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Due to inevitable corruption on signals across all subcarriers, strong and frequently occurring impulses pose a grim threat to Orthogonal Frequency Division Multiplexing (OFDM) systems, where system performance is likely to be further degraded by multipath fading phenomenon. Recently, a deep neural network (DNN) receiver is shown to be capable of not only implicitly estimating channel state information but also explicitly recovering the transmitted symbols without assuming the signal-to-noise ratio (SNR) level. In principle, the DNN model is trained using data generated from computer simulations–according to WINNER II channel model as fading channels, and the additive white Gaussian noise (AWGN) as background noise. As with its conventional peers which assume the inter- fering source from AWGN only, the aforementioned DNN receiver is prone to substantial performance loss when subject to impulse noise. To address this obstacle, this paper incor- porates the fine-tuning notion into the DNN model–by substituting impulse noise-laced data samples for succeeding training data–with an aim at enhancing representation learning. To attest the efficacy of the proposed DNN receiver, the bit error rate (BER) result derived from a compressive sensing-based receiver, enabled by consensus alternating direction method of multipliers (ADMM), is used as a benchmark for performance comparison. Noteworthily, the induced BER performance is comparable to that of the clipping-featured receiver, the threshold of which however solicits the knowledge of the SNR value, an assumption that is otherwise relaxed by the proposed DNN receiver. Furthermore, through extensive simulations, the deep learning based approach demonstrates promising robustness against impulse noise model mismatch between training and testing counterparts.

An efficient approach to alleviating performance loss caused by unknown (non-stationary) impulse noise, which is typically sparse and whose statistics are difficult to model accurately, was made possible in power line communications systems when the clipping operation is properly implemented. As reported in numerous papers, the clipping threshold, which impacts system performance profoundly, is closely related with the probability of impulse occurrence and the strength of the background noise. This paper first highlights that the least-absolute-shrinkage-and-selection-operator (LASSO) algorithm, aimed at estimating the regularization parameter $\lambda$ and sparse vector of regression coefficients, can be interpreted as an effective clipping operation. Subsequently, a hybrid of the Bayesian LASSO and the maximum a posteriori algorithm using the Monte Carlo expectation maximization (MCEM) is proposed to simultaneously estimate the regularization parameter and detect the convolutional codeword in single-carrier coded systems subject to the impulse noise. Moreover, by exploiting information exchangeable between those two aforementioned entities, a novel termination rule on the EM iteration stage is devised. Numerical results attest the efficacy of the proposed scheme, despite a lack of statistical knowledge on impulse noise models.

Strong impulse noise is widely known to adversely affect conventional receivers designed only to consider background noise. Although sophisticated receivers offer substantial performance improvements, fully exploiting the impulse statistics, which are generally not time invariant and are difficult to model accurately, can be unrealistic. Alternatively, without making assumptions regarding the underlying impulse channel model, the authors' previously developed robust decoding schemes can achieve performance equivalent to that of their optimal counterparts in impulse noise channels. In this letter, a robust turbo decoding metric is proposed to address the inherent memory in an impulse channel: a 2-D trellis is used to adapt for channel state transitions when statistics on the memory impulse channel model are lacking. The simulation results verified the robustness of the proposed decoder in harsh environments.

Communication systems are susceptible to impulse noise, particularly when the impulse statistics are not time invariant and are difficult to model accurately. To address the challenge of impulse noise, a robust and efficient decoding scheme was devised for single-carrier convolutionally coded transmissions over memory impulse noise channels. By accommodating channel states but without relying on statistical knowledge of impulses, the Viterbi algorithm (VA) based on an expanded set of trellis states was employed to perform maximum-likelihood decoding. A detailed analysis of complexity was offered; the analytical results reinforced the efficiency of the proposed scheme compared with the traditional VA. The simulation results indicated that the proposed decoding scheme is compellingly robust: The bit error probability performance level attained using the proposed decoder is remarkably close to that of an optimal decoder, which uses impulse statistics; furthermore, the proposed decoder was superior to the alpha-penalty function decoder, which neglects the channel memory property and experiences an error floor under fairly general circumstances.

Communication systems subjected to strong impulse noise are prone to performance degradation when the impulse occurrence is neglected in the decoding process: turbo decoders are likely to exhibit error propagation because the decision-making is dictated by excessive samples corrupted by impulses when the conventional decision metric, which is based on the assumption of additive white Gaussian noise, is used. A sophisticated decoder is widely known to yield a substantial performance improvement by exploiting impulse statistics; however, impulse statistics are difficult to model accurately and are generally not time invariant, likely leading the receiver complexity to be prohibitive. Based on our previous work — which proved the robustness of a soft limiter despite a lack of impulse statistics, this study incorporated a clipping operation into a single-carrier turbo-coded system in memoryless impulse noise channels. Further, the performance of the proposed decoder was evaluated according to the bit error rate to demonstrate the performance gain derived from various clipping thresholds, each of which reflects a different degree of impulse information to which the proposed decoder can leverage.

Numerous studies have shown that a generic communication system is prone to substantial performance loss in the context of strong impulse noise, which is generally not time invariant and is hard to model accurately. A turbo coded orthogonal frequency division multiplexing system in memory impulse channels is investigated in this work. Upon reception of the transmitted data, each of the two proposed receivers aims at detecting corrupted samples associated with impulse noise using the maximum a posteriori rule-with an adaptation to the inherent memory in the impulse channel. Sequentially, the refined receiver, assuming full statistics of impulse noise, clips excessively large samples-identified at the detection stage-with a proper clipping threshold exhaustively sought offline. In contrast, an efficient receiver, which lacks access to using impulse statistics, trades off a slight performance loss (as evidenced by simulation results) for complexity reduction attributable to forgoing the clipping operation as well as memory impulse channel model estimation.

Extrinsic information transfer (EXIT) chart is a useful tool for analyzing iterative decoding behavior. One crucial challenge in realizing it is that a precise estimate of the probability density function (PDF) of the extrinsic information at the output of a soft-input soft-output decoder must be made to obtain mutual information. To achieve this, we employed a kernel density estimator (KDE), as opposed to using the traditional histogram estimator, when a fairly small interleaver size is available. Simulation results showed that the KDE approach outperformed the histogram method for estimating the PDF in the EXIT band analysis in terms of bit error rate prediction.

Proposed is a zero-inserting precoder and a two-stage linear equaliser, to shorten the guard interval in block-based single-carrier modulation. The first-stage equaliser consists of a linear single-tapper-subcarrier frequency-domain equaliser. The second-stage equaliser maximises the SINR, in the time-domain, based on the interference-plus-noise estimated from the zero-padded sub-intervals of the single-carrier modulation. This proposed scheme is applicable even without cyclic prefixing.

Previously, a hybrid mechanism employing clipping and turbo coding was vindicated to be remarkably robust against impulse noise, characterized by strong power and frequent occurrence, in Orthogonal Frequency Division Multiplexing systems. This work aims at addressing the impulse noise together with the distortion induced by a more realistic transmitter configuration: Clipping and filtering for the peak-to-average power ratio reduction and nonlinearity of the power amplifier. Without assuming the statistics of impulse, the proposed receiver, after adequately adapting to all attenuating effects to measure the clipping threshold of clipper and the bit metric of decoder, is compellingly on par with its refined counterpart, which uses the statistical knowledge of impulse, under fairly general circumstances.

Powerline communications, an enabling technology for data networking alternative, susceptible to impulse noise such that it is likely to miss an assured quality of service. Turbo coding has long served as an essential tool to overcome this obstacle. However, this comes at the cost of estimating the statistics of impulse noise, which is generally not time-invariant and may change rapidly over time, further increasing receiver complexity. This study proposes clipping on trellis to avoid this problem. A corresponding decoder metric dictated by the clipping threshold, which is a function of the probability of guessing for the unknown impulse arrival probability, was properly formulated to leverage the coding gain. Regardless of the impulse noise model assumed, the simulation results in this study, in terms of bit error probability, show that the proposed decoding scheme is robust to impulse and performs fairly close to its sophisticated counterpart, which however fully exploits the statistics of the impulse noise.

Using cooperative transmission allows energy-harvesting active networked tags (EnHANTs) to cooperate regarding information transmission and reception, achieving higher throughput compared with non-cooperative counterparts. In this paper, a battery-threshold-based optimal cooperative transmission strategy is proposed for EnHANTs, allowing EnHANT-equipped objects to communicate with the destination (i.e., the reader) either directly or by cooperating with neighbouring objects. A strategy adapted to request states and available energy resources is considered, and an optimal transmission policy for EnHANTs is developed to maximise the long-term average throughput by formulating a Markov decision process (MDP) model. The simulated results indicate that the proposed strategy yields superior performance compared with single-link transmission.

It is well known that communication systems are susceptible to strong impulsive noises. To combat this, convolutional coding has long served as a cost-efficient tool against moderately frequent memoryless impulses with given statistics. Nevertheless, impulsive noise statistics are difficult to model accurately and are typically not time-invariant, making the system design challenging. In this paper, because of the lack of knowledge regarding the probability density function of impulsive noises, an efficient decoding scheme was devised for single-carrier narrowband communication systems; a design parameter was incorporated into recently introduced joint erasure marking and Viterbi decoding algorithm, dubbed the metric erasure Viterbi algorithm (MEVA). The proposed scheme involves incorporating a well-designed clipping operation into a Viterbi algorithm, in which the clipping threshold must be appropriately set. In contrast to previous publications that have resorted to extensive simulations, in the proposed scheme, the bit error probability performance associated with the clipping threshold was characterized by deriving its Chernoff bound. The results indicated that when the clipping threshold was judiciously selected, the MEVA can be on par with its optimal maximum-likelihood decoding counterpart under fairly general circumstances.

For broadband power line communication systems, which may be incorporated with Orthogonal Frequency Division Multiplexing (OFDM) technique, the detrimental effect arising from strong and frequently occurred impulses is paramount as signaling on each sub-carrier is simultaneously corrupted thanks to frequency-domain transformation on a per-OFDM symbol basis at the front-end receiver. In this perspective, channel coding epoch on the basis of per-OFDM symbol cannot effect the coding gain. Recently, clipping operation on received samples has been addressed as an effective approach to mitigate this incurring performance loss subject to impulsive noise, given the knowledge of the probability density function (PDF) of impulsive noise at receiver. In this paper, we forgo the a-priori knowledge of the PDF of impulsive noise but devise the threshold to clipping, only relying on the probability of occurrence of impulses. To attest our method, we conduct computer simulations in compliance with the IEEE 1901 standard over commonly adopted memoryless impulsive noise models. Promisingly, the proposed scheme is on par with its counterpart, where the threshold of a limiter is realized by assuming the PDF of impulsive channels perfectly known to receiver. In addition, the devised clipping scheme is shown to be robust against impulses of frequent occurrence characterized by excessively high energy in the signal-to-noise ratio range of interest, regardless of the impulsive noise model assumed.

Power-line communications, conceived as an enabling technology for data networking alternative, is known to be susceptible to impulsive noises to attain assured transmission rate. To combat this obstacle, we design an efficient decoding scheme to incorporate erasures into the Viterbi algorithm. A unique feature of the decoding scheme is that it does not need the detailed knowledge of impulse statistics and only requires an estimate of impulse arrival probability. Surprisingly, our simulation results show that the proposed decoding scheme is on par with its optimal counterpart, where the exact impulse statistics is assumed to be known at receiver, in terms of bit error rate.

Space-time modulation exploiting diversity or/and coding gains has been a major focus in high-speed wireless communications systems against channel fading. We employ noncoherent space-time modulation in cyclic-prefix (CP) based single-carrier (SC) block systems over frequency selective Rayleigh block fading channels, where channel order, upper-bounded by the CP length, is assumed known. Capitalizing on multi-channel input-output relation for time-domain processing, we reveal that employing complex orthogonal m-sequences can enable a simplified maximum-likelihood (ML) detector. Further, code construction is addressed so as to adaptation of a variety of transmission rates and trellis-coded unitary space-time modulation is also investigated. Numerical results suggest that this noncoherent space-time modulation for SC block system, coupled with time-domain ML detection, can realize multipath diversity under certain circumstances.