Unmanned aerial vehicles (UAVs) have been widely applied in wireless communications because of its high flexibility and line-of-sight transmission. In this study, we develop low-complexity and robust device-to-device (D2D) link scheduling in UAV-assisted industrial-Internet-of-Things (IIoT) networks. First, we propose a sparse convolutional neural network (SCNN) model that uses the geographical map of transmission links as input. The model consists of three main blocks: 1) generic feature filtering, 2) speed–accuracy balancing, and 3) deep feature processing. Unlike other state-of-the-art methods, the proposed SCNN directly processes the geographical map collected using a connected UAV. Second, we propose a deep deterministic policy gradient-based reinforcement learning model that processes the output feature map from the SCNN to optimize the D2D scheduling decision and maximize the achievable system rate in the long run. Extensive simulations revealed that the proposed scheme significantly improved the achievable rate over other benchmark comparison schemes, such as transmitters and receivers density-based deep learning (DL), ResNet-based DL, VGGNet-based DL, random scheduling, and all-active schemes, respectively. The simulations also demonstrated that the proposed scheme reduces computational complexity. With reduced complexity and nearly optimal performance, the proposed solution can be more efficiently applied to large-scale and dense IIoT networks.

Compared to static time-division duplexing (TDD), dynamic TDD (D-TDD) has significantly increased the spectral efficiency of cellular networks. However, conventional systems operate based on exact channel state information, resulting in high communication overhead and delay. Spatial deep learning refers to using spatial geographical information as the training data. This study investigates a spatial deep learning-based D-TDD scheme for 6G hotspot networks. First, we represent geographical location information in forms of traffic demand density grid matrices. Second, we use spatial convolution filters to extract discriminative features of uplink and downlink service gains and harms, taking the traffic demand density grid matrices as the input. Subsequently, extracted feature matrices are processed with sparse convolution blocks to reduce computation cost for the classification. Finally, we develop novel deep dueling neural networks, leveraging the extracted features to efficiently learn the near-optimal radio slot configurations for all base stations. Numerical results show that the proposed approach improves average rate per user by 2.5%, 6%, and 523.3% over those achieved in state-of-the-art centralized D-TDD, the competitive reinforcement learning, and greedy approaches, respectively. In addition, the proposed approach achieves up to 98.7% of the data rate performance of the optimum scheme with an exhaustive search algorithm.

One of the essential factors for enabling sixth-generation systems is efficiently ensuring diverse quality-of-service (QoS) performance metrics to support the upcoming massive ultra-reliable low-latency communication (URLLC). This work proposes efficient transmission control in intelligent reflecting surface (IRS)-assisted nonorthogonal multiple access (NOMA) networks in the finite blocklength (FBL) regime that statistically guarantee stringent URLLC QoS requirements. Thus, we formulate a nonconvex problem that maximizes the sum effective capacity (SEC) while ensuring statistical delay QoS constraints. To make the problem more tractable, we propose a tight upper bound for the objective function based on Jensen’s inequality and employ the concept of opportunistically minimizing an expectation. Then, we decompose the problem into two subproblems: active beamforming at the base station and phase-shift optimization at the IRS. Each subproblem is convexified by employing slack variables, penalty functions, and linear approximation, and solved using successive convex approximations. The subproblems are iteratively solved until convergence using alternating optimization. The convergence to a suboptimal stationary solution and the computing complexity of the proposed algorithm are rigorously analyzed. Finally, extensive numerical evaluations confirm that the proposed control in the FBL regime significantly improves the SEC under various QoS parameters compared to existing benchmark schemes. In particular, as the number of antennas and IRS elements increases, the proposed method becomes more efficient than the semi-definite relaxation-based approach in terms of complexity and performance.

Federated learning (FL) is a framework for realizing distributed machine learning in an environment where training samples are distributed to each device. Recently, FL has employed over-the-air computation enabling all devices to transmit learning model updates simultaneously. This work proposes a communication-efficient sparse one-bit analog aggregation (SOBAA) method, incorporating new power control, layer-wise scaled one-bit quantization, layer-wise sparsification, and an error-feedback mechanism. We derive a tight upper bound of the expected convergence rate of the proposed SOBAA as a closed-form expression. From this expression, we explicitly identify the relationship between the convergence rate and compression and aggregation errors. Based on the theoretical convergence analysis, we formulate a joint optimization problem of the compression ratio and power control to minimize compression and aggregation errors, leading to the fastest convergence. In each communication round, the optimization problem is decomposed, and solved in a computationally efficient and feasible way. From this solution, we characterize the trade-off between learning performance and communication cost. Through extensive experiments on well-known MNIST and CIFAR-10 datasets, we confirm that the proposed method provides an enhanced trade-off performance between test accuracy and communication costs and a faster convergence rate than the other state-of-the-art methods. In addition, it is proven that the proposed method is more effective for more complex datasets and learning models.

This paper proposes a flexible and efficient access control scheme that combines the orthogonal frequency division multiple access and rate-splitting multiple-access techniques for enhancing the uplink transmission in a digital twin edge network system. We formulate a non-convex mixed integer optimization problem that minimizes the energy consumption of all Internet of Things devices (IoTDs) and maximizes the number of successful IoTD tasks. To this end, we propose a deep reinforcement learning (DRL) framework by normalizing a DRL training algorithm named deep deterministic policy gradient for efficiently designing the variables while ensuring the problem constraints. However, in the inference stage, the proposed DRL method may encounter different devices and services. Therefore, we design an exhaustive-improved DRL method that can improve the proposed DRL effectively using information from a digital-twin module. We also propose a mathematical approximation-based solution employing two convexification approach: Dinkelbach’s method and relaxed Linear Matrix Inequality (LMI). Through extensive simulations over different parameters and scenarios, we identify the polynomial complexity, stable convergence, and operating regime of the proposed solutions. It is also confirmed that the proposed approaches work well even with digital twin defects and provide improved performance in terms of energy consumption and number of successful tasks in comparison with benchmark schemes.

Recently, network coding has become a promising transmission approach to support high throughput and low latency in distributed multihop networks. In this article, we develop a delay-controlled distributed route establishment scheme that can provide maximal bidirectional transmission to enhance network coding gain while satisfying a time-critical route setup. The scheme is called network coding-aware delayed store and forwarding (NC-DSF). It delays the received route information packets before forwarding them according to the link status and network topology. We propose a tight delay function derived using a strict end-to-end delay bound for delay control. Subsequently, we suggest a relaxed delay function derived using realistic and practical conditions. Finally, we propose a load-weighted delay function considering the tradeoff between bidirectionality and network-load balancing. The simulations confirm that the proposed scheme offers increased throughput and decreased latency in mesh and random multihop networks. The proposed transmission scheme, NC-DSF, can be efficiently employed in the future industrial Internet of Things networks requiring a time-constrained route setup, high throughput, and low latency.

In this study, we consider an energy-efficient and low-complexity transmission control in a SWIPT-NOMA-based green cellular network (GCN) that consists of a green base station (GBS) and green users (GUEs). First, we formulate a non-convex problem that minimizes transmit power consumption while supporting minimum downlink user service rate, downlink data queue stability, and user battery availability. Then, we transform the problem into a Lyapunov-drift-penalty minimization problem, which can determine a new resource allocation scheme that balances transmit power consumption and queue stability. Second, the Lyapunov-drift-penalty problem is decomposed into subchannel assignment, power allocation, and power splitting (PS) ratio control problems. The subchannel assignment problem is solved using a matching theory-based low-complexity algorithm. The power allocation and PS ratio control problems are solved using the alternating optimization (AO) approach and bisection method. This decomposed subproblem-based control also enables distributed control between the GBS and GUEs. Third, we prove the convergence, optimality, and polynomial computation complexity of the proposed algorithm. Lastly, we demonstrate that the proposed control outperforms the benchmark controls regarding transmit power consumption and the achievable rate. Owing to the optimality and low complexity, the proposed control can be efficiently applied to large-scale and distributed GCNs in sixth-generation environments.

To meet the stringent demands of emerging Internet-of-Things (IoT) applications, such as smart home, smart city, and virtual reality in 5G/6G IoT networks, edge content caching for mobile/multiaccess edge computing (MEC) has been identified as a promising approach to improve the quality of services in terms of latency and energy consumption. However, the limitations of cache capacity make it difficult to develop an effective common caching framework that satisfies diverse user preferences. In this article, we propose a new content caching strategy that maximizes the cache hit ratio through flexible prediction in dynamically changing network and user environments. It is based on a hierarchical deep learning architecture: long short-term memory (LSTM)-based local learning and ensemble-based meta-learning. First, as a local learning model, we employ an LSTM method with seasonal-trend decomposition using loess (STL)-based preprocessing. It identifies the attributes for demand prediction on the contents in various demographic user groups. Second, as a metalearning model, we employ a regression-based ensemble learning method, which uses an online convex optimization framework and exhibits sublinear “regret” performance. It orchestrates the obtained multiple demographic user preferences into a unified caching strategy in real time. Extensive experiments were conducted on the popular MovieLens data sets. It was shown that the proposed control provides up to a 30% higher cache hit ratio than conventional representative algorithms and a near-optimal cache hit ratio within approximately 9% of the optimal caching scheme with perfect prior knowledge of content popularity. The proposed learning and caching control can be implemented as a core function of the 5G/6G standard’s network data analytic function (NWDAF) module.

Mobile edge computing and nonorthogonal multiple access (NOMA) have been considered as promising technologies that can satisfy rigorous requirements of industrial Internet of Things systems. However, system dynamics, including channel states and computation task requests, may continuously change NOMA decoding order and computation uploading time, making it difficult to reduce latency using conventional highly complex optimization methods. In this article, we investigate a novel scheme that effectively reduces the average task delay to improve the quality of service for all users by jointly optimizing subchannel assignment (SA), offloading decision (OD), and computation resource allocation (CRA). To deal with the high complexity, the original multiserver problem is first decomposed into multiple single-server problems. Subsequently, each single-server problem is decoupled into CRA and SA/OD subproblems. Using convex optimization, a closed-form solution is derived for the optimal CRA action. Concurrently, the optimal SA/OD action is obtained using a distributed multiagent deep reinforcement learning algorithm. Simulation results reveal that the proposed scheme significantly outperforms the state-of-the-art schemes. In particular, it reduces the action decision duration by 30 times while achieving a near-optimal performance of up to 97% of the optimum under the exhaustive search scheme.

Intelligent reflecting surface (IRS) is a promising 6G technology that can improve wireless communication capacity in a cost-effective and energy-efficient manner, by adjusting a large number of passive reflectors to appropriately change the signal propagation. In this study, we identified the achievable rate region of a two-hop interference channel with distributed multiple IRS relays. To do so, we formulated a non-convex problem that characterizes the rate-profile, and found its solution using successive convex approximation (SCA). We then proposed an alternating direction method of multipliers (ADMM) and alternating optimization (AO) based distributed and low-complex IRS control that maximizes the achievable sum-rate, and proved its convergence and optimality. We then compared the proposed IRS control with semi-definite relaxation (SDR)-, random phase-, deep reinforcement learning (DRL)- based IRS controls, and optimal amplify-and-forward (AF)-, interference neutralization (IN)-, and decode-and-forward (DF) based relaying schemes. We demonstrated that the proposed control with multiple IRS elements outperforms the benchmark controls in terms of the achievable rate region, achievable sum-rate, and energy efficiency under same power budget. We also confirmed that the discrete phase approximation of the proposed control provides near-optimal performance with fewer bits, and the proposed control is robust under imperfect CSI condition. The proposed controls can be efficiently applied to large-scale multi-pair multihop device-to-device and machine-type device communications in the interference-limited or low-powered dense networks of 5G and 6G environments.

Mobile-edge computing (MEC) and nonorthogonal multiple access (NOMA) have been regarded as promising technologies for beyond fifth-generation (B5G) and sixth-generation (6G) networks. This study aims to reduce the computational overhead (weighted sum of consumed energy and latency) in a NOMA-assisted MEC network by jointly optimizing the computation offloading policy and channel resource allocation under dynamic network environments with time-varying channels. To this end, we propose a deep reinforcement learning algorithm named ACDQN that utilizes the advantages of both actor-critic and deep Q-network methods and provides low complexity. The proposed algorithm considers partial computation offloading, where users can split computation tasks so that some are performed on the local terminal while some are offloaded to the MEC server. It also considers a hybrid multiple access scheme that combines the advantages of NOMA and orthogonal multiple access to serve diverse user requirements. Through extensive simulations, it is shown that the proposed algorithm stably converges to its optimal value, provides approximately 10%, 27%, and 69% lower computational overhead than the prevalent schemes, such as full offloading with NOMA, random offloading with NOMA, and fully local execution, and achieves near-optimal performance.

Future wireless and mobile network services must accommodate highly dynamic downlink and uplink traffic asymmetry. To fulfill this requirement, the third-generation partnership project (3GPP) introduced the enhanced interference mitigation and traffic adaptation strategy in addition to dynamic time division duplexing (TDD). In this study, we develop a reinforcement learning (RL)-based dynamic TDD framework that effectively controls interference and serves various traffic demands. First, we introduce an interference-penalty model that evaluates interference indirectly based on the duplexing policy. This can significantly reduce overhead for measuring and exchanging channel information in a dense network. Second, we design a new mixed-reward model that consists of the achievable data rate and the implicit interference penalty. Third, we implement deep RL algorithms that base station (BSs) use to train their radio frame configurations (RFCs). The training process at each BS takes into account the traffic demand and the RFCs of the surrounding BSs. The BSs are coordinated in a single-leader multi-follower Stackelberg game, which achieves a global RFC setup that maximizes the data rate and minimizes the interference. Extensive simulations show that the proposed framework stably converges in various environments and provides near-optimal performance equivalent to 95% or more of the full-search-based optimal performance, which is 48.84%, 41.92%, and 62.11% higher than the currently utilized random RFC, fixed RFC, and traffic-matched RFC approaches.

In a full-duplex (FD) cellular network, a base station transmits data to the downlink (DL) user and receives data from uplink (UL) users at the same time; thereby the interference from UL users to DL users occurs. One of the possible solutions to reduce this interuser interference in the FD cellular network is user pairing, which pairs a DL user with a UL user so that they use the same radio resource at the same time. In this paper, we consider a user pairing problem to minimize outage probability and formulate it as a nonconvex optimization problem. As a solution, we design a low-complexity user pairing algorithm, which first controls the UL transmit power to minimize the interuser interference and then allows the DL user having a worse signal quality to choose first its UL user giving less interference to minimize the outage probability. Then, we perform theoretical outage analysis of the FD cellular network on the basis of stochastic geometry and analyze the performance of the user pairing algorithm. Results show that the proposed user pairing significantly decreases the interuser interference and thus improves the DL outage performance while satisfying the requirement of UL signal-to-interference-plus-noise ratio, compared to the conventional HD mode and a random pairing. We also reveal that there is a fundamental tradeoff between the DL outage and UL outage according to the user pairing strategy (e.g., throughput maximization or outage minimization) in the FD cellular network.

Heterogeneous cellular networks which consist of macrocells and small cells can offer significant capacity gain by utilizing the resources of the small cells. However, to achieve this, the interference between the macrocells and the small cells must be carefully managed. In this work, we propose an uplink intercell interference control (ICIC) scheme which is a unified ICIC approach of handover based interference control and rate-split based interference control. The handover based interference control scheme is a win-win strategy which enhances both interfering user's rate and interfered user's rate. On the other hand, the rate-split based interference control scheme is a yield-win strategy where an interfering user sacrifices his rate to save the interfered user's rate. In this paper, we assume that users have their target QoS such as minimum rate when they send their data. The proposed uplink ICIC scheme reduces the interference as best as possible while it guarantees the minimum QoS. Simulation results shows that the proposed ICIC scheme offers enhanced rate or fairness than legacy ICIC schemes which are not considering user QoS. The proposed ICIC scheme works in distributed manner with low-complexity so that it can be applied to self-organizing network and mobile ad-hoc networks as well as heterogeneous cellular networks.

In heterogeneous cellular networks (HCN) which consists of macrocells and numerous picocells, efficient interference management schemes between macrocells and picocells are so crucial to the overall system performance. We propose a dynamic cooperative silencing control (DCS) scheme for intercell interference control (ICIC). It is a low-complex, low-feedback and distributed algorithm using only strongly interfered neighboring users' information. The system simulation shows that the system performance and in particular the cell-edge throughput is significantly increased with the proposed silencing scheme. It offers 420% and 190% higher average spectral efficiency and edge-user spectral efficiency in compared to macrocell only case, respectively.

This paper focuses on interference issues arising in the downlink of a heterogeneous network (HetNet), where small cells are deployed within a macrocell. Interference scenario in a HetNet varies based on the type of small cell access modes, which can be classified as either closed subscriber group (CSG) or open subscriber group (OSG) modes. For these two types of modes, we propose hierarchical interference alignment (HIA) schemes, which successively determine beamforming matrices for small cell and macro base stations (BSs) by considering a HetNet environment in which the macro BS and small cell BSs have different numbers of transmit antennas. Unlike prior work on interference alignment (IA) for homogeneous networks, the proposed HIA schemes compute the beamforming matrices in closed-form and reduce the feedforward overhead through a hierarchical approach. By providing a tight outer bound of the degrees-of-freedom (DoF), we also investigate the optimality of the proposed HIA schemes with respect to the number of antennas without any time expansion. Furthermore, we propose a new optimization process to maximize the sum-rate performance of each cell while satisfying the IA conditions. The simulation results show that the proposed HIA schemes provide an additional DoF compared to the conventional interference coordination schemes using a time domain-based resource partitioning. Under multi-cell interference environments, the proposed schemes offer an approximately 100% improvement in throughput gain compared to the conventional coordinated beamforming schemes when the interference from coordinated BSs is significantly stronger than the remaining interference from uncoordinated BSs.

In heterogeneous cellular networks (HTN) which consists of macro-cells and numerous femto-cells, efficient interference management schemes between macro-cells and femto-cells are so crucial to the overall system performance. To mitigate inter-cell interference in the HTN, we propose a new rate-split transmission scheme which has following characteristics. First, it guarantees serving user''s QoS by deciding common message power for an interfered user. Second, it is a low complex scheme using only ISNR (Interference to Signal and Noise Ratio) feedback between a macro-base station and a femto-base station. Third, it operates in a distributed manner. The performance evaluation shows that the proposed algorithm significantly reduces the interference for severely interfered users while guaranteeing serving user''s QoS.

In this paper, we study a distributed resource control problem achieving fairness and information theoretic capacity in an OFDMA system. For practicality, we consider an OFDMA system with incomplete information. The information theoretic MAC capacity region is known to be achieved by successive decoding schemes. Therefore, the problem can be formulated as a stackelberg optimization problem. As a solution of the problem, we provide a distributed game-theoretic resource allocation scheme based on iterative multi-unit second price auction. We prove the algorithm's convergence, stability and optimality through analytic model and simulations.

This paper proposes a new on-demand multi-path routing protocol considering path redundancy as one of route selection criteria. Path redundancy implies how many possible redundant paths may exist on a route that contains more redundant paths toward the destination by involving intermediate nodes with relatively mode adjacent nodes in a possible route. Our approach can localize the effects of route failures, and reduce control traffic overhead and route reconfiguration time by enhancing the reachability to the destination node without source-initiated route rediscoveries at route failures. We have evaluated the performance of our routing scheme through a series of simulation using the network simulator 2 (ns-2).