In the era of Industry 4.0, the proliferation of dataintensive industrial applications poses a tremendous challenge on current edge computing networks. The collaboration of computing powers across standalone edge servers is vital to provision such delay-sensitive services for smart devices (SDs), enabling the converged computing and networking at the edge. In this paper, we propose a new computing paradigm named edge-driven computing power networks (CPNs) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we present an architecture that integrates digital twin (DT) technology into the edge-driven CPNs, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. The objective is to investigate the system average weighted utility maximization through the joint design of the computing power assignment at the edge server as well as the service association, task partition, and transmit power control at the SD. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the resultant problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, we develop a hybrid soft actor-critic based deep reinforcement learning algorithm, which is a continuous-discrete converged variant of soft actor-critic. Simulation results validate the superiority of our proposed algorithm over the benchmarks, and further show the significant gains can be obtained by integrating DTs into the edge-driven CPNs.

Underwater acoustic backscatter communication, which allows an underwater sensor node (USN) to communicate with the surface sink node (SN) by reflecting the acoustic signal from the SN, is a promising solution for enabling the Internet of Underwater Things. However, underwater backscattering is vulnerable to jamming attacks, especially from a gliding autonomous underwater vehicle (AUV) that can inject jamming signal to the underwater acoustic channel accordingly. This paper considers the attack-defense interactions between the AUV jammer and the SN defender in their transmit power allocations of jamming signals and acoustic signals over multiple USNs. We formulate the strategic power allocation problem for the SN and AUV as an asymmetric anti-jamming Colonel Blotto (CB) game model with a finite budget of total transmit powers. In particular, the competitive interactions between the SN and the AUV is modeled as the competition of two players in the game for limited resource budgets over the battlefield set. We analyze the mixedstrategy Nash equilibrium to the game, and obtain the closedform performance bound of the defense power allocation strategy. Numerical results show the superiority of the proposed CB game based multi-USN secure transmission scheme as compared to the benchmarks in terms of the expected sum-utility.

This letter considers a reconfigurable intelligent surface (RIS)-aided indoor visible light communication system, where a mirror array-based RIS is deployed to assist the communication from a light-emitting diode (LED) to multiple user terminals (UTs). We aim to maximize the sum-rate in an entire serving period by jointly optimizing the orientation of the RIS reflecting unit, the time fraction for the UT, and the transmit power at the LED, subject to the communication and illumination intensity requirements. To solve this high-dimensional non-convex problem, we transform it as a constrained Markov decision process. Then, a soft actor-critic (SAC)-based deep reinforcement learning algorithm is proposed with the goal of maximizing both the average reward and the expected policy entropy. Simulation results prove the effectiveness of the proposed SAC-based joint optimization design in improving the sum-rate and long-term average reward.

In this paper, we address the problem of energy efficient resource optimization for downlink transmission in user-centric ultra-dense networks enabled by wireless access via nonorthogonal multiple access and wireless backhaul via beamforming. Our objective is to maximize the system energy efficiency by optimizing user/access point scheduling, subchannel assignment, and power allocation jointly. The problem is formulated as a nonconvex mixed-integer nonlinear programming problem which is NP-hard. We then transform it into a convex subproblem using the sum-of-ratios decoupling and the iterative successive convex approximation method. An overall algorithm is further developed to solve the subproblem iteratively. Simulation results show that the proposed algorithm has improved the system-wide energy efficiency significantly when compared to the benchmark scheme.

In this paper, we attempt to deal with the routing problem in a cognitive unmanned aerial vehicle (UAV) swarm (CU-SWARM), which applies the cognitive radio into a swarm of UAVs within a three-hierarchical aerial-ground integrated network architecture for emergency communications. In particular, the flexibly converged architecture utilizes a UAV swarm and a high-altitude platform to support aerial sensing and access, respectively, over the disaster-affected areas. We develop a Q-learning framework to achieve the intelligent routing with maximum utility for CU-SWARM. To characterize the reward function, we take into account both the routing metric design and the candidate UAV selection optimization. The routing metric is determined by maximizing the utility, which jointly captures the achievable rate of UAV pair and the residual energy of UAV. Besides, under the location, arc, and direction constraints, the circular sector is modeled by properly choosing the central angle and the acceptable signal-to-noise ratio for the UAV. With this setup, we further propose a low-complexity iterative algorithm using the dynamic learning rate to update Q-values during the training process for achieving a fast convergence speed. Extensive simulation results are provided to assess the potential of the Q-learning framework of intelligent routing as well as to verify our overall iterative algorithm via the dynamic learning rate for training procedure. Our findings reveal that the proposed algorithm can significantly increase the accumulated rewards significantly with practical complexity compared to other benchmark schemes with fixed and decaying learning rates.

In this letter, we study the pilot assignment scheme in the uplink cell-free massive multiple-input multiple-output system. To accurately depict the potential co-pilot interference (CPI) among the user terminals (UTs), we introduce the normalized contribution value of AP to the received signal strength at the UT. A weighted conflict clustering graph is constructed by using the potential CPI to define the weight of the edge that connects the co-pilot UTs. Then, the pilot assignment optimization is transformed into a Min-Cut problem, which is further converted as a more effective normalized cut to find a partition of the UTs into disjoint clusters for minimizing the overall CPI. We develop a two-stage algorithm via spectral clustering to solve the relaxed problem efficiently. Numerical results are provided to validate the superiority of the proposed algorithm as compared to the existing methods in terms of the uplink average sum-rate and the quality of channel estimation.

Integrating digital twins (DTs) and multi-access edge computing (MEC) is a promising technology that realizes edge intelligence in 6G, which has been recognized as the key enabler for Industrial Internet of Things (IIoT). In this paper, we explore a DT-assisted MEC system for the IIoT scenario where a DT server is created as a digital counterpart of the MEC server, via estimating the computation state of the MEC server within the DT modelling cycle. To achieve energy and spectrally efficient offloading, we consider that IIoT devices communicate with industrial gateways (IGWs) through a non-orthogonal multiple access (NOMA) protocol. Each IIoT device has an industrial computation task that can be executed locally or fully offloaded to IGW. We aim to minimize the total task completion delay of all IIoT devices by jointly optimizing the IGW’s subchannel assignment as well as the computation capacity allocation, edge association, and transmit power control of IIoT device. The resulting problem is shown to be a mixed integer non-convex optimization problem, which is NP-hard and challenging to solve. We decompose the original problem into four solvable sub-problems, and then propose an overall alternating optimization algorithm to solve the sub-problems iteratively until convergence. Validated via simulations, the proposed scheme shows superiority to the benchmarks in reducing the total task completion delay and increasing the percentage of offloading IIoT devices.

Combination of the industrial Internet of Things (IIoT) and federated learning (FL) is deemed as a promising solution to realizing Industry 4.0 and beyond. In this paper, we focus on a hierarchical collaborative FL architecture over the IIoT systems, where the three-layer architectural design is conceived for supporting the training process. To effectively balance among the learning speed, energy consumption, and packet error rate for edge aggregation with regard to the participating IIoT devices, a weighted learning utility function is developed from the perspective of the fusing multiple performance metrics. An optimization problem is formulated to maximize the weighted learning utility by jointly optimizing the edge association as well as the allocations of resource block (RB), computation capacity, and transmit power of each IIoT device, under the practical constraints of the FL training process. The resulting problem is a non-convex and mixed integer optimization problem, and consequently it is difficult to solve. By resorting to the block coordinate descent method, we propose an overall alternating optimization algorithm to solve this problem in an iterative way. Specifically, in each iteration, for given transmit power and computation capacity, the sub-problem of joint RB assignment and edge association is transformed to a three-uniform weighted hypergraph model, which is solved by the local search-based three-dimensional hypergraph matching algorithm. Second, given RB assignment, edge association, and computation capacity, we employ the successive convex approximation method to tackle the sub-problem for optimizing the transmit power by turning it into a convex approximation problem. After the proposed alternating optimization algorithm converges to a tolerance threshold, a locally optimal solution of the original problem can be found. Numerical results reveal that our proposed joint optimization scheme can increase the system-wide learning utility and achieve significant performance gains over the four benchmark schemes.

In this paper, a joint spectrum allocation and device association problem is investigated for a federated learning aided hierarchical Industrial Internet of Things (IIoT) system for smart factory. To achieve the optimization jointly, we design a weighted learning utility maximization problem, which is a 0-1 integer linear programming problem. To solve this problem, we convert it into a weighted 3D hypergraph model by capturing the 3D mapping relation for IIoT device, subchannel, and edge server. A local search algorithm is then presented to find a 3D hypergraph matching with maximum total weights as the suboptimal solution. Simulation results demonstrate the superior performance of the proposed algorithm compared with the greedy algorithm in the system learning utility.

Multi-access edge computing (MEC) has been recently considered in challenging environments lacking available terrestrial infrastructures by extending the computing resources to the air for further enhancing the computation capability of the new aerial user equipment (AUE). Additionally, wireless power transfer (WPT) is a promising solution to prolong the battery lifetime of energy-constrained wireless devices like AUEs. In this paper, we investigate the integration of laser-beamed WPT in the high-altitude platform (HAP) aided MEC systems for the HAP-connected AUEs. By discretizing the three-dimensional coverage space of the HAP, we present a multi-tier tile grid-based spatial structure to provide aerial locations for laser charging. With this setup, we identify a new privacy vulnerability caused by the openness during the air-to-air transmission of WPT signaling messages in the presence of a terrestrial adversary. A privacy-aware laser-powered aerial MEC framework is developed that addresses this vulnerability and enhances the location privacy of AUEs for laser WPT. Specifically, the interaction between the HAP as a defender and the adversary in their tile grid allocation as charging locations to AUEs is formulated as a Colonel Blotto game, which models the competition of the players for limited resources over multiple battlefields for a finite time horizon. Moreover, we derive the mixed-strategy Nash equilibria of the tile grid allocation game for both symmetric and asymmetric tile grids between the defender and the adversary. Simulations results show that the proposed framework significantly outperforms the design baselines with a given privacy protection level in terms of system-wide expected total utilities.

By decoupling network functions from the underlying physical machines (PMs) at the edge of the networks, the virtualized multi-access edge computing (MEC) enables deployment of new network services and elastic network scaling to reduce maintenance costs in a more flexible, scalable and cost-effective manner. Although there are appealing performance gains to be achieved, the placement of virtual machines (VMs) on top of the sharing PMs to support computation-intensive applications for the smart mobile devices becomes a major challenge, especially for an increasing network scale. In this paper, we attempt to deal with the VM placement problem in virtualized MEC system, which is targeted for finding a performance balance between energy consumption and computing/offloading delay. To capture such a tradeoff for VM placement, we formulate a weighted sum based cost minimization problem as a pure 0-1 integer linear programming problem, which is NP-complete and very complex to solve with lower complexity. Based on the one-to-one mapping relation constraint, the VM placement problem is converted into a many-to-many two-sided matching problem between the VM instances and the PMs. Motivated by the student project allocation problem, we develop an extended two-sided matching algorithm with lower computational complexity for solving the many-to-many matching problem. Simulation results are presented to demonstrate the effectiveness of our proposed matching algorithm, and the normalization factor is of great significance to obtain lower total cost.

In this paper, a laser-powered aerial mobile edge computing (MEC) architecture is proposed, where a high-altitude platform (HAP) integrated with an MEC server transfers laser energy to charge aerial user equipments (AUEs) for offloading their computation tasks to the HAP. Particularly, we identify a new privacy vulnerability caused by the transmission of wireless power transfer (WPT) signaling in the presence of a malicious smart attacker (SA). To address this vulnerability, the interaction between the HAP and the SA in their allocation of tile grids as charging points to the AUEs in laser-enabled WPT is formulated as a Colonel Blotto game (CBG), which models the competition of two players for limited resources over multiple battlefields for a finite time horizon. Moreover, the utility function that each player receives over a battlefield is developed by identifying the tradeoff between privacy protection level and energy consumption of each AUE. We further obtain the mixed-strategy Nash equilibrium for the modified CBG with asymmetric players. Simulation results are presented to show the effectiveness of this game framework.