Reconfigurable intelligent surface (RIS) plays an essential role in alleviating severe path loss in millimeter wave communication systems. Its performance hinges on the precise modeling of high-dimensional cascaded channels. However, traditional modeling approaches require extensive experience in radio propagation, resulting in complex and inefficient processes. To overcome these limitations, we transform the RIS channel modeling into a channel distribution transport mapping problem and introduce a generative model based on rectified flow. Our approach integrates distance information into a diffusion transformer (DiT) architecture through cross-attention mechanisms, resulting in a conditional DiT capable of synthesizing target channels from distance inputs. We further optimize the rectified flow into a single-step generator via reflow techniques. Building on this framework, we design a generative digital twin (DT) channel model that serves as a high fidelity data generator for downstream tasks. The proposed model acts as a virtual replica of the propagation environment, enabling efficient channel data synthesis for training communication algorithms such as channel state information feedback and channel estimation. Simulation results show that our approach generates channels with minimal distribution discrepancy compared to real channels (a maximum mean discrepancy < 0.01), outperforming existing generative methods. Furthermore, the reflow-driven DT channel model achieves the shortest generation time among all evaluated benchmarks.

Functional and anatomical image fusion plays an important role in medical applications by combining information from multiple imaging modalities to retain functional features and anatomical details. Although deep learning-based methods have advanced the field, existing methods often struggle to capture local details and global context, especially when features span multiple scales. To address these limitations, we propose FAMAFuse, a novel multiscale attention mechanism designed specifically for functional and anatomical image fusion. FAMAFuse integrates three key innovations to overcome these challenges. First, the spatial attention residual module (SARM) models long-range global context, ensuring the fusion of relevant anatomical features across modalities. Second, the inter-modal feature fusion (IMFF) module fuses multi-source features, enhancing interaction between local details and broader structures. Finally, the multiscale gaussian attention-infused module (M-GAIM) leverages a learnable gaussian kernel to extract multiscale features, improving fusion quality across various imaging modalities. We validated FAMAFuse on SPECT-MRI, PET-MRI, and CT-MRI datasets. Experimental results demonstrate significant improvements over state-of-the-art fusion methods. In quantitative evaluations, FAMAFuse outperforms existing techniques by 4% to 10% in fusion quality and 0.5% to 6% in structural preservation. Furthermore, FAMAFuse exhibits excellent generalisation across different modalities, making it a suitable tool for clinical imaging. This method represents a promising solution for more accurate and informative medical diagnoses and clinical research. The source code is available at: https://github.com/Alphaalimamy/famafuse.

With the emergence of extremely large-scale antenna arrays (ELAAs), the next generation of wireless communication is likely to occur in the radiating near-field region of base stations (BS). In such regions, beam training needs to search both the angle and distance dimensions, leading to a prolonged training process and coverage hole (dead zone). To cope with those issues, we propose a novel codebook design guideline for the uniform circular array by maximizing the overlapping coverage between the beam coverage (BC) and near-field region, where the energy spread effect in the near-field region is exploited to obtain the optimal focusing point to improve the beam gain inside the dead zone. Based on this guideline, we construct the beam coverage-based codebook structure and two-stage beam training (TSBT) scheme. Numerical simulations show that the TSBT scheme with the proposed codebook can potentially reduce beam training overhead while improving the success rate and patching the dead zone.

High-dimensional time series data are becoming more widespread in many domains, including large-scale wireless networks for communication. However, because of its high dimensionality, label scarcity, and complicated temporal connections, anomaly detection in such data is difficult. This work proposes a Bayesian network conditional normalizing flow model for multivariate time series anomaly detection, called Graphormer-based Bayesian Network Conditional Normalizing Flow (GBNCNF), based on a graph Transformer (Graphormer) to convert the spatial and temporal dependencies of high-dimensional time series into simple evaluable conditional densities. It models the causal links between numerous time series using a Bayesian network, and it obtains representations of the interdependencies between different time series by combining LSTM modules with Graphormer modules. These representations are introduced as conditional information into the normalizing flow for density estimation, and data corresponding to low density are judged as anomalies. Experiments are conducted on two real datasets and show that our method detects anomalies more accurately than baseline methods, accurately captures the correlations between sensors, and allows users to infer the root causes of detected anomalies.

Network slicing is a key enabler for 6G mobile networks. Guaranteeing the service level agreement with the smallest amount of radio resources is a challenging problem in network slicing scenarios due to random traffic patterns and the channel environment. To this end, we propose a novel deep reinforcement learning algorithm named CGDSAC based on the conditional generative diffusion model to achieve the optimization objective while capturing the underlying environment distribution. Subsequently, we further design a digital twin (DT) enhanced version of CGDSAC named CGDSAC-DT, to address issues that CGDSAC is unsafe or has lower performance than the default strategy in the early training stages, and converges slowly. Numerical results show that our proposed method can solve the issues encountered and outperform the baseline algorithm regarding performance metrics.

Extremely large-scale antenna arrays (ELAAs) introduce a new communication paradigm called near-field communications, where users are likely to operate in the near-field region of the base-stations (BSs). In such a region, beam training needs to search both the angle and distance dimensions, leading to a prolonged training process and a coverage hole (dead zone). To cope with this issue, we developed a beam depth-based codebook and training scheme for near-field ELAA systems. As the performance of codebook design is mainly dictated by the array configurations, we study the codebook design considering uniform linear, circular and planar antenna arrays. Specifically, we first offer an integrated model to characterize the near-field channel for the considered array configurations. Then, we propose a novel codebook design guideline by maximizing the overlap depth between the near-field codeword (beam) coverage and near-field region, where the energy spread effect is exploited to obtain the optimal focusing point to improve the beam gain inside the dead zone. Based on this guideline, we respectively construct the beam depth based on two-stage and hierarchical codebooks as well as the corresponding beam training schemes. Numerical simulations show that the proposed codebook based beam training schemes can potentially reduce beam training overhead while improving the success rate and beam gain inside the dead zone.

Network slicing is essential for the sixth-generation mobile networks. Minimizing radio resources while guaranteeing service level agreement (SLA) remains challenging due to random traffic patterns and channel conditions, making policy security enforcement and underlying traffic distribution inference critical research goals. In this paper, to facilitate the design of policy agents for radio resource management, we first design a high-fidelity conditional generative diffusion model (CGDM)-driven digital twin network (DTN) to provide closed-loop interaction and pre-validation capabilities. The DTN consists of a safety-bound coarse correction method to enhance strategy SLA compliance, a model market for agent warm-up and decision-level pre-validation, and a virtual interaction environment for high-fidelity agent pre-optimization. Then, a CGDM-driven safe reinforcement learning agent based on constrained multi-agent Markov decision process, termed CGD safe actor-critic (CGDSAC), is proposed to manage inter-slice radio resources. CGDSAC balances safety and strategy quality via Lagrangian primal-dual optimization and a behavior cloning objective targeting DTN-corrected strategies, while capturing latent traffic patterns. Furthermore, CGDSAC comprehensively leverages policy warm-up, decision-level pre-validation, and policy-level pre-optimization capabilities of DTN to resolve early inferior performance, SLA jitter, and slow convergence. Numerical results confirm that the built DTN exhibits good fidelity. Under fixed slices and stable traffic pattern, the DTN-enhanced approaches outperform the best baseline with an average SLA violation relative reduction of 71.3% and an average resource block utilization relative degradation of 10.9%, achieve about convergence speed enhancement of 80% compared to native CGDSAC, and is capable of adapting to the scenarios of dynamic number of slices and varying traffic patterns through knowledge transfer and pre-validation.

Colorectal cancer (CRC) is the most common malignant neoplasm in the digestive system and a primary cause of cancer-related mortality in the United States, exceeded only by lung and prostate cancers. The American Cancer Society estimates that in 2024, there will be approximately 152,810 new cases of colorectal cancer and 53,010 deaths in the United States, highlighting the critical need for early diagnosis and prevention. Precise polyp segmentation is crucial for early detection, as it improves treatability and survival rates. However, existing methods, such as the UNet architecture, struggle to capture long-range dependencies and manage the variability in polyp shapes and sizes, and the low contrast between polyps and the surrounding background. We propose a multiscale dynamic polyp-focus network (MDPNet) to solve these problems. It has three modules: dynamic polyp-focus (DPfocus), non-local multiscale attention pooling (NMAP), and learnable multiscale attention pooling (LMAP). DPfocus captures global pixel-to-polyp dependencies, preserving high-level semantics and emphasizing polyp-specific regions. NMAP stabilizes the model under varying polyp shapes, sizes, and contrasts by dynamically aggregating multiscale features with minimal data loss. LMAP enhances spatial representation by learning multiscale attention across different regions. This enables MDPNet to understand long-range dependencies and combine information from different levels of context, boosting the segmentation accuracy. Extensive experiments on four publicly available datasets demonstrate that MDPNet is effective and outperforms current state-of-the-art segmentation methods by 2-5% in overall accuracy across all datasets. This demonstrates that our method improves polyp segmentation accuracy, aiding early detection and treatment of colorectal cancer.

Cell-free (CF) networks are a promising architecture poised to revolutionize future wireless communication systems. To enhance performance, designing effective transmission strategies for CF networks is of practical significance. In this paper, we study the downlink beamforming design to maximize the resource efficiency (RE) of CF networks, which encompasses both energy efficiency (EE) and spectral efficiency (SE) optimization, thereby enabling the realization of an EE-SE tradeoff. Specifically, we formulate a RE maximization problem taking into account both the quality of service (QoS) requirements of the users and the power constraint of the network. The RE optimization problem is a non-convex fractional program. To solve it efficiently, we first equivalently decompose the challenging RE problem into two more tractable problems, a subproblem and a primary problem. Then, we show that the subproblem is equivalent to a power minimization problem and propose two effective methods to obtain the optimal primal and dual solutions simultaneously. After that, we derive the gradient of the optimal value function of the subproblem exploiting the obtained primal and dual solutions that facilitates the design of efficient algorithms with rapid convergence to address the primary problem. Finally, numerical results demonstrate that the proposed algorithms can effectively balance the EE-SE tradeoff, surpassing existing approaches in terms of either EE or SE.

Native artificial intelligence (AI) has played a pivotal role in shaping the evolution of 6G networks. It must meet stringent real-time requirements and therefore deploying lightweight AI models is necessary. However, as wireless networks generate a multitude of data fields and only a fraction of them imposes significant impact on the AI models, it is essential to accurately identify a small amount of critical data that significantly impacts communication performance. In this paper, we propose the pervasive multi-level (PML) native AI architecture, which incorporates knowledge graph (KG) into mobile network operations to establish a wireless data KG. Leveraging the wireless data KG, we analyze the relationships among various data fields and provide the on-demand generation of minimal and effective datasets, referred to as feature datasets. Consequently, it not only enhances AI training, inference, and validation processes but also significantly reduces resource wastage and overhead for communication networks. The proposed solution includes a spatio-temporal heterogeneous graph attention neural network model (STREAM) and a feature dataset generation algorithm. Experimental results validate the exceptional capability of STREAM in handling spatio-temporal data and demonstrate that the proposed architecture effectively reduces data scale and computational costs of AI training by almost an order of magnitude.

Abstract The challenges posed by the future wireless communication network, which will be a huge system with more complex structures, diverse functions, and massive communication ends, will be addressed by intelligent wireless communication technologies. These technologies are playing an increasingly important role in network architecture, computing architecture, resource allocation algorithm design, etc., thanks to the rapid development of artificial intelligence technologies, particularly the deep learning technologies, and their extensive application in various domains. In this paper, an endogenous intelligent architecture is developed to effectively clarify and understand in-depth the relationship among the factors by constructing wireless knowledge graph for the air interface transmission, the core network, as well as the network environment, and so on. Furthermore, the knowledge graph simultaneously reveals the structure and operation mechanism of the whole wireless communication networks. Cause tracing, intelligent optimization, and performance evaluation are sequentially implemented based on the knowledge graph, thus forming a complete closed-loop for endogenous intelligent wireless communication networks.

Extremely large-scale multiple-input multiple-output (XL-MIMO) communication system with extremely large-scale antenna arrays can achieve ultra-high spectral efficiency. However, the conventional far-field beam codebooks may be mismatched with the near-field spherical-wavefront channel caused by large array aperture, which results in severe performance loss. To address this problem, we develop a criterion of code book design to maximize the worst-case beam gain within the beam coverage. Then, a closed-form expression of the near-field full dimensional (FD) codebook with non-orthogonal structure is derived, which can realize the spatial oversampling regardless of the number of antennas at the transceiver. Simulation results show that our proposed non-orthogonal codebook can potentially improve the accuracy of near-field beam training, compared with existing codebooks.

Joint access point (AP) clustering and beamforming design is an effective way to improve system performance and reduce signaling overhead for cell-free networks. However, conventional optimization methods usually solved the joint AP clustering and beamforming design by separately handling them, at the cost of high computing resources, especially when quality of service (QoS) constraint is also considered. To this end, this paper proposes a low-complexity unsupervised deep learning method to jointly optimize AP clustering and beamforming design, called as joint clustering and beamforming network (JcbNet). The JcbNet also designs a neural network to handle the QoS constraint to reduce the hyperparameters of loss function, and it introduces a learnable safety distance parameter in the loss function to reduce the violation rate of QoS constraint. In addition, the JcbNet is scalable since the dimensions of parameters and output beamforming vary with the dimension of input channel state information (CSI). The experimental results show that the JcbNet is low-complexity, and achieves a higher sum rate under a smaller number of AP clustering compared to traditional and deep learning algorithms such as weighted minimum mean square error (WMMSE), sparse WMMSE (S-WMMSE) and convolutional neural network (CNN).

User scheduling and beamforming design are two crucial yet coupled topics for multiuser wireless communication systems. They are usually addressed separately with conventional optimization methods. In this paper, cross-layer optimization problem is considered, namely, the user scheduling and beamforming are jointly discussed, subjecting to the requirement of per-user quality of service and the maximum allowable transmit power for multicell multiuser joint transmission networks. To achieve the goal, a mixed discrete-continuous variables combinational optimization problem is investigated with aiming at maximizing the sum rate of the communication system. To circumvent the original non-convex problem with dynamic solution space, we first transform it into a 0–1 integer and continuous variables optimization problem, and then obtain a tractable form with continuous variables by exploiting the characteristics of the 0–1 integer constraints. Finally, the scheduled users and the optimized beamforming vectors are simultaneously calculated by an alternating optimization algorithm. We also theoretically prove that the base stations allocate zero power to the unscheduled users. Furthermore, two heuristic optimization algorithms are proposed respectively based on brute-force search and greedy search. Numerical results validate the effectiveness of our proposed methods, and the optimization approach gets relatively balanced results compared with the other two approaches.

In this paper, we investigate joint user (UE) scheduling, base station (BS) clustering and beamforming design in a dense network. To reduce the computation burden, we propose a deep unfolding UE scheduling, BS clustering and beamforming (DU-USBCB) method based on the iterative weighted minimum mean square error (WMMSE) algorithm, where the UE scheduling and BS clustering are represented by the group sparsity of the transmit beamforming vectors. The proposed DU-USBCB neural network layer comprises receive coefficient, weight and transmit beamforming vector modules, the former two of which have closed-form expressions. For the third module, the group sparse transmit beamforming vectors are obtained by multiple steps of projected gradient descent and nonlinear sparsification, where the step-sizes are learned through unsupervised learning. We also propose a distributed UE selection (DUS) algorithm, which helps reduce the computation workload. Simulation results verify the effectiveness of the proposed DU-USBCB and DUS methods. The learned step-sizes are directly applied in the testing scenarios with different numbers of UEs, BSs and antennas as well as incomplete channel state information. Besides, our proposed methods can achieve comparable performance but with about 53% and 89% computation reduction compared to the RSRP-WMMSE and SWMMSE methods respectively.

In multiuser communication systems, user scheduling and beamforming (US-BF) design are two fundamental problems that are usually studied separately in the existing literature. In this work, we focus on the joint US-BF design with the goal of maximizing the set cardinality of scheduled users, which is computationally challenging due to the non-convex objective function and the coupled constraints with discrete-continuous variables. To tackle these difficulties, a successive convex approximation based US-BF (SCA-USBF) optimization algorithm is firstly proposed. Then, inspired by wireless intelligent communication, a graph neural network based joint US-BF (J-USBF) learning algorithm is developed by combining the joint US and power allocation network model with the BF analytical solution. The effectiveness of SCA-USBF and J-USBF is verified by various numerical results, the latter achieves close performance and higher computational efficiency. Furthermore, the proposed J-USBF also enjoys the generalizability in dynamic wireless network scenarios.

With the application of the fifth-generation wireless communication technologies, more smart terminals are being used and generating huge amounts of data, which has prompted extensive research on how to handle and utilize these wireless data. Researchers currently focus on the research on the upper-layer application data or studying the intelligent transmission methods concerning a specific problem based on a large amount of data generated by the Monte Carlo simulations. This article aims to understand the endogenous relationship of wireless data by constructing a knowledge graph according to the wireless communication protocols, and domain expert knowledge and further investigating the wireless endogenous intelligence. We firstly construct a knowledge graph of the endogenous factors of wireless core network data collected via a 5G/B5G testing network. Then, a novel model based on graph convolutional neural networks is designed to learn the representation of the graph, which is used to classify graph nodes and simulate the relation prediction. The proposed model realizes the automatic nodes classification and network anomaly cause tracing. It is also applied to the public datasets in an unsupervised manner. Finally, the results show that the classification accuracy of the proposed model is better than the existing unsupervised graph neural network models, such as VGAE and ARVGE.

In this paper, we consider the problem of joint beam selection and link activation across a set of communication pairs to effectively control the interference between communication pairs via inactivating part communication pairs in ultra-dense device-to-device (D2D) mmWave communication networks. The resulting optimization problem is formulated as an integer programming problem that is nonconvex and NP-hard. Consequently, the global optimal solution, even the local optimal solution, cannot be generally obtained. To overcome this challenge, this paper resorts to design a deep learning architecture based on graph neural network to finish the joint beam selection and link activation, with taking the network topology information into account. Meanwhile, we present an unsupervised Lagrangian dual learning framework to train the parameters of the GBLinks model. Numerical results show that the proposed GBLinks model can converge to a stable point with the number of iterations increases, in terms of the weighted sum rate. Furthermore, the GBLinks model can reach near-optimal solutions through comparing with the exhaustive scheme in small-scale ultra-dense D2D mmWave communication networks and outperforms GreedyNoSched and the SCA-based method. It also shows that the GBLinks model can generalize to varying network densities and network coverage regions of ultra-dense D2D mmWave communication networks.

In wireless networks, the optimization problems generally have complex constraints and are usually solved via utilizing the traditional optimization methods that have high computational complexity and need to be executed repeatedly with the change of network environments. In this paper, to overcome these shortcomings, an unsupervised deep unrolling framework based on projection gradient descent (PGD), i.e., unrolled PGD network (UPGDNet), is designed to solve a family of constrained optimization problems. The set of constraints is divided into two categories according to the coupling relations among optimization variables and the convexity of constraints. One category of constraints includes convex constraints with decoupling among optimization variables, and the other category of constraints includes non-convex or convex constraints with coupling among optimization variables. Then, the first category of constraints is directly projected onto the feasible region, while the second category of constraints is projected onto the feasible region using a neural network. Finally, an unrolled sum rate maximization network (USRMNet) is designed based on UPGDNet to solve the weighted SR maximization problem for the multiuser ultra-reliable low latency communication system. Numerical results show that USRMNet has a comparable performance with low computational complexity and an acceptable generalization ability in terms of the user distribution.

High-throughput satellites (HTSs) play an important role in future millimeter-wave (mmWave) aeronautical communication to meet high speed and broad bandwidth requirements. This paper investigates the outage performance of an aeronautical broadband satellite communication system’s forward link, where the feeder link from the gateway to the HTS uses free-space optical (FSO) transmission and the user link from the HTS to aircraft operates at the mmWave band. In the user link, spot beam technology is exploited at the HTS and a massive antenna array is deployed at the aircraft. We first present a location-based beamforming (BF) scheme to maximize the expected output signal-to-noise ratio (SNR) of the forward link with the amplify-and-forward (AF) protocol, which turns out to be a phased array. Then, by supposing that the FSO feeder link follows Gamma-Gamma fading whereas the mmWave user link experiences shadowed Rician fading, we take the influence of the phase error into account, and derive the closed-form expression of the outage probability (OP) for the considered system. To gain further insight, a simple asymptotic OP expression at a high SNR is provided to show the diversity order and coding gain. Finally, numerical simulations are conducted to confirm the validity of the theoretical analysis and reveal the effects of phase errors on the system outage performance.

Millimeter-wave (mmWave) frequency bands, which offer abundant underutilized spectral resources, have been explored and exploited in the past several years to meet the requirements of emerging wireless services highlighted by high data rates, ultrareliability, and ultralow delivery latency. Yet, the unique characteristics of mmWave, e.g., continuous wide bandwidth, large path, and penetration losses, along with hardware constraints, call for innovative technologies for mmWave communication. Recently, an extensive amount of work on mmWave communication has been carried out by researchers and practitioners from both academia and industry, and various technologies have been developed for mmWave communication systems to fulfill the full potential of mmWave frequency bands. In this article, we present a comprehensive survey of the standardization of mmWave communication, the latest progress and outcomes of the research on mmWave communication technologies, and the emerging applications of mmWave communication. In particular, we provide a timely and in-depth summary of the state-of-the-art technology specifications of mmWave communication with an emphasis on the physical (PHY) layer. Then, we elaborate on a number of well-established or promising antenna architectures in mmWave communication systems and investigate the enabling PHY layer transmission technologies. Finally, we show some existing and emerging applications of mmWave communication and discuss the potential open research issues.

Driven by the explosive growth of Internet of Things (IoT) devices with stringent requirements on latency and reliability, ultra-reliability and low latency communication (uRLLC) has become one of the three key communication scenarios for the 5th generation (5G) and 6G communication systems. In this paper, we focus on the beamforming design problem for the downlink multiuser uRLLC system. Since the strict demand on the reliability and latency, in general, short packet transmission is a favorable way for uRLLC systems, which indicates the classical Shannon’s capacity formula is no longer applicable. With the finite blocklength transmission, the achievable rate is greatly influenced by the reliability and finite blocklength. Using the developed achievable rate formula for finite blocklength transmission, we respectively formulate the problems of interest as the weighted sum rate maximization, energy efficiency maximization, and user fairness optimization by considering the maximum allowable transmission power and minimum rate requirement. These problems considered are non-convex and are hard to obtain the global optimal solution, even for the local optimal solution. To overcome these difficulties, some important insights have been discovered by analyzing the function of achievable rate. For example, an analytical solution of the minimum rate requirement is provided with respective to the signal-to-interference-plus-noise ratio. Based on the discovered results, we provide algorithms to optimize the beamforming vectors and power allocation, which are guaranteed to converge to a local optimum solution to the formulated problems with low computational complexity. Our simulation results reveal that our proposed beamforming algorithms outperform the zero-forcing beamforming algorithm with equal power or water filling allocation widely used in the existing literatures.

With the rapid development of the Internet, watermarks are widely used in images to protect copyright. This implies that the robustness of watermark is very important. In recent years, there have been some studies to evaluate watermark performance by removing the watermark. Among them, some methods need to mark the watermark position in advance, and some require multiple images with the same watermark. Moreover, when the colour of thewatermark is similar to that of the background, the existing methods can hardly remove the watermark from the watermarked image. In the proposed work, the authors presented a watermark removal structure consisting of watermark extraction and image inpainting to address the aforementioned issues. In particular, the extraction network is used to extract the watermark in the watermarked image, and the inpainting network is used to inpainting image for a better watermark removal image, respectively. Finally, the authors train and test the developed network architecture by constructing two data sets, i.e. white watermarked image data set (WW‐data set) and colour watermarked image data set (CW‐data set). The proposed method not only has better performance on the WW‐data set than the current latest methods (on the CW‐data set, other methods have almost failed) but also effectively removes the watermarks.

In recent years, network densification and edge caching become effective approaches to reduce the burden on the fronthaul links and the content delivery latency for wireless communication systems. However, maximizing system spectral efficiency cannot directly provide any insight on their energy requirements/efficiency for cache-enabled millimeter-wave (mmWave) radio access networks (RANs). In this paper, we study the design of energy-efficient transceiver, consisting of analog and digital precoder/combiner, for the delivery phase of the downlink of cache-enabled mmWave RANs. Due to the non-convexity of the delivery rate and objective, the coupling between the digital and analog precoders/combiners, and the constant module constraint on the elements of analog precoders/combiners, the problem of interest is non-convex and hard to obtain the global optimal solution, even the local optimal solution. To this end, we first overcome these challenges one-by-one and then transform the original problem into tractable one. Finally, an algorithmic framework that converges to the Karush-Kuhn-Tucker solution with provable is developed to achieve the design of energy-efficient transceiver. Numerical results are provided to evaluate the performance of the proposed algorithm, where fully digital precoding is used as benchmark.

This paper investigates the optimal design of multiple panel arrays (MPAs) for line-of-sight (LoS) multiple-input multiple-output (MIMO) communication systems. We use the spherical wave channel model and give a geometric model to model the LoS channel, which allows the receive antenna arrays to have azimuth rotation, elevation angle rotation, up-down offset and left-right offset distance. Based on the geometric model, we derive the optimal antenna design conditions for achieving the maximum channel capacity and spatial multiplexing gain according to the effective degrees of freedom. The results show that the proposed antenna design can achieve high channel space freedom when the receive antennas have angle rotation and offset, and is suitable for the case where the receive antennas have a large left-right offset distance.

Different from the conventional receive scheme for millimetre wave (mmWave) communication, this study proposes a multi‐beam receive scheme to improve the quality of the received signal and enhance the robustness of the system. The authors proposal is firstly formulated as an optimisation problem, where each signal received by different beams is combined through phase compensation to harvest more transmission energy. Then the original problem is divided into a series of sub‐problems and an analytical solution is further obtained for each sub‐problem. Furthermore, considering the spatial sparsity of the mmWave channel, they propose further a low‐computational complexity algorithm by choosing a few candidate codewords with non‐negligible receive signal power. Numerical results show that their proposal achieves remarkable performance improvements even with a small size codebook and is more robust for different scenarios, especially for a non‐line of sight scenario. Compared with doubling receive antennas to obtain diversity gain, their proposal obtains larger signal‐to‐noise ratio gain while using fewer hardware resources.

Recently, edge caching and multicasting arise as two promising technologies to support high-data-rate and low-latency delivery in wireless communication networks. In this paper, we design three transmission schemes aiming to minimize the delivery latency for cache-enabled multigroup multicasting networks. In particular, full caching bulk transmission scheme is first designed as a performance benchmark for the ideal situation where the caching capability of each enhanced remote radio head (eRRH) is sufficient large to cache all files. For the practical situation where the caching capability of each eRRH is limited, we further design two transmission schemes, namely partial caching bulk transmission (PCBT) and partial caching pipelined transmission (PCPT) schemes. In the PCBT scheme, eRRHs first fetch the uncached requested files from the baseband unit (BBU) and then all requested files are simultaneously transmitted to the users. In the PCPT scheme, eRRHs first transmit the cached requested files while fetching the uncached requested files from the BBU. Then, the remaining cached requested files and fetched uncached requested files are simultaneously transmitted to the users. The design goal of the three transmission schemes is to minimize the delivery latency, subject to some practical constraints. Efficient algorithms are developed for the low-latency cloud-edge coordinated transmission strategies. Numerical results are provided to evaluate the performance of the proposed transmission schemes and show that the PCPT scheme outperforms the PCBT scheme in terms of the delivery latency criterion.

This letter investigates the robust beamforming design for multigroup multicast in a backhaul-limited cloud radio access network. Users requesting the same content form a multicast group, served by remote radio heads (RRHs) cooperatively. Each RRH acquires the requested contents from baseband unit via backhaul links. We first formulate the robust beamforming design as maximizing the sum of the minimum rate of users in each multicast group under the transmission power and backhaul constraints. Due to the introduction of the channel estimation error and inter-user interference, the considered problem becomes more complex and difficult to address directly. To overcome these difficulties, convex approximation methods are adopted to transform the original problem into convex one. Then, an effective optimization algorithm is developed to address the resulting problem. Numerical results demonstrate the effectiveness of the proposed robust beamforming design of multigroup multicast transmission.

In this paper, we investigate the downlink transmission for cache-enabled fog radio access networks aiming at maximizing the delivery rate under the constraints of fronthaul capacity, maximum transmit power, and size of files. To reduce the delivery latency and the burden on fronthaul links and make full use of the local cache and baseband signal processing capabilities of enhanced remote radio heads (eRRHs), a two-level transmission scheme including cache-level and network-level transmission is proposed. In cache-level transmission, only requested files cached at the local cache are transmitted to the corresponding users. The duration of cache-level transmission is the delay caused by the transfer between the baseband unit (BBU) and eRRHs as well as the signal processing at the BBU. The remaining requested files are jointly transmitted to the corresponding users at network-level transmission. For cache-level transmission, a centralized optimization algorithm is first presented and then a decentralized optimization algorithm is provided to avoid the exchange of signaling among eRRHs. Meanwhile, another centralized optimization algorithm is presented to tackle the optimization problem for network-level transmission. All presented algorithms are proved to converge to the Karush-Kuhn-Tucker solutions of the problems. Numerical results are provided to validate the effectiveness of the proposed transmission scheme as well as evaluating the system performance.

In this paper, we study the design of a hybrid precoder, consisting of an analog and a digital precoder, for the delivery phase of downlink cache-enabled millimeter-wave (mm-wave) radio access networks (CeMm-RANs). In CeMm-RANs, enhanced remote radio heads (eRRHs), which are equipped with local cache and baseband signal processing capabilities in addition to the basic functionalities of conventional RRHs, are connected to the baseband processing unit via fronthaul links. Two different fronthaul information transfer strategies are considered, namely, hard fronthaul information transfer, where hard information of uncached requested files is transmitted via the fronthaul links to a subset of eRRHs, and soft fronthaul information transfer, where the fronthaul links are used to transmit quantized baseband signals of uncached requested files. The hybrid precoder is optimized for maximization of the minimum user rate under a fronthaul capacity constraint, an eRRH transmit power constraint, and a constant-modulus constraint on the analog precoder. The resulting optimization problem is non-convex, and hence, the global optimal solution is difficult to obtain. Therefore, convex approximation methods are employed to tackle the non-convexity of the achievable user rate, the fronthaul capacity constraint, and the constant modulus constraint on the analog precoder. Then, an effective algorithm with provable convergence is developed to solve the approximated optimization problem. The simulation results are provided to evaluate the performance of the proposed algorithms, where fully digital precoding is used as the benchmark. The results reveal that except for the case of a large fronthaul link capacity, soft fronthaul information transfer is preferable for CeMm-RANs. Furthermore, surprisingly, hybrid precoding outperforms fully digital precoding with soft fronthaul information transfer for medium-to-large file sizes and fronthaul capacity limited mm-wave cloud RANs.

Millimeter wave (mmWave) communication emerges as an enabling technology for Gbps transmission. A further performance enhancement can be achieved by incorporating mmWave communication into fog radio access networks (F-RANs), which alleviate the large path loss of mmWave signals by shortening the distance between transmitters and users and reduce the latency by caching at enhanced remote radio heads (eRRHs). The full benefit of mmWave F-RANs is leveraged on a joint design of signal processing at the centralized baseband unit (BBU) and distributed eRRHs. In this paper, we propose an energy efficient hybrid precoding design for the downlink mmWave F- RANs with wireless fronthaul links, where digital and hybrid precoders are exploited at the BBU and eRRHs, respectively. We develop an effective method to solve the resulting difficult precoding optimization problem and provide numerical results to demonstrate the effectiveness of the proposed mmWave F-RAN design.

This paper studies beam training design for a codebook- based beamforming millimeter wave (mmwave) system where multiple antenna arrays are employed and each array is capable of beamforming independently. To reduce the training overhead and the complexity of subsequent beam direction search, we propose a cooperative multi- subarray beam training method. Specifically, from the perspective of excluding noneffective beam direction combinations and thus reducing search space, method and criterion of beam superposition are proposed to construct a wide beam from multiple narrow beams corresponding to multiple subarrays. Then, a cooperative multisubarray beam training scheme is proposed based on the proposed criterion. Finally, simulation results show that the proposed scheme achieves a spectral efficiency close to that of the optimal exhaustive search scheme, while has greatly reduced training overhead and computational complexity.

Non-orthogonal multiple access (NOMA) enables user multiplexing in the power domain via successive interference cancellation (SIC). The key to achieve the full benefit of NOMA is resource allocation, including power allocation and channel assignment for all users, which leads to difficult mixed integer programs. In the literature, the optimal power allocation has only been investigated for users on a single channel (or in one group), while the joint optimization of power allocation and channel assignment generally requires an exhaustive research. In this paper, we investigate resource allocation in downlink NOMA systems. We analytically characterize the optimal power allocation in closed-form for sum rate maximization with weights or quality of service (QoS) constraints. Furthermore, we also propose a low-complexity efficient method to jointly optimize channel assignment and power allocation in NOMA systems by incorporating the matching algorithm with the optimal multichannel power allocation. Simulation results show that the joint resource optimization using our optimal power allocation yields better performance than the existing schemes.

In cloud radio access network, a baseband unit (BBU) performs the baseband processing for a cluster of low‐power low‐cost remote radio heads (RRHs) that are connected to the BBU through low‐latency fronthaul links. In this study, the authors study the optimisation of two energy‐efficient compression and precoding strategies which take transmit power constraint, fronthaul capacity constraint and user specific rate constraint into account. To overcome the non‐convexity nature of the original problem, they first transform the objective of the original problem into a parameterised subtractive form and obtain an approximate convex problem via the successive convex approximation. Then, an effective optimisation algorithm with provable convergence is designed to solve the effective problem. Numerical results reveal that the proposed scheme outperforms the conventional maximum sum rate and minimum total power consumption schemes in terms of the energy‐efficiency criterion. In particular, compression after precoding strategy outperforms compression before precoding strategy when both of their RRHs perform the same user scheduling, while the opposite conclusion can be drawn otherwise.

Non-orthogonal multiple access (NOMA) enables power-domain multiplexing via successive interference cancellation (SIC) and has been viewed as a promising technology for 5G communication. The full benefit of NOMA depends on resource allocation, including power allocation and channel assignment, for all users, which, however, leads to mixed integer programs. In the literature, the optimal power allocation has only been found in some special cases, while the joint optimization of power allocation and channel assignment generally requires exhaustive search. In this paper, we investigate resource allocation in downlink NOMA systems. As the main contribution, we analytically characterize the optimal power allocation with given channel assignment over multiple channels under different performance criteria. Specifically, we consider the maximin fairness, weighted sum rate maximization, sum rate maximization with quality of service (QoS) constraints, and energy efficiency maximization with weights or QoS constraints in NOMA systems. We also take explicitly into account the order constraints on the powers of the users on each channel, which are often ignored in the existing works, and show that they have a significant impact on SIC in NOMA systems. Then, we provide the optimal power allocation for the considered criteria in closed or semi-closed form. We also propose a low-complexity efficient method to jointly optimize channel assignment and power allocation in NOMA systems by incorporating the matching algorithm with the optimal power allocation. Simulation results show that the joint resource optimization using our optimal power allocation yields better performance than the existing schemes.

SYM-ILDL is a numerical software package that computes incomplete LDL T (ILDL) factorizations of symmetric indefinite and real skew-symmetric matrices. The core of the algorithm is a Crout variant of incomplete LU (ILU), originally introduced and implemented for symmetric matrices by Li and Saad [2005]. Our code is economical in terms of storage, and it deals with real skew-symmetric matrices as well as symmetric ones. The package is written in C++ and is templated, is open source, and includes a M atlab ™ interface. The code includes built-in RCM and AMD reordering, two equilibration strategies, threshold Bunch-Kaufman pivoting, and rook pivoting, as well as a wrapper to MC64, a popular matching-based equilibration and reordering algorithm. We also include two built-in iterative solvers: SQMR, preconditioned with ILDL, and MINRES, preconditioned with a symmetric positive definite preconditioner based on the ILDL factorization.

Hybrid multiple input multiple output (MIMO) beamforming can be divided into shared and split MIMO beamforming architectures, according to different concatenations of radio frequency (RF) chains and antennas. This paper considers split MIMO beamforming for millimeter wave system, i.e., each antenna subarray is only connected with one RF chain. To obtain analog precoding matrix and combining matrix, an algorithm based on signal to leakage plus noise ratio (SLNR) is proposed to align the transmitter's and receiver's antenna subarrays in one- to-one way. The effectiveness of our proposed subarray alignment algorithm is validated by simulation, and the hybrid and purely digital beamforming are compared in terms of channel capacity. Numerical results show that for the number of transmit and receive antennas, the performance gap between purely digital and hybrid beamforming decreases by increasing the number of RF chains. Moreover, effective degree of freedom (EDOF) is introduced to analyze the channel characteristic.

To combat bursty errors caused by wireless channels, interleaving is usually employed to randomize these errors with an aim to make error correction codes more effective. Subsequently, the design of efficient interleaving strategies becomes an attractive topic. Our main contributions of this paper consist: i) put forward equivalent distance sum from the physical scenarios as the metric of an interleaving sequence, which can give an overall but meticulous characterization of interleaving; ii) combine unequal grouping strategy with group interleaving to exploit the potential of interleaving which mostly outperforms classical block interleaving; iii) develop a practical algorithm to generate the near-optimal interleaving sequence with arbitrary length based on underlying insights. Numerical results under IEEE 802.11aj (45 GHz) millimeter-wave system in single carrier mode validate the effectiveness of the algorithm.

This paper studies joint beamforming and power allocation for multicell multiuser multi‐antenna systems with the objective of maximising the minimum signal‐to‐interference‐plus‐noise ratio (max–min SINR). The authors first consider developing an iterative algorithm to achieve the optimal performance by extending the uplink–downlink duality for finite‐scale wireless communication systems. The solution is then generalised to achieve the asymptotically optimal multicell beamforming with the aim to reduce the overhead of signalling exchange between coordinated base stations based on large dimension random matrix theory. Based on that, an efficient multicell beamforming algorithm is proposed to asymptotically achieve the max–min SINR. To further solve the complexity issue of large dimensional matrix inversion involved in the calculation of beamforming vectors, they propose a low‐complexity beamforming calculator based on truncated polynomial expansion approach. Numerical results validate the effectiveness of the authors’ proposed algorithms and show that they can achieve the optimal or asymptotically optimal performance in a massive multi‐input multi‐output system with low complexity and small backhaul overhead.

Wireless networks face the challenge of increasing energy consumption while satisfying the unprecedented demand for higher data rates. Energy-efficient transmission has been regarded as a key technology for the next-generation wireless system. Meanwhile, to reduce the cost, in practice, a base station usually has less radio chains than the antennas, which makes antenna selection an appealing transmission strategy. This letter addresses the problem of joint optimization of energy-efficient beamforming and antenna selection for downlink multiuser systems. The nonconvexity arising from both the nonlinear fractional programming and the ℓ0-(quasi)norm presents the main difficulty in solving the joint optimization problem. Nevertheless, we develop an effective algorithm to address this problem. Numerical results are given to validate the effectiveness and the performance of the developed algorithm.

This paper studies joint power allocation and beam-forming for energy efficient communication in coordinated multi-cell multi-user downlink systems. The considered energy efficiency maximization problem which takes both dynamic and static power consumption into account is non-convex and hard to tackle. To address it, the optimization problem is first transformed into a parametric subtractive form using the classical fractional programming method. Then, by introducing the concept of the interference temperature used in cognitive radio networks, the parameterized subtractive form optimization problem is further decomposed into a master problem and a set of subproblems. Based on that, we exploit the convex approximation to develop a decentralized multi-cell multi-user algorithm, which is shown to converge and only needs limited information exchange between the coordinated BSs. Numerical results show that the proposed decentralized energy efficiency algorithm outperforms conventional power allocation algorithms and exhibits a performance close to the optimal centralized solution.

In this paper, we investigate joint beamforming and power allocation in multicell multiple-input single-output (MISO) downlink networks. Our goal is to maximize the utility function defined as the ratio between the system weighted sum rate and the total power consumption subject to the users’ quality of service requirements and per-base-station (BS) power constraints. The considered problem is nonconvex and its objective is in a fractional form. To circumvent this problem, we first resort to an virtual uplink formulations of the the primal problem by introducing an auxiliary variable and applying the uplink-downlink duality theory. By exploiting the analytic structure of the optimal beamformers in the dual uplink problem, an efficient algorithm is then developed to solve the considered problem. Furthermore, to reduce further the exchange overhead between coordinated BSs in a large-scale antenna system, an effective coordinated power allocation solution only based on statistical channel state information is reached by deriving the asymptotic optimization problem, which is used to obtain the power allocation in a long-term timescale. Numerical results validate the effectiveness of our proposed schemes and show that both the spectral efficiency and the energy efficiency can be simultaneously improved over traditional downlink coordinated schemes, especially in the middle-high transmit power region.

Spectral efficiency (SE) and energy efficiency (EE) are both essential in future wireless communications. To improve the system performance on these two metrics, in this paper we consider the weighted sum rate maximization (WSRMax) problem subject to per-cell EE constraints and per-BS transmit power constraints in multicell multiuser downlink systems. This problem is difficult in its original form due to the introduction of new EE constraints. We first reveal that the original problem can be transformed into an equivalent parameterized polynomial form by introducing some auxiliary variables. By exploiting the concavity property with respect to each variable in the equivalent problem, an efficient block coordinate ascent algorithm is then proposed with guaranteed convergence property. Numerical results show that compared with the conventional WSRMax algorithm, in addition to fulfilling the EE requirement of each cell, our algorithm achieves a better system EE performance at the cost of a slight sum rate performance loss at a certain region and offers a new insight on the SE-EE tradeoff in wireless communication systems.

This study considers the joint transmission precoding design for downlink multicell multiuser multiple‐input single‐output systems where imperfect channel variances are available at the base stations. The authors aim to tackle the robust signal‐to‐interference‐plus‐noise ratio (SINR) balancing problem to maximise the minimum worst‐case user rate. To solve the non‐convex problem, a duality relationship between the downlink max–min worst‐case SINR optimisation problem and the virtual uplink min–max worst‐case SINR optimisation problem is first revealed. Based on this, a new algorithm is developed to solve the virtual problem by using jointly the sub‐gradient method and the geometric programming methods, whose achieved solution is finally converted to the downlink. Their analysis shows that the proposed algorithm is guaranteed to converge and has lower computational complexity than conventional approaches. Moreover, computer simulations validate the effectiveness of the proposed method and show that the proposed algorithm has fast convergence and achieves a performance close to that of the brute search method.

Energy-efficient communications has attracted much interest in the research of 5G cellular systems. In this paper, we study energy-efficient coordinated beamforming design for heterogeneous multicell multiuser downlink systems. The considered problem is formulated as maximizing the weighted sum per-cell energy efficiencies (WSPEEMax) subject to predefined per-user target rate demands, maximum leakage interference power constraints, and per-BS transmit power constraints. This formulation is more general than the conventional EE optimization problem and provides a unified way to consider the EE of heterogeneous networks. However, it is hard to tackle due to the weighted sum-of-ratios form of the objective function and the non-convex nature of per-user target rate constraints. To address it, we propose to first transform the original problem into a polynomial form optimization by introducing some auxiliary variables and then further reveal their equivalence in finding the solution. Then, an efficient block coordinate ascent optimization algorithm is developed to solve the equivalent problem by exploiting the concave nature of the considered problem with respect to each optimization variable. To further improve the network EE, we also develop an energy-efficient transmission method for each small-cell network. Finally, extensive numerical results are provided to verify the effectiveness of the proposed schemes and show that both the EE and spectral efficiency (SE) of heterogeneous network can be significantly improved by energy-efficient coordinated multiple-input multiple-output (MIMO) transmission.

This paper investigates the coordinated beam-forming design for multi-cell MISO downlink beamforming system, aiming at maximizing the sum rate. In the proposed scheme, convex approximation approach is used to first recast the primal non-convex problem into an approximate problem of minimizing the sum of weighted inverse SINR. Then, an alternating optimization method is developed to address the approximate problem based on uplink-downlink duality. We show that our solution is globally optimal in the case of two-BS cooperation with a total power constraint, and is also effective in a general case. When extending to per-BS power constraints, an alternating optimization algorithm with provable convergence to stationary point is proposed following a similar procedure. Our simulation results show that the proposed scheme has a fast convergence and achieves a sum rate performance very close to the optimal performance obtained by exhaustive search.

This paper considers robust beamforming design for coordinated multiple point joint transmission systems with imperfect channel state information at the base stations (BSs). A robust lower bound duality relation between the downlink max-min worst-case SINR optimization problem and the virtual uplink min-max worst-case SINR optimization problem is first revealed. Based on this, an iterative optimization method is then proposed using the subgradient theory to solve the virtual uplink problem, by which the solution to the downlink optimization problem is easily obtained. It is proved that the convergence of the proposed algorithm can be guaranteed with monotonic boundary sequence theorem and the subgradient theory. Numerical simulation verifies the effectiveness of the proposed method.

In this paper we study energy efficient joint power allocation and beamforming for coordinated multicell multiuser downlink systems. The considered optimization problem is in a non-convex fractional form and hard to tackle. We propose to first transform the original problem into an equivalent optimization problem in a parametric subtractive form, by which we reach its solution through a two-layer optimization scheme. The outer layer only involves one-dimension search for the energy efficiency parameter which can be addressed using the bi-section search, the key issue lies in the inner layer where a non-fractional sub-problem needs to tackle. By exploiting the relationship between the user rate and the mean square error, we then develop an iterative algorithm to solve it. The convergence of this algorithm is proved and the solution is further derived in closed-form. Our analysis also shows that the proposed algorithm can be implemented in parallel with reasonable complexity. Numerical results illustrate that our algorithm has a fast convergence and achieves near-optimal energy efficiency. It is also observed that at the low transmit power region, our solution almost achieves the optimal sum rate and the optimal energy efficiency simultaneously; while at the middle-high transmit power region, a certain sum rate loss is suffered in order to guarantee the energy efficiency.

In this paper, a new analytical expression of the max-min SINR duality between the multi-cell downlink and the virtual uplink subject to per-BS power constraints is firstly given. Based on that, a hierarchical iterative scheme is proposed to solve the virtual uplink optimization problem. The uplink solution is then converted to achieve the solution to the multi-cell downlink beamforming problem. Simulation results show that, in contrast to existing multi-cell beamforming schemes, the proposed scheme achieves better performance in terms of both the worst-user rate and the rate per energy.

In this paper, we address the problem of the coordinated beamforming design for multi-cell multiple input single output (MISO) downlink system subject to per-BS power constraints. The objective is taken as the maximization of the minimum signal-to-interference plus noise ratio (SINR), while a complete analysis of the duality between the multi-cell downlink and the virtual uplink optimization problems is provided. A hierarchical iterative scheme is proposed to solve the virtual uplink optimization problem, whose solution is then converted to derive the one of the multi-cell downlink beamforming problem. The proposed algorithm is proved to converge to a stable point. Additional, the complexity of the proposed algorithm is analyzed. Simulation results show that, in contrast to existing multi-cell beamforming schemes, the proposed algorithm achieves better performance in terms of both rate per energy (RPE) and the worst-user rate.