This paper addresses the challenges of image transmission in satellite communication networks, where bandwidth constraints, high interference, and latency issues significantly limit conventional transmission methods. We propose a novel semantic communication framework that adapts to various computational capabilities of receiving terminals in Internet of Everything (IoE). Our approach leverages the Swin Transformer V2 architecture to extract and transmit task-relevant semantic features rather than raw image data, significantly reducing bandwidth requirements while maintaining high reconstruction quality. The proposed system dynamically adjusts its encoding and decoding processes based on receiver computational capacities, enabling efficient image transmission to heterogeneous terminals ranging from high-performance stations to resource-constrained devices. Extensive experiments on various datasets demonstrate that our framework outperforms conventional JPEG+LDPC schemes and state-of-the-art deep learning-based approaches in terms of both PSNR performance and semantic communication utility across various signal-to-noise ratios. The framework shows particular robustness in low-SNR and low-CBR environments, addressing the “efficiency-compatibility” dilemma in resource-constrained satellite communications.

Nowadays, Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) has been recognized as a promising technique for flexibly handling computation tasks in 5G advanced and 6G networks. This paper investigates the joint optimization of service caching and computation offloading within a dual time-scale framework. We maximize the caching utility and minimize the task processing delay by jointly optimizing service caching policies, UAV flight trajectory, and computation offloading decisions. Specifically, for the long-term problem, we use the Latent Dirichlet Allocation (LDA) model to predict user preferences, and propose a Lagrangian dual decomposition based algorithm. For the short-term problem, a self-attention based Multi-Agent Proximal Policy Optimization (MAPPO) algorithm is designed. Under the Centralized Training with Decentralized Execution (CTDE) framework, this algorithm integrates a multi-head self-attention mechanism with curriculum learning. Each UAV is regarded as an agent, and a self-attention encoder is integrated at the front-end of each Actor network. This enables the agent to dynamically capture the relative importance between itself and all users, and context-aware features are extracted to make more intelligent and trajectory designs. Through extensive simulation experiments, the long-term algorithm yields the performance improvements of 76.5% in cache hit rate and 66% in caching utility, as compared to the second best baseline. In dynamic scenarios, the short-term algorithm achieves a 16% reduction in total processing latency with respect to the proximal policy optimization policy.

Orthogonal Time Frequency Space (OTFS) enables reliable communication in high-speed mobility scenarios, making it ideal for Low Earth Orbit (LEO) satellite communication. Furthermore sparse Code Multiple Access (SCMA) supports massive connection in uplink mobile communications. This paper proposes an OTFS-SCMA scheme for LEO satellite communications and the corresponding cross domain detection algorithm. At the transmitter, users are grouped, and a practical codebook is employed. At the receiver, cross-domain detection is utilized to obtain initial estimates, which are then refined using the Message Passing Algorithm (MPA) for optimized detection. Comparative analysis with other baseline schemes demonstrates the performance gain.

In this paper, we propose a secure content caching and delivery strategy to support multi-level viewing services based on scalable video coding (SVC). With the existence of multiple eavesdroppers (Eves), the base layer (BL) and enhancement layers (ELs), that are encoded by SVC are cached in the macro-cell base station (MBS) and small-cell base stations (SBSs), respectively, using a probabilistic caching strategy. Power domain non-orthogonal transmission is utilized to deliver ELs for the serving SBS. We analyze the transmission secrecy performance for the end user. Specifically, employing the tool of stochastic geometry, the successful transmission probabilities (STPs) from the serving MBS and SBS are derived, and the worst-case secrecy outage probability (SOP) is also analyzed. After obtaining the expressions for STPs and SOP, a more comprehensive performance metric to evaluate the system security and caching efficiency, called the secrecy cache-aided data rate (SCADR), is analyzed and derived. Simulation results validate the accuracy of our derived STPs and SOP, and show how the SCADR varies with important system parameters under different caching schemes.

Reconfigurable Intelligent Surface (RIS) has emerged as a pivotal technology in the development of sixth-generation (6G) mobile communication systems. In this paper, a wireless communication system assisted by RIS in the air is studied, in which RIS is installed on the UAV as a mobile relay (RIS-UAV) to re-establish the line-of-sight (LoS) link between the base station and users. Considering the active beamforming vector of base station, UAV trajectory and phase shift of RIS, a joint optimization scheme is proposed to maximize the energy efficiency of the system. Aiming at this optimization problem, a dual DDQN structure algorithm (PER-TDDQN) based on priority experience replay mechanism is proposed to solve this multi-objective joint optimization problem. The simulation results show that the proposed algorithm can effectively improve the energy efficiency of the system.

To address the high dynamics and diverse user preferences in edge caching systems, this paper proposes a personalized recommendation and cache optimization strategy based on location-aware federated graph learning. Specifically, a Bi-directional Long Short-Term Memory(Bi-LSTM) model is first employed to predict the real time user locations. Subsequently, federated graph learning is utilized to locally construct and train the user preference model, effectively capturing the diversity of individual preferences. By integrating location predictions with user preference models, personalized recommendation lists for pre-cached contents are generated, improving recommendation accuracy while preserving user privacy. To optimize both the recommendation and caching strategies, a one-to-one matching algorithm and a greedy algorithm are applied in an alternating optimization procedure. Simulation results demonstrate that the proposed approach significantly improves the recommendation accuracy, reduces the average content transmission latency, and enhances the overall caching performance.

Space-air-ground integrated networks (SAGIN) are a key technology in 6G, enabling seamless global connectivity through a unified communication framework, while integrated sensing and communication (ISAC) mitigates spectrum congestion by reusing spectrum and transceivers for dual communication and sensing functions. Despite its potential, existing ISAC research has largely focused on terrestrial networks, with limited exploration in SAGIN environments. This paper proposes a novel ISAC framework for SAGIN, incorporating orthogonal time frequency space (OTFS) modulation and non-orthogonal multiple access. OTFS enhances the system's robustness in doubly-dispersive channels and supports precise communication and sensing integration. These channels experience both time dispersion caused by multipath propagation and frequency dispersion caused by Doppler shifts. In this framework, high-altitude platform stations serve as relay nodes, amplifying communication signals and processing echo signals for accurate target parameter estimation. To further enhance system performance, we design a beamforming optimization strategy using successive convex approximation to improve communication energy efficiency. Simulation results demonstrate that the proposed scheme significantly enhances the overall performance of the SAGIN system.

Reconfigurable intelligent surface (RIS) and nonorthogonal multiple access (NOMA) are two promising technologies for future wireless communication networks. This paper proposes a new transmission design for short-packet communications (SPCs) in a downlink RIS-assisted NOMA system, considering hardware impairments at the transceiver nodes, non-optimal continuous phase shifts (CPSs) with phase estimation errors at the RIS, and imperfect successive interference cancellation (SIC) for a pair of NOMA users. Using the method of moment matching, we approximate the end-to-end channel gain with a Gamma distribution, and derive the closed-form approximate expressions for the average block error rates (BLERs) for NOMA users. Moreover, the system throughput is obtained to characterize the transmission efficiency of the network. Analytical results of the proposed scheme indicate that the phase estimation errors can seriously affect the system performance. Therefore, when conducting theoretical analysis, the non-optimal CPSs should be considered for RIS deployment. Numerical results validate our theoretical analysis, and confirm the performance improvement of transmission reliability and effectiveness of the proposed scheme, as compared to existing transmission schemes.

This paper investigates a covert communication system enhanced by a reconfigurable intelligent surface (RIS), where an unmanned aerial vehicle (UAV) serves as the transmitter while a Warden moves freely within a certain range. To enhance both communication covertness and energy efficiency, we jointly optimize the UAV trajectory and RIS phase shifts to maximize system energy efficiency. The optimization problem is formulated as a Markov decision process (MDP), and a reinforcement learning-based approach, LSTM-DDQN, is proposed to exploit deep reinforcement learning (DRL) in dynamic environments. By integrating long short-term memory (LSTM) to capture temporal dependencies and double deep Q-network (DDQN) to mitigate Q-value overestimation, the proposed algorithm enhances stability and decision-making effectiveness. Simulation results demonstrate that LSTM-DDQN significantly improves energy efficiency while effectively reducing the risk of detection compared to conventional DDQN, validating its superiority in RIS-assisted UAV covert communication.

This paper proposes a novel framework for downlink multi-user cluster communications in non-orthogonal multiple access (NOMA) systems assisted by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). We focus on the energy splitting (ES) protocol and account for practical issues such as hardware impairments (HIs) and error propagation from imperfect successive interference cancellation (I-SIC). The objective is to maximize the total user throughput by jointly optimizing user clustering, dynamic decoding order, base station active beamforming, and the STAR-RIS’s transmission and reflection beamforming. Furthermore, to address user mobility„ we design an efficient two-step algorithm: first, K-means is used for periodic user clustering, followed by proximal policy optimization (PPO), a deep reinforcement learning(DRL) algorithm, is applied to dynamically solve the aforementioned joint optimization problem. Simulation results show that the proposed algorithm significantly outperforms baseline DRL approaches in total throughput and that the STAR-RIS-NOMA system significantly exceeds conventional RIS-NOMA and OMA systems in performance.

The utilization of data buffer in unmanned aerial vehicle (UAV) introduces a dual role in the age of information (AoI) performance. Despite the enhancement of data delivery quality resulting from flexible and efficient data transmission, buffering may also risk increasing AoI by causing data aging. Against this background, this paper investigates the AoI minimization problem in buffer-aided UAV wireless communications while incorporating the effects of UAV buffering dynamics and limited buffer size. Specifically, we formulate the problem as a partially observable Markov decision process (POMDP) and propose a deep recurrent Q-network (DRQN) -based algorithm to jointly optimize UAV trajectory planning and buffering decisions. Simulation results exhibit the superiority of the proposed algorithm in terms of reducing the average AoI. Moreover, the study sheds light on the impact of UAV buffer size on AoI optimization, yielding essential design for buffer-aided UAV communications.

To reduce the heavy traffic burden over backhaul links, this paper studies a new reconfigurable intelligent surface (RIS) aided wireless caching system. The RIS is equipped with a smart controller to control reflective elements. The functions of content caching and energy harvesting (EH) are endowed to the controller. A two-phase transmission policy is designed. In the first phase, the requested contents that are not locally cached are delivered by the base station (BS) to the RIS, and the smart controller harvests the energy from ambient radio frequency (RF) signals. In the second phase, the smart controller distributes cached contents utilizing non-orthogonal multiple access (NOMA) transmission with the aid of the harvested energy, while the RIS forwards the fetched contents. The beamforming vectors of the BS and the power allocation factors for NOMA transmission are jointly optimized to maximize the achievable sum rate of the system. The original problem is difficult to solve owing to coupled variables. After being processed by fractional programming (FP) and quadratic transformation (QT), it is divided into two sub-problems. The first sub-problem is solved by introducing auxiliary variables, and the second one is converted to be convex by rational transformations to the quality of service constraint. The optimal solution is achieved by alternatively solving the two sub-problems. Simulation results reveal that the original problem can converge quickly, and the proposed algorithm outperforms benchmarks in terms of the achievable sum rate.

This paper proposes a novel content-centric framework for joint content update, push, and delivery in wireless caching networks. We employ a two-stage transmission strategy by grouping users based on their distribution locations and content caching status of the content server (CS). Users can be served either by the base station (BS) through multicast transmission or by the CS covering them and caching the requested contents. The content update scheme is designed based on the caching function that takes the request popularity and file size into account. Afterwards, the content push and delivery process is conducted in two stages. In the first stage, the requested and updated contents for multicast groups and the CS are delivered, while the CS harvests energy by employing the function of simultaneous wireless information and power transfer (SWIPT). In the second stage, the CS serves its covering users utilizing non-orthogonal multiple access (NOMA) transmission. Based on the proposed caching and transmission designs, we formulate the sum rate optimization problem, which is challenging due to its non-convexity. Thereafter, we convert it into a convex second-order cone programming problem using semi-definite relaxation (SDR), successive convex approximation (SCA), and other approximation techniques, and solve it with CVX solvers. Simulation results exhibit the fast convergence property of the proposed algorithm and demonstrate the advantages of our twostage content transmission approach, particularly in terms of the content hit rate and the sum rate.

Reconfigurable intelligent surface (RIS) assisted cell-free multiple-input multiple-output (MIMO) systems have emerged as a promising paradigm for future wireless communications. To overcome the multiplicative fading effect inherent in the passive RIS, a novel active RIS equipped with reflection-type power amplifiers has been proposed, which however is confronted with high hardware cost and power consumption under the fully-connected architecture. To address this issue, we consider a cost-and power-efficient sub-connected active RIS assisted cell-free system in this paper, where the whole active RIS is divided into multiple sub-RISs, each being connected to a dedicated power amplifier. We aim to maximize the system sum rate under imperfect channel state information (CSI) by jointly optimizing the AP transmit beamforming matrices and the RIS reflection coefficient matrix. Since the traditional centralized beamforming scheme may lead to a high computational burden at the central processing unit (CPU), we propose a two-stage distributed iterative algorithm to efficiently find high-quality suboptimal solutions. Specifically, in stage 1, users apply the classical weighted minimum mean-square error (WMMSE) method to optimize their local variables in parallel. Then in stage 2, APs optimize their respective transmit beamforming matrices, the RIS reflection phase shift vector and the RIS reflection amplification vector sequentially. The corresponding semi-closed-form optimal solutions are available by jointly leveraging the Lagrange duality theory, majorization minimization (MM) and symmetric alternating direction method of multiplier (S-ADMM) techniques. Moreover, we develop a simplified distributed algorithm to further reduce system communication overhead. Numerical results demonstrate that the two proposed distributed algorithms can achieve comparable sum rate performance to the centralized scheme while attaining lower computational overhead.

This paper proposes a new joint random caching and hierarchical transmission scheme for delivering multimedia content in cache-assisted heterogeneous networks. We use scalable video coding (SVC) and wireless edge caching to offer personalized video-watching services to end users. Resorting to stochastic geometry, we derive new expressions for successful transmission probabilities (STPs). Using the derived STPs, the achievable transmission rates (ATRs) and transmission delay experienced by multimedia users are obtained. The Gaussian-Chebyshev quadrature is employed to approximate the STPs and ATRs in closed form to improve mathematical tractability. We also attain the user satisfaction index (USI) concerning user’s delay experience. A transmission delay minimization problem is formulated and solved efficiently using the standard gradient projection approach to determine the optimal layer placement design under a random caching criterion. Simulation results confirm the correctness of the derived STPs, show the negligible approximation loss of Gaussian-Chebyshev quadratures, and manifest the performance superiority of the devised SVC-based caching design to the energy-optimal caching and the non-caching approach.

Integrated sensing and communication (ISAC) and space-air-ground integrated networks (SAGIN) have been considered as key technologies of 6G. The challenge of achieving ISAC in uplink massive access scenarios within the SAGIN has become a major research topic. This paper introduces the Resilient Massive Access (RMA) protocol, which deeply integrates S-ALOHA and NOMA, effectively enhancing the system's access success probability. Additionally, a cascaded uplink detection algorithm based on LS and MUSIC (MUSIC-NOMA-TSA) is proposed, enabling signal decoding and target sensing even in the presence of collisions at the receiver. Simulation results demonstrate that, compared to traditional algorithms, the proposed algorithm not only achieves more accurate signal decoding and target sensing but also significantly improves the access success probability.

This paper investigates the joint computation offloading and content caching scheme in fog radio access networks (F-RANs), where users and fog nodes (FNs) are all endowed with caching and computing capabilities. The Kuhn-Munkres (KM) algorithm is first used to make the user-and-FN matching decisions. We then comprehensively reveal the service modes when a user receives the requested data to perform a computation task. The optimization problem of minimizing the task response latency is formulated under the constraints of limited cache sizes for users and FNs, aiming to yield the optimal decisions for computation offloading and content caching. The formulated problem is NPhard, and transformed into the convex form by introducing the methods of difference of convex (DC) function, successive convex approximation (SCA), and arithmetic geometric mean (AGM) inequality. Simulation results demonstrate the fast convergence speed of the proposed algorithm, and show superior latency performance of our designed scheme to the benchmarks.

Wireless edge caching can alleviate the transmission pressure and improve the quality of experience (QoE) provided to users, which has been proposed as a promising technique in the sixth generation (6G) communication systems. This paper considers a clustered device-to-device (D2D) caching network, where users having similar request preference are classified into the same cluster. Due to the fact that recommendation policy will have a significant effect on user requests, the joint design of content recommendation and caching is investigated, aiming to enhancing the system caching efficiency (CE). Under the constraints of the recommendation quality, recommendation quantity and cache size, we formulate the CE maximization problem. The original problem is an NP-hard integer programming problem, making it difficult to solve. Therefore, it is partitioned into two sub-problems. The binary variables in the first sub-problem, namely, the recommendation design sub-problem, is relaxed to be continuous, and then the relaxed problem is solved by applying the methods of successive convex approximation and arithmetic geometric mean inequality. The recommendation decisions are recovered following a greedy strategy based principle. The second sub-problem, i.e., the caching design sub-problem, is addressed by using the smooth function to replace the $l_{0}$ norm. Simulation results demonstrate the fast convergence speed of the proposed algorithm, and show the obtained CE performance is superior to existing homogeneous recommendation method.

Sparse code multiple access (SCMA) is a new nonorthogonal multiple access scheme, which effectively exploits the constellation shaping gain of multi-dimensional codebook. In this paper, we propose a new SCMA architecture of uplink satellite communication system. At the transmitter, we group the users and design a practical spherical codebook. At the receiver, a low-complexity multi-user detection algorithm, namely logarithm domain message passing algorithm (Log-MPA) is implemented. The results show that the reliability of the proposed codebook outperforms the existing SCMA codebook schemes in both AWGN and Rayleigh channels.

In the emergency communication scenario, unmanned aerial vehicle (UAV)-based relaying and mobile edge computing (MEC) networks can quickly collect ground users' (GUs) information and provide high-quality computing services. In this paper, we investigate a UAV and reconfigurable intelligent surface (RIS) assisted uplink non-orthogonal multiple access MEC networks for emergency communications, where the UAV serves as a relay to assist the transmission from GUs to base station and provides MEC services. For the considered system, the computation efficiency (CE) is maximized by jointly optimizing the phase shift of RIS, communication and computation resources, bandwidth allocation coefficients, and the space location of UAV. The formulated problem is non-convex, and we divide the problem into four sub-problems. An alternating optimization-based algorithm is proposed to deal with it. In particular, with the aid of Dinkelbach's method, semi-definite relaxation, first-order Taylor expansion, successive convex approximation and improved particle swarm optimization, the decoupled non-convex sub-problems are effectively solved. Simulation results show that the proposed scheme can effectively improve the CE of the system, as compared with the conventional schemes.

Cell-free (CF) massive MIMO is considered one of the key technologies for 6G to achieve high spectral efficiency (SE) and ultralow latency. However, as the number of users increases, pilot contamination becomes more serious, and the optimal SE can not be achieved when the number of users exceeds the access points (APs). Therefore, we study the CF massive MIMO-NOMA system. Specifically, we design a user clustering algorithm based on the average Signal to Interference plus Noise Ratio (SINR), using orthogonal pilots between different clusters, and different users in the cluster using the same pilot, thereby reducing pilot contamination. Then we propose a flexible power allocation problem to maximize the system SE while taking into account user fairness. We model the problem as a Markov Decision Process (MDP) and then solve it using the asynchronous advantage actor-critic (A3C) algorithm in deep reinforcement learning. Simulation results show that the proposed A3C based power allocation scheme in CF massive MIMO-NOMA outperforms the baseline schemes in terms of fairness and SE.

Internet of Medical Things (IoMT) is regarded as an imperative technology for intelligent healthcare in the foreseeable 6G era. Due to the limited computing power of edge devices and task-related coupling, IoMT faces significant challenges. Considering the associative relationship among tasks, this paper proposes a computing offloading policy for multiple-user devices (UDs) under a multi-access edge computing (MEC) system. Specifically, we formulate the offloading scheme as a mixed-integer nonconvex optimization problem to minimize the total delay and energy consumption. To obtain the optimal solution, a double deep Q-network (Double DQN) based associative tasks computing offloading (DDATO) algorithm is then proposed, which can make the best offloading decision under the condition that tasks of UDs are associative. In addition, we use a dynamic ε−greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods, the proposed algorithm can lower the total cost more efficiently within the maximum delay and energy consumption tolerance.

In this paper, we investigate the physical layer security for simultaneous transmission and reflection (STAR) reconfigurable intelligent surface (RIS) aided systems. We consider a more practical case where multiple eavesdroppers can wiretap the transmitted signals from the access point (AP) to the legitimate users located in the transmission and reflection regions, respectively. An artificial noise (AN) aided secrecy transmission scheme is then proposed, aiming to improve the secrecy performance. The transmit beamforming and AN vectors of AP as well as the transmission and reflection coefficients are jointly optimized to maximize the sum secrecy rate. To effectively solve the formulated problem, it is firstly divided into two subproblems, and then the methods of semi-definite relaxation and Sion’s minimax theorem are employed to deal with the subproblems. By alternatively solving the sub-problems, the optimal solutions are obtained. Simulation results demonstrate the effectiveness of the proposed scheme compared to the conventional scheme without AN and STAR-RIS, and also bring useful insights into the design of secrecy communications in STAR-RIS aided systems with multiple eavesdroppers.

As complementary way of mobile communication, Mobile Ad Hoc Network (MANET) has developed rapidly and been utilized for various scenarios, where the energy consumption and load balancing are considered as key issues. To address this problem, based on Ad hoc On-demand Multipath Distance Vector (AOMDV)routing protocol, we propose a multipath routing protocol based on energy aware and constrained queue length (AOMDV-EC). We define the congestion state into several levels according to the queue length in the MAC layer, and select paths considering residual energy and hop count. The simulation results show that the performance of the proposed routing protocol is significantly improved in terms of packet delivery ratio, average end-to-end delay, throughput, routing overhead and energy exhausted nodes.

With the introduction of edge caching to fog radio access networks (F-RANs), users do not need to fetch the preferred contents from a far distant cloud center, which effectively reduces content downloading latency. Inspired by the adoption of deep reinforcement learning (DRL) in solving caching-related issues, this paper proposes a latency-oriented cooperative caching strategy for F-RANs based on the asynchronous advantage actor-critic (A3C) algorithm. Specifically, content popularity is first predicted in an online manner. Aims at minimizing the average download latency, the A3C algorithm is then employed to learn the optimal caching strategy based on the obtained content popularity results. Simulation results demonstrate that the proposed algorithm achieves a lower average download latency compared to other baseline policies.

This paper investigates a computation offloading and resource allocation policy for multiple vehicle user equipments (VUEs) in the Internet of Vehicles (IoV). Aiming at balancing the delay and energy consumption during the offloading procedure, a Support Vector Machine (SVM) is initially adopted to classify the offloading tasks into two categories according to different delay and energy consumption requirements. Consequently, VUEs can choose to offload the tasks to the mobile edge computing (MEC) server or other VUEs for completion. In particular, to further decrease the task offloading time in the MEC processing mode, the non-orthogonal multiple access (NOMA) scheme is adopted, which makes it possible for the MEC server to serve two VUEs simultaneously on the same sub-channel. To minimize the total cost, a Dueling Double Deep Q-Network (D3QN) based resource allocation algorithm is proposed, which can allocate the corresponding radio or computing resources under different task processing modes. Simulation results demonstrate that the proposed scheme can effectively reduce the total offloading cost within the maximum delay tolerance compared with existing methods.

Nonorthogonal multiple access (NOMA) and symbiotic ambient backscatter communications (AmBCs) are both considered promising technologies for beyond 5G mobile communication technology by enabling low-powered and spectrum-efficient access in large-scale Internet of Things. In this article, we investigate the uplink symbiotic communication in AmBC enabled NOMA system. In contrast with existing works, we assume the carrier transmitter (CT) transmits information while providing energy to the backscatter devices (BDs), which improves the energy efficiency. However, the signal power transmitted by the CT to integrated receiver (IR) through the direct link is much larger than the signal reflected by the BD, which can seriously affect the detection of the reflected signal by BD. Therefore, we jointly design the signal waveforms of CT and BDs, and propose a multiuser blind detection algorithm based on interference cancellation at IR. The simulation results demonstrate that the proposed multiuser detection algorithm achieves improved performance even the active BD number is unknown or without direct link.

Traditional cellular system deploys base station at the center of each cell, which leads to inter-user/cell interference and limited spectral efficiency. In contrast, the cell-free massive MIMO can effectively mitigate these interference by deploying multiple access points distributed in the coverage that jointly serve all the users. In this paper, we investigate a cell-free massive MIMO system assisted by the non-orthogonal multiple-access (NOMA) and propose a sparse signal reconstruction algorithm based on extended approximate message-passing (EAMP). The simulation results show that the proposed algorithm outperforms the traditional baselines in terms of recovery rate, calculation time and system capacity.

In the emergency communication scenario, how to quickly collect ground users (GUs) information and carry out reliable transmission has become a difficult point. In this paper, we consider the unmanned aerial vehicle (UAV)-assisted emergency communication system based on uplink non-orthogonal multiple access (NOMA). For the considered system, an alternative optimization scheme for the space location of UAV and resource allocation is proposed to maximize the uplink throughput from the UAV to base station (BS). The original joint optimization problem turns out to be non-convex and is divided into two sub-problems. Firstly, we optimize the space location of UAV by using particle swarm optimization algorithm, which has significant advantages in solving the optimal space location problem. Secondly, by using the tools of the first-order Taylor expansion and successive convex approximation, we jointly optimize the bandwidth allocation coefficients and transmit powers of GUs to maximize the throughput from the UAV to BS. Finally, simulation results verify our proposed scheme is superior in terms of the throughput for UAV-assisted emergency communication system compared with the existing scheme and the baseline schemes.

In this paper, the non-orthogonal multiple access (NOMA) and simultaneous wireless information and power transfer (SWIPT) are applied to wireless caching networks (WCN). Particularly, in the content pushing stage, base station sends the most popular contents to helpers with NOMA and the helpers harvest energy from the received signals. Then, the helpers send the requested files to the users with NOMA in the content delivery stage. In the content pushing stage, we minimize the maximum delay of the helpers by jointly optimizing the power allocation and time switching factors. Due to the non-convexity of the problem, the bisection method and two-layer iterative algorithm are devised to solve the problem. In the content delivery stage, the energy efficiency (EE) optimization problem is formulated under the quality-of-service, energy harvesting, and transmit power constraints. By using the nonlinear fractional programming theory and the Lagrange dual method, the original optimization problem is solved and the optimal power and time allocation coefficients are obtained. Simulation results show that the proposed algorithm reduces the delay of the helpers and improves the EE performance of helpers compared to the benchmark schemes.

In this paper, we investigate the user pairing and power allocation scheme for multiple cellular users (CUs) under the downlink non-orthogonal multiple access (NOMA) system. To maximize the sum-rate of all CUs, we formulate the resource allocation issue through optimizing the user pairing relationship and power allocation method. However, due to the nonconvex property of the formulated problem, the original problem is decoupled into two subproblems. First, the optimal power allocation scheme with a given subchannel assignment is obtained via a closed-form solution. Furthermore, based on the obtained optimal allocation scheme, a classical deep reinforcement learning (DRL) method called Deep Q-Network (DQN) algorithm is adopted to find the optimal user pairing scheme, where the DQN algorithm is characterized by higher learning efficiency and better performance of the features extraction ability compared with traditional reinforcement learning (RL) schemes. Simulation results validate the effectiveness of our proposed resource allocation, as compared against the RL based scheme and conventional orthogonal multiple access (OMA) method.

In this paper, we investigate a computing offloading policy for multiple User Equipments (UEs) in Fog Radio Access Networks (F-RANs). Aimming at maximizing the total utility of UEs in dynamically changing wireless environment, we formulate task offloading problem as a mixed integer nonlinear programming (MINP) problem. To solve the nonconvex problem, we first utilize a centralized deep reinforcement learning (DRL) algorithm called Dueling Deep Q-Network (DDQN) to obtain the most appropriate offloading mode for each UE with unknown Channel State Information (CSI). Especially, a pre-processing procedure is initially proposed to reduce the complexity of the DDQN algorithm. Then, combining with the training results of DDQN and the delay requirements of each UE's task, we obtain the final optimal offloading policy for each UE. Simulation results demonstrate the performance gains of the proposed scheme compared with other existing baseline schemes.

Fog radio access networks (F-RANs) is foreseen as a possible solution to provide the storage and computing capacities at the network edge. To reduce the burden on fronthaul link and provide low latency service, this paper proposes a Device-to-Device (D2D)-enabled cooperative caching strategy for fog radio access network (F-RAN). Aim at maximizing the total cache hit rate, we first formulate the cooperative caching problem as a probability-triggered combinatorial multi-armed bandit problem (CMAB). Next, an enhanced multi-agent reinforcement learning (EMARL) algorithm is designed to solve the above issue combined with user preference and content popularity prediction. Based on a real dataset from MovieLens, simulation results demonstrate that the proposed cooperative caching algorithm can improve the cache hit rate compared with existing caching schemes.

The deep reinforcement learning is investigated for predicting coordinated beamforming strategy in ultra dense network (UDN). The balancing coefficients regulate the strategy between selfish and altruistic beamforming in this case. As balancing coefficients of one user also depend on the beamforming vectors of users in other cells, iterations are inevitable. To address this problem, deep reinforcement learning (DL) based on Deep Q-network is proposed in this paper to predict the balancing coefficients. Moreover, the scheme to discretize the theoretically infinite strategy space with limited levels and granularity in defining the action set of the Deep Q-network is examined. The distinguished feature of the proposed approach is the exploration of the beamforming structure, in consequence, complexity problem brought by predicting the beamforming matrix directly is avoided. Experiment results confirm the feasibility of the proposed discretizing scheme. Simulation results prove the effectiveness of the proposed beamforming scheme by comparing with the classical algorithms.

As a promising solution of spectrum shortage, spectrum sharing has received tremendous interests recently. However, under different sharing policies of different licensees, the shared spectrum is heterogeneous both temporally and spatially, and is usually uncertain due to the unpredictable activities of incumbent users. In this paper, considering the spectrum uncertainty, we propose a spectrum sharing based delay-tolerant traffic off-loading (SDTO) scheme. To capture the available heterogeneous shared bands, we adopt a mesh cognitive radio network and employ the multi-hop transmission mode. To statistically guarantee the end-to-end (E2E) session request under the uncertain spectrum supply, we formulate the SDTO scheme into a stochastic optimization problem, which is transformed into a mixed integer nonlinear programming (MINLP) problem. Then, a coarse-fine search based iterative heuristic algorithm is proposed to solve the MINLP problem. Simulation results demonstrate that the proposed SDTO scheme can well schedule the network resource with an E2E session guarantee.

The fast development on emerging services makes our telecommunications networks witness two key problems. One is the flexibility to fulfill the diverse service requests and the other is the shortage on spectrum. Network slicing and spectrum sharing have been regarded as two prominent solutions, which, however, are barely jointly studied. When taking the shared spectrum into account, its unique feature of heterogeneity and uncertainty will bring new challenges for the slicing. In this paper, we propose a service-oriented spectrum-aware RAN-slicing trading (SSRT) scheme with a comprehensive consideration on both aspects. For the SSRT scheme, we jointly slice three kinds of resources, namely, time, spectrum (including both licensed one and shared one), and network facilities, according to the diverse traffic requests, which are classified into delay-tolerant (DT) ones and delay-sensitive (DS) ones, as well as the willing payments from different service providers (SPs). To achieve both inter-slice and intra-slice isolation, we construct a three-dimensional (3D) conflict graph and formulate the SSRT scheme into a mixed-integer nonlinear programming (MINLP) problem with a cross-layer spectrum-aware resource allocation and a hybrid transmission mode (including both single-hop and multi-hop). Since finding all the maximum independent sets (MIS) for the 3D conflict graph is an NP-hard problem, we further develop an iterative heuristic algorithm for the MIS determination.

The explosive growth on emerging Internet-of-Things (IoT) applications makes our telecommunications networks confront twofold challenges. One is to provide sufficient flexibility for service diversity. The other is the shortage on spectrum. Network slicing and spectrum sharing have been deemed as two prominent solutions, which, however, are barely jointly studied in the literature. In this article, standing on both aspects, we propose a service-oriented spectrum-aware RAN-slicing trading (SSRT) scheme to achieve a dynamic on-demand RAN slicing under the spectrum sharing scenario. For the SSRT scheme, we jointly slice multidimensional resources, including heterogeneous spectrums (licensed and shared), time, and network facilities (nodes, radios, and powers). In particular, considering the uncertainty of shared spectrums, we distinguish them from the traditional licensed ones to fulfill different types of sessions, which are classified into delay tolerant and delay sensitive. To achieve an effective isolation, we construct a 4-D conflict graph and formulate the slice generation problem into a mixed-integer nonlinear programming (MINLP) problem, where a cross-layer resource allocation based on a hybrid transmission mode is designed for the customization. To cope with the difficulties when solving the problem, we employ the column generation algorithm to obtain the final slicing result over all the resources. The simulation results have shown the effectiveness of the proposed scheme.

In industry 4.0, the most popular way to identify and track objects is to add tags. Because the cost of smart tag is still high, most companies still use cheap barcodes or QR codes. In order to solve the real-time positioning problem of upgrading traditional tags into smart tags, this paper proposes a QR tag location method based on Binarized Normed Gradients (BING) and AdaBoost-SVM. BING algorithm is the fastest general object detection algorithm at present, but its disadvantage is that the recall rate decreases sharply with the increase of Intersection-over-Union (IoU) threshold. In the proposed algorithm, Adaboost-SVM method is introduced to make up for the shortcomings of BING algorithm. More specifically, before training and prediction process of Adaboost-SVM, Contrast Limited Adaptive Histogram Equalization (CLAHE) mechanism is used for image enhancement, and thus can greatly shorten the training time and improve the precision of prediction. As a result, the precision of low-quality image prediction is significantly improved. Compared with the existing methods based on Neural Network (NN), the proposed algorithm does not depend on the parallel acceleration hardware such as GPU, hence, it can significantly reduce the hardware cost, and expand QR code automatic positioning algorithm utilization in the field of lower hardware requirements.

In order to overcome the shortcomings of orthogonal frequency division multiplexing (OFDM) and prolong the battery life of devices in the Internet of Things, a joint subcarrier and subsymbol allocation-based simultaneous wireless information and power transfer scheme for multiuser generalized frequency division multiplexing (GFDM) system is proposed in this paper. According to the 2-D time-frequency block structure of GFDM, we investigate the problem to maximize sum information decoding (ID) rate by optimizing subcarrier and subsymbol allocation, power allocation and power splitting ratio under the constraints of total transmit power and harvested energy. To solve the nonconvex problem, an iterative algorithm is developed to obtain its optimal solution. The performances of sum ID rate and harvested energy are simulated and evaluated. Simulation results show that the proposed algorithm converges fast. Moreover, the proposed algorithm can not only allocate the subcarriers, subsymbols, and power based on different channel conditions of users, but also outperform the conventional OFDM in sum ID rate on the premise of satisfying the minimum harvested energy of each user.

As a key feature of the next generation wireless networks (5G), heterogeneous networks (HetNets) architecture is embraced as the fundamental network technology to meet the immensely diverse service requirements and characteristics of various devices. By introducing underlaying device-to-device (D2D) communication into HetNets, it is possible to offload the unevenly distributed load from the overloaded macrocell to the uncongested femtocell in multitier HetNets. However, the performance of load balancing (LB) scheme heavily depends on the D2D relay selection method as well as the potential resource reuse interference brought by underlay D2D relaying. In order to improve resources utilization ratio and mitigate the resource reuse interference, an LB strategy based on D2D relaying is proposed. To accommodate more new users into the already overloaded macrocell, the macro user equipment with poor link quality will be first transferred into nearby uncongested femtocells by D2D relaying. Then, the released macrocell resource can be allocated to new users who cannot access femtocell due to location restriction. Furthermore, we also propose a two-stage relay selection and resource allocation scheme which not only minimizes the potential interference caused by resource reuse but also guarantees the transmission requirement of different users. Extensive simulation results demonstrate that by taking advantage of D2D relaying, the proposed LB algorithm can effectively adjust the unbalanced load between macrocell and femtocells, which achieves improved performance of the whole HetNets.

Cooperative relaying is accepted as a promising solution to achieve high data rates over large areas in the future 4G wireless system. In this paper, a cell-cluster based traffic load balancing strategy is proposed to solve the problem of cell congestion in cooperative cellular networks. In the paper, a dynamic cell-cluster construction method is first studied based on the traffic distribution among the hot spot cell and its neighboring cells, which is followed by the calculation of transferred traffic from the hot spot cell to each of the noncongested cells in the cell-cluster in order to minimize the average blocking probability, then, after the study of spectral efficiency for both direct and cooperative relaying transmissions, a mathematic model is formulated to jointly optimize routing and radio resource allocation in traffic load balancing, and a greedy based CJRR (cell-cluster based joint routing and radio resource allocation) algorithm is proposed to find a suboptimal solution. Simulation results show that our proposed traffic load balancing strategy has a satisfying performance in system spectral efficiency and blocking probability comparing with other transmission schemes.

MIMO-relay technology is accepted as a promising solution to improve cell edge performance for the future 4G wireless system. In this paper, an inter-cell interference mitigation scheme based on MIMO-relay technique is proposed to improve the average capacity for cell edge users. In the paper, a new network structure with shared relay station (SRS) is first studied, and a two-tier channel decomposition method is proposed based on the structure; then the BS (base station) precoding and relay filtering scheme is designed based on the proposed method, and the power allocation scheme at SRS is further discussed with two solutions of average power allocation algorithm and genetic based optimization algorithm. Numerical results show that compare with other inter-cell interference mitigation schemes, the proposed scheme can effectively cancel the inter-cell interference and significantly improve the capacity performance for cell edge users.

In this paper, a game theory based load-balancing routing (GBLBR) protocol with cooperation stimulation is presented for delay sensitive traffic in wireless ad hoc networks. By calculating the game theory based delay utility function, GBLBR fully utilizes the link capacity across different paths, and obtains the optimal arrival rate for each path. This minimizes the average delay of each packet. Extensive simulation results have shown that the proposed routing protocol can reduce the average end-to-end delay and the percentage of packet loss. This is shown to be better than the conventional shortest path routing with the minimum hops approach. Moreover, the suggested game theory based cooperation stimulation strategy can enforce cooperation among nodes and improve network fairness.

In this paper, an effective relay based load balancing scheme, employing traffic transferring and channel borrowing, is presented for two-hop cellular relaying networks. In the proposed scheme, each relay station is allocated a set of real-time traffic channels so as to perform call relaying from a congested cell to another non-congested cell. Moreover, owing to the limited capacity of each relay station, dynamic channel borrowing is adopted to further facilitate load balancing between neighboring cells. With two different relay deployment scenarios considered, a multi-dimensional markov chain model is then developed to evaluate the performance of the proposed scheme. Simulation results demonstrate that the proposed scheme achieves better performance compared with that of the conventional cellular network in terms of call blocking rate.