Integrated sensing and communication (ISAC) emerges as an advanced technology to improve the spectrum efficiency by sharing the same spectrum for both communication and sensing. However, the open nature and the shared spectrum make the privacy a critical issue. Fortunately, covert communication can tackle this issue and provide an additional privacy protection for ISAC. In this paper, we propose a novel multi-user covert ISAC scheme against collusive wardens. Specifically, a dual-functional transmitter senses the wardens while communicating with multiple legitimate users covertly, where the more practical Rician fading is considered. First, we analyze the global detection performance of collusive wardens, where we employ the moment matching to handle the intractable theoretical analysis and computation introduced by Rician fading. Then, we optimize each warden’s detection threshold to achieve the greatest detection, creating the worst scenario for legitimate communication. Under this threat, we maximize the average covert transmission rate through jointly optimizing the power allocation and beamforming. To solve this non-convex optimization problem, semidefinite relaxation and successive convex approximation are adopted to transform it into a convex problem, and a convergence-guaranteed iteration algorithm is developed to obtain the optimal solutions. Simulation results show the superiority of the proposed multi-user covert ISAC scheme while revealing the inherent trade-off among covertness, sensing, and communication.

Collaborative perception allows each agent to enhance its perceptual abilities by exchanging messages with others. It inherently results in a trade-off between perception ability and communication costs. Previous works transmit complete full-frame high-dimensional feature maps among agents, resulting in substantial communication costs. To promote communication efficiency, we propose only transmitting the information needed for the collaborator's downstream task. This pragmatic communication strategy focuses on three key aspects: i) pragmatic message selection, which selects task-critical parts from the complete data, resulting in spatially and temporally sparse feature vectors; ii) pragmatic message representation, which achieves pragmatic approximation of high-dimensional feature vectors with a task-adaptive dictionary, enabling communicating with integer indices; iii) pragmatic collaborator selection, which identifies beneficial collaborators, pruning unnecessary communication links. Following this strategy, we first formulate a mathematical optimization framework for the perception-communication trade-off and then propose PragComm, a multi-agent collaborative perception system with two key components: i) single-agent detection and tracking and ii) pragmatic collaboration. The proposed PragComm promotes pragmatic communication and adapts to a wide range of communication conditions. We evaluate PragComm for both collaborative 3D object detection and tracking tasks in both real-world, V2V4Real, and simulation datasets, OPV2V and V2X-SIM2.0. PragComm consistently outperforms previous methods with more than 32.7 K× lower communication volume on OPV2V.

Energy efficiency is essential for federated learning (FL) over mobile devices and its potential prosperous applications. Different from existing communication efficient FL research efforts, which regard communication energy consumption as the bottleneck, we have observed that with ever increasing wireless transmission speed (e.g., Wi-Fi 5 or 5G), the energy consumption of wireless communications for model updates in FL is significantly reduced and sometimes is smaller than that of local on-device training. Motivated by such observations, in this paper, we propose a high-speed wireless communications inspired energy efficient federated learning over mobile devices (EEFL), whose goal is to reduce the overall energy consumption (computing + communication). In particular, we design a novel energy-aware adaptive local update policy for mobile devices by jointly considering FL performance and energy saving of high-speed wireless transmissions. Furthermore, given the device’s local update policy in each FL global round, we advance the dynamic voltage and frequency scaling (DVFS) strategy to minimize local training’s energy consumption by keeping GPU and CPU working at appropriate frequencies without triggering thermal throttling. Extensive experimental results with various learning models, datasets, and wireless transmission environments demonstrate the proposed EEFL’s superiority over the peer designs in terms of energy efficiency.

As a popular distributed learning paradigm, federated learning (FL) over mobile devices fosters numerous applications, while their practical deployment is hindered by participating devices’ computing and communication heterogeneity. Some pioneering research efforts proposed to extract subnetworks from the global model, and assign as large a subnetwork as possible to the device for local training based on its full computing and communications capacity. Although such fixed size subnetwork assignment enables FL training over heterogeneous mobile devices, it is unaware of (i) the dynamic changes of devices’ communication and computing conditions and (ii) FL training progress and its dynamic requirements of local training contributions, both of which may cause very long FL training delay. Motivated by those dynamics, in this paper, we develop a wireless and heterogeneity aware latency efficient FL (WHALE-FL) approach to accelerate FL training through adaptive subnetwork scheduling. Instead of sticking to the fixed size subnetwork, WHALE-FL introduces a novel subnetwork selection utility function to capture device and FL training dynamics, and guides the mobile device to adaptively select the subnetwork size for local training based on (a) its computing and communication capacity, (b) its dynamic computing and/or communication conditions, and (c) FL training status and its corresponding requirements for local training contributions. We provide a theoretical convergence analysis for WHALE-FL with heterogeneous subnetwork assignment, based on which subnetwork structures can be dynamically optimized to reduce the resulting gap to standard full-model FL. Our evaluation shows that, compared with peer designs, WHALE-FL effectively accelerates FL training without sacrificing learning accuracy.

Semantic communications (SemComs) have emerged as a promising paradigm for joint data and task-oriented transmissions, combining the demands for both the bit-accurate delivery and end-to-end (E2E) distortion minimization. However, current joint source-channel coding (JSCC) in SemComs is not compatible with the existing communication systems and cannot adapt to the variations of the sources or the channels, while separate source-channel coding (SSCC) is suboptimal in the finite blocklength regime. To address these issues, we propose an adaptive source-channel coding (ASCC) scheme for SemComs over parallel Gaussian channels, where the deep neural network (DNN)-based semantic source coding and conventional digital channel coding are separately deployed and adaptively designed. To enable efficient adaptation between the source and channel coding, we first approximate the E2E data and semantic distortions as functions of source coding rate and bit error ratio (BER) via logistic regression, where BER is further modeled as functions of signal-to-noise ratio (SNR) and channel coding rate. Then, we formulate the weighted sum E2E distortion minimization problem for joint source-channel coding rate and power allocation over parallel channels, which is solved by the successive convex approximation. Finally, simulation results demonstrate that the proposed ASCC scheme outperforms typical deep JSCC and SSCC schemes for both the single- and parallel-channel scenarios while maintaining full compatibility with practical digital systems.

Combining integrated sensing and communication (ISAC) and an unmanned aerial vehicle (UAV) can not only save the wireless resource but also enhance the air-ground coverage. However, the high-quality air-ground link of ISAC network is more prone to exposure, and its security is challenging. In this paper, we design a covert air-ground transmission scheme for ISAC, where the sensing signal can be utilized as a mask to disrupt the detection of communication by Willie. Since it is difficult to obtain the accurate knowledge about Willie’s location, we employ the norm-bounded model to describe the uncertainty of location at Willie. To further enhance the covertness, a full-duplex (FD) UAV user is considered to receive the covert signal while transmitting the artificial jamming to confuse Willie. We first calculate the minimum detection error probability (MDEP) by deriving the optimal detection threshold, and we obtain the analytic expression of average MDEP. Then, the covert transmission rate is maximized by controlling beamforming vectors and the UAV trajactory while satisfying the target detection constraint, the covertness constraint as well as the transmit power constraint, which can be resolved by an alternating optimization algorithm. Finally, we present simulation results to verify that the proposed scheme with the FD jamming can better guarantee the covertness of air-ground ISAC.

As a popular distributed learning paradigm, federated learning (FL) over mobile devices fosters numerous applications, while their practical deployment is hindered by participating devices' computing and communication heterogeneity. Some pioneering research efforts proposed to extract subnetworks from the global model, and assign as large a subnetwork as possible to the device for local training based on its full computing capacity. Although such fixed size subnetwork assignment enables FL training over heterogeneous mobile devices, it is unaware of (i) the dynamic changes of devices' communication and computing conditions and (ii) FL training progress and its dynamic requirements of local training contributions, both of which may cause very long FL training delay. Motivated by those dynamics, in this paper, we develop a wireless and heterogeneity aware latency efficient FL (WHALE-FL) approach to accelerate FL training through adaptive subnetwork scheduling. Instead of sticking to the fixed size subnetwork, WHALE-FL introduces a novel subnetwork selection utility function to capture device and FL training dynamics, and guides the mobile device to adaptively select the subnetwork size for local training based on (a) its computing and communication capacity, (b) its dynamic computing and/or communication conditions, and (c) FL training status and its corresponding requirements for local training contributions. Our evaluation shows that, compared with peer designs, WHALE-FL effectively accelerates FL training without sacrificing learning accuracy.

This paper proposes an adaptive source-channel coding (ASCC) scheme for point-to-point digital semantic communications over parallel Gaussian channels, where the deep neural network (DNN)-based semantic source coding and conventional digital channel coding are separately deployed and adaptively designed. To enable efficient adaptation between the source and channel coding, we first approximate the E2E data and semantic distortions as functions of source coding rate and bit error ratio (BER) via logistic regression, where BER is further modeled as functions of signal-to-noise ratio (SNR) and channel coding rate. Then, we formulate the weighted sum E2E distortion minimization problem for joint source-channel coding rate and power allocation over parallel channels, which is solved by the successive convex approximation. Finally, simulation results demonstrate that the proposed ASCC scheme outperforms typical separate and deep joint source-channel coding schemes while maintaining full compatibility with practical digital systems.

In this paper, we propose a covert air-ground transmission scheme for integrated sensing and communication (ISAC), where the sensing signal can be utilized as a mask to disrupt the detection of communication by Willie. To further enhance the covertness, a full-duplex (FD) unmanned aerial vehicle (UAV) user is deployed to receive the covert signal while transmitting the artificial jamming to confuse Willie. The minimum detection error probability (MDEP) is first calculated by deriving the optimal detection threshold, and the analytic expression of average MDEP is obtained. Then, the covert transmission rate is maximized while satisfying the target detection constraint, the covertness constraint as well as the transmit power constraint, which can be resolved by an alternating optimization algorithm. Finally, simulation results are presented to demonstrate that the proposed scheme with the FD jamming can better guarantee the covertness of air-ground ISAC.

In the research of over-the-air (OTA) performance testing, the multi-probe anechoic chamber (MPAC) method is crucial, which reconstructs the target channel by controlling the position and power weight of the probes. Investigating and optimizing the combination of probes and their power weights constitutes an indispensable step in the testing process of MPAC systems. Under the premise of a selected probe combination, most existing related studies typically employ a convex optimization toolbox called CVX to solve for the probe weights. However, with the trend of massive and large-scale wireless communication, CVX may not be the best option for large-scale optimization due to memory and speed limitations. This paper proposes a decomposed and alternating updated augmented Lagrangian multiplier (DAU-ALM) method for probe weighting, which decomposes the target problem and optimizes it. Besides, a dynamic adjustment penalty parameter mechanism is used to accelerate the convergence speed. Simulation results show that the proposed algorithm improves the computational efficiency of probe weighting with the same accuracy as CVX.

Multi-modal learning improves model robustness and accuracy by integrating complementary information from multiple modalities, addressing the limitations of uni-modal approaches in handling complex tasks. To address privacy and communication constraints, federated learning (FL) has been adopted for distributed multi-modal model training, where only model parameters instead of raw data are uploaded. Traditional multi-modal FL methods face performance degradation due to the intricate interplay of three types of modality heterogeneity: modality quantity, modality feature emphasis, and modality statistical distribution. In this paper, we propose HeteroPMMFL, a novel personalized multi-modal FL framework. HeteroPMMFL proposes a model decoupling approach to effectively address modality combination differences and designs personalized collaboration graphs to strengthen cooperation among similar clients. This framework is applicable to any multi-modal dataset and can be easily extended to accommodate various modality combinations. Experimental results show that HeteroPMMFL outperforms both traditional FedAvg and the state-of-the-art Harmony framework in model accuracy, under the presence of three types of modality heterogeneity.

The synergy between Federated Learning (FL) and Foundation Models (FMs) holds great promise in enhancing privacy protection and improving the generalization capabilities of AI systems. However, the high computational and communication overhead of FMs hinders effective deployment in real-world scenarios. Although some pioneering research has proposed using proxy sub-Foundation Models (sub-FMs) to reduce the computational and communication costs when fine-tuning FMs in FL environments, it overlooks the challenges posed by heterogeneous mobile devices with varying computational and communication capabilities, and by dynamic changes in their operational conditions, which cause very long FL training delay. Motivated by these challenges, we propose a novel federated fine-tuning of Foundation Models design via adaptive pruning (FedFTAP). FedFTAP introduces a pruning method specifically designed for FMs, combined with parameter-efficient fine-tuning modules to enhance communication and computational efficiency. FedFTAP further addresses system heterogeneity and system dynamic changes by adaptively tailoring heterogeneous sub-FMs suitable for local training on mobile devices. Moreover, FedFTAP introduces a method for aligning heterogeneous sub-FMs with the global FM. The experimental results show that FedFTAP effectively reduces computational and communication costs in federated fine-tuning scenarios.

We propose “MaskDSC”, a novel system designed to facilitate robust visual data transmission over unreliable wireless channels. MaskDSC effectively balances compression efficiency and transmission resilience by leveraging contextual modeling within the semantic latent space, complemented by unequal error protection mechanism at the physical layer, ensuring compatibility with existing digital communication systems. The novelty of our approach lies in a dual-functional masked Transformer architecture that exploits causal-order contextual dependencies among visual tokens. This architecture not only enhances compression efficiency through improved contextual entropy modeling but also provides robust error concealment capabilities to address diverse transmission error patterns inherent in volatile wireless channels. Our experimental evaluations conducted on image datasets demonstrate that MaskDSC outperforms state-of-the-art transmission systems, especially in terms of efficiency and resilience under dynamic wireless channel conditions.

To support the large-scale connectivity of massive intelligent devices in the Internet of Things (IoT) scenario, in this paper, an uplink multi-user semantic communication system based on model division multiple access (MDMA) is investigated. A model resource pool consisting of multiple mutually exclusive semantic models is built as one new kind of access resource, and a global optimization problem is formulated to make the mapping of different semantic models have stronger mutual exclusion. To solve this problem, a joint deep adversarial semantic decomposition (JDASD) algorithm is proposed to enhance the ability of the semantic models to eliminate interference from other devices. Simulation results demonstrate that the proposed JDASD algorithm achieves higher anti-interference capability in the MDMA system than the traditional independent training scheme applied in most works, showing the advantages of the proposed scheme for the IoT scenario with massive devices.

Mobile edge caching (MEC) is a promising technique to improve the quality of service (QoS) for mobile users (MU) by bringing data to the network edge. However, optimizing the crucial QoS aspects of message freshness and service promptness, measured by age of information (AoI) and service delay, respectively, entails a tradeoff due to their competition for shared edge resources. This article investigates this tradeoff by formulating their weighted sum minimization as a sequential decision-making problem, incorporating high-dimensional, discrete-valued, and linearly constrained design variables. First, to assess the feasibility of the considered problem, we characterize the corresponding achievable region by deriving its superset with the rate stability theorem and its subset with a novel stochastic policy, and develop a sufficient condition for the existence of solutions. Next, to efficiently solve this problem, we propose a mixed-order drift-plus-penalty algorithm by jointly considering the linear and quadratic Lyapunov drifts and then optimizing them with dynamic programming (DP). Finally, by leveraging the Lyapunov optimization technique, we demonstrate that the proposed algorithm achieves an$O(1/V)$versus$O(V)$tradeoff for the average AoI and average service delay.

End-to-end visual communication systems typically optimize a trade-off between channel bandwidth costs and signal-level distortion metrics. However, under challenging physical conditions, this traditional coding and transmission paradigm often results in unrealistic reconstructions with perceptible blurring and aliasing artifacts, despite the inclusion of perceptual or adversarial losses for optimizing. This issue primarily stems from the receiver’s limited knowledge about the underlying data manifold and the use of deterministic decoding mechanisms. To address these limitations, this paper introducesDiffCom, a novel end-to-endgenerative communicationparadigm that utilizes off-the-shelf generative priors and probabilistic diffusion models for decoding, thereby improving perceptual quality without heavily relying on bandwidth costs and received signal quality. Unlike traditional systems that rely on deterministic decoders optimized solely for distortion metrics, ourDiffComleverages raw channel-received signal as a fine-grained condition to guide stochastic posterior sampling. Our approach ensures that reconstructions remain on the manifold of real data with a novel confirming constraint, enhancing the robustness and reliability of the generated outcomes. Furthermore,DiffComincorporates a blind posterior sampling technique to address scenarios with unknown forward transmission characteristics. Extensive experimental validations demonstrate thatDiffComnot only produces realistic reconstructions with details faithful to the original data but also achieves superior robustness against diverse wireless transmission degradations. Collectively, these advancements establishDiffComas a new benchmark in designing generative communication systems that offer enhanced robustness and generalization superiorities.

In this paper, we propose “SoundSpring”, a cutting-edge error-resilient audio transceiver that marries the robustness benefits of joint source-channel coding (JSCC) while also being compatible with current digital communication systems. Unlike recent deep JSCC transceivers, which learn to directly map audio signals to analog channel-input symbols via neural networks, our SoundSpring adopts the layered architecture that delineates audio compression from digital coded transmission, but it sufficiently exploits the impressive in-context predictive capabilities of large language (foundation) models. Integrated with the casual-order mask learning strategy, our single model operates on the latent feature domain and serve dual-functionalities: as efficient audio compressors at the transmitter and as effective mechanisms for packet loss concealment at the receiver. By jointly optimizing towards both audio compression efficiency and transmission error resiliency, we show that mask-learned language models are indeed powerful contextual predictors, and our dual-functional compression and concealment framework offers fresh perspectives on the application of foundation language models in audio communication. Through extensive experimental evaluations, we establish that SoundSpring apparently outperforms contemporary audio transmission systems in terms of signal fidelity metrics and perceptual quality scores. These new findings not only advocate for the practical deployment of SoundSpring in learning-based audio communication systems but also inspire the development of future audio semantic transceivers.

Recent advancements in neural image codecs (NICs) are of significant compression performance, but limited attention has been paid to their error resilience. These resulting NICs tend to be sensitive to packet losses, which are prevalent in real-time communications. In this paper, we investigate how to elevate the resilience ability of NICs to combat packet losses. We propose ResiComp, a pioneering neural image compression framework with feature-domain packet loss concealment (PLC). Motivated by the inherent consistency between generation and compression, we advocate merging the tasks of entropy modeling and PLC into a unified framework focused on latent space context modeling. To this end, we take inspiration from the impressive generative capabilities of large language models (LLMs), particularly the recent advances of masked visual token modeling (MVTM). In specific, ResiComp develops a bi-directional masked Transformer to model the contextual dependencies among latents with dual-functionality: 1) iteratively acts as a conditional entropy model to boost compression efficiency; 2) operates latent PLC to improve resilience. During training, we integrate MVTM to mirror the effects of packet loss, enabling a dual-functional Transformer to restore the masked latents by predicting their missing values and conditional probability mass functions. Our ResiComp jointly optimizes compression efficiency and loss resilience. Moreover, ResiComp provides flexible coding modes, allowing for explicitly adjusting the efficiency-resilience trade-off in response to varying Internet or wireless network conditions. Extensive experiments demonstrate that ResiComp can significantly enhance the NIC’s resilience against packet losses, while exhibits a worthy trade-off between compression efficiency and packet loss resilience. Additionally, packet-level simulations, conducted using diverse network models based on real traces, demonstrate that ResiComp exhibits much better robustness to fluctuating network conditions compared to redundancy-based approaches like VTM + FEC.

Training latency is critical for the success of numerous intrigued applications ignited by federated learning (FL) over heterogeneous mobile devices. By revolutionarily overlapping local gradient transmission with continuous local computing, FL can remarkably reduce its training latency over homogeneous clients, yet encounter severe model staleness, model drifts, memory cost and straggler issues in heterogeneous environments. To unleash the full potential of overlapping, we propose, FedEx, a novelfederated learning approach toexpedite FL training over mobile devices under data, computing and wireless heterogeneity. FedEx redefines the overlapping procedure with staleness ceilings to constrain memory consumption and make overlapping compatible with participation selection (PS) designs. Then, FedEx characterizes the PS utility function by considering the latency reduced by overlapping, and provides a holistic PS solution to address the straggler issue. FedEx also introduces a simple but effective metric to trigger overlapping, in order to avoid model drifts. Experimental results show that compared with its peer designs, FedEx demonstrates substantial reductions in FL training latency over heterogeneous mobile devices with limited memory cost.

Multicasting is an efficient technique for simultaneously transmitting common messages from the base station (BS) to multiple mobile users (MUs). Multicast scheduling over multiple channels, which aims to jointly minimize the energy consumption of the BS and the latency of serving asynchronized requests from the MUs, is formulated as an infinite-horizon Markov decision process (MDP) problem with a large discrete action space, multiple time-varying constraints, and multiple time-invariant constraints. To address these challenges, this paper proposes a novel distribution-embedding multi-agent proximal policy optimization (DE-MAPPO) algorithm, which consists of one modified MAPPO and one distribution-embedding module. The former one handles the large discrete action space and time-varying constraints by modifying the structure of the actor networks and the training kernel of the conventional MAPPO; and the latter one iteratively adjusts the action distribution to satisfy the time-invariant constraints. Moreover, a performance upper bound of the considered MDP is derived by solving a two-step optimization problem. Finally, numerical results demonstrate that our proposed algorithm outperforms the existing ones in terms of applicability, effectiveness, and robustness, and achieves comparable performance to the derived upper bound.

In non-orthogonal multiple access (NOMA) networks, intelligent reflecting surface (IRS) and artificial noise (AN) can provide a double guarantee to achieve the secure transmission, especially essential for the far user with poor channel. However, AN is often eliminated via successive interference cancellation (SIC), which severely mitigates the secrecy energy efficiency. To tackle this issue, we propose a constructive interference precoding (CIP) enabled secure IRS-NOMA scheme, leveraging both the inter-user interference and AN to boost the legitimate transmission of far user, while inducing the eavesdropper to decode the deceptive information. In the CIP-NOMA scheme, we minimize the transmit power under the perfect channel state information (CSI), subject to the CIP constraint for the far user and eavesdropper, while guaranteeing the quality of service and SIC for the near user and the IRS unit modulus constraint. To handle this non-convex problem, we propose an alternating optimization algorithm. Specifically, by alternately optimizing the precoding vectors at the base station and the IRS reflecting matrix via the successive convex approximation and the penalty-based algorithm, respectively, a reliable solution can be obtained. Furthermore, to ensure the robustness, we also extend the scheme to a more practical case of imperfect CSI, where we utilize the S-procedure to deal with the channel uncertainty. Simulation results demonstrate that the proposed scheme can achieve better security performance with less energy consumption compared to the conventional NOMA in both cases.

This paper investigates the sensing performance of two intelligent reflecting surface (IRS)-enabled non-line-of-sight (NLoS) sensing systems with fully- and semi-passive IRSs, respectively. In particular, we consider a fundamental setup with one base station (BS), one uniform linear array (ULA) IRS, and one point target in the NLoS region of the BS. Accordingly, we analyze both the sensing signal-to-noise ratio (SNR) and the Cramér-Rao bound (CRB) for estimating the target’s direction-of-arrival (DoA) with joint transmit and reflective beamforming optimization. First, we characterize the maximum sensing SNR when the BS-IRS channel follows line-of-sight (LoS) and Rayleigh fading, respectively. It is revealed that when the number of reflecting elementsNequipped at the IRS becomes sufficiently large, the maximum sensing SNR increases proportionally toN2andN4for the semi- and fully-passive IRSs, respectively. Then, we analyze the minimum CRB performance when the BS-IRS channel follows Rayleigh fading. It is shown that whenNgrows, the minimum CRB decreases inversely proportionally toN4andN6for the semi- and fully-passive IRS, respectively. Finally, numerical results are presented to corroborate our analysis across general channel conditions. It is shown that the fully-passive IRS outperforms the semi-passive counterpart whenNexceeds a certain threshold due to the additional reflective beamforming gain in the IRS-BS path, which efficiently compensates for the path loss.

Benefiting from the performance enhancement brought by multistatic cooperative sensing, integrated sensing and communication (ISAC) can capture more environmental information to address the security risk of information leakage caused by the openness of wireless channels. In this paper, we study the multistatic cooperative sensing assisted secure transmission via intelligent reflecting surface (IRS). In particular, we propose a multistatic cooperative sensing scheme to obtain the angle of arrival and the localization of the eavesdropping target to achieve the beam alignment accurately. The goal is to maximize the sum secrecy rate by jointly optimizing the association variables of base station (BS) and users, the BS beamforming and the IRS phase shifts, subject to the requirement of target sensing. Due to the coupling of variables and non-convex objective function, the formulated problem is intractable to solve directly. As such, we decompose it into three subproblems and develop an alternative optimization algorithm to solve them iteratively. The association variables are first optimized by successive convex approximation. Then, the BS transmit beamforming can be derived via the semidefinite relaxation. Finally, we adopt an alternating direction method of multiplier for the IRS phase-shift design. Simulation results indicate the feasibility of the proposed scheme, and the multistatic cooperative sensing via IRS can enhance the sensing performance and guarantee the secure transmission.

Quantum computing is a promising paradigm for efficiently solving large and high-complexity problems. However, ensuring privacy within this quantum computing necessitates innovative approaches. Existing research has introduced the concept of quantum differential privacy (QDP) to protect data privacy in quantum computing by leveraging quantum noise. Yet, this method faces limitations due to the fixed and uncontrollable nature of the inherent noise, which directly affects the privacy budget of QDP. Addressing this critical gap, our study proposes a novel approach that utilizes quantum error correction (QEC) techniques not only to mitigate quantum computing errors but also to adjust QDP protection levels precisely. By selectively applying QEC to single or multiple qubit gates, we introduce a method to manipulate the quantum noise error rate effectively. Moreover, we derive a new formula for calculating the overall error rate in a quantum circuit and the adjusted privacy budget after QEC operation. Through extensive numerical simulations, we validate the efficacy of utilizing QEC in tuning privacy protection levels within quantum computing.

Quantum computing revolutionizes the way of solving complex problems and handling vast datasets, which shows great potential to accelerate the machine learning process. However, data leakage in quantum machine learning (QML) may present privacy risks. Although differential privacy (DP), which protects privacy through the injection of artificial noise, is a well-established approach, its application in the QML domain remains under-explored. In this paper, we propose to harness inherent quantum noises to protect data privacy in QML. Especially, considering the Noisy Intermediate-Scale Quantum (NISQ) devices, we leverage the unavoidable shot noise and incoherent noise in quantum computing to preserve the privacy of QML models for binary classification. We mathematically analyze that the gradient of quantum circuit parameters in QML satisfies a Gaussian distribution, and derive the upper and lower bounds on its variance, which can potentially provide the DP guarantee. Through simulations, we show that a target privacy protection level can be achieved by running the quantum circuit a different number of times.

Mobile edge caching (MEC) is a promising technique to improve the quality of service (QoS) for mobile users (MU) by bringing data to the network edge. However, optimizing the crucial QoS aspects of message freshness and service promptness, measured by age of information (AoI) and service delay, respectively, entails a tradeoff due to their competition for shared edge resources. This paper investigates this tradeoff by formulating their weighted sum minimization as a sequential decision-making problem, incorporating high-dimensional, discrete-valued, and linearly constrained design variables. First, to assess the feasibility of the considered problem, we characterize the corresponding achievable region by deriving its superset with the rate stability theorem and its subset with a novel stochastic policy, and develop a sufficient condition for the existence of solutions. Next, to efficiently solve this problem, we propose a mixed-order drift-plus-penalty algorithm by jointly considering the linear and quadratic Lyapunov drift and then optimizing them with dynamic programming (DP). Finally, by leveraging the Lyapunov optimization technique, we demonstrate that the proposed algorithm achieves an O(1/V) versus O(V) tradeoff for the average AoI and average service delay.

With the significant progress in recent decades, wireless rechargeable sensor networks (WRSNs) have been widely deployed in smart cities, lakes, forests and other intelligent scenarios to perceive physical environment. To remotely power these sensor nodes (SNs), unmanned aerial vehicle (UAV) can provide far-field wireless charging by electromagnetic waves. Designing a trajectory for the UAV to “visit” all sensors with the shortest time is an important issue to be resolved in UAV-facilitated WRSNs. Existing researches have proposed massive novel algorithms to solve this issue but cannot realize a desirable solution for charging large-scale WRSNs within a tolerable time. In this work, we leverage UAV to wirelessly power the sensors and relay their sensed data timely. We first propose the UAV moving direction model, UAV-to-SN charging model, data backhauling model and UAV flying speed model to calculate the time consumed by the UAV for charging SNs and relaying data. In order to reduce the work time (equivalent to energy consumption) of the UAV, a flying and hovering time minimization problem is formulated. As this problem is proved as an NP-hard problem, we further propose the Fly Forward (FF) algorithm to efficiently solve it. Through extensive simulations, we have validated that the FF algorithm can effectively reduce the UAV's flying and hovering time, thus realizing an efficient and economical flying trajectory.

Quantum computing has revolutionized the approach to solving complex problems and handling vast datasets. However, data leakage in quantum computing may present privacy risks. While differential privacy (DP) has been a classical solution to protect privacy by injecting artificial noises, the implementation of DP within the quantum domain remains an under-explored area. We observe that there is a potential to leverage the inherent noises generated by Noisy Intermediate-Scale Quantum (NISQ) devices during quantum operations for achieving DP in quantum computing. Given that these inherent noises are uncontrollable, we take the lead to utilize quantum error mitigation (QEM) techniques to manage quantum differential privacy (QDP) protection levels. In our approach, we offer a tangible description of the "closeness" between neighboring quantum datasets and introduce a novel QDP definition based on observables of interest. Our simulations reveal that factors like the distance between neighboring quantum states and the number of circuit executions influence the privacy budget. Notably, by controlling the QEM samples while keeping the number of circuit executions fixed, QEM allows for substantial noise control to meet a desired privacy budget. Furthermore, we extend analysis from single-qubit to multiple qubits and discuss the impact of observables of interest on the privacy budget.

We study a semi-passive intelligent reflecting surface (IRS) enabled ISAC, where IRS is employed to assist the secure communication and perform target sensing. Specifically, we model two types of targets, namely point targets and extended targets. The direction-of-arrival (DoA) of the former and the complete target response matrix of the latter should be estimated. We derive the Cramér-Rao bound (CRB) as the performance metric of target estimation. To achieve the performance tradeoff, we design a weighted optimization problem that balances maximizing the secrecy rate and minimizing the CRB, via jointly optimizing the transmit beamforming and phase shifts of IRS. Then, we employ the alternating optimization, successive convex approximation and semi-definite relaxation to tackle the non-convex problems for the two target cases. Simulation results show the effectiveness of the proposed schemes.

In order to meet the requirements of 6G communication for environmental adaptability and interference resistance, obtaining accurate Channel State Information (CSI) is of paramount importance. However, traditional communication methods struggle to fulfill these demands, leading to a growing interest in deep learning-based channel estimation solutions among researchers. This paper introduces a solution to the channel estimation problem in OFDM systems, employing a deep learning approach based on the MLP-Mixer block, referred to as CENet. CENet's channel-mixing and token-mixing structures enable better capturing of both temporal and spectral channel characteristics. The proposed channel estimation method consists of two parts: firstly, preliminary channel estimation results are generated using the LS algorithm, and then CENet is employed to further refine these preliminary results. Simulation results demonstrate the superiority of the proposed approach over other deep learning methods. Additionally, this paper extends the method to MIMO scenarios and introduces pruning techniques to reduce redundant parameters in the MLP layers, thereby reducing computational complexity.

With the further study of 6G, the development of Internet of Things (IoT) network with 6G draws more attention. Making the system sustainable and enhancing the performance of the system are the current important research directions. This paper introduces a cooperative communication network based on energy-harvesting (EH) decode-and-forward (DF) relays. To solve the current problem of node self-sustaining capacity and achieve sustainable communication, the relay nodes within this system adopt a harvest-storage-use (HSU) structure, allowing them to extract energy from the surrounding environment through energy buffering. To enhance the communication system's performance, the paper incorporates the opportunistic routing algorithm and the generalized selection combining (GSC) algorithm. Further-more, utilizing a discrete-time continuous-state space Markov chain model (DCSMC), the paper derives a theoretical expression for the energy limiting distribution stored in infinite buffers. Through the utilization of probability distribution and the state transition matrix, the paper provides theoretical expressions for system outage probability and throughput. Simulation verification confirms the theoretical results' robust agreement with the simulated outcomes. At last, there is a maximum improvement of about 10% failure probability lower than the existing model.

Reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) has recently emerged as a promising candidate to improve the energy and spectral efficiency of Internet of Things (IoT) systems. This paper aims to develop an efficient channel estimation scheme for RIS-assisted MIMO IoT systems within structured Bayesian learning framework. However, the high-dimensional channel matrix with considering its underlying structured sparsity makes efficient channel estimation scheme design a challenging task. To deal with it, we firstly formulate the cascaded RIS-assisted MIMO channel estimation as a generic sparse signal recovery problem with considering the constructed two-level block-structured sparsity of channels. Secondly, we design a flexible prior model to characterize such structured sparsity of channels, in which hierarchical hyperparameters are introduced, and the iterative Bayesian learning-based method is developed to autonomously estimate channels and the hyperparameters associated with the prior model. Thirdly, to relieve the high-computational complexity involving matrix inversion when calculating the posterior of channels, we develop efficient methods from two perspectives. On the one hand, an inverse-free method is developed by relaxed evidence lower bound (ELBO) maximization with an adjustable factor of reducing the gap between the standard ELBO and relaxed ELBO. On the other hand, a method of reducing the dimension of sparse representation matrix aided by external block-structured sparsity is developed. Finally, the computational complexity and convergence properties of the proposed methods are analyzed in detail. Simulation results are provided to verify the superiority of the devised channel estimation methods.

With the explosive growth of communication requirements for real-time intelligent tasks, mobile communication is shifting from the traditional communication to task-oriented communication, where the transmitted data is shifting from undifferentiated transmission to value-oriented transmission. To maximize the value of transmitted data, it is urgent to match the source decisions with the task demands and wireless channel state. In this article, we focus on the joint source-channel optimization problem in task-oriented communication, and we design the timeliness-accuracy degradation (TAD) metric to measure the value of transmitted source. Moreover, we design a source value-based resource allocation scheme to minimize the TAD through joint optimization of task data generation and compression strategies, bandwidth allocation, and transmit power selection. Furthermore, to avoid the curse of dimensionality, we propose dimension-refined reinforcement learning (DRRL) algorithm to obtain the optimal solution of the problem in a stable and low-complexity manner. Numerical results demonstrate that the designed scheme can effectively improve the task performance and verify the low complexity and stability of the algorithm.

Semantic communications are expected to accomplish various semantic tasks with relatively less spectrum resource by exploiting the semantic feature of source data. To simultaneously serve both the data transmission and semantic tasks, joint data compression and semantic analysis has become a pivotal issue in semantic communications. This article proposes a deep separate source-channel coding (DSSCC) framework for the joint task and data-oriented semantic communications (JTD-SCs) and utilizes the variational autoencoder approach to solve the rate-distortion problem with semantic distortion. First, by analyzing the Bayesian model of the DSSCC framework, we derive a novel rate-distortion optimization problem via the Bayesian inference approach for general data distributions and semantic tasks. Next, for a typical application of joint image transmission and classification, we combine the variational autoencoder approach with a forward adaption scheme to effectively extract image features and adaptively learn the density information of the obtained features. Finally, an iterative training algorithm is proposed to tackle the overfitting issue of deep learning models. Simulation results reveal that the proposed scheme achieves better coding gain as well as data recovery and classification performance in most scenarios, compared to the classical compression schemes and the emerging deep joint source-channel schemes.

We investigate the coexistence of task-oriented and data-oriented communications in a IoT system that shares a group of channels, and study the scheduling problem to jointly optimize the weighted age of incorrect information (AoII) and throughput, which are the performance metrics of the two types of communications, respectively. This problem is formulated as a Markov decision problem, which is difficult to solve due to the large discrete action space and the time-varying action constraints induced by the stochastic availability of channels. By exploiting the intrinsic properties of this problem and reformulating the reward function based on channel statistics, we first simplify the solution space, state space, and optimality criteria, and convert it to an equivalent Markov game, for which the large discrete action space issue is greatly relieved. Then, we propose a Whittle’s index guided multi-agent proximal policy optimization (WI-MAPPO) algorithm to solve the considered game, where the embedded Whittle’s index module further shrinks the action space, and the proposed offline training algorithm extends the training kernel of conventional MAPPO to address the issue of time-varying constraints. Finally, numerical results validate that the proposed algorithm significantly outperforms state-of-the-art age of information (AoI) based algorithms under scenarios with insufficient channel resources.

As the proliferation of sophisticated task models in 5G empowered digital twin, it yields significant demands on fast and accurate model training over resource-limited wireless networks. It is vital to investigate how to accelerate the training process based on the salient features of practical systems, including heterogeneous data distributions and system resources both across devices and over time. To study the non-trivial coupling between participating device selection and their appropriate training parameters, we first characterize the dependency of convergence performance bound on system parameters, i.e., statistical structure of local data, mini-batch size and gradient quantization level. Based on the theoretical analysis, a training efficiency optimization problem is formulated subject to heterogeneous communication and computation capabilities among devices. To realize online control of training parameters, we propose an adaptive batch-size assisted device scheduling strategy, which prioritizes the selection of devices that offer good data utility and dynamically adjust their mini-batch sizes and gradient quantization levels adapting to network conditions. Simulation results demonstrate our proposed strategy can effectively speed up the training process as compared with benchmark algorithms.

The randomness and complexity of the wireless channel is challenging to meet the various quality of service (QoS) for different wireless communication application scenarios. Reconfigurable intelligent surface (RIS) technology has been proposed to achieve dynamic control of signal propagation over the wireless medium, and thus enables intelligent reconstruction of the channel environment. Moreover, age of information (AoI) has been proposed to quantify the timeliness of status update information accurately, which is new QoS metric. However, the AoI-driven statistical QoS guarantee problem in the RIS-enabled wireless network is not trivial and needs to be solved. In this paper, we employ the AoI violation probability to measure the reliability requirement for maintaining the freshness of status updates and derive its upper bound. Then, we formulate the AoI-driven effective capacity maximization problem. Finally, we transform the formulated problem into a signal-to-noise ratio (SNR) maximization problem, and further propose a twin delayed deep deterministic policy gradient (TD-DDPG) based joint optimization algorithm for obtaining the effective decisions on transmission power of device and the phase shift of RIS. Simulation results show that the TD-DDPG-based scheme has better performance than other traditional schemes.

To support large-scale connectivity of massive machine-type communication (mMTC) devices in Industrial Internet of Things (IIoT) scenario, a grant-free (GF) transmission aided non-orthogonal multiple access (NOMA) system is considered in this paper, where the devices with diverse behavior characteristics coexist. In order to reflect the diversity of IIoT scenario, multiple types of MTC devices are taken into account, and the network environment is divided into stationary mode and overload mode based on the differences in behavior characteristics of devices. Further, the successful access probability maximization problem is established to obtain the priority-aware access strategy. To solve this problem, we propose a novel distributed Q-learning algorithm and an adaptive update (AdaUpdate) aided priority-aware deep Q network (PA-DQN) algorithm for the stationary mode and the overload mode, respectively. Simulation results demonstrate that the proposed algorithms achieve better performance than that of traditional reinforcement learning algorithms and random access algorithm in terms of overall access efficiency and satisfying the access requirements of emergency devices preferentially.

In industrial edge caching networks, to ensure long-term accurate decision making of industrial applications, it is critical to obtain fresh sensing contents with low sensor energy consumption. The acquisition of sensing contents consists of cache update and content delivery, jointly determining the Age of Information (AoI) of applications. However, cache update suffers from the large sensor energy consumption and the mismatch between content offerings and demands. Content delivery suffers from the limited fronthaul capacity. Furthermore, contents from multiple sensors typically need to be aggregated, allowing the AoI of applications to be determined by the co-AoI of all correlated sensors. It is challenging to make the tradeoff between the energy efficiency of each sensor and the co-AoI performance of all correlated sensors. In our work, the weighted sum of application AoI and sensor energy consumption is minimized by jointly optimizing cache update and content delivery, which is formulated as a long-term stochastic optimization problem. Next, two caching schemes, access point centric scheme (APCS) and request adaptive caching scheme (RACS), are presented. In APCS, we fully decouple cache update and content delivery by applying statistical probability of application requests to control update. In RACS, cached contents are updated along with content delivery according to real-time requests. Thus, we introduce the concept of decision reward to transform the stochastic problem into the per-time slot reward maximization problem and propose online algorithms to solve it. Simulation results show that proposed schemes can reduce the sensor energy consumption by 40% while guaranteeing the application AoI.

Semantic communication shows great promise in reducing network traffic and alleviating spectrum shortage. While many semantic theories have been put forward, how to measure the importance of semantic information theoretically remains an open issue. In this paper, we propose semantic value, a metric that measures the importance of semantic information, for text transmission. First, we model a semantic communication system for text transmission, in which semantic information is represented by semantic triplets. Then, we propose a hybrid communication mechanism to ensure the success of text transmission. Finally, we compare the performances of the conventional mode and the semantic mode in terms of latency and derive conditions leading to minimum latency.

This artilce aims to develop a differential private federated learning (FL) scheme with the least artificial noises added while minimizing the energy consumption of participating mobile devices. By observing that some communication efficient FL approaches and even the nature of wireless communications contribute to the differential privacy (DP) preservation of training data on mobile devices, in this paper, we propose to jointly leverage gradient compression techniques (i.e., gradient quantization and sparsification) and additive white Gaussian noises (AWGN) in wireless channels to develop a piggyback DP approach for FL over mobile devices. Even with the piggyback DP approach, information distortion caused by gradient compression and noise perturbation may slow down FL convergence, which in turn consumes more energy of mobile devices for local computing and model update communications. Thus, we theoretically analyze FL convergence and formulate an energy efficient FL optimization under piggyback DP, transmission power, and FL convergence constraints. Furthermore, we propose an efficient iterative algorithm where closed-form solutions for artificial DP noise and power control are derived. Extensive simulation and experimental results demonstrate the effectiveness of the proposed scheme in terms of energy efficiency and privacy preservation.

Participant selection (PS) helps to accelerate federated learning (FL) convergence, which is essential for the practical deployment of FL over mobile devices. While most existing PS approaches focus on improving training accuracy and efficiency rather than residual energy of mobile devices, which fundamentally determines whether the selected devices can participate. Meanwhile, the impacts of mobile devices heterogeneous wireless transmission rates on PS and FL training efficiency are largely ignored. Moreover, PS causes the staleness issue. Prior research exploits isolated functions to force long-neglected devices to participate, which is decoupled from original PS designs. In this paper, we propose aresidualenergy andwirelessaware PS design for efficientFLtraining over mobile devices (REWAFL). REWAFL introduces a novel PS utility function that jointly considers global FL training utilities and local energy utility, which integrates energy consumption and residual battery energy of candidate mobile devices. Under the proposed PS utility function framework, REWAFL further presents a residual energy and wireless aware local computing policy. Besides, REWAFL buries the staleness solution into its utility function and local computing policy. The experimental results show that REWAFL is effective in improving training accuracy and efficiency, while avoiding flat battery of mobile devices.

Federated learning (FL) has been recognized as a promising distributed learning paradigm to support intelligent applications at the wireless edge, where a global model is trained iteratively through the collaboration of the edge devices without sharing their data. However, due to the relatively large communication cost between the devices and parameter server (PS), direct computing based on the information from the devices may not be resource efficient. This paper studies the joint communication and learning design for the over-the-air computation (AirComp)-based two-tier wireless FL scheme, where the lead devices first collect the local gradients from their nearby subordinate devices, and then send the merged results to the PS for the second round of aggregation. We establish a convergence result for the proposed scheme and derive the upper bound on the optimality gap between the expected and optimal global loss values. Next, based on the device distance and data importance, we propose a hierarchical clustering method to build the two-tier structure. Then, with only the instantaneous channel state information (CSI), we formulate the optimality gap minimization problem and solve it by using an efficient alternating minimization method. Numerical results show that the proposed scheme outperforms the baseline ones.

The scope of the 6G wireless communication system is envisioned to expand beyond delivering data to humans and towards connecting machines that constantly upload computation-intensive status updates to obtain real-time situational awareness. Under dynamic environments, the amount of useful information contained in status updates degrades over time, which could be measured based on the concept of age of information. In this paper, we develop an analytical framework to investigate the temporal value of status updates, in terms of the peak age of information. Given the temporal dynamics of observed physical process, the procedure of transmission and computing is modeled as tandem queues for both parallel processing and series processing modes at the edge server. The obtained closed-form expressions explicitly characterize the coupling among information generation, transmission, and usage, which can be exploited as performance metrics for task-oriented resource optimization. The accuracy of our analysis is verified with simulation results. Based on the theoretical analysis, we formulate an optimization problem to simultaneously minimize the age of status updates and energy consumption for multiple devices. Numerical results reveal that the computation and transmission time could be traded off to obtain timely status updates at low energy cost.

This paper investigates an intelligent reflecting surface (IRS) enabled multiuser integrated sensing and communications (ISAC) system, which consists of one multi-antenna base station (BS), one IRS, multiple single-antenna communication users (CUs), and one target at the non-line-of-sight (NLoS) region of the BS. The IRS is deployed to not only assist the communication from the BS to the CUs, but also enable the BS’s NLoS target sensing based on the echo signals from the BS-IRS-target-IRS-BS link. We consider two types of targets, namely the extended and point targets, for which the BS aims to estimate the complete target response matrix and the target’s direction-of-arrival (DoA) with respect to the IRS, respectively. To provide full degrees of freedom for sensing, we consider that the BS sends dedicated sensing signals in addition to the communication signals. Accordingly, we model two types of CU receivers, namely Type-I and Type-II CU receivers, which do not have and have the capability of canceling the interference from the sensing signals, respectively. Under each setup, we jointly optimize the transmit beamforming at the BS and the reflective beamforming at the IRS to minimize the Cramér-Rao bound (CRB) for target estimation, subject to the minimum signal-to-interference-plus-noise ratio (SINR) constraints at the CUs and the maximum transmit power constraint at the BS. We present efficient algorithms to solve the highly non-convex SINR-constrained CRB minimization problems, by using the techniques of alternating optimization, semi-definite relaxation, and successive convex approximation. Numerical results show that the proposed design achieves lower estimation CRB than other benchmark schemes, and the sensing signal interference cancellation at Type-II CU receivers is beneficial when the number of CUs is greater than one.

Integrated sensing and communication (ISAC) has evolved into an influential technique to ameliorate energy and spectrum scarcity via co-designing these two functionalities. However, the target can be a potential eavesdropper aiming at wiretapping the information transmitted to the communication user. This paper studies a semi-passive intelligent reflecting surface (IRS) enabled ISAC system, where the IRS is employed to assist the secure communication and simultaneously perform the target sensing based on the echo signals received by the dedicated sensor at the IRS. Specifically, we model two types of targets, namely point targets and extended targets. The direction-of-arrival (DoA) of the former and the complete target response matrix of the latter should be estimated. Under this configuration, we derive the Cramér-Rao bound (CRB) as the performance metric of target estimation. To achieve an optimal performance trade-off, we formulate a weighted optimization problem that balances maximizing the secrecy rate and minimizing the CRB, via jointly optimizing the transmit beamforming and the phase shifts of IRS. Then, we employ the alternating optimization, successive convex approximation and semi-definite relaxation to tackle the proposed non-convex problems for the two target cases. Simulation results show the effectiveness of the proposed schemes compared with benchmarks.

This paper proposes a deep separate source-channel coding (DSSCC) scheme for the semantic-aware image transmission, where image is lossily compressed and transmitted to receiver for recovery and processing certain semantic tasks. To improve the compression efficiency, the forward adaption (FA) method is incorporated into the DSSCC scheme to capture the density information of compressed features as side information. For a typical application of image classification task, we derive a novel rate-distortion optimization problem by analyzing the Bayesian model of the FA-based DSSCC framework. Then, a variational autoencoder approach is proposed to effectively compress image for semantic-aware transmission by minimizing the proposed rate-distortion problem. Simulation results reveal that the proposed FA-based DSSCC scheme achieves better image recovery and classification performance in most scenarios, compared to the classical compression schemes and the emerging deep joint source-channel schemes.

Energy efficiency is essential for federated learning (FL) over mobile devices and its potential prosperous applications. Different from existing communication efficient FL research efforts, which regard communication energy consumption as the bottleneck, we have observed that with ever increasing wireless transmission speed (e.g., Wi-Fi 5 or 5G), the energy consumption of wireless communications for model updates in FL is significantly reduced and sometimes is smaller than that of local on-device training. Motivated by such observations, in this paper, we propose a high-speed wireless communications inspired energy efficient federated learning over mobile devices (EEFL), whose goal is to reduce the overall energy consumption (computing + communication). In particular, we design a novel energy-aware adaptive local update policy for mobile devices by jointly considering FL performance and energy saving of high-speed wireless transmissions. Furthermore, given the device's local update policy in each FL global round, we advance the dynamic voltage and frequency scaling (DVFS) strategy to minimize local training's energy consumption by keeping GPU and CPU working at appropriate frequencies without triggering thermal throttling. Extensive experimental results with various learning models, datasets, and wireless transmission environments demonstrate the proposed EEFL's superiority over the peer designs in terms of energy efficiency.

Abstract Unmanned aerial vehicles (UAVs) are employed as mobile relay nodes to enable timely remote monitoring by collecting information from monitoring devices and transferring the collected information to base station. The freshness of delivered information is critical to system performance, which can be quantified by the concept of age of information (AoI). Nevertheless, the fresher information comes at the cost of higher energy consumption at UAVs. Considering the limited onboard energy, it is essential to strike a balance between the age of delivered information and the required energy budget. here, both straight trajectory and circular trajectory of UAV are considered, and study a problem with the goal of supporting timely data collection while minimizing the energy consumption at UAV. The problem is formulated as a multi‐criteria optimization problem by jointly considering the aging of collected information, the trajectory planning of UAV and energy consumption at UAV. To solve the formulated problem, a solution procedure to find a sequence of Pareto‐optimal points is proposed. Simulation results demonstrate the Pareto‐optimal curve, which yields the energy‐efficient UAV trajectory for timely data collection.

Abstract Remote monitoring systems has emerged to automate public services, where Internet of Things (IoT) devices are deployed to collect and relay streams of status updates to a remote control centre. The freshness of collected data is of critical importance to system performance, which can be quantified by the concept of age of information (AoI). To obtain timely perception of the monitored area, each sensor tends to sample status updates at the maximum frequency, which may lead to device drop‐outs due to limited battery capacity. Simultaneous wireless information and power transfer (SWIPT) is considered as a promising technology to prolong network lifetime. In this paper, a SWIPT‐enabled two‐hop relay network, where a relay node uses power splitting protocol to assist data transmissions from multiple source nodes to the destination is considered. The impact of power splitting coefficients, data arrival pattern and time‐varying channel conditions on the averaged age performance and expected lifetime of the system is theoretically analysed. Moreover, a lightweight relay strategy which selectively forward packets based on a reward function consisting of energy level at relay node and expected age reduction is developed. Simulation results show that the proposed strategy outperforms existing schemes in collecting status updates in a timely and energy‐efficient manner.

The fast development of mobile communication and artificial intelligence (AI) technologies greatly promotes the prosperity of the Internet of Things (IoT), where various types of IoT devices can perform more intelligent tasks. Considering the privacy leakage and limited communication resources, federated learning (FL) has emerged to enable devices to collaboratively train AI models based on their local data without raw data exchanges. Nevertheless, it is still challenging for guaranteeing any FL models to be effective due to the sluggish willingness of IoT devices and the model poisoning attacks in the FL. To address these issues, in this article, we introduce blockchain technology and propose a blockchain-based FL framework for supporting a trustworthy and reliable FL paradigm in IoT. In the proposed framework, we design a committee-based participant selection mechanism that selects the aggregate node and local model updates dynamically to construct the global model. Moreover, considering the tradeoff between the energy consumption and the convergence rate of the FL model, we perform the channel allocation, block size adjustment, and block producer selection jointly. Since the remaining resources, handling transactions, and channel conditions are dynamically varying (i.e., stochastic environment), we formulate the problem as a Markov decision process (MDP) and adopt a deep reinforcement learning (DRL)-based algorithm to solve it. The simulation results demonstrate the effectiveness of the proposed framework and show the superior performance of the DRL-based resource allocation algorithm compared with other baseline methods in terms of energy consumption.

Based on the panel data of 29 provinces and cities in China from 2013 to 2020, the mediating effect model and the moderating effect model are used to empirically analyze the transmission role of technological innovation and industrial structure optimization in the digital economy influencing the green transformation of manufacturing industry, and the moderating role of environmental regulation in the digital economy promoting the green transformation of the manufacturing industry. The results of the empirical study show that the digital economy promotes the green transformation of the manufacturing industry through technological innovation and industrial structure optimization. On the other hand, the intensity of environmental regulation has a significant negative moderating effect on the green transformation of the manufacturing industry promoted by the digital economy.

Federated learning (FL) over mobile devices has fostered numerous intriguing applications/services, many of which are delay-sensitive. In this paper, we propose a service delay efficient FL (SDEFL) scheme over mobile devices. Unlike traditional communication efficient FL, which regards wireless communications as the bottleneck, we find that under many situations, the local computing delay is comparable to the communication delay during the FL training process, given the development of high-speed wireless transmission techniques. Thus, the service delay in FL should be computing delay + communication delay over training rounds. To minimize the service delay of FL, simply reducing local computing/communication delay independently is not enough. The delay trade-off between local computing and wireless communications must be considered. Besides, we empirically study the impacts of local computing control and compression strategies (i.e., the number of local updates, weight quantization, and gradient quantization) on computing, communication and service delays. Based on those trade-off observation and empirical studies, we develop an optimization scheme to minimize the service delay of FL over heterogeneous devices. We establish testbeds and conduct extensive emulations/experiments to verify our theoretical analysis. The results show that SDEFL reduces notable service delay with a small accuracy drop compared to peer designs.

The emerging field semantic communication is driving the research of end-to-end data transmission. By utilizing the powerful representation ability of deep learning models, learned data transmission schemes have exhibited superior performance than the established source and channel coding methods. While, so far, research efforts mainly concentrated on architecture and model improvements toward a static target domain. Despite their successes, such learned models are still suboptimal due to the limitations in model capacity and imperfect optimization and generalization, particularly when the testing data distribution or channel response is different from that adopted for model training, as is likely to be the case in real-world. To tackle this, in this paper, we propose a novel online learned joint source and channel coding approach that leverages the deep learning model’s overfitting property. Specifically, we update the off-the-shelf pre-trained models after deployment in a lightweight online fashion to adapt to the distribution shifts in source data and environment domain. We take the overfitting concept to the extreme, proposing a series of implementation-friendly methods to adapt the codec model or representations to an individual data or channel state instance, which can further lead to substantial gains in terms of the end-to-end rate-distortion performance. Accordingly, the streaming ingredients include both the semantic representations of source data and the online updated decoder model parameters. The system design is formulated as a joint optimization problem whose goal is to minimize the loss function, a tripartite trade-off among the data stream bandwidth cost, model stream bandwidth cost, and end-to-end distortion. The proposed methods enable the communication-efficient adaptation for all parameters in the network without sacrificing decoding speed. Extensive experiments, including user study, on continually changing target source data and wireless channel environments, demonstrate the effectiveness and efficiency of our approach, on which we outperform existing state-of-the-art engineered transmission scheme (VVC combined with 5G LDPC coded transmission).

Moving beyond just interconnected devices, the increasing interplay between communication and computation has fed the vision of real-time networked control systems. To obtain timely situational awareness, IoT devices continuously sample computation-intensive status updates, generate perception tasks and offload them to edge servers for processing. In this sense, the timeliness of information is considered as one major contextual attribute of status updates. In this paper, we derive the closed-form expressions of timeliness of information for computation offloading at both edge tier and fog tier, where two-stage tandem queues are exploited to abstract the transmission and computation process. Moreover, we exploit the statistical structure of Gauss-Markov process, which is widely adopted to model temporal dynamics of system states, and derive the closed-form expression for process-related timeliness of information. The obtained analytical formulas explicitly characterize the dependency among task generation, transmission and execution, which can serve as objective functions for system optimization. Based on the theoretical results, we formulate a computation offloading optimization problem at edge tier, where the timeliness of status updates is minimized among multiple devices by joint optimization of task generation, bandwidth allocation, and computation resource allocation. An iterative solution procedure is proposed to solve the formulated problem. Numerical results reveal the intertwined relationship among transmission and computation stages, and verify the necessity of factoring in the task generation process for computation offloading strategy design.

In this paper, we design a new class of high-efficiency deep joint source-channel coding methods to achieve end-to-end video transmission over wireless channels. The proposed methods exploit nonlinear transform and conditional coding architecture to adaptively extract semantic features across video frames, and transmit semantic feature domain representations over wireless channels via deep joint source-channel coding. Our framework is collected under the name deep video semantic transmission (DVST). In particular, benefiting from the strong temporal prior provided by the feature domain context, the learned nonlinear transform function becomes temporally adaptive, resulting in a richer and more accurate entropy model guiding the transmission of current frame. Accordingly, a novel rate adaptive transmission mechanism is developed to customize deep joint source-channel coding for video sources. It learns to allocate the limited channel bandwidth within and among video frames to maximize the overall transmission performance. The whole DVST design is formulated as an optimization problem whose goal is to minimize the end-to-end transmission rate-distortion performance under perceptual quality metrics or machine vision task performance metrics. Across standard video source test sequences and various communication scenarios, experiments show that our DVST can generally surpass traditional wireless video coded transmission schemes. The proposed DVST framework can well support future semantic communications due to its video content-aware and machine vision task integration abilities.

In a multi-user system, system resources should be allocated to different users. In traditional communication systems, system resources generally include time, frequency, space, and power, so multiple access technologies such as time division multiple access (TDMA), frequency division multiple access (FDMA), space division multiple access (SDMA), code division multiple access (CDMA), and non-orthogonal multiple access (NOMA) are widely used. In semantic communication, which is considered a new paradigm of the next-generation communication system, we extract high-dimensional features from signal sources in a model-based artificial intelligence approach from a semantic perspective and construct a model information space for signal sources and channel features. From the high-dimensional semantic space, we excavate the shared and personalized information of semantic information and propose a novel multiple access technology, named model division multiple access (MDMA), which is based on the resource of the semantic domain. From the perspective of information theory, we prove that MDMA can attain more performance gains than traditional multiple access technologies. Simulation results show that MDMA saves more bandwidth resources than traditional multiple access technologies, and that MDMA has at least a 5-dB advantage over NOMA in the additive white Gaussian noise (AWGN) channel under the low signal-to-noise (SNR) condition.

The proliferation of IoT devices, with various capabilities in sensing, monitoring, and controlling, has prompted diverse emerging applications, highly relying on effective delivery of sensitive information gathered at edge devices to remote controllers for timely responses. To effectively deliver such information/status updates, this paper undertakes a holistic study of AoI in multi-hop networks by considering the relevant and realistic factors, aiming for optimizing information freshness by rapidly shipping sensitive updates captured at a source to its destination. In particular, we consider the multi-channel with OFDM (orthogonal frequency-division multiplexing) spectrum access in multi-hop networks and develop a rigorous mathematical model to optimize AoI at destination nodes. Real-world factors, including orthogonal channel access, wireless interference, and queuing model, are taken into account for the very first time to explore their impacts on the AoI. To this end, we propose two effective algorithms where the first one approximates the optimal solution as closely as we desire while the second one has polynomial time complexity, with a guaranteed performance gap to the optimal solution. The developed model and algorithms enable in-depth studies on AoI optimization problems in OFDM-based multi-hop wireless networks. Numerical results demonstrate that our solutions enjoy better AoI performance and that AoI is affected markedly by those realistic factors taken into our consideration.

This paper studies the joint device selection and power control for wireless federated learning (FL), considering both the analog downlink and over-the-air computation (AirComp)-based uplink communications between the parameter server (PS) and the terminal devices. First, we propose an AirComp-based adaptive reweighing scheme for the aggregation of local updated models, where the model aggregation weights are directly determined by the uplink transmit power values of the selected devices. Furthermore, we provide a convergence analysis for the proposed wireless FL algorithm and derive the upper bound on the expected optimality gap between the expected and optimal global loss values with respect to (w.r.t.) the selected devices and downlink and uplink transmit power values. With instantaneous channel state information (CSI), we formulate the optimality gap minimization problem, which is solved by using the semidefinite programming (SDR) technique. Numerical results reveal that our proposed wireless FL algorithm achieves close to the best performance by using the ideal FedAvg scheme with error-free model exchange and full device participation.

We investigate the coexistence of task-oriented and data-oriented communications in a IoT system that shares a group of channels, and study the scheduling problem to jointly optimize the weighted age of incorrect information (AoII) and throughput, which are the performance metrics of the two types of communications, respectively. This problem is formulated as a Markov decision problem, which is difficult to solve due to the large discrete action space and the time-varying action constraints induced by the stochastic availability of channels. By exploiting the intrinsic properties of this problem, we first simplify it and convert it to an equivalent Markov game, for which the large and discrete action space issue is greatly relieved. Then, we propose a Whittle's index guided multi-agent proximal policy optimization (WI-MAPPO) algorithm to solve the considered game, where the embedded Whittle's index module further shrinks the action space, and the proposed offline training algorithm extends the training kernel of the conventional MAPPO to address the issue of time-varying constraints.

The ubiquitous computing resource at UAVs and IoT devices can be exploited in conjunction to form a UAV-IoT edge computing system for low-cost and responsive environmental monitoring. Under stochastic computational task arrival at IoT devices, one major challenge is how to realize path control for multiple UAVs and energy efficient computation offloading in real time. Moreover, the freshness of obtained updates is of critical importance to the system performance under such time-critical scenarios. In this paper, we employ the concept of age of information (AoI) to quantify the timeliness of updates at IoT devices, and formulate an energy minimization problem by jointly considering UAV path planning, energy consumption of computation offloading and age evolution of updates. To solve the formulated problem, we propose a deep reinforcement learning based solution to achieve fast decision making. Simulation results show that the performance of proposed solution is competitive in terms of obtaining fresh updates at low energy cost.

Federated learning (FL) through its novel applications and services has enhanced its presence as a promising tool in the Internet of Things (IoT) domain. Specifically, in a multiaccess edge computing setup with a host of IoT devices, FL is most suitable since it leverages distributed client data to train high-performance deep learning (DL) models while keeping the data private. However, the underlying deep neural networks (DNNs) are huge, preventing its direct deployment onto resource-constrained computing and memory-limited IoT devices. Besides, frequent exchange of model updates between the central server and clients in FL could result in a communication bottleneck. To address these challenges, in this article, we introduce GWEP, a model compression-based FL method. It utilizes joint quantization and model pruning to reap the benefits of DNNs while meeting the capabilities of resource-constrained devices. Consequently, by reducing the computational, memory, and network footprint of FL, the low-end IoT devices may be able to participate in the FL process. In addition, we provide theoretical guarantees of FL convergence. Through empirical evaluations, we demonstrate that our approach significantly outperforms the baseline algorithms by being up to 10.23 times faster with 11 times lesser communication rounds, while achieving high-model compression, energy efficiency, and learning performance.

In Industrial Internet of Things (IIoT), it is essential to acquire timely device status information to ensure efficient operation. In this article, we consider a wireless monitoring system in IIoT and employ the concept of Age of Information (AoI) to characterize the timeliness of device status information in the system. Considering the impact of resource constraints on information acquisition, we apply a pull-based model to control the entire process of sampling, transmission, and processing associated with device status updates, which constitutes a system-wide AoI minimization problem. The formulated problem is a mixed-integer nonconvex problem, due to the temporal correlation of AoI and the intractability of the implicit AoI-associated objective function. We introduce the concept of average AoI earnings to equivalently substitute the optimization objective. The original problem in consecutive time slots is decomposed into the per-time slot average AoI earnings maximization problem to deal with the temporal correlation of AoI. Then, an online slot-by-slot optimization algorithm (SBSA) is proposed to control device status updates without long-term system state information. Simulation results show that SBSA can significantly improve the AoI performance of the system. However, the problem decomposition in SBSA inevitably brings approximation error. Hence, based on the actual transmission and processing in the system, we get the lower bound of the system total AoI by designing a multislot optimization algorithm (MSA) and analyze the approximate error caused by SBSA. Through simulation results, SBSA has a substantially lower computational complexity, while maintaining acceptable approximation error in comparison to MSA.

In this paper, we propose a class of high-efficiency deep joint source-channel coding methods that can closely adapt to the source distribution under the nonlinear transform, it can be collected under the name nonlinear transform source-channel coding (NTSCC). In the considered model, the transmitter first learns a nonlinear analysis transform to map the source data into latent space, then transmits the latent representation to the receiver via deep joint source-channel coding. Our model incorporates the nonlinear transform as a strong prior to effectively extract the source semantic features and provide side information for source-channel coding. Unlike existing conventional deep joint source-channel coding methods, the proposed NTSCC essentially learns both the source latent representation and an entropy model as the prior on the latent representation. Accordingly, novel adaptive rate transmission and hyperprior-aided codec refinement mechanisms are developed to upgrade deep joint source-channel coding. The whole system design is formulated as an optimization problem whose goal is to minimize the end-to-end transmission rate-distortion performance under established perceptual quality metrics. Across test image sources with various resolutions, we find that the proposed NTSCC transmission method generally outperforms both the analog transmission using the standard deep joint source-channel coding and the classical separation-based digital transmission. Notably, the proposed NTSCC method can potentially support future semantic communications due to its content-aware ability and perceptual optimization goal.

This paper studies the joint device selection and power control scheme for wireless federated learning (FL), considering both the downlink and uplink communications between the parameter server (PS) and the terminal devices. In each round of model training, the PS first broadcasts the global model to the terminal devices in an analog fashion, and then the terminal devices perform local training and upload the updated model parameters to the PS via over-the-air computation (AirComp). First, we propose an AirComp-based adaptive reweighing scheme for the aggregation of local updated models, where the model aggregation weights are directly determined by the uplink transmit power values of the selected devices and which enables the joint learning and communication optimization simply by the device selection and power control. Furthermore, we provide a convergence analysis for the proposed wireless FL algorithm and the upper bound on the expected optimality gap between the expected and optimal global loss values is derived. With instantaneous channel state information (CSI), we formulate the optimality gap minimization problems under both the individual and sum uplink transmit power constraints, respectively, which are shown to be solved by the semidefinite programming (SDR) technique. Numerical results reveal that our proposed wireless FL algorithm achieves close to the best performance by using the idealFedAvgscheme with error-free model exchange and full device participation.

The digital twin (DT) bridges the physical world with the digital world in real-time for the Industrial Internet of Things (IIoT) and federated learning (FL) enables edge intelligence services for IIoT under the premise of avoiding privacy leakage. The fusion of two technologies can extremely accelerate the development of Industry 4.0 by enabling instant intelligence services. However, in the resource-constrained IIoT, the energy consumption of performing FL and maintaining the virtual object in the digital space by DT technology become the bottlenecks and can not be ignored. To address these issues, in this paper, we proposed an energy-efficient FL framework for DT-enabled IIoT. In the proposed framework, IIoT devices choose different training methods considering dynamic time-varying environment status to achieve energy-efficient FL, i.e., either train locally or connect to the virtual object by DT in the corresponding server of a small base station (SBS) to train mapped data using computing resources of SBS. Then, we investigate the joint training method selection and resource allocation problem to minimize the energy consumption while satisfying the convergence rate of the training model. Considering the problem is intractable using traditional approaches, we use a deep reinforcement learning (DRL)-based algorithm to solve it. Simulation results show that the proposed framework decreases greatly energy consumption compared with the static framework while satisfying the convergence rate of FL.

The ever-growing number of Internet-of-Things (IoT) devices makes multiaccess edge computing (MEC)-assisted status update system more and more attractive, which can be deployed to enable remote data acquisition and analysis from urban space. The ambient computing resource at edge automatically extracts valuable status update information from the data collected by IoT devices, which supports the real-time remote monitoring applications. In this article, we employ the concept of Age of Information (AoI) to quantify the freshness of status updates. To combat the limited battery capacity at IoT devices, energy harvesting (EH) is leveraged to capture the green energy from ambient environment. Specifically, we investigate an age minimization problem by considering the randomness in energy arrivals, heterogeneity in harvesting mode, and the stochasticity in transmission and computing process. The formulated problem is a long-term stochastic optimization problem. Then, we transform the original problem into a series of per-time slot deterministic optimization problem. An online scheduling policy is proposed to obtain the energy management decisions at devices, and the transmission and computing scheduling decisions among multiple devices without any prior knowledge on the network dynamics, which is facilitated to be implemented. Simulation results show that the performance of our proposed algorithm is competitive when compared with other existing schemes.

The advent of vehicle-to-everything communication facilitates the emergence of vehicular sensing networks, where vehicles equipped with advanced sensors continuously sample informative status updates of its surroundings and forward the sampled data to roadside infrastructure based on a certain routing strategy. The collected data is analyzed to obtain real-time situational awareness to impose certain behaviors on the vehicles. In such networked control systems, the timeliness of collected data is of critical importance to system performance, which can be quantified by the concept of Age of Information. Note that to obtain timely perception of its surroundings, each vehicle tends to sample status updates at the maximum frequency, which may congest the network due to limited communication resource. Moreover, the highly dynamic nature of vehicular network poses a great challenge in finding a reliable route for timely data forwarding. Therefore, the data collection scheme should be carefully designed to balance the timeliness of collected information and network stability. In this article, we study an age optimization problem by jointly considering the data sampling at source vehicles and the data forwarding process for multiple information flows across the network. We employ the Lyapunov optimization technique to develop a distributed age-aware data collection scheme consists of a threshold-based sampling strategy at source vehicles and a learning-based data forwarding strategy. Simulation results show that our proposed scheme outperforms existing strategies in collecting status updates in a timely manner.

Thanks to the inherent characteristics of flexible mobility and autonomous operation, unmanned aerial vehicles (UAVs) will inevitably be integrated into 5G/B5G cellular networks to assist remote sensing for real-time assessment and monitoring applications. Most existing UAV-assisted data collection schemes focus on optimizing energy consumption and data collection throughput, which overlook the temporal value of collected data. In this article, we employ Age of Information (AoI) as a performance metric to quantify the temporal correlation among data packets consecutively sampled by the Internet of Things (IoT) devices, and investigate an AoI-energy-aware data collection scheme for UAV-assisted IoT networks. We aim to minimize the weighted sum of expected average AoI, propulsion energy of UAV, and the transmission energy at IoT devices, by jointly optimizing the UAV flight speed, hovering locations, and bandwidth allocation for data collection. Considering the system dynamics, the optimization problem is modeled as a Markov decision process. To cope with the multidimensional action space, we develop a twin-delayed deep deterministic (TD3) policy gradient-based UAV trajectory planning algorithm (TD3-AUTP) by introducing the deep neural network (DNN) for feature extraction. Through simulation results, we demonstrate that our proposed scheme outperforms the deep$Q$-network and actor–critic-based algorithms in terms of achievable AoI and energy efficiency.

As the emergence of small cell densification and cache-enabled smart devices, mobile edge caching is regarded as a promising tool to relieve traffic burden of core network and reduce end-to-end delay. To fully utilize the limited caching capacity, cooperative caching has been proposed to further improve user experience by exploiting caching diversity. Under such paradigm, popular contents are prefetched and stored in small base stations (SBSs) or user devices. However, the popularity of a certain content may change over time due to human factors. In this paper, we study the cooperative content caching problem from a reinforcement learning perspective. We investigate a delay minimization problem by jointly considering the spatiotemporal variation of content variation, the cost of content sharing between user devices, and the cost of cooperative caching among BSs. To address this problem, we propose a two-stage multi-armed bandit learning based online cooperative (MAB-LOC) algorithm. In the first stage, we design a MAB based algorithm to estimate the content popularity. In the second stage, we design a semidefinite relaxation based approach to obtain the caching strategy. Through simulation results, we show that the performance of the proposed algorithm is competitive in terms of caching-hit probability and end-to-end delay.

Grant-free transmission is considered as a promising technology to support sporadic data transmission in massive machine-type communications (mMTC). Due to the distributed manner, high collision probability is an inherent drawback of grant-free access techniques. Non-orthogonal multiple access (NOMA) is expected to be used in uplink grant-free transmission, multiplying connection opportunities by exploiting power domain resources. However, it is usually applied for coordinated transmissions where the base station performs coordination with full channel state information, which is not suitable for grant-free techniques. In this paper, we propose a novel distributed layered grant-free NOMA framework. Under this framework, we divide the cell into different layers based on predetermined inter-layer received power difference. A distributed layered grant-free NOMA based hybrid transmission scheme is proposed to reduce collision probability. Moreover, we derive the closed-form expression of connection throughput. A joint access control and NOMA layer selection (JACNLS) algorithm is proposed to solve the connection throughput optimization problem. The numerical and simulation results reveal that, when the system is overloaded, our proposed scheme outperforms the grant-free-only scheme by three orders of magnitude in terms of expected connection throughput and outperforms coordinated OMA transmission schemes by 31.25% with only 0.0189% signaling overhead of the latter.

Machine-type communication (MTC) is endorsed in the fifth-generation (5G) networks to realize innovative IoT based applications, such as smart city and intelligent manufacturing. MTC devices with sensing and communication capabilities can monitor the surrounding environment and transmit the collected information back to Base Station (BS) for further data analysis. The dense deployment of sensing devices calls for a clustering structure to preprocess the redundant data to avoid traffic overload. Moreover, due to limited battery capacity, the energy cost remains a critical concern in such IoT systems. In this paper, we propose an energy-efficient clustering routing algorithm. Considering the non-uniform traffic distribution, we propose an uneven cluster formation scheme for load balancing and energy efficiency. Moreover, we propose a distributed cluster head (CH) rotation mechanism to balance energy consumption within each cluster. As for long distance transmission to BS, we design a dynamic multi-hop routing algorithm among CH nodes based on a proposed distance-and-energy-aware cost function to avoid the energy hole problem. Simulation results show that the performance of our proposed algorithm is competitive in terms of network lifetime, throughput and energy efficiency.

Orthogonal matching pursuit (OMP) and its improved algorithms are widely used as hybrid precoding solutions in millimeter wave massive MIMO systems. The existing modified hybrid precoding schemes reduce computational complexity, however, they cause the loss of spectral efficiency to some extent. In this paper, we propose a novel generalized orthogonal matching pursuit (gOMP) algorithm based on order-recursive least squares (ORLS) in order to balance both computational cost and spectral efficiency. Compared to OMP algorithm, the maximum iteration of gOMP-ORLS algorithm can be reduced by choosing more than one vector in each iteration. Meanwhile, it has much lower implementation complexity due to avoiding matrix inversion operation. More importantly, the realized spectral efficiency of the proposed algorithm is higher than other existing algorithms. The simulation results as well as our detailed analysis demonstrate that a) the proposed gOMP-ORLS algorithm can achieve the approximately same spectral efficiency as OMP algorithm; b) it can reduce computational complexity and improve precoding efficiency prominently.

Interference neutralization (IN) is regarded as a promising interference management techniques for multi-hop wireless networks. Yet most existing results of IN are limited to two-hop networks such as the relay-aided cellular network. Little progress has been made so far in the exploration of IN in generic multi-hop (more than two hops) networks. This paper aims to bridge this gap by developing an optimization framework for IN in a generic multi-hop network with the objective of maximizing the end-to-end throughput of multiple coexisting communication sessions. We first derive a mathematical model for IN in a special one-hop network to characterize the capability of IN, and then generalize this model to a multi-hop network. Based on the IN model, we develop a cross-layer optimization framework for a multi-hop network with the objective of fully translating the benefits of IN to the end-to-end throughput of the multi-hop sessions. To evaluate the performance of IN in multi-hop networks, we compare its performance against the case where IN is not employed. Simulation results show that the use of IN can significantly (more than 50%) increase the session throughput and, more notably, the throughput gain of IN increases with the node density and traffic intensity in the network.

Millimeter-wave communication in 60 GHz band is considered a promising technology to meet the explosive growth of data demand in Wi-Fi based WLAN. To address potential blockage for 60 GHz signals, multiple APs are proposed for such WLAN. This paper addresses the important problem of AP assignment and transmission scheduling for a multi-AP 60 GHz WLAN. We propose two AP assignment schemes with different complexity and study how to maximize user throughput with joint consideration of AP assignment and transmission scheduling. We advocate to use one-shot AP assignment-based scheduling due to its simplicity for implementation. To address real-time online traffic and human blockage, we propose an online algorithm to implement the one-shot AP assignment scheme without altering the AP assignment for other existing users. Through performance evaluation, we show that the proposed online algorithm is competitive when compared to the offline algorithm.

Co-localization of mobile users combines methods of detecting nearby users and providing them interesting and useful services or information. By exploiting the massive use of smartphones, nearby users can be co-localized using only their captured ambient radio signals. In this paper, we propose a real-time co-localization system, in a centralized manner, that leverages co-located users with high accuracy. We exploit the similarity of radio frequency measurements from users' mobile terminal. We do not require any further information about them. Our co-localization system is based on a nonparametric Bayesian method called infinite Gaussian mixture model that allows the model parameters to change with observed input data. In addition, we propose a modified version of Gibbs sampling technique with an average similarity threshold to better fit user's group. We design our system in a completely centralized manner. Hence, it enables the network to control and manage the formation of the users' groups. We first evaluate the performance of our proposal numerically. Then, we carry out an extensive experiment to demonstrate the feasibility, and the efficiency of our approach with data sets from a real-world setting. Results on experiment favor our algorithm over the state-of-the-art community detection-based clustering method.

In recent years, there has been great interest from the cellular service providers to use the unlicensed spectrum for their service offerings. On the other hand, existing unlicensed users in these bands (e.g., Wi-Fi in the 5-GHz band) have serious concern that such coexistence will jeopardize their service quality. Although there are some proposals on how to achieve coexistence, they are driven by the service providers and as such there remain many issues and skepticism. In this paper, we take a novel human-centric approach to understand coexistence between Wi-Fi and LTE by focusing on human satisfaction. Through mathematical modeling, problem formulation, and extensive simulations studies, we show that in terms of maximizing total human satisfaction function, there does not appear to be any advantage with the coexistence of unlicensed spectrum for Wi-Fi and LTE under static partitioning of unlicensed spectrum. This finding serves as a powerful counter argument to some LTE service providers' proposal to share the unlicensed spectrum with Wi-Fi through static partitioning. On the other hand, we find that there is a significant improvement in human satisfaction in coexistence between Wi-Fi and LTE under adaptive spectrum partitioning. Since adaptive spectrum partitioning may require a user to change its service provider whenever there is a change among the users, we propose a practical (semi-adaptive) algorithm for implementation without affecting existing users' service providers. Through performance evaluation, we show that the proposed semi-adaptive algorithm is highly competitive.

There have been some rapid advances on the design of full duplex (FD) transceivers in recent years. Although the benefits of FD have been studied for single-hop wireless communications, its potential on throughput performance in a multi-hop wireless network remains unclear. As for multi-hop networks, a fundamental problem is to compute the achievable end-to-end throughput for one or multiple communication sessions. The goal of this paper is to offer some fundamental understanding on end-to-end throughput performance limits of FD in a multi-hop wireless network. We show that through a rigorous mathematical formulation, we can cast the multi-hop throughput performance problem into a formal optimization problem. Through numerical results, we show that in many cases, the end-to-end session throughput in a FD network can exceed 2x of that in a half duplex (HD) network. Our finding can be explained by the much larger design space for scheduling that is offered by removing HD constraints in throughput maximization problem. The results in this paper offer some new understandings on the potential benefits of FD for end-to-end session throughput in a multi-hop wireless network.

Existing spectrum sharing paradigms have set clear boundaries between the primary and secondary networks. There is either no or very limited node-level cooperation between the primary and secondary networks. In this paper, we develop a new and bold spectrum-sharing paradigm beyond the state of the art for future wireless networks. We explore network cooperation as a new dimension for spectrum sharing between the primary and secondary users. Such network cooperation can be defined as a set of policies under which different degrees of cooperation are to be achieved. The benefits of this paradigm are numerous, as they allow integrating resources from two networks. There are many possible node-level cooperation policies that one can employ under this paradigm. For the purpose of performance study, we consider a specific policy called United cooperation of Primary and Secondary (UPS) networks. UPS allows a complete cooperation between the primary and secondary networks at the node level to relay each other's traffic. As a case study, we consider a problem with the goal of supporting the rate requirement of the primary network traffic while maximizing the throughput of the secondary sessions. For this problem, we develop an optimization model and formulate a combinatorial optimization problem. We also develop an approximation solution based on a piece-wise linearization technique. Simulation results show that UPS offers significantly better throughput performance than that under the interweave paradigm.

Interference neutralization (IN) is an interference management technique that allows simultaneous transmission of multiple links by nullifying their mutual interference in the air via cooperation among the transmitters. Although IN has been studied from information theoretic perspective, its potential for a general multi-hop wireless network has not been explored. The goal of this paper is to understand IN in a multi-hop wireless network from networking perspective. We first establish an IN reference model. Based on this reference model, we develop a set of feasibility constraints for a subset of links to be active simultaneously. By identifying each eligible neutralization node (called neut), we study IN in a general multi-hop network and develop a set of necessary constraints to characterize neut selection, IN, and scheduling. These constraints allow us to study the performance of multi-hop networks without the need of getting involved into onerous signal design issues at the physical layer. Finally, we apply our IN model and constraints to study a throughput maximization problem and show that the use of IN can generally increase network throughput. In particular, throughput gain is most significant when the node density increases.

The classical approach to interference management in wireless medium access is based on avoidance. Recently, there is a growing interest in exploiting interference (rather than avoiding it) to increase network throughput. This was made possible by a number of advances at the physical layer. In particular, the so-called successive interference cancellation (SIC) scheme appears very promising, due to its ability to enable concurrent receptions from multiple transmitters as well as interference rejection. Although SIC has been extensively studied as a physical layer technology, its research and advances in the context of multi-hop wireless network remain limited. In this paper, we aim to close this gap by offering a systematic study of SIC in a multi-hop wireless network. After gaining a fundamental understanding of SIC's capability and limitation, we propose a cross-layer optimization framework for SIC that incorporates variables at physical, link, and network layers. We use numerical results to affirm the validity of our optimization framework and give insights on how SIC behaves in a multi-hop wireless network.

Recently, degree-of-freedom (DoF)-based models have been widely used to study MIMO network performance. Existing DoF-based models differ in their interference cancellation (IC) behavior and many of them suffer from either loss of solution space or possible infeasible solutions. To overcome these limitations, a new DoF-based model, which employs an IC scheme based on node-ordering was proposed. In this paper, we apply this new DoF IC model to study a throughput maximization problem in a multihop MIMO network. The problem formulation involves joint consideration of flow routing and DoF allocation and falls in the form of a mixed-integer linear program (MILP). Our main contribution is an efficient polynomial time algorithm that offers a competitive solution to the MILP through a series of linear programs (LPs). The algorithm employs a sequential fixing framework to obtain an initial feasible solution and then improves the solution by exploiting: 1) the impact of node ordering on DoF consumption for IC at a node and 2) route diversity in the network. Simulation results show that the solutions obtained by our proposed algorithm are competitive and feasible.

The transparent coexistence (TC) paradigm allows simultaneous activation of the secondary users with the primary users as long as their interference to the primary users can be properly canceled. This paradigm has the potential to offer much more efficient spectrum sharing than the traditional interweave paradigm. In this paper, we design a distributed algorithm to achieve this paradigm for a secondary multi-hop network. For interference cancelation (IC), we employ MIMO at secondary nodes. We present a distributed iterative algorithm to maximize each secondary session's throughput while meeting all IC requirements under TC. By maintaining two local sets for each node, we can keep track of the node's IC responsibility. Although no explicit node ordering is maintained in our distributed algorithm, we prove that our distributed data structure at each node (with the use of two local sets) can be mapped to an explicit global node ordering for IC among all nodes in the network. This guarantees that each active node's degree-of-freedoms allocated for IC is feasible at the physical layer. Our algorithm is iterative in nature and all steps can be accomplished based on local information exchange among the neighboring nodes. We present the simulation results to show that the performance of our distributed algorithm is highly competitive when compared with an upper bound solution from the corresponding centralized problem.

There has been a growing interest to employ the so-called degree-of-freedom (DoF) based models to study multihop MIMO networks. Existing DoF-based models differ in their interference cancelation (IC) behavior and suffer from either loss of solution space or possible infeasible solutions. Recently, a DoF model based on a novel node-ordering concept was proposed to overcome the limitations of the exiting DoF models. In this paper, we apply this new DoF model to study a throughput maximization problem in a multi-hop network. The problem formulation jointly considers half duplex, node ordering, DoF consumption constraints and flow routing and is in the form of a mixed integer linear program (MILP). Our main contribution is the development of an efficient polynomial time algorithm that offers a competitive solution to the MILP through a series of linear programs (LPs). The key idea in the algorithm is to explore (i) the impact of node ordering on DoF consumption for IC at a node, and (ii) route diversity in the network while ensuring DoF constraints are satisfied at each node throughout the iterations. Simulation results show that our solutions by the proposed algorithm are competitive and feasible.