On Energy Efficiency of Hybrid NOMA

This paper aims to prove the significant superiority of hybrid non-orthogonal multiple access (NOMA) over orthog onal multiple access (OMA) in terms of energy efficiency. In particular, a novel hybrid NOMA scheme is proposed in which a user can transmit signals not only by using its own time slot but also by using the time slots of other users. The data rate maximization problem is studied by optimizing the power allocation, where closed-form solutions are obtained. Further more, the conditions under which hybrid NOMA can achieve a higher instantaneous data rate with less power consumption than OMA are obtained. It is proved that the probability that hybrid NOMA can achieve a higher instantaneous data rate with less power consumption than OMA approaches one in the high SNR regime, indicating the superiority of hybrid NOMA in terms of power efficiency. Numerical results are also provided to verify the developed analysis and also to demonstrate the superior performance of hybrid NOMA.

Exploring the Impact of RIS on Cooperative NOMA URLLC Systems: A Theoretical Perspective

In this paper, we conduct a theoretical analysis of how to integrate reconfigurable intelligent surfaces (RIS) with cooperative non-orthogonal multiple access (NOMA), considering URLLC. We consider a downlink two-user cooperative NOMA system employing short-packet communications, where the two users are denoted by the central user (CU) and the cell-edge user (CEU), respectively, and an RIS is deployed to enhance signal quality. Specifically, compared to CEU, CU lies nearer from BS and enjoys the higher channel gains. Closed-form expressions for the CU's average block error rate (BLER) are derived. Furthermore, we evaluate the CEU's BLER performance utilizing selective combining (SC) and derive a tight lower bound under maximum ratio combining (MRC). Simulation results are provided to our analyses and demonstrate that the RIS-assisted system significantly outperforms its counterpart without RIS in terms of BLER. Notably, MRC achieves a squared multiple of the diversity gain of the SC, leading to more reliable performance, especially for the CEU. Furthermore, by dividing the RIS into two zones, each dedicated to a specific user, the average BLER can be further reduced, particularly for the CEU.

Model-Based GNN Enabled Energy-Efficient Beamforming for Ultra-Dense Wireless Networks

This paper investigates deep learning enabled beamforming design for ultra-dense wireless networks by integrating prior knowledge and graph neural network (GNN), named model-based GNN. A energy efficiency (EE) maximization problem is formulated subject to power budget and quality of service (QoS) requirements, which is reformulated based on the minimum mean square error scheme and the hybrid zero-forcing and maximum ratio transmission schemes. Based on the reformulated problem, the model-based GNN to realize the mapping from channel state information to beamforming vectors. Particular, the multi-head attention mechanism and residual connection are adopted to enhance the feature extracting, and a scheme selection module is designed to improve the adaptability of GNN. The unsupervised learning is adopted, and a various-input training strategy is proposed to enhance the stability of GNN. Numerical results demonstrate the millisecond-level response with limited performance loss, the scalability to different users and the adaptability to various channel conditions and QoS requirements of the model-based GNN in ultra-dense wireless networks.

Hybrid NOMA Assisted OFDMA Uplink Transmission

Hybrid non-orthogonal multiple access (NOMA) has recently received significant research interest due to its ability to efficiently use resources from different domains and also its compatibility with various orthogonal multiple access (OMA) based legacy networks. Unlike existing studies on hybrid NOMA that focus on combining NOMA with time-division multiple access (TDMA), this work considers hybrid NOMA assisted orthogonal frequency-division multiple access (OFDMA). In particular, the impact of a unique feature of hybrid NOMA assisted OFDMA, i.e., the availability of users' dynamic channel state information, on the system performance is analyzed from the following two perspectives. From the optimization perspective, analytical results are developed which show that with hybrid NOMA assisted OFDMA, the pure OMA mode is rarely adopted by the users, and the pure NOMA mode could be optimal for minimizing the users' energy consumption, which differs from the hybrid TDMA case. From the statistical perspective, two new performance metrics, namely the power outage probability and the power diversity gain, are developed to quantitatively measure the performance gain of hybrid NOMA over OMA. The developed analytical results also demonstrate the ability of hybrid NOMA to meet the users' diverse energy profiles.

Exploring Age-of-Information Weighting in Federated Learning under Data Heterogeneity

This paper investigates federated learning in a wireless communication system, where random device selection is employed with non-independent and identically distributed (non-IID) data. The analysis indicates that while training deep learning networks using federated stochastic gradient descent (FedSGD) on non-IID datasets, device selection can generate gradient errors that accumulate, leading to potential weight divergence. To mitigate training divergence, we design an age-weighted FedSGD to scale local gradients according to the previous state of devices. To further improve learning performance by increasing device participation under the maximum time consumption constraint, we formulate an energy consumption minimization problem by including resource allocation and sub-channel assignment. By transforming the resource allocation problem into convex and utilizing KKT conditions, we derived the optimal resource allocation solution. Moreover, this paper develops a matching based algorithm to generate the enhanced sub-channel assignment. Simulation results indicate that i) age-weighted FedSGD is able to outperform conventional FedSGD in terms of convergence rate and achievable accuracy, and ii) the proposed resource allocation and sub-channel assignment strategies can significantly reduce energy consumption and improve learning performance by increasing the number of selected devices.

On the Reliability and Security of Ambient Backscatter Uplink NOMA Networks

A fundamental objective of the forthcoming sixth-generation wireless networks is to concurrently serve a vast array of devices many of which, such as Internet-of-Things (IoT) sensors, are projected to have low power requirements or even operate in a battery-free manner. To achieve this goal, non-orthogonal multiple access (NOMA) and ambient backscatter communications (AmBC) are regarded as two pivotal and promising technologies. In this work, we present a novel analytical framework for studying the reliability and security of uplink NOMA-based AmBC systems. Specifically, closed-form analytical expressions for both NOMA-users' and IoT backscatter device's (BD's) outage probabilities (OPs) are derived for both cases of perfect and imperfect successive interference cancellation (SIC). In addition, assuming that one NOMA-user transmits an artificial noise in order to enhance system's security, the physical layer security (PLS) of the system is investigated by extracting analytical expressions for NOMA-users' and BD's intercept probabilities (IPs). To gain insightful understandings, an asymptotic analysis is carried out by focusing on the high signal-to-noise (SNR) regime, which reveals that NOMA-users and BDs face outage floors in the high SNR regime as well as that IPs reach constant values at high SNR. Additionally, practical insights regarding how different system parameters affect these OP floors and IP constant values are extracted. Numerical results verify the accuracy of othe developed theoretical framework, offer performance comparisons between the presented NOMA-based AmBC system and a conventional orthogonal multiple access-based AmBC system, and reveal the impact of different system parameters on the reliability and security of NOMA-based AmBC networks.

Next Generation Multiple Access for IMT Towards 2030 and Beyond

Multiple access techniques are fundamental to the design of wireless communication systems, since many crucial components of such systems depend on the choice of the multiple access technique. Because of the importance of multiple access, there has been an ongoing quest during the past decade to develop next generation multiple access (NGMA). Among those potential candidates for NGMA, non-orthogonal multiple access (NOMA) has received significant attention from both the industrial and academic research communities, and has been highlighted in the recently published International Mobile Telecommunications (IMT)-2030 Framework. However, there is still no consensus in the research community about how exactly NOMA assisted NGMA should be designed. This perspective is to outline three important features of NOMA assisted NGMA, namely multi-domain utilization, multi-mode compatibility, and multi-dimensional optimality, where important directions for future research into the design of NOMA assisted NGMA are also discussed.

BackCom Assisted Hybrid NOMA Uplink Transmission for Ambient IoT

Hybrid non-orthogonal multiple access (H-NOMA) has recently received significant attention as a general framework of multiple access, where both conventional orthogonal multiple access (OMA) and pure NOMA are its special cases. This paper focuses on the application of H-NOMA to ambient Internet of Things (IoT) with energy-constrained devices, where a new backscatter communication (BackCom) assisted H-NOMA uplink scheme is developed. Resource allocation for H-NOMA uplink transmission is also considered, where an overall power minimization problem is formulated. Insightful understandings for the key features of BackCom assisted H-NOMA and its difference from conventional H-NOMA are illustrated by developing analytical results for the two-user special case. For the general multi-user scenario, two algorithms, one based on the branch-bound (BB) principle and the other based on successive convex approximation (SCA), are developed to realize different tradeoffs between the system performance and complexity. The numerical results are also provided to verify the accuracy of the developed analytical results and demonstrate the performance gain of H-NOMA over OMA.

Secure Communication of Active RIS Assisted NOMA Networks

As a revolutionary technology, reconfigurable intelligent surface (RIS) has been deemed as an indispensable part of the 6th generation communications due to its inherent ability to regulate the wireless channels. However, passive RIS (PRIS) still suffers from some pressing issues, one of which is that the fading of the entire reflection link is proportional to the product of the distances from the base station to the PRIS and from the PRIS to the users, i.e., the productive attenuation. To tackle this problem, active RIS (ARIS) has been proposed to reconfigure the wireless propagation condition and alleviate the productive attenuation. In this paper, we investigate the physical layer security of the ARIS assisted non-orthogonal multiple access (NOMA) networks with the attendance of external and internal eavesdroppers. To be specific, the closed-form expressions of secrecy outage probability (SOP) and secrecy system throughput are derived by invoking both imperfect successive interference cancellation (ipSIC) and perfect SIC. The secrecy diversity orders of legitimate users are obtained at high signal-to-noise ratios. Numerical results are presented to verify the accuracy of the theoretical expressions and indicate that: i) The SOP of ARIS assisted NOMA networks exceeds that of PRIS-NOMA, ARIS/PRIS-assisted orthogonal multiple access (OMA); ii) Due to the balance between the thermal noise and residual interference, introducing excess reconfigurable elements at ARIS is not helpful to reduce the SOP; and iii) The secrecy throughput performance of ARIS-NOMA networks outperforms that of PRIS-NOMA and ARIS/PRIS-OMA networks.

Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks

This study explores the benefits of integrating the novel clustered federated learning (CFL) approach with non-orthogonal multiple access (NOMA) under non-independent and identically distributed (non-IID) datasets, where multiple devices participate in the aggregation with time limitations and a finite number of sub-channels. A detailed theoretical analysis of the generalization gap that measures the degree of non-IID in the data distribution is presented. Following that, solutions to address the challenges posed by non-IID conditions are proposed with the analysis of the properties. Specifically, users' data distributions are parameterized as concentration parameters and grouped using spectral clustering, with Dirichlet distribution serving as the prior. The investigation into the generalization gap and convergence rate guides the design of sub-channel assignments through the matching-based algorithm, and the power allocation is achieved by Karush-Kuhn-Tucker (KKT) conditions with the derived closed-form solution. The extensive simulation results show that the proposed cluster-based FL framework can outperform FL baselines in terms of both test accuracy and convergence rate. Moreover, jointly optimizing sub-channel and power allocation in NOMA-enhanced networks can lead to a significant improvement.