Stochastic Channel Modeling for Deep Neural Network-aided Sparse Code Multiple Access Communications

5 G networks may not be competent in fully supporting the high data rates, system capacity, and spectral efficiency expected in the upcoming 6 G networks. An enabling factor to achieve these requirements is the adoption of the Sparse Code Multiple Access (SCMA), which is one of the most promising code-domain Non-Orthogonal Multiple Access (NOMA) techniques. Nowadays, there are two major challenges for the implementation of SCMA in practical networks. First, the design of a near-optimal codebook set at the transmitter, that heavily impacts the network performance. Second, an efficient decoding algorithm at the receiver, that dominates the computational complexity of the system. The two aspects are not independent from each other; therefore the optimization of an SCMA network should address jointly both the Encoder and the Decoder side. Moreover, such solution is far from optimal when designed for general purpose use, since it does not exploit the channel statistical information known at the transmitter and the receiver side. However, most of the works available in the literature address only one aspect and also are not tailored for a given radio channel. In this paper, we design an end-to-end SCMA en/deconding structure based on the integration between a state-of-the-art AutoEncoder (AE) architecture and a novel Wasserstein Generative Adversarial Network (WGAN). The aim is to jointly create a near-optimal codebook design and a non-computationally heavy Decoder that are robust to the given channel statistics. Finally, we trained our model to be flexible to support different channel conditions and compared its performance against the state-of-the-art AE and the best conventional Decoders. The simulation results show that our innovative design exhibits decoding performance very similar to conventional Decoders with a reduced decoding latency.

The sparse code multiple access (SCMA) is a promising candidate for bandwidth-efficient next generation wireless communications, since it can support more users than the number of resource elements. On the same note, faster-than-Nyquist (FTN) signaling can also be used to improve the spectral efficiency. Hence in this paper, we consider a combined uplink FTN-SCMA system in which the data symbols corresponding to a user are further packed using FTN signaling. As a result, a higher spectral efficiency is achieved at the cost of introducing intentional inter-symbol interference (ISI). To perform joint channel estimation and detection, we design a low complexity iterative receiver based on the factor graph framework. In addition, to reduce the signaling overhead and transmission latency of our SCMA system, we intrinsically amalgamate it with grant-free scheme. Consequently, the active and inactive users should be distinguished. To address this problem, we extend the aforementioned receiver and develop a new algorithm for jointly estimating the channel state information, detecting the user activity and for performs data detection. In order to further reduce the complexity, an energy minimization based approximation is employed for restricting the user state to Gaussian. Finally, a hybrid message passing algorithm is conceived. Our Simulation results show that the FTN-SCMA system relying on the proposed receiver design has a higher throughput than conventional SCMA scheme at a negligible performance loss.

The implementation of Multiple Input Multiple Output (MIMO) on the Sparse Code Multiple Access (SCMA) can enhance performance in 5G future communication. Low complexity detector for the MIMO-SCMA system to achieve near-optimum bit error rate (BER) performance is still hot topic in this research field. This paper is investigate low complexity MIMO-SCMA detector by combining a joint factor graph (JFG) and sphere decoding (SD) algorithm based on message passing algorithm (MPA) called by SD-JFG-MPA. SD-JFG-MPA is combine n-th single factor graph become one virtual factor graph to deal with MIMO detector. SD algorithm is used to reduce high complexity of the JFG-MPA by calculating possible combination codeword within search radius R SD . SD-JFG-MPA is investigated under Rayleigh channel model with overloading factor 150%. The simulation result shows that complexity and BER performance is determined by a selected radius of SD-JFG-MPA. Optimum performance with less processing time for one bit error on single SCMA when $R_{SD} = \sqrt {E_b} \times 3/2 \times r_{max}$ meanwhile for 2x2 MIMO-SCMA when $R_{SD} = \sqrt {E_b} \times N_{mimo} \times r_{max}$. 2×2 MIMO-SCMA performance is better with 5 dB gap at BER = 10−3 compared to single SCMA.

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.

In this work, the polar coded sparse code multiple access (SCMA) scheme based on chaotic scrambled selective mapping (CS_SLM) is proposed for future passive optical networks (PONs). The original bits are coded by polar and mapped into constellation symbols by sparse code. Then orthogonal chirp division multiplexing (OCDM) modulation is adopted which has better anti-interference ability than orthogonal frequency division multiplexing (OFDM). At the receiver, the joint iterative detection and decoding for polar and SCMA is used to further improve the BER performance. The CS_SLM is helpful to improve the reliability and security, while keeping high spectral efficiency of SCMA-OCDM modulation. A 49.42 Gb/s coded SCMA-OCDM PON over 2 km seven-core fiber is demonstrated in the experiment. The results show that the SCMA-OCDM scheme gets a receiver sensitivity of 0.8 dB at a bit error rate (BER) of 3.8 × 10-3, compared with SCMA-OFDM. Because of peak to average power ratio (PAPR), the polar coded SCMA-OCDM based on CS_SLM is compared with clipping and filtering (CAF) method, about 2.08 dB receiver sensitivity gain is obtained when BER is lower than 10-5. The polar coded SCMA-OCDM based on CS_SLM also takes security into the consideration, due to the downlink broadcasting of PON. It can be a promising candidate for the next generation PONs.

Non-orthogonal multiple access (NOMA) is a promising technique for future wireless communication. Compared with orthogonal multiple access (OMA), it provides high spectral efficiency and the ability to support a large number of users. A novel DNN-NOMA scheme is proposed in this paper. Both the encoder and decoder of it are composed of deep neural networks (DNN). This DNN-NOMA scheme combines power-domain NOMA, sparse code multiple access (SCMA) and multi-user shared access (MUSA), which means all of these NOMA schemes can be regarded as a special case of it. DNN-based encoders and decoders are able to generate appropriate codebooks and decode received signals automatically. The orthogonal resources (OR) used by each user are automatically determined in the process of optimizing network parameters. The simulation results prove that the BER of this novel DNN-NOMA scheme is lower than other NOMA schemes. Moreover, because there is no need to design the codebook manually, it can easily adapt to different numbers of users and ORs.KeywordsNOMADeep learningSCMA

Many researchers tend to evaluate the performance of their state-of-the-art MIMO antenna array designs using either stochastic or ray-tracing channel modeling tools. These channel models are mainly categorized into deterministic and stochastics channel models. In this paper, we investigate these two categories in terms of the MIMO channel capacity and evaluate the performance of a miniaturized MIMO antenna array in an indoor offices scenario. The effects of the number of transmitters and receivers, spatial location, 3D radiation patterns, polarization, and S-parameters are evaluated for an indoor office model and the IEEE TGn-B indoor stochastic channel model. The ray-tracing software, Wireless InSite, is used as the deterministic channel modeling tool while MIMObit is used as the stochastic channel modeling tool.

Multi-user spatial modulation (SM) assisted by sparse code multiple access (SCMA) has been recently proposed to provide uplink high spectral efficiency transmission. The message passing algorithm (MPA) is employed to detect the transmitted signals, which suffers from high complexity. This paper proposes three low-complexity algorithms for the first time to the SM-SCMA. The first algorithm is referred to as successive user detection (SUD), while the second algorithm is the modified version of SUD, namely modified SUD (MSUD). Then, for the first time, the tree-search of the SM-SCMA is constructed. Based on that tree-search, another variant of the sphere decoder (SD) is proposed for the SM-SCMA, referred to as fixed-complexity SD (FCSD). SUD provides a benchmark for decoding complexity at the expense of bit-error-rate (BER) performance. Further, MSUD slightly increases the complexity of SUD with a significant improvement in BER performance. Finally, FCSD provides a near-optimum BER with a considerable reduction of the complexity compared to the MPA decoder and also supports parallel hardware implementation. The proposed algorithms provide flexible design choices for practical implementation based on system design demands. The complexity analysis and Monte-Carlo simulations of the BER are provided for the proposed algorithms.

Sparse code multiple access (SCMA) is a kind of code-domain non-orthogonal multiple access (NOMA) scheme, which can support the increasing requirements for high spectral efficiency and massive connections. Meanwhile, multi-access edge computing (MEC) is a promising technology for providing resource-constrained users with computing resources. The integration of these two technologies can efficiently improve the computation service. In this paper, we propose a novel optimization scheme in the SCMA-based MEC network from the perspective of energy and latency for the Internet of Things (IoT) devices. To minimize the total overhead of devices, the communication and computation resources allocation, as well as computation offloading are jointly considered. Primarily, a joint SCMA codebook assignment based on the bidirectional matching principle and optimal power allocation algorithm is proposed to maximize the uplink transmit rate. Moreover, we put forward CPU frequency scheduling strategies utilizing convex optimization to optimize the computing resources allocation of local devices and the MEC server. Finally, a low-complexity task offloading policy based on simulated annealing is presented. Numerical results show that our proposed joint optimization algorithm for resource allocation and computation offloading can achieve a good compromise between time delay and energy consumption for IoT devices.

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.

A structural-multipath approach has been developed for constructing a spatiotemporal model of a single-mode decameter communication channel, which makes it possible to determine the two-frequency spatial correlation function of a single-mode decameter communication channel and to establish the dependences of frequency and spatial correlation intervals of fading on the operating frequency, diffuse ionosphere parameters, and radio link geometry. The method for constructing this model of a communication channel using the structural multipath approach includes four stages: 1) development of a multibeam spatiotemporal decameter model of a communication channel; 2) development of a radiophysical space-time model of radio wave propagation in a decameter radio link, taking into account the influence of inhomogeneities (diffusion) of the ionosphere based on the method of smooth perturbations; 3) identification of multipath and radiophysical models to determine the two-frequency spatial correlation function of the decameter of the communication channel; 4) determining the dependence of frequency and spatial correlation intervals of fading in a single-mode decameter communication channel on the choice of operating frequency, diffuse ionosphere parameters, and radio link geometry.

As vehicular communications for beyond fifth‐generation (B5G) and sixth‐generation (6G) is picking up interests from academia and industry recently, more and more research and development have been devoted towards the establishment of vehicular communications for B5G and 6G that is capable of supporting the ever more intelligent transportation systems. One key facilitating the design and improvement of vehicular communications for B5G and 6G is channel modelling, which is widely regarded as the foundation of all communication and networking systems. In this paper, the authors focus on the research and analysis of B5G and 6G vehicular channel measurements and modelling. By emphasising the new requirements and challenges that the emerging B5G and 6G technologies and frequency bands bring to vehicular communication channel measurements and modelling, they present an overview of the existing work and identify the limitations therein, and provide guidelines on the channel model development and adoption for various system development and verification objectives. Finally, future challenges related to vehicular channel measurements, modelling, and their application for B5G and 6G are addressed.

Разработана компьютерная модель спутникового канала связи с учетом влияния доплеровского сдвига по частоте и стороннего спутника связи. Компьютерная модель построена в графической среде имитационного моделирования Simulink, которая входит в программный пакет MATLAB. В статье рассматривается имитационная модель спутникового канала связи с использованием сигнала MSK (Minimum Shift Keying). В ходе работы изучены и применены алгоритмы цифровой обработки сигналов при разработке компьютерной модели спутникового канала связи. По заданным параметрам принимаемого сигнала определены основные параметры цифрового демодулятора сигнала MSK, такие как частота повторения бита, частота дискретизации, минимальная разрядность аналого-цифрового преобразователя (АЦП), количество уровней квантования, частота среза цифрового фильтра-дифференциатора. В результате компьютерного моделирования спутникового канала связи было исследовано влияние отстройки по частоте сигнала MSK и уровня мешающего сигнала (стороннего спутника) на помехоустойчивость модема. По результатам моделирования получены графики зависимости вероятности ошибки приема бита от уровня мешающего сигнала и вероятности ошибки от отстройки по частоте. Разработанная компьютерная модель существенно расширяет круг задач, связанных с оперативной оценкой параметров электромагнитной совместимости (ЭМС) и радиоподавления в группировках подвижных РЭС.

This chapter is attempted to provide a survey of the advanced concepts and related issues involved in Multiple Input Multiple Output (MIMO) systems. MIMO system technology has been considered as a really significant foundation on which to build the next and future generations of wireless networks. The chapter addresses advanced MIMO techniques such as polarization diversity and antenna selection. We gradually provide an overview of the MIMO features from basic to more advanced topics. The first sections of this chapter start by introducing the key aspects of theMIMO theory. TheMIMO systemmodel is first presented in a genericway. Then, we proceed to describe diversity schemes used in MIMO systems. MIMO technology could exploit several diversity techniques beyond the spatial diversity. These techniques essentially cover frequency diversity, time diversity and polarization diversity. We further provide the reader with a geometrically based models for MIMO systems. The virtue of this channel modeling is to adopt realisticmethods for modeling the spatio-temporal channel statistics from a physical wave-propagation viewpoint. Two classes for MIMO channel modeling will be described. These models involve the Geometry-based Stochastic ChannelModels (GSCM) and the Stochastic channel models. Besides the listedMIMO channel models already described, we derive and discuss capacity formulas for transmission over MIMO systems. The achieved MIMO capacities highlight the potential of spatial diversity for improving the spectral efficiency of MIMO channels. When Channel State Information (CSI) is available at both ends of the transmission link, the MIMO system capacity is optimally derived by using adaptive power allocation based on water-filling technique. The chapter continues by examining the combining techniques for multiple antenna systems. Combining techniques are motivated for MIMO systems since they enable the signal to noise ratio (SNR) maximization at the combiner output. The fundamental combing techniques are the Maximal Ratio Combining (MRC), the Selection Combining (SC) and the Equal Gain Combining(EGC). Once the combining techniques are analyzed, the reader is introduced to the beamforming processing as an optimal strategy for combining. The use of multiple antennas significantly improves the channel spectral efficiency. Nevertheless, this induces higher system complexity of the communication system and the communication system performance is effected due to correlation between antennas that need to be deployed at the same terminal. As such, the antenna selection algorithm for MIMO systems is presented. To elaborate on this point, we introduce Space time coding techniques for MIMO systems and we evaluate by simulation the performance of the communication system. Next, we emphasis on multi polarization techniques for MIMO systems. As a background, we presume that the reader has a thorough understanding of antenna theory. We recall the basic antenna theory and concepts that are used throughout the rest of the chapter. We rigorously introduce the 3D channel model over the Non-Line of Sight (NLOS) propagation channel for MIMO system with polarized antennas. We treat the depolarization phenomena and we study its effect on MIMO system capacity. The last section of the chapter provides a scenario for collaborative sensor nodes performing distributed MIMO system model which is devoted to sensor node localization in Wireless Sensor Networks. The localization algorithm is based on beamforming processing and was tested by simulation. Our chapter provides the reader by simulation examples for almost all the topics that have been treated for MIMO system development and key issues affecting achieved performance.

The wireless channel model is a deterministric or stochastic numerical description of physical propagation enviroments based on channel measurement or analysis and simulation on the basis of propagation theories. It provides an effective and simple means to approximately express the channel characteristics of the wireless transmission. Channel models can be used for link and system-level performance evaluation, optimization, and comparative studies on wireless communications technologies, algorithms, products and system. Channel model is of great importance to all wireless communications (including 5G mobile communication) research. This chapter focuses on the progress in 5G channel measurement and channel modeling. Section 3.1 introduces the requirements for 5G wireless channel model. Section 3.2 introduces and compares five commonly used channel modeling methods. Section 3.3 introduces the knowledge on channel measurement, channel measurement system and the currently ongoing 5G wireless channel measurement activities. Section 3.4 describes several channel parameter extraction algorithms and statistical analysis of channel parameters. Section 3.5 introduces several existing channel models. Section 3.6 gives an example to show the generating process of stochastic channel models. And the last section is the summary of this chapter.