Successive Interference Cancellation Decoding Research Articles

Asynchronous Code Division Multiplexing-Based Visible Light Positioning and Communication Network Using Successive Interference Cancellation Decoding.

In the evolving landscape of sixth-generation wireless communication, the integration of visible light communication (VLC) and visible light positioning (VLP), known as visible light positioning and communication (VLPC), emerges as a pivotal technology. This study addresses the challenges of asynchronous code division multiplexing (ACDM) in VLPC networks, focusing on the enhancement of data transmission quality and positioning accuracy. Firstly, we propose an orthogonal pseudo-random code (OPRC) for ACDM-based VLP systems. Leveraging its excellent correlation properties, VLP signals preserve orthogonality even amidst asynchronous transmissions, achieving sub-centimeter average positioning errors. Next, by combining OPRC with successive interference cancellation decoding (SICD), we propose an enhanced ACDM-based VLPC system that utilizes OPRC for improved signal orthogonality and SICD for progressive elimination of multiple access interference (MAI) among VLPC signals. The results show substantial improvements in bit-error rate (BER) and positioning error (PE), approaching the performance levels observed in synchronized VLPC systems. Specifically, the SICD-OPRC scheme reduces average BER to 4.3 × 10-4 and average PE to 2.7 cm, demonstrating its robustness and superiority in complex asynchronous scenarios.

Deep-Reinforcement-Learning-Based NOMA-Aided Slotted ALOHA for LEO Satellite IoT Networks

The low earth orbit (LEO) satellites have received extensive attention as an essential supplement to the terrestrial network for supporting global Internet of Things (IoT) services. Considering the rapid growth of IoT devices and the significant satellite-to-ground latency, proposing low-latency, low-overhead access protocols for LEO satellite IoT systems is challenging. In this paper, we propose a multi-beam random access (RA) framework and deploy the deep reinforcement learning (DRL) algorithm to control the non-orthogonal multiple access (NOMA) aided random access strategy. First, we divide the satellite coverage region into multiple beams and assume that the adjacent beams share parts of regions. Hence, the devices in the sharing region are allowed to transmit packets in two periods allocated for the two beams. Then, packets in multiple beams can be decoded jointly by an inter-slot successive interference cancellation (SIC) decoder. In addition, we consider the heterogeneity among devices and assign different power levels for heterogeneous types of devices, which enables power-domain NOMA and the intra-slot SIC decoder in this system to mitigate the collision resolution. To maximize the average throughput, the deep deterministic policy gradient (DDPG) algorithm is adopted to achieve an online decision to optimize the random access protocol where the packet repetition strategies of devices are adjusted dynamically. The simulation results show that the proposed scheme outperforms the traditional benchmark schemes with significant throughput gain.

Integration of NOMA With Reflecting Intelligent Surfaces: A Multi-Cell Optimization With SIC Decoding Errors

Reflecting intelligent surfaces (RIS) has gained significant attention due to its high energy and spectral efficiency in next-generation wireless networks. By using low-cost passive reflecting elements, RIS can smartly reconfigure the signal propagation to extend the wireless communication coverage. On the other hand, non-orthogonal multiple access (NOMA) has been proven as a key air interface technique for supporting massive connections over limited resources. Utilizing the superposition coding and successive interference cancellation (SIC) techniques, NOMA can multiplex multiple users over the same spectrum and time resources by allocating different power levels. This paper proposes a new optimization scheme in a multi-cell RIS-NOMA network to enhance the spectral efficiency under SIC decoding errors. In particular, the power budget of the base station and the transmit power of NOMA users while the passive beamforming of RIS is simultaneously optimized in each cell. Due to objective function and quality of service constraints, the joint problem is formulated as non-convex, which is very complex and challenging to obtain the optimal global solution. To reduce the complexity and make the problem tractable, we first decouple the original problem into two sub-problems for power allocation and passive beamforming. Then, the efficient solution of each sub-problem is obtained in two-steps. In the first-step of For power allocation sub-problem, we transform it to a convex problem by the inner approximation method and then solve it through a standard convex optimization solver in the second-step. Accordingly, in the first-step of passive beamforming, it is transformed into a standard semi-definite programming problem by successive convex approximation and different of convex programming methods. Then, penalty based method is used to achieve a Rank-1 solution for passive beamforming in second-step. Numerical results demonstrate the benefits of the proposed optimization scheme in the multi-cell RIS-NOMA network.

Secure beamforming designs for maximizing secrecy sum rate in MISO-NOMA networks

In this paper, the application of Non-Orthogonal Multiple Access (NOMA) is investigated in a multiple-input single-output network consisting of multiple legitimate users and a potential eavesdropper. To support secure transmissions from legitimate users, two NOMA Secrecy Sum Rate Transmit BeamForming (NOMA-SSR-TBF) schemes are proposed to maximise the SSR of a Base Station (BS) with sufficient and insufficient transmit power. For BS with sufficient transmit power, an artificial jamming beamforming design scheme is proposed to disrupt the potential eavesdropping without impacting the legitimate transmissions. In addition, for BS with insufficient transmit power, a modified successive interference cancellation decoding sequence is used to reduce the impact of artificial jamming on legitimate transmissions. More specifically, iterative algorithm for the successive convex approximation are provided to jointly optimise the vectors of transmit beamforming and artificial jamming. Experimental results demonstrate that the proposed NOMA-SSR-TBF schemes outperforms the existing works, such as the maximized artificial jamming power scheme, the maximized artificial jamming power scheme with artificial jamming beamforming design and maximized secrecy sum rate scheme without artificial jamming beamforming design.

Secure wireless communications for STAR‐RIS‐assisted millimetre‐wave NOMA uplink networks

AbstractThis paper explores the potential application of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR‐RIS) in improving the physical layer security (PLS) of millimetre wave (mmWave) non‐orthogonal multiple access (NOMA) uplink communications. In particular, the legitimate users on both sides of STAR‐RIS send confidential information simultaneously to the base station (BS) while keeping it secret from the eavesdroppers near the BS by exploiting the STAR‐RIS to proactively regulate the electromagnetic propagation environment. By jointly designing the transmit power, the active beamforming at the BS, and the reflecting/transmitting coefficients at the STAR‐RIS, the authors' goal is to maximize the minimum secrecy capacity subject to the successive interference cancellation decoding order constraints. Due to the non‐convexity of the formulated problem, an efficient algorithm is proposed by capitalizing on alternating optimization, a penalty‐based approach, successive convex approximation, and semi‐definite relaxation. Firstly, a two‐layer iterative algorithm based on the penalty for the joint beamforming optimization sub‐problem is proposed. Then, the non‐convex problem is transformed into a convex positive semi‐definite programming problem for the transmit power sub‐problem. Numerical simulation results reveal that the STAR‐RIS‐aided system has more tremendous advantages and effectiveness in improving the PLS of mmWave NOMA uplink communication compared with the benchmark schemes.

Cluster-Free NOMA Communications Toward Next Generation Multiple Access

A generalized downlink multi-antenna non-orthogonal multiple access (NOMA) transmission framework is proposed with the novel concept of cluster-free successive interference cancellation (SIC). In contrast to conventional NOMA approaches, where SIC is successively carried out within the same cluster, the key idea is that the SIC can be flexibly implemented between any arbitrary users to achieve efficient interference elimination. Based on the proposed framework, a sum rate maximization problem is formulated for jointly optimizing the transmit beamforming and the SIC operations between users, subject to the SIC decoding conditions and users’ minimal data rate requirements. To tackle this highly-coupled mixed-integer nonlinear programming problem, an alternating direction method of multipliers-successive convex approximation (ADMM-SCA) algorithm is developed. The original problem is first reformulated into a tractable biconvex augmented Lagrangian (AL) problem by handling the non-convex terms via SCA. Then, this AL problem is decomposed into two subproblems that are iteratively solved by the ADMM to obtain the stationary solution. Furthermore, to reduce the computational complexity and alleviate the parameter initialization sensitivity of ADMM-SCA, a Matching-SCA algorithm is proposed. The intractable binary SIC operations are solved through an extended many-to-many matching, which is jointly combined with an SCA process to optimize the transmit beamforming. The proposed Matching-SCA can converge to an enhanced exchange-stable matching that guarantees the local optimality. Numerical results demonstrate that: i) the proposed Matching-SCA algorithm achieves comparable performance and a faster convergence compared to ADMM-SCA; ii) the proposed generalized framework realizes scenario-adaptive communications and outperforms traditional multi-antenna NOMA approaches in various communication regimes.

Distributed Auto-Learning GNN for Multi-Cell Cluster-Free NOMA Communications

A multi-cell cluster-free NOMA framework is proposed, where both intra-cell and inter-cell interference are jointly mitigated via flexible cluster-free successive interference cancellation (SIC) and coordinated beamforming design. The joint design problem is formulated to maximize the system sum rate while satisfying the SIC decoding requirements and users’ minimum data rate requirements. To address this highly complex and coupling non-convex mixed integer nonlinear programming (MINLP), a novel distributed auto-learning graph neural network (AutoGNN) architecture is proposed to alleviate the overwhelming information exchange burdens among base stations (BSs). The proposed AutoGNN can train the GNN model weights whilst automatically optimizing the GNN architecture, namely the GNN network depth and message embedding sizes, to achieve communication-efficient distributed scheduling. Based on the proposed architecture, a bi-level AutoGNN learning algorithm is further developed to efficiently approximate the hypergradient in model training. It is theoretically proved that the proposed bi-level AutoGNN learning algorithm can converge to a stationary point. Numerical results reveal that: 1) the proposed cluster-free NOMA framework outperforms the conventional cluster-based NOMA framework in the multi-cell scenario; and 2) the proposed AutoGNN architecture significantly reduces the computation and communication overheads compared to the conventional convex optimization-based methods and the conventional GNNs with fixed architectures.

Energy-Efficient Backscatter-Assisted Coded Cooperative NOMA for B5G Wireless Communications

In this manuscript, we propose an alternating optimization framework to maximize the energy efficiency of a backscatter-enabled cooperative Non-orthogonal multiple access (NOMA) system by optimizing the transmit power of the source, power allocation coefficients (PAC), and power of the relay node under imperfect successive interference cancellation (SIC) decoding. A three-stage low-complexity energy-efficient alternating optimization algorithm is introduced which optimizes the transmit power, PAC, and relay power by considering the quality of service (QoS), power budget, and cooperation constraints. Subsequently, a joint channel coding framework is introduced to enhance the performance of far user which has no direct communication link with the base station (BS) and has bad channel conditions. In the destination node, the far user data is jointly decoded using a Sum-product algorithm (SPA) based joint iterative decoder realized by jointly-designed Quasi-cyclic Low-density parity-check (QC-LDPC) codes. Simulation results evince that the proposed backscatter-enabled cooperative NOMA system outperforms its counterpart by providing an efficient performance in terms of energy efficiency. Also, proposed jointly-designed QC-LDPC codes provide an excellent bit-error-rate (BER) performance by jointly decoding the far user data for considered BSC cooperative NOMA system with only a few decoding iterations.

Energy-Efficient Utility Function-Based Channel Resource Allocation and Power Control for D2D Clusters With NOMA Enablement in Cellular Networks

Device-to-device(D2D) communication combined with non-orthogonal multiple access(NOMA) can further improve the quality of service for future communications systems and increase spectrum utilization and connection density in cellular networks. In this paper, an effective power allocation scheme is designed to optimize the performance of a D2D communication system based on NOMA for imperfect Successive Interference Cancellation(SIC) decoding. To solve the non-convexity of the problem due to integer constraints and coupling variables, an alternate optimization algorithm was designed to obtain the optimal solution of each subproblem by Lagrange duality analysis and the sub-gradient descent method. Finally, the numerical simulation results demonstrate the performance advantages of the proposed joint optimization algorithm for channel resource allocation and power control in energy efficiency.

Outage Performance Analysis of RSMA-Aided Semi-Grant-Free Transmission Systems

In this paper, we analyze the outage performance of a rate-splitting multiple access (RSMA)-aided semi-grant-free (SGF) transmission system, in which a grant-based user (GBU) and multiple grant-free users (GFUs) access the base station by sharing the same resource blocks. In the RSMA-aided SGF (RSMA-SGF) transmission system, the GBU and admitted GFU are respectively treated as the primary and secondary users by using the cognitive radio principle. With the aid of RSMA, the admitted GFU’s transmit power allocation, target rate allocation, and successive interference cancellation decoding order are jointly optimized to attain the maximum achievable rate for the admitted GFU, without deteriorating the GBU’s outage performance compared to orthogonal multiple access. Taking into account the extended non-outage zone achieved by rate-splitting, a closed-form expression is derived for the outage probability of the admitted GFU in the considered RSMA-SGF system. Asymptotic analysis for the admitted GFU’s outage probability is also provided. The superior outage performance and full multiuser diversity gain achieved by the RSMA-SGF transmission system are verified by the analytical and simulation results.

Learning-Assisted Energy Minimization for MEC Systems With Noncompletely Overlapping NOMA

This article investigates the long-term energy minimization of mobile edge computing (MEC) systems with multiuser noncompletely overlapping nonorthogonal multiple access (NOMA) when time varying channel is considered. Different from the existing work with multiuser noncompletely overlapping NOMA that adopted the delay-based successive interference cancellation (SIC) decoding order, we adopt the channel gain-based SIC decoding order, which is widely used and has been proved to be more energy-efficient. We formulate the problem as a joint transmit power, transmission time, and computing resource optimization problem with multiple deeply coupled variables. To ensure the real-time decision-making, the considered problem is characterized as a deep reinforcement learning (DRL) problem by introducing penalty mechanism into the immediate reward function when the constraints are violated, and we propose a soft actor-critic (SAC) algorithm to solve it. Simulations show that the proposed algorithm can significantly improve the system's performance, and, compared with the noncompletely overlapping NOMA mechanism with delay-based SIC decoding order, the completely overlapping NOMA mechanism and time division multiple access (TDMA) mechanism, the noncompletely overlapping NOMA mechanism with channel gain-based SIC decoding order can achieve an average energy consumption reduction of 32.4%, 49.9%, and 74.4%, respectively.

Reinforcement Learning Based Latency Minimization in Secure NOMA-MEC Systems With Hybrid SIC

In this paper, physical layer security (PLS) in a non-orthogonal multiple access (NOMA)-based mobile edge computing (MEC) system is investigated, where hybrid successive interference cancellation (SIC) decoding is considered. Specifically, users intend to complete confidential tasks with the help of the MEC server, while an eavesdropper attempts to intercept the offloaded tasks. By jointly designing computational resource allocation, task assignment, and power allocation, a latency minimization problem is formulated. Based on the interactions between local computing time and MEC processing time, the closed-from solutions of computational resource allocation and task assignment are derived. After that, a strategy selection mechanism is established to select offloading strategies based on the corresponding conditions. Moreover, according to the analysis of hybrid SIC decoding, the conditions of different decoding orders in secure NOMA networks are derived. Furthermore, a reinforcement learning based algorithm is proposed to solve the power allocation problems for NOMA and OMA offloading strategies. This work is extended to a multi-user scenario, in which a matching-based algorithm is proposed to solve the formulated sub-channel assignment problem. Simulation results indicate that: i) the proposed solution can significantly reduce the latency and provide dynamic strategy selection for various scenarios; ii) the NOMA offloading strategy with hybrid SIC decoding can outperform other strategies in the considered system.

Joint Intra/Inter-Slot Code Design for Unsourced Multiple Access in 6G Internet of Things

Unsourced multiple access (UMA) is the technology for massive, low-power, and uncoordinated Internet-of-Things in the 6G wireless system, improving connectivity and energy efficiency on guaranteed reliability. The multi-user coding scheme design is a critical problem for UMA. This paper proposes a UMA coding scheme based on the T-Fold IRSA (irregular repetition slotted Aloha) paradigm by using joint Intra/inter-slot code design and optimization. Our scheme adopts interleave-division multiple access (IDMA) to enhance the intra-slot coding gain and the low-complexity joint intra/inter-slot SIC (successive interference cancellation) decoder structure to recover multi-user payloads. Based on the error event decomposition and density evolution analysis, we build a joint intra/inter-slot coding parameter optimization algorithm to minimize the SNR (signal-to-noise ratio) requirement at an expected system packet loss rate. Numerical results indicate that the proposed scheme achieves energy efficiency gain by balancing the intra/inter-slot coding gain while maintaining relatively low implementation complexity.

Performance comparison of successive interference cancellation decoding order for base station association and power control in small cell networks incorporating handoff optimization

The co-channel interference and frequent handoff are two important issues that have a great impact on the spectrum and energy efficiency in the non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) in the small cell networks (SCN). This paper investigates the performance of SIC decoding order based on the users’ channel state information (CSI) and random SIC decoding order under the perfect and imperfect conditions for the handoff optimization in SCNs with NOMA. The effect of SIC decoding order in the joint base station association and power control problem is studied by considering the motion of mobile users (MUs). Two optimization problems are formulated using the number of associated MUs, the number of handoffs, and the transmit power of all MUs, under the maximum allowable transmit power and minimum average-rate constraints. Two objectives are combined into a single-stage optimization problem through the weighted sum method. The formulated problem is solved using a game theory-based algorithm and primal decomposition theory. The simulation results show that changing the SIC decoding order can lead to performance improvement in the uplink NOMA- based SCNs. Also, it is shown that the proposed algorithm can significantly reduce the frequent handoffs in small cell networks.

Backscatter Sensors Communication for 6G Low-Powered NOMA-Enabled IoT Networks Under Imperfect SIC

The combination of nonorthogonal multiple access (NOMA) using power-domain with backscatter communication (BC) is expected to connect large-scale Internet of things (IoT) devices in the future sixth-generation era. This article introduces a BC in a multicell IoT network, where a source in each cell transmits a superimposed signal to its associated IoT devices using NOMA. The backscatter sensor tag (BST) also transmits data to IoT devices by reflecting and modulating the superimposed signal of the source. A new optimization framework is provided that simultaneously optimizes the total power of each source, power allocation coefficient of IoT devices, and RC of BST under imperfect successive interference cancellation decoding. This work aims to maximize the total energy efficiency (EE) of the IoT network subject to the quality of services of each IoT device. The problem is first transformed using the Dinkelbach method and then decoupled into two subproblems. The Karush–Kuhn–Tucker conditions and dual Lagrangian method are employed to obtain efficient solutions. In addition, we also calculate the EE of the conventional NOMA network without BC as a benchmark framework. Simulation results unveil the advantage of our considered NOMA BC network over the conventional NOMA network in terms of system total EE.

On the Secrecy Design of STAR-RIS Assisted Uplink NOMA Networks

This paper investigates the secure transmission in a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted uplink non-orthogonal multiple access system, where the legitimate users send confidential signals to the base station by exploiting STAR-RIS to reconfigure the electromagnetic propagation environment proactively. Depending on the availability of the eavesdropping channel state information (CSI), both the full CSI and statistical CSI of the eavesdropper are considered. For the full eavesdropping CSI scenario, we adopt the adaptive-rate wiretap code scheme with the aim of maximizing minimum secrecy capacity subject to the successive interference cancellation decoding order constraints. To proceed, we propose an alternating hybrid beamforming (AHB) algorithm to jointly optimize the receive beamforming, transmit power, and reflection/transmission coefficients. While for the statistical eavesdropping CSI scenario, the constant-rate wiretap code scheme is employed to minimize the maximum secrecy outage probability (SOP) subject to the quality-of-service requirements of legitimate users. Then, we derive the exact SOP expression under the constant-rate coding strategy and develop an extended AHB algorithm for the joint secrecy beamforming design. Simulation results demonstrate the effectiveness of the proposed scheme. Moreover, some useful guidance about the quantification of phase shift/amplitude and the deployment of STAR-RIS is provided.

Intelligent reflecting surface-assisted cognitive radio-inspired rate-splitting multiple access systems

Intelligent reflecting surface (IRS) has been widely regarded as a promising technology for configuring wireless propagation environments. In this paper, we utilize IRS to assist transmission of a secondary user (SU) in a cognitive radio-inspired rate-splitting multiple access (CR-RSMA) system in which a primary user's (PU's) quality of service (QoS) requirements must be guaranteed. Without introducing intolerable interference to deteriorate the PU's outage performance, the SU conducts rate-splitting to transmit its signal to the base-station through the direct link and IRS reflecting channels. For the IRS-assisted CR-RSMA (IRS-CR-RSMA) scheme, we derive the optimal transmit power allocation, target rate allocation, and successive interference cancellation decoding order to enhance the outage performance of the SU. The closed-form expression for the SU's outage probability achieved by the IRS-CR-RSMA scheme is derived. Various simulation results are presented to clarify the enhanced outage performance achieved by the proposed IRS-CR-RSMA scheme over the CR-RSMA scheme.

Achieving Near Ideal Covertness in NOMA Systems With Channel Inversion Power Control

In this letter, we propose two resource allocation schemes for covert communication in a downlink NOMA system, where the transmitter can send information simultaneously both to the public and the covert receiver, while maintaining ideal (or near-ideal) covertness with respect to the warden. The covert transmission is concealed by combining NOMA and channel inversion power control, acting as a random source of uncertainty at the warden. The transmission scheme supports zero outage transmission to the public user and non-zero outage transmission to the covert user, while guaranteeing an ideal or near-ideal covertness at the warden, depending on whether the covert user employs single-user decoding or successive interference cancellation (SIC) decoding. In the latter case, we determine the optimal power sharing factor between the public and covert signals that maximizes the average covert throughput, given a predefined fixed transmission rate to the public user. If the channel between the transmitter and the covert receiver is moderate or strong, the SIC decoding, compared to the single-user decoding, leads to much higher covert throughput at the cost of minor degradation of covertness.

NOMA-Enabled Optimization Framework for Next-Generation Small-Cell IoV Networks Under Imperfect SIC Decoding

To meet the demands of massive connections, diverse quality of services (QoS), ultra-reliable and low latency in the future sixth-generation (6G) Internet-of-vehicle (IoV) communications, we propose non-orthogonal multiple access (NOMA)-enabled small-cell IoV network (SVNet). We aim to investigate the trade-off between system capacity and energy efficiency through a joint power optimization framework. In particular, we formulate a nonlinear multi-objective optimization problem under imperfect successive interference cancellation (SIC) detecting. Thus, the objective is to simultaneously maximize the sum-capacity and minimize the total transmit power of NOMA-enabled SVNet subject to individual IoV QoS, maximum transmit power and efficient signal detecting. To solve the nonlinear problem, we first exploit a weighted-sum method to handle the multi-objective optimization and then adopt a new iterative Sequential Quadratic Programming (SQP)-based approach to obtain the optimal solution. The proposed optimization framework is compared with Karush-Kuhn-Tucker (KKT)-based NOMA framework, average power NOMA framework, and conventional OMA framework. Monte Carlo simulation results unveil the validness of our derivations. The presented results also show the superiority of the proposed optimization framework over other benchmark frameworks in terms of system sum-capacity and total energy efficiency.

Performance Bounds of Coded Slotted ALOHA Over Erasure Channels

In this paper, we derive an upper bound on load-threshold of coded slotted ALOHA (CSA) for slot erasure channel (SEC) and that for packet erasure channel (PEC), respectively. In SEC, by exploiting the analogy between Gaussian elimination of solving linear equations and the iterative successive interference-cancellation (SIC) decoding procedure of CSA, we derive the upper performance bound for SEC. Subsequently, in PEC, utilizing density evolution to track the average probability of packet-decoded failure within the SIC decoding process, we obtain the upper bound for PEC. Moreover, we propose a numerical search algorithm of these bounds under a given calculating precision. The numerical results indicate that the asymptotic upper limit on load-threshold of CSA over SEC is always less than that over PEC with any given erasure probability and average transmission rate.