widely used in various wireless communication systems these days. QR factorization is a fundamental module yet computationally intensive used in many MIMO detection schemes. In this paper, a complex-valued QR factorization (CQRF) scheme realized via a sequence of real-value Givens rotations is first presented. An efficient CQRF design using coordinate rotation digital computer (CORDIC) modules is next developed. The design features a highly parallel architecture to support high throughput operations. One CQRF can be obtained in every 8 clock cycles. To reduce the circuit complexity, a pipelined CORDIC structure is also applied. The implementation results in TSMC 0.18-µm CMOS process indicate that the proposed design can achieve a throughput rate of 25MCQRFs per second while consuming only 103.7k gates in circuit complexity. Performance evaluation based on a composite index consisting of area and throughput rate also shows the advantages of the proposed design against other similar works.

Efficient highly-parallel turbo decoder for 3GPP LTE-Advanced

Turbo codes have been widely adopted in latest wireless communication systems due to their excellent error correction capability. In 3GPP LTE-Advanced systems, a peak data rate of up to 1 Gbps should be satisfied. To meet this throughput requirement, several turbo decoding algorithms aimed at achieving highly parallel architecture have been investigated. However, the resulting hardware cost of turbo decoders is increased considerably with increasing parallelism. This paper presents a modified parallel-window decoding algorithm to reduce the warm-up computation ratio per each decoding window. In addition, a dual-mode computing schedule is proposed to support the requirement of various code rates and block lengths. Experimental results reveal that the proposed design, implemented in the TSMC 90-nm CMOS process, can achieve the highest throughput rate of 1.45 Gbps and improve the normalized area efficiency by about 24.53% compared to the existing 3GPP-LTE-Advanced turbo decoders.

Subspace-Based Blind Channel Estimation for MIMO-OFDM Systems with New Signal Permutation Method

Subspace-based blind channel estimation for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with repetition index has been proposed in the literature. However, the full-row-rank probability of the signal matrix using the MIMO-based repetition index method is extremely low, especially when low-order modulation or few received OFDM symbols are used. In this paper, the MIMO-based repetition index method with a new signal permutation method is proposed. The proposed method solves the problem of previous MIMO-based repetition index method. Additionally, the proposed method performs well even if low-order modulation or few revived OFDM symbols are applied. Simulation results show that proposed method not only has high full-row-rank probability, but also outperforms conventional blind channel estimation approaches in terms of normalized mean-square error (NMSE).

Subspace-Based Blind Channel Estimation for MIMO-OFDM Systems with Repetition Index

A subspace-based blind channel estimation algorithm for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems is proposed in this paper. The proposed algorithm for MIMO-OFDM systems generalizes the conventional repetition index approach, which is only suitable for single-input single-output (SISO)-OFDM systems. Additionally, the necessary conditions of the proposed algorithm to be full row rank are discussed. Simulation results show that the probability of full row rank of the proposed signal matrix is close to one for different constellation schemes. The proposed generalized approach also outperforms the conventional approach with virtual carriers (VCs) in terms of normalized mean-squared error (NMSE) under static channel environments even if the number of OFDM symbols is small.

Blind channel estimation with periodicity for OFDM systems without cyclic prefix

Generation of the signal and noise subspaces is a critical problem in subspace-based algorithms for orthogonal frequency division multiplexing (OFDM) systems. Some special characteristics, such as virtual carriers (VCs), real symbols, and/or cyclic prefix (CP), can be exploited to construct the required noise subspace in conventional subspace approaches. In this paper, a blind channel estimation algorithm with periodicity property is proposed for OFDM systems without CP. Using the time-domain periodicity, which can be obtained by inserting zeros at some positions of frequency-domain OFDM symbols, a method for constructing the noise subspace is developed based on the proposed signal model. Simulation results show that the proposed blind channel estimation method has better normalized mean-squared error (NMSE) performance than that of a conventional approach with VCs.

A signal permutation method for cyclic-prefix-free OFDM channel estimation

A subspace approach for blind channel estimation in SIMO-OFDM systems without cyclic prefix is proposed in this paper. By exploiting a repetition index in the signal model, our algorithm generalizes the conventional subspace approach. The computational complexity of the singular value decomposition (SVD) operation in the proposed method is lower than that in other subspace approaches. Additionally, the proposed approach is suitable for cyclic-prefix-free systems. An extra benefit is that fewer OFDM blocks are needed to satisfy the necessary condition of the signal matrix to be full rank. Simulation results show that the proposed method is better than existing approaches in normalized mean square error.