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A modification of the BCJR algorithm is derived for convolutionally-coded transmissions over the time-selective, frequency-flat, Rayleigh fading channel with pilot-symbol-assisted channel estimation. The derivation demonstrates why the minimum mean-square error (MMSE) channel estimator is required and how the acquired channel estimate and the estimation error variance should be used in the maximum a posteriori probability decoding. Using the LTE turbo code as an example, we show that the newly derived algorithm outperforms the conventional BCJR algorithm in both standard turbo decoding and iterative channel estimation and decoding. The work demonstrates the importance of incorporating channel estimation accuracy in turbo decoding.

In this paper, we consider transmissions over the flat fading channel with no pilot symbols, and propose code-aided channel estimation, which utilizes the structure of the error-correction codes for channel estimation. Using a repeat-accumulate code as an example, we demonstrate how pilot-free communication can be achieved effectively with higher information throughput and negligible performance loss. The work aims to motivate the study on code and transceiver design specific to fading channels, which are suitable for efficient low-complexity joint channel estimation and decoding.

Accurate channel state information (CSI) is crucial for reliable decoding over fading channels. In many existing works, the CSI acquisition relies greatly on accurate knowledge of the channel model information (CMI), which includes, in particular, the power spectrum of the fading process and the statistical distribution of the fading gain. In practice, it may be difficult to obtain or keep track of the CMI accurately. In this paper, we consider the decoding of Reed- Solomon convolutional concatenated (RSCC) codes over the quasi-static fading channel, and propose a robust, decision-directed, soft-output convolutional decoder which does not require explicit CSI acquisition. Simulation result shows that the proposed decoder has a lower bit-error rate for convolutional decoding than the conventional soft-output Viterbi algorithm and the BCJR algorithm with pilot-symbol-assisted channel estimation. The advantage of the former in terms of the accuracy of soft-decision output is demonstrated by the superiority of word-error performance in the RSCC decoding.

We consider iterative decoding of repeat- accumulate (RA) codes over frequency-flat, quasi- static fading channels. A soft-input, soft-output decoder is proposed for the inner convolutional decoding, which fuses the decoding approach of the soft-output Viterbi algorithm and the estimation approach of the maximum-likelihood sequence detector. The decoder deploys trellis search algorithm based on the generalized likelihood ratio test, whereby the channel state information is acquired implicitly using both the pilot and data signals during the decoding process. Through simulations, we show that the RA decoding with the proposed decoder has much better error performance than standard RA decoding with pilot-symbol- assisted channel estimation, while having approximately the same computational complexity. Compared with the conventional scheme of iterative channel estimation and decoding, the proposed decoder has much simpler structure and requires significantly less computational power, although it incurs some loss in error performance.

We derive the exact log-likelihood ratio (LLR) for iterative decoding of low-density parity-check codes for pilot- symbol-assisted BPSK transmission over flat Rayleigh fading channels. The bit-error rate (BER) performances of this metric are studied for various interleavers and different pilot spacings. We further compare this metric with the approximate metric, which has been commonly used in the literature. Simulation results show that the newly derived LLR metric results in better BER performances. In addition, the effect of signal-to-noise ratio estimation error is investigated and our new LLR shows more robustness.

In order to opportunistically utilize the licensed frequency band, secondary radios need to sense the spectrum for the presence of the primary radio. Non-cooperative and cooperative sensing schemes have been reported in the literature. In this paper, an improved spectrum sensing scheme, where cooperative sensing is performed only selectively according to a predefined criterion, is proposed. Our studies show that by introducing such intelligence between a pair of secondary radios, a significant improvement when performing primary radio detection can be achieved. We extend our study to the case when primary radio is a multiband (or multicarrier) system with multiple secondary radios each transmits through one subcarrier. A cross-band selective cooperation scheme, which takes the advantage that a radio can be the relay to multiple secondary radios, is proposed. Through analytical studies and simulations, we show that further improvement in primary user detection can be achieved.

Firstly an interval extending and merging technology is introduced and then an improved association rules mining method with temporal restraint is proposed. The association analysis which takes use of interval extending and merging technology is implemented based on the data with temporal restraint. Finally the corresponding algorithm is presented.

This paper first overviews the concept of temporal association rules mining associated methods. It proposes an innovative approach of data mining for temporal association rules mining. According to the ordinary related rules, our method considers the time-constrains. Finally, the paper provides the performance estimation of this algorithm.