Per-survivor Processing Research Articles

Joint equalization and decoding with per-survivor processing based on super-trellis in time-varying underwater acoustic channels.

This Letter proposes a low-complexity joint equalization and decoding reception scheme based on super-trellis per-survivor processing, making it possible to apply maximum likelihood sequence estimation in high-order underwater acoustic communications under fast time-varying channels. The technique combines trellis-coded modulation states and intersymbol interference states and uses per-survivor processing to track channel parameters. Furthermore, a general trellis configuration for arbitrary order quadrature amplitude modulation signal is provided when truncate the channel is used to describe the intersymbol interference state to 1. Sea trials results show that the performance of proposed method can be more than 1.4 dB superiority than conventional schemes.

Single-Channel Blind Separation of Unsynchronized Multiuser PSK Signals With Non-Identical Sampling Frequency Offsets

Single-channel blind source separation (SCBSS) of uncoordinated, non-spread, co-frequency interfering multi-user signals is a challenging task. When the multi-user signals have sampling frequency offset (SFO) due to the imperfection of the local oscillator (LO), this problem becomes even more difficult to be solved. In this letter, a select-stitch data-aided channel estimation (CE) initialized per-survivor processing (SS-DCE-PSP) algorithm is proposed for SCBSS of multi-user signals with SFOs caused by imperfect LOs. By dividing a long sequence into a number of segments and selecting the segments with highest detection reliability to stitch together, the time slip and symbol slip due to the non-identical SFOs can be resolved. The data-aided CE in SS-DCE-PSP further enhances the performance of PSP, especially for the initial symbols. Simulation results show that the proposed SS-DCE-PSP algorithm is robust to the normalized SFO up to 10−4, and the BERs obtained by the SS-DCE-PSP algorithm are close (within 0.5dB) to the BERs obtained for multi-user signals without SFOs.

Low Complexity Adaptive Detection of Short CPM Bursts for Internet of Things in 6G.

With the standardization and commercialization of 5G, research on 6G technology has begun. In this paper, a new low-complexity soft-input-soft-output (SISO) adaptive detection algorithm for short CPM bursts is proposed for low-power, massive Internet of Things (IoT) connectivity in 6G. First, a time-invariant trellis is constructed on the basis of truncation in order to reduce the number of states. Then, adaptive channel estimators, recursive least squares (RLS), or least mean squares (LMS), are assigned to each hypothetical sequence by using the recursive structure of the trellis, and per-survivor processing (PSP) is used to improve the quality of channel estimation and reduce the number of searching paths. Then, the RLS adaptive symbol detector (RLS-ASD) and LMS adaptive symbol detector (LMS-ASD) could be acquired. Compared to using a least-squares estimator, the RLS-ASD avoids matrix inversion for the computation of branch metrics, while the LMS-ASD further reduces the steps in the RLS-ASD at the cost of performance. Lastly, a soft information iteration process is used to further improve performance via turbo equalization. Simulation results and analysis show that the RLS-ASD improves performance by about 1 dB compared to the state-of-the-art approach in time-variant environments while keeping a similar complexity. In addition, the LMS-ASD could further significantly reduce complexity with a power loss of approximately 1 dB. Thus, a flexible choice of detectors can achieve a trade-off of performance and complexity.

Single-channel blind separation of co-frequency signals based on convolutional network

Single-channel blind separation (SCBS) of co-frequency signals has been a challenging task in the blind signal processing field. Most effective approaches were still formulated in signal space, triggering complicated signal processing and high computational cost. To reduce the complexity and improve the separation accuracy of SCBS, in this paper, we propose a new waveform separation-demodulation scheme. As the first quintessential step, we design a convolutional time-domain network with squeeze-and-excitation blocks (CNSE) to carry out waveform separation in latent feature space, then the separated results are demodulated by the low-complexity per-survivor processing (LC-PSP) method. Our network adopts the backbone structure of encoder, separator, and decoder. And, it is worth noting that we deepen the encoder/decoder of the network to emphasize the low-level features and further weigh these features by means of the channel attention module. The encoder-decoder structure helps to eliminate the influence of noise during the stage of signal feature extraction and signal reconstruction. In our experiment, CNSE surpasses long short-term memory network and convolutional time-domain audio separation network as evaluated by signal-to-distortion ratio, signal-to-interferences ratio, and symbol error rate. Additionally, to reduce the influence of the truncation effect in separation, we also propose a data pre-processing method called redundancy protection (RP), and our trials reveal that RP can compensate for the performance of CNSE in the whole signal-to-noise ratio range. It is also verified that, compared with the traditional PSP algorithm, our method has better noise robustness and higher separation efficiency, especially when processing more complex mixtures.

BEM-PSP for Single-Carrier and SC-FDMA Communication Over a Doubly Selective Fading Channel

In this paper, we consider pilot-aided channel estimation and equalization for single-carrier and single-carrier frequency division multiple-access (SC-FDMA) transmission over doubly-selective channels (DSC). To reduce the channel estimation (CE) parameters, the DSC is modelled using a complex-exponential basis expansion model (CX-BEM) with non-uniform BEM frequencies. We optimize the CX-BEM basis functions using CE error minimization as the objective. As a result, the channel modelling error is greatly reduced. Next, we propose a BEM-based per-survivor processing (PSP) technique and combine it with a decision-directed channel estimator to obtain a channel-tracking equalizer at the receiver. The resultant BEM-PSP receiver significantly improves the channel estimation mean square error (MSE) and the bit error rates (BER) performance in fast fading multi-path channel, thanks to the channel tracking capability embedded within its Viterbi equalizer. Finally, we employ cross-frame channel interpolation, and power distribution between the data and pilot symbols to further improve the system performance at high fading rates. Extensive simulation results show that our BEM-PSP receiver outperforms many existing methods and approaches close to an ideal receiver with perfectly known CSI under various fading scenarios.

A Demodulation Based on Channel Prediction Employing Soft-Output Per-Survivor Processing

A Demodulation Based on Channel Prediction Employing Soft-Output Per-Survivor Processing

An Off-Line Underwater Acoustic Software Modem for Multiple Differential Detection Employing Per-Survivor Processing with Channel Prediction

An Off-Line Underwater Acoustic Software Modem for Multiple Differential Detection Employing Per-Survivor Processing with Channel Prediction

Improving THz wireless communication data transmission rates

The working frequency of wireless communication systems will inevitably be developed for the higher-frequency terahertz (THz) band. An effective way to improve data transmission rates in this band is to adopt higher-order modulation. However, existing THz wireless communication systems struggle to realise higher-order modulation and demodulation at high speeds. Baseband digital modulation mixes signals with the same modulation and symbol rate. Different bit streams are sent on the 0.34 THz band and the receiver uses a per-survivor processing algorithm to separate and demodulate the mixed signal. This doubles the transmission rate of THz data whilst avoiding the problem of modulation and demodulation at high speeds resulting from higher-order modulation. Both simulation and measured data is used to verify the feasibility of the method.

Low-Complexity Single-Channel Blind Separation of Co-Frequency Coded Signals

Single-channel blind source separation (SCBSS) of uncoordinated, non-spread, co-frequency interfering signals with non-zero carrier frequency offset and timing offset, and without training sequence for channel estimation, is a challenging task. Iterative SCBSS of coded signals leads to good performance but is computationally expensive as it involves the turbo processing of multi-user per-survivor processing and soft-input soft-output channel decoding. In this letter, we propose a low-complexity SCBSS (LC-SCBSS) scheme, which reduces the computational complexity of the conventional iterative SCBSS by using interference-cancellation from the second iteration onward, and adaptive channel truncation for certain users. Simulation results show that the proposed LC-SCBSS reduces the computational complexity by more than 99%, with only a marginal degradation in performance.

Generalization of channel estimation weights for multiple differential detection based on per-survivor processing

Generalization of channel estimation weights for multiple differential detection based on per-survivor processing

More general performance evaluation for single‐channel PCMA signals blind separation

The single-channel blind separation performance of the mixed signals with parameters estimation error in paired carrier multiple access (PCMA) satellite communication systems is analysed in this study. Current literatures have theoretically analysed the blind separation performance using the symbol detection error probability of PCMA signals. However, the parameters of PCMA signals are assumed to be accurate during their derivations, which are not practical in blind separation scenario. In this study, the parameters' estimation error is considered during the derivation of the blind separation performance of PCMA signals. Thus, the derived performance is more general. The simulation separation result is obtained by per-survivor processing method. It is in accordance with the derived performance.

Low-complexity single-channel blind source separation

For the time-frequency overlapped signals, a low-complexity single-channel blind source separation (SBSS) algorithm is proposed in this paper. The algorithm does not only introduce the Gibbs sampling theory to separate the mixed signals, but also adopts the orthogonal triangle decomposition-M (QRD-M) to reduce the computational complexity. According to analysis and simulation results, we demonstrate that the separation performance of the proposed algorithm is similar to that of the per-survivor processing (PSP) algorithm, while its computational complexity is sharply reduced.

An Adaptive State Assignment Mechanism Based on Joint Data Detection and Channel Estimation on Fading Meteor Channel

Integrating adaptive modulation and coding techniques into time-varying meteor burst channels with joint data detection and channel estimation at the receiver can greatly increase the average throughput of a meteor burst communication system (MBCS). Although the per-survivor processing algorithm (PSPA) based on joint data and channel estimation provides superior performance and robustness for MBCS, it is very difficult to implement it into practice due to the extremely high computational complexity. An adaptive state reduction scheme using a dimension-down PSPA (ADPSP) with few states in the trellis diagram is proposed based on the slow-fading characteristic of the meteor burst channel. It has the advantages of the dimension-down PSPA (D-PSPA) and the adaptive state reduction scheme based on PSPA proposed in our previous work. The scheme is proposed to reduce the computation complexity and memory size for exponentially decaying meteor burst channels and make the maximum likelihood sequence detection available for adaptive data transmission. Based on the estimation of parameters of the meteor burst channel, the adaptive threshold is obtained in order to decrease the computation compleixty of PSPA, where the states close to the correct one in the trellis is selected. Simulation results are presented to validate the theoretical analysis. It is shown that the proposed adaptive state reduction scheme using a D-PSPA can obtain a good tradeoff between the achievable performance and its dynamically computation complexity, as well as provide reliable data transmission for MBCS using adaptive coding and modulation.

Receiver design for spread-spectrum communications with a small spread in underwater clustered multipath channels.

This paper studies a direct-sequence spread-spectrum communication system with a small spreading factor (e.g., a single digit) in underwater acoustic multipath channels. Exploiting the channel characteristics that the propagation paths can be grouped into distinct clusters, a receiver with a set of parallel branches is proposed, where per-survivor processing (PSP) is applied on each branch to deal with the signal from one cluster while explicitly treating the signals from other clusters as structured interference. As such, it overcomes a major limitation of an existing PSP based receiver [Zhou, Morozov, and Preisig, J. Acoust. Soc. Am. 133, 2746-2754 (2013)], avoiding exponential complexity increase when the spreading factor decreases. On the first branch, joint channel estimation and interference cancellation are performed based solely on the survivor paths. On the other branches, joint channel estimation and interference cancellation are carried out based on the survivor paths and the tentatively decoded data. The bit log-likelihood ratios from different branches are combined for channel decoding. Performance results based on simulations and collected data sets validate the superior performance of the proposed receiver over the conventional RAKE receiver, which is effective only when the spreading factor is large.

Non-coherent sequence detection scheme for satellite-based automatic identification system

The satellite-based automatic identification system (AIS) receiver has to encounter the frequency offset caused by the Doppler effect and the oscillator instability. This paper proposes a non-coherent sequence detection scheme for the satellite-based AIS signal transmitted over the white Gaussian noise channel. Based on the maximum likelihood estimation and a Viterbi decoder, the proposed scheme is capable of tolerating a frequency offset up to 5% of the symbol rate. The complexity of the proposed scheme is reduced by the state-complexity reduction, which is based on per-survivor processing. Simulation results prove that the proposed non-coherent sequence detection scheme has high robustness to frequency offset compared to the relative scheme when messages collision exists.

The Rotating-Target Algorithm for Jointly Detecting Asynchronous Tracks

Two-dimensional magnetic recording promises to increase areal density through the joint detection of multiple tracks of interest. This paper concerns the problem of joint detection of multiple tracks that are written asynchronously, meaning that neither the bit boundaries (phase) nor the bit rate (frequency) are aligned between neighboring tracks. We propose the rotating-target algorithm for jointly detecting multiple asynchronous tracks from one or more readback waveforms. The proposed approach is based on the joint Viterbi algorithm and a time-varying target that results when the asynchrony of the tracks is absorbed into the underlying target. Timing estimation for the tracks being detected is embedded inside the joint Viterbi detector using per-survivor processing. Performance results show that the proposed algorithm closely matches the performance of a fictitious system in which neighboring tracks are synchronous, and further that it significantly outperforms a previously reported detector that separately detects the two tracks.

The DFF‐PSP Iterative Separation and Theoretical Bound for PCMA with Long Memory

This paper proposes the Decision feedbackfeedforward per-survivor processing (DFF-PSP) iterative separation algorithm and the Monte Carlo integrationbased method to compute the theoretical bound for Paired carrier multiple access (PCMA) Single channel blind separation (SCBS) which is highly complex with long memory. The truncated PSP is employed while the pre- and post- cursors are disregarded to control the complexity. The delayed decisions feedforward and local decisions feedback filters are designed to process the pre- and post- cursors. The Bit-interleaved code modulation iterative decoder (BICM-ID) is combined with the second soft decision of the truncated PSP to obtain a better performance. The mixture signal model is regarded as a Multiple-access channel (MAC) with memory, and the theoretical performance bound is obtained via entropy inequality, Monte Carlo integration and Fano inequality. Simulation results show that the DFF-PSP iterative separation provides the good tradeoff between complexity and performance.

Multiple trellis-coded modulation, per-survivor processing and Reed–Solomon coding in the presence of phase noise

The aim of this paper is to consider trellis coded modulation (TCM) in the presence of phase noise. The starting point is a two-dimensional 32 point cross (32-CR) signal set that has served as a benchmark for other power and bandwidth efficient modulation schemes. Per-survivor processing (PSP) is used for carrier phase estimation, and both the phase acquisition (transient state) and phase tracking (steady state) performances are considered in an additive white Gaussian noise (AWGN) channel, in which phase offset or time varying phase noise is introduced. Based on the theoretical and simulation results it can be said that the 32-CR constellation is itself more sensitive to a phase error rather than the PSP algorithm. Due to the phase error the TCM scheme becomes a catastrophic code. Possible solutions include stringent phase noise requirements for oscillators, multiple trellis-coded modulation (MTCM) and concatenated TCM and Reed–Solomon (RS) coding. Our simulation results show that a simple combined MTCM, PSP and RS scheme has around 3 dB advantage compared to the pure TCM and PSP in the presence of phase noise. A semianalytical approach is used for performance evaluation, and the method is a particularly convenient tool to determine the rate of RS coding in detail.

Receiver structure and estimation of the modulation index for tamed frequency modulated (TFM) signals

Tamed frequency modulation (TFM) is a spectrally efficient constant amplitude continuous phase modulation (CPM) scheme which can be simply realized by using a frequency modulator (FM). In the implementation the modulation index of TFM is calibrated to have a nominal value of 0.5, but due to temperature variations it can drift causing time varying phase jitter. In this paper we present novel algorithms and performance results to measure and control the modulation index in a coherent receiver based on the joint reduced state sequence detector (RSSD) and per-survivor processing (PSP) carrier phase estimation. The modulation index estimator measures phase transitions in the receiver and derives estimates by comparing the result to the coding rule of the TFM signal. The estimator has acquisition and tracking ability, and the current estimate can be used to replace the nominal index value. Our simulation results show that the proposed coherent receiver with the novel modulation index estimator has less than 1 dB performance degradation compared to around 4.5 dB exploiting only the PSP carrier phase estimation.

An improved PSP scheme using state cooperation for phase synchronization

The conventional per-survivor-processing (PSP) scheme suffers from the error propagation problem because it does not fully use the state message provided by a hidden Markov process. This study proposes a vertical cooperation among states to enhance the estimate reliabilities in the PSP scheme. The key idea of the proposed algorithm is to estimate the system uncertainty at each time stage in a maximum likelihood (ML) manner that chooses the parameter estimate of the state with the minimum cumulative branch metric as the survival estimate of the time stage. Computer simulations show that with the improved phase estimates, the proposed algorithm significantly outperforms the conventional PSP scheme in data decoding.