This paper addresses the challenge of training federated learning (FL) algorithms over practical wireless networks enhanced by non-orthogonal multiple access (NOMA). In FL procedures, model parameters are transmitted over wireless links, and factors such as packet errors can significantly impact training quality, especially with non-independent and identically distributed (non-IID) datasets. To address this issue, we formulate the complex learning and wireless resource allocation problem as an optimization task aimed at minimizing an FL loss function, thereby capturing the performance of the FL algorithm. Under mild assumptions, we derive the expected convergence rate of the FL algorithm, quantifying the impact of wireless factors on FL. Leveraging this convergence analysis, we determine the optimal transmit power for each user through quadratic transform (QT) and a proposed low-complexity iterative method with successive convex approximation (SCA) serving as benchmark. Extensive simulation results demonstrate that the proposed scheme significantly outperforms traditional schemes from both the optimization and FL performance perspectives.

Pinching antennas, as a novel flexible-antenna technology capable of establishing line-of-sight (LoS) connections and effectively mitigating large-scale path loss, have recently attracted considerable research interest. However, the implementation of ideal pinching-antenna systems involves determining and adjusting pinching antennas to an arbitrary position on waveguides, which presents challenges to both practical deployment and related optimization. This paper investigates a practical pinching-antenna system in multi-waveguide scenarios, where pinching antennas are installed at pre-configured discrete positions to serve downlink users with non-orthogonal multiple access (NOMA). To improve system throughput, a sophisticated optimization problem is formulated by jointly considering waveguide assignment, antenna activation, successive interference cancellation (SIC) decoding order design, and power allocation. By treating waveguide assignment and antenna activation as two coalition-formation games, a novel game-theoretic algorithm is developed, in which the optimal decoding order is derived and incorporated. For power allocation, monotonic optimization and successive convex approximation (SCA) are employed to construct global optimal and low-complexity solutions, respectively. Simulation results demonstrate that the NOMA based pinching-antenna system exhibits superior performance compared to the considered benchmark systems, and the proposed solutions provide significant improvement in terms of sum rate and outage probability.

Large language models (LLMs) have shown great promise in many domains, yet their potential to transform wireless communications, where the escalating complexity of the network outpaces traditional model-based methods, remains largely untapped. Addressing this gap is critical for the next generation of intelligent and adaptive 6G systems. In this work, we develop a specialized dataset aimed at enhancing the evaluation and fine-tuning of LLMs specifically for wireless communication applications. The dataset includes a diverse set of multi-hop questions, including true/false and multiple-choice types, spanning varying difficulty levels from easy to hard. By utilizing advanced language models for entity extraction and question generation, rigorous data curation processes are employed to maintain high quality and relevance. Additionally, we introduce a Pointwise V-Information (PVI) based fine-tuning method, providing a detailed theoretical analysis and justification for its use in quantifying the information content of training data with 2.24% and 1.31% performance boost for different models compared to baselines, respectively. To demonstrate the effectiveness of the fine-tuned models with the proposed methodologies on practical tasks, we also consider different tasks, including summarizing optimization problems from technical papers and solving the mathematical problems related to non-orthogonal multiple access (NOMA), which are generated by using the proposed multi-agent framework. Simulation results show significant performance gain in summarization tasks with 20.9% in the ROUGE-L metrics. We also study the scaling laws of fine-tuning LLMs and the challenges LLMs face in the field of wireless communications, offering insights into their adaptation to wireless communication tasks. This dataset and fine-tuning methodology aim to enhance the training and evaluation of LLMs, contributing to advancements in LLMs for wireless communication research and applications.

The combination of non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) has recently received great attention for maximizing energy efficiency (EE) in wireless networks. Previous studies mainly focus on single-ratio EE maximization using pure orthogonal multiple access (OMA) or NOMA scheme in MEC networks. This paper proposes a dynamic hybrid NOMA scheme that dynamically transforms between pure OMA and pure NOMA under the general network status in the multi-user MEC network. An offloading multi-ratio and non-convex EE problem is formulated. Two iterative algorithms, i.e., the successive Dinkelbach method-based algorithm and the closed-form nested fractional programming (FP) algorithm, are proposed to efficiently solve the offloading EE over multiple users’ time and power within polynomial time based on different FP techniques, which provides feasible ideas to solve such multi-ratio optimization problems. The Dinkelbach method-based algorithm decomposes and optimizes the offloading EE user-wise. The nested FP algorithm jointly maximizes multiple users’ EE by transforming the multi-ratio problem into equivalent linear forms, where the closed-form power solution is obtained. Simulation results show the superiority of the dynamic hybrid NOMA strategy and the feasibility of the two proposed algorithms. Critical insights are obtained over the dynamic hybrid NOMA strategies in the general MEC network.

This paper investigates wireless federated learning in data heterogeneous scenarios, where device selection usually leads to a degradation in learning performance. This paper is motivated by the fact that while training deep learning networks using federated stochastic gradient descent (FedSGD) on non-independent and identically distributed (non-IID) datasets, device selection can generate gradient errors that accumulate, leading to potential weight divergence, which is further exacerbated with low device participation. To mitigate weight divergence, an age-weighted FedSGD algorithm is designed in this paper to scale local gradients according to the previous device selection results. Furthermore, by revealing the relationship between device participation and latency, an energy consumption minimization problem is formulated accordingly, which consists of resource allocation and sub-channel assignment. By transforming the resource allocation problem into convex and utilizing KKT conditions, we derive the optimal resource allocation solution. Moreover, this paper develops a matching based algorithm to generate the enhanced sub-channel assignment. Simulation results indicate that 1) age-weighted FedSGD is able to outperform conventional FedSGD in terms of convergence rate and achievable accuracy, and 2) the proposed resource allocation and sub-channel assignment strategies can significantly reduce energy consumption and improve learning performance by increasing device participation.

The combination of non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) has received considerable attention for maximizing energy efficiency (EE) in wireless networks. Previous studies focus on EE maximization based on pure OMA or NOMA offloading in MEC networks. In this paper, a hybrid NOMA (H-NOMA) scheme which dynamically transforms between pure OMA and pure NOMA offloading strategies is proposed under the general network status in the multi-user MEC network to maximize EE. An offloading multi-ratio EE problem is formulated. Two iterative algorithms, the successive Dinkelbach method-based algorithm and the quadratic transform-based fractional programming (FP) algorithm, are proposed to efficiently maximize the EE over multiple users' time and power within polynomial time via different FP techniques. Simulation results verify the properties of the proposed dynamic H-NOMA offloading strategy and show the superior performance of the H-NOMA strategy over EE in MEC systems.

Although Federated Learning (FL) has garnered increasing attention from researchers, the development of ad-vanced FL frameworks incorporating multiple access techniques remains relatively underexplored. This paper investigates the integration of a novel clustered federated learning (CFL) framework with non-orthogonal multiple access (NOMA) in environments with non-independent and identically distributed (non-iid) datasets. To explore the potential benefits of the proposed framework, the optimization problem is formulated as an energy minimization problem, which includes sub-channel and power allocation. The formulated problem is divided into two sub-problems, respectively solved by the matching-based algorithm and Karush-Kuhn-Tucker (KKT) conditions, in which the closed-form solution is derived. Our simulation results demon-strate that jointly optimizing sub-channel and power allocation in NOMA-enhanced networks can lead to a significant improvement in test accuracy and convergence speed in the proposed FL framework.

This paper investigates a backscatter communication (BackCom) based non‐orthogonal multiple access (NOMA) system in a multiple‐input and single‐output (MISO) scenario, where two decoding methods are deployed, including the sum‐capacity approach and QR decomposition. The goal is to maximize energy efficiency (EE) through the optimization of the beamforming matrix and the reflection coefficient of the BackCom devices. Two algorithms, Dinkelbach based on penalty semidefinite relaxation (SDR) and successive convex approximation (SCA), are proposed as high‐performance and low‐complexity solutions, respectively. Simulation results indicate that the combination of the sum‐capacity approach and Dinkelbach yields the best performance, though at the highest complexity, while the amalgamation of QR decomposition and SCA offers the lowest performance but with minimal complexity.

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.

In this paper, a federated learning (FL) based system is investigated with non-independent and identically distributed (non-IID) dataset, where multiple devices participate in the global model aggregation through a limited number of sub-channels. By analyzing weight divergence and convergence rate, a new metric is proposed based on age-of-information (AoI), which incorporates latency and can provide an advanced device selection standard. After that, device selection, sub-channel assignment and resource allocation are jointly designed in an overall AoI minimization problem under the maximum energy consumption constraint. The formulated problem is decoupled into two sub-problems. After analyzing the feasibility, the resource allocation problem is transformed to a convex problem, and the closed-from solution is obtained based on KKT conditions. By introducing virtual sub-channels, device selection and sub-channel assignment are jointly solved by a matching based algorithm. Simulation results indicate that the proposed scheme is able to outperform all baselines in terms of both test accuracy and sum AoI, and the developed strategies can achieve significant improvements for all schemes.

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.

The combination of non-orthogonal multiple access (NOMA) and multi-access edge computing (MEC) can significantly improve the system performance including communication coverage, spectrum efficiency, etc. In this article, we focus on energy-efficient resource allocation for a multi-user multi-BS NOMA-MEC network with imperfect channel state information (CSI), where each user can upload its tasks to multiple base stations (BSs) for remote executions. We propose an optimization scheme, including task assignment, power allocation and user association, to minimize energy consumption. Specifically, we transform the probabilistic problem into a non-probabilistic one. To efficiently solve this nonconvex energy minimization problem, we first investigate the one-user two-BS case and derive the optimal closed-form expressions of task assignment and power allocation via the bilevel programming method. Subsequently, based on the derived optimal solution, we propose a low complexity algorithm for the user association in the multi-user multi-BS scenario. Simulations demonstrate that the proposed algorithm can yield much better performance than the conventional OMA scheme and the identical results with lower complexity from the exhaustive search with the small number of BSs.

In this article, a two-user scenario of a non-orthogonal multiple access (NOMA)-based mobile edge computing (MEC) network is investigated. By treating the users and the MEC server as leader and follower, respectively, a Stackelberg game is formulated. More specifically, the leader tends to minimize the total energy consumption for task offloading and local computing by optimizing the task assignment coefficients and transmit power. On the other side, the follower aims to minimize the total execution time by allocating different computational resources for processing the offloaded tasks. In order to solve the formulated problem, the Stackelberg equilibrium is considered. Based on the given insights, a closed-form solution of the follower level problem is obtained and included in the leader level one. Furthermore, by analyzing the leader's strategies, the leader level problem is solved through the Karush-Kuhn-Tucker (KKT) conditions, and closed-form expressions for the optimal task assignment coefficients and offloading time, are derived. Finally, this work is extended to the multi-user scenario, where a matching-based user pairing algorithm is proposed to assign users into different sub-channels. Simulation results indicate that: i) the derived closed-form solutions and the proposed user pairing algorithm can significantly improve energy efficiency; ii) the different task assignment strategies can be dynamically implemented to handle the varying wireless environment.

In this paper, sub-channel scheduling, task assignment and power allocation are investigated for orthogonal multiple access (OMA)-based and non-orthogonal multiple access (NOMA)-based mobile edge computing (MEC) systems. Based on different channel conditions and computational capacities, computational tasks are partially offloaded to the MEC server via OMA or NOMA protocols. In order to minimize the total energy consumption, an optimization problem under the task execution latency constraint is formulated and divided into two sub-problems. By utilizing matching theory, the formulated sub-channel allocation problem is solved by a proposed low-complexity algorithm, where the joint optimization of task assignment and power allocation is performed at each iteration. Based on the delay constraint, some insights are obtained, and the closed-form solutions of task assignment coefficients and transmit power are derived. Furthermore, the offloading strategy in both OMA and NOMA schemes is analyzed, which shows that the optimal task assignment coefficient is decided by the energy consumption efficiency (ECE). Simulation results indicate that: i) the proposed sub-channel allocation algorithm and derived closed-form solutions can significantly improve the MEC system in terms of the energy consumption; ii) the provided offloading strategy can be dynamically and efficiently employed with different channel conditions and computational capacities.

In this paper, task assignment and power allocation are investigated for the the non-orthogonal multiple access (NOMA)-aided mobile edge computing (MEC) system. Based on the different channel conditions and central processing units (CPUs), users can offload computational tasks to the MEC server or process tasks locally. In order to minimize the energy consumption of the proposed NOMA- aided MEC system, the task assignment and power allocation optimization problem is formulated. Based on the insight derived from the delay constraint, the closed-form expressions of task ratios and transmit power are derived. Furthermore, the optimality of the derived closed-form solutions is analyzed, which shows that the optimal task assignment of any user is based on the energy consumption efficiency (ECE) of offloading and local computing. Simulation results indicate that: i) the derived closed-form solutions can significantly reduce the energy consumption of the NOMA-MEC system, and ii) the analysis of the derived closed-form solutions is confirmed.

In this paper, we investigate a full duplex (FD) multi-user non-orthogonal multiple access (NoMA) communication system based on the optimization of received signal-to-interference-plus-noise ratio (SINR) per unit power. Since the communication system operates in the FD mode, co-channel interference (CCI) and self-interference (SI) dominate the system's performance. Accordingly, to combat the CCI, we adopt a game-theoretic approach and propose users' clustering algorithms and to suppress the SI, we formulate an optimization problem to maximize the power-normalized SINR (PN-SINR). While the user clustering optimization problem is constrained by: 1) the successive interference cancellation (SIC) constraint and 2) two binary constraints for the allocations of uplink (UL) and downlink (DL) users, the PN-SINR problem is constrained by: 1) total transmit power budget at the base station and UL users; 2) the fundamental condition for the implementation of successive interference cancellation in the NoMA; and 3) the minimum fairness condition for the UL users. The original PN-SINR problem is non-convex and hence is converted into an equivalent subtractive-form problem, after which we propose an iterative algorithm to find the optimal power allocation policy. Properties of all the proposed algorithms are thoroughly investigated and the numerical results are provided. Based on the channel conditions and suppression level of SI and CCI, the superiority of the proposed FD-NoMA system over half-duplex NoMA and FD orthogonal multiple access systems is verified.

In this paper, the joint design of user clustering and power allocation is investigated in a downlink non-orthogonal multiple access-based millimeter wave (mm-wave-NOMA) system. To reduce the system overhead, hybrid precoding techniques are adopted at the base station by using the channel state information of cluster heads (CHs) only. In order to maximize the sum rate of the system, Stackelberg game-based optimization problems are formulated for two cases with different quality of some targets: Case 1 focuses on improving the data rates of CHs; and Case 2 aims to increase the rates of cluster members when the CHs' data rates are caped. With the aid of coalitional game theory, a low complexity algorithm is proposed to dynamically allocate users into different clusters. Then, the optimal power allocation coefficients of the users in each cluster are obtained by the derived closed-form expressions. The properties of the proposed joint algorithms are analyzed in terms of Stackelberg equilibrium, the complexity, the convergence and the stability. The simulation results demonstrate that: 1) the proposed algorithms can significantly improve the sum rate and reduce the outage probability of the mm-wave-NOMA system and 2) the application of NOMA in mm-wave systems is capable of achieving promising gains over conventional orthogonal multiple access-based frameworks in both cases.

In this paper, user association and power allocation are investigated in a non-orthogonal multiple access (NOMA)-based multi-cell network. In order to perform successive interference cancellation (SIC) techniques for removing the intra-base station (BS) interference, the optimal decoding order is derived for all users associated with the same BS. In an effort to improve the system, a sum rate maximization problem is formulated by jointly designing user association and power allocation. Two game theory based algorithms are proposed to obtain the stable user structure by dividing users into different BSs' clusters, where the sub-optimal and global optimal solutions can be achieved. The properties of the proposed algorithms, including complexity, convergence, stability and optimality, are analyzed. Based on the quality-of-service (QoS) constraint, the closed-from solutions for power allocation are derived, and thus the expressions for the sum rate of all users in each cluster is obtained. Moreover, the case that the QoS threshold cannot be achieved by all users in each cluster is considered. Simulation results demonstrate that: i) the proposed user association algorithms and the closed-form solutions for power allocation can significantly enhance the sum rate and outage probability; and ii) the proposed NOMA-based system is capable of achieving promising gains over the conventional orthogonal multiple access (OMA)-based framework in the multi-cell scenario.

In this paper, the joint design of user clustering and power allocation is investigated in a downlink non-orthogonal multiple access based millimeter wave (mmWave-NOMA) system. In order to maximize the sum rate of the system, the Stackelberg game based optimization problem is formulated. With the aid of game theory, a low complexity algorithm is proposed to dynamically allocate users into different clusters. By deriving the closed-form expressions, the optimal power allocation coefficients of the users in each cluster are obtained. The properties of the proposed joint algorithm, including complexity, convergence and stability, are analyzed. Simulation results demonstrate that: i) the proposed algorithm can significantly improve the sum rate and reduce the outage probability of the mmWave- NOMA system; ii) the application of NOMA in mmWave systems is capable of achieving promising gains over conventional orthogonal multiple access (OMA) based frameworks in both cases.

In this paper, the flexible user association is inves- tigated in non- orthogonal multiple access (NOMA)-based multi- ple base stations (BSs) networks. More particularly, users are partitioned into multiple orthogonal clusters to allocate into different resource blocks (RBs) for avoiding serious co-channel interferences. In an effort to maximize the weighted sum rate of the system, a user association optimization problem is formulated. Two algorithms based on coalitional game are proposed for obtaining suboptimal and global optimal solutions, respectively. The properties of the proposed algorithms, including complexity, convergence, stability and optimality are analyzed. Simulation re- sults demonstrate that: i) the proposed algorithms can significantly enhance the weighted sum rate of the considered systems; ii) the proposed NOMA-based system is capable of achieving promising gains over conventional orthogonal multiple access (OMA)-based framework, and iii) the considered distance based weight factor can greatly improve the fairness of users.