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In this paper, a symbol error rate (SER) study is presented for the dual-hop relay networks employing space–time block code (STBC) from coordinate interleaved orthogonal design (STBC-CIOD), where a relay with either a decode-and-forward (DF) or an amplify-and-forward (AF) protocol is equipped with multiple antennas. More specifically, we provide the union upper and lower bounds on the average SER by deriving an accurate closed-form formula for the corresponding symbol pairwise error rate (SPER). In addition, the asymptotic diversity order is analyzed. The theoretically derived bounds are in good agreement with the simulation results. [ABSTRACT FROM PUBLISHER]

The use of open-loop coding can be easily extended to a closed-loop concatenated code if the transmitter has access to feedback. This can be done by introducing a feedback transmission scheme as an inner code. In this paper, this process is investigated for the case when a linear feedback scheme is implemented as an inner code and, in particular, over an additive white Gaussian noise (AWGN) channel with noisy feedback. To begin, we look to derive an optimal linear feedback scheme by optimizing over the received signal-to-noise ratio (SNR). From this optimization, a linear feedback scheme is produced that is asymptotically optimal in the sense of blocklength-normalized SNR; it is then compared to other well-known schemes. Then, the linear feedback scheme is implemented as an inner code to a concatenated code over the AWGN channel with noisy feedback. This code shows improvements not only in error exponent bounds, but also in bit error rate (BER) and frame error rate (FER). It is also shown that if the concatenated code has total blocklength L and the inner code has blocklength, N, the inner code blocklength should scale as N = O\left(C\over R\right), where C is the capacity of the channel and R is the rate of the concatenated code. Simulations with low-density parity-check (LDPC) and turbo codes are provided to display practical applications and their error rate benefits. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present simulation results for Reed–Muller (RM) turbo codes over AWGN and Rayleigh fading channels. We also compare the simulation results of turbo product codes and block turbo codes with RM component codes over an AWGN channel. We show that minimum distance is not important as far as the BER performance of long codes is concerned. The weight distribution of RM codes of different lengths and turbo product codes with first-order RM component codes are obtained and analyzed for their good performance. We show that the weight distribution asymptotically approaches that of random coding as the code length increases. [Copyright &y& Elsevier]

In this paper, a new channel-estimation algorithm based on maximum-likelihood (ML) algorithm for estimation and tracking of the multiple-input-multiple-output (MIMO) channels is presented. The ML algorithm presents the optimum estimation when the exact channel model is known. The derived channel-estimation algorithm is very efficient, with a computational complexity comparable to the least mean square and much lower than the recursive least squares and the Kalman algorithms. The proposed algorithm is analyzed, and the effect of the channel-tracking error is applied as a modifying component for the derived algorithm. The proposed algorithm is simulated for half- and full-rank flat-fading time-varying MIMO channels for the different values of fDT, Eb/No, and training lengths via Monte Carlo simulation technique. The minimum mean-square-error (mmse) joint detector is considered as the detection algorithm. The output of the mmse receiver is considered as the virtual training data in the blind mode of operation: the same as in the decision-directed algorithm. By various simulations, the bit error rate and the mse of tracking the proposed algorithm for different values of fDT, presenting the speed of channel variations, are evaluated and compared with the Kalman filtering approach. By simulating the proposed algorithm for different values of the training length, the minimum training length required for different channel conditions is extracted. [ABSTRACT FROM AUTHOR]