Movable antennas (MAs) and reconfigurable intelligent surfaces (RISs) have emerged as two promising technologies for enhancing wireless communication performance, owing to their capability to dynamically reshape and manipulate the propagation environment. Motivated by this potential, this paper investigates the joint utilization of the additional degrees of freedom introduced by MAs (through antenna repositioning) and RIS (via optimized reflection) to effectively mitigate computation distortion in over-the-air computation (AirComp) systems. Specifically, we formulate an optimization problem aimed at minimizing the mean square error (MSE) between the target function values and their estimates, through jointly optimizing the receive beamformer at the access point, RIS reflection phase shifts, and transmit coefficients as well as antenna positions of AirComp users. To address the non-convex nature of the formulated problem, we develop a computationally efficient algorithm capitalizing alternating optimization technique, the penalty-dual decomposition method, and the particle swarm optimization enhanced by a dynamic neighborhood pruning mechanism. Next, we further extend the optimization framework to a more practical case with discrete MA positions. Extensive simulation results demonstrate that the joint optimization of RIS beamforming and MA positioning substantially reduces the computation MSE, compared to the separate MA-enhanced AirComp and RIS-aided AirComp schemes. Moreover, the proposed algorithm achieves comparable performance to the penalty function-based method, while incurring significantly lower computational complexity.

The joint design of sensing, communication, computing, and control (SC3) is crucial for supporting environment-aware Industrial Internet of Things (IIoT) applications. Considering the uncontrollable wireless propagation environments and limited spectrum resources, wireless communication performance often becomes the primary design bottleneck for such an integrated system. To address this challenge, this paper presents a design framework for reconfigurable intelligent surface (RIS)-enhanced semantic-aware SC3networks, where RIS and semantic communication technologies are employed to improve wireless communication efficiency. To facilitate real-time closed-loop control, we further formulate a weighted sum execution latency minimization problem, while imposing constraints on maximum execution latency and energy consumption of individual IoT device, as well as minimum information entropy to meet specific control requirements measured by linear quadratic regulator cost. In addition, the design framework aims at optimizing bandwidth allocation, RIS phase shift matrix, time scheduling, transmit power, and CPU-cycle frequency for IoT devices and the base station (BS). To handle the coupled multi-dimensional optimization variables, the block coordinate descent method is utilized to decompose the formulated problem into more tractable subproblems, which are then solved using a penalty-function-based approach and geometric programming technique. Simulation results demonstrate the performance advantages achieved by our proposed method compared to several benchmark approaches. Additionally, we explore the impact of various parameters on SC3systems, offering deeper insights and meaningful research observations.

This article investigates a joint optimization framework for intelligent reflecting surface (IRS)-enhanced integrated sensing, communication, and powering systems. In this framework, the base station transmits signals for simultaneous radar sensing, as well as multi-user information and power transmissions. We aim at maximizing the minimum harvested power among all users, while satisfying beampattern gain requirements for multi-target sensing and signal-to-interference-plus-noise constraints of users. To tackle this strictly non-convex problem, we employ the block coordinate descent technique to iteratively optimize the transmit beamformer of the base station, the phase shift matrix of the IRS, and the power splitting ratios of users. The semi-definite relaxation method is utilized to obtain the optimal transmit beamformer of the base station, and the tightness of the rank-one relaxation is demonstrated. Furthermore, we develop a penalty function-based algorithm and use successive convex approximation techniques to determine the optimal phase shift matrix of the IRS. Additionally, closed-form expressions are derived for the optimal power splitting ratios. Moreover, by exploiting the Bernstein-type inequality, we further designed the robust beamforming and power splitting scheme for considered systems under stochastic channel estimation errors. Numerical results demonstrate that the proposed IRS-enhanced method outperforms several benchmark methods in terms of the minimum harvested power among all users.

The integration of intelligent transportation systems in vehicular edge computing (VEC), powered by 5G, plays a vital role in enhancing supply chain systems (SCS). It facilitates real-time communication and offloading tasks to edge servers, boosting logistics and optimizing transportation efficiency. However, conventional task offloading techniques in VEC cannot dynamically adapt or retrain models to respond to the ever-changing supply chain environment. To address this challenge, this study proposes a Vehicular Multitasking Edge Computing Offloading Algorithm based on meta reinforcement learning (VMRL). This algorithm models complex vehicular tasks in SCS, using a directed acyclic graph (DAG), to meet the processing requirements of high-demand applications. Furthermore, by leveraging meta reinforcement learning, VMRL quickly adapts to dynamically changing logistics demands and transportation conditions in the supply chain, enhancing the generalization capabilities of the VEC offloading strategy. Experiment results demonstrate that the VMRL algorithm significantly improves system efficiency, reduces the offload delay, improves overall responsiveness and stability of the supply chain, and facilitates more intelligent and automated management of the SCS.

This article investigates the uplink performance of reconfigurable intelligent surface (RIS)-aided cell-free (CF) massive multiple-input-multiple-output (mMIMO) systems over spatially correlated Rayleigh fading channels. We consider multiple RISs and low-resolution analog-to-digital converters (ADCs) to improve the system energy efficiency (EE). We first provide an aggregated channel estimation technique with less pilot overhead. By exploiting the statistical channel state information (CSI), we further optimize the RISs’ phase shifts with the goal of minimizing the total normalized mean square error (NMSE) of the estimated aggregated channels. Subsequently, we derive the closed-form expression of the uplink spectral efficiency (SE) for quantization-aware minimum mean-square error (MMSE) combining. Third, based on the closed-form SE expression and power consumption model, we formulate and solve an optimization problem that maximizes the uplink EE under the constraints of transmit power and total ADC quantization bits. Specifically, by leveraging the Dinkelbach transform, Lagrangian dual transform, and fractional programming (FP) techniques, an alternating optimization (AO)-based algorithm is proposed to jointly obtain the bit allocation (BA) scheme among all access points (APs) and the uplink power control (PC) strategy for all users. Finally, numerical results validate the correctness of the closed-form SE expression and show the effectiveness of the proposed optimization methods for phase shift design and EE maximization.

To support emerging environmentally-aware intelligent applications, a massive amount of data needs to be collected by sensor devices and transmitted to edge/cloud servers for further computation and analysis. However, due to the high deployment and operational cost, only depending on terrestrial infrastructures cannot satisfy the communication and computation requirements of sensor devices in the unexpected and emergency situations. To tackle this issue, this paper presents a digital twin-enabled space-air-ground integrated sensing, communication and computation network framework, where unmanned aerial vehicles (UAVs) serve as aerial edge access point to provide wireless access and edge computing services for ground sensor devices, and satellites provide access to cloud data center. In order to tackle the complex network environments and coupled multi-dimensional resources, the digital twin technique is utilized to realize real-time network monitoring and resource management, and the mapping deviation is also considered. To realize real-time data sensing and analysis, we formulate a maximum execution latency minimization problem while satisfying the energy consumption constraints and network resource restrictions. Based on the block coordinate descent method and successive convex approximation technique, we develop an efficient algorithm to obtain the optimal sensing time, transmit power, bandwidth allocation, UAV deployment position, data assignment strategy, and computation capability allocation scheme. Simulation results demonstrate that the proposed method outperforms several benchmark methods in terms of maximum execution latency among all sensor devices.

To satisfy the heterogeneous service requirements in future internet of things (IoT), this paper investigates the novel framework for intelligent reflecting surface (IRS)-aided wireless powered over-the-air computation (AirComp) and communication networks, where the IoT devices first harvest energy from the downlink signal sent by the base station, and then conduct the information transmissions and AirComp in the uplink. In particular, the IRS is used to improve the efficiency of wireless energy transfer, and alleviate the harmful interference between the communication and AirComp signals. To balance the performance of such an integrated system, we present two joint beamforming and reflection optimization problems via minimizing the computation distortion and maximizing the sum rate, respectively. To solve the non-convex problems, we develop the alternating optimization framework with proved convergence, in which the penalty function-based method and variable substitution technique are exploited to acquire the optimal solutions of beamformers and reflection parameters. Finally, simulation results show that the proposed method realizes significantly higher computation accuracy and communication rate, in comparison with several existing benchmark methods.

Benefitting from the breakthrough of wireless power transfer technology, the lifetime of Wireless Sensor Networks (WSNs) can be significantly prolonged by scheduling a mobile charger (MC) to charge sensors. Compared with omnidirectional charging, the MC equipped with directional antenna can concentrate energy in the intended direction, making charging more efficient. However, all prior arts ignore the considerable energy leakage behind the directional antenna (i.e.,back lobe), resulting in energy wasted in vain. To address this issue, we study a fundamental problem of how to utilize the neglected back lobe and schedule the directional MC efficiently. Towards this end, we first build and verify a directional charging model considering both main and back lobes. Then, we focus on jointly optimizing the number of dead sensors and energy usage effectiveness. We achieve these by introducing a scheduling scheme that utilizes both main and back lobes to charge multiple sensors simultaneously. Finally, extensive simulations and field experiments demonstrate that our scheme reduces the number of dead sensors by$49.5\%$and increases the energy usage effectiveness by$10.2\%$on average as compared with existing algorithms.

Intelligent reflecting surface (IRS) is envisioned as a promising technique to improve the performance of full-duplex wireless-powered communication networks (FD-WPCNs). This article investigates the joint phase beamforming design and resource management for IRS-assisted FD-WPCNs, where multiple wireless devices (WDs) can harvest downlink radio-frequency energy and transmit uplink information to the hybrid access point (HAP) over the same band with the aid of IRS. We first formulate a total transmission time minimization problem subject to the minimum transmit rate and energy causality constraints of WDs. In particular, the random phase error of IRS is integrated into our optimization model. Furthermore, we develop an alternating optimization method to obtain the optimal solution of the formulated nonconvex problem by iteratively solving two subproblems. For the phase beamforming optimization subproblem, we first convert the random phase errors to a deterministic expression, and then utilize the successive convex approximation method to solve the phase beamforming optimization problem. For the transmit power and time-slot allocation subproblem, the optimal transmit power of WDs is derived in closed-form expressions, and the approximation method and variable substitution technique are adopted to obtain the optimal time-slot allocation and transmit power of HAP. Finally, numerical results are provided to evaluate the performance of our proposed method and reveal the benefits introduced by the IRS technique as compared to benchmark methods.

Federated learning (FL) is an emerging technique to support privacy-aware and resource-constrained machine learning, where a base station (BS) will coordinate a set of distributed Internet of Things (IoT) devices to train a shared machine learning model with their local data sets. Nevertheless, due to the frequent interactions between BS and distributed IoT devices for the aggregating/distributing learning model parameters, the performance of FL is fundamentally restricted by the randomness of channel condition. To address this issue, we utilize the intelligent reflecting surface (IRS) to improve the efficiency of learning model aggregation/distribution. In addition, we consider two transmission protocols to enable the model aggregation from IoT devices to BS, i.e., frequency division multiple access (FDMA) and nonorthogonal multiple access (NOMA). For both protocols, we formulate the total training latency minimization problem under the available energy constraints of IoT devices, to jointly optimize the phase shifts of IRS, communication resource scheduling, and transmit power and local computing frequencies of IoT devices. Moreover, we further develop the efficient multidimensional resource management algorithms to solve the formulated training latency minimization problems. Numerical results demonstrate that the proposed IRS-assisted FL systems can achieve significant latency reduction as compared with other benchmark methods, and the NOMA-based model aggregation method exhibits a lower total training latency than the FDMA-based counterpart.

This article proposes an electrical signature analysis method for open-circuit faults (OCFs) detection of inverter with various disturbances in distribution grid. According to the fault mechanism, the fundamental value, rated harmonics, and direct current component of three-phase currents are used as fault electrical signatures. The signatures are estimated by unscented Kalman filter (UKF) and recognized by extreme learning machine (ELM) for fault detection. Both OCF of single switch and OCFs of multiple switches are tested with consideration of direct disturbances such as load change, overcurrent and bias current, and indirect disturbances such as grid frequency variation, background harmonics, and unbalanced voltage dip. The simulations and experiments show that the OCF detection of the new method is still accurate even with these disturbances, and reveal that only the signatures of the faulty phase current are immune to the disturbances while the ones of unfaulty phases are not. The robustness when facing the various disturbances and all explainable detection results make the new method suitable and effective for OCF of inverter detection in complicated distribution grid environment.

Traffic load-balancing is important to data center performance. However, existing data center load-balancing solutions are either limited to simple topologies or cannot provide satisfactory performance. In this paper, we propose a multipath-aware TCP (MA-TCP) which can sense the path migration of TCP flows. With this new mechanism, the reduction in TCP congestion window due to packet reordering during the path migration can be avoided. This, in turn, makes the path migration more timely as soon as the original path is congested. Furthermore, if the new path is congested (again), the flow can securely continue to migrate without worrying about transmitting rate reduction. Through NS-3 simulations, we show that MA-TCP achieves better flow completion time (FCT) than existing data center load-balancing solutions.

Blockchain is a mainstream technology in which many untrustworthy nodes work together to maintain a distributed ledger with advantages such as decentralization, traceability, and tamper-proof. The network layer communication mechanism in its architecture is the core of the networking method, message propagation, and data verification among blockchain nodes, which is the basis to ensure blockchain’s performance and key features. When blocks are propagated in peer-to-peer (P2P) networks with gossip protocol, the high propagation delay of the protocol itself reduces the propagation speed of the blocks, which is prone to the chain forking phenomenon and causes double payment attacks. To accelerate the propagation speed and reduce the fork probability, this paper proposes a blockchain network propagation mechanism based on proactive network provider participation for P2P (P4P) architecture. This mechanism first obtains the information of network topology and link status in a region based on the internet service provider (ISP), then it calculates the shortest path and link overhead of peer nodes using P4P technology, prioritizes the nodes with good local bandwidth conditions for transmission, realizes the optimization of node connections, improves the quality of service (QoS) and quality of experience (QoE) of blockchain networks, and enables blockchain nodes to exchange blocks and transactions through the secure propagation path. Simulation experiments show that the proposed propagation mechanism outperforms the original propagation mechanism of the blockchain network in terms of system overhead, rate of data success transmission, routing hops, and propagation delay.

Mobile Edge Computing (MEC) can significantly promote the development of Internet of Vehicles (IoV) for providing a low-latency and high-reliability environment. Nevertheless, a huge amount of sensor data or computing requirements generated by massive vehicles in adjacent area may be duplicated. In order to realize the efficient diffusion of information, we propose a hierarchical end-edge framework with the aid of deep collaboration among data communication, computation offloading and content caching to minimize network overheads. Specially, duplicated perceived data and computation results are cached in advance to decrease repeated data uploading and duplicated computation in offloading process. In addition, the problem is formulated as a mixed integer non-linear programming (MINLP) problem, and the deep deterministic policy gradient (DDPG)-based resource allocation scheme is utilized to obtain a sub-optimal solution with low computation complexity. Performance evaluation demonstrates that the proposed scheme can significantly reduce network overheads compared with other benchmark methods.

The integration of Mobile-edge Computing (MEC) and Wireless Energy Transfer (WET) has been recognized as a promising technique to enhance computation capability and to prolong battery lifetime of resource-constrained wireless devices in the Internet of Things (IoT) era. However, it is challenging to jointly schedule energy, radio, and computational resources for coordinating heterogeneous performance requirements in wireless powered MEC systems. To fill this gap, this paper investigates the fundamental tradeoff between Energy Efficiency (EE) and delay in a multi-user wireless powered MEC system. Considering the random channel conditions and task arrivals, we formulate a stochastic optimization problem to study the EE-delay tradeoff, which optimizes network EE subject to network stability, maximum central processing unit frequency, peak transmission power, available communication resource, and energy causality constraints. Further, we propose the online computation offloading and resource allocation algorithm by transforming the original problem into a series of deterministic optimization problems in each time block based on Lyapunov optimization theory. In addition, theoretical analysis shows that the algorithm achieves the EE-delay tradeoff as [O(1/V), O(V)] and introduces a control parameter V to balance the EE-delay performance. Numerical results verify the theoretical analysis and reveal the impact of various parameters to the system performance.

Mobile-edge computing (MEC) and non-orthogonal multiple access (NOMA) has been envisioned as two promising technologies in the future wireless networks. In this paper, we concentrate on the joint computation offloading and result downloading strategy for a NOMA-assisted MEC system, where uplink/downlink NOMA is used for computation task offloading or result downloading, respectively. The energy consumption minimization problem is formulated with joint optimization of time assignment, power control, CPU frequency, and computation offloading scheme. By exploiting block coordinate descent (BCD) method, we develop a joint communication and computation resource allocation algorithm to address the original nonconvex problem. Specifically, the optimal solution is obtained in closed form. Furthermore, extensive numerical results are provided to demonstrate the effectiveness of the NOMA-assisted MEC system, when compared to the OMA-based scheme.

This paper studies the sustainable resource allocation for energy harvesting (EH) powered cloud radio access network (C-RAN), where EH powered remote radio units (RRUs) cooperatively transmit wireless energy and information to the energy-constrained mobile devices. To investigate network resource allocation problem, firstly, a general system utility optimization framework is proposed for the design of coordinated beamforming and power splitting algorithm based on Lyapunov optimization theory. The randomness of channel conditions and energy arrivals are considered in the framework. Secondly, an online algorithm is designed to maximize the system utility subject to energy sustainable constraints at the RRUs, signal-to-interference-plus-noise (SINR) and energy harvesting requirements of mobile devices. Specifically, there is no requirements of prior distribution knowledge about channel condition or energy arrival in the proposed online algorithm. Finally, performance analysis demonstrate that the proposed algorithm can achieve close-to-optimal system utility. In addition, extensive simulation results are provided to validate the theoretical analysis and to evaluate the performance of the proposed algorithm.

This paper investigates energy-efficient resource allocation for cooperative wireless powered cellular networks (WPCNs), where the cellular and device-to-device (D2D) users first harvest energy from the HAP and then the D2D user consumes a portion of power to help the cell-edge cellular user relay the data in exchange for some time from cellular user for D2D communications. Under the proposed cooperation scheme, we formulate an energy efficiency (EE) maximization problem. The energy beamforming vector, time assignment and power allocation are jointly optimized under the transmission rate requirements and available energy constraints of both D2D and cellular users. Based on the fractional programming theory and semi-definite relaxation (SDR) method, we transform the originally non-convex EE maximization problem into a standard convex problem. This allows us to design efficient resource allocation algorithm for achieving optimal solution. Extensive simulation results are provided to show the convergence rate of the proposed iterative algorithm and to demonstrate the EE improved by the proposed system than that of two baseline systems.

Prolonging battery lifetime, enhancing computation capability and improving spectral efficiency have been the key design challenges in Internet of Things (IoT) era. This paper provides a novel solution to jointly optimize the allocation of the communication, computing and energy resources in IoT, with the aid of some advanced wireless communication technologies including Wireless Energy Transfer (WET), Mobile-Edge Computing (MEC) and Full-Duplex (FD). Specifically, the Hybrid Access-Point (HAP) (integrated with a MEC server) operates in FD mode to simultaneously broadcast energy and receive computation tasks to/from the mobile devices in the same band. Each mobile relies on the harvested energy to accomplish computation tasks by locally executing or (partial) offloading to the HAP. We concentrate on max-min energy efficiency optimization problem (MMEP) with the joint the optimization of the transmission power at the HAP, computation energy consumption and offloaded bits at each mobile device, time slots for energy transfer and computation offloading. We study the cases with perfect and imperfect self-interference cancellation at the HAP. To solve the non-convex MMEP, we apply the fractional programming theory and Block Coordinate Descent (BCD) method to design the algorithms with low complexity. Numerical results demonstrate that the proposed solutions outperform the baseline scheme in terms of the worst-case mobile EE. Moreover, the proposed algorithms can converge to the optimal solution through a few iterations.

Prolonging battery lifetime and enhancing computation capability have been the key challenges for designing the mobile devices in the Internet of Things (IoT) era. The investigation of Mobile-Edge Computing (MEC) with Wireless Energy Transfer (WET) is a promising solution to overcome such challenges. In this paper, we study the fundamental tradeoff between Energy Efficiency (EE) and delay in the multi-user wireless powered MEC systems. In order to tackle the randomness of channel conditions and task arrivals, we formulate a stochastic optimization problem to achieve the EE-delay tradeoff, which optimizes the network energy efficiency subject to the network stability, Central Processing Unit (CPU)-cycle frequency, peak transmission power, and energy causality constraints. Furthermore, we propose a joint computation allocation and resource management algorithm by transforming the original problem into a series of deterministic optimization problems in each time block based on Lyapunov optimization theory, whose convexity is further proved. Specifically, the proposed algorithm with low complexity requires no prior distribution knowledge of channel conditions and task arrivals. In addition, theoretical analysis shows that the algorithm achieves the EE-delay tradeoff as [O(1/V ),O(V )] and provides a control parameter V to balance the EE-delay performance. Numerical results verify the theoretical analysis and reveal the impacts of various parameters to the system performance.