Soft Interference Cancellation Research Articles

Iterative signal detection based soft interference cancellation for satellite-terrestrial downlink NOMA systems

Iterative signal detection based soft interference cancellation for satellite-terrestrial downlink NOMA systems

Virtual Space-Time DiversityTurbo Equalization Using Cluster Sparse Proportional Recursive Least Squares Algorithm for Underwater Acoustic Communications

The oceanic positioning, navigation and timing (PNT) network requires high-quality underwater acoustic message transmission. Turbo equalization technology has exhibited superior performance for underwater acoustic (UWA) communications compared with conventional channel equalizers. To overcome the performance reduction caused by severe doubly selective UWA channels, the virtual space-time diversity soft direct-adaptation turbo equalization is proposed for UWA communications. The proposed scheme improves the ability of the typical turbo equalizer to deal with both Doppler and multipath effects for time varying channels. We utilize a fractionally spaced soft interference cancellation equalizer (FS-SE) instead of a hard decision to constitute the soft-input soft-output (SISO) equalizer. Combined with another virtual time-reversal mirror equalizer component, we can obtain virtual space and time diversity with only a single receiving transducer and mitigate the error propagation phenomenon of the feedback filter. To satisfy the sparse UWA channel, the ℓp,q-PRLS algorithm is applied to adaptive updates for FS-SE. In the proposed scheme, an adjustable interpolator and digital phase-locked loop are embedded into the equalizer to overcome the residual Doppler frequency shift and recover the timing distortion. Results of simulations and field lake trial show that the proposed scheme achieves better performance than existing ones under the same equalizer order.

Enhanced deep soft interference cancellation for multiuser symbol detection

AbstractThe detection of all the symbols transmitted simultaneously in multiuser systems using limited wireless resources is challenging. Traditional model‐based methods show high performance with perfect channel state information (CSI); however, severe performance degradation will occur if perfect CSI cannot be acquired. In contrast, data‐driven methods perform slightly worse than model‐based methods in terms of symbol error ratio performance in perfect CSI states; however, they are also able to overcome extreme performance degradation in imperfect CSI states. This study proposes a novel deep learning‐based method by improving a state‐of‐the‐art data‐driven technique called deep soft interference cancellation (DSIC). The enhanced DSIC (EDSIC) method detects multiuser symbols in a fully sequential manner and uses an efficient neural network structure to ensure high performance. Additionally, error‐propagation mitigation techniques are used to ensure robustness against channel uncertainty. The EDSIC guarantees a performance that is very close to the optimal performance of the existing model‐based methods in perfect CSI environments and the best performance in imperfect CSI environments.

Low-complexity hybrid interference cancellation for sparse code multiple access

Sparse Code Multiple Access (SCMA) is one of Non Orthogonal Multiple Access (NOMA) candidates for 5G, the fifth generation mobile network. Multiple overlapped blocks of SCMA code are transmitted per frequency layer which compels the receiver to cancel unwanted SCMA code blocks. Thanks to the sparsity of SCMA, Message Passing Algorithm (MPA) achieves near-optimal performance. Nevertheless, this process is time-consuming and has a high degree of complexity even if Max-Log Algorithm is used. The major contribution of this paper is to propose a new decoding scheme called Hybrid Interference Cancellation (HIC) to reduce time processing without degrading a target Bit Error Rate (BER). The HIC decodes and removes the strongest user, reducing the complexity of Log Domain MPA decoding of remaining users, followed by a Low Density Parity Code (LDPC) decoder. The interference cancellation decoder is either a hard interference cancellation or a soft interference cancellation. The originality of the paper is to calculate a threshold value based on power measurements to decide whether hard decoder or soft decoder should be used to reach the BER target with a lower complexity. Results show a significant reduction in terms of complexity as compared to the traditional MPA process for the same values of BER.

Low-Complexity LMMSE-Based Iterative Soft Interference Cancellation for MIMO Systems

This paper presents a low-complexity linear minimum mean-squared-error-based iterative soft interference cancellation (LMMSE-ISIC) scheme for multiple-input multiple-output (MIMO) systems. To avoid direct matrix inversion in the conventional LMMSE-ISIC scheme, expressions are derived to obtain the filtering vector and estimate of the conventional scheme; the direct inverse operation is then replaced by the vector-based operations in the proposed scheme. Thus, for the worst-case scenario in the proposed scheme, the complexity of estimating each transmit symbol is approximately proportional to the square of the number of receive antennas; in the conventional scheme, it is approximately proportional to the cube of the number of receive antennas. In addition, because there are no approximations for deriving the filtering vector and estimate, the proposed LMMSE-ISIC scheme achieves near-optimum performance identical to that of the conventional scheme, which is close to the matched filter bound of the channel. Further, the extension of the proposed scheme for iterative detection and decoding is developed for coded systems. The simulated results confirm that the conventional and proposed schemes outperform the approximated matrix inversion based schemes and achieve the identical error performance in both uncoded and coded systems, while the complexity order of the proposed scheme is similar to or even lower than those of the approximation schemes. Therefore, the proposed scheme can be considered an effective near-optimum LMMSE-based iterative detection approach for MIMO systems, especially for massive MIMO systems with a high load factor.

Soft Interference Cancellation for Random Coding in Massive Gaussian Multiple-Access.

In 2017, Polyanskiy showed that the trade-off between power and bandwidth efficiency for massive Gaussian random access is governed by two fundamentally different regimes: low power and high power. For both regimes, tight performance bounds were found by Zadik et al., in 2019. This work utilizes recent results on the exact block error probability of Gaussian random codes in additive white Gaussian noise to propose practical methods based on iterative soft decoding to closely approach these bounds. In the low power regime, this work finds that orthogonal random codes can be applied directly. In the high power regime, a more sophisticated effort is needed. This work shows that power-profile optimization by means of linear programming, as pioneered by Caire et al. in 2001, is a promising strategy to apply. The proposed combination of orthogonal random coding and iterative soft decoding even outperforms the existence bounds of Zadik et al. in the low power regime and is very close to the non-existence bounds for message lengths around 100 and above. Finally, the approach of power optimization by linear programming proposed for the high power regime is found to benefit from power imbalances due to fading which makes it even more attractive for typical mobile radio channels.

Bayesian Iterative Channel Estimation and Turbo Equalization for Multiple-Input–Multiple-Output Underwater Acoustic Communications

This article investigates a robust receiver scheme for a single carrier, multiple-input-multiple-output (MIMO) underwater acoustic (UWA) communications, which uses the sparse Bayesian learning algorithm for iterative channel estimation embedded in Turbo equalization (TEQ). We derive a block-wise sparse Bayesian learning framework modeling the spatial correlation of the MIMO UWA channels, where a more robust expectation-maximization algorithm is proposed for updating the joint estimates of channel impulse response, residual noise, and channel covariance matrix. By exploiting the spatially correlated sparsity of MIMO UWA channels and the second-order a priori channel statistics from the training sequence, the proposed Bayesian channel estimator enjoys not only relatively low complexity but also more stable control of the hyperparameters that determine the channel sparsity and recovery accuracy. Moreover, this article proposes a low complexity space-time soft decision feedback equalizer (ST-SDFE) with successive soft interference cancellation. Evaluated by the undersea 2008 Surface Processes and Acoustic Communications Experiment, the improved sparse Bayesian learning channel estimation algorithm outperforms the conventional Bayesian algorithms in terms of the robustness and complexity, while enjoying better estimation accuracy than the orthogonal matching pursuit and the improved proportionate normalized least mean squares algorithms. We have also verified that the proposed ST-SDFE TEQ significantly outperforms the low-complexity minimum mean square error TEQ in terms of the bit error rate and error propagation.

DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection

Digital receivers are required to recover the transmitted symbols from their observed channel output. In multiuser multiple-input multiple-output (MIMO) setups, where multiple symbols are simultaneously transmitted, accurate symbol detection is challenging. A family of algorithms capable of reliably recovering multiple symbols is based on interference cancellation. However, these methods assume that the channel is linear, a model which does not reflect many relevant channels, as well as require accurate channel state information (CSI), which may not be available. In this work we propose a multiuser MIMO receiver which learns to jointly detect in a data-driven fashion, without assuming a specific channel model or requiring CSI. In particular, we propose a data-driven implementation of the iterative soft interference cancellation (SIC) algorithm which we refer to as DeepSIC. The resulting symbol detector is based on integrating dedicated machine-learning methods into the iterative SIC algorithm. DeepSIC learns to carry out joint detection from a limited set of training samples without requiring the channel to be linear and its parameters to be known. Our numerical evaluations demonstrate that for linear channels with full CSI, DeepSIC approaches the performance of iterative SIC, which is comparable to the optimal performance, and outperforms previously proposed learning-based MIMO receivers. Furthermore, in the presence of CSI uncertainty, DeepSIC significantly outperforms model-based approaches. Finally, we show that DeepSIC accurately detects symbols in non-linear channels, where conventional iterative SIC fails even when accurate CSI is available.

Low-Complexity LMMSE–SIC Turbo Receiver for Continuous Phase Modulation, Based on a Multiaccess–Multipath Analogy

Low-complexity iterative reception of Continuous Phase Modulation (CPM) is addressed using Linear Minimum Mean-Square Error (LMMSE) filtering empowered by Soft Interference Cancellation (SIC). The transmitter emits a convolutionally encoded, true CPM signal with a constant envelope. The receiver exploits Laurent–Mengali–Morelli’s (LMM) decomposition to reduce the detection complexity, where the transmitted true CPM signal is approximated as a superposition of a small number of parallel Pulse Amplitude Modulated (PAM) and phase–encoded terms. Pulses and phase–encoded pseudo-symbols arising from the LMM decomposition are treated, respectively, as parallel Inter–Symbol Interference (ISI) channels and Multiple Access Interference (MAI) components. Hence the transmitted signal is regarded to inherently contain controlled ISI and MAI, which are equalized for at the receiver front-end using LMMSE–SIC filters. The receiver back-end comprises a serially concatenated turbo scheme encompassing a Soft Input–Soft Output (SISO) CPM detector and a SISO channel decoder. Simulations conducted on various CPM schemes show that the proposed receiver provides significant complexity reduction for a small performance penalty. Consequently, CPM waveforms with long pulses, which have traditionally been deemed infeasible due to the prohibitive complexity, can now be implemented employing the proposed receiver. Hence substantial spectral side-lobe compaction offered by long CPM pulses can be harnessed.

Discrete Fourier transform‐based block transmission for multi‐carrier faster‐than‐Nyquist signalling

Multi-carrier faster-than-Nyquist (MC-FTN) signalling, which is a non-orthogonal data transmission scheme, is broadly viewed as one of the potential candidates for the future high spectral-efficient communications. In this study, a discrete time-frequency packing scheme based on the discrete Fourier transform (DFT) block transmission for MC-FTN signalling (DBT-MC-FTN) is proposed. Firstly, by transforming the blockwise MC-FTN signalling into the frequency domain, an efficient implementation transceiver structure incorporating a bank of DFTs, circular expanding operation, and cyclic shift summation operation is obtained. Thus, flexible packing in both time and frequency domains can be achieved efficiently within a DBT-MC-FTN block. Secondly, to deal with the inter-symbol interference (ISI) introduced by time packing and inter-carrier interference (ICI) introduced by frequency packing in the proposed DBT-MC-FTN scheme, a subcarrier-level equalisation combined with a soft interference cancellation scheme operated in the frequency domain is further proposed to detect the DBT-MC-FTN signals at the receiver. Simulation results show that the proposed DBT-MC-FTN scheme performs close to the corresponding orthogonal system with negligible bit error rate performance loss in the case of moderate ISI and ICI. Additionally, the post-processing signal-to-interference-plus-noise ratio of the proposed scheme is also given in detail.

Discrete Fourier Transform-Based Block Faster-Than- Nyquist Transmission for 5G Wireless Communications

Faster-than-Nyquist (FTN) signaling is regarded as a potential candidate for improving data rate and spectral efficiency of 5G new radio (NR). However, complex detectors have to be utilized to eliminate the inter symbol interference (ISI) introduced by time-domain packing and the inter carrier interference (ICI) introduced by frequency-domain packing. Thus, the exploration of low complexity transceiver schemes and detectors is of great importance. In this paper, we consider a discrete Fourier transform (DFT) block transmission for multi-carrier FTN signaling, i.e., DBT-MC-FTN. With the aid of DFTs/IDFTs and frequency domain windowing, time- and frequency domain packing can be implemented flexibly and efficiently. At the receiver, the inherent ISI and ICI can be canceled via a soft successive interference cancellation (SIC) detector. The effectiveness of the detector is verified by the simulation over the additive white Gaussian noise channel and the fading channel. Furthermore, based on the characteristics of the efficient architecture of DFT-MC-FTN, two pilot-aided channel estimation schemes, i.e., time-division-multiplexing DBT-MC-FTN symbol-level pilot, and frequency-division-multiplexing subcarrier-level pilot within the DBT-MC-FTN symbol, respectively, are also derived. Numerical results show that the proposed channel estimation schemes can achieve high channel estimation accuracy.

Iterative-Detection-Aided Tomlinson-Harashima Precoding for Faster-Than-Nyquist Signaling

Faster-than-Nyquist(FTN) is a promising technique due to higher spectral efficiency, but at a cost of introducing the inter-symbol interference (ISI) which usually requires the computationally demanding detection algorithms. In order to reduce the detection complexity at the receiver, in this paper, a novel Tomlinson-Harashima precoding (THP) scheme is proposed for FTN system. In the conventional THP (CTHP) scheme, the ISI introduced by time-packing can only be diminished from several preceding information symbols, which results in large residual ISI and an unattractive bit-error-rate (BER) performance. Moreover, due to the suboptimal receiver - a simple modulo operation which ignores the correlation between received symbols, CTHP faces obvious capacity loss and is ineffective to execute interference cancellation for FTNS. In this paper, residual ISI of FTNS in CTHP is formulated in detail and modeled mathematically. Further more, an improved THP method with an optimized receiver based on the soft interference cancelation (SIC) algorithm and iterative turbo process is proposed to combat residual ISI for FTNS. Computational complexity analysis and numerical simulation results show that the proposed scheme not only has inexpensive computational cost but also greatly outperforms CTHP and other cited schemes. Moreover, for moderate time-packing, it can approach the ISI-free BER performance boundary and is also competitive to the MAP equalization technique.

Efficient Method for Three Loop MMSE-SIC based Iterative MIMO Systems

Iterative decoding is one of the promising methods to improve the performance of MIMO systems. In iterative processing channel decoder and MIMO detector share the information in order to enhance the overall system performance. However, iterative processing requires a lot of computations therefore it is considered as a computationally complex approach due to complex detection schemes involving iterative processing. There are several promising detection methods that require further improvements and they can be candidates in order to practically implement iterative processing. In this paper, the propose method to improve the efficiency of three loop-based minimum mean squared errors with soft interference cancellation (MMSE-SIC) method by reducing its complexity with a single inverse operation. Simulation results are given in order to provide detail analysis of the proposed MMSE-SIC based approach for iterative detection and decoding (IDD).

Min-Sum Soft Symbol Estimation for Iterative MMSE Detection

Soft symbol estimation (SSE) is the first process in soft interference cancellation minimum mean squared error (SIC-MMSE) detection for multiple-input multiple-output (MIMO) systems. SSE requires the sum of exhaustive multiplications of probability that occupies a non-negligible amount of the entire SIC-MMSE complexity. This paper proposes two approaches to reduce the complexity of SSE. The first is to find the approximation of SSE by investigating the estimation in the log-domain, which leads to a simple min-sum operation. The second is to approximate the hyperbolic tangent with low-order piecewise polynomial functions. These two approaches are then integrated. The simulation results show that the proposed methods do not degrade the performance in terms of bit error rate (BER), and that they can greatly reduce the number of multiplications, from $O(K2^{K})$ to $O(K)$ .

Priori-Aided Detection for Generalized Space-and-Frequency Index Modulation Systems With Multi-Carrier Frequency Offset

In this paper, two priori -aided detectors are proposed for generalized space-and-frequency index modulation (GSFIM) systems with the objective of mitigating the effect of the multi-carrier frequency offset (CFO). Specifically, based on the priori information, the first- and second-order moments of the sparse GSFIM transmit signal are calculated, and the inter-carrier interference (ICI) is cancelled via soft interference cancellation. Furthermore, unlike cancellation-based detectors only treating the signals from other transmit frequency-interval as interference to be cancelled, a multipath-based iterative detector is proposed by treating all the signals from the same transmit frequency-interval as multi-path gains, yielding a favorable system performance. Simulation results demonstrate that the proposed detectors are capable of effectively mitigating the effect of multiple CFOs and of providing considerable system performance improvements at a moderate expense of the complexity.

Improved proportionate FONLMS algorithm based direct adaptive Turbo equalization for MIMO underwater acoustic communications

In this paper, a novel normalized least mean squares (NLMS) algorithm that jointly updates the efficient of the linear equalizer and soft interference canceller (SIC) in an adaptive turbo equalizer for multiple-input multiple-output (MIMO) underwater acoustic (UWA) communications. To exploit the sparsity of MIMO UWA channels and enhance the convergence speed of adaptive equalization, improved proportionate fast self-optimized NLMS algorithm (IPFONLMS), is proposed to well adapt to sparse channel with the similar complexity as improve proportionate NLMS (IPNLMS) algorithm. Then we extend the proposed algorithm to the adaptive turbo equalization for MIMO UWA communications. The performance of the proposed adaptive algorithm is evaluated by numerical results. Simulation results show that the improved data efficiency and bit error ratio (BER) performance of the proposed receiver is achieved over adaptive turbo equalizer based on the IPNLMS algorithm.

Evaluation of MMSE-Based Iterative Soft Detection Schemes for Coded Massive MIMO System

There have been a number of researches on soft iterative detection and decoding scheme for coded multiple-input-multiple-output (MIMO) systems. Minimum mean square error (MMSE)-based detection schemes were often considered for massive MIMO systems, due to their reasonable complexity and performance tradeoff. This paper evaluates a number of MMSE-based soft iterative detection schemes for massive MIMO systems, with new proposals to improve the performances and to reduce the complexity. We consider joint iterative detection and decoding schemes for a coded massive MIMO system, and various kinds of MMSE-based soft detection methods proposed in the literatures are investigated. By utilizing the diagonal approximation of the channel matrix, we propose efficient soft MMSE filtering methods in combination with soft interference cancellation techniques and a compact soft bit estimation method. In addition, new matrix inversion-less soft MMSE methods are proposed for joint iterative detections. The simulation results demonstrate that the proposed methods clearly contribute to the reduction of the complexity of the conventional methods, and performance enhancement.

Performance Analysis and Improvement for VAMP Soft Frequency-Domain Equalizers

The vector approximate message passing (VAMP) soft frequency-domain equalizer (SFDE) has been proposed for turbo equalization. It consists of an inner soft equalizer (ISE) and an inner soft slicer (ISS), which iteratively exchange block-wise extrinsic information to improve the equalization performance, analogous to the outer turbo iteration between a soft equalizer and a soft decoder. However, a comparison of the VAMP-SFDE with other types of self-iterative SFDEs, including the self-iterative block-wise soft interference cancellation (SI-BSIC) SFDE and the generalized approximate message passing (GAMP) SFDE, is lacking. Moreover, comprehensive performance analysis for the VAMP-SFDE is yet to be performed. In this paper, relations among the VAMP-SFDE, the SI-BSIC-SFDE, and the GAMP-SFDE are first revealed. After that, the performance analysis is provided for the VAMP-SFDE and it leads to an improved semi-adaptive damping (SAD) scheme for maintaining the independence between the ISS and ISE. The resulting SAD-VAMP-SFDE shows improved mean square error (MSE) evolution curves compared with the original VAMP-SFDE. The SAD-VAMP-SFDE-based turbo equalization outperforms those using the SI-BSIC-SFDE or GAMP-SFDE and approaches the matched filter bound (MFB) over low signal-to-noise ratio region. To further demonstrate its superiority, the SAD-VAMP-SFDE is employed to enable a near-capacity transceiver design, which adopts the serial concatenation of an irregular convolutional code and a unity-rate code on the transmitter side.

Interference Cancellation for Layered Asymmetrically Clipped Optical OFDM With Application to Optical Receiver Design

In this paper, we study a novel two-stage receiver to demodulate layered asymmetrically clipped optical orthogonal frequency division multiplexing for intensity modulation direct detection based visible light communications. Designed for avoiding the error propagation of the conventional receiver, the first stage of the receiver is a soft interference cancellation (SIC) module which evaluates the minimum mean square error (MMSE) estimates of the signals in different layers. For this stage, we derive the exact formula of the MMSE estimator, and compare the achieved mean square error and bit error rate (BER) with those of the existing simplified SIC receiver. We show that the estimation error in a layer has negligible impact on the design of estimators in the subsequent layers. Using the outputs of the SIC module, the second stage performs noise clipping to suppress the additive noise. For this stage, we present two schemes, the SIC-based iterative noise clipping (SIC-INC) and the SIC-based direct noise clipping (SIC-DNC). The simulation results show that SIC-INC can achieve BERs similar to those of the SIC-based diversity combining receiver with optimum combining coefficients. It is also shown that SIC-DNC outperforms the existing advanced receivers by up to 0.8 dB at the BER of $10^{-4}$ .

Low-complexity SIC-MMSE for joint multiple-input multiple-output detection

Iterative detection and decoding based on a soft interference cancellation–minimum mean squared error (SIC-MMSE) scheme provides efficient performance for coded MIMO systems. The critical computational burden for a SIC-MMSE detector in a MIMO system lies in the multiple inverse operations of the complex matrix. In this paper, we present a new method to reduce the complexity of the SIC-MMSE scheme based on a MIMO detection scheme that uses a single universal matrix with a non-layer-dependent inversion process. We apply the Taylor series expansion approach and derive a simple non-layer-dependent inverse matrix. The simulation results reveal that the utilization of the universal matrices presented in this paper produces almost the same performance as the conventional SIC-MMSE scheme but with low computational complexity.