Multiple Access Research Articles

Max-Min Fairness and PHY-Layer Design of Uplink MIMO Rate-Splitting Multiple Access With Finite Blocklength

Rate-Splitting Multiple Access (RSMA) has emerged as a potent and reliable multiple access and interference management technique in wireless communications. While downlink Multiple-Input Multiple-Ouput (MIMO) RSMA has been widely investigated, uplink MIMO RSMA has not been fully explored. In this paper, we investigate the performance of uplink RSMA in short-packet communications with perfect Channel State Information at Transmitter (CSIT) and Channel State Information at Receiver (CSIR). We propose an uplink MIMO RSMA framework and optimize both precoders and combiners with Max-Min Fairness (MMF) metric and Finite Blocklength (FBL) constraints. Due to the high coupling between precoders and combiners, we apply the Alternating Optimization (AO) to decompose the optimization problem into two subproblems. To tackle these subproblems, we propose a Successive Convex Approximation (SCA)-based approach. Additionally, we introduce a low-complexity scheme to design the decoding order at the receiver. Subsequently, the Physical (PHY)-layer of the uplink MIMO RSMA architecture is designed and evaluated using multi-user Link-Level Simulations (LLS), accounting for finite constellation modulation, finite length polar codes, message splitting, adaptive modulation and coding, and Successive Interference Cancellation (SIC) at the receiver. Numerical results demonstrate that applying RSMA in uplink MIMO with FBL constraints not only achieves MMF gains over conventional transmission schemes such as Space Division Multiple Access (SDMA) and Non-orthogonal Multiple Access (NOMA) but also exhibits robustness to network loads. The benefits of splitting messages from multiple users are also illustrated. LLS results confirm the improved max-min throughput benefits of RSMA over SDMA and NOMA.

Low Complexity Robust Beamforming for Heterogeneous MIMO Rate-Splitting Multiple Access

Low Complexity Robust Beamforming for Heterogeneous MIMO Rate-Splitting Multiple Access

Max-Min Fairness for Uplink Rate-Splitting Multiple Access With Finite Blocklength

Max-Min Fairness for Uplink Rate-Splitting Multiple Access With Finite Blocklength

RNN Beamforming Optimizer for Rate-Splitting Multiple Access and Cell-Free Massive MIMO

RNN Beamforming Optimizer for Rate-Splitting Multiple Access and Cell-Free Massive MIMO

Energy Efficiency Optimization for UAV-RIS-Assisted Wireless Powered Communication Networks

In urban environments, unmanned aerial vehicles (UAVs) can significantly enhance the performance of wireless powered communication networks (WPCNs), enabling reliable communication and efficient energy transfer for urban Internet of Things (IoTs) nodes. However, the complex urban landscape characterized by dense building structures and node distributions severely hampers the efficiency of wireless power transmission. To address this challenge, this paper presents a novel framework for urban WPCN systems assisted by UAVs equipped with reconfigurable intelligent surfaces (UAV-RISs). The framework adopts time division multiple access (TDMA) technology to coordinate the transmission process of information and energy. Considering two TDMA methods, the paper jointly optimizes the flight trajectory of the UAV, the energy harvesting scheduling of ground nodes, and the phase shift matrix of the RIS with the goal of improving the energy efficiency of the system. Furthermore, deep reinforcement learning (DRL) is introduced to effectively solve the formulated optimization problem. Simulation results demonstrate that the proposed optimized scheme outperforms benchmark schemes in terms of average throughput and energy efficiency. Experimental data also reveal the applicability of different TDMA strategies: dynamic TDMA exhibits superior performance in achieving higher average throughput at ground nodes in urban scenarios, while traditional TDMA is more advantageous for total energy harvesting efficiency. These findings provide critical theoretical insights and practical guidelines for UAV trajectory design and communication network optimization in urban environments.

An Exact Analysis on Security-Reliability Tradeoff in Downlink Rate Splitting Multiple Access

An Exact Analysis on Security-Reliability Tradeoff in Downlink Rate Splitting Multiple Access

Uplink Rate Splitting Multiple Access With Imperfect Channel State Information and Interference Cancellation

Uplink Rate Splitting Multiple Access With Imperfect Channel State Information and Interference Cancellation

Communication Performance Analysis and Research of UAV Cooperative Non-Orthogonal Multiple Access System

In the case of poor communication such as major disasters and forest fire, the use of unmanned aerial vehicle (UAV) as emergency communication relays can realize the normal transmission of signals. In this paper, the performance of cooperative non-orthogonal multiple access (NOMA) communication system based on UAV is studied. First of all, the average achievable rate of the UAV cooperative NOMA communication system under the independent Rayleigh fading channel is analyzed. Mathematical methods are employed to derive expressions for the average achievable rate, outage probability, and throughput. The accuracy of the theoretical results is verified through Monte Carlo simulations. Secondly, the impact of UAV flight altitude on system performance is further analyzed. The results indicate that the communication performance of UAV cooperative NOMA scheme exhibits significant improvement compared to the traditional cooperative communication system.

Joint time delay and frequency offset estimation scheme for multiple ONUs' activation in point-to-multipoint coherent PON system.

For the next-generation access network standard, the coherent passive optical network (PON) using digital subcarrier multiplexing (DSCM) is a competitive candidate. The architecture employs time and frequency division multiple access (TFDMA) to improve network flexibility and access efficiency. Initial access to PON requires the activation of multiple optical network units (ONUs) to complete upstream synchronization. To meet the stringent latency requirements, the activation process of ONU should not cause significant delay and degradation for the transmission of upstream data. Therefore, we propose what we believe to be a novel ONU activation method for TFDMA coherent PON to jointly estimate time delay and frequency offset. The registration signal used for the ONU activation is designed as a combination of Zadoff-Chu sequence and Gold sequence, and the registration signal can be transmitted together with the data signal without the need for a quiet window. At the receiver, the time delay and frequency offset are estimated by calculating correlation values. Furthermore, successive interference cancellation is introduced to support simultaneous registration of multiple ONUs. The proposed activation scheme is validated in an coherent PON system with 6 × 10 GHz subcarriers. The experimental results show that the scheme can achieve a time delay estimation accuracy of 1 ns and frequency offset estimation accuracy of 1 MHz. Moreover, it does not cause any delay for upstream signal transmission, and the resulting SNR penalty is less than 0.2 dB.

Incomplete lineage sorting and introgression among genera and species of Liliaceae tribe Tulipeae: insights from phylogenomics

BackgroundPhylogenetic research in Tulipa (Liliaceae), a genus of significant economic and horticultural value, has relied on limited nuclear (mostly nuclear ribosomal internal transcribed spacer, nrITS) and plastid DNA sequences, resulting in low-resolution phylogenetic trees and uncertain intrageneric classifications. The genus, noted for its large genome, presents discordant relationships among Amana, Erythronium, and Tulipa, likely due to incomplete lineage sorting (ILS) and/or reticulate evolution. Thus, phylogenomic approaches are needed to clarify these relationships and the conflicting signals within the tribe Tulipeae.ResultsWe newly sequenced 50 transcriptomes of 46 species of tribe Tulipeae (including multiple accessions of all four genera) and one outgroup species of the sister tribe Lilieae (Notholirion campanulatum), and downloaded 15 previously published transcriptomes of tribe Tulipeae to supplement the sampling. One plastid dataset (74 plastid protein-coding genes, PCGs) and one nuclear dataset (2594 nuclear orthologous genes, OGs) were constructed, with the latter used for species tree inference based on maximum likelihood (ML) and multi-species coalescent (MSC) methods. To investigate causes of gene tree discordance, “site con/discordance factors” (sCF and sDF1/sDF2) were calculated first, after which phylogenetic nodes displaying high or imbalanced sDF1/2 were selected for phylogenetic network analyses and polytomy tests to determine whether ILS or reticulate evolution best explain incongruence. Key relationships not resolved by this technique, especially those among Amana, Erythronium, and Tulipa, were further investigated by applying D-statistics and QuIBL.ConclusionsWe failed to reconstruct a reliable and unambiguous evolutionary history among Amana, Erythronium, and Tulipa due to especially pervasive ILS and reticulate evolution, likely caused either by obscured minority phylogenetic signal or differing signals among genomic compartments. However, within Tulipa we confirmed the monophyly of most subgenera, with the exception of two species in the small subgenus Orithyia, of which Tulipa heterophylla was recovered as sister to the remainder of the genus, whereas T. sinkiangensis clustered within subgenus Tulipa. In contrast, most traditional sections of Tulipa were found to be non-monophyletic.

Block-Cipher No-Hit-Zone Sequence-Assisted Spectrum Control Scheme for Distributed Systems

In distributed systems, the dense access of wireless devices introduces significant challenges, including severe quasi-synchronous multiple access interference (MAI) and transmission security threats, which limit the effectiveness of traditional orthogonality-based spectrum control schemes. To address these challenges, this paper proposes a new block-cipher no-hit-zone (BC-NHZ) sequence-assisted spectrum control transmission scheme, aimed at enhancing privacy protection and improving overall communication capacity for distributed systems. The BC-NHZ scheme employs block cipher encryption and establishes control sequences to represent the spectrum usage scheme. Moreover, we mathematically prove that the parameters of the BC-NHZ scheme achieve optimal results with respect to the bound of the Hamming correlation. Numerical analysis and simulation results validate the practical feasibility of the BC-NHZ scheme, demonstrating its reliability to relax synchronous requirements while providing enhanced transmission privacy protection performance.

CNN-Based End-to-End CPU-AP-UE Power Allocation for Spectral Efficiency Enhancement in Cell-Free Massive MIMO Networks

Cell-free massive multiple-input multiple-output (MIMO) networks eliminate cell boundaries and enhance uniform quality of service by enabling cooperative transmission among access points (APs). In conventional cellular networks, user equipment located at the cell edge experiences severe interference and unbalanced resource allocation. However, in cell-free massive MIMO networks, multiple access points cooperatively serve user equipment (UEs), effectively mitigating these issues. Beamforming and cooperative transmission among APs are essential in massive MIMO environments, making efficient power allocation a critical factor in determining overall network performance. In particular, considering power allocation from the central processing unit (CPU) to the APs enables optimal power utilization across the entire network. Traditional power allocation methods such as equal power allocation and max–min power allocation fail to fully exploit the cooperative characteristics of APs, leading to suboptimal network performance. To address this limitation, in this study we propose a convolutional neural network (CNN)-based power allocation model that optimizes both CPU-to-AP power allocation and AP-to-UE power distribution. The proposed model learns the optimal power allocation strategy by utilizing the channel state information, AP-UE distance, interference levels, and signal-to-interference-plus-noise ratio as input features. Simulation results demonstrate that the proposed CNN-based power allocation method significantly improves spectral efficiency compared to conventional power allocation techniques while also enhancing energy efficiency. This confirms that deep learning-based power allocation can effectively enhance network performance in cell-free massive MIMO environments.

Channel and Power Allocation for Multi-Cell NOMA Using Multi-Agent Deep Reinforcement Learning and Unsupervised Learning.

Among the 5G and anticipated 6G technologies, non-orthogonal multiple access (NOMA) has attracted considerable attention due to its notable advantages in data throughput. Nevertheless, it is challenging to find the near-optimal allocation of the channel and power resources to maximize the performance of the multi-cell NOMA system. In addition, due to the complex and dynamically changing wireless communication environment and the lack of the near-optimal labels, conventional supervised learning methods cannot be directly applied. To address these challenges, this paper proposes a framework of MDRL-UL that integrates the multi-agent deep reinforcement learning with the unsupervised learning to allocate the channel and power resources in a near-optimal manner. In the framework, a multi-agent deep reinforcement learning neural network (MDRLNN) is proposed for channel allocation, while an attention-based unsupervised learning neural network (ULNN) is proposed for power allocation. Furthermore, the joint action (JA) derived from the MDRLNN for channel allocation is used as a representation to be fed into the ULNN for power allocation. In order to maximize the energy efficiency of the multi-cell NOMA system, the expectation of the energy efficiency is used to train both the MDRLNN and the ULNN. Simulation results indicate that the proposed MDRL-UL can achieve higher energy efficiency and transmission rates than other algorithms.

(185) REFERRAL RELATIONSHIPS FOR HOLISTIC SEXUAL HEALTH

Abstract Introduction The physician/sex therapist/sexuality educator referral relationship has the potential to enhance sexual health through a holistic approach to care. This type of business relationship thrives when the participants understand and respect each other’s scope of work. Through a collaborative practice, our center exemplifies the biopsychoeducational model of sexual healthcare, which can help providers build and maintain referral relationships, address legal and ethical concerns, and maintain appropriate professional boundaries. Objective The biopsychoeducational model can be initiated by psychotherapists, physicians, or educators as a practice-building opportunity and way to ensure holistic client care. One of the chief values of the model is that it recognizes that sexual health encompasses physical, psychological, relational, and situational aspects (Levy, 2002), and because of its complexity, individuals may be unable to locate an individual provider capable of providing comprehensive care. By affiliating even loosely, professionals in sexual medicine, psychotherapy, and sexuality education can offer complementary diagnosis, assessment, and treatment or counseling based on a biopsychoeducational model of sexual healthcare (Davis, 2010). Methods Through case consultation of four patients within our practice model, we outline how the referral process works. The partners (physician, sex therapist, and sexuality educator) address the patient/client’s needs related to sexual health, function, pain, knowledge, relationships, desire, pleasure, and other issues. Patients have multiple access points to the practice and are not required to start with a medical examination. The multiple entry points to our practice highlight our determination to make sexual healthcare accessible. Results The biopsychoeducational model relies on each provider’s appreciation of the benefits of referrals as well as familiarity with each other’s scopes of practice. When each provider understands and values the others’ sexual healthcare specialty, the appropriateness of referrals increases. This enhances patient care and helps in practice building. Conclusions The complex nature of sexuality may benefit from an integrative approach to sexual healthcare that is based on professional relationships among physicians, psychotherapists, and sexuality educators (Davis, 2010). This approach, best described by the biopsychoeducational model, can enhance client sexual health if referral relationships are fostered among physicians, sex therapists, and sexuality educators. The biopsychoeducational model can help providers build their practice and increase their credibility, specifically among other medical practitioners as the providers involved, and their patients, share word of the efficacy of the practice model. Disclosure No.

Performance Analysis of Blockchain Consensus Algorithm in Unmanned Aerial Vehicle Ad Hoc Networks

Blockchain technology is a competitive solution to address the prevailing data security concerns in unmanned aerial vehicle (UAV) networks. However, wireless communication links between UAV nodes are vulnerable to electromagnetic interference and competition due to limited spectrum resources. Consequently, a comprehensive analysis of blockchain performance within UAV network environments is imperative for the effective deployment and optimization of blockchain technology. This paper presents two theoretical models. The first model assesses the impact of the Carrier Sense Multiple Access with Collision Avoid (CSMA/CA) channel access protocol on the latency and throughput of the chained HotStuff consensus algorithm. The second model considers the movement characteristics of UAV nodes in three-dimensional space and the complexity of the communication environment. The aim of the second model is to calculate the consensus failure probability of UAV networks under electromagnetic interference. The results demonstrate that the theoretical values closely match the actual simulation outcomes.

Securing Intelligent Reflecting Surface (IRS)–Aided NOMA Networks in Covert Wireless Communication Using Jammer

ABSTRACTIn this paper, an intelligent reflecting surface (IRS)–assisted non‐orthogonal multiple access (NOMA) network is proposed in a multi‐user scenario where the transmitter communicates with its receiver covertly by helping a friendly jammer. In this network, an adversary detects the communication existence of the users in the frequency band while the jammer sends the jamming signals to the adversary to degrade its performance. In this case, the analytical expressions for the secrecy outage probability (SOP), false alarm probability, and the missed detection probability at adversary are obtained. Rayleigh fading channel is assumed as the channel model while the covert communication performance is improved. For this purpose, the total effective rates are maximized by optimization of the transmission power, power allocation to multiple users, IRS reflection matrix, and also transmission probability adjustment with constraints on the detection performance and SOP. The problem is non‐convex; therefore, we present the genetic algorithm (GA) method to find the suboptimal solution for the problem with lower complexity. Numerical results show the performance improvement of the proposed algorithm in comparison to the benchmark algorithms.

Evaluation of the implementation and the associated operating costs of a medicine regulatory harmonisation model in Africa: the case of ZaZiBoNa, a SADC work-sharing initiative.

To improve the access to and affordability of medicines, countries in the Southern African Development Community (SADC) established ZaZiBoNa, a medicine regulatory harmonisation initiative. Studies have evaluated the initiative's technical and operational efficiencies and effectiveness. This study evaluated ZaZiBoNa from project implementation and cost perspectives, at the 5-year mark, to establish a business case for further investment. A quantitative and desk review were undertaken. Two surveys, one with 7 National Regulatory Authorities (NRAs) that participated in the initiative in the initial 5 years were conducted and the other with 40 pharmaceutical companies who submitted applications in the same period were recruited to share their perceived experiences and expectations with the collaborative initiative. All 7 NRAs agreed the initiative had generally achieved its objectives and participation was beneficiary. As of June 2022, the 7 NRAs had registered 152 of the 235 (64.7%) products assessed during the study period, with a median time from joint assessment recommendations to registration of 7 months, against a target of 3 months (range: 4 to 14 months). Of the industry respondents (n = 13), 61.5% indicated the initiative had had a positive impact on their business, though expectations were only met to some degree (31%-46%). Failure to create a less expensive process and to facilitate simultaneous multiple market access (31% and 23% respectively) were the most common unmet expectations. Nonetheless, 62% of the industry respondents expressed willingness to continue utilising the collaborative process. The cost per joint assessment recommendation during the study period was USD 2,768. The study also highlighted that implementation of initiatives could take time and that some goals can only be achieved in the medium to long term. This is a lesson for other collaboration initiatives in Africa, such as the African Medicines Agency.

Throughput analysis of optical NOMA waveform through RNN and CNN neural networks with 256-QAM

Abstract Optical non-orthogonal multiple access (NOMA) is a critical communication technology that allows multiple users to access the same frequency spectrum at the same time, greatly improving spectral efficiency. This paper explores neural network-based encryption methods to protect NOMA systems with high throughput. In particular, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are used to study and improve encryption mechanisms. CNNs learn spatial features of the data, whereas RNNs are good at modeling temporal dependencies. Simulation outcomes show that RNN-based encryption provides better performance in dynamic scenarios with increased security against eavesdropping and lower bit error rate (BER). Under a Rayleigh fading channel, RNN encryption lowers the BER at SNR to 6.1 dB, which is better than CNN (8.1 dB), MIMO-NOMA (10.2 dB), OFDM (13 dB), and OTFS (15 dB). In the same way, in Rician channel conditions, RNN attains 5 dB BER, outperforming CNN (7.1 dB), MIMO-NOMA (9.1 dB), OFDM (12.1 dB), and OTFS (10.1 dB). RNN also enhances power spectral density (PSD), reducing it to −810 dB from −700 dB of CNN, −590 dB of MIMO-NOMA, and −400 dB and −510 dB of OFDM and OTFS, respectively. These advancements emphasize the compromises between complexity, throughput, and present a promising avenue for optical NOMA systems using deep learning-based methods.

Energy‐Efficient Blockchain‐Based Secure Model to Share Medical Data Using Mobile Edge Computing

ABSTRACTBlockchain technology is gaining importance in different sectors like healthcare, finance, agriculture, and many more. The important capabilities of blockchain like decentralization, immutability, consensus mechanism, etc. provide security, privacy, transparency, accountability, and many other benefits. On the other hand, Mobile Edge Computing (MEC) is a distributed framework that provides cloud computing capabilities to mobile devices. The existing studies combining blockchain technology and MEC often do not consider the delay and energy consumption for data offloading. In this paper, a blockchain‐based scheme has been proposed for sharing Internet of Medical Things (IoMT) data between a patient and a doctor, which offloads tasks to the MEC server to achieve energy efficiency. In the proposed scheme, the Non‐Orthogonal Multiple Access (NOMA) protocol is used to share a channel among several users. Here, NOMA offers some advantages in the system like low cost, latency, and power consumption. In the proposed scheme, the energy consumption is optimized based on the task delegation decision and resource distribution in the MEC server. Additionally, operations of the blockchain network are automated using various smart contracts. The efficiency of the proposed scheme is analyzed in terms of energy consumption, average transmission rate, and offloading delay in processing healthcare data. The experimental results demonstrate that the proposed model enhances energy efficiency and optimizes performance compared to the state‐of‐the‐art offloading schemes.

Comparison of the Effects of Multiple Retrograde Intrarenal Surgery and Single Access Coventional Percutaneous Nephrolithotomy Operation on Early Acute Kidney Injury

Comparison of the Effects of Multiple Retrograde Intrarenal Surgery and Single Access Coventional Percutaneous Nephrolithotomy Operation on Early Acute Kidney Injury