Iterative Receiver Design for FTN Signaling Aided Sparse Code Multiple Access

In this paper, we investigated channel estimation with decision feedback for sparse code multiple access (SCMA) system with diversity. In this scheme, symbols of SCMA codeword from J users are spread over K < J orthogonal resources with uncorrelated fading. For block fading model (flat fading) in uplink channel, it is required to estimate smaller number of channel coefficients per resource block than in the scheme without diversity where fading is identical for all orthogonal resources. The minimal length of orthogonal pilot signal for system with diversity can be decreased proportionally to the sparsity factor of SCMA codewords. Furthermore, the proposed scheme allows choosing flexible pilot lengths, while in a scheme without diversity this length should be a multiple of K. Data-feedback-aided algorithm with hard decisions from SCMA detector and from decoder of turbo code is used for channel estimation. The simulation results show performance gain when turbo decoder is included in feedback loop.

Channel estimation is one of the most important aspects of wireless communication. Especially in Sparse Code Multiple Access (SCMA) system, the accuracy of channel estimation has a significant impact on decoding performance. Various methods, so far, have been developed for channel estimation. Most of these methods regard channel estimation as a parameter estimation problem of linear models. However, these methods require lots of time-frequency resources to ensure high estimation accuracy. In massive connection scenarios, high pilot overhead makes the spectrum resource more scarce. Therefore, the drawback of conventional channel estimation methods limits the further improvement of system capacity in the Internet of Things(IoT) when time-frequency resources is restricted. To address this problem, in this paper, we propose an efficient channel estimation scheme and sparse pilot structure design method in SCMA system based on complex-valued sparse autoencoder which is effective to learn features of wireless channel. Complex-valued sparse autoencoder is a kind of neural network with complex-valued weights. It contains two parts: encoder and decoder. In our work, the encoder part is used to realize pilot design. Channel estimation is implemented by the decoder. Complex-valued weights obtained from training are used as baseband pilots. Compared with maximum likelihood channel estimation (MLE) of linear model, the proposed method can achieve higher channel estimation accuracy with more sparse pilot structure. The bit-error rates performance of the SCMA receiver in our work is very close to that of the perfect channel state information (CSI).

Recently, sparse code multiple access (SCMA) is proposed as one of the important candidacies for the fifth generation (5G) radio access technique. SCMA is a non-orthogonal multiple access (NOMA) technique which allows multiple users share the same radio resource and thus increase spectral efficiency largely. In SCMA, a set of binary coded bits is mapped directly into a complex vector by use of a predefined multi-dimensional codebook. The sparsity of the codewords makes the near-optimal detection feasible through iterative message passing algorithm (MPA). The pursuit of higher spectral efficiency motivates us to study the performance of multiple-input-multiple-output (MIMO) SCMA system in this paper. Simulation results show that compared with the conventional MIMO-OFDM system, MIMO-SCMA can provide a gain up to 50% in data throughput with the aid of MPA technique.

Sparse code multiple access (SCMA) has been recognized as a promising candidate technique for fifth generation (5G) cellular network to improve spectral efficiency (SE). In this paper, physical layer security (PLS) in a downlink single-input single-output (SISO) SCMA system is investigated, which includes one base station (BS), multiple legitimate users and one eavesdropper. The aim of this paper is to maximize the ergodic secrecy sum rate (ESSR) of the SCMA system under the constraint of the transmit power of BS. A novel many-to-many two-side matching algorithm is then proposed. After a limited number of iterations, the algorithm converges to a pair-stable matching. Finally, simulation results indicate that the proposed algorithm outperforms the random scheme and the orthogonal multiple access (OMA) scheme.

The existing multiuser detection schemes for uplink (UP) sparse code multiple access (SCMA) systems are based on a parallel message update for message passing algorithm (MPA). In this letter, a shuffled MPA (S-MPA) scheme for UP SCMA systems is proposed, based on a serial message update strategy. Since the updated messages can join the message propagation immediately in current iteration, the convergence rate is accelerated, so that the complexity of the proposed S-MPA scheme can be substantially reduced with negligible bit error rate (BER) degradation. Simulations show that the proposed S-MPA scheme with two iterations provides similar BER performance to the original MPA scheme with six iterations. Furthermore, a parallel form of the proposed S-MPA scheme, termed group S-MPA, is developed to decrease the detection delay of the proposed S-MPA scheme, which offers a good BER-latency tradeoff.

With the exponential growth of cellular traffic in recent times, larger bandwidth and higher spectral efficiency (SE) have become indispensable for higher data rate applications. Discrete Fourier transform spreading orthogonal frequency division multiplexing (DFT-s-OFDM) waveform is being considered as one of the potential candidates for the next generation communications, because of its low peak to average power ratio (PAPR), which is more suitable for large bandwidth systems that are limited by inefficient power amplifiers. However, DFT-s-OFDM suffers from low SE, thus affecting the system throughput. Recently, faster than Nyquist (FTN) signaling combined with DFT-s-OFDM is being studied for its improved spectral efficiency with less PAPR. In this paper, we analyze the performance benefits of applying FTN signaling on DFT-s-OFDM waveform with 5G new radio uplink system. We show that the FTN combined with DFT-s-OFDM has 2% &#x223C; 75% more throughput than that of of DFT-s-OFDM for the same signal to noise ratio (SNR), using fading channel model. We further show through link adaptation method that the SE of DFT-s-OFDM waveform improves significantly when combined with FTN signaling. We also discuss about the implementation aspects and the performance trade-off with FTN integration into beyond 5G systems.

In this paper, we first study the average sum rate of sparse code multiple access (SCMA) systems, where a general scenario is considered under the assumption that the distances between the mobile users and the base station are not necessarily identical. Closed-form analytical results are derived for the average sum rate based on which an optimal factor graph matrix is designed for maximizing the capacity of the SCMA systems. Moreover, we propose a low-complexity iterative algorithm to facilitate the design of the optimal graph matrix. Finally, Monte Carlo simulations are provided to corroborate the accuracy of the theoretical results and the efficiency of the proposed iterative algorithm.

Next-generation wireless networks require higher spectral efficiency and lower latency to meet the demands of various upcoming applications. Recently, non-orthogonal multiple access (NOMA) schemes are introduced in the literature for 5G and beyond. Various forms of NOMA are considered like power domain, code domain, pattern division multiple access, etc. to enhance the spectral efficiency of wireless networks. In this chapter, we introduce the code domain-based sparse code multiple access (SCMA) NOMA scheme to enhance the spectral efficiency of a wireless network. The design and detection of an SCMA system are analyzed in this chapter. Also, the method for codebooks design and its impact on system performance are highlighted. A hybrid multiple access scheme is also introduced using both code-domain and power-domain NOMA. Furthermore, simulation results are included to show the impact of various SCMA system parameters.

The joint transmission scheme of polar codes and sparse code multiple access (SCMA) has been regarded as a promising technology for future wireless communication systems. However, most of the existing polar-coded SCMA (PC-SCMA) systems suffer from high latency caused by the feedback iteration and list decoding. In addition, the error performance of PC-SCMA systems is unsatisfactory for ultra-reliable transmission. Inspired by the compelling benefits of non-binary polar codes, in this paper, we design a non-binary polar-coded SCMA (NB-PC-SCMA) system with a free order matching strategy to address the issues of delay and reliability. Specifically, we first formulate a joint factor graph for NB-PC-SCMA and propose a non-binary successive cancellation list (NB-SCL) and damping based joint iterative detection and decoding (NSD-JIDD) multiuser receiver to improve the BER and latency performance. Then, a lazy-search based NB-SCL (L-NB-SCL) decoding is proposed to reduce the computational complexity by simplifying the path search pattern of the list decoder. After that, we modify the update of user nodes for SCMA detection to improve the convergence error and finally propose the improved NSD-JIDD (ISD-JIDD) algorithm, which can avoid redundant operations by exploiting L-NB-SCL decoding. Simulation results show that the proposed NB-PC-SCMA system achieves better bit error rate (BER) performance and considerable latency gain when compared to its counterparts. In particular, the proposed ISD-JIDD can achieve similar BER performance of NSD-JIDD with less complexity.

Sparse code multiple access (SCMA) is a promising non-orthogonal multiple access (NOMA) scheme for massive machine-type communications for 5G-and-beyond communication systems. Innovative use of orthogonal frequency division multiplexing with index modulation (OFDM-IM) takes advantage of using tone indices in data transmission. In this study, we propose a novel multiplexing and multiple access scheme, namely SCMA based on OFDM-IM (SCMA-IM). SCMA-IM method can be implemented in uplink NOMA systems in an efficient way with similar computational complexity as SCMA. In this method constellation differences and diversity are obtained via SCMA codebooks (CBs) and the employment of CBs is transferred to index modulation (IM). Performance improvement is achieved via CBs so that the number of data bits carried by indices increases more than one bit with different constellations, and also with the number of CBs assigned. In order to determine the indices used in IM, the receiver structure is simplified by the message passing algorithm and log-likelihood ratio detection process used in SCMA receivers without indexing the receiver. Thus, a new NOMA scheme is obtained by coding with IM. Bit error rate performance of the system is studied for 150% and 200% overloading factors.

Sparse code multiple access (SCMA) is able to provide high spectral efficiency and massive connectivity, hence it is considered as a promising scheme for the fifth generation (5G) systems. This paper proposed a radio resource allocation scheme based on deep learning for SCMA systems, with the aim to automatically avoid the inter-cell interference. A long short term memory (LSTM) network is adopted to learn the past interference characteristics and predict the interference power in the current subframe. Radio resource blocks with less predicted interference power are then selected for users to transmit signals. Simulation results show that the proposed scheme outperforms the moving average prediction method and has significant gains over the random radio resource block allocation in terms of achievable bit error rate in SCMA systems.

This paper is devoted to detection of signals with intentionally introduced intersymbol interference (ISI). Faster-than-Nyquist (FTN) signaling and optimal partial response signaling (PRS) are considered. As the detection algorithm it is used a sub-optimal modification of BCJR algorithm, named Max-Log-M-BCJR. Signals are compared in the plane of spectral efficiency and energy consumptions for the fixed value of bit error rate (BER) and different grades of detection algorithm complexity. It is shown that using the sub-optimal BCJR algorithm provides a noticeable decrease in the computational complexity of the detection. For deep ISI optimal PR signaling provides higher spectral efficiency as compared with FTN signaling and vise versa.

Sparse code multiple access (SCMA) has been recently proposed for the future wireless networks, which allows non-orthogonal spectrum resource sharing and enables system overloading. In this paper, we apply SCMA into device-to-device (D2D) communication and cellular hybrid network, targeting at using the overload feature of SCMA to support massive device connectivity and expand network capacity. Particularly, we develop a stochastic geometry based framework to model and analyze SCMA, considering underlaid and overlaid mode. Based on the results, we analytically compare SCMA with orthogonal frequency-division multiple access (OFDMA) using area spectral efficiency (ASE) and quantify closed-form ASE gain of SCMA over OFDMA. Notably, it is shown that system ASE can be significantly improved using SCMA and the ASE gain scales linearly with the SCMA codeword dimension. Besides, we endow D2D users with an activated probability to balance cross-tier interference in the underlaid mode and derive the optimal activated probability. Meanwhile, we study resource allocation in the overlaid mode and obtain the optimal codebook allocation rule. It is interestingly found that the optimal SCMA codebook allocation rule is independent of cellular network parameters when cellular users are densely deployed. The results are helpful in the implementation of SCMA in the hybrid system.

Sparse code multiple access (SCMA) has excellent application prospects due to its high spectral efficiency and accsess capacity. However, due to the nonorthogonal characteristic of SCMA in code domain, the codebook needs to be manually designed for all communication scenarios, and the receiver has high computational complexity. To address this issue, deep neural network-aided SCMA (DNN-SCMA) is proposed, but it is difficult to capture channel state information (CSI) in the dynamic and time-varying communication scenarios, which hinders the overall learning and optimization fot end-to-end communications. This paper proposes a stochastic channel model with conditional generative adversarial network (CGAN) for DNN-aided SCMA in a data-driven way. Particularly, a model-free learning method is adopted to accurately learn different types of random channel models, which realizes effective acquisition of dynamic channel information. Finally, the end-to-end training is achieved through the use of back propagation (BP), and then by an iterative training of the composed networks, the end-to-end loss can be optimized in a supervised manner. Results show the feasibility of CGAN-based channel modeling in end-to-end DNN-SCMA.

Numerous works are dedicated to Faster-than-Nyquist (FTN) signaling providing symbol rate higher than “Nyquist limit” without energy losses. It is necessary to use infinite pulses or pulses with rather long duration for FTN signaling. Using limited duration of FTN pulses allows to simplify the structure of transceiver but causes challenges with measurement of spectral efficiency. Finite time optimal pulses with controlled level of intersymbol interference (ISI) may be used for FTN signaling. In this work it is proposed to solve optimization problem taking into account limitation on Euclidean distance between different realizations of two random FTN signals. Obtained optimal FTN signals provide the gain in energy efficiency about 2 dB and 4 times higher spectral efficiency comparing to the known spectrally efficient signals based on RRC pulses.