In this paper, we consider a novel optimization design for multi-waveguide pinching-antenna systems, aiming to maximize the weighted sum rate (WSR) by jointly optimizing beamforming coefficients and antenna position. To handle the formulated non-convex problem, a gradient-based meta-learning joint optimization (GML-JO) algorithm is proposed. Specifically, the original problem is initially decomposed into two sub-problems of beamforming optimization and antenna position optimization through equivalent substitution. Then, the convex approximation methods are used to deal with the nonconvex constraints of sub-problems, and two sub-neural networks are constructed to calculate the sub-problems separately. Different from alternating optimization (AO), where two sub-problems are solved alternately and the solutions are influenced by the initial values, two sub-neural networks of proposed GML-JO with fixed channel coefficients are considered as local sub-tasks and the computation results are used to calculate the loss function of joint optimization. Finally, the parameters of sub-networks are updated using the average loss function over different sub-tasks and the solution that is robust to the initial value is obtained. Simulation results demonstrate that the proposed GML-JO algorithm achieves 5.6 bits/s/Hz WSR within 100 iterations, yielding a 32.7% performance enhancement over conventional AO with substantially reduced computational complexity. Moreover, the proposed GML-JO algorithm is robust to different choices of initialization and yields better performance compared with the existing optimization methods.

Recently, an emerging flexible-antenna technology, termed pinching antennas, has attracted growing academic interest. By inserting discrete dielectric materials, pinching antennas can be activated at arbitrary points along waveguides, allowing for flexible customization of channel conditions. This paper investigates a multi-waveguide pinching-antenna integrated sensing and communications (ISAC) system, where transmit pinching antennas (TPAs) and receive pinching antennas (RPAs) coordinate to simultaneously detect one potential target and serve one downlink user. We formulate a communication rate maximization problem subject to radar signal-to-noise ratio (SNR) requirement, transmit power budget, and the allowable movement region of the TPAs, by jointly optimizing TPA locations and transmit beamforming design. To address the non-convexity of the problem, we propose a novel fine-tuning approximation method to reformulate it into a tractable form, followed by a successive convex approximation (SCA)-based algorithm to obtain the solution efficiently. Furthermore, we derive the closed-form optimal solution for a special multi-waveguide case involving a single TPA. Extensive simulations validate both the system design and the proposed algorithm. Results show that the proposed method achieves near-optimal performance compared with the computational-intensive exhaustive search-based benchmark, and pinching-antenna ISAC systems exhibit a distinct communication-sensing trade-off compared with conventional systems.

In the literature of pinching-antenna systems, in-waveguide attenuation is often neglected to simplify system design and enable more tractable analysis. However, its effect on overall system performance has received limited attention in the existing literature. While a recent study has shown that, in line-of-sight (LoS)-dominated environments, the data rate loss incurred by omitting in-waveguide attenuation is negligible when the communication area is not excessively large, its effect under more general conditions remains unclear. This work extends the analysis to more realistic scenarios involving arbitrary levels of LoS blockage. We begin by examining a single-user case and derive an explicit expression for the average data rate loss caused by neglecting in-waveguide attenuation. The results demonstrate that, even for large service areas, the rate loss remains negligible under typical LoS blockage conditions. We then consider a more general multi-user scenario, where multiple pinching antennas, each deployed on a separate waveguide, jointly serve multiple users. The objective is to maximize the average sum rate by jointly optimize antenna positions and transmit beamformers to maximize the average sum rate under probabilistic LoS blockage. To solve the resulting stochastic and nonconvex optimization problem, we propose a dynamic sample average approximation (SAA) algorithm. At each iteration, this method replaces the expected objective with an empirical average computed from dynamically regenerated random channel realizations, ensuring that the optimization accurately reflects the current antenna configuration. Extensive simulation results are provided to the proposed algorithm and demonstrate the substantial performance gains of pinching-antenna systems, particularly in environments with significant LoS blockage.

Pinching-antenna systems have emerged as a promising flexible-antenna architecture for next-generation wireless networks, enabling enhanced adaptability and user-centric connectivity through antenna repositioning along waveguides. However, existing studies often overlook in-waveguide signal attenuation and in the literature, there is no comprehensive analysis on whether and under what conditions such an assumption is justified. This paper addresses this gap by explicitly incorporating in-waveguide attenuation into both the system model and algorithm design, and studying its impact on the downlink user data rates. We begin with a single-user scenario and derive a closed-form expression for the globally optimal antenna placement, which reveals how the attenuation coefficient and the user-to-waveguide distance jointly affect the optimal antenna position. Based on this analytical solution, we further provide a theoretical analysis identifying the system conditions under which in-waveguide attenuation has an insignificant impact on the user achievable rate. The study is then extended to the multi-user multiple-input multiple-output setting, where two efficient algorithms are developed, based on the weighted minimum mean square error method and the maximum ratio combining method, to jointly optimize beamforming and antenna placement. Simulation results validate the efficacy of the proposed algorithms and demonstrate that pinching-antenna systems substantially outperform conventional fixed-antenna baselines, underscoring their potential for future flexible wireless communications.

Pinching antennas, realized through position-adjustable radiating elements along dielectric waveguides, have emerged as a promising flexible-antenna technology thanks to their ability to dynamically reshape large-scale channel conditions. However, most existing studies focus on idealized LoS-dominated environments, overlooking the stochastic nature of realistic wireless propagation. This paper investigates a more practical multiuser pinching-antenna system under a composite probabilistic channel model that captures distance-dependent LoS blockage and NLoS scattering. To account for both efficiency and reliability aspects of communication, two complementary design metrics are considered: an average signal-to-noise ratio (SNR) metric characterizing long-term link quality and fairness, and an outage-constrained metric ensuring a prescribed reliability level. Based on these metrics, we formulate two optimization problems: the first seeks to maximize the minimum average SNR across users, while the second seeks to maximize a guaranteed SNR threshold subject to per-user outage constraints. Although both problems are inherently nonconvex, we exploit their underlying monotonic structures and develop low-complexity, bisection-based algorithms that achieve globally optimal solutions using only simple scalar evaluations. Extensive simulations validate the effectiveness of the proposed methods and demonstrate that pinching-antenna systems significantly outperform conventional fixed-antenna designs even under random LoS and NLoS channels.

Transformer-Based models have turned out to be very successful in many artificial intelligence (AI) tasks, outperforming traditional convolutional neural networks (CNNs), especially in the field of Natural Language Processing (NLP). Their success relies upon a self-attention mechanism which, when compared to CNNs, has a global rather than a local receptive domain. This article proposes an energy-efficient edge-device Transformer inference processor termed Smart Transformer Processing Element (STPE). Firstly, STPE sets up a Multi-Mode Indexing and Sparsity Scheme (MISS) for token association, and further reduces the computational load through in-situ computation; secondly, STPE exploits the Local Properties of Attention Mechanism (LPAM) to further reduce redundant and repetitive calculations in Transformer operations by means of a search band calculation and error correction mechanism; thirdly, STPE has designed a Quantization and Compression Parallel Method (QCPM) to improve the computing speed and hardware utilization under weak related (WR) token. Employing 28nm CMOS synthesis tools, the area of the proposed STPE processor is 7.33 mm2. Its peak energy efficiency is 84.15TOPS/W, which is 14.7 times higher than that of the H100 graphics processing unit (GPU) and 3.06 times higher than that of the most advanced Transformer processor.

Recently, Graph Neural Networks (GNNs) have shown great potential in terms of accuracy for problems that are well-described by graph representations, such as problems of path planning. However, implementing GNNs on mobile platforms is challenging as it requires a significant amount of computation and large memory. This article proposes a High-Performance Edge GNN Inference Processor termed GPE (GNN Processing Element). Firstly, GPE sets up Multi-Dimensional Indexing and Dynamic Pruning Schemes (MIDPS) for GNN networks, and achieves cross layer interconnection of multiple neighboring nodes via NOC (Network on Chip); secondly, GPE utilizes Graph Structure Adjacency Table Information (GSATI) of a GNN to further reduce redundant and repetitive calculations by means of repeated matching and difference transfer mechanisms; thirdly, GPE has a graph-based Multi Parallelism Simplification and Operation Method (MPSOM) to improve computing speed and hardware utilization under small data volumes. Using 28nm CMOS synthesis tools, the area of the proposed GPE processor is 5.37 square millimeters. Its peak energy efficiency is 21.5TOPS/W, which is 3.76 times higher than that of the H100 GPU (Graphics Processing Unit), while the energy consumption of GNN is 80.9% lower than the previous SOTA (State of Art) work.

Pinching antennas, implemented by applying small dielectric particles on a waveguide, have emerged as a promising flexible-antenna technology ideal for next-generation wireless communications systems. Unlike conventional flexible-antenna systems, pinching antennas offer the advantage of creating line-of-sight (LoS) links by enabling antennas to be activated on the waveguide at a position close to the users. This paper investigates a typical two-user non-orthogonal multiple access (NOMA) down-link scenario, where multiple pinching antennas are activated on a single dielectric waveguide to assist NOMA transmission. We formulate the problem of maximizing the data rate of one user subject to the quality-of-service (QoS) requirement of the other user by jointly optimizing the antenna positions and power allocation coefficients. The formulated problem is nonconvex and difficult to solve due to the impact of antenna positions on large-scale path loss and two types of phase shifts, namely in-waveguide phase shifts and free space propagation phase shifts. To this end, we propose an iterative algorithm based on block coordinate descent and successive convex approximation techniques. Moreover, we consider the special case with a single pinching antenna, which is a simplified version of the multi-antenna case. Although the formulated problem is still nonconvex, by using the inherent features of the formulated problem, we derive the global optimal solution in closed-form, which offers important insights on the performance of pinching-antenna systems. Simulation results demonstrate that the pinching-antenna system significantly outperforms conventional fixed-position antenna systems, and the proposed algorithm achieves performance comparable to the computationally intensive exhaustive search based approach.

Distributed channel estimation (DCE) algorithm design is pivotal for exploiting the potential of cell-free massive multiple-input multiple-output (CF-mMIMO) systems, while reducing fronthaul costs and computational complexities inherent in conventional centralized algorithms. Most existing DCE algorithms utilize the linear minimum mean square error (LMMSE) estimator for its efficiency, but this method depends on precise channel covariance acquisition. To address these limitations, federated learning (FL)-based DCE algorithms, which use the widely adopted federated averaging protocol, have been developed for CF-mMIMO systems. However, in practical scenarios, small-scale fading at different access points often follows non-independent and identically distributed (non-IID) patterns. This non-IID nature causes existing FL-based DCE algorithms to suffer from model update variance during training, leading to slow convergence rates and poor inference performance. To address these challenges, we propose an efficient DCE algorithm built on a novel FL algorithm, specifically developed for this purpose, named Federated Variance Reduction (FedVR). Unlike federated averaging-based algorithms,FedVRcan reduce the variance of model updates between access points during training, resulting in faster convergence rates and improved channel estimation accuracy. Additionally, we theoretically prove the convergence of theFedVRalgorithm, and conduct extensive simulations to validate its efficacy.

Federated learning (FL) has been recognized as a viable solution for local-privacy-aware collaborative model training in wireless edge networks, but its practical deployment is hindered by the high communication overhead caused by frequent and costly server-device synchronization. Notably, most existing communication-efficient FL algorithms fail to reduce the significant inter-device variance resulting from the prevalent issue of device heterogeneity. This variance severely decelerates algorithm convergence, increasing communication overhead and making it more challenging to achieve a well-performed model. In this paper, we propose a novel communication-efficient FL algorithm, named FedQVR, which relies on a sophisticated variance-reduced scheme to achieve heterogeneity-robustness in the presence of quantized transmission and heterogeneous local updates among active edge devices. Comprehensive theoretical analysis justifies that FedQVR is inherently resilient to device heterogeneity and has a comparable convergence rate even with a small number of quantization bits, yielding significant communication savings. Besides, considering non-ideal wireless channels, we propose FedQVR-E which enhances the convergence of FedQVR by performing joint allocation of bandwidth and quantization bits across devices under constrained transmission delays. Extensive experimental results are also presented to demonstrate the superior performance of the proposed algorithms over their counterparts in terms of both communication efficiency and application performance.

With the wide application of deep learning (DL) across various fields, deep joint source–channel coding (DeepJSCC) schemes have emerged as a new coding approach for image transmission. Compared with traditional separated source and CC (SSCC) schemes, DeepJSCC is more robust to the channel environment. To address the limited sensing capability of individual devices, distributed cooperative transmission is implemented among edge devices. However, this approach significantly increases communication overhead. In addition, existing distributed DeepJSCC schemes primarily focus on specific tasks, such as classification or data recovery. In this paper, we explore the wireless semantic image collaborative nonorthogonal transmission for distributed edge networks, where edge devices distributed across the network extract features of the same target image from different viewpoints and transmit these features to an edge server. A two‐view distributed cooperative DeepJSCC (two‐view‐DC‐DeepJSCC) with or without information disentanglement scheme is proposed. In particular, the two‐view‐DC‐DeepJSCC with information disentanglement (two‐view‐DC‐DeepJSCC‐D) is proposed for achieving balancing performance between multitasking of image semantic communication; while the two‐view‐DC‐DeepJSCC without information disentanglement only pursues outstanding data recovery performance. Through curriculum learning (CL), the proposed two‐view‐DC‐DeepJSCC‐D effectively captures both common and private information from two‐view data. The edge server uses the received information to accomplish tasks such as image recovery, classification, and clustering. The experimental results demonstrate that our proposed two‐view‐DC‐DeepJSCC‐D scheme is capable of simultaneously performing image recovery, classification, and clustering tasks. In addition, the proposed two‐view‐DC‐DeepJSCC has better recovery performance compared to the existing schemes, while the proposed two‐view‐DC‐DeepJSCC‐D not only maintains a competitive advantage in image recovery but also has a significant improvement in classification and clustering accuracy. However, the proposed two‐view‐DC‐DeepJSCC‐D will sacrifice some image recovery performance to balance multiple tasks. Furthermore, two‐view‐DC‐DeepJSCC‐D exhibits stronger robustness across various signal‐to‐noise ratios.

As a novel distributed learning paradigm, federated learning (FL) faces serious challenges in dealing with massive clients with heterogeneous data distribution and computation and communication resources. Various client-variance-reduction schemes and client sampling strategies have been respectively introduced to improve the robustness of FL. Among others, primal-dual algorithms such as the alternating direction of method multipliers (ADMM) have been found being resilient to data distribution and outperform most of the primal-only FL algorithms. However, the reason behind remains a mystery still. In this paper, we firstly reveal the fact that the federated ADMM is essentially a client-variance-reduced algorithm. While this explains the inherent robustness of federated ADMM, the vanilla version of it lacks the ability to be adaptive to the degree of client heterogeneity. Besides, the global model at the server under client sampling is biased which slows down the practical convergence. To go beyond ADMM, we propose a novel primal-dual FL algorithm, termed FedVRA, that allows one to adaptively control the variance-reduction level and biasness of the global model. In addition, FedVRA unifies several representative FL algorithms in the sense that they are either special instances of FedVRA or are close to it. Extensions of FedVRA to semi/un-supervised learning are also presented. Experiments based on (semi-)supervised image classification tasks demonstrate superiority of FedVRA over the existing schemes in learning scenarios with massive heterogeneous clients and client sampling.

This paper proposes a hierarchical sparse estimation of spatial non-stationarity channel for uplink massive multiple-input multiple-output (MIMO) systems without prior information. Especially, the non-zero rows of non-stationarity channel matrix are estimated according to the in-row correlation in the first layer; while the non-zero elements of the estimated non-zero rows are further refined in the second layer. A row-wise sparse adaptive matching pursuit (SAMP) is used to find the non-zero rows in the first layer of the proposed algorithms, and multiple non-zero rows can be estimated in one iteration, which has higher precision and lower complexity, compared to the conventional SAMP. Different from the existing two-layer iteration algorithms, a threshold is designed to estimate the non-zero elements replacing the iterative algorithm in the second layer. Further, the computation complexity is analyzed and compared. The simulation results demonstrate that the proposed threshold-enhanced hierarchical spatial non-stationary channel estimation algorithms achieve better performance compared to various state-of-the-art baselines in terms of channel coefficient estimation, and computational efficiency.

The multi-antenna non-orthogonal multiple access (NOMA) technique is a promising method to enhance energy and spectrum efficiencies of wireless communication systems through advanced precoding algorithms. However, traditional NOMA schemes encounter high complexity issues due to the successive interference cancellation (SIC) process at the receiver end. Moreover, conventional precoding designs for multi-antenna NOMA systems only utilize user channel state information and overlook the modulation details of transmitted data symbols, which may result in suboptimal performance. To overcome these disadvantages, we propose a symbol-level precoding (SLP) scheme to maximize the energy efficiency of the system, which has been little studied in the literature. Furthermore, the proposed SLP scheme makes the “interference signals” to fall within the decoding region of the “desired signal”, eliminating the need for an SIC receiver, thereby reducing the complexity of the NOMA system in practical applications. To resolve the optimization problem associated with the SLP scheme, we develop a fractional programming and successive upper-bound maximization based algorithm. Our simulation results demonstrate the effectiveness of the proposed SLP scheme and algorithms in improving the energy efficiency of the system.

Recently, federated learning (FL) has been increasingly appealing in distributed signal processing and machine learning. Nevertheless, the practical challenges of label deficiency and client heterogeneity form a bottleneck to its wide adoption. Although numerous efforts have been devoted to semi- supervised FL, most of the adopted algorithms follow the same spirit as FedAvg, thus heavily suffering from the adverse effects caused by client heterogeneity. In this paper, we boost the semi-supervised FL by addressing the issue using model personalization and client-variance-reduction. In particular, we propose a novel and unified problem formulation based on pseudo-labeling and model interpolation. We then propose an effective algorithm, named FedCPSL, which judiciously adopts the schemes of a novel momentum-based client- variance-reduction and normalized averaging. Convergence property of FedCPSL is analyzed and shows that FedCPSL is resilient to client heterogeneity and obtains a sublinear convergence rate. Experimental results on image classification tasks are also presented to demonstrate the efficacy of FedCPSL over the benchmark algorithms.

To cope with the bottlenecks of the high computational complexity and excessive inter-connection communication in the conventional centralized baseband processing architecture, the decentralized baseband processing (DBP) architecture has been proposed, where the antennas are partitioned into multiple clusters, each connected to a local baseband unit (BBU). In this paper, we are interested in the distributed channel estimation (DCE) method under such DBP architecture, which is rarely studied in the literature. Our goal is to devise a DCE algorithm that can perform as well as the centralized scheme but with a small inter-connection communication cost. Specifically, based on the low-complexity diagonal minimum mean square error channel estimator, we propose an aggregate-then-estimate based DCE algorithm. In contrast to the existing DCE algorithm which requires iterative information exchanges among BBUs, our algorithm only requires one round-trip communication between the nodes. Experiment results are presented to demonstrate the efficacy of the proposed DCE algorithm.

Decentralized multi-cell coordinated beamforming (D-MCBF) is a promising technique to improve the system spectral efficiency, but it usually requires the base stations (BSs) to frequently exchange large amounts of information in order to approach the centralized MCBF solution. However, in practical environments with time-varying channels and limited backhaul bandwidth, frequent information exchange causes delays and thus the existing D-MCBF methods suffer significant performance loss. In this paper, we aim to design a D-MCBF method that can approach the centralized MCBF solution while with only a few inter-BS information exchanges. By assuming that the BSs can exchange the interference channel powers, we firstly formulate a virtual power control based sum rate maximization (VPC-SRM) problem where each BS individually optimizes the beamformers for its served users and at the same time “virtually” optimizes the transmission powers of other BSs. Thus, the VPC-SRM problem is a surrogate of the centralized SRM problem and can be efficiently handled by existing iterative algorithms. To provide a good initial point, we further propose a fully decentralized leakage-based SRM formulation. Simulation results show that the proposed D-MCBF algorithms can perform closely with the centralized method with only two times of information exchange even when the channel is time-varying.

Millimeter-wave (mmW) automotive radar imaging technology has great potential in advanced driver assistance systems (ADAS). Existing super-resolution imaging methods can improve angular (azimuth) resolution for automotive radar with limited aperture. However, these super-resolution methods have high computational complexity meanwhile have poor imaging performance in single-snapshot. In this paper, combined with CS based on ℓ1-norm regularization, we proposed a fast super-resolution imaging method via subspace detection and near-field phase compensation. Frist, the range-pulse-compression (RPC) data in the near and far field is formed by using range FFT. Then, the subspace detection is presented to reduce the size of the measurement matrix by using angle FFT to obtain the potential angle subspace of the target with all RPC data. After, the near-field phase compensation is utilized to make the measurement matrix applicable to all RPC data. Finally, the iterative shrinkage-thresholding algorithm (ISTA) algorithm is utilized to form the super-resolution images based on the measurement matrices and all RPC data. The simulation results show the proposed method can significantly improve imaging resolution with lower computational complexity than other imaging methods.

Federated learning (FL) has been recognized as a viable distributed learning paradigm which trains a machine learning model collaboratively with massive mobile devices in the wireless edge while protecting user privacy. Although various communication schemes have been proposed to expedite the FL process, most of them have assumed ideal wireless channels which provide reliable and lossless communication links between the server and mobile clients. Unfortunately, in practical systems with limited radio resources such as constraint on the training latency and constraints on the transmission power and bandwidth, transmission of a large number of model parameters inevitably suffers from quantization errors (QE) and transmission outage (TO). In this paper, we consider such non-ideal wireless channels, and carry out the first analysis showing that the FL convergence can be severely jeopardized by TO and QE, but intriguingly can be alleviated if the clients have uniform outage probabilities. These insightful results motivate us to propose a robust FL scheme, namedFedTOE, which performs joint allocation of wireless resources and quantization bits across the clients to minimize the QE while making the clients have the same TO probability. Extensive experimental results are presented to show the superior performance ofFedTOEfor deep learning-based classification tasks with transmission latency constraints.

Multi-access edge computing (MEC) can enhance the computing capability of mobile devices, while non-orthogonal multiple access (NOMA) can provide high data rates. Combining these two strategies can effectively benefit the network with spectrum and energy efficiency. In this paper, we investigate the task delay minimization in multi-user NOMA-MEC networks, where multiple users can offload their tasks simultaneously through the same frequency band. We adopt the partial offloading policy, in which each user can partition its computation task into offloading and locally computing parts. We aim to minimize the task delay among users by optimizing their tasks partition ratios and offloading transmit power. The delay minimization problem is first formulated, and it is shown that it is a nonconvex one. By carefully investigating its structure, we transform the original problem into an equivalent quasi-convex. In this way, a bisection search iterative algorithm is proposed in order to achieve the minimum task delay. To reduce the complexity of the proposed algorithm and evaluate its optimality, we further derive closed-form expressions for the optimal task partition ratio and offloading power for the case of two-user NOMA-MEC networks. Simulations demonstrate the convergence and optimality of the proposed algorithm and the effectiveness of the closed-form analysis.

In this paper, we aim to design an adaptive power allocation scheme to minimize the average transmit power of a hybrid automatic repeat request with chase combining (HARQ-CC) enabled non-orthogonal multiple access (NOMA) system under strict outage constraints of users. Specifically, we assume that the base station only knows the statistical channel state information of the users. To achieve power efficient design and cope with the reliable transmissions of users, a partial HARQ-CC scheme is proposed. We first focus on the two-user case. To evaluate the performance of the two-user partial HARQ-CC enabled NOMA system, we first analyze the outage probability of each user. Then, an average power minimization problem is formulated. However, the attained expressions of the outage probabilities are nonconvex, and thus make the problem challenging to solve. Hence, we propose to use a successive convex approximation (SCA) based algorithm to solve the problem iteratively. Meanwhile, we prove that the proposed algorithm can converge to a Karush-Kuhn-Tucker point of the original problem. For more practical applications, we also investigate the partial HARQ-CC enabled transmissions in the multi-user scenario. The user pairing and power allocation problem is considered. With the aid of matching theory, a low complexity algorithm is presented to first handle the user pairing problem. Then the power allocation problem for each user pair is solved by the proposed SCA-based algorithm. Simulation results show the efficiency of the proposed transmission strategy and the near-optimality of the proposed algorithms.

This paper investigates the transmission energy minimization problem for the two-user downlink with strictly heterogeneous latency constraints. To cope with the latency constraints and to explicitly specify the trade-off between blocklength (latency) and reliability the normal approximation of the capacity of finite blocklength codes (FBCs) is adopted, in contrast to the classical Shannon capacity formula. We first consider the non-orthogonal multiple access (NOMA) based transmission scheme. However, due to heterogeneous latency constraints and channel conditions at receivers, the conventional successive interference cancellation may be infeasible. We thus study the problem by considering heterogeneous receiver conditions under different interference mitigation schemes and solve the corresponding NOMA design problems. It is shown that, though the energy function is not convex and does not have closed form expression, the studied NOMA problems can be globally solved semi-analytically and with low complexity. Moreover, we propose a hybrid transmission scheme that combines the time division multiple access (TDMA) and NOMA. Specifically, the hybrid scheme can judiciously perform bit and time allocation and take TDMA and NOMA as two special instances. To handle the more challenging hybrid design problem, we propose a concave approximation of the FBC rate/capacity formula, by which we obtain computationally efficient and high-quality solutions. Simulation results show that the hybrid scheme can achieve considerable transmission energy saving compared with both pure NOMA and TDMA schemes.

Mobile edge computing (MEC) can provide considerable computing capabilities for Internet of Things (IoT) devices, especially for applications with latency sensitive tasks. By applying non-orthogonal multiple access (NOMA) in MEC, multiple users can offload their tasks simultaneously on the same frequency band. In this paper, the minimization problem of task completion time is investigated for the NOMA enabled multi-user MEC networks. We adopt \emph{partial offloading}, in which each user's task can be partitioned, while the formulated problem is quasi-convex. Thus a bisection search (BSS) algorithm is proposed to achieve the minimum task completion time for the multi- user case. To reduce the complexity and evaluate the optimality of the BSS algorithm, we further derive closed- form expressions for the optimal task partition ratio and offloading power for a two-user NOMA-MEC network. Simulations demonstrate the convergence and optimality of the proposed BSS algorithm and the effectiveness of the optimal approach.

This paper investigates the performance of non-orthogonal multiple access (NOMA) systems over time-correlated Rayleigh fading channels, where the users have heterogeneous quality of service requirements, e.g., a latency-critical user with a low target rate and a delay-tolerant user with a large target rate. In order to meet the different requirements of the users, two partial hybrid automatic repeat request (HARQ) schemes, including partial HARQ with chase combining (HARQ-CC) and HARQ with incremental redundancy (HARQ-IR), are proposed. The closed-form expressions of outage probabilities for NOMA with and without re-transmission are derived. With the developed outage probabilities, a condition on the superiority of NOMA to orthogonal multiple access (OMA) is obtained. In particular, the condition is characterized by the transmit powers for NOMA without re-transmission and is obtained by using the bisection method in the case with re-transmission. To further improve the performance of the HARQ enabled NOMA schemes, we consider an average transmit power minimization problem by optimizing the transmit power among different transmission rounds with outage constraints. However, due to the complexity of the developed outage probabilities, the formulated problem is non-convex and challenging to solve. Then we approximate the original problem by deriving the upper-bound approximations of the outage probabilities and solve it by using the geometric programming method. Simulation results demonstrate the accuracy of the developed analytical results. It is shown that the performance of NOMA is superior to OMA, only when the obtained condition is satisfied. HARQ-CC and HARQ-IR can enhance the outage performance of NOMA over time-correlated fading channels and the HARQ-IR has excellent performance in terms of energy efficiency.

A novel cooperative non-orthogonal multiple access (NOMA) strategy is proposed, where a full-duplex cell-center user acts as a friendly relay to help a cell-edge user. An advanced energy harvesting technology based on simultaneous wireless information and power transfer (SWIPT) is used at the cell-center user to harvest energy used at the cooperative stage. We propose a joint design to optimize the power splitting (PS) ratio and the beamforming vectors. The proposed design aims to maximize energy efficiency (EE) of the system while guaranteeing the minimum required target rate of the cell-edge user and the successful decoding rate at the cell-center user. To make the problem tractable, we use Dinkelbachs method to address the fractional functions and the semidefinite relaxation (SDR) technique to deal with the rank constraint. Then, an iterative algorithm based on successive convex approximation (SCA) is proposed to finally solve the reformulated problem. In order to rich the application of the proposed power allocation algorithm, we extend the proposed strategy to an imperfect channel state information (CSI) mode. S-Procedure is introduced to approximate the channel uncertainties. Numerical results reveal that our proposed iterative algorithms have a good convergence rate. In addition, the proposed strategy offers a significant increase in energy efficiency compared to the existing strategies, especially at the low power region.

In this paper, we aim to design a power allocation strategy to minimize the average consumed power of a hybrid automatic repeat request with chase combining (HARQ-CC) enabled system under a strict outage constraint for each user. In particular, the non-orthogonal multiple access (NOMA) is incorporated to further improve the system spectrum efficiency and we assume the base station only knows the statistical channel state information of the users. To evaluate the performance of the HARQ-CC enabled NOMA system, we first analyze the outage probability of each user. Then, based on the derived outage probabilities, by optimizing the transmit power, an average power minimization problem is considered. However, the attained expressions of the outage probabilities are nonconvex and extremely complicated, and thus make the formulated problem difficult to deal with. To efficiently solve the original problem, we first conservatively approximate it by a tractable one and then use a successive convex approximation based algorithm to handle the relaxed problem iteratively. And the presented algorithm can be guaranteed to converge to at least a stationary point of the problem. The simulation results show the efficacy of the proposed transmission strategy and the near-optimality of the proposed approximation approach and algorithm.

This paper investigates the application of simultaneous wireless information and power transfer (SWIPT) to nonorthogonal multiple access (NOMA). A new cooperative multiple-input-single-output (MISO) SWIPT NOMA protocol is proposed, where user 2 who has a strong channel condition acts as an energy-harvesting relay to help user 1 who has a poor channel condition. Our objective is to maximize the data rate of user 2 while satisfying the QoS requirement of user 1. The formulated problem boils down to a complicated nonconvex problem. We first use the semidefinite relaxation (SDR) technique to relax the nonconvex problem. Then, an iterative algorithm with the successive convex approximation (SCA) method is proposed to solve the relaxed problem. As a result, a local optimal solution is obtained. Motivated by the practical applications, the cooperative SWIPT NOMA protocol design in SISO case is investigated and a semiclosed-form solution is derived, which can strictly guarantee the global optimality. It is worth pointing out that the SCA method also admits a global optimal solution in SISO case. Simulation results show that the SWIPT-aided NOMA protocol outperforms the existing transmission protocols.

With the rapid development of high-speed railway (HSR) system, there is an increasing demand on providing high quality multimedia services for HSR passengers. In this paper, we investigate the downlink resource allocation problem for multimedia services delivery in HSR orthogonal frequency division multiple access (OFDMA) system. Taking the statistical delay-QoS requirements into account, we formulate the problem as a mixed integer non-linear programming (MINLP), which aims at maximizing the system throughput over a trip of the train. With the help of integer constraint relaxation, this problem can be simplified into a convex optimization problem. To solve it, a stochastic approximation based dynamic resource allocation algorithm is developed. Finally, the numerical results are presented to show the effectiveness of the proposed resource allocation algorithm.