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2008

Linear

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Full-Text Articles in Science and Technology Studies

Impact Of Signaling Schemes On Iterative Linear Minimum-Mean-Square-Error Detection, Li Ping, Jun Tong, Xiaojun Yuan, Qinghua Guo Jan 2008

Impact Of Signaling Schemes On Iterative Linear Minimum-Mean-Square-Error Detection, Li Ping, Jun Tong, Xiaojun Yuan, Qinghua Guo

Faculty of Engineering and Information Sciences - Papers: Part A

In this paper, we study the iterative detection problem for a coded system with multi-ary modulation. We show that, with iterative linear minimum-mean-square-error (LMMSE) detection, superposition coded modulation (SCM) can provide performance superior to that with other traditional signaling schemes used in trellis coded modulation (TCM) and bit-interleaved coded modulation (BICM). This finding provides a useful guideline for system design considering inter-symbol interference (ISI) and other forms of interference. Simulation results are provided to illustrate the efficiency of the iterative LMMSE detection with different signaling schemes. © 2008 IEEE.


Evolution Analysis Of Low-Cost Iterative Equalization In Coded Linear Systems With Cyclic Prefixes, Xiaojun Yuan, Qinghua Guo, Xiaodong Wang, Li Ping Jan 2008

Evolution Analysis Of Low-Cost Iterative Equalization In Coded Linear Systems With Cyclic Prefixes, Xiaojun Yuan, Qinghua Guo, Xiaodong Wang, Li Ping

Faculty of Engineering and Information Sciences - Papers: Part A

This paper is concerned with the low-cost iterative equalization/detection principles for coded linear systems with cyclic prefixes. Turbo frequency-domain-equalization (FDE) is applied to systems that may contain the joint effect of multiple-access interference (MAI), cross-antenna interference (CAI) and inter-symbol interference (ISI). We develop an SNK-variance evolution technique for the performance evaluation of the proposed systems. Numerical results in various channel environments demonstrate excellent agreement between the predicted and simulated system performance. © 2008 IEEE.


Performance Analysis Of Multi-Ary Systems With Iterative Linear Minimum-Mean-Square-Error Detection, Li Ping, Jun Tong, Xiaojun Yuan, Qinghua Guo Jan 2008

Performance Analysis Of Multi-Ary Systems With Iterative Linear Minimum-Mean-Square-Error Detection, Li Ping, Jun Tong, Xiaojun Yuan, Qinghua Guo

Faculty of Engineering and Information Sciences - Papers: Part A

This paper is concerned with coded multi-ary systems over linear channels. Based on a semi-analytical evolution technique, the impact of signaling schemes on the performance of low-cost iterative linear minimum-mean-square-error (LMMSE) detection is studied. It is shown that superposition coded modulation (SCM) maximizes the output signal-to-noise ratio (SNR) of LMMSE detectors. Consequently, SCM may potentially outperform other conventional signaling schemes when LMMSE detectors are used. Numerical examples are provided to verify the theoretical analysis. © 2008 IEEE.


Joint Linear Interleaver Design For Concatenated Zigzag Codes, D S. Lin, S Tong, S Q. Li Jan 2008

Joint Linear Interleaver Design For Concatenated Zigzag Codes, D S. Lin, S Tong, S Q. Li

Faculty of Engineering and Information Sciences - Papers: Part A

The design of a class of well-structured low-density parity-check (LDPC) codes, namely linear interleaver based concatenated zigzag (LICZ) codes, is investigated. With summary distances as the design metric, short LICZ codes with large minimum distances can be constructed. Moreover, an efficient cycle-based method is proposed to compute the minimum distances of LICZ codes. Simulation results show that LICZ codes outperform both CZ codes with random interleavers and LDPC codes by the progressive edge growth algorithm.


Psdboost: Matrix-Generation Linear Programming For Positive Semidefinite Matrices Learning, Chunhua Shen, Alan Welsh, Lei Wang Jan 2008

Psdboost: Matrix-Generation Linear Programming For Positive Semidefinite Matrices Learning, Chunhua Shen, Alan Welsh, Lei Wang

Faculty of Engineering and Information Sciences - Papers: Part A

In this work, we consider the problem of learning a positive semidefinite matrix. The critical issue is how to preserve positive semidefiniteness during the course of learning. Our algorithm is mainly inspired by LPBoost [1] and the general greedy convex optimization framework of Zhang [2]. We demonstrate the essence of the algorithm, termed PSDBoost (positive semidefinite Boosting), by focusing on a few different applications in machine learning. The proposed PSDBoost algorithm extends traditional Boosting algorithms in that its parameter is a positive semidefinite matrix with trace being one instead of a classifier. PSDBoost is based on the observation that any ...