Large language models (LLMs) are emerging as key enablers of automation in domains such as telecommunications, assisting with tasks including troubleshooting, standards interpretation, and network optimization. However, their deployment in practice must balance inference cost, latency, and reliability. In this work, we study an edge-cloud-expert cascaded LLM-based knowledge system that supports decision-making through a question-and-answer pipeline. In it, an efficient edge model handles routine queries, a more capable cloud model addresses complex cases, and human experts are involved only when necessary. We define a misalignment-cost constrained optimization problem, aiming to minimize average processing cost, while guaranteeing alignment of automated answers with expert judgments. We propose a statistically rigorous threshold selection method based on multiple hypothesis testing (MHT) for a query processing mechanism based on knowledge and confidence tests. The approach provides finite-sample guarantees on misalignment risk. Experiments on the TeleQnA dataset –a telecom-specific benchmark – demonstrate that the proposed method achieves superior cost-efficiency compared to conventional cascaded baselines, while ensuring reliability at prescribed confidence levels.

To promote the practicality of deep learning-based localization, existing studies aim to address the issue of scenario dependence through meta-learning. However, these studies primarily focus on variations in environmental layouts while overlooking the impact of changes in device configurations, such as bandwidth, the number of access points (APs), and the number of antennas used. Unlike environmental changes, variations in device configurations affect the dimensionality of channel state information (CSI), thereby compromising neural network usability. To address this issue, we propose Meta-SimGNN, a novelWiFi localization system that integrates graph neural networks with meta-learning to improve localization generalization and robustness. First, we introduce a fine-grained CSI graph construction scheme, where each AP is treated as a graph node, allowing for adaptability to changes in the number of APs. To structure the features of each node, we propose an amplitude-phase fusion method and a feature extraction method. The former utilizes both amplitude and phase to construct CSI images, enhancing data reliability, while the latter extracts dimension-consistent features to address variations in bandwidth and the number of antennas. Second, a similarity-guided meta-learning strategy is developed to enhance adaptability in diverse scenarios. The initial model parameters for the fine-tuning stage are determined by comparing the similarity between the new scenario and historical scenarios, facilitating rapid adaptation of the model to the new localization scenario. Extensive experimental results over commodity WiFi devices in different scenarios show that Meta-SimGNN outperforms the baseline methods in terms of localization generalization and accuracy.

Extremely large-scale antenna arrays (XL-arrays) and ultra-high frequencies are two fundamental technologies for future sixth-generation (6G) wireless networks, providing enhanced system capacity and substantial bandwidth expansion. To fully leverage these technological advancements, conventional far-field models must be replaced by more accurate near-field spherical-wave propagation models. This paper investigates a near-field communication system comprising a hybrid analog-digital beamforming base station (BS) and multiple mobile users, aiming to maximize system sum-rate through optimized codebook design, beam selection, and digital precoding. To accommodate dynamic user distributions, we propose two model-agnostic meta-learning (MAML)-based frameworks that enable prompt adaptation by learning well-initialized models for fine tuning. The first framework integrates the MAML method with a deep neural network (DNN) to design near-field codebooks tailored to the user distributions, addressing the limitations of conventional uniform codebooks. The second framework employs a joint neural network (NN) for beam selection and digital precoding, combining deep reinforcement learning (DRL) and deep unfolding. The DRL NN formulates beam selection as a Markov Decision Process, while the deep-unfolding NN approximates optimal digital precoding through a lightweight iterative algorithm without matrix inversion. Simulation results show that the proposed frameworks significantly outperform conventional methods, achieving superior generalization and overall performance in dynamic near-field scenarios.

In 6G mobile communications, acquiring accurate and timely channel state information (CSI) becomes increasingly challenging due to the growing antenna array size and bandwidth. To alleviate the CSI feedback burden, the channel knowledge map (CKM) has emerged as a promising approach by leveraging environment-aware techniques to predict CSI based solely on user locations. However, how to effectively construct a CKM remains an open issue. In this paper, we propose F4-CKM, a novel CKM construction framework characterized by four distinctive features: radiance Field rendering, spatial-Frequency-awareness, location-Free usage, and Fast learning. Central to our design is the adaptation of radiance field rendering techniques from computer vision to the radio frequency (RF) domain, enabled by a novel Wireless Radiator Representation (WiRARE) network that captures the spatial-frequency characteristics of wireless channels. Additionally, a novel shaping filter module and an angular sampling strategy are introduced to facilitate CKM construction. Extensive experiments demonstrate that F4-CKM significantly outperforms existing baselines in terms of wireless channel prediction accuracy and efficiency.

WiFi-based indoor localization, supported by comprehensive infrastructure, is considered a highly promising solution. However, practical applications of existing WiFi-based methods often struggle due to dynamic antenna configurations and potential influence from obstacles, which can undermine the reliability of channel state information and frustrate localization. Even worse, environmental changes may lead to a domain shift, further degrading localization accuracy and system robustness. To address these problems, this paper introduces GraphFi, a novel system that leverages graph neural networks (GNNs) to deliver accurate and robust localization using nearby access points (APs). GraphFi designs two types of graph structures: intra-AP graph and inter-AP graph, to maximize the use of information from all available APs. They aggregate the local features among antennas within each AP and global features across APs, effectively addressing the problem of dynamic antenna configurations. Two specialized GNNs are utilized to derive accurate user locations from these graphs. Additionally, we introduce an anomaly detection method to identify and exclude obstacle-affected APs. This method also employs a tailored GNN to mitigate influence from unpredictable obstacles. Furthermore, we integrate an unsupervised domain adaptation mechanism based on a gradient reversal layer into GNNs. This helps maintain localization performance in a cost-efficient manner and ensures sustained effectiveness in a cross-domain setting. We prototype GraphFi using commodity WiFi devices and conduct extensive experiments in various scenarios. The results demonstrate that GraphFi achieves average localization errors of 0.17 m in a single-domain setting and 0.2851 m in a cross-domain setting, surpassing existing solutions in both precision and robustness.

Developing channel-adaptive deep joint source-channel coding (JSCC) systems is a critical challenge in wireless image transmission. While recent advancements have been made, most existing approaches are designed for static channel environments, limiting their ability to capture the dynamics of channel environments. As a result, their performance may degrade significantly in practical systems. In this paper, we consider time-varying block fading channels, where the transmission of a single image can experience multiple fading events. We propose a novel coarse-to-fine channel-adaptive JSCC framework (CFA-JSCC) that is designed to handle both significant fluctuations and rapid changes in wireless channels. Specifically, in the coarse-grained phase, CFA-JSCC utilizes the average signal-to-noise ratio (SNR) to adjust the encoding strategy, providing a preliminary adaptation to the prevailing channel conditions. Subsequently, in the fine-grained phase, CFA-JSCC leverages instantaneous SNR to dynamically refine the encoding strategy. This refinement is achieved by re-encoding the remaining channel symbols whenever the channel conditions change. Additionally, to reduce the overhead for SNR feedback, we utilize a limited set of channel quality indicators (CQIs) to represent the channel SNR and further propose a reinforcement learning (RL)-based CQI selection strategy to learn this mapping. This strategy incorporates a novel reward shaping scheme that provides intermediate rewards to facilitate the training process. Experimental results demonstrate that our CFA-JSCC provides enhanced flexibility in capturing channel variations and improved robustness in time-varying channel environments.

Recently, semantic communication (SC) has garnered increasing attention for its efficiency, yet it remains vulnerable to semantic jamming attacks. These attacks entail introducing crafted perturbation signals to legitimate signals over the wireless channel, thereby misleading the receivers’ semantic interpretation. This paper investigates the above issue from a practical perspective. Contrasting with previous studies focusing on power-fixed attacks, we extensively consider a more challenging scenario of power-variable attacks by devising an innovative attack model named Adjustable Perturbation Generator (APG), which is capable of generating semantic jamming signals of various power levels. To combat semantic jamming attacks, we propose a novel framework called Robust Model Ensembling (ROME) for secure semantic communication. Specifically, ROME can detect the presence of semantic jamming attacks and their power levels. When high-power jamming attacks are detected, ROME adapts to raise its robustness at the cost of generalization ability, and thus effectively accommodating the attacks. Furthermore, we theoretically analyze the robustness of the system, demonstrating its superiority in combating semantic jamming attacks via adaptive robustness. Simulation results show that the proposed ROME approach exhibits significant adaptability and delivers graceful robustness and generalization ability under power-variable semantic jamming attacks.

In this paper, we introduce an innovative hierarchical joint source-channel coding (HJSCC) framework for image transmission, utilizing a hierarchical variational autoencoder (VAE). Our approach leverages a combination of bottom-up and top-down paths at the transmitter to autoregressively generate multiple hierarchical representations of the original image. These representations are then directly mapped to channel symbols for transmission by the JSCC encoder. We extend this framework to scenarios with a feedback link, modeling transmission over a noisy channel as a probabilistic sampling process and deriving a novel generative formulation for JSCC with feedback. Compared with existing approaches, our proposed HJSCC provides enhanced adaptability by dynamically adjusting transmission bandwidth, encoding these representations into varying amounts of channel symbols. Additionally, we introduce a rate attention module to guide the JSCC encoder in optimizing its encoding strategy based on prior information. Extensive experiments on images of varying resolutions demonstrate that our proposed model outperforms existing baselines in rate-distortion performance and maintains robustness against channel noise.

Recent deep learning-based localization methods leverage meta-learning to enhance adaptability across diverse environments. However, most existing approaches focus on variations in environmental layouts while overlooking the changes in device configurations—such as the number of access points, antennas, or bandwidth. Unlike environmental variations, changes in device configurations fundamentally alter the dimensionality of channel state information (CSI), which can significantly hinder the usability and generalizability of neural networks. To address this problem, we propose Meta-GLoc, an adaptive and robust WiFi localization system that combines meta-learning with graph neural networks to effectively handle variations in CSI dimensionality. Specifically, we introduce an amplitude-phase fusion method and a feature extraction method to construct fine-grained CSI graphs. The former fuses the cleaned amplitude and phase in a carefully determined ratio to construct robust CSI images, while the latter extracts dimension-consistent features to mitigate the impact of varying bandwidth and antenna configurations. Moreover, meta-learning is employed to realize adaptive localization in different environments. Experiment results on commodity WiFi devices across different configurations demonstrate that Meta-GLoc effectively improves localization accuracy and robustness.

There has been a line of work characterizing the generalization performance of neural networks using norm-based methods. However, when compared with empirical results, norm-based generalization bounds tend to be overly loose and even become vacuous. This paper seeks to unravel this phenomenon for equivariant tensor networks through the lens of topology, by investigating the relation between operator norms and compactness, as well as the null space of equivariant linear operators. We proved that while the boundedness of the operator norm is a sufficient condition for a class of equivariant tensor networks to be compact, it is not necessary. This observation sheds light on occasions where norm-based methods may lead to an arbitrarily loose generalization bound. Furthermore, we demonstrate that due to the sparsity of equivariant linear operators, their common null space can be significantly larger than that of ordinary linear operators. This might encourage larger operator norms to compensate for the "energy loss" caused by mapping into the null spaces, leading to larger and looser norm-based generalization bounds.

Deep learning-based joint source-channel coding (JSCC) is emerging as a promising technology for effective image transmission. However, most existing approaches focus on transmitting clear images, overlooking real-world challenges such as motion blur caused by camera shaking or fast-moving objects. Motion blur often degrades image quality, making transmission and reconstruction more challenging. Event cameras, which asynchronously record pixel intensity changes with extremely low latency, have shown great potential for motion deblurring tasks. However, the efficient transmission of the abundant data generated by event cameras remains a significant challenge. In this work, we propose a novel JSCC framework for the joint transmission of blurry images and events, aimed at achieving high-quality reconstructions under limited channel bandwidth. This approach is designed as a deblurring task-oriented JSCC system. Since RGB cameras and event cameras capture the same scene through different modalities, their outputs contain both shared and domain-specific information. To avoid repeatedly transmitting the shared information, we extract and transmit their shared information and domain-specific information, respectively. At the receiver, the received signals are processed by a deblurring decoder to generate clear images. Additionally, we introduce a multi-stage training strategy to train the proposed model. Simulation results demonstrate that our method significantly outperforms existing JSCC-based image transmission schemes, addressing motion blur effectively.

Despite the remarkable success of semantic image transmission, existing approaches face the challenge of distortion accumulation. Specifically, as a received image is further forwarded to other devices, reconstruction distortion will accumulate, leading to decreased system stability. In this paper, we propose an idempotent semantic communication system for image transmission to enhance stability. We systematically analyze the factors contributing to this accumulation effect and propose several strategies to mitigate it. First, we design the system using a right-invertible neural network to achieve idempotence, ensuring the decoder functions as the right inverse of the encoder. Second, we introduce a feature discretization mechanism to further reduce distortion accumulation, leveraging the benefits of digitalization over analog transmission. Finally, we employ a recursive training strategy, which incorporates the reconstructed images into the training process to significantly improve overall stability. Empirical results demonstrate that our proposed strategies effectively enhance system stability, minimizing quality degradation and enhancing output consistency across multiple transmissions.

With the widespread deployment of WiFi devices, utilizing beamforming feedback for sensing has become a popular trend in WiFi systems. However, the singular value decomposition (SVD)-based feedback method adopted in existing WiFi standards performs poorly in sensing performance since it only aims at maximizing the communication performance. To address this, we propose an integrated sensing and communication (ISAC)-oriented beamforming feedback protocol, which provides different sensing information based on the sensing indicator. Accordingly, we develop different ISAC-oriented CSI compression methods for different sensing applications requiring different feedback information. Taking the angle of departure (AoD) as an example, an optimization problem is formulated to maximize the data rate while preserving the complete AoD information. To resolve it, we propose an iterative algorithm to obtain the suboptimal solution and a heuristic algorithm to reduce the computational complexity. We further extend the proposed compression method for the AoD information to the compression of the angle of arrival (AoA) and time of flight (ToF). Test results show that our proposal achieves both excellent communication and sensing performance and can be applied to various sensing applications, such as localization and action recognition.

The millimeter wave (mmWave) communication becomes a key enabler for the future Internet of Things (IoT) due to its capability for supporting high rate and low-latency traffic. However, beamforming in the mmWave band faces issues of low efficiency since the narrow beam of mmWave devices would increase the search delay and overhead. Inspired by this, we utilize localization over sub-6 GHz band to assist the mmWave base station in performing fast and robust adaptive beamforming (RABF). Different from existing works, we focus on the indoor scenario and consider the effects of several practical issues, including localization errors and hardware defects. Specifically, a novel two-step access scheme is proposed. During the first step, we design a novel localization method customized for indoor scenarios, jointly considering the time of flight and angle of arrival. The localization error is further analyzed to determine the mmWave scanning angle and an optimal beamwidth expression is derived in closed-form to maximize system throughput with the considerations of the search delay. Moreover, considering the mismatch of the steering vector caused by the hardware defects, we propose an RABF method in closed-form. Simulation results demonstrate that the proposed scheme can effectively reduce the search delay and realize robust beamforming to enhance the mmWave communication performance.

WiFi-based gesture recognition (WGR) has emerged as a promising technology due to its potential for integration with communication systems under the concept of integrated sensing and communication (ISAC). However, current WGR systems face two primary challenges: limited scalability for recognizing new gestures and poor compatibility with ISAC. These systems typically require extensive data collection and retraining for each new gesture and struggle to handle the dimensional variability of channel state information (CSI) caused by fluctuating data traffic in communication networks. To overcome these limitations, we introduce OneSense, a one-shot WGR system designed for seamless integration with communication systems. OneSense designs a data enrichment technique based on the law of signal propagation to generate virtual gestures. Based on enriched dataset, OneSense leverages an aug-meta learning (AML) framework to facilitate efficient and scalable FSL. OneSense also incorporates a data cropping strategy to enhance gesture feature prominence and a dynamic size-adaptive backbone model that ensures compatibility with CSI samples exhibiting dimensional inconsistencies. Experimental results show that OneSense achieves over 94% accuracy in one-shot gesture recognition. A case study further illustrates its effectiveness in ISAC contexts. Furthermore, our proposed AML framework reduces pre-training latency by more than 86% compared to conventional meta-learning approaches.

In recent years, the application of artificial intelligence (AI) in wireless communications has demonstrated inherent robustness against wireless channel distortions. Most existing works empirically leverage this robustness to yield considerable performance gains through AI architectural designs. However, there is a lack of direct theoretical analysis of this robustness and its potential to enhance communication efficiency, which restricts the full exploitation of these advantages. In this paper, we adopt an information-theoretic approach to evaluate the robustness in wireless distributed learning by deriving an upper bound on the task performance loss due to imperfect wireless channels. Utilizing this insight, we define task outage probability and characterize the maximum transmission rate under task accuracy guarantees, referred to as the task-aware ϵ-capacity resulting from the robustness. To achieve the utility of the theoretical results in practical settings, we present an efficient algorithm for the approximation of the upper bound. Subsequently, we devise a robust training framework that optimizes the trade-off between robustness and task accuracy, enhancing the robustness against channel distortions. Extensive experiments validate the effectiveness of the proposed upper bound and task-aware ϵ-capacity and demonstrate that the proposed robust training framework achieves high robustness, thus ensuring a high transmission rate while maintaining inference performance.

Recently, decentralized learning has been proposed for model training among mobile devices without center nodes. However, large resource overhead for model aggregation and synchronization would be incurred, which may reduce the learning performance under a given resource budget. To cope with these issues, we propose a novel cluster-based collaborative learning framework over device-to-device (D2D) network, where one device is selected as the cluster head for model aggregation. Within the proposed framework, the learning performance (evaluated by model divergence) and learning latency are analyzed with the consideration of imbalanced data and unreliable D2D communication. Then, an optimization problem is formulated to maximize the learning performance under a given latency constraint by joint cluster head selection and resource allocation. To solve this problem, a lower bound on latency constraint is first obtained for error-free model aggregation. The optimal learning performance is also derived with different degrees of data distribution. After that, an adaptive cluster head selection and resource allocation algorithm is developed for erroneous case by introducing the outage probability. Finally, comprehensive experiments are conducted on well-known models and datasets to illustrate the effectiveness of the proposed algorithm. The results show that our proposal can improve the learning performance while reducing communication and signaling overheads.

Recent studies in joint source-channel coding (JSCC) have fostered a fresh paradigm in end-to-end semantic communication. Despite notable performance achievements, present initiatives in building semantic communication systems primarily hinge on the transmission of continuous channel symbols, thus presenting challenges in compatibility with established digital systems. In this paper, we introduce a novel approach to address this challenge by developing a multi-order digital joint coding-modulation (MDJCM) scheme for semantic communications. Initially, we construct a digital semantic communication system by integrating a multi-order modulation/demodulation module into a nonlinear transform source-channel coding (NTSCC) framework. Recognizing the non-differentiable nature of modulation/demodulation, we propose a novel substitution training strategy. Herein, we treat modulation/demodulation as a constrained quantization process and introduce scaling operations alongside manually crafted noise to approximate this process. As a result, employing this approximation in training semantic communication systems can be deployed in practical modulation/demodulation scenarios with superior performance. Additionally, we demonstrate the equivalence by analyzing the involved probability distribution. Moreover, to further upgrade the performance, we develop a hierarchical dimension-reduction strategy to provide a gradual information extraction process. Extensive experimental evaluations demonstrate the superiority of our proposed method over existing digital and non-digital JSCC techniques.

External disks (henceforth referred to as disks) are commonly used data storage peripherals for hosts. Verifying the legitimacy of these disks is essential to mitigate security risks, such as privacy breaches and virus propagation, before initiating interactions with a host. To address this challenge, we proposeDiskPrint, a novel replay-resistant, few-shot disk authentication system that relies on unintentional electromagnetic (EM) emanations from the internal components of disks. The core idea ofDiskPrintis that EM signals emitted during data writing operations can reveal unique hardware discrepancies among different disks. Building on electromagnetic theory, we develop a theoretical model that links EM signals to the underlying electronic components of the disk, demonstrating the feasibility of extracting distinctive disk fingerprints from these emanations. We also propose a set of signal enhancement techniques aimed at mitigating EM interface noise and improving the signal-to-noise ratio (SNR) of the EM measurements. To further strengthen the security ofDiskPrint, we introduce a device-agnostic, replay-resistant approach by incorporating randomness into the leaked EM signals. Real-world experiments with 60 disks, spanning both hard disk drives (HDDs) and solid-state drives (SSDs) from seven brands and 14 different models, indicate thatDiskPrintachieves an authentication success rate exceeding 99.6% with only three registration samples. A robustness analysis confirms its stability over time, while a security evaluation shows its resilience against various attack scenarios.

We study the Out-of-Distribution (OOD) generalization in machine learning and propose a general framework that establishes information-theoretic generalization bounds. Our framework interpolates freely between Integral Probability Metric (IPM) andf-divergence, which naturally recovers some known results (including Wasserstein- and KL-bounds), as well as yields new generalization bounds. Additionally, we show that our framework admits an optimal transport interpretation. When evaluated in two concrete examples, the proposed bounds either strictly improve upon existing bounds in some cases or match the best existing OOD generalization bounds. Moreover, by focusing onf-divergence and combining it with the Conditional Mutual Information (CMI) methods, we derive a family of CMI-based generalization bounds, which include the state-of-the-art ICIMI bound as a special instance. Finally, leveraging these findings, we analyze the generalization of the Stochastic Gradient Langevin Dynamics (SGLD) algorithm, showing that our derived generalization bounds outperform existing information-theoretic generalization bounds in certain scenarios.

For safety guard and crime prevention, video surveillance systems have been pervasively deployed in many security-critical scenarios, such as the residence, retail stores, and banks. However, these systems could be infiltrated by the adversary and the video streams would be modified or replaced, i.e., under the video forgery attack. The prevalence of Internet of Things (IoT) devices and the emergence of Deepfake-like techniques severely emphasize the vulnerability of video surveillance systems under such attacks. To secure existing surveillance systems, in this paper we propose a vision-WiFi cross-modal video forgery detection system, namelyWiSil. Leveraging a theoretical model based on the principle of signal propagation,WiSilconstructs wave front information of the object in the monitoring area from WiFi signals. With a well-designed deep learning network,WiSilfurther recovers silhouettes from the wave front information. Based on a Siamese network-based semantic feature extractor,WiSilcan eventually determine whether a frame is manipulated by comparing the semantic feature vectors extracted from the video’s silhouette with those extracted from the WiFi’s silhouette. We enhance the basic version ofWiSilFang et al. 2023 by developing a model compression method and a forgery trace localization method. Extensive experiments show thatWiSilachieves 95%$+$accuracy in detecting tampered frames.

Integrated sensing, communication, and computation (ISCC) has been regarded as a prospective technology for the next-generation wireless network, supporting human-centric intelligent applications. However, the delay sensitivity of these computation-intensive applications, especially in a multi-device ISCC system with limited resources, highlights the urgent need for efficient sensing task execution frameworks. To address this, we propose a resource-efficient sensing framework in this paper. Different from existing solutions, it features a novel action detection module deployed at each device to detect the onset of an action. Only time windows filled with signals of interest are offloaded to the edge server and processed by the edge recognition module, thus reducing overhead. Furthermore, we quantitatively analyze the sensing performance of the proposed sensing framework and formulate a sensing accuracy maximization problem under power, delay, and resource limitations for the multi-device ISCC system. By decomposing it into two subproblems, we develop an alternating direction method of multipliers (ADMM)-based distributed algorithm. It alternatively solves a sensing accuracy maximization subproblem at each device and employs a closed-form computation resource allocation strategy at the edge server till convergence. Finally, a real-world test is conducted using commodity wireless devices to validate the sensing performance analysis. Extensive test results demonstrate that our proposal achieves higher sensing accuracy under the limited resource compared to two baselines.

In modern wireless network architectures, such as O-RAN, artificial intelligence (AI)-based applications are deployed at intelligent controllers to carry out functionalities like scheduling or power control. The AI “apps” are selected on the basis of contextual information such as network conditions, topology, traffic statistics, and design goals. The mapping between context and AI model parameters is ideally done in a zero-shot fashion via an automatic model selection (AMS) mapping that leverages only contextual information without requiring any current data. This paper introduces a general methodology for the online optimization of AMS mappings. Optimizing an AMS mapping is challenging, as it requires exposure to data collected from many different contexts. Therefore, if carried out online, this initial optimization phase would be extremely time consuming. A possible solution is to leverage a digital twin of the physical system to generate synthetic data from multiple simulated contexts. However, given that the simulator at the digital twin is imperfect, a direct use of simulated data for the optimization of the AMS mapping would yield poor performance when tested in the real system. This paper proposes a novel method for the online optimization of AMS mapping that corrects for the bias of the simulator by means of limited real data collected from the physical system. Experimental results for a graph neural network-based power control app demonstrate the significant advantages of the proposed approach.

In the realm of semantic communication, the significance of encoded features can vary, while wireless channels are known to exhibit fluctuations across multiple subchannels in different domains. Consequently, critical features may traverse subchannels with poor states, resulting in performance degradation. To tackle this challenge, we introduce a framework called Feature Allocation for Semantic Transmission (FAST), which offers adaptability to channel fluctuations across both spatial and temporal domains. In particular, an importance evaluator is first developed to assess the importance of various features. In the temporal domain, channel prediction is utilized to estimate future channel state information (CSI). Subsequently, feature allocation is implemented by assigning suitable transmission time slots to different features. Furthermore, we extend FAST to the space-time domain, considering two common scenarios: precoding-free and precoding-based multiple-input multiple-output (MIMO) systems. An important attribute of FAST is its versatility, requiring no intricate fine-tuning. Simulation results demonstrate that this approach significantly enhances the performance of semantic communication systems in image transmission. It retains its superiority even when faced with substantial changes in system configuration.

We study the Out-of-Distribution (OOD) generalization in machine learning and propose a general framework that provides information-theoretic generalization bounds. Our framework interpolates freely between Integral Probability Metric (IPM) and$f$-divergence, which naturally recovers some known results (including Wasserstein- and KL-bounds), as well as yields new generalization bounds. Moreover, we show that our framework admits an optimal transport interpretation. When evaluated in two concrete examples, the proposed bounds either strictly improve upon existing bounds in some cases or recover the best among existing OOD generalization bounds.

Recently, semantic communication has received increasing attention for its potential to enhance efficiency, yet research on semantic security is still in its infancy. Due to the open nature of wireless channels, semantic communication systems are susceptible to wireless adversarial attacks. These attacks entail introducing deliberately crafted perturbation signals to legitimate signals over the wireless channel, which misleads the semantic interpretation at the receiver. This paper explores defense approaches from a practical perspective. To better characterize real-world wireless adversarial attacks, we first introduce an effective attack model named Adjustable Perturbation Generator (APG), designed to generate perturbation signals of various power levels. To combat these attacks, we propose a novel framework called Robust Model Ensembling for Semantic Communication (ROME-SC). Specifically, a Multi-level Perturbation Detector (MPD) is developed to detect the presence of attacks and measure their power levels. Then, the robust model ensembling approach is proposed to handle wireless adversarial attacks adaptively with the assistance of the MPD. Simulation results show that the proposed ROME-SC significantly enhances the overall performance of semantic communication systems under wireless adversarial attacks.

In recent developments, deep learning (DL)-based joint source-channel coding (JSCC) for wireless image transmission has made significant strides in performance enhancement. Nonetheless, the majority of existing DL-based JSCC methods are tailored for scenarios featuring stable channel conditions, notably a fixed signal-to-noise ratio (SNR). This specialization poses a limitation, as their performance tends to wane in practical scenarios marked by highly dynamic channels, given that a fixed SNR inadequately represents the dynamic nature of such channels. In response to this challenge, we introduce a novel solution, namely deep refinement-based JSCC (DRJSCC). This innovative method is designed to seamlessly adapt to channels ex-hibiting temporal variations. By leveraging instantaneous channel state information (CSI), we dynamically optimize the encoding strategy through re-encoding the channel symbols. This dynamic adjustment ensures that the encoding strategy consistently aligns with the varying channel conditions during the transmission process. Specifically, our approach begins with the division of encoded symbols into multiple blocks, which are transmitted progressively to the receiver. In the event of changing channel conditions, we propose a mechanism to re-encode the remaining blocks, allowing them to adapt to the current channel conditions. Experimental results show that the DRJSCC scheme achieves comparable performance to the other mainstream DL-based JSCC models in stable channel conditions, and also exhibits great robustness against time-varying channels.

Although existing semantic communication systems have achieved great success, they have not considered that the channel is time-varying wherein deep fading occurs occasionally. Moreover, the importance of each semantic feature differs from each other. Consequently, the important features may be affected by channel fading and corrupted, resulting in performance degradation. Therefore, higher performance can be achieved by avoiding the transmission of important features when the channel state is poor. In this paper, we propose a scheme of Feature Arrangement for Semantic Transmission (FAST). In particular, we aim to schedule the transmission order of features and transmit important features when the channel state is good. To this end, we first propose a novel metric termed feature priority, which takes into consideration both feature importance and feature robustness. Then, we perform channel prediction at the transmitter side to obtain the future channel state information (CSI). Furthermore, the feature arrangement module is developed based on the proposed feature priority and the predicted CSI by transmitting the prior features under better CSI. Simulation results show that the proposed scheme significantly improves the performance of image transmission compared to existing semantic communication systems without feature arrangement.

The rise in renewable energy has driven the widespread use of large-scale energy storage batteries, which makes the risk of overheating more threatening. To ensure battery safety, it is essential to build a monitoring system with a comprehensive evaluation of large quantities of batteries. However, existing battery management systems exhibit significant limitations in terms of monitoring scope, analytical precision, and transmission efficiency. As an applicable solution, cloud–edge technology is an advanced integrated method that provides low-latency data access, accurate analysis capabilities, and adjustable monitoring ranges. In this work, the Kubernetes-orchestrated battery monitoring platform (KBMP), which integrates Kubernetes and cloud–edge technology, is proposed to provide comprehensive battery management. Specifically, Kubernetes is used to ensure low latency in data transmission and analysis, while the K-Means clustering algorithm is applied to provide accurate thermal runaway (TR) warnings. To validate the performance of KBMP, four sets of real battery TR data are fed to test its accuracy and latency. The experimental findings reveal that KBMP is capable of providing battery TR warnings in advance within 30 min. Additionally, the platform concurrently decreases data transmission latency by up to 20% and reduces replica scaling latency by 50% compared to the platform without integrating Kubernetes.

To satisfy the requirements of many industrial applications, realizing ultrareliable low-latency communication (URLLC) has become one of the major challenges for future wireless networks. This article considers a downlink multiuser multiple-input-single-output (MISO) system in the Internet of Things (IoT) networks, in which a multiantenna base station (BS) serves multiple delay-sensitive IoT users, each equipped with a single antenna. To minimize the overall end-to-end delay, we jointly optimize the beamforming vectors and the packet blocklength to balance the queuing delay and the transmission delay. The problem is formulated as a Markov decision process (MDP), whose optimal solution can be theoretically found. However, the complexity on finding the optimal resource allocation and blocklength selection strategy is prohibitively high for real-system deployments due to the large state and action space. To overcome this issue, we simplify the original problem and develop an iterative algorithm to solve the simplified problem based on the uplink-downlink duality theory. Since solving the simplified problem would result in suboptimal solutions and may degrade the latency performance, we further develop a deep-reinforcement-learning (DRL)-based beamforming and blocklength selection framework to efficiently learn the optimal strategy of the original MDP. Simulation results demonstrate that the proposed algorithms can effectively improve the latency performance compared with the benchmark algorithm.

This article investigates an integrated sensing-communication-computation (ISCC)-based multiview-multitask (MVMT) edge artificial intelligence inference system. Each device senses a narrow view of a target area and processes the echo signal to generate real-time sensory data. An edge server receives and combines multiple views of data from multiple devices to complete several downstream inference tasks. Compared with existing designs where dedicated sensory data are obtained, transmitted, and processed for each task, this ISCC-based MVMT framework enjoys reduced costs of sensing, on-device computation, and communication overhead due to data sharing among different tasks. The challenges of improving all tasks’ inference accuracy lie in the tight coupling of sensing, communication, and computation among different devices and sensory view competition among different tasks. These two challenges intertwine, making the multitask optimization problem mixed-integer nonconvex programming. To tackle this problem, we propose a joint device scheduling and resource allocation (JDSRA) scheme, which alternatively solves a subproblem of joint device scheduling and time allocation and a subproblem of resource allocation till convergence. Particularly, in addition to a dynamic-programming-based optimal device scheduling algorithm, a low-complexity suboptimal algorithm is proposed based on sorting a derived closed-form indicator, which represents the increase of all tasks’ inference accuracy per time unit consumption. Besides, a low-complexity optimal resource allocation algorithm is proposed by parallelly solving multiple simple convex subproblems. Numerical results based on jointly completing three tasks of human motion recognition, human height recognition, and localization in smart home scenarios are conducted to verify the performance of our proposed schemes.

Given the fact that WiFi-based sensing can be realized through the reuse of WiFi communication facilities and frequency bands, integrated sensing and communication (ISAC) emerges as a pivotal direction for future WiFi standards, such as IEEE 802.11bf. Traditional WiFi sensing systems extract channel state information (CSI) from exclusive WiFi packets to quantify the characteristics of the sensing target. This poses challenges for existing WiFi systems originally designed for communication purposes, as it demands high-quality and sufficient CSI measurements. In this paper, we propose SenCom as a step towards forward-compatible ISAC solution. SenCom extracts CSI from general WiFi packets, enabling CSI calibration across different WiFi communication modes and delivering quality CSI measurements for upper-layer sensing applications. A fitting-resampling scheme and an incentive strategy are also developed. The former one is to obtain evenly sampled CSI with consistent dimensionality and the latter one is to guarantee sufficient CSI measurements over time. We build a prototype of SenCom and conduct extensive experiments involving 15 participants. The results show that SenCom’s competence for a variety of sensing tasks while making minimal compromises to WiFi communication performance.

Recently, the ever-increasing demand for bandwidth in multi-modal communication systems requires a paradigm shift. Powered by deep learning, semantic communications are applied to multi-modal scenarios to boost communication efficiency and save communication resources. However, the existing end-to-end neural network (NN) based framework without the channel encoder/decoder is incompatible with modern digital communication systems. Moreover, most end-to-end designs are task-specific and require re- design and re- training for new tasks, which limits their applications. In this paper, we propose a distributed multi-modal semantic communication framework incorporating the conventional channel encoder/decoder. We adopt NN-based semantic encoder and decoder to extract correlated semantic information contained in different modalities, including speech, text, and image. Based on the proposed framework, we further establish a general rate-adaptive coding mechanism for various types of multi-modal semantic tasks. In particular, we utilize unequal error protection based on semantic importance, which is derived by evaluating the distortion bound of each modality. We further formulate and solve an optimization problem that aims at minimizing inference delay while maintaining inference accuracy for semantic tasks. Numerical results show that the proposed mechanism fares better than both conventional communication and existing semantic communication systems in terms of task performance, inference delay, and deployment complexity.

Task-oriented semantic communications have achieved significant performance gains. However, the employed deep neural networks in semantic communications have to be updated when the task is changed or multiple models need to be stored for performing different tasks. To address this issue, we develop a unified deep learning-enabled semantic communication system (U-DeepSC), where a unified end-to-end framework can serve many different tasks with multiple modalities of data. As the number of required features varies from task to task, we propose a vector-wise dynamic scheme that can adjust the number of transmitted symbols for different tasks. Moreover, our dynamic scheme can also adaptively adjust the number of transmitted features under different channel conditions to optimize the transmission efficiency. Particularly, we devise a lightweight feature selection module (FSM) to evaluate the importance of feature vectors, which can hierarchically drop redundant feature vectors and significantly accelerate the inference. To reduce the transmission overhead, we then design a unified codebook for feature representation to serve multiple tasks, where only the indices of these task-specific features in the codebook are transmitted. According to the simulation results, the proposed U-DeepSC achieves comparable performance to the task-oriented semantic communication system designed for a specific task but with significant reduction in both transmission overhead and model size.

Recently, deep learning (DL) based semantic communication systems have shown great potential to improve transmission efficiency in various tasks. However, the coexisting mechanism between semantic communications and other services remains unexplored, which limits the application of semantic communications in practical communication systems. In this paper, we propose a dynamic multiplexing and co-scheduling scheme for the semantic and ultra-reliable low-latency communication (URLLC) traffic coexisting systems. In particular, a joint resource allocation and model training problem is formulated, which aims at maximizing the utility of semantic service while satisfying the latency requirement of URLLC traffic. To reduce the computational complexity, the original problem is simplified and decoupled into a joint resource allocation and model selection problem and a robust model training problem. In the resource allocation and model selection phase, the original problem is decomposed into three subproblems and an alternating optimization algorithm is then proposed to obtain the optimal resource allocation result. In the model training phase, a two-stage semantic communication network is designed, which can efficiently mitigate the impact of feature erasure brought by the random arrival of URLLC traffic. Simulation results show that the proposed method can effectively improve the quality of semantic service while satisfying the latency requirement of URLLC traffic.

Traditional scheduling algorithms make decisions based solely on the channel conditions, without considering the transmitted semantic content. However, in multi-modal semantic communication systems, there is redundancy and variance in the importance of semantic features across modalities for task performance. To address this issue, we propose a novel feature scheduling and error probability control scheme that balances channel diversity and semantic diversity in semantic communication systems. Specifically, we first introduce a semantic feature importance metric to measure each feature’s contribution to the inference performance of semantic task. Using this metric, we formulate and solve an optimization problem to reduce overall latency while guaranteeing semantic task performance. Our detailed analysis examines feature selection strategies and transmission rate optimization to illustrate scheduling decisions based on both channel fading and semantic content. Consequently, we develop both optimal and low-complexity feature transmission scheduling schemes based on the optimization solution. Extensive experiments over multi-modal semantic communication systems validate that the semantic feature importance metric can reveal the importance of features from different modalities and accordingly affect their transmission rate. Additionally, the proposed schemes significantly reduce the system latency compared to the traditional methods.

Integrated sensing, computation, and communication (ISCC) has been recently considered as a promising technique for beyond 5G systems. In ISCC systems, the competition for communication and computation resources between sensing tasks for ambient intelligence and computation tasks from mobile devices becomes an increasingly challenging issue. To address it, we first propose an efficient sensing framework with a novel action detection module. In this module, a threshold is used for detecting whether the sensing target is static and thus the overhead can be reduced. Subsequently, we mathematically analyze the sensing performance of the proposed framework and theoretically prove its effectiveness with the help of the sampling theorem. Based on sensing performance models, we formulate a sensing performance maximization problem while guaranteeing the quality-of-service (QoS) requirements of tasks. To solve it, we propose an optimal resource allocation strategy, in which the minimum resource is allocated to computation tasks, and the rest is devoted to the sensing task. Besides, a threshold selection policy is derived and the results further demonstrate the necessity of the proposed sensing framework. Finally, a real-world test of action recognition tasks based on USRP B210 is conducted to verify the sensing performance analysis. Extensive experiments demonstrate the performance improvement of our proposal by comparing it with some benchmark schemes.

Integrated sensing and communication (ISAC) has emerged as a pivotal technology for next-generation mobile networks to embed sensing function on communication waveforms. A major challenge in ISAC is the effective integration of sensing and communication functions. Addressing this, this paper introduces a dual-functional waveform design that builds on the existing orthogonal frequency division multiplexing (OFDM) waveform. Unlike prior approaches that generally sacrifice communication performance to enhance sensing performance, our design contains a null-space sensing precoder that utilizes the null space of the communication channel to project additional sensing signals, thus improving the sensing functionality of the OFDM waveform without degrading any communication performance. We formulate a waveform optimization problem aimed at maximizing the sensing performance under the null-space sensing precoder and then propose a majorization-minimization (MM)-based waveform design algorithm. Additionally, to meet the real-time communication requirement in practice, we analyze the intrinsic characteristics of the high-performance sensing waveform and then develop a low-complexity waveform design algorithm. Simulation results show that the proposed MM-based algorithm can dramatically improve sensing performance without incurring any additional sensing power and degrading the communication performance. Furthermore, the low-complexity algorithm achieves substantial improvements in the sensing performance with much reduced computational complexity.

In recent years, the application of neural networks (NNs) in wireless communication has garnered widespread attention and proven successful. However, conventional learning-based NNs often suffer from poor calibration, meaning that they struggle to reliably quantify prediction confidence and lack proper uncertainty estimation. This limitation becomes especially critical for next generation communication systems, particularly in complex industrial scenarios with stringent reliability requirements. Previous efforts to enhance model calibration have primarily centered on modifying NNs’ training processes. However, these methods often demand significant computing resources, making them impractical for resource-constrained scenarios. In this paper, we investigate a distributed wireless communication system involving multiple users and propose a novel approach to improve model calibration. Our method focuses on enhancing calibration during the inference stage of NNs by introducing a power control mechanism. Notably, existing research indicates that many NNs exhibit overconfidence, i.e., the NN’s confidence exceeds its actual accuracy. Leveraging this insight, we exploit the inherent noise and fading in wireless systems to naturally reduce the NN’s confidence while preserving accuracy. To achieve this, we employ linear relaxation-based perturbation analysis (LiRPA) to approximate the relationship between the perturbed output and the input perturbation of the NN. Subsequently, we devise an optimization problem by leveraging the analyzed relationship and the definition of perfect calibration. It is aimed at finding the input perturbation that maximizes the probability of the model achieving perfect calibration. Finally, considering different channel conditions and a given specific modulation method, we derive the optimal transmission power based on bit error rate (BER) formula. Simulation results demonstrate that our proposed power control method exhibits significant advantages in model calibration compared to several traditional approaches.

As an efficient neural network model for graph data, graph neural networks (GNNs) recently find successful applications for various wireless optimization problems. Given that the inference stage of GNNs can be naturally implemented in a decentralized manner, GNN is a potential enabler for decentralized control/management in the next-generation wireless communications. Privacy leakage, however, may occur due to the information exchanges among neighbors during decentralized inference with GNNs. To deal with this issue, in this paper, we analyze and enhance the privacy of decentralized inference with GNNs in wireless networks. Specifically, we adopt local differential privacy as the metric, and design novel privacy-preserving signals as well as privacy-guaranteed training algorithms to achieve privacy-preserving inference. We also define the SNR-privacy trade-off function to analyze the performance upper bound of decentralized inference with GNNs in wireless networks. To further enhance the communication and computation efficiency, we adopt the over-the-air computation technique and theoretically demonstrate its advantage in privacy preservation. Through extensive simulations on the synthetic graph data, we validate our theoretical analysis, verify the effectiveness of proposed privacy-preserving wireless signaling and privacy-guaranteed training algorithm, and offer some guidance on practical implementation.

In integrated sensing, computation, and communication (ISCC) systems, the competition for communication and computation resources between sensing tasks for ambient intelligence and computation tasks from mobile devices becomes an increasingly challenging issue. To address it, we first propose an efficient sensing framework with a novel action detection module that can detect whether the sensing target is static. Subsequently, we analyze the sensing performance of the proposed framework and formulate a sensing accuracy maximization problem while guaranteeing the quality-of-service (QoS) requirements of tasks. To solve it, we propose an optimal resource allocation strategy and derive a threshold selection policy that demonstrates the necessity of the proposed sensing framework. Finally, a real-world test of action recognition tasks based on USRP B210 is conducted to verify the sensing performance analysis, and extensive experiments demonstrate the performance improvement of our proposal by comparing it with some benchmark schemes.

Recently, deep learning-enabled joint-source channel coding (JSCC) has received increasing attention due to its great success in image transmission. However, most existing JSCC studies only focus on single-input single-output (SISO) channels. In this paper, we first propose a JSCC system for wireless image transmission over multiple-input multiple-output (MIMO) channels. As the complexity of an image determines its reconstruction difficulty, the JSCC achieves quite different reconstruction performances on different images. Moreover, we observe that the images with higher reconstruction qualities are generally more robust to the noise, and can be allocated with less communication resources than the images with lower reconstruction qualities. Based on this observation, we propose an adaptive channel state information (CSI) feedback scheme for precoding, which improves the effectiveness by adjusting the feedback overhead. In particular, we develop a performance evaluator to predict the reconstruction quality of each image, so that the proposed scheme can adaptively decrease the CSI feedback overhead for the transmitted images with high predicted reconstruction qualities in the JSCC system. We perform experiments to demonstrate that the proposed scheme can significantly improve the image transmission performance with much-reduced feedback overhead.

Given the fact that WiFi-based sensing can be realized by reusing WiFi communication facilities and communication frequency bands, integrated sensing and communication (ISAC) is considered a crucial development direction for future WiFi standards, such as IEEE 802.11bf. Traditional WiFi sensing systems extract channel state information (CSI) from customized WiFi packets to quantify the characteristics of the sensing target. This poses challenges for existing WiFi systems originally designed for communication purposes, as it requires high-quality and sufficient CSI measurements. In this paper, we propose SenCom, which extracts CSI from general WiFi packets. SenCom enables CSI calibration across different WiFi communication modes and provides unified CSI measurements for upper-layer sensing applications. We also devise a fitting-resampling scheme to derive evenly sampled CSI with consistent dimensionality, and an incentive strategy to ensure sufficient CSI measurements over time. We build a prototype of SenCom and perform extensive experiments with 15 participants. The results show that SenCom is competent for a variety of sensing tasks, while incurring little compromise to the WiFi communication performance.

The integration of both sensing and communication functions is a crucial feature for future communication systems. This paper considers a novel scenario where a radar covers multiple small-cell base stations (BSs) which operate in different spectra. We propose a co-existence system of multiple-input multiple-output (MIMO) radar and multiple-input single-output (MISO) communication systems. We aim to minimize the system transmit power while maintaining the performance of both radar and communication. Due to the complexity of the original problem, we traverse the BS selection and transform the subproblem into a more tractable one by introducing auxiliary variables and propose a penalty dual decomposition (PDD)-based algorithm to solve it. In the inner loop, we propose a concave-convex procedure (CCCP)-based algorithm to deal with the optimization problem, and the block coordinate descent (BCD) algorithm is utilized to update the variables. In the outer loop, we update the penalty term or Lagrange multipliers. Finally, numerical simulations validate the superiority of our proposed algorithm over benchmark algorithms.

With the great success of deep learning (DL) in image classification, speech recognition, and other fields, more and more studies have applied various neural networks (NNs) to wireless resource allocation. Generally speaking, these artificial intelligent (AI) models are trained under some special learning hypotheses, especially that the statistics of the training data are static during the training stage. However, the distribution of channel state information (CSI) is constantly changing in the real-world wireless communication environment. Therefore, it is essential to study effective dynamic DL technologies to solve wireless resource allocation problems. In this paper, we propose a novel framework, named meta-gating, for solving resource allocation problems in an episodically dynamic wireless environment, where the CSI distribution changes over periods and remains constant within each period. The proposed framework, consisting of an inner network and an outer network, aims to adapt to the dynamic wireless environment by achieving three important goals, i.e., seamlessness, quickness and continuity. Specifically, for the former two goals, we propose a training method by combining a model-agnostic meta-learning (MAML) algorithm with an unsupervised learning mechanism. With this training method, the inner network is able to fast adapt to different channel distributions because of the good initialization. As for the goal of ‘continuity’, the outer network can learn to evaluate the importance of inner network’s parameters under different CSI distributions, and then decide which subset of the inner network should be activated through the gating operation. Additionally, we theoretically analyze the performance of the proposed meta-gating framework. Simulation results demonstrate that the proposed meta-gating framework can well achieve the three important goals compared with existing state-of-the-art algorithms.

Integrated sensing and communication (ISAC) has recently been considered as a promising approach to save spectrum resources and reduce hardware cost. Meanwhile, as information security becomes increasingly more critical issue, government agencies urgently need to legitimately monitor suspicious communications via proactive eavesdropping. Thus, in this paper, we investigate a wireless legitimate surveillance system with radar function. We seek to jointly optimize the receive and transmit beamforming vectors to maximize the eavesdropping success probability which is transformed into the difference of signal-to-interference-plus-noise ratios (SINRs) subject to the performance requirements of radar and surveillance. The formulated problem is challenging to solve. By employing the Rayleigh quotient and fully exploiting the structure of the problem, we apply the divide-and-conquer principle to divide the formulated problem into two subproblems for two different cases. For the first case, we aim at minimizing the total transmit power, and for the second case we focus on maximizing the jamming power. For both subproblems, with the aid of orthogonal decomposition, we obtain the optimal solution of the receive and transmit beamforming vectors in closed-form. Performance analysis and discussion of some insightful results are also carried out. Finally, extensive simulation results demonstrate the effectiveness of our proposed algorithm in terms of eavesdropping success probability.

Graph neural network (GNN) is an efficient neural network model for graph data and is widely used in different fields, including wireless communications. Different from other neural network models, GNN can be implemented in a decentralized manner during the inference stage with information exchanges among neighbors, making it a potentially powerful tool for decentralized control in wireless communication systems. The main bottleneck, however, is wireless channel impairments that deteriorate the prediction robustness of GNN. To overcome this obstacle, we analyze and enhance the robustness of the decentralized GNN during the inference stage in different wireless communication systems in this paper. Specifically, using a GNN binary classifier as an example, we first develop a methodology to verify whether the predictions are robust. Then, we analyze the performance of the decentralized GNN binary classifier in both uncoded and coded wireless communication systems. To remedy imperfect wireless transmission and enhance the prediction robustness, we further propose novel retransmission mechanisms for the above two communication systems, respectively. Through simulations on the synthetic graph data, we validate our analysis, verify the effectiveness of the proposed retransmission mechanisms, and provide some insights for practical implementation.

As an important part of the fifth generation (5G) mobile networks, unmanned aerial vehicles (UAVs) have been applied in various communication scenarios due to their high operability and low cost. In this paper, we investigate a multi-UAV communication system with moving users and consider the co-channel interference caused by the transmissions of all other UAVs. To ensure the fairness, we maximize the minimum average user rate during the observed time by jointly optimizing UAVs’ trajectories, transmission power, and user association. Considering that UAVs can cover a large area for communications, UAVs do not need to move as soon as the users move. Therefore, a two-timescale structure is proposed for the considered scenario, where the UAVs’ trajectories are optimized based on the channel state information (CSI) in a long timescale, while the transmission power and the user association are optimized based on the instantaneous CSI in a short timescale. To effectively tackle this challenging non-convex problem with both discrete and continuous variables, we propose a joint neural network (NN) design, where a deep reinforcement learning based Pointer Network named advantage pointer-critic (APC) is applied to optimize discrete variables and a deep-unfolding NN is used to optimize the continuous variables. Specifically, we first formulate a Markov decision process to model the user association, and then employ the APC network trained by the advantage actor-critic algorithm to address it. The APC network consists of a Pointer Network and a Multilayer Perceptron. As for the deep-unfolding NN, we first develop a block coordinate descent based algorithm to optimize the UAVs’ trajectories and transmission power, and then unfold the algorithm into a layer-wise NN with introduced trainable parameters. These two networks are jointly trained in an unsupervised fashion. Simulation results validate that the proposed joint NN significantly outperforms the optimization algorithm with much lower complexity, and achieves good performances on scalability and generalization ability.

Although semantic communications have exhibited satisfactory performance on a large number of tasks, the impact of semantic noise and the robustness of the systems have not been well investigated. Semantic noise refers to the misleading between the intended semantic symbols and received ones, thus causes the failure of tasks. In this paper, we first propose a framework for the robust end-to-end semantic communication systems to combat the semantic noise. In particular, we analyze sample-dependent and sample-independent semantic noise. To combat the semantic noise, the adversarial training with weight perturbation is developed to incorporate the samples with semantic noise in the training dataset. Then, we propose to mask a portion of the input, where the semantic noise appears frequently, and design the masked vector quantized-variational autoencoder (VQ-VAE) with the noise-related masking strategy. We use a discrete codebook shared by the transmitter and the receiver for encoded feature representation. To further improve the system robustness, we develop a feature importance module (FIM) to suppress the noise-related and task-unrelated features. Thus, the transmitter simply needs to transmit the indices of these important task-related features in the codebook. Simulation results show that the proposed method can be applied in many downstream tasks and significantly improve the robustness against semantic noise with remarkable reduction on the transmission overhead.

Device-to-device (D2D)-assisted decentralized learning has been proposed for mobile devices to collaboratively train artificial intelligence networks without the centralized parameter server. However, a densely connected network will cause large learning latency and energy consumption due to the limited computation and communication resources. In addition, link selection and aggregation weight have a significant impact on the learning performance. To cope with these challenges, we propose a joint computing power adjustment, wireless resource allocation, link selection, and aggregation weight adaptation mechanism to improve both communication and energy efficiencies. Specifically, the learning performances including the convergence rate, per-iteration learning latency, and per-iteration energy consumption are first analyzed. Then, an optimization problem is formulated to minimize the total learning cost, which is defined as the weighted sum of total learning latency and energy consumption. Given a network topology, the computing power and wireless resource allocation are optimized by the alternating optimization algorithm. Moreover, the optimal aggregation weight is obtained by semidefinite programming. With respect to link selection, we propose a tabu search based meta-heuristic algorithm to approximately achieve feasible solutions with a low computational complexity. Finally, extensive experiments demonstrate that the proposed link selection algorithm can significantly reduce the learning cost under the given learning accuracy requirement.

Graph neural network (GNN) is widely applied in various fields, especially for graph data. Moreover, it is an effective technique for decentralized inference tasks, where information exchange among neighbors relies on wireless communications. However, wireless channel impairments and noise decrease the accuracy of prediction. To remedy imperfect wireless transmission and enhance the prediction robustness, we propose a novel retransmission mechanism with adaptive modulation that could select the appropriate modulation order adaptively for each retransmission. Compared to the traditional method that determines the modulation order based on bit error ratio (BER), we bring in a new indicator called robust prediction to select an appropriate modulation order for each transmission. Under the requirement of prediction robustness, the error-tolerance of GNNs is exploited and a higher modulation order can be used in the proposed mechanism compared with the traditional method, thus reducing the communication overhead and improving the data rate. Meanwhile, we combine the signals of different retransmission with the soft-bit maximum ratio combine (SBMRC) technique. Simulation results verify the effectiveness of the proposed retransmission mechanism.

In this paper, we investigate a multi-UAV communication system with moving users and consider the co-channel interference caused by the transmissions of all other UAVs. To ensure the fairness of moving users, we maximize the minimum average user rate during the observed time by jointly optimizing UAVs' trajectories, transmission power, and user association. To effectively tackle this non-convex problem with both discrete and continuous variables, we propose a joint neural network (NN) design, where a network named advantage pointer-critic (APC) is applied to optimize discrete variables and a deep-unfolding NN is used to optimize continuous variables. Specifically, we first elaborately formulate a Markov decision process to model the user association, and then use the APC network trained by the advantage actor-critic algorithm to address it. As for the deep-unfolding NN, we first develop a block coordinate descent based algorithm to optimize UAVs' trajectories and transmission power, and then unfold this algorithm into a layer-wise NN with introduced trainable parameters. These two networks are jointly trained in an unsupervised fashion. Simulation results validate that the proposed joint NN significantly outperforms the mathematical optimization algorithm with much lower complexity.

The task-oriented semantic communication sys-tems have achieved significant performance gain, however, the paradigm that employs a model for a specific task might be limited, since the system has to be updated once the task is changed or multiple models are stored for serving various tasks. To address this issue, we firstly propose a unified deep learning enabled semantic communication system (U-DeepSC), where a unified model is developed to serve various transmission tasks. To jointly serve these tasks in one model with fixed parameters, we employ domain adaptation in the training procedure to specify the task-specific features for each task. Thus, the system only needs to transmit the task-specific features, rather than all the features, to reduce the transmission overhead. Moreover, since each task is of different difficulty and requires different number of layers to achieve satisfactory performance, we develop the multi-exit architecture to provide early-exit results for relatively simple tasks. In the experiments, we employ a proposed U-DeepSC to serve five tasks with multi-modalities. Simulation re-sults demonstrate that our proposed U-DeepSC achieves compa-rable performance to the task-oriented semantic communication system designed for a specific task with significant transmission overhead reduction and much less number of model parameters.

Wireless hierarchical federated learning (HFL) has been proposed for large-scale model training over multi-cell network while preserving the data privacy. However, the imbalanced data distribution and load have a significant impact on the convergence rate, the learning accuracy, and the learning latency in wireless HFL with non-independent identically distributed training data. To cope with these challenges, we first derive the learning latency and the upper bound of the model error. Then, an optimization problem is formulated to minimize the weighted sum of total data distribution distance and learning latency. Joint user association and wireless resource allocation algorithms are investigated to achieve the optimal learning performance. Finally, the effectiveness of the proposed algorithms are demonstrated by the simulations.

Deep-unfolding has received great attention since it achieves satisfactory performance with relatively low complexity. Typically, the deep-unfolding networks are restricted to a fixed-depth for all inputs. However, the optimal number of layers required for convergence changes with different inputs. In this paper, we first develop a framework of deep deterministic policy gradient (DDPG)-driven deep-unfolding with adaptive depth for different inputs. Specifically, the optimization variables, trainable parameters, and the deep-unfolding architecture are designed as the state, action, and state transition of DDPG, respectively. Then, this framework is employed to deal with the channel estimation problem in massive multiple-input multiple-output systems. Specifically, first of all we formulate the channel estimation problem with off-grid basis and develop a sparse Bayesian learning (SBL)-based algorithm to solve it. Secondly, the SBL-based algorithm is unfolded into a layer-wise structure with a set of introduced trainable parameters. Thirdly, the proposed DDPG-driven deep-unfolding framework is employed to solve this channel estimation problem based on the unfolded structure of the SBL-based algorithm. Simulation results show that the proposed algorithm outperforms the conventional algorithms with much reduced number of layers.

Although the semantic communications have exhibited satisfactory performance in a large number of tasks, the impact of semantic noise and the robustness of the systems have not been well investigated. Semantic noise is a particular kind of noise in semantic communication systems, which refers to the misleading between the intended semantic symbols and received ones. In this paper, we first propose a framework for the robust end-to-end semantic communication systems to combat the semantic noise. Particularly, we analyze the causes of semantic noise and propose a practical method to generate it. To remove the effect of semantic noise, adversarial training is proposed to incorporate the samples with semantic noise in the training dataset. Then, the masked autoencoder (MAE) is designed as the architecture of a robust semantic communication system, where a portion of the input is masked. To further improve the robustness of semantic communication systems, we firstly employ the vector quantization-variational autoencoder (VQ-VAE) to design a discrete codebook shared by the transmitter and the receiver for encoded feature representation. Thus, the transmitter simply needs to transmit the indices of these features in the codebook. Simulation results show that our proposed method significantly improves the robustness of semantic communication systems against semantic noise with significant reduction on the transmission overhead.

The deployment of generative adversarial networks (GANs) in wireless networks faces three key challenges of limited devices’ computational capability, scarce communication resources, and severe data privacy leakage. To address these issues, this paper proposes a new distributed framework for training GANs based on ensemble learning. First, multiple discriminators are trained at many devices using their local datasets. A generator is then trained at a central server by aggregating devices’ discriminators in an ensemble manner. The per-round training time is established. Finally, simulation results show that the proposed framework can simultaneously reduce the training time and improve the learning performance as compared with an existing framework.

The massive onsite data produced by the Internet of Things (IoT) can bring valuable information and immense potentials, thus empowering a new wave of emerging applications. However, with the rapid increase of onsite IoT data streams, it has become extremely challenging to develop a scalable computing platform and provide a comprehensive workflow for processing IoT data streams with lower latency and more intelligence. To this end, we present a Kubernetes-based scalable fog computing platform (KFIML), integrating big data streaming processing with machine learning (ML)-based applications. We also provide a comprehensive IoT data processing workflow, including data access and transfer, big data processing, online ML, long-term storage, and monitoring. The platform is feasibly validated on a clustered testbed, which comprises a master node, IoT broker servers, worker nodes, and a local database server. By leveraging the lightweight orchestration system, namely Kubernetes, we can readily scale and manage containerized software frameworks on our testbed. The big data processing layer utilizes the advanced data flow frameworks such as Apache Flink, to support both streaming processing and statistical analysis with low latency. In addition, the specified long short-term memory (LSTM)-based ML pipelines are employed on the online ML layer, to enable the real-time predictive analysis of IoT data streams. The experiments on a real-world smart grid use case demonstrate that the container-based KFIML platform can be well-scaled with Kubernetes to efficiently perform big data processing increased onsite IoT data streams with lower latency and conduct ML-based applications.

Integrated sensing and communication (ISAC) has been regarded as one of the most promising technologies for future wireless communications. However, the mutual interference in the communication radar coexistence system cannot be ignored. Inspired by the studies of reconfigurable intelligent surface (RIS), we propose a double-RIS-assisted coexistence system where two RISs are deployed for enhancing communication signals and suppressing mutual interference. We aim to jointly optimize the beamforming of RISs and radar to maximize communication performance while maintaining radar detection performance. The investigated problem is challenging, and thus we transform it into an equivalent but more tractable form by introducing auxiliary variables. Then, we propose a penalty dual decomposition (PDD)-based algorithm to solve the resultant problem. Moreover, we consider two special cases: the large radar transmit power scenario and the low radar transmit power scenario. For the former, we prove that the beamforming design is only determined by the communication channel and the corresponding optimal joint beamforming strategy can be obtained in closed-form. For the latter, we minimize the mutual interference via the block coordinate descent (BCD) method. By combining the solutions of these two cases, a low-complexity algorithm is also developed. Finally, simulation results show that both the PDD-based and low-complexity algorithms outperform benchmark algorithms.

In this paper, we propose an end-to-end deep learning-based joint transceiver design algorithm for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, which consists of deep neural network (DNN)-aided pilot training, channel feedback, and hybrid analog-digital (HAD) precoding. Specifically, we develop a DNN architecture that maps the received pilots into feedback bits at the receiver, and then further maps the feedback bits into the hybrid precoder at the transmitter. To reduce the signaling overhead and channel state information (CSI) mismatch caused by the transmission delay, a two-timescale DNN composed of a long-term DNN and a short-term DNN is developed. The analog precoders are designed by the long-term DNN based on the CSI statistics and updated once in a frame consisting of a number of time slots. In contrast, the digital precoders are optimized by the short-term DNN at each time slot based on the estimated low-dimensional equivalent CSI matrices. A two-timescale training method is also developed for the proposed DNN with a binary layer. We then analyze the generalization ability and signaling overhead for the proposed DNN based algorithm. Simulation results show that our proposed technique significantly outperforms conventional schemes in terms of bit-error rate performance with reduced signaling overhead and shorter pilot sequences.

In massive multiple-input multiple-output (MIMO) systems, hybrid analog-digital beamforming is an essential technique for exploiting the potential array gain without using a dedicated radio frequency chain for each antenna. However, due to the large number of antennas, the conventional channel estimation and hybrid beamforming algorithms generally require high computational complexity and signaling overhead. In this work, we propose an end-to-end deep-unfolding neural network (NN) joint channel estimation and hybrid beamforming (JCEHB) algorithm to maximize the system sum rate in time-division duplex (TDD) massive MIMO. Specifically, the recursive least-squares (RLS) algorithm and stochastic successive convex approximation (SSCA) algorithm are unfolded for channel estimation and hybrid beamforming, respectively. In order to reduce the signaling overhead, we consider a mixed-timescale hybrid beamforming scheme, where the analog beamforming matrices are optimized based on the channel state information (CSI) statistics offline, while the digital beamforming matrices are designed at each time slot based on the estimated low-dimensional equivalent CSI matrices. We jointly train the analog beamformers together with the trainable parameters of the RLS and SSCA induced deep-unfolding NNs based on the CSI statistics offline. During data transmission, we estimate the low-dimensional equivalent CSI by the RLS induced deep-unfolding NN and update the digital beamformers. In addition, we propose a mixed-timescale deep-unfolding NN where the analog beamformers are optimized online, and extend the framework to frequency-division duplex (FDD) systems where channel feedback is considered. Simulation results show that the proposed algorithm can significantly outperform conventional algorithms with reduced computational complexity and signaling overhead.

The combinatorial auction (CA) is an efficient mechanism for resource allocation in different fields, including cloud computing. It can obtain high economic efficiency and user flexibility by allowing bidders to submit bids for combinations of different items instead of only for individual items. However, the problem of allocating items among the bidders to maximize the auctioneers’ revenue, i.e., the winner determination problem (WDP), is NP-complete to solve and inapproximable. Existing works for WDPs are generally based on mathematical optimization techniques and most of them focus on the single-unit WDP, where each item only has one unit. On the contrary, few works consider the multi-unit WDP in which each item may have multiple units. Given that the multi-unit WDP is more complicated but prevalent in cloud computing, we propose leveraging machine learning (ML) techniques to develop a novel low-complexity algorithm for solving this problem with negligible revenue loss. Specifically, we model the multi-unit WDP as an augmented bipartite bid-item graph and use a graph neural network (GNN) with half-convolution operations to learn the probability of each bid belonging to the optimal allocation. To improve the sample generation efficiency and decrease the number of needed labeled instances, we propose two different sample generation processes. We also develop two novel graph-based post-processing algorithms to transform the outputs of the GNN into feasible solutions. Through simulations on both synthetic instances and a specific virtual machine (VM) allocation problem in a cloud computing platform, we validate that our proposed method can approach optimal performance with low complexity and has good generalization ability in terms of problem size and user-type distribution.

Ultra-reliable low-latency communication (URLLC) is essential for future vehicle-to-vehicle (V2V) networks to improve traffic safety and enhance driving experience. Due to the fast-varying channel caused by high mobility, guaranteeing latency and reliability performance of the V2V links is a tremendous challenge. In this paper, we propose a novel resource allocation framework to support ultra-reliable low-latency V2V communications. The proposed framework includes both large-scale and small-scale resource optimizations. The large-scale resource allocation is performed at the central base station based on large-scale channel information periodically collected from vehicles. On the other hand, the small-scale resource allocation is performed at the vehicles according to instantaneous channel and queuing information. We develop optimal solutions for both resource allocation problems. With the proposed optimal solutions, the latency performance at the occurrence of extreme events is enhanced by enabling spectrum sharing among the vehicles. Simulation results demonstrate that the proposed algorithm can effectively improve the URLLC performance compared against the benchmark algorithm.

Driven by the demand for massive and accurate sensing data to achieve wireless network intelligence under a limited available spectrum, the coexistence between radar and communication systems has attracted public attention. In this paper, we investigate a novel dual-functional full-duplex relay aided radar-communication system where the phased-array radar is employed at the amplify-and-forward (AF) relay. A joint transceiver design is proposed to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all detection directions at the radar receiver under communication quality-of-service and total energy constraints. The formulated optimization problem is particularly challenging due to the highly nonconvex objective function and constraints. Based on the problem structure, we equivalently decompose it into the radar-energy and relay-energy minimization problems under SINR requirements. To solve the radar-energy minimization problem, we propose a low-complexity algorithm based on the alternating direction method of multipliers to optimize the radar transmit power and receiver. The relay-energy minimization problem can be simplified into an equivalent quadratic programming problem by introducing an insightful unitary matrix. Then, the closed-form expression for the AF relay beamforming matrix can be derived, which is jointly determined by the channel condition of relay communication and the detection direction of the radar. After that, we introduce the overall transceiver design algorithm to the original problem and discuss its optimality and computational complexity. Simulation results verify that the proposed algorithm significantly outperforms other benchmark algorithms.

In recent years, wirelessfederated learning(FL) has been proposed to support the mobile intelligent applications over the wireless network, which protects the data privacy and security by exchanging the parameter between mobile devices and thebase station(BS). However, the learning latency increases with the neural network scale due to the limited local computing power and communication bandwidth. To tackle this issue, we introduce model pruning for wireless FL to reduce the neural network scale. Device selection is also considered to further improve the learning performance. By removing the stragglers with low computing power or bad channel condition, the model aggregation loss caused by model pruning can be alleviated and the communication overhead can be effectively reduced. We analyze the convergence rate and learning latency of the proposed model pruning method and formulate an optimization problem to maximize the convergence rate under the given learning latency budget via jointly optimizing the pruning ratio, device selection, and wireless resource allocation. By solving the problem, the closed-form solutions of pruning ratio and wireless resource allocation are derived and the threshold-based device selection strategy is developed. Finally, extensive experiments are carried out to demonstrate that the proposed model pruning algorithm outperforms other existing schemes.

Distributed machine learning has been extensively employed in wireless systems, which can leverage abundant data distributed over massive devices to collaboratively train a high-quality global model. The research efforts of recent works have focused on improving performance (e.g., communication efficiency, energy efficiency, and scalability) of centralized architectures, which include a number of distributed devices and a server. However, centralized architectures may cause congestion at the central node, which is not applicable under some circumstances. To tackle this issue, we introduce a decentralized edge learning framework over wireless networks via unreliable device-to-device (D2D) links and improve its learning performance. The unreliable transmission caused by the channel uncertainty has a negative effect on model convergence. To enhance the performance, we formulate an optimization problem to minimize the overall model deviation under a given latency requirement by jointly optimizing the broadcast data rate and bandwidth allocation. Then, the optimal solution of broadcast data rate is derived and an algorithm for obtaining the optimal bandwidth allocation is developed. Besides, we also propose a decentralized edge learning protocol without a central server and provide the convergence analysis. Finally, extensive simulations are conducted to demonstrate the performance advantages of our proposed algorithm compared against the baseline algorithm.

In this paper, we investigate an intelligent reflecting surface (IRS) assisted multi-user multiple-input multiple-output (MIMO) full-duplex (FD) system. We jointly optimize the active beamforming matrices at the access point (AP) and uplink users, and the passive beamforming matrix at the IRS to maximize the weighted sum-rate of the system. Since it is practically difficult to acquire the channel state information (CSI) for IRS-related links due to its passive operation and large number of elements, we conceive a mixed-timescale beamforming scheme. Specifically, the high-dimensional passive beamforming matrix at the IRS is updated based on the channel statistics while the active beamforming matrices are optimized relied on the low-dimensional real-time effective CSI at each time slot. We propose an efficient stochastic successive convex approximation (SSCA)-based algorithm for jointly designing the active and passive beamforming matrices. Moreover, due to the high computational complexity caused by the matrix inversion computation in the SSCA-based optimization algorithm, we further develop a deep-unfolding neural network (NN) to address this issue. The proposed deep-unfolding NN maintains the structure of the SSCA-based algorithm but introduces a novel non-linear activation function and some learnable parameters induced by the first-order Taylor expansion to approximate the matrix inversion. In addition, we develop a black-box NN as a benchmark. Simulation results show that the proposed mixed-timescale algorithm outperforms the existing single-timescale algorithm and the proposed deep-unfolding NN approaches the performance of the SSCA-based algorithm with much reduced computational complexity when deployed online.

In this work, hierarchical federated learning (HFL) over wireless multi-cell networks is proposed for large-scale model training while preserving data privacy. However, the imbalanced data distribution has a significant impact on the convergence rate and learning accuracy. In addition, a large learning latency is incurred due to the traffic load imbalance among base stations (BSs) and limited wireless resources. To cope with these challenges, we first provide an analysis of the model error and learning latency in wireless HFL. Then, joint user association and wireless resource allocation algorithms are investigated under independent identically distributed (IID) and non-IID training data, respectively. For the IID case, a learning latency aware strategy is designed to minimize the learning latency by optimizing user association and wireless resource allocation, where a mobile device selects the BS with the maximal uplink channel signal-to-noise ratio (SNR). For the non-IID case, the total data distribution distance and learning latency are jointly minimized to achieve the optimal user association and resource allocation. The results show that both data distribution and uplink channel SNR should be taken into consideration for user association in the non-IID case. Finally, the effectiveness of the proposed algorithms are demonstrated by the simulations.

In this work, we investigate the joint design of beam selection and digital precoding matrices for millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems with discrete lens arrays (DLA) to maximize the sum-rate. To tackle this challenging problem with discrete variables and coupled constraints, we propose an efficient framework of joint neural network (NN) design. Specifically, the proposed framework consists of a deep reinforcement learning (DRL)-based NN and a deep-unfolding NN, which are employed to optimize the beam selection and digital precoding matrices, respectively. As for the DRL-based NN, we formulate the beam selection problem as a Markov decision process and a double deep Q-network algorithm is developed to solve it. Regarding the design of the digital precoding matrix, we develop an iterative weighted minimum mean-square error algorithm induced deep-unfolding NN, which unfolds this algorithm into a layer-wise structure. Simulation results show that our proposed jointly trained NN significantly outperforms the existing iterative algorithms.

In wireless federated learning, the unreliable communication has a significant impact on the convergence rate and learning latency, which cannot be ignored. To cope with this problem, we propose a novel modulation selection mechanism to achieve the balance between learning latency and convergence rate loss caused by stochastic channel error. Different from the traditional one, the modulation mode in wireless FL system should be adjusted according to devices’ computing power, channel conditions, and training data importance. Then, an optimization problem to maximize the learning efficiency is formulated to obtain the optimal modulation scheme. Finally, extensive experiments are implemented to demonstrate the effectiveness of the proposed mechanism.

Dual-layer iterative algorithms are generally required when solving resource allocation problems in wireless communication systems. Specifically, the spectrum efficiency maximization problem for hybrid precoding architecture is hard to solve by the single-layer iterative algorithm. The dual-layer penalty dual decomposition (PDD) algorithm has been proposed to address the problem. Although the PDD algorithm achieves significant performance, it requires high computational complexity, which hinders its practical applications in real-time systems. To address this issue, we first propose a novel framework for deep-unfolding, where a dual-layer deep-unfolding neural network (DLDUNN) is formulated. We then apply the proposed frame-work to solve the spectrum efficiency maximization problem for hybrid precoding architecture. An efficient DLDUNN is designed based on unfolding the iterative PDD algorithm into a layer-wise structure. We also introduce some trainable parameters in place of the high-complexity operations. Simulation results show that the DLDUNN presents the performance of the PDD algorithm with remarkably reduced complexity.

The millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems with discrete lens arrays (DLA) have received great attention due to their simple hardware implementation and excellent performance. In this work, we investigate the joint design of beam selection and digital precoding matrices for mmWave MU-MIMO systems with DLA to maximize the sum-rate subject to the transmit power constraint and the constraints of the selection matrix structure. The investigated non-convex problem with discrete variables and coupled constraints is challenging to solve and an efficient framework of joint neural network (NN) design is proposed to tackle it. Specifically, the proposed framework consists of a deep reinforcement learning (DRL)-based NN and a deep-unfolding NN, which are employed to optimize the beam selection and digital precoding matrices, respectively. As for the DRL-based NN, we formulate the beam selection problem as a Markov decision process and a double deep Q-network algorithm is developed to solve it. The base station is considered to be an agent, where the state, action, and reward function are carefully designed. Regarding the design of the digital precoding matrix, we develop an iterative weighted minimum mean-square error algorithm induced deep-unfolding NN, which unfolds this algorithm into a layer-wise structure with introduced trainable parameters. Simulation results verify that this jointly trained NN remarkably outperforms the existing iterative algorithms with reduced complexity and stronger robustness.

Training task in classical machine learning models, such as deep neural networks, is generally implemented at a remote cloud center for centralized learning, which is typically time-consuming and resource-hungry. It also incurs serious privacy issue and long communication latency since a large amount of data are transmitted to the centralized node. To overcome these shortcomings, we consider a newly-emerged framework, namely federated edge learning, to aggregate local learning updates at the network edge in lieu of users' raw data. Aiming at accelerating the training process, we first define a novel performance evaluation criterion, called learning efficiency. We then formulate a training acceleration optimization problem in the CPU scenario, where each user device is equipped with CPU. The closed-form expressions for joint batchsize selection and communication resource allocation are developed and some insightful results are highlighted. Further, we extend our learning framework to the GPU scenario. The optimal solution in this scenario is manifested to have the similar structure as that of the CPU scenario, recommending that our proposed algorithm is applicable in more general systems. Finally, extensive experiments validate the theoretical analysis and demonstrate that the proposed algorithm can reduce the training time and improve the learning accuracy simultaneously.

Many mission-critical and latency-sensitive applications require ultra-reliable and low-latency communications (URLLC), which has been listed as a new service category of 5G New Radio (NR). To guarantee stringent latency and reliability constraints, URLLC services always exclusively occupy the spectrum and have priority over enhanced mobile broadband (eMBB) communications in the current coexistence scenario, which will greatly affect the performance of eMBB services and degrade the utilization efficiency of the spectrum resource. On the other hand, millimeter-wave (mmWave) communications can fulfill the enormous throughput requirements of 5G cellular communications. In this paper, we introduce mmWave communications into URLLC systems to provide a more efficient coexistence for eMBB and URLLC. A novel mmWave URLLC system is first developed, where URLLC users are allowed to share the spectrum resources with eMBB users. Besides, multi-connectivity technology, which enables users to access multiple base stations simultaneously, is introduced to the mmWave URLLC system to enhance the reliability. Then, a resource management problem is formulated, which maximizes the throughput of eMBB users while guaranteeing the latency and reliability requirements of URLLC users. To obtain optimal solutions, we first divide it into three subproblems, i.e., power allocation, resource matching, and user paring, and then solve them respectively. Simulation results demonstrate the data rate improvement compared against the traditional coexistence scenario without reusing strategy. Moreover, the multi-connectivity functionality poses a great effect on guaranteeing the latency and reliability requirements for URLLC users.

Optimization theory assisted algorithms have received great attention for precoding design in multiuser multiple-input multiple-output (MU-MIMO) systems. Although the resultant optimization algorithms are able to provide excellent performance, they generally require considerable computational complexity, which gets in the way of their practical application in real-time systems. In this work, in order to address this issue, we first propose a framework for deep-unfolding, where a general form of iterative algorithm induced deep-unfolding neural network (IAIDNN) is developed in matrix form to better solve the problems in communication systems. Then, we implement the proposed deep-unfolding framework to solve the sum-rate maximization problem for precoding design in MU-MIMO systems. An efficient IAIDNN based on the structure of the classic weighted minimum mean-square error (WMMSE) iterative algorithm is developed. Specifically, the iterative WMMSE algorithm is unfolded into a layer-wise structure, where a number of trainable parameters are introduced to replace the high-complexity operations in the forward propagation. To train the network, a generalized chain rule of the IAIDNN is proposed to depict the recurrence relation of gradients between two adjacent layers in the back propagation. Moreover, we discuss the computational complexity and generalization ability of the proposed scheme. Simulation results show that the proposed IAIDNN efficiently achieves the performance of the iterative WMMSE algorithm with reduced computational complexity.

Generalized Benders decomposition (GBD) is a globally optimal algorithm for mixed integer nonlinear programming (MINLP) problems, which are NP-hard and can be widely found in the area of wireless resource allocation. The main idea of GBD is decomposing an MINLP problem into a primal problem and a master problem, which are iteratively solved until their solutions converge. However, a direct implementation of GBD is time- and memory-consuming. The main bottleneck is the high complexity of the master problem, which increases over the iterations. Therefore, we propose to leverage machine learning (ML) techniques to accelerate GBD aiming at decreasing the complexity of the master problem. Specifically, we utilize two different ML techniques, classification and regression, to deal with this acceleration task. In this way, a cut classifier and a cut regressor are learned, respectively, to distinguish between useful and useless cuts. Only useful cuts are added to the master problem and thus the complexity of the master problem is reduced. By using a resource allocation problem in device-to-device communication networks as an example, we validate that the proposed method can reduce the computational complexity of GBD without loss of optimality and has good generalization ability. The proposed method is applicable for solving various MINLP problems in wireless networks since the designs are invariant for different problems.

Link scheduling in device-to-device (D2D) networks is usually formulated as a non-convex combinatorial problem, which is generally NP-hard and difficult to get the optimal solution. Traditional methods to solve this problem are mainly based on mathematical optimization techniques, where accurate channel state information (CSI), usually obtained through channel estimation and feedback, is needed. To overcome the high computational complexity of the traditional methods and eliminate the costly channel estimation stage, machine leaning (ML) has been introduced recently to address the wireless link scheduling problems. In this article, we propose a novel graph embedding based method for link scheduling in D2D networks. We first construct a fully-connected directed graph for the D2D network, where each D2D pair is a node while interference links among D2D pairs are the edges. Then we compute a low-dimensional feature vector for each node in the graph. The graph embedding process is based on the distances of both communication and interference links, therefore without requiring the accurate CSI. By utilizing a multi-layer classifier, a scheduling strategy can be learned in a supervised manner based on the graph embedding results for each node. We also propose an unsupervised manner to train the graph embedding based method to further reinforce the scalability and develop a K-nearest neighbor graph representation method to reduce the computational complexity. Extensive simulation demonstrates that the proposed method is near-optimal compared with the existing state-of-art methods but is with only hundreds of training network layouts. It is also competitive in terms of scalability and generalizability to more complicated scenarios.

The implementation of artificial intelligence (AI) in wireless networks is becoming more and more popular because of the growing number of mobile devices and the availability of huge amount of data. Directly transmitting data for centralized learning will cause long communication latency and may incur severe privacy issue as well. To address these issues, we consider the importance-aware federated edge learning (FEEL) system in this paper. Based on the relation between loss decay and gradient norm, a learning efficiency maximization problem is formulated by jointly considering the communication resource allocation and data selection. The closed-form results for optimal communication resource allocation and data selection are both developed, where some insights are also highlighted. Finally, the test results show that the proposed algorithm can effectively reduce the training latency and improve the learning accuracy as compared with some benchmark algorithms.

In wireless federated learning system, wireless communication and local computation have a significant impact on the learning latency due to the limited bandwidth and computing power of mobile devices. To reduce the learning latency, local stochastic gradient methods and gradient compression can be applied, which however would decrease the convergence rate. To tackle such issues, in this paper, the trade-off between the convergence rate and the learning latency is taken into account. We first formulate an optimization problem to maximize the convergence rate under the given training latency constraint via jointly optimizing the batchsize, compression ratio, and spectrum allocation. Then, by decomposing the problem into two subproblems, an adaptive algorithm is proposed to obtain the optimal solution. The results show that batchsize and compression ratio should be selected according to the computing power and channel state information of the devices to improve the convergence rate. Finally, experimental results are presented to verify the effectiveness of the proposed algorithm.

With the combination of deep learning and mobile edge computing, the accuracy of the computer vision task in mobile augmented reality (AR) applications can be significantly improved along with the enhancement on the end-to-end latency and energy efficiency. However, no architecture-based delay model for convolutional neural networks (CNNs) has been proposed in edge computing. In this paper, we first develop a new delay model to characterize the relation between the processing delay and the input image size of general CNN models. Then, we formulate a non-convex optimization problem to maximize the learning accuracy under the communication and computation resource constraints. By problem transformation, the optimal resource allocation policy is derived in closed-form and low-complexity search algorithm is also developed. Finally, test results validate the applicability of the delay model and demonstrate the learning accuracy improvement of the proposed algorithm.

Link scheduling for device-to-device (D2D) communications is usually formulated as an NP-hard non-convex combinatorial problem, which is difficult to get the optimal solution. Traditional methods are mainly based on mathematical optimization techniques with the help of accurate channel state information (CSI), which is costly to obtain. In this paper, we propose a graph embedding based method to achieve link scheduling without CSI for D2D communications. We first construct a fully-connected directed graph for the D2D network, and then compute a low-dimensional feature vector for each node in the graph based on the distances of both communication and interference links. Finally, a scheduling strategy can be learned based on the graph embedding results by utilizing a multi-layer classifier. Extensive simulation demonstrates that the proposed method is near-optimal compared with the existing state-of-art methods and only needs hundreds of training network layouts. It is also competitive in terms of scalability and generalizability to more complicated scenarios.

Wireless link scheduling in D2D communication systems aims at maximizing the weighted sum rate of D2D pairs by determining which subset of D2D pairs should be activated. However, it is a non-convex combinatorial optimization problem, which is generally NP-hard and difficult to achieve the optimal solution. Inspired by the recent attempt of introducing machine learning and graph embedding to reach the general goal, we propose an efficient method to solve the weighted sum rate maximization problem. We first model the system as a one-nearest neighbor graph, in which each D2D pair is a node and the strongest interference link for each node is an edge. Then we compute the feature vectors of both weights and nodes by graph embedding and use the feature vectors as the input of a subsequent multi-layer classifier. The parameters of classifier and graph embedding are trained jointly in a supervised manner. Simulation result shows that the proposed method can obtain near-optimal performance with only hundreds of training samples and is capable to be generalized to more complicated scenarios.

Deploying device-to-device (D2D) communication over underlaying cellular network can significantly enhance the spectrum utilization. However, co-channel interference will occur when D2D pairs share the same radio resource with cellular users. To mitigate the interference within a reasonable range, a machine learning based resource reuse scheme for D2D and cellular users is proposed in this paper. Specifically, we formulate an optimization problem to maximize the total throughput of D2D pairs and cellular users by optimally allocating subcarrier and power within the limits of the interference threshold. Since the formulated problem is a mixed integer non-linear programming problem, we solve it in two steps. First, we assign the orthogonal subcarriers to different cellular users to maximize the total throughput of all cellular users. Then, D2D pairs are allowed to reuse different subcarriers to further enhance the throughput without affecting the performance of cellular users. The second step is still NP-hard and therefore we propose a low-complexity algorithm based on the pointer network, a specific neural network structure proposed recently. Results reveal that, with remarkably reduced complexity, the proposed scheme outperforms the conventional resource allocation algorithms.

This paper investigates the user pairing in a full-duplex (FD) communication system, aiming at maximizing the overall data rate of the system by reducing inter-user interference. The traditional user pairing methods usually suffer from high computational complexity and therefore are not suitable for practical implementation. Inspired by the recent innovation of deep reinforcement learning (DRL), we develop a low-complexity algorithm for the user pairing problem in FD networks. We first transform the problem into a Markov decision process (MDP) to facilitate the implementation of DRL. We then utilize the semi-supervised training paradigm to speed up the training process by adding some expert experience to the replay buffer. Finally, our proposal is extensively tested via numerical simulation, which demonstrates that the DRL-based user pairing algorithm can achieve a good performance with a significantly reduced computational complexity.

With the development of the Internet of Underwater Things (IoUT), two critical problems have been prominent, i.e., the energy constraint of underwater devices and large demand for data collection. In this article, we introduce an autonomous underwater vehicle (AUV)-aided underwater acoustic sensor networks (UWSNs) to solve these problems. To improve the performance of UWSNs, we formulate an optimization problem to maximize the energy consumption utility, which is defined to balance the energy consumption and network throughput. To solve this optimization problem, we decompose it into four parts. First, due to the constraint of communication distance, we construct a cluster-based network and formulate the selection of cluster heads as a maximal clique problem (MCP). Second, the clustering algorithm is proposed. Third, we design a novel media access control (MAC) protocol to coordinate data transmission between AUV and cluster heads, among intracluster nodes, as well as among intercluster nodes. Finally, path planning of AUV is formulated as a traveling salesman problem to minimize AUV travel time. Based on the above analysis, two algorithms, namely, AUV-aided energy-efficient data collection (AEEDCO) and approximate AUV-aided energy-efficient data collection (AEEDCO-A), are developed accordingly. The simulation results show that the proposed algorithms perform well and are very promising in UWSNs with demand for large-scale communication, large system capacity, long-term monitoring, and high data traffic load.

It has been a long-held belief that judicious resource allocation is critical to mitigating interference, improving network efficiency, and ultimately optimizing wireless communication performance. The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve the problem to a certain level of optimality. Nonetheless, as wireless networks become increasingly diverse and complex, for example, in the high-mobility vehicular networks, the current design methodologies face significant challenges and thus call for rethinking of the traditional design philosophy. Meanwhile, deep learning, with many success stories in various disciplines, represents a promising alternative due to its remarkable power to leverage data for problem solving. In this article, we discuss the key motivations and roadblocks of using deep learning for wireless resource allocation with application to vehicular networks. We review major recent studies that mobilize the deep-learning philosophy in wireless resource allocation and achieve impressive results. We first discuss deep-learning-assisted optimization for resource allocation. We then highlight the deep reinforcement learning approach to address resource allocation problems that are difficult to handle in the traditional optimization framework. We also identify some research directions that deserve further investigation.

Millimeter-wave (mmWave) communication has been regarded as one of the most promising means to improve the cellular system capacity in the fifth-generation (5G) era. Compared with the conventional microwave communication networks, mmWave terminals should connect with multiple base stations (BSs) simultaneously to prevent the signal blockage. Meanwhile, the accurate instantaneous channel state information (CSI) is difficult to estimate and collect due to the densification of mmWave BSs. These unique characteristics pose stiff challenges to user association in mmWave networks. To deal with these issues, we develop a novel machine learning based user association approach to support multi-connectivity in mmWave networks. Specifically, we first formulate the mmWave user association problem as a multi-label classification problem, which is then transformed into a series of single-label classification problems through efficient multi-label classification algorithms. To further reduce the requirement on the amount of training samples, we utilize graphical model to represent the user association scenario and adopt novel feature extraction methods to obtain appropriate features from both geographical location information and topological information. With appropriate features, each single-label classification problem can be trained in a supervised manner. Test results show that the proposed approach can achieve a good performance with only a few training samples and without the need of CSI.

It is an essential goal for future wireless networks to provide better artificial intelligent services. In this paper, we investigate the joint communication and computation resource optimization in the mobile edge learning system to support augmented reality applications, where the convolutional neural networks (CNNs) are deployed at the edge server. For such a system, we first develop a delay model to characterize the relation between the computation latency and the input image size of general CNN models. Then, we formulate a mixed integer nonlinear optimization problem to maximize the system computation capacity under the constraints of learning accuracy, end-to-end latency, and energy consumption. To solve this problem, we first investigate maximizing the system learning accuracy under the communication and computation resource constraints. The optimal resource allocation policy can be achieved by a low-complexity search algorithm. We further prove that the original problem is NP-hard and propose an efficient heuristic algorithm with a newly-developed offloading priority function. An upper bound for the proposed algorithm is also derived. Finally, test results validate the applicability of the delay model and demonstrate the performance improvement of the proposed algorithm as compared with the existing algorithms.

Unmanned aerial vehicles (UAVs) have been applied in many different communication scenarios due to their mobility and manipuility. In this paper, we investigate a UAV-aided relay network, where a number of ground users in the urban area with many obstructions need to collect data from a base station (BS), and a UAV could fly around above the users and serve as a decode-and-forward (DF) mobile relay to improve the transmission coverage and performance. In this situation, channel can be represented by the segmented ray-tracing model. To ensure fairness, we aim to maximize the minimum throughput among all the users by jointly optimizing the three-dimensional (3D) UAV trajectory, user scheduling, and bandwidth allocation. To tackle the non-convex objective function and coupling constraints, we first construct surrogate functions, and then approximate the problem into a convex one and develop a constrained successive convex approximation (CSCA) algorithm. In particular, through insightful auxiliary variables and linearly coupled equality (LCE) constraints, we propose a low-complexity algorithm based on the alternating direction method of multipliers (ADMM) to solve the approximated convex problem in the iteration of the proposed CSCA algorithm. Furthermore, we prove the convergence of the proposed algorithm and analyze its complexity. The proposed algorithm can be easily extended to the multi-UAV scenario. Simulation results show that the proposed design significantly outperforms the existing schemes.

In cellular federated edge learning (FEEL), multiple edge devices holding local data jointly train a neural network by communicating learning updates with an access point without exchanging their data samples. With very limited communication resources, it is beneficial to schedule the most informative local learning updates. This paper focuses on FEEL with gradient averaging over participating devices in each round of communication. A novel scheduling policy is proposed to exploit both diversity in multiuser channels and diversity in the “importance” of the edge devices' learning updates. First, a new probabilistic scheduling framework is developed to yield unbiased update aggregation in FEEL. The importance of a local learning update is measured by its gradient divergence. If one edge device is scheduled in each communication round, the scheduling policy is derived in closed form to achieve the optimal trade-off between channel quality and update importance. The probabilistic scheduling framework is then extended to allow scheduling multiple edge devices in each communication round. Numerical results obtained using popular models and learning datasets demonstrate that the proposed scheduling policy can achieve faster model convergence and higher learning accuracy than conventional scheduling policies that only exploit a single type of diversity.

In this paper, inspired by the minority game (MG), we propose a distributed user association mechanism for the heterogenous networks (HetNets) on unlicensed bands. Our proposal aims to achieve load balance under different resource contention schemes between the LTE-unlicensed (LTE-U) and Wi-Fi networks in a fully distributed fashion. To formulate the user association problem as MG, we first prove that there exists a unique cut-off value in the single-AP scenario for both listen-before-talk and duty cycle muting schemes. Meanwhile, both the pure strategy and the mixed strategy are developed and the Nash equilibria are achieved. We further extend our analysis into the scenario with multiple Wi-Fi access points and prove the existence and uniqueness of the cut-off value set. Numerical results show that the proposed MG-based user association algorithm can achieve load balance and fine spectrum utilization without channel state information (CSI). Some inspiring results are also highlighted through the numerical simulation. The proposed distributed mechanisms not only handle the LTE-U/Wi-Fi selection, but also give fascinating insights into user association and resource allocation in other scenarios of heterogenous networks.

In licensed assisted access (LAA) based small cell networks (SCNs), the small base station (SBS) can reuse the uplink licensed bands with the macro cell while sharing the unlicensed bands with the Wi-Fi networks to improve its throughput. To mitigate the severe co-channel interference to the macro cell and guarantee the harmonious coexistence with the Wi-Fi networks, the spectrum and power should be jointly allocated at the SBSs. Moreover, to overcome the overwhelming signaling overheads introduced by the traditional centralized scheme and adapt to the variable radio environments, an adaptive decentralized scheme is necessary. Therefore, in this paper, an adaptive semi-distributed scheme is proposed to jointly allocate the power and spectrum on both licensed and unlicensed bands, which can achieve the global optimal spectrum efficiency (SE) of the SCNs. The proposed scheme can enable the SBSs to work independently and adaptively without sharing the whole information of the SBSs, but requires some Lagrangian parameters exchange via the coordination of the macro base station (MBS). Theoretical analysis and numerical results are presented to show that the proposed scheme is capable of achieving the optimal SE on both licensed and unlicensed bands adaptively while confining the co-channel interference to the MBS and guaranteeing the fair coexistence with the Wi-Fi network.

In this paper, we study a UAV-aided relay network, where a number of ground users need to collect data from a base station (BS), and a UAV could fly around above the users and serve as a decode-and-forward (DF) mobile relay to improve the performance. Firstly, we develop a novel UAV-relay model, which maximizes the minimum throughput among users by optimizing the UAV trajectory, the user scheduling and bandwidth allocation. Moreover, we adopt the segmented ray- tracing channel model to characterize the practical urban channel. Then, by constructing surrogate functions of the nonconvex constraints, we approximate the problem into a convex one and develop a block successive convex approximation (BSCA) algorithm to solve it. In particular, through insightful auxiliary variables and linearly coupled equality constraints, we propose a low-complexity algorithm to solve the key subproblem in the iteration of the proposed BSCA algorithm. Finally, simulation results show that the proposed design significantly outperforms the existing algorithms.

The mobile edge computing (MEC) and device-to-device (D2D) communications take advantage of the proximity for supporting high-speed mobile computing and high-rate data communications, respectively. In this paper, we integrate both techniques to further improve the computation capacity of the cellular networks by proposing the D2D-MEC technique. We aim to maximize the number of supported devices and formulate a mixed integer non-linear problem. To solve it, we decouple it into two subproblems and prove that the optimal solutions to the two subproblems compose the optimal solution to the original problem. The first one minimizes the required edge computation resource for a given D2D pair while the second one maximizes the number of supported devices via optimal D2D pairing. Then, by solving two subproblems, the optimal algorithm is developed and some insightful results are also highlighted. Finally, numerical results show that combining D2D communications with MEC can significantly enhance the computation capacity of the system.

The long term evolution-unlicensed (LTE-U) has been introduced by the third Generation Partnership Project (3GPP) to meet the continued insufficient capacity challenges. As multi-radio access technology (RAT) HetNets gradually become the mainstream, RAT coordination and user association issues arise from the extensions of cellular networks to unlicensed bands, for fair and harmonious coexistence with other RATs such as WiFi. In this work, we propose for the first time a distributed method of RAT selection and user association based on minority game, where users self-organize to achieve a balanced state between the cellular and WiFi networks while seeking their own interests. This distributive method only requires minimal external information from the environment and low computational complexity. Numerical results demonstrate the effectiveness of our distributed method. Furthermore, our work could be extended to handle distributed RAT coordination and user association issues in other scenarios.

Principle component analysis (PCA) can be used to estimate the eigenvalues and eigenvectors of high dimensional data set with low complexity. By using this instinct feature, an adaptive PCA based channel parameter estimation and tracking method is proposed for two dimensional (2D) massive multiple-input and multiple-output (M-MIMO) systems with uniform rectangular arrays (URAs) of antennas in this paper. The online received data is used to estimate and track the channel parameters (including the direction of arrival angles and the path gain). Since the adaptive PCA method is used, the proposed scheme has low complexity and high accuracy for estimation and tracking which are verified via the numerical simulations.

In the paper, an inverse discrete Fourier transform-based Vandermonde-subspace frequency division multiplexing (IDFT-VFDM) waveform is proposed for the new radio (NR) supplementary uplink (SUL) to share the same time and frequency resources with the frequency division duplex (FDD) based long-term evolution (LTE) network. To avoid the co-channel interference to the LTE user equipment (UE) uplink transmission, the interference channel state information (CSI) is necessary for the NR UE to design the interference-free precoder. Since the operating band used for NR SUL corresponds to LTE FDD mode, the channel reciprocity condition in the time division duplex (TDD) mode is no longer held. To deal with it, the reciprocity on the channel related parameters for each path, i.e. amplitude, initial phase, propagation distance, angle of arrival, angle of departure, is exploited to estimate the uplink CSI from the NR UE to the LTE base station (BS) via the downlink CSI. Accordingly, the uplink waveform is designed for the NR UE to guarantee the absence of interference towards the LTE BS with the knowledge of uplink CSI. Numerical results are presented to validate the accuracy of the CSI estimation and the merit of the IDFT-VFDM as a potential waveform to achieve the LTE FDD-NR SUL co-existence.

Resource allocation for device-to-device (D2D) communications is usually formulated as mixed integer nonlinear programming (MINLP) problems, which are generally NP-hard and difficult to solve. Traditional methods are based on mathematical optimization techniques, which suffer from forbidding computational complexity or unsatisfactory optimality. In this paper, we introduce a machine leaning (ML) technique, imitation learning, to address the resource allocation in D2D communications. The key idea is learning a good prune policy to speed up the widely-used globally optimal algorithm for the MINLP problems, the branch- and-bound (B&B) algorithm. With appropriate feature selection, imitation learning can be converted into a binary classification problem, which can be solved by the classical support vector machine (SVM). Extensive simulation demonstrates that the proposed method can achieve good optimality and reduce computational complexity simultaneously. It only needs hundreds of training samples and has a good generalization ability. Our proposed method can be also applied to the MINLP problems in other wireless communication networks.

Facing the challenges brought by the surge in the demand for mobile data traffic and increasingly scarce spectrum resources, two well-known channel access mechanisms named as duty-cycle muting (DCM) and listen- before-talk (LBT) have been proposed. In this article, we propose a novel adaptive hybrid channel access scheme which takes advantages of both mechanisms. Based on the WiFi traffic and the available licensed spectrum resource, our proposal can adaptively adjust the important parameters, such as the back-off window size and the duty-cycle time fraction, while ensuring fair and harmonious network coexistence between the WiFi and LTE-U systems. It can realize the flexible handoff between the DCM and LBT mechanisms to meet the requirements of different markets as well. Moreover, joint transmission power and spectrum resource allocation is also studied to improve the spectral efficiency on both licensed and unlicensed bands. The effectiveness of the proposed scheme is finally validated by numerical simulations.

In this paper, we investigate the latency minimization resource allocation problem in a hierarchical cloud-edge coexistence system by optimally splitting tasks for partial cloud computing and partial edge computing. A joint communication and computation resource allocation problem is first formulated and the structural characteristics are further analyzed. Next, by defining two novel parameters: the normalized backhaul communication capacity and the normalized cloud computation capacity, an optimal task splitting strategy is developed. With the help of these definitions, the joint communication and computation resource allocation policy can be devised in closed-form. Finally, numerical results demonstrate that the proposed collaborative cloud-edge computing scheme performs better than some baseline schemes in terms of minimizing the end-to-end latency of mobile devices.

In this paper, a low complexity channel parameter estimation method is proposed for two-dimensional (2D) uniform rectangular array (URA) massive multiple-input and multiple-output (MIMO) systems. Instead of assuming independent fading between different transmit-receive antenna pairs, a physical channel which models the realistic scattering environment via the angles and gains associated with different propagation paths is studied. A novel 2D Fourier transform (FT) based on elevation and azimuth steering factors is designed to derive the spatial spectrum distribution of received signals at base-station (BS). Accordingly, the received data matrices are used to estimate the channel parameters, which includes the direction of arrival (DOA) angles and the channel gains respective to each resolvable path. Since the channel coefficients are estimated from the DOA perspective and the proposed 2D FT can be realized by Fast-Fourier-Transform (FFT), the computational complexity is reduced significantly. Simulation results are provided to verify the accuracy and the complexity of the proposed scheme.

Unmanned aerial vehicles (UAVs) have been considered in wireless communication systems to provide high-quality services for their low cost and high maneuverability. This paper addresses a UAV-aided mobile edge computing system, where a number of ground users are served by a moving UAV equipped with computing resources. Each user has computing tasks to complete, which can be separated into two parts: one portion is offloaded to the UAV and the remaining part is implemented locally. The UAV moves around above the ground users and provides computing service in an orthogonal multiple access manner over time. For each time period, we aim to minimize the sum of the maximum delay among all the users in each time slot by jointly optimizing the UAV trajectory, the ratio of offloading tasks, and the user scheduling variables, subject to the discrete binary constraints, the energy consumption constraints, and the UAV trajectory constraints. This problem has highly nonconvex objective function and constraints. Therefore, we equivalently convert it into a better tractable form based on introducing the auxiliary variables, and then propose a novel penalty dual decomposition-based algorithm to handle the resulting problem. Furthermore, we develop a simplified l0-norm algorithm with much reduced complexity. Besides, we also extend our algorithm to minimize the average delay. Simulation results illustrate that the proposed algorithms significantly outperform the benchmarks.

Licensed-assisted-access (LAA) has recently emerged as a heterogeneous network technology to help mitigate the scarcity of licensed spectrum by extending the long-term evolution (LTE) network to unlicensed spectrum. This work proposes an enhanced LAA (eLAA) as a practical technique by exploiting the inherent transmit opportunity (TXOP) reservation via the Clear-to-Send-to-Self (CTS-to-Self) frame in Enhanced Distributed Channel Access (EDCA) to seamlessly integrate eLAA transmission within existing WiFi protocol. Unlike other LAA proposals, our eLAA is non-intrusive to unlicensed WiFi users and beneficial to both eLAA and WiFi networks. To further improve throughput, we analyze and derive an optimized unlicensed resource allocation scheme before deriving several intrinsic properties. Our results demonstrate that tagging CTS-to-Self frames as a higher-priority access category can substantially improve eLAA throughput without seriously degrading the per-user WiFi throughput.

Millimeter-wave (mmWave) heterogeneous network (HetNet) has been regarded as a promising means to improve the cellular system capacity in the 5G era. In this paper, we investigate the joint user association and resource allocation problem in a multi-band mmWave HetNet where different bands have different propagation characteristics. According to whether a user can transmit on multiple mmWave bands simultaneously, two different access schemes are considered: the single-band access scheme and the multi-band access scheme. For the single-band access scheme, we first find a closed-form expression for the optimal time fraction allocation and then develop an iterative algorithm for joint user association and power allocation based on the Lagrangian dual decomposition methods and the Newton-Raphson method. For the multi-band access scheme, we develop a near-optimal solution based on the Markov approximation framework. Our analytical results reveal that different users can only access at most one band simultaneously although the multi-band access scheme allows a user to transmit on multiple bands. Finally, numerical results demonstrate that the multi-band access scheme performs better than the single-band access scheme, especially in the light load scenario.

The future 5G wireless networks aim to support high-rate data communications and high-speed mobile computing. To achieve this goal, the mobile edge computing (MEC) and device-to-device (D2D) communications have been recently developed, both of which take advantage of the proximity for better performance. In this paper, we integrate the D2D communications with MEC to further improve the computation capacity of the cellular networks, where the task of each device can be offloaded to an edge node and a nearby D2D device. We aim to maximize the number of devices supported by the cellular networks with the constraints of both communication and computation resources. The optimization problem is formulated as a mixed integer non-linear problem, which is not easy to solve in general. To tackle it, we decouple it into two subproblems. The first one minimizes the required edge computation resource for a given D2D pair, while the second one maximizes the number of supported devices via optimal D2D pairing. We prove that the optimal solutions to the two subproblems compose the optimal solution to the original problem. Then, the optimal algorithm to the original problem is developed by solving two subproblems, and some insightful results, such as the optimal transmit power allocation and the task offloading strategy, are also highlighted. Our proposal is finally tested by extensive numerical simulation results, which demonstrate that combining D2D communications with MEC can significantly enhance the computation capacity of the system.

LTE-Unlicensed (LTE-U) has been widely considered as one of the most up-to-date promising innovations to achieve more ambitious data rate under a unified networking architecture by extending the LTE network into the bandwidth-rich unlicensed spectrum. In this paper, we study the user association optimization for LTE-U systems with multiple small cell base stations (SBSs) and WiFi access points (APs). Aiming at maximizing the whole throughput of LTE-U systems, a non-convex combinatorial optimization problem is modeled, which is NP-hard. By relaxing the integer variables to continuous variables, an upper-bound algorithm is developed based on the theory of sum-of- ratios optimization. Moreover, a heuristic algorithm based on the student project allocation (SPA) modeling is also developed, whose computational complexity is linear. Numerical results demonstrate the effectiveness and efficiency of our proposed algorithms.

In this paper, we consider multi-homing in terms of the emerging long-term evolution in unlicensed band (LTE-U) technology. Accordingly, the unlicensed band can be simultaneously and dynamically shared by every single user between the two radio access technologies (RATs), WiFi and LTE-U, to boost the system performance and enhance the user experience as well. Because of the additive freedom on unlicensed band, the problem of multi-RAT selection and resource allocation is much more complicated. To realize multi-homing in LTE-U networks, we aim at maximizing the overall throughput of multi-homing users while guaranteeing the quality of service and the power limitation. First, the sufficient conditions for each user to select only one RAT for transmission are developed. Then, since the multi-RAT selection and resource allocation problem is NP-hard, an effective heuristic algorithm is proposed to solve it. Numerical results validate the effectiveness of our proposed algorithm and demonstrate that the proposed mechanism can enhance the system throughput compared with traditional single-homing LTE-U networks.

Mobile edge computing (MEC) has become a dominant technology in the upcoming era of the 5th generation mobile networks. By offloading tasks from mobile devices to edge clouds provided by cellular base stations, both energy consumption and end-to-end delay of mobile tasks can be reduced. In this paper, we aim to optimize the latency performance of TDMA-based MEC systems by joint allocation of computation and communication resource. Our goal is to minimize the maximal delay of all devices in the system. We first simplify the optimization problem and convert it into a convex one. Then we derive the closed-form expression for the optimal resource allocation strategy and investigate the relationship between uplink and downlink resource allocation. A subgradient algorithm is also developed to solve the joint resource allocation problem. Finally, numerical simulation results are shown to verify that our proposal can achieve a better performance compared with the traditional schemes.

This paper investigates a multiuser mobile edge computing system under interference channels, where mobile users can offload their latency-sensitive (computation-intensive) tasks to the mobile edge server via a base station (BS). In this work, we seek to jointly optimize the user selection indicators for offloading and the computation resources, as well as the transmit power level of the offloading users in order to minimize the system energy consumption under latency-sensitive, computation and transmit power budget, transmission quality, and user selection constraints. The proposed optimization problem is nonconvex and highly coupled, which is difficult to solve. By exploiting binary relaxation and introducing auxiliary variables, we first convert this problem into a more tractable form. We then propose a concave-convex procedure (CCCP) based algorithm to obtain the resulting problem. Furthermore, a simplified algorithm is proposed to reduce the computational complexity. Simulation results are proposed to verify the proposed algorithms.

In this paper, we investigate the latency minimization resource allocation for a multi-user augmented reality (AR) system based on mobile edge computing (MEC). First, we develop a novel data sharing model for the delay-sensitive AR tasks. Then, by integrating the partial offloading scheme into the task processing, we formulate a weighted-sum latency minimization problem to improve the quality of experience (QoE) for AR devices. Both the optimal task segmentation strategy and the optimal joint resource allocation are derived in closed-form. Finally, numerical results show that the proposed partial task offloading with data sharing scheme can achieve a better delay performance as compared against some benchmark schemes.

With the development of the fifth generation mobile communication, long-term evolution in unlicensed spectrum (LTE-U) has been proposed as a promising means to solve the spectrum scarcity problem. In this paper, we investigate the mobile data offloading in a LTE-U system where one LTE small cell base station (SBS) coexists with several WiFi APs. Different from the traditional unidirectional mobile offloading, the proposed bidirectional mobile offloading can improve the system throughput and achieve the load balance among different WiFi APs as well. We first formulate a multi-objective optimization problem (MOOP) to analyze the bidirectional mobile offloading. Then, we propose two different algorithms to achieve the effective solutions to the MOOP. The first one utilizes the Nash bargaining solution (NBS) to obtain the closed-form solution to the optimal unlicensed resource allocation. The second one leverages the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to develop a low-complexity heuristic algorithm. Our proposals are finally validated by numerical simulations.

We consider a single-cell network with a hybrid full-/half-duplex base station. For the practical scenario with N channels, K uplink users, and M downlink users (max{ K , M } ≤ N ≤ K + M ), we tackle the issue of user admission and power control to simultaneously maximize the user admission number and minimize the total transmit power when guaranteeing the quality-of-service requirement of individual users. We formulate a 0–1 integer programming problem for the joint-user admission and power allocation problem. Because finding the optimal solution of this problem is NP-hard in general, a low-complexity algorithm is proposed by introducing the novel concept of adding dummy users. Simulation results show that the proposed algorithm achieves performance similar to that of branch and bound algorithm and significantly outperforms the random pairing algorithm.

By offloading intensive computation tasks to the edge cloud located at the cellular base stations, mobile-edge computation offloading (MECO) has been regarded as a promising means to accomplish the ambitious millisecond-scale end-to-end latency requirement of fifth-generation networks. In this paper, we investigate the latency-minimization problem in a multi-user time-division multiple access MECO system with joint communication and computation resource allocation. Three different computation models are studied, i.e., local compression, edge cloud compression, and partial compression offloading. First, closed-form expressions of optimal resource allocation and minimum system delay for both local and edge cloud compression models are derived. Then, for the partial compression offloading model, we formulate a piecewise optimization problem and prove that the optimal data segmentation strategy has a piecewise structure. Based on this result, an optimal joint communication and computation resource allocation algorithm is developed. To gain more insights, we also analyze a specific scenario where communication resource is adequate while computation resource is limited. In this special case, the closed-form solution of the piecewise optimization problem can be derived. Our proposed algorithms are finally verified by numerical results, which show that the novel partial compression offloading model can significantly reduce the end-to-end latency.

In this paper, we consider latency-minimization resource allocation for a multi-user mobile edge computation offloading (MECO) system. First, we develop a novel partial computation offloading model and then formulate the weighted-sum latency-minimization problem by optimally allocating the communication and computation resources. After that, the closed-form expression for the optimal data segmentation strategy is derived. Based on this result, we transform the original problem into a piecewise convex optimization problem and propose a sub-gradient algorithm to find the optimal resource allocation solution. Moreover, we analyze a specific scenario where communication resource is adequate while computation resource is limited. In this special case, the closed-form solution is devised. Finally, numerical results show that the partial computation offloading model can achieve a better performance than other two baseline schemes.

By offloading computationally intensive tasks to the edge cloud provided by the cellular base stations, the mobile edge computing (MEC) technique has the potential to realize the critical millisecond-scale latency requirement of next generation mobile services. In this paper, we investigate the joint subcarrier and power allocation problem in an orthogonal frequency division multiple access (OFDMA) based MEC system to minimize the maximal delay of each mobile device. The partial data offloading scenario is considered where mobile data can be computed at both local devices and the edge cloud. Since the problem is a combinatorial optimization one, we first propose a lower-bound algorithm by relaxing the channel allocation indicators into continuous variables. Then, to find a low-complexity feasible solution, we further develop a heuristic algorithm which separates subcarrier assignment and power allocation. The performances of our proposed algorithms are finally tested by extensive numerical simulations.

LTE over unlicensed spectrum (LTE-U) is one of the promising approaches to further improve LTE network throughput. To maximize the benefit of LTE-U, in this work we study joint resource allocation for LTE over the legacy licensed spectrum and the sharing unlicensed spectrum in a multi-cell scenario. Specifically, we formulate a mixed-integer power-channel allocation problem aiming at maximizing the network throughput, with the constraints of protecting the coexisting Wi-Fi networks and hardware limitation of user equipments in the LTE-U networks. To solve the resource allocation problem efficiently, we exploit delay column generation approach to decompose the original optimization problem and then propose a novel algorithm based KKT conditions. Simulation results show the advantage of LTE-U networking and the effectiveness of the proposed algorithm in terms of convergence speed and network throughput.

As one of the 5G physical-layer technique candidates, universal filtered multi-carrier (UFMC) supports sliced spectrum communication by filtering a series of subcarriers. The filter design greatly affects the inter-sub-band interference in UFMC and hence is crucial to the overall system performance. Here we first propose a novel UFMC scheme, namely UFMC-AIC, which incorporates active interference cancellation (AIC) into UFMC to considerably reduce the inter-sub-band interference. Then we propose a modified version of UFMC-AIC which optimizes the weighting factors of the interference cancellation subcarriers (ICSs) under a total power constraint. Simulation results show that the modified UFMC-AIC scheme can achieve higher transmission rate and better BER performance than the conventional UFMC either for AWGN channel or for Rayleigh channel. Moreover, we validate that our proposal can significantly reduce the filter design requirements.

In this paper, an inter-cell interference coordination (ICIC) scheme for small cell networks with full-duplex (FD) base stations is proposed to maximize the overall system throughput. To be compatible with existing enhanced ICIC (eICIC) and further enhanced ICIC (FeICIC) techniques, the proposed ICIC scheme adopts cell range expansion and almost blank subframe to coordinate various kinds of interference caused by FD communications. In detail, the novel scheme includes four steps, namely, choosing the serving base station, pairing users for FD communication, resource block allocation, and power control of FD users. Numerical simulation results show that our proposed ICIC scheme can effectively harvest the FD gain for small cell networks.

Universal filtered multi-carrier (UFMC) is one of the enabling techniques for 5G, which supports sliced spectrum by filtering successive sub-bands. The filter design is an essential and challenging issue, which is critical to the performance of UFMC. In this paper, we propose a novel UFMC scheme, namely, UFMC-AIC, which incorporates active interference cancellation into UFMC so as to mitigate the inter-sub-band interference. A non-convex optimization problem is formulated, which aims to maximize the overall signal-to-interference-plus-noise ratio performance by adaptively optimizing the weighting factors of the interference cancellation subcarriers under the power constraints. The minorization-maximization method is applied to solve the problem in the ideal case when the sub-band states and the channel state information are perfectly known at the base station. A practical distributed solution is further obtained in a closed form by the Lagrangian dual method, which largely reduces the computational complexity with only slight performance loss compared with the ideal case. Simulation results show that our proposal with lower cost of filter can still achieve better bit error rate performance than the conventional UFMC under different carrier frequency offsets for the Rayleigh block-fading channel.

In this paper, we address the tradeoff between energy efficiency (EE) and spectral efficiency (SE) for cellular networks with full-duplex (FD) communications enabled base stations. To be backward compatible with legacy LTE systems, it is assumed that user devices still work in the conventional half-duplex (HD) mode. There usually exists residual self-interference (RSI) in FD communications after advanced interference suppression techniques are applied. In this paper, we consider two different RSI models: constant RSI model and linear RSI model. First, the necessary conditions for an FD transceiver to achieve better EE-SE tradeoff than an HD one are derived for both the RSI models. Then, for the constant RSI model, a closed-form EE-SE expression is obtained in the scenario of single pair of users. We further extend our result and prove that EE is a quasi-concave function of SE in the scenario of multiple user pairs. Accordingly, an optimal algorithm to achieve the maximum EE based on the Lagrange dual decomposition technique is developed. For the linear RSI model, the EE-SE relation is difficult to deal with and we develop a heuristic algorithm by decoupling the problem into two sub-problems: power control and resource allocation. Our analysis and algorithms are finally verified by comprehensive numerical results.

This paper investigates a cellular network powered by a micro-grid (MG) in the context of green communications, which integrates the conventional generators, energy storage devices, and renewable energy generators, so as to supply electricity to base stations (BSs). Under this model, we study the efficiency aspect of the MG-powered cellular network from the economical perspective. Specifically, the concept of cost efficiency (CE) is employed to measure the sum rate delivered per dollar. Then, our goal is to maximize this CE subject to a series of constraints, including multi-variable coupling and time coupling constraints. Particularly, we assume the zero-forcing beamforming scheme employed by the BSs. To address this established fractional CE optimization problem, we first apply the Dinkelbach method, and then propose a low-complexity algorithm based on the alternating direction method of multipliers approach to jointly schedule power generation in the MG and optimize transmit power for BSs. We introduce a number of auxiliary variables to design a special variable splitting scheme so that the coupling inequality constraints can be separable among two variable sets. Consequently, the proposed algorithm only incorporates simple updates in each step and thus can be implemented in a parallel and completely distributed fashion. Simulation results demonstrate the convergence and energy scheduling performance of the proposed algorithm.

In this paper, we investigate a network with N pairs of users transmitting on the same channel simultaneously from the energy efficiency (EE) perspective. We formulate a non-concave multi-objective optimization problem (MOOP) to investigate the EE tradeoff, taking into account the minimum data rate requirement of each user. The weighted Tchebycheff method is utilized to solve the MOOP by converting it into a single-objective optimization problem, which is then solved by the Dinkelbach method and the concave-convex procedure (CCCP) method. Based on the above, a power control algorithm is developed for the interference network to achieve at least a local optimum. The proposed algorithm is compared with the orthogonal bandwidth sharing, where each user orthogonally shares the whole bandwidth without interfering each other. The performance of the proposed algorithms is verified by numerical results, which show that it is better to share the bandwidth orthogonally rather than non-orthogonally if the interference between each user pair is stronger than a given threshold.

This paper investigates a multi-cell system powered by a micro-grid (MG), where the conventional generators (CGs), energy storage devices (ESDs) and renewable energy generators (REGs) are scheduled to supply electricity to base stations (BSs) at different prices respectively. Under this energy schedule model, we study the efficiency aspect of the MG-powered multi-cell systems from the economical perspective, and propose a new concept of efficiency, which is referred to as cost efficiency (CE). Specifically, we consider the ratio of the sum rate for all BSs to the total energy cost that the system spends in providing electricity for BSs. Assuming that the zero-forcing (ZF) beamforming scheme is employed by the BSs, our goal is to maximize the CE by jointly scheduling energy in the MG and allocating the transmit power at the BSs. We apply the Lagrange duality decomposition technique and Dinkelbach method to address the established CE optimization problem. Simulations are provided to validate the effectiveness of the proposed algorithm.

Small cell base stations (SBSs) have been deployed in heterogeneous networks to improve the spectrum efficiency on licensed channels by reusing the spectrum resource of the macro base station (MBS). To relief the shortage on the licensed spectrum resources, licensed-assisted access (LAA) has been introduced to LTE small cell systems to share the unlicensed channel with the Wi-Fi users. In this paper, we investigate the fundamental tradeoff between the collision probability (CP) to the Wi-Fi users and the co-channel interference (CI) power to the MBS in such a heterogeneous LAA system. A multi-objective resource allocation problem is first formulated while guaranteeing the quality-of-service (QoS) of small cell users (SUEs). Then, the double waterfilling-line power allocation on the licensed and unlicensed channels is developed to analyze the CI-CP tradeoff and the weighted Tchebycheff method is applied to convert the multi-objective optimization problem into a single objective optimization problem. To find the complete set of Pareto optimal solutions to the tradeoff problem, a novel feasibility method is proposed. Based on the simulation results, the proposed joint resource allocation algorithm can achieve a flexible CI-CP tradeoff according to the QoS of SUEs in LAA systems.

In order to expand the Long Term Evolution (LTE) to the unlicensed spectrum, its coexistence with WiFi must be carefully considered. In this paper, we propose a multiobjective optimization framework to balance the performances of WiFi and LTE in the unlicensed spectrum. In particular, three objectives are simultaneously optimized, namely, the WiFi throughput, the LTE throughput, and the average packet delay of WiFi. Furthermore, we utilize the non-dominated sorting genetic algorithm II (NSGA-II) to obtain the complete Pareto optimal solution set of the optimization problem. The algorithm has the merits of low- complexity, robustness, fast convergence, and completeness. Simulation results demonstrate that the proposed method is able to generate a satisfactory Pareto optimal solution plane, which provides a good reference for network operator to determine the appropriate system operation point.

The employment of Long-Term Evolution (LTE) in unlicensed spectrum, known as LTE-U, can alleviate the spectrum scarcity problem in the 5G networks. With this new technique, the traditional mobile data offloading schemes, which generally offload LTE users to the WiFi network, should be revisited. In this paper, we propose to transfer WiFi users to the LTE-U network and simultaneously allocate some unlicensed spectrum to LTE-U. In this way, a win-win situation could be generated since LTE can achieve better spectrum efficiency than WiFi in the unlicensed spectrum. To facilitate it, three important challenges are addressed in the paper: which WiFi users should be transferred; how many WiFi users need to be transferred; and how much unlicensed resource should be allocated to the LTE network. We utilize the Nash bargaining solution to design fair unlicensed spectrum allocation between WiFi and LTE-U and thereby a win-win strategy is developed, whose performance is demonstrated by numerical simulation.

In multi-user cooperative diversity systems, energy-efficient optimal power allocation (OPA) is an essential problem. However, most of the existing works only focus on optimizing the overall system energy efficiency (EE), rather than the individual EE for each user. In this paper, we investigate the OPA in multi-user amplify-and-forward cooperative systems aiming at maximizing the EE for each individual user while satisfying the quality-of-service (QoS) requirements. We utilize the multi-objective optimization framework to formulate the power allocation problem. Due to the NP-hardness of the problem, we further employ the non-dominated sorting genetic algorithm II (NSGA-II) to find the Pareto optimal solutions to the multi-objective optimization problem. Simulation results demonstrate that the proposed algorithm can obtain well-distributed Pareto optimal solutions, as well as yield fast convergence and flexible EE tradeoff.

Device-to-device (D2D) communications have been a promising technique for future long term evolution (LTE) systems. In this paper, we aim to maximize the system energy efficiency (EE) of a communication system including both conventional cellular users and D2D users. We consider two different D2D access cases depending on whether all D2D users can be accessed or not. In both cases, the EE maximization problem is formulated as a combinatorial fractional programming problem. We then use the fractional programming and the branch-and-bound (BnB) method to find the optimal solution. Furthermore, low complexity algorithms are proposed according to the network load. Simulation results demonstrate the effectiveness of the proposed algorithms.

Full‐duplex communication (FDC) can potentially double the network capacity by allowing a device to transmit and receive simultaneously on the same frequency band. In this study, a novel resource allocation and user scheduling algorithm is proposed to maximise the network throughput for a cellular network with full‐duplex (FD) base stations (BSs). The authors consider that FDC is utilised at the BS with imperfect self‐interference (SI) cancellation while user devices only work in the traditional half‐duplex (HD) way. In addition, to potentially cancel co‐channel interference caused by other users, the opportunistic interference cancellation (OIC) technique is applied at user side. Since FDC does not always perform better than HD due to residual SI (RSI), a joint mode selection, user scheduling, and channel allocation problem is formulated to maximise the system throughput. The optimisation problem is non‐convex and NP‐hard, thereby a suboptimal heuristic algorithm with low computational complexity is proposed. Numerical results demonstrate that user diversity gain, FD gain, and OIC gain can be achieved by the proposed algorithm, respectively. The performance of FDC depends on the intensity of RSI and the distribution of user devices.

Long-term evolution in unlicensed spectrum (LTE-U) is an emerging technology for expanding cellular network capacity without additional spectrum cost. This paper investigates effective spectrum sharing for coexisting Wi-Fi and LTE-U services. Based on a novel hyper access point (HAP) we introduced for effectively embedding LTE-U in unlicensed Wi-Fi band, LTE-U can directly take advantage of the Wi-Fi point coordination function protocol. To facilitate the coexistence, our HAP dedicates a contention-free period to LTE-U users and allows a contention period (CP) for traditional Wi-Fi users. We investigate the optimization of joint user association and resource allocation to further improve system throughput and user fairness. We formulate a network utility maximization problem based on the Nash bargaining solution (NBS), for which we derive a closed-form expression for the optimal CP length under a given user association. We analyze this NBS-based utility maximization and the performance of the proposed algorithm under log-normal fading, Rayleigh fading, and Rician fading channel models, respectively. Our numerical results corroborate our analysis and demonstrate effective improvement of the system performance by the proposed HAP algorithm against traditional LTE-U deployment.

To improve system capacity, licensed-assisted access (LAA) has been proposed for long-term evolution (LTE) systems to use unlicensed bands. However, the energy efficiency (EE) of the LTE system may be degraded by LAA since unlicensed bands are generally less energy-efficient than licensed bands. In this paper, we investigate the EE optimization of LAA systems. We first develop a criterion to determine whether unlicensed bands can be leveraged to improve the EE of LAA systems. We prove that unlicensed bands can be used to improve the EE only when the allocated licensed resource blocks (RBs) are not enough. We then investigate joint licensed and unlicensed RB allocation to maximize the EE of each small cell base station (SBS) in a multi-SBS scenario, taking into account fair resource sharing between LTE and WiFi networks. The complete Pareto optimal EE set can be obtained by the weighted Tchebycheff method. We also develop an algorithm to provide fair EE among different SBSs based on the Nash bargaining solution. Numerical results are presented to confirm our analysis and to demonstrate the effectiveness of the proposed algorithms.

Traditional mobile data offloading transfers cellular users to WiFi networks to relieve the cellular system from the pressure of the ever-increasing data traffic load. However, the spectrum utilization of the WiFi network is bound to suffer from potential packet collisions due to its contention-based access protocol, especially when the number of competing WiFi users grows large. To tackle this problem, we propose transferring some WiFi users to be served by the LTE system, in contrast to the traditional mobile data offloading which effectively offloads LTE traffic to the WiFi network. Meanwhile, leveraging the emerging LTE in unlicensed spectrum (LTE-U) technology, some unlicensed spectrum resources may be allocated to the LTE system in compensation for handling more WiFi users. In this way, a win-win situation would be generated since LTE can generally achieve better performance than WiFi due to its capability of centralized co-ordination. To facilitate it, three important challenging issues are addressed in the paper: which WiFi users should be transferred; how many WiFi users need to be transferred; and how much unlicensed resources should be relinquished to the LTE-U network. We investigate three different user transfer schemes according to the availability of channel state information (CSI): the random transfer, the distance-based transfer, and the CSI-based transfer. In each scheme, the minimum required amount of unlicensed resources under a given transferred user number is analyzed. Furthermore, we utilize the Nash bargaining solution (NBS) to develop joint user transfer and unlicensed resource allocation strategy to fulfill the win-win situation for both networks, whose performance is demonstrated by numerical simulation.

Traffic offloading and resource sharing are two common methods for delivering cellular data traffic over unlicensed bands. In this paper, we first develop a hybrid method to take full advantages of both traffic offloading and resource sharing methods, where cellular base stations (BSs) offload traffic to WiFi networks and simultaneously occupy certain number of time slots on unlicensed bands. Then, we analytically compare the cellular throughput of the three methods with the guarantee of WiFi per-user throughput in the single-BS scenario. We find that traffic offloading can achieve better performance than resource sharing when existing WiFi user number is below a threshold and the hybrid method achieves the same performance as the resource sharing method when existing WiFi user number is large enough. In the multi-BS scenario where the coverage of small cells and WiFi access points are mutually overlapped, we consider to maximize the minimum average per-user throughput of each small cell and derive a closed-form expression for the throughput upper bound in each method. Meanwhile, practical traffic offloading and resource sharing algorithms are also developed for the three methods, respectively. Numerical results validate our theoretical analysis and demonstrate the effectiveness of the proposed algorithms as well.

Small cell deployment in heterogeneous networks, whereby small cell base stations (SBS) are deployed alongside traditional macro-cell base stations, is a proven solution for enhancing spatial frequency reuse across licensed spectrum in long-term evolution (LTE) networks. In order to mitigate the shortage of licensed spectrum resources, licensed-assisted access (LAA) has been introduced to allow LTE SBSs to share the unlicensed channel with WiFi nodes. As such, a complex yet interesting optimization problem results from the joint utilization of licensed and unlicensed spectrum resources by the SBSs to meet the quality-of-service (QoS) requirements of small cell users (SUEs). In this paper, we highlight the fundamental tradeoff induced by the SBSs between the amount of co-channel interference (CI) resulting from the reuse of licensed spectrum resources and the collision probability (CP) imposed on the co-existing WiFi nodes due to the sharing of unlicensed spectrum resources in such a coexisting LTE LAA-WiFi heterogeneous network deployment. We find that this fundamental tradeoff can be analyzed by developing a power allocation rule with double water-filling lines and the complete set of Pareto optimal solution can be achieved by the weighted Tchebycheff method. Our simulation results show that the proposed joint resource allocation algorithm can achieve a flexible and suitable tradeoff between the licensed spectrum CI and the WiFi CP according to the QoS requirements of SUEs in LTE LAA networks.

Driven by the demand for more radio spectrum resources, mobile operators are looking to exploit the unlicensed spectrum as a complement to the licensed spectrum. LTE-unlicensed (LTE-U), also referred to as licensed-assisted access by the third generation partnership project, is an extension of the LTE standard operating on the unlicensed spectrum. To realize LTE-U, its coexistence with Wi-Fi systems is the main challenge and must be addressed. In this paper, a listen-before-talk access mechanism featuring an adaptive distributed control function protocol is adopted for the small base stations (SBSs), whereby the backoff window size is adaptively adjusted according to the available licensed spectrum bandwidth and the Wi-Fi traffic load to satisfy the quality-of-service requirements of small cell users and minimize the collision probability of Wi-Fi users. Meanwhile, both licensed and unlicensed spectrum bands are jointly allocated to optimize spectrum efficiency. An admission control mechanism is further developed for the SBS to limit collision with Wi-Fi traffic. Extensive simulation results show that the proposed schemes achieve fair and harmonious coexistence between LTE-U small cells and the surrounding Wi-Fi service sets and substantially outperform baseline non-adaptive channel access mechanisms in the unlicensed spectrum.

In this paper, two novel mechanisms are developed for the coexistence of cellular and WiFi systems in unlicensed spectrum. In the opportunistic method, the small cell base station opportunistically selects traffic offloading or resource sharing on each WiFi access point (AP). In the hybrid method, the base station simultaneously offloads users and shares the unlicensed spectrum of each AP. The performances of the proposed methods are analyzed and compared. We find that traffic offloading can achieve better performance than resource sharing when the number of existing WiFi users is below a threshold and the hybrid method achieves the same performance as the resource sharing method when existing WiFi user number is large enough. Numerical results are presented to demonstrate the effectiveness of the proposed methods.

In this paper, we address the power control problem in an interference network with multiple users transmitting simultaneously on the same channel. We aim at achieving the energy efficiency (EE) balance among difference users. First, a multi-objective optimization problem is formulated, which maximizes the EE of each individual user while guaranteeing their minimum data rate requirements. To find its solution, we adopt two different scalarization methods to combine multiple objectives into a single one, namely, the weighted-sum method and the weighted Tchebycheff method. The problem in the weighted-sum method turns out to be a non-concave sum of- ratios optimization and an effective algorithm is developed based on the concave-convex procedure (CCCP) method. On the other hand, the problem in the weighted Tchebycheff method becomes a generalized fractional programming and we utilize the Dinkelbach method and the CCCP method to solve it. Through numerical simulation, we find that both methods can effectively obtain the Pareto optimal solutions to the multiobjective optimization problem and achieve the EE balance among users as well.

In this paper, an adaptive channel access mechanism is proposed to optimize the performance of licensed-assisted access (LAA) long-term evolution (LTE) small cell networks through joint allocation of licensed and unlicensed spectrum resources all the while ensuring a fair coexistence with Wi-Fi service sets on the unlicensed spectrum. A listen-before- talk (LBT) access mechanism featuring an adaptive distributed control function (DCF) protocol is adopted for the small cell base stations (SBSs), whereby the minimum backoff window size is adaptively adjusted according to the available licensed spectrum bandwidth and Wi-Fi traffic load to satisfy the quality-of-service (QoS) requirements of small cell users (SUs) and minimize the collision probability of Wi-Fi users. Meanwhile, both licensed and unlicensed spectrum bands are jointly allocated to optimize spectrum efficiency. An admission control mechanism is further developed for the SBSs to limit collision with Wi-Fi traffic. Extensive numerical results are presented to demonstrate the effectiveness of the proposed schemes.

Multi-stream aggregation (MSA) allows users to receive data from multiple base stations simultaneously to increase their data rates. In this paper, we propose a joint user association and resource allocation algorithm for MSA systems to achieve energy efficiency (EE) balance among different base stations. The problem is formulated as a non-convex combinatorial sum-of-ratios optimization problem, which is very hard to solve directly. We first relax the combinatorial variables and then transform the problem into a convex optimization problem by the sum-of-ratios algorithm and the successive convex approximation (SCA) method. Based on this, a near-optimal algorithm is developed. Simulation results show that the proposed algorithm can achieve a good performance with a fast convergence speed.

A framework on decentralized interference coordination based on the pricing mechanism is developed for device-to-device (D2D) communication underlying cellular systems to guarantee quality of service (QoS) of both cellular users (CUs) and D2D links. We aim at coordinating two types of interference: inter-layer interference from D2D pairs to CUs and intra-layer interference among D2D pairs. The former is mitigated by the base station through setting a price on the channel being reused by D2D pairs while the latter is solved by a game-theoretic approach, in which the D2D pairs compete for the spectrum until a Nash Equilibrium (NE) is achieved. Finally, numerical results verify that the proposed distributed scheme is effective for the interference coordination and its performance is close to the centralized scheme.

Licensed-assisted access (LAA) has been developed to improve LTE system capacity by using unlicensed bands. However, the energy efficiency (EE) of the LTE system may be degraded by LAA since unlicensed bands are generally less energy-efficient than licensed bands. In this paper, we investigate the EE optimization of LAA systems. We first develop a criterion to determine whether unlicensed bands can be leveraged to improve the EE of LAA systems. We prove that unlicensed bands can be used to improve the EE only when the allocated licensed resource blocks (RBs) are not enough. We then investigate how to jointly allocate licensed and unlicensed RBs to achieve EE fairness among small cell base stations (SBSs), based on the Nash bargaining solution. Numerical results are presented to confirm our analysis and to demonstrate the effectiveness of the proposed algorithm.

When sharing the unlicensed band with Wi-Fi users in unlicensed LTE (U-LTE) systems, the most critical issue is how to guarantee the harmonious coexistence between the two systems. On the other hand, when sharing the licensed band with the micro base station (MBS) in the small cell base station (SBSs), the co-channel interference needs to be properly coordinated. To address these two issues, power control, licensed and unlicensed spectrum allocation are jointly considered at the SBS to maximize the spectrum efficiency while guaranteeing the QoS of small cell users (SUs) and at the same time providing fair resource sharing with Wi-Fi users. The convex optimization method is applied to design the optimal scheme. Then, numerical results are provided to verify the proposed scheme and demonstrate the tradeoff between the Wi-Fi performance and the achievable spectrum efficiency at the SBS.

This paper investigates joint bandwidth and power allocation for energy-efficient uplink communication in cellular networks. Instead of overall system energy efficiency (EE), we focus on maximizing the EE for each individual user while guaranteeing the quality-of-service. Therefore, a multi-objective optimization problem is formulated. To find its optimal solutions, we first utilize two different methods to convert the multi-objective optimization problem into single objective optimization problems. They are the scalarization method to maximize the weighted summation of users' EE and the max-min method to maximize the minimum EE among users. Then, we develop an effective algorithm based on the sum-of-ratios optimization to solve the scalarization problem, and an algorithm based on the generalized fractional programming to solve the max-min optimization. We also discuss a simple scenario with equivalent bandwidth allocation as a benchmark. Numerical results are provided to validate the effectiveness of the proposed algorithms.

Device-to-device (D2D) communication underlaying cellular network has recently been proposed as a featured technique for future LTE systems. In this paper, we consider the scenario that D2D users can share the resources with multiple cellular users and investigate the joint reuse partner selection and power allocation strategy to maximize the overall system throughput. A heuristic suboptimal algorithm is developed, which is based on the reuse partner selection criterion obtained by utilizing the Lagrange relaxation method. Numerical results show that the system throughput will be improved dramatically by enabling D2D users to share resources with multiple cellular users.

In this study, a distributed biasing scheme is designed to achieve load balancing for heterogeneous networks. Based on the limited backhaul capacity and user distribution in the system, each small cell base station adaptively and distributively changes its cell range by setting the bias value, to effectively utilise the wireless resource and achieve load balancing as well. The Q ‐learning algorithm is adopted to design the biasing scheme in each small cell base station. The tradeoff between the backhaul resource utilisation and the quality‐of‐service of users is considered in the reward function of the Q ‐learning model. To examine the performance of the distributed scheme, a centralised scheme aiming at maximising the backhaul resource utilisation is also proposed for comparison, whose performance lower bound is derived. Numerical results show that the proposed distributed scheme can effectively utilise the backhaul resource for load balancing, and achieve a close performance to the centralised one.

In this study, a distributed scheme based on the Nash bargaining model is designed to coordinate co‐channel interference for small‐cell networks. The authors consider a scenario that resource blocks can be reused among different small cells. Different to existing works where resource allocation is conducted at the base stations, they propose the scheme where user initialises resource bargaining request to the serving base station once its quality‐of‐service cannot be satisfied because of the severe co‐channel interference from other users. Since the general bargaining problem is a non‐linear integer optimisation, the genetic algorithm is utilised to solve it. They also develop a low‐complexity bargaining model which only takes into account the strongest co‐channel interference. Simulation results show that the proposed distributed scheme can effectively reduce the outage probability of users and improve the system throughput. In addition, the proposed low‐complexity bargaining solution can achieve a close performance to the genetic algorithm‐based solution.

Heterogeneous network(HetNet) integrated with multipleradio access technologies(RATs) is a promising technique for satisfying the exponentially increasing traffic demand of future cellular systems. In this paper, we investigate energy-efficient resource allocation in a multi-RAT HetNet, aimed at maximizing theenergy efficiency(EE) for each individual user while guaranteeing thequality-of-service(QoS) requirement. Since the EE cannot be simultaneously maximized for every user, amultiple-objective optimization problem(MOOP) is formulated. To find its Pareto optimal solution, we first introduce the concept of Utopia EE, defined as the maximum achievable EE, for each user. Then, using the weighted Tchebycheff method, asingle-objective optimization problem(SOOP) is formulated, which can achieve Pareto optimal solution of the original MOOP. The SOOP is a generalized fractional programming problem that aims to minimize the maximum of several quasiconvex fractional functions. We further transform the problem into an equivalent but better tractable one, and develop an iterative algorithm to effectively solve it. Numerical results demonstrate that the proposed algorithm yields fast convergence, high system EE, and flexible EE tradeoff.

This paper investigates energy-efficient device-to-device (D2D) communications in cellular networks. We aim to maximize the overall energy-efficiency (EE) of D2D users and regular cellular users (RCUs) while considering the circuit power consumption and the quality-of-service (QoS) requirements for both types of users as well as power constraints. Three transmission modes, namely, dedicated mode, reusing mode, and cellular mode, are considered for D2D users to share spectrum with RCUs. Parametric Dinkelbach method and concave-convex procedure (CCCP) are adopted to transform the original optimization problems into more tractable forms through sequential convex approximations. Then, interior point method is exploited to obtain the optimal solution. Simulation results show that system EE can be improved significantly with the proposed mode switching algorithm compared with the single mode transmission. Besides, it is also shown that the reusing mode is more preferred in the EE based mode switching while it is the dedicated mode in the spectrum-efficiency (SE) based mode switching in most situations.

In this paper, we investigate the energy-efficient resource allocation problem in a single-cell orthogonal frequency division multiple access (OFDMA) system to achieve the energy efficiency (EE) tradeoff among users. Rather than overall system EE, our objective is to maximize the EE for each individual user. Therefore, a multiple-objective optimization problem is formulated, which in general has many Pareto optimal solutions and is hard to solve. To find its solution, we first convert it into two different single-objective optimization problems using the weighted-sum approach and the max-min approach, respectively. The single-objective optimization problems are non-convex due to the combinatorial channel allocation variables. Therefore, for both problems, we first provide an upper bound algorithm by relaxing the combinatorial variables and then develop a suboptimal heuristic algorithm. The sum-of-ratios optimization and the generalized fractional programming are utilized for the weighted-sum problem and the max-min problem, respectively. Numerical results demonstrate that both the weighted-sum and the max-min approaches can effectively solve the EE maximization problem, and the suboptimal heuristic algorithms can achieve a close performance to the corresponding upper bound algorithm.

A pricing-based joint spectrum and power allocation framework is proposed for decentralized interference coordination among device-to-device (D2D) communications and cellular users (CUs), with the quality-of-service guarantee. The interlayer interference from D2D pairs to CUs is controlled by the base station through setting a price for each D2D channel usage. The intralayer interference among D2D pairs is mitigated distributively using a game-theoretic approach, where the D2D pairs compete for the spectrum until a Nash equilibrium is achieved. The effectiveness of the proposed strategy, including a practical scheme with limited signaling overhead, is demonstrated through comparing with a centralized scheme.

In this paper, joint energy-efficient resource allocation for both the base station and users is studied for time division duplex (TDD) systems with carrier aggregation (CA). We aim at balancing the energy efficiency (EE) between downlink and uplink, as well as the EEs among individual users, by joint bandwidth and power allocation on each carrier component (CC). We formulate the optimization problem into maximizing the weighted summation of EEs for the base station and different users, where the weights are used to reflect the levels of importance. The objective function of the problem is a sum of several fractional functions, therefore, nonlinear sum-of-ratios programming needs to be used to solve it, which has not been exploited in resource allocation problems yet. Specifically, a novel transformation is performed to formulate an equivalent but better tractable problem, based on which we develop an iterative algorithm to find the global optimum of the considered problem. Numerical results validate the feasibility, fast convergence, and flexibility of the proposed algorithm in terms of EE balancing.

In this paper, we propose a novel energy-efficient resource allocation method to simultaneously improve both downlink and uplink energy efficiency (EE) for time division duplex (TDD) systems with carrier aggregation (CA). We aim at EE tradeoff between downlink and uplink by optimizing the power and bandwidth allocation on each carrier component (CC) for each user. The objective function is a sum of several fractional functions, therefore, a novel nonlinear sum-of-ratios programming technique is used to solve it. We first transform the problem into an equivalent and better tractable one and then propose an iterative algorithm to find the global optimum solution. Numerical results show that our method can converge with an acceptable number of iterations and achieve flexible EE tradeoff between downlink and uplink.

Sleep mode control is essential to the energy efficiency of small cell networks. However, frequently switching on/off small cell base stations (SBSs) may cause the degradation to the quality‐of‐service of their users and the increase of network operational cost as well. In this study, the authors propose a novel dual‐threshold‐based sleep mode control strategy for small cell networks. The motivation of using dual‐thresholds to control the sleep mode is to minimise the network energy consumption while avoiding the frequent mode transitions of SBSs at the same time. They utilise the Markov chain method to analyse the performance of the proposed strategy. Optimisation problems are formulated to achieve the optimal dual‐thresholds for two different scenarios: the homogeneous threshold scenario in which uniform dual‐thresholds are applied to all SBSs and the heterogeneous threshold scenario where different dual‐thresholds are assigned to SBSs. For the homogeneous threshold scenario, they develop an optimal solution which is based on exhaustive searching. A reinforcement learning‐based algorithm and a heuristic algorithm are proposed for the heterogeneous threshold scenario, respectively. Simulation results are presented to demonstrate the performance of the author's proposed algorithms.

Device-to-device (D2D) communications have been recently proposed as an effective way to increase both spectrum and energy efficiency for future cellular systems. In this paper, joint mode selection, channel assignment, and power control in D2D communications are addressed. We aim at maximizing the overall system throughput while guaranteeing the signal-to-noise-and-interference ratio of both D2D and cellular links. Three communication modes are considered for D2D users: cellular mode, dedicated mode, and reuse mode. The optimization problem could be decomposed into two subproblems: power control and joint mode selection and channel assignment. The joint mode selection and channel assignment problem is NP-hard, whose optimal solution can be found by the branch-and-bound method, but is very complicated. Therefore, we develop low-complexity algorithms according to the network load. Through comparing different algorithms under different network loads, proximity gain, hop gain, and reuse gain could be demonstrated in D2D communications.

This paper addresses the optimal transmission scheduling problem in hybrid energy supply systems with the save-then-transmit protocol, where the energy supply of the transmitter comes from both the primary battery and the energy harvester. We first consider minimizing the outage probability for a given amount of battery energy by optimizing the saving factor. It is demonstrated that harvesting external energy is unnecessary for a large spectral efficiency requirement. Then, we consider joint packet scheduling and saving factor optimization to the battery energy consumption minimization (BECM) problem in both single packet arrival and burst packet arrival scenarios. Both optimal and suboptimal offline policies with full information on the traffic profile, the harvesting power, and the channel state are developed. We also propose an optimal online policy in the case that only causal information is available. Numerical results are presented that validate the effectiveness of the proposed algorithms.

In this paper, the interference mitigation problem in device-to-device (D2D) communication underlaying cellular networks using fractional frequency reuse (FFR) is studied. For such system, we propose a location based channel reusing scheme in which the D2D users in the inner region have a chance to reuse the channel resources of cellular users in the outer region of neighboring cells, and the D2D users in the outer region could reuse the channel resources of particular cellular users in the inner region of the same cell. Two novel concepts are introduced, namely the accessible region and the reusable region. To protect the outage probabilities of both cellular links and D2D links, only the D2D users in the accessible region could reuse the channel resources of cellular users in the reusable region. We present a specific method to calculate the boundary of the accessible region, as well as the reusable region. Simulation results demonstrate that the proposed scheme could effectively alleviate the interference between both users.

In this paper, we propose a novel power control scheme for small cells deployed within macro cells. Our aim is to find the optimal power level for each small cell according to the energy consumption tradeoff between network and User Equipments (UEs). Two different small cell deployment scenarios are considered: the non-dense scenario and the dense scenario. The multiagent decentralized Reinforcement Learning (RL) technique is applied to deal with the dense deployment scenario where the coverage of different small cells are overlapped. In the proposed multiagent RL algorithm, each small cell is modeled as an agent to learn the optimal policy from interaction with environment to dynamically change its transmit power. Simulation results are presented to validate the proposed method and show that the RL based algorithm could provide a satisfactory performance.

In this paper, we propose a three-stage rateless coded protocol for a half-duplex time-division two-way relay system, where two terminals send messages to each other through a relay between them. In the protocol, each terminal takes one of the first two stages respectively to encode its message using rateless code and broadcast the result until the relay acknowledges successful decoding. During the third stage, the relay combines and re-encodes both messages with a joint network-channel coding scheme based on rateless coding which provides incremental redundancy. Together with the packets received directly in previous stages, each terminal then retrieves the desired message using an iterative decoder. The degree profiles of the specific rateless codes, i.e., Raptor codes, implemented at both terminals and the relay, are jointly optimized for both the AWGN channel and the Rayleigh block fading channel through solving a set of linear programming problems. Simulation results show that, the system throughput as well as the error rate achieved by the optimized degree profiles always outperforms those achieved by the conventional degree profile optimized for Binary Erasure Channel (BEC) and the previous network coding scheme with rateless codes.

In this paper, we design a joint channel-network coding scheme based on rateless code for the three-stage two-way relay system, where two terminals send messages to each other through a relay between them. Each terminal takes one of the first two stages to encode its message using a Raptor Code and then broadcasts the result into the air, respectively. In the third stage, upon successfully decoding the corresponding messages, the relay node re-encodes them with the new Raptor Codes, and then XORs the outputs and broadcasts the result to both terminals. Together with the packets received directly in previous stages, each terminal then retrieves the desired message using an iterative decoder. Here, the degree profiles of the Raptor Codes used at each node are jointly optimized through solving a set of linear programming problems. Simulations show that, the system throughput achieved by the optimized degree profiles always outperforms the one with conventional degree profile optimized for binary erasure channel (BEC) and the conventional network coding scheme with rateless coding.

A new interference management strategy is proposed for device-to-device (D2D) underlaying cellular networks without the exact location information of cellular user equipments (CUEs). We consider the case where one D2D pair reusing the same channel with cellular users, both uplink and downlink. The base station (BS) only knows the distances that D2D user equipments (DUEs) and CUEs away from BS, instead of exact location information. First, we present a power control mechanism to limit the maximum transmit power of the D2D transmitter to control the harmful interference from D2D system to CNs. Then, an interference limited ring (ILR) control strategy is proposed to insure that the outage probability of the D2D communication caused by the interference from CUEs is less than a predetermined threshold ε. The exact radius of ILR is derived as a closed form. The D2D users can not reuse the channels of those CUEs which are located in the ILR to guarantee that the outage probability is less than ε. Through numerical simulation, we analyze the influence of three key parameters and come up with several practical suggestions for allocating channel resources.

Device-to-Device (D2D) communication as an underlaying cellular network empowers rich multimedia application, improves local communication, and enables local services, which would also bring out interference between cellular users and D2D terminals. In this paper, we study the challenges of the interference management in a hybrid network that D2D communication reuses uplink resource of cellular networks. We first introduce an interference coordination strategy for D2D communication. Then we design an optimal channel reusing selection algorithm for single cell scenario which is based on Hungarian algorithm. We also propose a heuristic algorithm to reduce the computational complexity. Our simulation results show that the heuristic algorithm has a close performance as the optimal algorithm with a significant decreasing of computational complexity, and both of the proposed algorithms could improve the system performance.

Self-organizing network (SON) is considered as a driving technology for the deployment of next generation radio access networks. This paper addresses the problem of load balancing (LB) for multi-hop cellular network (MCN) with fixed relays such as LTE-A network in the context of SON. The designed SON algorithm, namely RSPC-RL, is based on two ideas: relay node reference signal power control (RSPC) and multi-agent reinforcement learning (RL). In the proposed RSPC-RL algorithm, the relay node is modeled as an agent that learns an optimal policy of reference signal power control from its interaction with environment to balance the load distribution of the network through dynamically changing its coverage area. Numerical results show the significant performance gain brought about by the proposed algorithm RSPC-RL.

Relay assisted transmission could efficiently enhance the performance of Device-to-Device (D2D) communication when D2D user equipments (UEs) are too far away from each other or the quality of D2D channel is not good enough for direct communication. The relay selection problem for D2D communication underlaying cellular network is studied in this paper. We proposed a distributed relay selection method for relay assisted D2D communication system. The method firstly coordinates the interference caused by the coexistence of D2D system and cellular network and eliminates improper relays correspondingly. Next, the best relay is chosen among the optional relays using a simple distributed method. Numerical results show that performance of the proposed method is close to the optimal (centralized) method.

Handover decision is one of the technical challenges in the heterogeneous network (HetNet). Current researches on this topic concentrate mainly on the two-tier networks. In this paper, we investigates the handover decision scheme for a more complex network: three-tier macrocell/relay/femtocell network. The unique characteristics of the inter-tier handover in three-tier networks are analyzed in this paper and an effective handover algorithm is proposed to reduce the frequent and unnecessary handovers based on the ideas of dwell probability and handover priority. Simulation results show that the proposed algorithm significantly reduces the number of handovers while maintaining the call dropping rate at the same level.

This paper considers the power allocation problem in a relay-assisted Time-Division-Duplex-based multiple-cell system. In such a system, the achievable data rate of each user is not only coupled with those of others due to the inherent dynamic spatial frequency reuse therein, but also coupled between consecutive time slots due to the required two-hop transmission, which results in the so-called "spatial-coupling" and "time-coupling" nature, respectively. To address both of the two coupling effects in a relay assisted TDD cellular system, we formulate an optimal power allocation problem for the asynchronous scenario of the system where different cells work independently. We observe that the problem is non-convex and NP-hard, but can be converted to Geometric Programming (GP) problems and solved by interior-point methods. In case there is no central controller and/or massive information exchange among cells is prohibitive, game theory is employed to derive the distributed solutions. Finally, we validate the proposed algorithms by extensive simulations.

To improve the performance of a relay system with multiple channels, the following issues should be addressed. Namely, how to allocate the power at source and relay to subchannels, how to pair subchannels of the first and second hops, and which users should be scheduled to which subchannel pair. Considering these issues in the design of an optimal joint resource allocation scheme in orthogonal channels, in this paper we study a multi-user network with single regenerative relay node. A stochastic optimization problem to maximize system ergodic throughput with joint transmission power constraint and user average data rate is formulated. To satisfy user average data rate request, a scheme with a weighted factor associated to each user at each time slot is proposed. The stochastic approximation method is utilized to estimate this weighted factor and the proof of optimality is given. With the help of this weighted factor, the problem is converted into a deterministic optimization problem in each time slot and the Lagrange dual method can be employed to derive the optimal solution. Finally, the Stochastic Optimal Programming (SOP) is used to evaluate the performance by computer simulations.

Relay Stations (RSs) selection and spectrum allocation is a very important problem in cognitive Multi-hop Cellular Networks (MCN). In this paper, we propose a practical joint RS selection and spectrum allocation scheme to maximize the system capacity while fairness is guaranteed. In the proposed scheme, Mobile Stations (MS) are adaptively and optimally selected to be RS based on spatiality of spectrum sets and multi-hop links. Further, we simplify the optimal scheme and propose the RS non-deterministic heuristic algorithms and then compare with conventional RS predetermined algorithm in which RS are predefined before joint RS selection and resource allocation. Simulation results show that the RS non-deterministic algorithm outperforms the conventional RS predetermined algorithm in terms of spectrum efficiency.

In cognitive radio networks, multiple spectrum opportunities can be used together to satisfy the service requirement by spectrum aggregation. In this paper, an admission control algorithm and a spectrum assignment strategy are proposed in order for both increasing the spectrum aggregation aware access capacity and decreasing the channel switch times when the channel states change. Considering different bandwidth requirement of secondary users, the proposed greedy admission algorithm takes limited aggregation capability into account. The channel switch times of secondary users at sensing moments is minimized based on the prediction of primary activities and the corresponding channel state transitions. The concept of outage probability is introduced into the scheme to indicate the probability of channel switch. The numerical results show the performance improvement of the proposed algorithms.

In this paper, an optimal resource allocation scheme is proposed for multi-user multi-channel cognitive radio networks under imperfect spectrum sensing. The channel dynamic model and the sensing errors are considered together to derive the metric of mean delay for each user-channel combination based on the vacation queueing model. Finally, the optimal resource allocation is determined according to the average system delay by bipartite graph matching. The simulation results indicate that the proposed mean delay metric can represent the transmission performance successfully.

In this paper we study the problem of relay-enhanced cell (REC) coverage for which relay location is optimized to maximize the achievable REC radius. The problem is investigated for both Rayleigh and Rician relay fading channels, under a pre-determined user's outage probability constraint. We propose a statistical approach to formulate the problem and develop an optimization algorithm for it. The analytical derivation is justified by numerical simulations.

In this paper, we will study the joint problem of power allocation, relay selection and subchannel pairing in OFDM based amplify-and-forward multiple relays system. The optimization problem of maximizing system capacity under joint power constraint at source and relays is firstly formulated. Then, based on Lagrangian dual method, an optimal algorithm to the problem is derived with high SNR assumption. Both computational complexity and dual gap are analyzed. Through simulations, we show that the performance of the proposed algorithm is perfectly matched with that of exhaustive search method and the computational complexity of the proposed algorithm is acceptable for practical implementation.

In the presence of multiple non-regenerative relays, we derived optimal joint power allocation, relay selection, and subchannel pairing schemes in orthogonal frequency division multiplexing (OFDM) based wireless networks. The Lagrange dual method was employed to design the optimal algorithm. First, the optimization problem was formulated for the single-relay system and the optimal centralized algorithm was presented by resolving the dual problem. Next, the optimal algorithm for a multi-relay system was proposed in a similar way. Compared with the exhaustive search method, the computational complexity of the proposed optimal algorithms was reduced from non-polynomial to polynomial time. Finally, the centralized algorithm was extended to the distributed algorithm, which was more feasible for the practical system. Simulation results verify our analysis.

Relay selection and subchannel pairing are important issues in OFDM based cooperative wireless systems to improve the system performance and reliability. Most of the relay selection schemes in modern literatures are centralized under the assumption that the overall channel condition is known at the source node, each relay node and the destination node which is impractical. In this paper, we will propose a low computational complexity distributed relay selection and subchannel pairing algorithms under limited channel state information. Through the computer simulation, we found that under the limited channel state information constraint, the proposed algorithm can achieve a much better system performance than the traditional relay selection and subchannel matching schemes and earn most of the system performance of the optimal one.

Multi-channel spectrum sensing is prevailing but also very challenging in wideband cognitive radio systems. Conventional multi-channel spectrum detection such as channel-by-channel scan costs much time and energy. This paper aims to show a novel multi-user cooperative spectrum sensing method which can reduce the sensing ability requirement for secondary users while still guaranteeing the sensing accuracy and effectiveness in a multi-channel cognitive radio context. In our proposed method, each cognitive user chooses an Ideal-Soliton-Distributed number of channels to sense, and the partial detection results are then passed to a confusion center which uses a specially designed Belief Propagation (BP) algorithm to infer the spectrum activities of all the channels. A heuristic method to release the detected channels from the whole spectrum bands is also proposed to reduce the sensing complexity further. Simulation results show that the proposed sensing methods can obtain excellent performance.

This paper presents a cross-layer iterative decoder for irregular low-density parity-check (LDPC) codes which uses cyclic redundancy check (CRC) codes. The key idea of the decoder is to use correctly decoded frames as an aid for correcting the remaining erroneous frames. To accomplish this, the decoder exchanges the relevant information between layers by using the cross-layer design method and an iterative decoding architecture. Moreover, the unequal-error protection (UEP) property of irregular LDPC is exploited and both the multiple-error detection and single-error correction capabilities of the CRC code are used. Simulation results show that the proposed decoder outperforms the pure sum-product algorithm (SPA) decoder by a considerable gain while the increase in complexity is moderate. Furthermore, the error floor of irregular LDPC codes in the high Eb/NO regime can be lowered effectively. The proposed cross-layer iterative decoder can be used for any irregular LDPC coded wireless system to boost the performance and lower the error floor.

In this paper, spectrum sensing methods in a multiple antenna cognitive radio (CR) system are discussed. We firstly present a simplified cyclostationary features based detector for single antenna, with relatively low computation complexity compared to traditional cyclostationary feature detection method. In order to achieve better signal detection performance in low SNR regions where the cyclostationary features are overwhelmed by strong noise, we resort to multiple antenna technique. We then propose three signal detection methods for multiple antenna scheme, namely maximum ratio combination method, multiple decision result fusion method and comparison detection method, which are motivated by MIMO techniques and structure characteristic of spectral correlation function estimation. The simulation results show the performance improvement of signal detection obtained by utilizing the three proposed multiple antenna processing methods, in terms of detection probability.

Abstract In this paper, a cross‐layer analytical framework is proposed to analyze the throughput and packet delay of a two‐hop wireless link in wireless mesh network (WMN). It considers the adaptive modulation and coding (AMC) process in physical layer and the traffic queuing process in upper layers, taking into account the traffic distribution changes at the output node of each link due to the AMC process therein. Firstly, we model the wireless fading channel and the corresponding AMC process as a finite state Markov chain (FSMC) serving system. Then, a method is proposed to calculate the steady‐state output traffic of each node. Based on this, we derive a modified queuing FSMC model for the relay to gateway link, which consists of a relayed non‐Poisson traffic and an originated Poisson traffic, thus to evaluate the throughput at the mesh gateway. This analytical framework is verified by numerical simulations, and is easy to extend to multi‐hop links. Furthermore, based on the above proposed cross‐layer framework, we consider the problem of optimal power and bandwidth allocation for QoS‐guaranteed services in a two‐hop wireless link, where the total power and bandwidth resources are both sum‐constrained. Secondly, the practical optimal power allocation algorithm and optimal bandwidth allocation algorithm are presented separately. Then, the problem of joint power and bandwidth allocation is analyzed and an iterative algorithm is proposed to solve the problem in a simple way. Finally, numerical simulations are given to evaluate their performances. Copyright © 2008 John Wiley & Sons, Ltd.

In this paper, we study the problem of bit and power allocation in broadband cognitive radio system. In the broadband communication system, the primary transmitter employs Orthogonal Frequency Division Multiplexing (OFDM) technique on the whole bandwidth. A cognitive user, which has the ability of detecting the transmission of the primary link on each subcarrier, attempts to use the same bandwidth. The goal of this paper is to study how to optimally allocate bit and power on each subcarrier at the cognitive transmitter so that the sum-rate of the cognitive user is maximized under the condition that the primary transmission is not affected. Based on the framework presented by A. Jovicic and P. Viswanath, we first formulate the problem into an optimization problem with integer variables and then propose a greedy bit and power allocation algorithm. We also prove that the proposed algorithm is the optimal solution to the optimization problem.

IEEE 802.16e and 802.20 are emerging as two promising technologies for broadband wireless access systems. In order to improve capacity and coverage performance, both of them addressed a fractional frequency reuse (FFR) scheme to combat co-channel interference in multi-cell deployment, which is referred to as FFR16and FFR20respectively in this paper. As for the scheme of FFR20, virtual area partition is proposed in this paper, and fractional frequency reuse factor (FFRF) is achieved by tuning the parameter of signal strength ratio. The optimal combination of resource allocation strategy and signal strength ratio is determined through simulation. In order to compare the performance of FFR16and FFR20, three evaluation metrics are introduced, including average throughput, outage probability and spectrum efficiency. Simulation results show that outage probability is much lower under FFR20scheme, which goes beyond the acceptable range when FFRF is smaller than 2.4. The resource utilization efficiencies of both are continuously increasing with regard to FFRF within range [2.4, 3]. Besides, FFR20outperforms FFR16under given conditions, in terms of average throughput and spectrum efficiency.

In this paper, we consider a cognitive radio system in fading wireless channels and propose an opportunistic power control strategy for the cognitive users, which serves as an alternative way to protect the primary user's transmission and to realize spectrum sharing between the primary user and the cognitive users. The key feature of the proposed strategy is that, via opportunistically adapting its transmit power, the cognitive user can maximize its achievable transmission rate without degrading the outage probability of the primary user. If compared with the existing cognitive protocols, which usually try to keep the instantaneous rate of the primary user unchanged, our strategy relieves the cognitive users from the burden of detecting and relaying the message of the primary user and relaxes the system synchronization requirements. The achievable rate of a cognitive user under the proposed power control strategy is analyzed and simulated, taking into account the impact of imperfect channel estimation. A modified power control strategy is also proposed to reduce the sensitivity of our strategy to the estimation errors. Its effectiveness is verified by the simulations.

In this paper, a novel cross-layer design method for realtime video traffics in CDMA multi-hop wireless networks is proposed. First, the performances of application, physical, MAC, and network layers are modelled by some classical models under reasonable assumptions. Then, we present a framework in which source coding, power control, ARQ control, and delay partitioning functionalities at different layers can be jointly optimized. Our studied problem is to maximize the video quality under strict end-to-end delay constraints through adjusting the source coding rate, end-to-end delay distribution, and each node's transmit power. This optimization problem is proved to be a nonlinear but log-convex one. Finally, we propose a centralized solution based on the geometric programming theory, as well as a partially distributed solution based on the Lagrangian dual decomposition technique. And both solutions are proved to converge to the global optimum of the above problem.

In this paper, the problem about power control in multiuser cognitive networks is addressed. The scenario we study is a heterogeneous network, where multiple cognitive (without the spectrum license) users transmit simultaneously to their corresponding receivers through a common frequency channel in the presence of a primary (with the spectrum license) user communicating with its base station. The goal of this paper is to study how to minimize the sum-power of the above cognitive network given certain QoS requirements of all cognitive users. The power optimization problem is formulated as a nonlinear and non-convex problem. We first apply the classical power control algorithm to find the Pareto optimalefficientpowerfor each cognitive user and formulate the achievable power region of the cognitive networks. Then, the optimization problem is converted into a convex one and Lagrange multiplier method is utilized to solve it. Our work provides a theoretical lower bound on the power control performance that motivates further research on designing more practical transmission schemes in future cognitive networks.

In interference-limited wireless cellular systems, interference avoidance and interference averaging are widely adopted to combat co-channel interference. In different types of wireless networks, these two basic frequency planning methods are implemented under variable system deployment strategies. With respect to the IEEE 802.16 OFDMA network, interference avoidance is usually carried out by carefully designing the frequency reuse patterns, while interference averaging is implemented by distributed subcarrier permutation, which can be further divided into multiple operation modes. In this paper, based on the system level simulation of the average signal to interference-plus-noise ratio (SINR) in the 802.16 system, the performance metrics of system throughput and outage probability are estimated. According to the predetermined evaluation criterions, the system performances under variable combination patterns of frequency reuse and subcarrier permutation are evaluated. Finally, the optimal frequency planning strategies, in terms of the frequency reuse pattern and the subcarrier permutation mode, is obtained, which can serve as a reference in practical network deployments.

This paper studies the secrecy capacity of fading wireless channel in presence of multiple eavesdroppers. The eavesdroppers are mutually independent and the information is secure if it cannot be eavesdropped by any eavesdropper. We first generalize the work in (J. Barros and M.R.D. Rodrigues, 2006) into multiple-eavesdropper case and investigate the secrecy capacity in terms of outage probability and outage capacity. We show that the results presented in (J. Barros and M.R.D. Rodrigues, 2006)serves as some special cases of our works. We also characterize the ergodic secrecy capacity of fading wireless channel with multiple eavesdroppers. Our work gives the analytical results of secrecy capacity with different number of eavesdroppers.This paper studies the secrecy capacity of fading wireless channel in presence of multiple eavesdroppers. The eavesdroppers are mutually independent and the information is secure if it cannot be eavesdropped by any eavesdropper. We first generalize the work in (J. Barros and M.R.D. Rodrigues, 2006) into multiple-eavesdropper case and investigate the secrecy capacity in terms of outage probability and outage capacity. We show that the results presented in (J. Barros and M.R.D. Rodrigues, 2006) serves as some special cases of our works. We also characterize the ergodic secrecy capacity of fading wireless channel with multiple eavesdroppers. Our work gives the analytical results of secrecy capacity with different number of eavesdroppers.

Efficient network selection and call admission control (CAC) strategies are the key components of the next generation heterogeneous wireless networks (HWN). In this paper, we propose a distributed uplink call admission control and network selection scheme for hybrid CDMA-OFDMA networks, which aims to efficiently utilize the overall radio resource of the HWN, on condition that the incoming call blocking probability and the ongoing call dropping probability are both within the acceptable ranges. When a new call arrives, based on the information obtained from the resource management modules of the two radio access networks (RANs), the process of network load estimation can be implemented in the corresponding mobile terminal (MT) through power estimation: 1) estimation of the required transmit power from the MT if it is admitted to either RAN; 2) estimation of the total uplink power consumptions in the two RANs after admitting the incoming call. Accordingly, CAC decisions can be made distributedly in the MT by comparing the total uplink power consumptions with the predetermined uplink power thresholds of the corresponding RANs. If the incoming call can be admitted by both RANs, the estimated terminal transmit powers in the two RANs are compared, and the RAN requiring less transmit power from the MT is selected as the target network. Simulation results indicate that our scheme is practical and effective in achieving high resource utilization with low probability of overload.

Traditional admission control algorithms are based on bandwidth or channel reservation policy, which may be incompetent in IEEE 802.16e OFDMA systems for two reasons: (1) WiMAX system supports dynamic and flexible resource allocation, and (2) there exists a fundamental tradeoff between bandwidth resource and power resource. In this paper, we propose a novel admission control scheme based on power reservation. Foremost, we introduce a joint subchannel and power allocation algorithm for WiMAX system, which achieves high power efficiency by minimizing the overall downlink transmit power and utilizing all available subchannels. Based on this, we propose two power reservation schemes for inter-cell handoff calls and intra-cell handoff calls, respectively. Correspondingly, two reservation factors are introduced, the values of which are determined by optimizing the metric of grade of service(GoS). Computer simulation is carried out to evaluate the performance of the proposed admission control scheme.

In this paper, the issue of partial channel state information (CSI) based cooperative relaying and partner selection is discussed. First, we propose a new selection relaying scheme which switches between two fixed modes: direct transmission, and regenerative relaying cooperation. Unlike the existing scheme which employs instantaneous source-relay channel gain as criteria of the switching, we utilize only the geographical information of source, relay and destination terminals. Then, in order to apply our proposed scheme to large-scale networks, a cooperative partner selection protocol based on min-max criteria is introduced. We consider the scenario that all the nodes try to transmit packets to the same access point (AP) in a round-robin manner, and the AP tries to select a cooperative partner for each node utilizing the geographical information of nodes. Numerical simulation results show that the proposed selection relaying scheme can efficiently minimize the outage probability by properly choosing the dominant transmission mode. And our partner selection protocol based on the min-max criteria outperforms both the random partner selection strategy and the fixed non-cooperative strategy in terms of outage probability while ensuring fairness among users.

The adaptive subcarrier and bit allocation in OFDMA systems supporting multiple services is investigated. A suboptimal algorithm is developed, which aims to maximize the system throughput under the overall transmit power constraint while guaranteeing the QoS requirement of realtime users and supporting proportional fairness among non-realtime users. Firstly, the algorithm allocates subcarriers and power to realtime users so that the required power for them is minimized and the average power per subcarrier of realtime users is approximately equal to that of non-realtime users. Then, the remaining subcarriers and power are allocated to non- realtime users by a proposed rate adaptive resource allocation algorithm, which achieves a suboptimal overall throughput while providing proportional fairness among users. Simulation results show that the proposed algorithm achieves better performances than existing algorithms.

Cooperative transmission is an efficient technique to realize diversity gain in wireless fading channels via a distributed way. In this paper, we consider a wireless network composed of a source, a relay and a destination terminal. We exploit the limited feedback from the destination and propose three different cooperative ARQ protocols, which combine the incremental relaying and selection relaying protocols. An analysis model to analyze and compare the data link layer packet error rate (PER) of different ARQ protocols in slow fading wireless channel is established. Furthermore, the spatial diversity performances of the various protocols are investigated and it is demonstrated that full spatial diversity (second-order in this case) can be achieved by the three proposed protocols. Simulation results are given, which verify the performance analysis. The tradeoff between performance and implementation complexity of the three protocols is also discussed.

Cooperative diversity is an effective technique to combat multipath fading. When this technique is applied to ad hoc networks, it is a key issue to design distributed and power-efficient relay selection strategies. In this paper, we propose the power-aware relay selection (PARS) strategies to maximize the network lifetime. The PARS strategies are composed of the optimal power allocation(OPA) and three power-aware selection criteria. The OPA aims at minimizing the overall transmit power, while the three criteria select the relay that best help extend the network lifetime, based on both the OPA results and the residual power levels of the nodes. We combine our strategies with the opportunistic relay model, which is based on a 802.11b-like media access control (MRC) protocol, so the selection process can be done in a totally distributed way. Simulation results show that the proposed strategies have great improvement in both network lifetime and power efficiency

In this paper, a cross-layer analytical framework is proposed to analyze the throughput and packet delay of a two-hop wireless link. The adaptive modulation and coding (AMC) is employed at the physical layer and finite-length buffers is considered at the network layer. Firstly, we model the wireless channel as a finite state Markov chain (FSMC). Then, a method is proposed to calculate the steady-state output traffic distribution of the source node. Based on this, a modified queuing FSMC model, which utilizes the non-Poisson distribution of the entering traffic, is proposed to analyze the performance at the mesh gateway. The results of the performance analysis are discussed and verified by numerical simulations. In addition, the proposed analytical framework can be easily extended to multi-hop wireless networks

OFDM modulation and multihop transmission are two important techniques for future wireless networks. This paper discusses the adaptive subcarrier and power allocation in OFDM based multihop networks. Both OFDM-TDMA and OFDM-FDMA access methods are considered. For OFDM-TDMA based multihop networks, a power allocation algorithm is proposed to maximize the end-to-end capacity for the constraint of the overall transmit power of all hops. For OFDM-FDMA based multihop networks, a joint subcarrier and power allocation algorithm is proposed. The performances of the proposed algorithms are evaluated, in both symmetric case (each hop has same average channel gain) and asymmetric case (the hops have different average channel gain). The results show that the OFDM-FDMA access method achieves a higher end-to-end capacity than the OFDM-TDMA access method, and the proposed adaptive resource allocation algorithms perform better than the fixed resource allocation strategy, in both cases.

The resource allocation algorithm to minimize the overall required transmit power while satisfying the QoS requirements of real-time services in OFDMA systems is discussed in this paper. We develop the concept of marginal utility for each subcarrier, which corresponds to the maximal power reduction when the very subcarrier is allocated to a user. We also propose a low computational complexity algorithm to calculate the marginal utility. Based on this, a novel subcarrier and bit allocation algorithm is presented. In order to achieve the minimization of overall required power, the subcarrier with the largest marginal utility is assigned to its corresponding user at each subcarrier allocation iteration. Part of the bits required by the user are then redistributed to the newly assigned subcarrier by the proposed marginal utility calculation algorithm. Simulation results show that the proposed algorithm can achieve a better performance than Zhang's algorithm [6] at approximately the same computatotional complexity.

A novel subcarrier and bit allocation algorithm for multiuser OFDM system is presented in this paper. The algorithm aims to achieve a maximum transmit rate subject to the overall transmit power constraint and user rate proportionality constraint. The algorithm consists of two steps. In the subcarrier allocation step, the power is assumed to be uniformly allocated on each subcarrier and the subcarriers are allocated to users under the proportional rate constraint. Then, at each bit assignment iteration of the bit allocation step, the algorithm assigns one bit to the user with the least normalized transmit rate and the user greedily allocates the bit and corresponding power to the subcarrier which requires the least additional power to carry one more bit. The computational complexity of the proposed algorithm is analyzed, as well as the fairness performance. Simulation results show that the algorithm can achieve a better performance than the existing algorithms