Recently, unmanned aerial vehicles (UAVs) are crucial in the low-altitude economy due to their high mobility and operational efficiency, which enable rapid and flexible operations in various applications. To support the flight control and management of low-altitude UAVs, ground base stations need to ensure communication quality that is high-capacity, ultra-low latency, and highly reliable. Extremely large-scale multiple-input multiple-output (XL-MIMO) systems, capable of forming high-gain directional beams, are highly suited for supporting the communication requirements of UAVs in the low-altitude economy. However, the communication signal may quickly deviate from the main lobe of the beam due to the mobility of UAVs, resulting in beam misalignment and significant degradation in communication quality. To address this problem, this paper first analyzes the near-field beam pattern of XL-MIMO system serving low-altitude UAVs and then decomposes phase of beam pattern into linear and nonlinear components to derive the angular and distance half-power beamwidth in the near field. Then, the prediction error is used to determine the number of activated antennas for dynamic beamwidth adjustment based on UAV position prediction. In addition, a near-field adaptive beamwidth control-aided tracking (NF-ABCT) algorithm is proposed to improve the effectiveness of beam tracking. Finally, simulation results demonstrate that the proposed NF-ABCT achieves a higher signal-to-noise ratio compared to the existing fixed beam scheme and channel estimation methods, while maintaining beam traking robustness and stability in both near-field and far-field.

5G-Advanced (5G-A) has enabled the vibrant development of low altitude integrated sensing and communication (LA-ISAC) networks. As a core component of these networks, unmanned aerial vehicles (UAVs) have witnessed rapid proliferation in recent years. However, due to the lag in traditional industry regulatory norms, unauthorized flight incidents occur frequently, posing a severe security threat to LA-ISAC networks. To surveil the non-cooperative UAVs, in this paper, we propose a multi-domain supervised contrastive learning (MD-SupContrast) framework for UAV radio frequency (RF) open-set recognition. Specifically, first, the texture features and the time-frequency position features from the ResNet and the TransformerEncoder (TE) are fused, and then the supervised contrastive learning is applied to optimize the feature representation of the closed-set samples. Next, to surveil the invasive UAVs that appear in real life, we propose an improved generative OpenMax (IG-OpenMax) algorithm and construct an open-set recognition model, namely Open-RFNet. According to the unknown samples, we first freeze the feature extraction layers and then only retrain the classification layer, which achieves excellent recognition performance both in closed-set and open-set recognitions. We analyze the computational complexity of the proposed model. Experiments are conducted with a large-scale UAV open dataset. The results show that the proposed Open-RFNet outperforms the existing benchmark methods in terms of recognition accuracy between the known and the unknown UAVs, as it achieves 95.12% in closed-set and 96.08% in open-set under 25 UAV types, respectively.

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.

Distributed computing has been widely applied in distributed edge networks for reducing the processing burden of high-dimensional data centralization, where a high-dimensional computational task is decomposed into multiple low-dimensional collaborative processing tasks or multiple edge nodes use distributed data to train a global model. However, the computing power of a single-edge node is limited, and collaborative computing will cause information leakage and excessive communication overhead. In this paper, we design a parallel collaborative distributed alternating direction method of multipliers (ADMM) and propose a three-phase parallel collaborative ADMM privacy computing (3P-ADMM-PC2) algorithm for distributed computing in edge networks, where the Paillier homomorphic encryption is utilized to protect data privacy during interactions. Especially, a quantization method is introduced, which maps the real numbers to a positive integer interval without affecting the homomorphic operations. To address the architectural mismatch between large- integer and Graphics Processing Unit (GPU) computing, we transform high-bitwidth computations into low-bitwidth matrix and vector operations. Thus the GPU can be utilized to implement parallel encryption and decryption computations with long keys. Finally, a GPU-accelerated 3P-ADMM-PC2 is proposed to optimize the collaborative computing tasks. Meanwhile, large-scale computational tasks are conducted in network topologies with varying numbers of edge nodes. Experimental results demonstrate that the proposed 3P-ADMM-PC2 has excellent mean square error performance, which is close to that of distributed ADMM without privacy-preserving. Compared to centralized ADMM and distributed ADMM implemented with Central Processing Unit (CPU) computation, the proposed scheme demonstrates a significant speedup ratio.

Rate-splitting multiple access (RSMA) is a key technology for next-generation multiple access systems due to its robustness against imperfect channel state information (CSI). This makes RSMA particularly suitable for high-mobility autonomous driving, where ultra-reliable and low-latency communication (URLLC) is essential. To address the stringent requirements, this study enables RSMA finite blocklength (FBL) transmissions and explicitly evaluates the ergodic performance. We derive the closed-form lower bound for the ergodic sum-rate of RSMA, considering vital factors such as the vehicle velocities, vehicle positions, power allocation of each stream, blocklengths, and block error rates (BLERs). To further enhance the ergodic sum-rate while complying with quality of service (QoS) rate constraints, we jointly optimize the global power coefficient, private power distribution, and common rate splitting. Guided by gradient descent, we first adjust the global power coefficient based on its sum-rate solution. This parameter regulates the power state of the common stream, allowing for dynamic activation or deactivation: if active, we optimize the private power distribution and adjust the common rate splitting to meet minimum transmission constraints; if inactive, we use the sequential quadratic programming for private power distribution optimization. Simulation results confirm that our RSMA scheme significantly improves the ergodic performance, reduces blocklength and BLER, surpassing the RSMA counterpart with average private power and space division multiple access (SDMA). Furthermore, our approach is validated to guarantee the rates for users with the poorest channel conditions, thereby enhancing fairness across the network.

Radio Frequency Fingerprinting Identification (RFFI) is a lightweight physical layer identity authentication technique. It identifies the radio frequency device by analyzing the signal feature differences caused by the inevitable minor hardware impairments. However, existing RFFI methods based on closed set recognition struggle to detect unknown unauthorized devices in open environments. Moreover, the feature interference among legitimate devices can further compromise identification accuracy. In this paper, we propose a joint radio frequency fingerprint prediction and siamese comparison (JRFFP-SC) framework for open set recognition. Specifically, we first employ a radio frequency fingerprint prediction network to predict the most probable category result. Then a detailed comparison among the test sample's features with registered samples is performed in a siamese network. The proposed JRFFP-SC framework eliminates inter-class interference and effectively addresses the challenges associated with open set identification. The simulation results show that our proposed JRFFP-SC framework can achieve excellent rogue device detection and generalization capability for classifying devices.

In distributed edge networks employing frequency division duplex, the feedback of channel state information (CSI) from the edge devices to the edge server always consumes a lot of spectrum resources, resulting in a serious communication burden. In this paper, we first propose an end-to-end unfolding neural network framework inspired by the soft threshold iterative algorithm (U-ISTANet). The proposed U-ISTANet integrates the advantages of compression awareness and neural networks. Especially, the compression matrix and sparse transformation of channel matrix can be learned for accurate CSI compression and recovery. And a lightweight version of U-ISTANet, called U-ISTANet-L, is proposed to reduce the training parameters. To reduce the data transmission overhead in the centralized learning framework, we extend the proposed U-ISTANet-L to a federated U-ISTANet-L (FU-ISTANet-L), which can train a more generalizable model by increasing the number of edge devices to enlarge the data set in a distributed learning manner. The proposed FU-ISTANet-L reduces the transmission overhead and increases the training speed while achieving a performance close to that of centralized learning. Furthermore, we propose a personalized FU-ISTANet-L (P-FU-ISTANet-L) to solve the heterogeneous data training problem in different communication environments. Specifically, we first obtain a pre-trained model by federation unfolding learning, and then each edge device fine-tunes the model using only a small amount of train data to obtain a personalized model for local channel environment. Extensive experimental results are provided to show that the proposed networks achieve a significant performance over the benchmarking schemes in terms of the normalized mean square error.

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.

This article investigates the use of unmanned aerial vehicles (UAVs) in assisting hybrid non‐orthogonal multiple access (NOMA) systems to enhance spectrum efficiency and communication connectivity. A joint optimization problem is formulated for UAV positioning and user grouping to maximize the sum rate. The formulated problem exhibits non‐convexity, calling for an effective solution. To address this issue, a two‐stage approach is proposed. In the first stage, a particle swarm optimization algorithm is employed to optimize the UAV positions without considering user grouping. With the UAV positions optimized, a game theory‐based approach is utilized in the second stage to optimize user grouping and improve the sum rate of the hybrid NOMA system. Simulation results demonstrate that the proposed two‐stage method achieves solutions close to the global optimum of the original problem. By optimizing the positions of UAVs and user groups, the sum rate can be effectively improved. Additionally, optimizing the deployment of UAVs ensures better fairness in providing communication services to multiple users.

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.

In highly interconnected large-scale event and other Internet of Things (IoT) device-intensive scenarios, traditional terrestrial base stations have difficulty meeting the requirements of IoT devices for network speed and security, and have exacerbated carbon pollution. To this end, a blockchain-enabled unmanned aerial vehicles (UAVs)-assisted mobile edge computing (MEC) system is introduced to enhance communication efficiency and ensure the privacy of IoT devices. In this system, the Byzantine consensus algorithm is applied in the blockchain. Considering the pollution of reducing carbon dioxide emissions, a strategy for jointly optimizing the flight trajectories of UAVs, task offloading scheduling, and MEC computing resource allocation is formulated to minimize the system’s carbon emissions and time delay while meeting MEC and blockchain computing tasks. However, due to the coupling of variables, this problem is very complex. Therefore, the original problem is decoupled into multiple subproblems, and the block coordinate descent method (BCD) and successive convex approximation method (SCA) are used for solving. Specifically, the UAV flight trajectories, task offloading scheduling, and MEC computing resource allocation are alternately optimized until convergence. Simulation results verify the effectiveness and good performance of the proposed algorithm in this article.

Spatial non-stationarity channel estimation for uplink massive MIMO systems can be formulated as a non-uniform block sparse signal recovery problem, in which the hierarchical sparsity of the channel matrix is classified as row sparsity and in-row sparsity. This paper proposes an efficient threshold-enhanced hierarchical estimation (TEHE) algorithm without prior information. More precisely, the non-zero rows of spatial 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. Different from the existing two-layer iteration algorithms, an adaptive threshold is designed to estimate the non-zero elements replacing the iterative algorithm in the second layer. In the proposed TEHE algorithm, row-wise sparse adaptive matching pursuit (SAMP) is used to find the non-zero rows in the first layer, which has high precision and lower complexity, compared to the conventional SAMP. To further improve the efficiency of the row estimation for larger antenna array, an adaptive threshold-enhanced hierarchical estimation (A-TEHE) algorithm is proposed. In addition, a sufficient condition and a halting condition for theoretical guarantee to obtain accurate row estimation are developed. Finally, 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 support set estimation, channel coefficient estimation, and computational efficiency. Specifically, the proposed algorithms are robust to the in-row sparsity.

Hierarchical federated learning (HFL) shows great advantages over conventional two-layer federated learning (FL) in reducing network overhead and interaction latency while still retaining the data privacy of distributed FL clients. However, the communication and energy overhead still pose a bottleneck for HFL performance, especially as the number of clients raises dramatically. To tackle this issue, we propose a non-orthogonal multiple access (NOMA) enabled HFL system under semi-synchronous cloud model aggregation in this paper, aiming to minimize the total cost of time and energy at each HFL global round. Specifically, we first propose a novel fuzzy logic based client selection policy considering client heterogeneity in multiple aspects, including channel quality, data quantity and model staleness. Subsequently, given the fuzzy based client-edge association, a joint edge server scheduling and resource allocation problem is formulated. Utilizing problem decomposition, we firstly derive the closed-form solution for the edge server scheduling subproblem via the penalty dual decomposition (PDD) method. Next, a deep deterministic policy gradient (DDPG) based algorithm is proposed to tackle the resource allocation subproblem considering time-varying environments. Finally, extensive simulations demonstrate that the proposed scheme outperforms the considered benchmarks regarding HFL performance improvement and total cost reduction.

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.

Brain signals are important quantitative data for understanding physiological activities and diseases of human brain. Meanwhile, rapidly developing deep learning methods offer a wide range of opportunities for better modeling brain signals, which has attracted considerable research efforts recently. Most existing studies pay attention to supervised learning methods, which, however, require high-cost clinical labels. In addition, the huge difference in the clinical patterns of brain signals measured by invasive (e.g., SEEG) and non-invasive (e.g., EEG) methods leads to the lack of a unified method. To handle the above issues, in this paper, we propose to study the self-supervised learning (SSL) framework for brain signals that can be applied to pre-train either SEEG or EEG data. Intuitively, brain signals, generated by the firing of neurons, are transmitted among different connecting structures in human brain. Inspired by this, we propose MBrain to learn implicit spatial and temporal correlations between different channels (i.e., contacts of the electrode, corresponding to different brain areas) as the cornerstone for uniformly modeling different types of brain signals. Specifically, we represent the spatial correlation by a graph structure, which is built with proposed multi-channel CPC. We theoretically prove that optimizing the goal of multi-channel CPC can lead to a better predictive representation and apply the instantaneou-time-shift prediction task based on it. Then we capture the temporal correlation by designing the delayed-time-shift prediction task. Finally, replace-discriminative-learning task is proposed to preserve the characteristics of each channel. Extensive experiments of seizure detection on both EEG and SEEG large-scale real-world datasets demonstrate that our model outperforms several state-of-the-art time series SSL and unsupervised models, and has the ability to be deployed to clinical practice.

The loss function of Generative adversarial network (GAN) is an important factor that affects the quality and diversity of the generated samples for anomaly detection. In this paper, we propose an unsupervised multiple time series anomaly detection algorithm based on the GAN with message importance measure (MIM-GAN). In particular, the time series data is divided into subsequences using a sliding window. Then a generator and a discriminator designed based on the Long Short-Term Memory (LSTM) are employed to capture the temporal correlations of the time series data. To avoid the local optimal solution of loss function and the model collapse, we introduce an exponential information measure into the loss function of GAN. Additionally, a discriminant-reconstruction score is composed of discrimination and reconstruction loss. The global optimal solution for the loss function is derived and the model collapse is proved to be avoided in our proposed MIM-GAN-based anomaly detection algorithm. Experimental results show that the proposed MIM-GAN-based anomaly detection algorithm has superior performance in terms of precision, recall, and F1-score.

Unmanned Aerial Vehicle (UAV) networks have huge potential for a variety of military and civilian uses, such as intelligent transportation system, smart city, and so on. The 6th Generation (6G) communication technology is expected to provide 3-Dimensional (3D) wireless coverage and greatly improve the performance of UAV networks. The UAV-to-UAV (U2U) message exchange (or message exchange for short) is an important basis of multi-UAV cooperation. However, due to the unique characteristics of UAV networks, the U2U messages (or messages for short) are vulnerable to both the external and internal attackers. In this work, we propose a Lightweight Trustworthy Message Exchange (LTME) scheme for UAV networks by efficiently aggregating the cryptography and trust management technologies. In the LTME scheme, a centralized Ground Control Station (GCS) periodically updates the reputation levels of registered UAVs (or UAVs for short) and securely distributes secret values to the UAVs. Based on the received secret values, each trustworthy broadcasting UAV can generate its encrypted messages so that only trustworthy receiving UAVs can decrypt them, and each trustworthy receiving UAV can accurately judge whether the received messages and the corresponding broadcasting UAVs are trustworthy in a lightweight manner. Furthermore, we present a simplified LTME (sLTME) scheme and conduct a comprehensive theoretical analysis and simulation evaluation for the LTME and sLTME schemes. The results demonstrate that the proposed schemes can provide rich functionality and strong robustness with low computation and communication overheads, and are significantly superior to the existing schemes in several aspects.

Recently, unmanned aerial vehicle (UAV) swarm communication has drawn much attention in search and rescue (SAR) missions owing to its wide wireless coverage and increasing autonomy in navigation. In this article, we consider maximizing the downlink wireless coverage of a UAV swarm in an unknown mission area by controlling the quasistationary deployments of UAVs. Particularly, the stochastic wireless link failures caused by channel fading and noise in UAV-to-UAV communication links are considered in coverage control. Specifically, due to delay sensitivity and onboard energy limitation of UAV-enabled SAR networks, we study a distributed control strategy where swarm UAVs can address the coverage problem by exchanging only the local information. In this case, the wireless coverage problem is divided into several distributed optimization subproblems. However, due to the integer variable and nonlinear constraints, each subproblem is nonconvex and mutually coupling, which makes it difficult to solve via standard convex optimization solvers. Thus, we model the UAV swarm network as an undirected random graph and then solve the optimization subproblems by formulating a UAV swarm wireless coverage game. As per the designed utility function and potential function of the formulated game, existence of the pure Nash equilibrium is discussed and a distributed algorithm is developed to achieve the best Nash equilibrium. We analyze the convergence property and computational complexity of the proposed algorithm. Meanwhile, we analyze effects of the initial learning rate and step size on algorithm performance from both theoretical and simulation results. Simulation results show that the proposed algorithm improves the coverage by around 58% when compared with initial performance.

In this paper, we investigate a multi-vehicle multi-task non-orthogonal multiple access (NOMA) based mobile edge computing (MEC) system with passive eavesdropping vehicles. To enhance the performance of edge vehicles, we propose a vehicle grouping and pairing method (GPM) to employ the vehicle near to MEC acting as a full-duplex (FD) relay to assist edge vehicles. In order to improve the transmission security, artificial noise (AN) is used to interfere with eavesdropping vehicles. The approximate expression of secrecy outage probability of the system (SOPS) is derived in closed form. This paper aims to minimize the total delay of task completion of edge vehicles by jointly optimizing vehicle task division, power allocation and transmit beamforming. We design a power allocation and task scheduling algorithm relying on genetic algorithm (GA-PATS) to solve our formulated mixed-integer non-linear programming (MINP) problem. Numerical results demonstrate the superiority of our proposed scheme in the aspect of system security and transmission delay.

This letter considers the problem of a joint estimation for channel fading and user activity in an uplink grant-free massive MIMO system equipped with low-precision analog-to-digital converters (ADCs). Different from existing works, the joint estimation is formalized as a non-overlapping group problem, where the components of compound channel involving user activity indicator and channel fading are independent with condition distribution rather than independent Bernoulli-Gaussian. Based on this new formulation, a new algorithm leveraging hybrid generalized approximate passing (HyGAMP) is then developed including GAMP part (channel estimation) and loopy belief propagation (LBP) part (user activity detection), where the strong correlation among elements in each row of the channel matrix can be decoupled in LBP part. By exchanging the information between the GAMP part and the LBP part, the proposed algorithm improves the performance of channel estimation and user activity detection as compared to earlier results. In addition, the simulation results verify that the proposed algorithm develops the performance of conventional methods dramatically.

This letter considers a joint user activity, channel and data estimation problem in an uplink grant-free massive MIMO systems with low-precision analog-to-digital converters (ADCs). This joint estimation is firstly formalized as a non-overlapping group sparse problem, in which the components of compound channel follow independent conditional distribution. To address this problem, a new algorithm consists of the celebrated bilinear generalized approximate message passing (BiG-AMP) and loop belief propagation (LBP) is then proposed, where the strong connection of compound channel can be decoupled in LBP part. By exchanging the information between BiG-AMP part and LBP part, the proposed algorithm improves the performance of channel estimation compared with HyGAMP based method, in which the estimated payload data of proposed algorithm are utilized to aid channel estimation and it leads to relatively few pilot symbols to achieve equivalent channel and data estimation performances. The simulation results confirm that our proposed joint estimation algorithm improves the performance of the existing works in terms of user activity, channel and data estimation.

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 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.

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.

Sparse code multiple access (SCMA) is a new non- orthogonal multiple access scheme, which effectively exploits the shaping gain of multi-dimensional codebook. In this paper, a multi-dimensional SCMA (MD-SCMA) codebook design based on constellation rotation and interleaving method is proposed for downlink SCMA systems. In particular, the first dimension of mother constellation is constructed by subset of lattice Z2. Then the other dimensions are obtained by rotating the first dimension. Further, the interleaving is used for even dimensions to improve the performance in fading channels. In this way, we can design different codebooks for the aim of spectral efficiency or power efficiency. And the simulation results show that the bit error rate (BER) performance of MD-SCMA codebooks outperforms that of the existing SCMA codebooks and low density signature (LDS) in downlink Rayleigh fading channels.