The communication performance of traditional two-dimensional (2D) antenna arrays is approaching its theoretical limit under constraints of physical size and hardware costs, thus failing to meet the escalating demands of wireless communications. While double-layer three-dimensional (3D) antenna arrays presents a breakthrough for overcoming this bottleneck by exploiting the additional degrees of freedom, its implementation is hindered by several challenges, notably the issues of codebook design. In this paper, we propose a novel codebook scheme tailored for 3D antenna array structures. Specifically, an angle-distance-aware codebook for 3D antenna arrays is designed to cater to both near-field and far-field scenarios by minimizing inter-beam interference, with proven asymptotic orthogonality. Furthermore, evanescent codewords for both regions are effectively eliminated to improve codebook construction efficiency. Simulation results illustrate the superior performance of the proposed codebook over 2D baselines, with a 29% and 12% narrower angular and distance beamwidth ofh=λ, and a 27% gain in spectral efficiency ofh=0.5λ, owing to the vertical dimension. Moreover, practical mutual coupling that manifests as beam deviations and broadening is analyzed to establish a basis for future work.

In this paper, we introduce a novel simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) model designed for non-line-of-sight (NLoS) scenarios. To ensure energy self-sustainability, an unmanned aerial vehicle (UAV) is introduced for wireless energy transfer. In the proposed model, ground users (GUs) situated in communication-obstructed environments are supported by STAR-RIS to connect with the base station (BS). Energy harvested from the UAV is utilized to enable prolonged communication with 360° full spatial coverage. An optimization problem is formulated to maximize the system’s sum-rate and is decomposed into three subproblems: phase-shift optimization, power allocation, and time allocation. These subproblems are solved using semidefinite relaxation (SDR), Dinkelbach’s method, and game theory, respectively. A joint resource allocation algorithm based on the block coordinate descent (BCD) method is then proposed. Simulation results show that the proposed UAV-assisted STAR-RIS-NOMA scheme, combined with the BCD algorithm, achieves a 43.64% improvement in system capacity compared to existing approaches.

The paper proposes a joint optimization algorithm based on the semidefinite relaxation method and Dinkelbach (JOASDRD) algorithm for reflective intelligent surface (RIS) assisted MEC-MIMO systems. First, we define the energy consumption minimization function. Since the optimization function is an NP-hard problem, we decompose it into two subproblems: computing setting and communication setting using the block coordinate descent (BCD) iterative algorithm. The task offloading, transmit power, and phase shift matrix subproblems are solved iteratively using convex optimization, SDR, and Dinkelbach methods. The optimization simulation results show that the JOASDRD algorithm achieves lower energy consumption than existing methods.

Autonomous aerial vehicles (AAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications. However, the Line of Sight (LoS) dominance in air-to-ground (A2G) communications often leads to significant interference with terrestrial networks, reducing communication efficiency among terrestrial terminals. This article explores a novel uplink interference mitigation approach based on the collaborative beamforming (CB) method in multi-AAV network systems. Specifically, the AAV swarm forms an AAV-enabled virtual antenna array (VAA) to achieve the transmissions of gathered data to multiple base stations (BSs) for data backup and distributed processing. However, there is a tradeoff tradeoff between the effectiveness of CB-based interference mitigation and the energy conservation of AAVs. Thus, by optimizing the excitation current weights and hover position of AAVs as well as the sequence of data transmission to various BSs, we formulate an uplink interference mitigation multiobjective optimization problem (MOOP) to decrease interference affection, enhance transmission efficiency, and improve energy efficiency, simultaneously. In response to the computational demands of the formulated problem, we introduce an evolutionary computation method, namely chaotic nondominated sorting genetic algorithm II (CNSGA-II) with multiple improved operators. The proposed CNSGA-II efficiently addresses the formulated MOOP, outperforming several other comparative algorithms, as evidenced by the outcomes of the simulations. Moreover, the proposed CB-based uplink interference mitigation approach can significantly reduce the interference caused by AAVs to nonreceiving BSs.

In this paper, we propose a novel reconfigurable intelligent surface (RIS) -assisted non-orthogonal multiple access (NOMA) model, where an unmanned aerial vehicle (UAV) is employed for energy transfer. In this framework, the ground users (GUs) utilize energy from the UAV for information transmission to enable long-duration communication self-sufficiency. A sum-rate maximization problem is proposed, which is decomposed into four sub-problems: phase-shift optimization, power allocation, time allocation, and UAV trajectory optimization. These are solved using the SDR algorithm, CVX toolbox, game theory, and PSO algorithm, respectively. Subsequently, a joint resource allocation algorithm based on the block coordinate descent (BCD) method is introduced. Simulation results show that the UAV-assisted RIS-NOMA scheme, along with the proposed BCD algorithm, can increase the system capacity by 48.6% compared to the TDMA scheme as well as the alternating direction multiplier method (ADMM) and whale optimization algorithm (WOA).

In this paper, a novel device-to-device (D2D)-assisted cooperative non-orthogonal multiple access (NOMA) model with a two-stage transmission scenario is proposed, which consists of 1) partial decoding and forwarding from the transmitter to relay nodes; 2) transmission from relay nodes to the receivers. A sum-rate maximization problem is formulated, which is decoupled into subchannel selection and two-stage channel link power allocation. A joint optimization algorithm based on game theory and successive convex approximation (JOAGS) is proposed, which can efficiently utilize network resources and increase spectrum efficiency. The algorithm proposed in this paper has been validated through simulation results, demonstrating its substantial capability to amplify system capacity, diminish the outage probability of the communication link, and extend the communication distance. The findings reveal that when compared to the existing scheme, the system’s sum-rate is augmented by 10.8%, and the outage probability registers a notable reduction of 23.6%.

In the paper, we propose a joint optimization algorithm based on the block coordinate descent (JOABCD) algorithm for reflective intelligent surface (RIS) assisted MEC-MIMO systems. First, we define the delay minimization function for both single user with multi-antenna and multiple users with single-antenna scenarios. Since the optimization function is an NP-hard problem, we decompose it into two subproblems: computing setting and communication setting using the block coordinate descent (BCD) iterative algorithm. The subproblem of resource allocation is solved using a bisection method, while the subproblem of transmit power and phase shift matrix is solved alternately. The optimal simulation results show that the JOABCD algorithm can realize a lower time latency and a higher sum achievable rate compared with the existing methods.

An intelligent reflecting surface (IRS)-enabled wireless power transfer (WPT) and data collection scheme for unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) network is investigated in this paper. Specifically, IoT devices (IoTDs) first harvest energy from the UAV and then upload the sensed data by applying time-division multiple access (TDMA), where an IRS is deployed to improve the transmission quality. We aim to minimize the age of information (AoI) and energy consumption of the UAV. For achieving this, we formulate an optimization problem by jointly optimizing the UAV trajectory, IRS phase shits, charging time allocation, and binary IoTD scheduling, which is a mixed-integer non-convex optimization problem. To address the issue, we first formulate our problem into a Markov decision process (MDP) and then propose an alternating optimization-double parameterized deep Q-network (AO-DPDQN) approach to solve the optimization problem. Specifically, an AO-based method is adopted to optimize the phase shifts of IRS to simplify the action space of MDP, and then double parameterized deep Q-network (DPDQN) is employed to optimize UAV trajectory, charging time allocation, and IoTD scheduling. Simulation results demonstrate the effectiveness and superiority of the proposed approach compared to various baselines.

In the sixth generation (6G) era, unmanned aerial vehicle (UAV) and satellite communications offer promising prospects in terms of the increasing demand for network coverage by the explosive growth of Internet of Things (IoT) devices. However, the lack of spectrum resources has become the bottleneck affecting network service performance. In this paper, we seek to use cognitive radio (CR) technology to assist the satellite-UAV networks. Specifically, we consider an integrated satellite-aerial network (SAN) and UAV-enabled MEC system in which CR is applied for the allocation and management of spectrum resources. Then, we formulate an optimization problem to maximize task execution and data transmission in the SAN system and minimize the energy consumption of UAVs by jointly optimizing the UAV trajectory and the strategy of task offloading. The problem is non-convex with high dynamic and hybrid action space, and thus we propose a matching-aided multi-agent deep reinforcement learning (MADRL)-based algorithm to solve the problem. Simulation results show that the proposed algorithm can improve the efficiency of task collection and reduce energy consumption while ensuring the anti-jamming ability.

The collaborative communication of reuse mode and NOMA mode acts on the improvement of spectrum efficiency with a corresponding more complex interference. In this paper we proposed a game theory based approach to overcome the critical issue. An optimization function of maximizing system capacity is defined, which is an NP-hard problem. Therefore, we transform the problem into two sub-problems: D2D user mode selection and power allocation. The user communication mode selection is constructed as a potential game process. And the successive convex approximation is used to solve the user power allocation problem. The simulation results indicate that the proposed algorithm can improve system capacity by approximately 19% compared to other schemes.

Unmanned aerial vehicles (UAVs) deployed as an aerial assisted base station has the characteristics of flexibility and mobility. As an effective way to reduce the communication pressure of network center, content edge caching combined with UAV-assisted network is a promising solution to release the surge of network data traffic pressure. This paper studies the probabilistic caching strategy in UAV-assisted IoT system which supports device-to-device (D2D) communication and edge caching. Firstly, a three-tier heterogeneous model including user devices (UDs), ground small base stations (SBSs) and UAV is proposed. Considering the random characteristics of user movement and the interference characteristics between different nodes, the cache hit probability and successful transmission probability under different content transmission modes are derived by using stochastic geometry. On this basis, the total offloading probability is derived. The joint caching strategy of UD, SBS and UAV is solved with the goal of maximizing cache hit probability and successful offloading probability, respectively. For the mobile deployment of UAV, considering the limited computing resources and battery endurance of UAV, to enable the UAV to provide services to requesting UDs as soon as possible, this paper first uses tiny machine learning (TinyML) to predict the requesting probability of UDs, and then designs a UAV path planning algorithm to cover all users with high requesting probability in the shortest time. Through simulation analysis, we compared the performance of the two proposed caching strategies and found that the strategy of maximizing successful offloading probability has more advantages.

With the development of the Internet of Things (IoT) and 5G/6G technologies, there has been significant interest in the applications of the Internet of Vehicles (IoV) and multiaccess edge computing (MEC) in intelligent transportation systems. The significant increase in the number of vehicles currently accessing the Internet has highlighted the inability of some existing resource-constrained vehicles to adequately meet the demands of computationally intensive and latency-sensitive applications. There is a significant challenge in designing efficient task offloading strategies to enhance the utilization of computational resources and deliver high-quality services to vehicle users. In this article, we propose a four-tier computing architecture with local computing, vehicle-to-vehicle (V2V) computing, MEC computing, and mobile cloud computing (MCC), which can provide heterogeneous computing resources for multiple task vehicles and flexible offloading options of different types of vehicle tasks. We optimize the offloading decision and resource allocation with the objective function of minimizing the system cost. The nonconvex objective function and constraints both contain binary variables, which leads to NP-hard property. To solve this critical problem, we propose an alternating direction method of multipliers (ADMM)-based multivehicle task offloading scheme for IoV-MEC (AMTOS), to transform the nonconvex problem into a convex one by relaxing the binary variables, and provide an approximate optimal solution. Afterward, a binary variable recovery algorithm is used to recover the binary variables. Simulation results show that the algorithm can significantly reduce the system cost, compared with existing literature.

Indoor positioning technology based on the Wireless Local Area Network (WLAN) fingerprinting method is becoming a promising choice as for ubiquitous WLAN infrastructure. The technology mainly compares the received signal strength (RSS) of a mobile device with an RSS fingerprint in the fingerprint database, and uses the matching rule to find the closest match as the estimated position of the device. The quality of the fingerprint database construction can directly affect the positioning results. This work proposes a three-stage fingerprint database processing method. In the first stage, the original fingerprint database is divided into several small sub-fingerprint databases according to the specified rules. In the second stage, every sub-fingerprint database is processed using the principal component analysis method to achieve a reduced dimension fingerprint dataset. In the third stage, the k-d tree method is used to process each dimension-reduced sub-fingerprint database for obtaining a hierarchical sub-fingerprint database. In addition, in the online phase, the best bin first (BBF) method is applied to the search engine of sub-fingerprint database to complete the location determination of the device. This method can improve positioning performance through simulation research.

In WLAN fingerprint-based indoor localization, signal noise in the measurement of received signal strength indicator (RSSI) often results in matching a set of disperse reference points (RPs), leading to unsatisfactory estimation and weak robustness. To mitigate the noise problem, we propose a novel indoor positioning strategy, torus intersection localization (TILoc), aiming to improve the robustness and accuracy of fingerprint-based indoor localization. In the online phase, we design a new type of online RSSI fingerprints by filtering out unstable access points (APs). We use part of robust APs to construct RP torus and take the RPs in the intersection of RP tori as the nearest RPs. For reducing sparse spikes noise, we apply robust principal component analysis (RPCA) to train offline and online fingerprints. In addition, we take the AP's effect into consideration when we position a target. Our simulation and experiments show that the proposed algorithm outperforms other recent state-of-the-art algorithms in robustness and accuracy.

The rate-requirement of device-to-device (D2D) users is associated with the context information of velocity and data size of users to some extent. In this study, an efficient context-aware resource allocation scheme based on rate requirement (RARR) is proposed. This scheme consists of two allocation phases. In the rate-ensuring resource allocation phase, D2D pairs are allocated a certain amount of spectrum resource according to their rate requirement. In the allocation, the interference restricted area is limited to exclude cellular users that bring a negative capacity gain to the communication system. In the residual resource reallocation phase, surplus resources are assigned to D2D pairs according to the system fairness. Simulation results indicate that the proposed RARR scheme efficiently leads to superior performance in terms of system throughput and fairness and exhibits low complexity relative to traditional resource allocation.

In heterogeneous wireless sensor networks, the data collection method based on compressed sensing technology is susceptible to packet loss and noise, which leads to a decrease in data reconstruction accuracy in unreliable links. Combining compressed sensing and matrix completion, we propose a clustering optimization algorithm based on structured noise matrix completion, in which the cluster head transmits the compressed sampling data and compression strategy to the base station. The algorithm we proposed can reduce the energy consumption of the node in the process of data collection, redundant data and transmission delay. The rank-1 matrix completion algorithm constructs an extremely sparse observation matrix, which is adopted by the sink node to complete the reconstruction of the whole network data. Simulation experiments show that the proposed algorithm reduces network transmission data, balances node energy consumption, improves data transmission efficiency and reconstruction accuracy, and extends the network life cycle.