Low complexity iterative decision feedback equalizer for 8PSK modulation in time dispersive channels

Faster-than-Nyquist (FTN) signaling was originally proposed by Mazo, which provides the opportunity of increasing data transmission rate effectively. However, this induces unavoidable ISI effects at the receiver and leads to higher receiver complexity. So far, a number of time domain equalization receiver algorithms have been proposed, which have higher demodulation complexity in FTN system with infinite ISI. Against this background, people put forward low complexity iterative frequency domain decision feedback equalization and simulate to show that frequency domain equalization can reduce computational complexity of FTN. In order to promote the practical application of FTN, this paper expands low complexity IBDFE to frequency-selective fading channels. At the same time, simulation results verify its feasibility.

Compared to wired channels, time dispersive radio channels possess more frequent nulls in their spectral characteristics. Thus, the performance of linear filters to compensate intersymbol interference degrades dramatically. The well-known nonlinear decision feedback equalizer (DFE) is one approach to improve this behavior. However, in systems with observations of high dimensionality, the optimum DFE structure is computational intensive. In this paper, we apply the method of the multi-stage Wiener filter (MSWF) to a conventional minimum mean square error (MMSE) DFE in order to reduce computational complexity. The application of the new algorithm to an Enhanced Data rates for GSM Evolution (EDGE) system demonstrates the ability to outperform the even more computational intensive linear Wiener filter (WF).

Recently, an efficient error performance scheme named as labeling diversity has been proposed in the literature for wireless local area network (WLAN). This paper proposes labeling diversity for 8-phase shift keying (8-PSK) modulation named as boosted space-time scheme. A pair of labeling map is opted for this scheme by maximizing the utility function in asymptotic coding gain under the supposition of error-free feedback. Furthermore, labeling diversity is employed with the space-time forward error correction (FEC) codes like convolution and turbo codes for point to point communication scenarios. Since non-line of sight (NLOS) point to point communication scenario is assumed therefore Rayleigh distributed channel model is selected which remains constant for single space-time block code (STBC). The STBC used in boosted space-time scheme is non-orthogonal therefore maximum likelihood (ML) soft de-mapper is employed at the receiver. The convergence of iterative decoding between soft de-mapper and decoder is analyzed by the extrinsic information transfer (EXIT) chart. The validity of the convergence of iterative decoding is also verified by Monte Carlo simulation. It is evident from the simulation results that bit error rate (BER) performance of boosted space-time scheme is superior to the conventional spacetime scheme under identical conditions. Moreover, the boosted space-time scheme with convolution code performs better for low signal-to-noise ratio (SNR) regime in comparison to boosted space-time scheme with turbo code. However, for high SNR regime, turbo coding scheme outperforms the convolution scheme. In addition, turbo coding scheme provides better BER performance to the convolution scheme under non-iterative decoding.

In this paper, we investigate in detail the performance of turbo codes in quasi-static fading channels both with and without antenna diversity. First, we develop a simple and accurate analytic technique to evaluate the performance of turbo codes in quasi-static fading channels. The proposed analytic technique relates the frame error rate of a turbo code to the iterative decoder convergence threshold, rather than to the turbo code distance spectrum. Subsequently, we compare the performance of various turbo codes in quasi-static fading channels. We show that, in contrast to the situation in the AWGN channel, turbo codes with different interleaver sizes or turbo codes based on RSC codes with different constraint lengths and generator polynomials exhibit identical performance. Moreover, we also compare the performance of turbo codes and convolutional codes in quasi-static fading channels under the condition of identical decoding complexity. In particular, we show that turbo codes do not outperform convolutional codes in quasi-static fading channels with no antenna diversity; and that turbo codes only outperform convolutional codes in quasi-static fading channels with antenna diversity

Digital communication systems offer high data rate and efficient transmission through the channel. Low bit error rates assess the quality of any communication system. Bit Errors occur as the result of Adjacent Channel Interference (ACI), Co¬Channel Interference (CCI) and Inter Symbol Interference (ISI) in the presence of Noise. These interferences are more prominent in mobile cellular systems owing to dispersive nature of the channel. Channel dispersion is attributable to multiple reflections in Mobile cellular systems. Equalizer technique is one of the important methods of countering the ill effects of channel. The response of the equaliser should be closely inverse of the channel response. Then the interferences are reduced to great extent by proper equalizers in the receiver end. It attempts to recover the original transmitted symbols at the receiver. In the absence of Channel knowledge, Adaptive equalizer offers superior performance. In this paper, Steepest Descent algorithm is discussed. This algorithm applied in Adaptive equaliser for time invariant and Mobile cellular channels. The efficiency of above algorithm in terms of Bit Error Rate versus SIR is analysed for both channels.

The subject of turbo coding in the presence of an intersymbol interference (ISI) channel whose characteristics are unknown is investigated. An iterative blind turbo detector, is presented, which combines the channel estimation, the channel equalization, and the turbo decoding. Simulation results are presented for rate 1/2 recursive systematic convolutional (RSC) codes and rate 1/3 turbo codes with BPSK modulation, transmitted over an ISI channel having severe frequency distortion. We can improve the performance when we use a good choice of RSC encoder of which the denominator is a primitive polynomial.

In this paper, we present a semi‐analytical approach for analyzing the serial concatenation (SCC) of a high rate convolutional code and a precoded intersymbol interference (ISI) channel. The significance of the proposed approach is that it can be used to identify, for a given outer high rate code and fixed ISI channel, the precoder that results in the lowest error rate floor. In estimating the bit error performance in the floor region, we use analytical techniques developed for trellis codes to compute the overall system minimum squared Euclidean distance, and then use a semi‐analytical approach to find the corresponding multiplicity. We also demonstrate via simulations that the proposed technique may be extended to fading ISI channels with favorable results. We give examples that support our analysis. Copyright © 2006 John Wiley & Sons, Ltd.

This paper proposes a frequency-domain iterative soft decision feedback equalization (SDFE) scheme for multiple-input---multiple-output orthogonal frequency division multiplexing communication systems in multi-path fading channels. Error propagation can seriously affect the performance of the adaptive decision feedback equalization. We propose an iterative SDFE to not only combat error propagation but also be suitable for multilevel modulation systems. Under a Gaussian approximation of the a priori information and the SDFE output, the output of an equivalent AWGN channel and expressions for the a posteriori log likelihood ratio (LLR) and the extrinsic LLR are derived for multilevel modulation. In addition, we also investigate the iterative receivers by employing irregular low-density parity check codes (LDPC) and turbo codes. The performance of the LDPC-coded iterative receiver is better than that of the turbo-coded iterative receiver. Some simulation examples are given to show the effectiveness and comparisons of the proposed receiver.

Data pages retrieved from a volume holographic storage channel (VHSC) suffer from inter-symbol interference (ISI) and noise. Assuming ISI does not change spatially (i.e., stationary), 2-D equalization/detection schemes has been previously developed for quadratic channels like VHSC. In practice, however, the point spread function of a VHSC changes spatially due to magnification and misalignment errors. In this paper, we develop a channel model for magnification error (ME) dominated VHSC and also evaluate the effect of ME on the equalization/detection performance. Results indicate that the recently introduced iterative magnitude-squared decision feedback equalization (IMSDFE) method is more robust to magnification error compared to linear minimum mean squared-error (LMMSE) equalization and adaptive threshold detection (ATD).

In this manuscript, we consider the uplink of a single carrier frequency division multiple access (SC-FDMA) based system, where a set of small-cells coexist with a macro-cell using the same spectrum. To deal with both the inter-carrier interference (ICI) and inter-system interference we combine interference alignment (IA) precoding at the small-cell transmitters with iterative decision feedback equalization at the macroreceiver. The transmitter and receiver design is performed jointly, by considering both full-coordination and limited inter-system information exchange between macro and small-cells. At the small cells side, we assume that the access points are connected through a limited capacity backhaul network to a central unit, where the separation of a quantized version of the small-cell signals is performed. For this case the iterative feedback equalizer is designed by explicitly taking into account the impact of signal quantization. Moreover, a semi-analytical approach for the performance of the proposed schemes is provided. The results show that the proposed schemes are robust to both inter-system and inter-carrier interferences and thus are able to efficiently separate the macro and small cells spatial streams under limited information exchange.

We propose an efficient Adaptive Random Convolutional Network Coding (ARCNC)\nalgorithm to address the issue of field size in random network coding. ARCNC\noperates as a convolutional code, with the coefficients of local encoding\nkernels chosen randomly over a small finite field. The lengths of local\nencoding kernels increase with time until the global encoding kernel matrices\nat related sink nodes all have full rank. Instead of estimating the necessary\nfield size a priori, ARCNC operates in a small finite field. It adapts to\nunknown network topologies without prior knowledge, by locally incrementing the\ndimensionality of the convolutional code. Because convolutional codes of\ndifferent constraint lengths can coexist in different portions of the network,\nreductions in decoding delay and memory overheads can be achieved with ARCNC.\nWe show through analysis that this method performs no worse than random linear\nnetwork codes in general networks, and can provide significant gains in terms\nof average decoding delay in combination networks.\n

A precoding technique is proposed for single-carrier block transmission with cyclic prefix (CP) insertion over inter-symbol interference (ISI) channels. The precoder is based on circular convolution that can be implemented in the frequency domain, so that the detection complexity is kept low using the linear-minimum-mean-square-error (LMMSE) frequency domain equalization (FDE) technique. The precoder can be designed in a low-cost way using the water-filling principle, or optimized based on the signal-to-noise-ratio- (SNR-) variance evolution. Numerical results show that the precoding gain roughly corresponds with the water-filling gain.

This paper investigates the detection schemes for single-carrier underwater acoustic communications over the severe time-dispersive channels. A low-density parity-check (LDPC) code is employed for the improvement of the equalization performance. A detection scheme with Turbo conception is proposed which includes a LDPC decoder in the feedback loop of an iterative block decision feedback equalizer (IB-DFE), combining the iterative frequency domain equalization of IB-DFE with the iterative LDPC decoding. The external messages are fully exchanged between the IB-DFE and the decoder during the processing of the joint iterative equalization and decoding. The numerical results demonstrate that the decision signals with higher reliability from the LDPC decoder accelerate the convergence of the equalizer and enhance the detection performance, yielding up to 6 dB performance gains when compared with the scheme without LDPC code. Compared with the concatenated scheme in which the process of the iterative frequency domain equalization is serial concatenated with that of the iterative LDPC decoding, the combined scheme obtains about 2 dB performance gains.

We analyze a class of LDPC convolutional codes that are constructed from tightly braided convolutional base codes by a lifting procedure. For these braided protograph convolutional codes, we show that the distances grow linearly with the constraint length, and we present lower bounds on their asymptotic segment distance and free distance as well as on the asymptotic minimum distance of their tail-biting versions. With some constraints imposed on the lifting permutations, braided protograph convolutional codes can also be decoded as turbo-like codes by iterative application of the BCJR algorithm. For this case, we derive an explicit upper-bound on the asymptotic decoding error probability as a function of the number of iterations.

In this paper we present a study of Bit Error Rate (BER), for Digital Audio Broadcasting (DAB) system, employing Coded OFDM with different channel coding schemes. Analysis is carried out for convolutional coded and turbo coded data in an Additive White Gaussian Channel (AWGN) based on different constraint lengths and code generator polynomials used for coding. A comparative study on the computational complexity is also done by applying an audio signal and measuring the data processing time per frame, on computers with different processor speeds. It is shown that a coding gain of approximately 6 dB is achieved using turbo coding when compared to convolutional coding, at a cost of higher computational complexity.