Sparse Code Multiple Access Networks Research Articles

Coverage Probability and Area Potential Spectral Efficiency Analysis of 3D Dense SCMA Cellular Networks

This paper investigates the dense multi-user sparse code multiple access (SCMA) cellular networks, where base stations (BSs) and users are randomly deployed in a 3D space by using stochastic geometry tools. First, a method increasing the dimension of the assignment matrix is presented to provide massive connectivity in each cell. The random pattern of arrangement of non-zero elements access (RPA) policy is utilized to allocate the available SCMA resources. Then the coverage probability is characterized under the nearest-BS (NBS) association mechanism by developing a multi-user connectivity model based on the RPA policy and average connectivity ratio (ACR). A compact closed-form expression of the coverage probability and its approximation are derived by utilizing the Toeplitz matrix and finite sum of power series, respectively. We then extract the area potential spectral efficiency (APSE) based on the coverage probability, and provide new insights into the 3D dense multi-cell SCMA networks. Simulation results confirm the precision of the theoretical results and demonstrate the advantage of the SCMA scheme.

SWIPT-Aided Device-to-Device Communications for Massive IoT Networks: A Novel Resource Allocation With Sparse Code Multiple Access

Spectral efficiency (SE) and energy efficiency (EE) are crucial for next-generation wireless Internet of things networks. Device-to-device (D2D) communications and sparse code multiple access (SCMA) are promising solutions to meet the high quality of service. This requires a proper interference management for an ultra-dense user system. Considering both resource and power allocation, we herein aim to optimize the SE and improve the EE. First, rate fairness is considered to assign SCMA codewords to both cellular and D2D users. Subsequently, we propose a beamforming design and power allocation algorithm for D2D communications underlaying downlink and uplink operation of cellular networks, wherein energy harvesting is involved as a simultaneous wireless information and power transfer to assist an SCMA network.

A Flexible Encoding/Decoding Procedure for 6G SCMA Wireless Networks via Adversarial Machine Learning Techniques

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.

Sparse Code Multiple Access Scheme Based on Variational Learning

Sparse code multiple access (SCMA) technology has been widely studied thanks to its outstanding overload performance, which provides greater device access on limited time-frequency resources. We propose a variational learning based end-to-end SCMA network model termed as V-SCMA. In our model, SCMA codebooks are generated by a deep neural network (DNN) encoder. Motivated by the idea of variational learning, we derive the posterior probability of SCMA codeword send by each user from the point view of binary coding using the variational method. Then, we design an SCMA decoding network to learn this process of approaching the real posterior probability. The SCMA decoding network designed by us is a truncated recurrent neural network, which is simpler than other networks. In addition, a novel loss function is proposed to optimize V-SCMA. Compared with previous works, the new loss function is a tighter variational lower bound which improves the BER performance. Simulation results show that the proposed V-SCMA offers a better bit error rate (BER) performance and a lower computational complexity than conventional schemes.

A Novel Multitask Learning Empowered Codebook Design for Downlink SCMA Networks

Sparse code multiple access (SCMA) is a promising code-domain non-orthogonal multiple access (NOMA) scheme for the enabling of massive machine-type communication. In SCMA, the design of good sparse codebooks and efficient multiuser decoding have attracted tremendous research attention in the past few years. This letter aims to leverage deep learning to jointly design the downlink SCMA encoder and decoder with the aid of autoencoder. We introduce a novel end-to-end learning based SCMA (E2E-SCMA) design framework, under which improved sparse codebooks and low-complexity decoder are obtained. Compared to conventional SCMA schemes, our numerical results show that the proposed E2E-SCMA leads to significant improvements in terms of error rate and computational complexity.

User capacity for uplink SCMA system

Sparse Code Multiple Access (SCMA) is a non-orthogonal multiple access (NOMA) scheme which allows multi-user communication with massive connectivity. In this paper, we consider an uplink SCMA network of limited resources (time/frequency/power). We seek to answer the following question: what is the maximum number of users that can be admitted in uplink SCMA subject to user-rate constraints? This is termed “user capacity” in communication network. We first obtain a closed form for the lower bound of user capacity which is the first contribution of this work. We then study the general problem of optimizing the resource allocation in terms of codebook assignment and power allocation to maximize the number of users, as well as the total sum rate subject to power, codebook and user-rate constraints. This optimization problem turns out to be non-convex and NP-hard; therefore, we propose an iterative algorithm that provides sub-optimal codebook assignment and power allocation which can efficiently solve this problem. Specifically, another contribution of this work is showing that using swap matching for codebook assignment and Difference of two Convex functions (DC) programming for power allocation efficiently solve the problem yielding better results compared with, e.g., cooperative co-evolutionary particle swarm optimization (CCPSO) algorithm or simple equal power and conventional codebook allocation. Finally, we verify our analytical bound on the user capacity via Monte Carlo simulations and show that the proposed resource allocation algorithm provides significant gains in the number of scheduled users in the system with higher sum rate compared with other schemes. The proposed algorithm can achieve an increment in the number of scheduled users of 45%, and 80% compared with CCPSO and simple equal power and regular codebook allocation, respectively. We also compare our proposed swap matching criterion based on sum rate maximization with the two-sided exchange stable swap matching and simulation results show the effectiveness of the proposed criterion for our problem.

Analysis and Design of SCMA-Based Hybrid Unicast-Multicast Relay-Assisted Networks.

This paper studies a multi-user network model based on sparse code multiple access (SCMA), where both unicast and multicast services are considered. In the direct transmission scheme, the communication between the base station (BS) and the users is completed with one stage, in which the relay is inexistent. In the two-stage cooperative transmission scheme, any number of relays are placed to improve the reliability of wireless communication system. The BS broadcasts the requested message to users and relays in the first stage, and the successful relays forward the message to unsuccessful users in the second stage. To characterize the performance of these two schemes, we derive the exact and approximate expressions of average outage probability. Furthermore, to take full advantage of the cooperative diversity, an optimal power allocation and relay location strategy in the high signal-to-noise ratio (SNR) regime is studied. The outage probability reaches the minimum value when the first stage occupies half of the total energy consumed. Simulation and analysis results are presented to demonstrate the performance of these two schemes. The results show that the two-stage cooperative scheme effectively reduce the average outage probability in SCMA network, especially in the high SNR region.

Resource allocation for ultra-reliable low latency communications in sparse code multiple access networks

In this paper, we propose an optimal resource allocation policy for sparse code multiple access (SCMA) networks supporting ultra-reliable low-latency communications (URLLC). The network is assumed to operate with finite blocklength (FBL) codes, which is opposed to the classical information-theoretic works with infinite blocklength (IBL) codes. In particular, we aim at maximizing the average transmission rate in the FBL regime while guaranteeing the transmission reliability. A joint design is proposed, which combines the power allocation with the codebook assignment. The convexity of the corresponding optimization problem is analyzed and an iterative search algorithm is further provided. We study the impact of reliability and short blocklength constraints on the performance of the proposed optimal resource allocation policy through numerical simulations. In addition, we evaluate the FBL performance of the proposed joint design in comparison to the scenario with an IBL assumption.

Adaptive Codebook Design and Assignment for Energy Saving in SCMA Networks

Sparse code multiple access (SCMA) has been proposed as a candidate air interface (AI) technique for 5G wireless networks. However, the existing resource management schemes with predesigned SCMA codebooks cannot fully exploit user diversities in the frequency domain, thus degrading the performance of SCMA systems. To fully exploit the potential of SCMA, in this paper, we design a more flexible and configurable SCMA through adaptively adjusting the codebook design and assignment according to the user’s features. Specifically, for the uplink networks, first we formulate a detection complexity minimization problem by jointly considering the codebook design (i.e., mapping matrix and constellation graph design) and codebook assignment, which is an integer linear program and NP-hard in general. To tackle this hard problem effectively, first we borrow the idea of dual coordinate search to devise a suboptimal but computational efficient algorithm to determine the mapping matrix and codebook assignment. Based on the obtained mapping matrix, we use the multi-dimensional modulation characteristic of SCMA to carefully design the constellations for each codebook to further reduce the detection complexity. For the downlink networks, we formulate a total power consumption minimization problem by jointly considering the codebook design and assignment and power allocation. Exploiting the special structure of the problem, we employ the Lagrangian dual decomposition technique to propose a fast iterative algorithm, which can solve the problem optimally with low complexity. Finally, we present extensive simulations to exhibit the performance improvement against other algorithms in terms of detection complexity and power consumption. The modified SCMA in this paper can be intelligently optimized based on service and user awareness, which can provide some guidelines for the design of software-defined AI in future wireless networks.

Rate and Energy Maximization in SCMA Networks With Wireless Information and Power Transfer

In this letter, we investigate the fundamental tradeoff between rate and energy for sparse code multiple access (SCMA) networks with wireless power transfer. A weighted rate and energy maximization problem by jointly considering power allocation, codebook assignment, and power splitting, is formulated. To solve the hard problem, an iterative algorithm based on the univariate search technique is proposed, which has good performance with low complexity. Specifically, we analyze the special structure of the problem and exploit it to obtain the optimal power splitting ratio and resource allocation strategy when one of them is fixed. Simulation results indicate that our algorithm achieves a better rate-energy tradeoff compared to other schemes.