Semantic communication has shown exceptional performance in various tasks, such as image classification, owing to the advancements in deep learning technologies. However, due to the openness of wireless channels and the vulnerability of neural networks, semantic communication faces significant challenges from semantic impairment in the physical channel. In this paper, semantic impairment refers to the minor perturbations that cause discrepancies between the received features and the expected ones, which can lead to errors in image classification. We design four constraints from the perspectives of semantic level, concealment level and efficiency level to simulate potential malicious semantic impairment. These constraints are employed to generate adversarial perturbations specifically targeting semantic communication systems, ensuring that the perturbations can more effectively disrupt the normal function of the systems. Moreover, we innovatively propose knowledge graph enhanced anti-impairment cognitive semantic communication, which combines knowledge graph and adversarial training to boost robustness against semantic impairment. Specifically, we leverage the shared knowledge graph to transmit triplet information from the transmitter to the receiver in the form of indices and introduce the triplet information as additional information into the decoder to facilitate the decoding process. Simulation results show that our proposed knowledge graph enhanced cognitive semantic communication system achieves higher classification accuracy and robustness in environments with low signal-to-noise ratio and semantic impairment, compared to existing Better Portable Graphics (BPG) and Joint Source-Channel Coding(JSCC) schemes.

The collaboration between aerial and ground computing is effective for supporting computation-intensive and latency-critical applications, especially in the infrastructure-less scenarios. However, the main challenge lies in task allocation, as well as optimizing the trajectories to complete tasks fast. This paper investigates a collaborative computing network where heterogeneous unmanned aerial vehicles (UAVs) and a ground base station work together to provide computation services for smart mobile devices on the ground. An optimization framework to minimize the system processing delay is established by jointly designing computing UAV deployment, computation task allocation, and relay UAVs’ trajectories. However, the formulated problem is a non-convex mixed integer non-linear programming problem. We reformulate it into a tractable one and further divide it into three sub-problems, i.e., computing UAV deployment, computation task allocation, and UAVs’ trajectories scheduling. We further propose a K-Medoids-based alternate iterative algorithm, which uses the K-Medoids clustering algorithm to determine UAV deployment and optimize task allocation and relaying UAV trajectories iteratively with successive convex approximation (SCA). Simulation results show that the proposed approach reduces system processing delay compared to other benchmarks.

Thanks to the customization of channel propagation, reconfigurable intelligent surface (RIS)-aided cell-free (CF) massive multiple-input-multiple-output (MIMO) is recognized as a competitive candidate technique for the future communication system. However, only the limited performance gain can be afforded by passive RIS due to the double-fading effect in RIS-aided links. In this article, we consider the CF massive MIMO system with the assistance of the active RIS, which is capable of reflecting and amplifying the incident signal, to enable the Internet of Things network. We analyze the tradeoff between the number of active RIS reflecting elements (REs) and the amplification coefficient. Considering the power constraint at the active RIS, we formulate a sum-rate maximization problem to jointly optimize the user transmission power, the receive beamforming, and the RIS reflecting precoding. Since the original problem is nonconvex, we decouple it into three subproblems and then design an alternating optimization algorithm to solve them iteratively. Using the Lagrangian dual reformulation and generalized Rayleigh quotient theory, we derive the closed-form solutions for both the user transmission power and the uplink receive beamforming. We also develop a low-complexity method to acquire the RIS reflecting precoding based on the primal-dual subgradient theory. Compared to the passive RIS, the active RIS can significantly improve the system performance with fewer REs. Moreover, the proposed optimization scheme effectively mitigates the drawbacks of the active RIS under the high-transmission power regime and enhance its benefits. Finally, the proposed alternating optimization algorithm is validated by numerical results.

In the time-division-duplexing (TDD)-based cell-free (CF) massive multiple-input multiple-output (MIMO) system, the channel reciprocity needs to be recovered via a reciprocity calibration operation due to the random circuit impact on the transceiver radio frequency. In this paper, we study the effect of the calibration error on the TDD CF massive MIMO system under non-ideal calibrations. Assuming the spatially correlated Ricean fading channel, we derive the closed-form expression of the downlink achievable rate, which takes both the channel estimation error and the calibration error into account. Some novel insights of the calibration error in the CF massive MIMO system are gathered from the analytical results. It is shown that the downlink achievable rate is more sensitive to the calibration error at the user side. In order to provide a uniformly good service for each user, we employ the geometric programming (GP) to solve the max-min power optimization problem to maximize the minimum user rate. Additionally, we utilize the scaled alternating direction method of multipliers to develop a calibration error-aware beamforming scheme to mitigate the impact of calibration errors, improving the downlink sum-rate. Numerical results demonstrate that the proposed GP-based algorithm significantly improves the 95%-likely per-user downlink achievable rate with a fast convergence behavior. Moreover, the proposed calibration error-aware beamforming scheme enhances the downlink sum-rate and outperforms other benchmark schemes.

Semantic communication is envisioned as a potential communication paradigm enabled by artificial intelligence and is promising to break the Shannon limit for future 6G networks. This paradigm benefits uninhabited aerial vehicles (UAVs) to conserve communication resources and minimize latency by only transmitting task-relevant semantic information. However, resource allocation in the multiple collaborative UAV scenarios remains unexplored, particularly regarding multi-modal semantic communication. To tackle this challenge, this paper investigates a semantic-aware intelligent resource allocation method for multi-UAV-assisted semantic communication networks in the UAV image-sensing task-oriented scenario. A multi-modal semantic communication framework with multi-UAV relay collaboration is developed. At the semantic level, a novel quality of experience (QoE) and the transmission cost model are introduced, based on which a semantic-aware resource allocation problem is formulated, aiming to maximize QoE while minimizing the transmission cost by jointly optimizing the UAV trajectory, the spectrum bandwidth, the transmit power and the number of the transmitted semantic symbols. To deal with optimization challenges involving hybrid variables and coordination among UAVs, a multi-UAV hybrid decision-controlled deep reinforcement learning (DRL) scheme is proposed. Simulation results demonstrate the effectiveness of the proposed scheme compared with the benchmark schemes in achieving a good balance between the QoE and the transmission cost.

In this paper, we consider the cell-free massive multiple-input multiple-output (MIMO) system for supporting ultra-reliable and low-latency communication (URLLC) transmission, where a large number of access points (APs) serve a small number of users in the short packet regime. Assuming channel aging and channel spatial correlation, we derive the closed-form expression of the downlink achievable rate with the normalized conjugate beamforming (NCB). Under the goal of maximizing the minimum user rate, we formulate a max-min power optimization problem with a power constraint at each AP. However, it is challenging to solve this problem because the objective function is a complicated function of power coefficients. To tackle this difficulty, we use a path-following method to approximate the objective function to a logarithmic function and transform the polynomial constraint into a monomial. Thus, we can iteratively solve the original problem by reformulating it as a series of geometric programming problems. Numerical results verify the tightness of the closed-form expression for the downlink achievable rate in the short packet regime. Both channel aging and channel spatial correlation significantly degrade the system performance of CF massive MIMO URLLC systems. Moreover, Using NCB and the proposed max-min power allocation can effectively alleviate this impairment and improve the system performance.

In the context of Internet of Things, this letter considers a cell-free (CF) massive multiple-input–multiple-output (MIMO) system for ultrareliability and low-latency communication (URLLC) assisted by multiple reconfigurable intelligent surfaces (RISs). We derive the closed-form expression of the downlink achievable rate under multiple correlated RISs and pilot contamination. To mitigate the impact of pilot contamination and improve the fairness among users, we minimize the maximum normalized mean-squared error (NMSE) of the channel estimation by jointly optimizing the pilot power coefficient and the RIS phase shifts. Due to the nonconvexity of the original problem, we design an alternating optimization algorithm to solve the substitutable two subproblems using fractional programming and sequential convex approximation. Numerical results validate the proposed algorithm in terms of decreasing the maximum user NMSE and converging. Moreover, the 95%-likely per-user downlink achievable rate is also improved.

Unmanned aerial vehicle (UAV) swarms have found widespread applications in executing high-complexity and remote-risk missions. However, the limited onboard resources and energy of UAV swarms may hinder their support for computation-intensive yet delay-sensitive applications, especially when faced with exponentially growing big data. This article focuses on investigating a UAV swarm-assisted aerial–ground collaborative computing system, where one UAV swarm is divided into different groups and collaborates with a remote ground base station (BS) to provide computation services for ground smart mobile devices (SMDs). A comprehensive optimization framework is presented to maximize the system’s computation efficiency by jointly designing group formation, UAV trajectories, and resource allocation. The formulated problem involves a fractional structure with nonlinear coupling of different variables, rendering it highly nonconvex. To address this challenge, we propose a Dinkelbach-based looped iterative optimization (DLIO) algorithm. Specifically, Dinkelbach’s method is initially adopted to reformulate the original problem into a parametric structure, which is then decomposed into subproblems for group formation, resource allocation, and UAVs’ trajectory scheduling. Subsequently, these subproblems are addressed by a looped iterative optimization (LIO) algorithm. The outer loop determines group forming and resource allocation, while the inner loop utilizes the method of successive convex approximation (SCA) to solve trajectory scheduling for UAVs. Simulation results validate the effectiveness of our proposed DLIO algorithm, ensuring rapid convergence and significant improvements in the system’s computation efficiency compared to other benchmarks.

Semantic communication is envisioned as a potential communication paradigm based on artificial intelligence that holds the promise of breaking the Shannon limit for future 6G networks. This paradigm offers a promising opportunity for Unmanned Aerial Vehicles (UAVs) to conserve communication resources and minimize latency by only transmitting relevant semantic information. Despite the promising potential of UAV semantic communication networks, resource allocation in this context remains largely unexplored, particularly regarding multi-modal communication that adjusts the types of transmitted information (image, text, video, etc.) according to the task objectives and the available resources. This paper addresses the semantic-oriented resource allocation for multi-modal semantic communication with a focus on the UAV image-sensing task-oriented scenario. Firstly, a multi-modal semantic communication for the original image-sensing tasks of UAVs is designed. Subsequently, a semantic-level resource allocation problem based on the approximate semantic entropy and the semantic rate is formulated in terms of the transmit power allocation, channel assignment, and the number of transmitted semantic symbols. To solve the problem formulated, which involves a hybrid discrete-continuous action space, a novel algorithm called Hybrid-Decision-Controlled Deep Reinforcement Learning-based Semantic Communication Allocation (HDCD-SC) is introduced. The simulation results demonstrate that the proposed HDCD-SC algorithm can dynamically adjust the transmission modal according to the available resources, and achieve better performance in terms of latency, amount of semantic information, and notable reductions in energy and bandwidth costs when compared to other benchmarks.

In this paper, we study the resource allocation problem for an intelligent reflecting surface (IRS)-assisted OFDM system. The system sum rate maximization framework is formulated by jointly optimizing subcarrier allocation, base station transmit beamforming and IRS phase shift. Considering the continuous and discrete hybrid action space characteristics of the optimization variables, we propose an efficient resource allocation algorithm combining multiple deep Q networks (MDQN) and deep deterministic policy-gradient (DDPG) to deal with this issue. In our algorithm, MDQN are employed to solve the problem of large discrete action space, while DDPG is introduced to tackle the continuous action allocation. Compared with the traditional approaches, our proposed MDQN-DDPG based algorithm has the advantage of continuous behavior improvement through learning from the environment. Simulation results demonstrate superior performance of our design in terms of system sum rate compared with the benchmark schemes.

Abstract Here, we investigate the precoding schemes for the downlink data transmission of a time‐division duplex cell‐free massive multiple‐input multiple‐output (MIMO) system with channel aging, which arises from the user mobility. Closed‐form spectral efficiency (SE) expressions of the downlink with the normalized conjugate beamforming (NCB), and the full‐pilot zero‐forcing (FZF), are derived, which are used for the analytical system performance evaluation. Then, a novel combinatorial precoding scheme with enhanced system SE performance, which adopts either NCB or FZF according to each user channel aging condition, is proposed. Moreover, a pilot allocation strategy is proposed to alleviate the extra interference brought by the combinatorial precoding scheme. Also, a statistical channel cooperative power control is employed to further improve the performance for all the above precoding schemes. Numerical results show that the proposed precoding scheme can substantially improve the average downlink SE.

Mobile-edge computing (MEC) has recently emerged as a promising technology in the 5G era. It is deemed an effective paradigm to support computation intensive and delay-critical applications even at energy-constrained and computation-limited Internet of Things (IoT) devices. To effectively exploit the performance benefits enabled by MEC, it is imperative to jointly allocate radio and computational resources by considering nonstationary computation demands, user mobility, and wireless fading channels. This article aims to study the tradeoff between energy efficiency (EE) and service delay for multiuser multiserver MEC-enabled IoT systems when provisioning offloading services in a user mobility scenario. Particularly, we formulate a stochastic optimization problem with the objective of minimizing the long-term average network EE with the constraints of the task queue stability, peak transmit power, maximum CPU-cycle frequency, and maximum user number. To tackle the problem, we propose an online offloading and resource allocation algorithm by transforming the original problem into several individual subproblems in each time slot based on the Lyapunov optimization theory, which are then solved by convex decomposition and submodular methods. Theoretical analysis proves that the proposed algorithm can achieve a$[O(1/V), O(V)]$tradeoff between EE and service delay. Simulation results verify the theoretical analysis and demonstrate our proposed algorithm can offer much better EE-delay performance in task offloading challenges, compared to several baselines.

Mobile edge computing (MEC) has recently emerged as an enabling technology to support computation-intensive and delay-critical applications for energy-constrained and computation-limited Internet of Things (IoT). Due to the time-varying channels and dynamic task patterns, there exist many challenges to make efficient and effective computation offloading decisions, especially in the multi-server multi-user IoT networks, where the decisions involve both continuous and discrete actions. In this paper, we investigate computation task offloading in a dynamic environment and formulate a task offloading problem to minimize the average long-term service cost in terms of power consumption and buffering delay. To enhance the estimation of the long-term cost, we propose a deep reinforcement learning based algorithm, where deep deterministic policy gradient (DDPG) and dueling double deep Q networks (D3QN) are invoked to tackle continuous and discrete action domains, respectively. Simulation results validate that the proposed DDPG-D3QN algorithm exhibits better stability and faster convergence than the existing methods, and the average system service cost is decreased obviously.

Cognitive radio (CR) and energy harvesting (EH) have been deemed two promising technologies in the spectrum-scarce and energy-limited wireless networks. In this paper, the cooperative cognitive radio network (CRN) with EH is considered, where a secondary user (SU) close to the secondary base station (SBS) employs power splitting for EH and assists to relay the data for another SU far away from the SBS. A SU sum-rate maximization problem is formulated under the constraints of the power budget at the SBS, the interference threshold of the primary network, and SU QoS. To tackle this problem, a resource allocation algorithm based on an improved deep neural network (DNN) is proposed. In order to accelerate the convergence of the DNN loss function, transfer learning is exploited to initialize the DNN weights. The loss between the DNN output and the optimal transmit power obtained by the conventional solution is stored in the memory pool, where the samples with large losses are used to train the DNN. Simulation results show the efficiency of our proposed DNN-based resource allocation scheme, which outperforms the normal DNN-based resource allocation and conventional resource allocation scheme in terms of the computation time.

Mobile-edge computing (MEC)-enabled Internet of Things (IoT) networks have been deemed a promising paradigm to support massive energy-constrained and computation-limited IoT devices. Energy harvesting (EH) further enhances the operating capabilities of IoT devices that normally only possess very limited energy support. Nevertheless, many studies show that IoT devices using EH can experience uncertainty and unpredictability, which can complicate the EH-based IoT network design. Furthermore, with many new services in 5G and the forthcoming 6G eras, such as autonomous driving and vehicular communications, mobility consideration in IoT networks becomes more and more important. In this article, we study the computing offloading and resource allocation problems in an IoT network that supports both mobility and EH. The long-term average sum service cost of all the mobile IoT devices (MIDs) is minimized by optimizing the harvested energy, task-partition factors, the central process unit frequencies, the transmit power, and the association vector of MIDs. An online mobility-aware offloading and resource allocation (OMORA) algorithm is proposed based on the Lyapunov optimization and semidefinite programming (SDP). This online algorithm optimizes the offloading scheme without the need to have prior knowledge of the user mobility, EH model, and channel condition. Theoretical analysis shows that the proposed OMORA algorithm can achieve asymptotic optimality. Simulation results demonstrate that the proposed algorithm can effectively balance the system service cost and energy queue length, and outperform other offloading benchmark algorithms on the system service cost and packet losses.

Energy efficiency and security are two critical issues for mobile edge computing (MEC) networks. With stochastic task arrivals, time-varying dynamic environment, and passive existing attackers, it is very challenging to offload computation tasks securely and efficiently. In this paper, we study the task offloading and resource allocation problem in a non-orthogonal multiple access (NOMA) assisted MEC network with security and energy efficiency considerations. To tackle the problem, a dynamic secure task offloading and resource allocation algorithm is proposed based on Lyapunov optimization theory. A stochastic non-convex problem is formulated to jointly optimize the local-CPU frequency and transmit power, aiming at maximizing the network energy efficiency, which is defined as the ratio of the long-term average secure rate to the long-term average power consumption of all users. The formulated problem is decomposed into the deterministic sub-problems in each time slot. The optimal local CPU-cycle and the transmit power of each user can be given in the closed-from. Simulation results evaluate the impacts of different parameters on the efficiency metrics and demonstrate that the proposed method can achieve better performance compared with other benchmark methods in terms of energy efficiency.

Mobile edge computing (MEC)-enabled Internet of Things (IoT) networks have been deemed a promising paradigm to support massive energy-constrained and computation-limited IoT devices. IoT with mobility has found tremendous new services in the 5G era and the forthcoming 6G eras such as autonomous driving and vehicular communications. However, mobility of IoT devices has not been studied in the sufficient level in the existing works. In this paper, the offloading decision and resource allocation problem is studied with mobility consideration. The long-term average sum service cost of all the mobile IoT devices (MIDs) is minimized by jointly optimizing the CPU-cycle frequencies, the transmit power, and the user association vector of MIDs. An online mobility-aware offloading and resource allocation (OMORA) algorithm is proposed based on Lyapunov optimization and Semi-Definite Programming (SDP). Simulation results demonstrate that our proposed scheme can balance the system service cost and the delay performance, and outperforms other offloading benchmark methods in terms of the system service cost.

In this paper, we study the hybrid beamforming for the wideband mmWave MIMO-OFDM system with observation of beam squint. Firstly, we present the beam squint effect in the wideband mmWave system. We further characterize the mmWave wideband channel from Saleh-Valenzuela model. Secondly, in the full-connected hybrid architecture, we seek for the optimal hybrid precoder in the mmWave MIMO-OFDM system, which aims to maximize the spectral efficiency. The precoder design problem is formulated as the matrix factorization in this paper. To find the optimal precoder, wideband hybrid precoding (WHP) algorithm is proposed by using the manifold optimization. Finally, simulation results show the proposed algorithm could approximate to the optimal digital precoder in term of spectral efficiency for the wideband mmWave MIMO-OFDM system.