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.

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.

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.

Noise suppression is a crucial step before seismic data analysis. The noise in desert areas has the characteristics of nonstationary, non-Gaussian, and low-frequency, which makes some traditional methods cannot suppress the noise well. The weighted nuclear norm minimization (WNNM), one of the most effective methods to suppress noise in all low-rank matrix approximation methods, assigns different weights to different singular values. The good denoising performance of the WNNM is based on the accuracy of block matching, and the theoretical basis of block matching is the nonlocal self-similarity of clean signals. However, the noise will destroy the nonlocal self-similarity and greatly affect the accuracy of block matching. In this article, to reduce the influence of noise on block matching, we use a bandpass filter to process each noisy block before block-matching and use the similarity between the filtered blocks to represent the similarity between the original noisy blocks, then stack the most similar noisy blocks to construct a matrix for further denoising. Experimental results demonstrate the efficiency of the proposed method for low-frequency noise suppression.

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.

This paper presents a novel chain structure Bluetooth scatternet topology formation algorithm: all Bluetooth nodes enter into INQUIRY or INQUIRY states with identical probability, 0.5, then set up point-to-point connections simultaneously. As a result, as large as possible numbers of temporary piconets are made up, and furthermore, some larger piconets and groups composed of several piconets are made up through varieties of merging and recomposing repeatedly. Finally, a chain structure scatternet bearing only one group are formed by operating the algorithm after several times. Simulations and performance analysis indicate that the scatternet formed by this algorithm has excellent characteristics: comparably less number of piconets, less average number of every node's roles and less number of maximum degree of nodes, shorter networking establishment time, easier to maintain topology dynamically and dispensable for all nodes to be in communication range, etc