In this study, we consider the selective combining in hybrid cooperative networks (SCHCNs scheme) with one source node, one destination node and @math relay nodes. In the SCHCN scheme, each relay first adaptively chooses between amplify-and-forward protocol and decode-and-forward protocol on a per frame basis by examining the error-detecting code result, and @math ( @math ) relays will be selected to forward their received signals to the destination. We first develop a signal-to-noise ratio (SNR) threshold-based frame error rate (FER) approximation model. Then, the theoretical FER expressions for the SCHCN scheme are derived by utilizing the proposed SNR threshold-based FER approximation model. The analytical FER expressions are validated through simulation results.
In @cite_23 , the SNR threshold-based FER approximation model is applied to the iteratively decoded systems with turbo codes. It is shown that the optimal SNR threshold coincides with the convergence threshold of the iterative turbo decoder. Furthermore, the SNR threshold-based FER model is extended to non-iterative coded and uncoded systems in @cite_18 @cite_33 . The is adopted in @cite_18 @cite_33 to minimize the sum of absolute error where @math denotes the average SNR, and the SNR threshold is given as Since the does not consider the fact that the FER decreases more quickly at high SNR region in high diversity order systems, it needs to be improved for general diversity order systems.
@cite_18 @cite_33 @cite_23
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We study a distributed antenna system where L antenna terminals (ATs) are connected to a central processor (CP) via digital error-free links of finite capacity R0, and serve K user terminals (UTs). This model has been widely investigated both for the uplink (UTs to CP) and for the downlink (CP to UTs), which are instances of the general multiple-access relay and broadcast relay networks. We contribute to the subject in the following ways: 1) For the uplink, we consider the recently proposed “compute and forward” (CoF) approach and examine the corresponding system optimization at finite SNR. 2) For the downlink, we propose a novel precoding scheme nicknamed “reverse compute and forward” (RCoF). 3) In both cases, we present low-complexity versions of CoF and RCoF based on standard scalar quantization at the receivers, that lead to discrete-input discrete-output symmetric memoryless channel models for which near-optimal performance can be achieved by standard single-user linear coding. 4) We provide extensive numerical results and finite SNR comparison with other “state of the art” information theoretic techniques, in scenarios including fading and shadowing. The proposed uplink and downlink system optimization focuses specifically on the ATs and UTs selection problem. In both cases, for a given set of transmitters, the goal consists of selecting a subset of the receivers such that the corresponding system matrix has full rank and the sum rate is maximized. We present low-complexity ATs and UTs selection schemes and demonstrate through Monte Carlo simulation that the proposed schemes essentially eliminate the problem of rank deficiency of the system matrix and greatly mitigate the noninteger penalty affecting CoF RCoF at high SNR. Comparison with other state-of-the art information theoretic schemes, show competitive performance of the proposed approaches with significantly lower complexity. In this letter, we propose a novel forward error correction (FEC) coding method using a modified Reed-Muller (RM) code for providing accurate dimming control in on-off keying (OOK) based visible light communication (VLC) systems. The proposed FEC scheme has a property that produces exactly same occurrence probability of 1's and 0's. Therefore, it can contain minimal compensation symbols in supporting multiple dimming target values. Moreover, VLC with our proposed scheme provides more coding gain than VLC scheme with the conventional FEC codes [the first-order RM and Reed-Solomon (RS) codes]. Simulation results for various dimming target values show that the proposed scheme leads to at least 2 dB or more signal-to-noise ratio (SNR) gain in 1 bit error rate (BER) than two FEC codes. It is known that the frame error rate of turbo codes on quasi-static fading channels can be accurately approximated using the convergence threshold of the corresponding iterative decoder. This paper considers quasi-static fading channels and demonstrates that non-iterative schemes can also be characterized by a similar threshold based on which their frame error rate can be readily estimated. In particular, we show that this threshold is a function of the probability of successful frame detection in additive white Gaussian noise, normalized by the squared instantaneous signal-to-noise ratio. We apply our approach to uncoded binary phase shift keying, convolutional coding and turbo coding and demonstrate that the approximated frame error rate is within 0.4 dB of the simulation results. Finally, we introduce performance evaluation plots to explore the impact of the frame size on the performance of the schemes under investigation. We introduce a simple technique for analyzing the iterative decoder that is broadly applicable to different classes of codes defined over graphs in certain fading as well as additive white Gaussian noise (AWGN) channels. The technique is based on the observation that the extrinsic information from constituent maximum a posteriori (MAP) decoders is well approximated by Gaussian random variables when the inputs to the decoders are Gaussian. The independent Gaussian model implies the existence of an iterative decoder threshold that statistically characterizes the convergence of the iterative decoder. Specifically, the iterative decoder converges to zero probability of error as the number of iterations increases if and only if the channel E sub b N sub 0 exceeds the threshold. Despite the idealization of the model and the simplicity of the analysis technique, the predicted threshold values are in excellent agreement with the waterfall regions observed experimentally in the literature when the codeword lengths are large. Examples are given for parallel concatenated convolutional codes, serially concatenated convolutional codes, and the generalized low-density parity-check (LDPC) codes of Gallager and Cheng-McEliece (1996). Convergence-based design of asymmetric parallel concatenated convolutional codes (PCCC) is also discussed.