This paper proposes a multi-level sleep mode control technique based on safe deep reinforcement learning (SDRL), which integrates long short-term memory (LSTM)-based traffic prediction to reduce the energy consumption of small cell base stations, as they account for a large proportion of heterogeneous networks. To mitigate quality of service (QoS) degradation caused by prolonged sleep states, the proposed framework incorporates safety constraints that proactively avoid excessive sleep continuity by predicting future traffic, while simultaneously restricting unacceptable mode transitions. The framework learns to balance these objectives through a reward structure that explicitly captures the trade-off between energy saving and QoS, and is implemented using a deep Q-network (DQN). Simulation results show that the proposed method ensures efficient and stable operation in dynamic traffic environments, achieving reduced energy consumption while stably satisfying QoS requirements compared to benchmark methods.

This paper proposes a hierarchical multi-agent deep reinforcement learning (HDRL) framework for heterogeneous Low Earth Orbit (LEO) satellite networks. The framework addresses the timing mismatch between the execution and the decision by incorporating delay-aligned traffic predictions into the agent state, structuring the control so that an upper-level agent selects an active set of cells while lower-level agents allocate beams and channels under visibility constraints per-satellite. Using a shared reward composed of demand-weighted efficiency, a duplication penalty for identical cell-channel pairs, and a coverage bonus, the framework reduces early exploration loss and converges to higher rewards, while suppressing resource duplication and jointly improving efficiency and coverage—yielding a practical cooperative policy for heterogeneous LEO systems.

In beam hopping-based low earth orbit (LEO) satellite systems, spatially imbalanced and temporally varying ground traffic requires dynamic wireless resource allocation. This study proposes a long short-term memory (LSTM)-based approach that leverages real-world mobile network data to predict percell traffic demand. Based on these predictions, two-types of deep Q-network (DQN) agents are assigned to each beam to jointly optimize cell selection and bandwidth allocation. The proposed dual-agent DQN architecture decouples the complex action space, thereby enhancing learning efficiency. In addition, a unified reward function is designed to simultaneously capture traffic service efficiency, average queueing delay, and the number of beam switchings. Simulation results show that the proposed algorithm significantly outperforms existing benchmarks in terms of overall resource utilization efficiency.

This paper proposes a safe reinforcement learning (RL) method for fixed-wing unmanned aerial vehicles (UAVs) to reach their destination along an optimal collision-free path. The proposed method allows UAVs to avoid surrounding obstacles by adjusting their attitude based solely on locally sensed information. A key feature is a safe RL mechanism that manages collision risk by maintaining the received signal strength indicator (RSSI) between agents below predefined thresholds. Simulation-based performance comparisons with benchmark method demonstrate that the proposed method effectively prevents inter-agent collisions while maintaining a high success rate.

Integrated sensing and communication (ISAC) is a key sixth-generation (6G) technology that enhances spectrum efficiency, but its application in multi-unmanned aerial vehicle (UAV) systems faces challenges such as limited flight time and severe mutual interference. To address these issues, this paper proposes a multi-agent deep reinforcement learning (DRL) framework for a network of tethered UAVs (TUAVs). In our scheme, each TUAV acts as an independent agent, leveraging a double deep Q-network (DDQN) to autonomously and jointly optimize its three-dimensional (3D) position and transmit power. A key feature of our approach is the use of a unified reward function—a weighted sum of the total communication throughput to a single base station and the total sensing mutual information (MI) from all sensed targets. This structure effectively induces cooperative behavior among the agents to maximize the overall system performance without requiring explicit information exchange. Simulation results demonstrate that the proposed framework significantly outperforms various benchmark algorithms in both communication and sensing performance, validating its effectiveness for next-generation multi-UAV ISAC networks.

This paper aims to achieve Gbps-level transmission using frequency bands in the tens of GHz range. However, such high-frequency bands inherently suffer from limited propagation distance and strong susceptibility to physical obstructions. To address these challenges, this study proposes an integrated access and backhaul (IAB) network architecture that dynamically utilizes frequency resources to provide multi-hop, relay-based wireless backhaul and access links in 5G/6G environments. In the proposed system model, the satellite is defined as the IAB-donor, the ground base stations (GBSs) as IAB-nodes, and the ground devices (GDs) as user equipment (UE). Furthermore, to mitigate the latency caused by low earth orbit (LEO) satellites, a hierarchical and distributed deep Q-network (HDDQN) algorithm is employed for dynamic resource allocation, thereby maximizing the overall network sum rate.

In this paper, we propose a novel sequence index grouping-based multiple access (SIGMA) technique for multi-user orthogonal frequency division multiplexing-based integrated sensing and communication (OFDM-ISAC) systems. The proposed technique ensures collision-free access by performing a pre-assignment of orthogonal sequences (OSs) between the base station (BS) and each user equipment (UE), where each UE modulates its communication data onto the allocated sequence index and transmits it to the BS. By exploiting the autocorrelation property of the received sequences, the BS estimates the distance to each UE, while the direction of each user is obtained using a uniform linear array (ULA) antenna equipped at the BS. Furthermore, to achieve accurate localization even in non-line-of-sight (NLoS) multipath environments, a clustering algorithm and an oversampling technique are incorporated. Extensive simulation results demonstrate that the proposed system achieves robust localization and communication performance in multi-user scenarios.

To overcome energy limitation of unmanned aerial vehicle base stations (UAV-BSs), tethered UAV-BS (TUAV-BSs), which is continuously powered through a tether connected to ground stations (GSs), have been proposed. Since the performance of TUAV-BS based system is highly dependent on their three-dimensional (3D) position, optimizing the TUAV-BS’ 3D position is important. However, when the GS is fixed, the service coverage of TUAV-BS is limited. Therefore, this paper proposes hierarchical multi-agent deep Q-Network (DQN) for optimal resource allocation and position control of both TUAV-BS and movable GS to maximize the total sum rate while minimizing the number of outage user equipment (UE). Simulation demonstrates that the proposed algorithm outperforms other benchmarks.

Solar energy harvesting offers an energy-efficient solution to reduce the growing power consumption of mobile networks. However, the intermittent nature of solar energy and the spatio-temporal variability of traffic demand make stable operation and resource management challenging. To address these issues, we propose a prediction-based distributed deep Q- network (DQN) approach that dynamically controls transmission power based on predicted traffic and harvested energy. Simulation results demonstrate improved energy efficiency and network performance over benchmark methods.

In massive random access-based tactical flying adhoc networks (FANETs), the rapid mobility of unmanned aerial vehicles (UAVs) leads to highly dynamic topological changes that frequently cause collisions of control and data packets and ultimately degrade network performance. To address the collision problem, this study proposes an online safe multi-agent reinforcement learning (OSMAR)-based massive random access method, incorporating an artificial Q-adjustment (AQA) mechanism to optimally allocate transmission slots to multiple UAVs within each frame. Specifically, a safe reinforcement learning (RL) technique is employed to design the action selection policy, which balances exploration and exploitation by considering long-term rewards and risks based on a Boltzmann distribution. With this, a blacklist mechanism is incorporated to prevent UAVs from choosing the time slots with high collision risk. Also, the AQA mechanism, composed of an artificial Q-decrement (AQD) that lowers the Q-values of slots already used by other UAVs and an artificial Q-initialization (AQI) that resets the Q-values of idle slots, leverages overheard channel status information to accelerate convergence and enhance adaptation under dynamic network conditions. Extensive simulations demonstrate that the proposed OSMAR method outperforms existing approaches in terms of collision probability, while also enhancing fairness and maintaining robustness under various network conditions.

Operating in flying ad-hoc networks (FANETs) is challenging due to the highly dynamic nature of the network environment. Energy efficiency is crucial in these networks, particularly because unmanned aerial vehicles (UAVs) have limited battery capacity. In slotted-ALOHA (S-ALOHA)-based FANETs, frequent packet collisions, driven by changes in the network environment, can significantly degrade energy efficiency. Therefore, accurately estimating the number of active UAVs is essential for improving the performance and energy efficiency of S-ALOHA-based networks. Several estimation methods, such as low-bound, Schoute, max-probability, and Bayesian estimation, have been explored and perform well in static network environments. However, their estimation accuracy decreases significantly in dynamic environments. To address this issue, this study proposes a hybrid pseudo-Bayesian estimation method designed to improve estimation accuracy in highly dynamic environments. Specifically, this method combines the strengths of pure-Bayesian and pseudo-Bayesian estimation methods to overcome limitations such as the pure-Bayesian method's inadequacy in dynamic environments and the lower estimation accuracy of the pseudo-Bayesian method. This paper compares the performance of the proposed method with that of benchmark methods in terms of estimation error and the number of successful packets, considering variation periods and step sizes. The results demonstrate that the proposed method is more adaptable to dynamic changes in network environments.

In this study, we consider wireless covert communication within unmanned aerial vehicle (UAV) environments. Here, the UAV functions as a covert transmitter, sending data to predetermined ground receivers while avoiding detection by ground-based detectors. We aim to maximize the UAVs' through-put and the detector's minimum detection error probability by optimizing the UAV's transmission power and positioning through Q-learning. We utilize reinforcement learning to de-termine UAVs' optimal transmission power and location in complex environments, ensuring effective problem-solving even in challenging scenarios.

The integration of solar energy harvesting into small-cell networks is a promising solution for achieving energy-efficient and sustainable wireless communications. However, the inherent variability and intermittency of solar energy, coupled with precise intercell interference management, significantly hinder efficient network operation. To resolve these challenges, we propose a multiagent distributed deep Q-network framework, where the distributed base stations learn the optimal transmit power control policies. To further enhance adaptability under varying solar conditions, we present a daily model transfer with a fine-tuning approach, enabling efficient deployment without extensive training overhead. Simulation results demonstrate that the proposed methods remarkably improve energy efficiency while maintaining robust adaptability under dynamic solar conditions, revealing their potential for sustainable small-cell network deployments.

Recently, Internet of Things (IoT) devices have been installed everywhere, and these devices are connected through wireless communication networks. In particular, unmanned aerial vehicles (UAVs), one of the most promising IoT devices, are expected to be used actively in Internet of Battlefield-Things (IoBT) networks due to their flexible three-dimensional (3D) mobility. To react to enemy UAV attacks in the IoBT networks, the ground-to-air (G2A) or air-to-air (A2A) radio jamming can be an effective counterattack technique that disrupts the communication and control signals of adversary equipment. That is, it can be a very effective means of coping with attacks by UAVs in modern battlefields characterized by electronic warfare. Accordingly, this paper proposes a hierarchical distributed deep reinforcement learning-based cooperative jamming (HDRL-CJ) method for secure air-ground integrated networks. The proposed method uses two types of jammers: ground jammers (GJ) and UAV jammers (UJ). The GJ optimizes the beamwidth to maximize the effectiveness of jamming, and the UJ tracks the malicious UAV (MU) and controls the jamming power to minimize the MU’s signal-to-jamming-plus-noise ratio (SJNR) while considering the UJ’s limited battery capacity. Moreover, to reduce the computational complexity of reinforcement learning (RL) method, we devise a hierarchical RL architecture that separates the UJ’s movement control and transmit power control. Through extensive simulations, we demonstrate that the proposed HDRL-CJ method converges to the optimal solution obtained by the optimal exhaustive search algorithm. Furthermore, by comparing the jamming performance with several benchmark methods, we validate the proposed method’s superior performance under various 3D network environments.

Wireless-powered communications (WPCs) are increasingly crucial for extending the lifespan of low-power Internet of Things (IoT) devices. Furthermore, reconfigurable intelligent surfaces (RISs) can create favorable electromagnetic environments by providing alternative signal paths to counteract blockages. The strategic integration of WPC and RIS technologies can significantly enhance energy transfer and data transmission efficiency. However, passive RISs suffer from double-fading attenuation over RIS-aided cascaded links. In this article, we propose the application of an active RIS within WPC-enabled IoT networks. The enhanced flexibility of the active RIS in terms of energy transfer and information transmission is investigated using adjustable parameters. We derive novel closed-form expressions for the ergodic rate and outage probability by incorporating key parameters, including signal amplification, active noise, power consumption, and phase quantization errors. Additionally, we explore the optimization of WPC scenarios, focusing on the time-switching factor and power consumption of the active RIS. The results validate our analysis, demonstrating that an active RIS significantly enhances WPC performance compared to a passive RIS.

In uncrewed aerial vehicle (UAV)-aided Internet of Things (IoT) networks, providing seamless and reliable wireless connectivity to ground devices (GDs) is difficult owing to the short battery lifetimes of UAVs. Hence, we consider a deep reinforcement learning (DRL)-based UAV base station (UAV-BS) control method to maximize the network-wide energy efficiency of UAV-aided IoT networks featuring continuously moving GDs. First, we introduce two centralized DRL approaches; round-robin deep Q-learning (RR-DQL) and selective-k deep Q-learning (SK-DQL), where all UAV-BSs are controlled by a ground control station that collects the status information of UAV-BSs and determines their actions. However, significant signaling overhead and undesired processing latency can occur in these centralized approaches. Hence, we herein propose a quasi-distributed DQL-based UAV-BS control (QD-DQL) method that determines the actions of each agent based on its local information. By performing intensive simulations, we verify the algorithmic robustness and performance excellence of the proposed QD-DQL method based on comparison with several benchmark methods (i.e., RR-DQL, SK-DQL, multiagent Q-learning, and exhaustive search method) while considering the mobility of GDs and the increase in the number of UAV-BSs.

This paper proposes an algorithm to overcome the limitations of traditional ground-based stations (GBS) in providing communication services to the satellite-air-terrestrial integrated network (SATIN). An algorithm integrates low earth orbit (LEO) satellites and unmanned aerial vehicle base stations (UBS) to create a dynamic communication network with extensive coverage. Challenges arise due to LEO propagation delay and computational complexity, and cross-tier channel interference issues with efficient use of frequency resources through integrated access and backhaul (IAB) [1]. To overcome these challenges, this research proposes the hierarchical distributed Q-learning algorithm that maximizes the network sum rate through resource allocation and UBS control. To overcome these challenges, this research proposes the hierarchical distributed Q-learning algorithm that maximizes the network sum rate through resource allocation and UBS control.

This study focuses on a laser charging-based multi-unmanned aerial vehicles (UAV) network, where UAVs serve as UAV base stations (UBSs) to provide downlink coverage to ground users. To address the battery constraints of UAVs, this paper aims to enhance the energy efficiency, communication quality, and fairness in a laser-charged multi-UBS network and proposes a deep Q-network (DQN)-based algorithm to control the UBS deployment and communication transmit power. Simulation results confirm that the proposed algorithm outperforms other benchmark algorithms.

The integration of the integrated access and back-haul (IAB) network and tethered flying platform (TFP) solves the performance degradation problem of airborne base stations (ABS) due to battery constraints and provides flexibility in topology. Therefore, this study proposes a distributed deep Q-Network (DQN)-based resource allocation and tethered unmanned aerial vehicles (TUAVs) placement control (RAPC) joint optimization scheme to maximize the total sum rate of IAB network supported by TUAVs and tethered balloon (TB). Simulations demonstrate that the RAPC achieves a high aggregate total sum rate compared to several benchmarks, and has robust performance maintained in various ground users (GUs) moving speed environments.

Non-orthogonal multiple access (NOMA) techniques can recover collided signals simultaneously transmitted from different users that select different target received signal strength (RSS) levels. In this study, we apply NOMA to the slotted ALOHA protocol in an air-to-ground communication environment, where multiple unmanned aerial vehicles (UAVs) attempt random access to a ground control station (GCS). In such a wide airspace, the channel gain from UAVs to the GCS exhibits significant disparities and thus the UAVs far from the GCS are restricted to selecting lower target RSS levels due to the practical limitation on the maximum transmit power of UAVs. This limitation increases the probability that UAVs will choose lower target RSS levels and leads to a fairness issue between near and far UAVs. To address this challenge, we enhance the basic NOMA-ALOHA protocol in which the number of UAVs selecting each RSS level is adjusted and the probability of selecting each RSS level is determined in order that the selected RSS levels are distributed as evenly as possible. Subsequently, we present the operation of the proposed NOMA-ALOHA protocol between the GCS and UAVs, and analyze the throughput of NOMA-ALOHA protocols, taking into account the impacts of the maximum transmit power limit and our adjustment algorithm. Analysis and simulation results show that the proposed NOMA-ALOHA improves both throughput and fairness performances against the conventional NOMA-ALOHA and also enhances the trade-off between throughput and coverage in air-to-ground communication environments with a maximum transmit power limit.

Sixth-generation (6G) networks pose substantial security risks because confidential information is transmitted over wireless channels with a broadcast nature, and various attack vectors emerge. Physical layer security (PLS) exploits the dynamic characteristics of wireless environments to provide secure communications, while reconfigurable intelligent surfaces (RISs) can facilitate PLS by controlling wireless transmissions. With RIS-aided PLS, a lightweight security solution can be designed for low-end Internet of Things (IoT) devices, depending on the design scenario and communication objective. This article discusses RIS-aided PLS designs for 6G-IoT networks against eavesdropping and jamming attacks. The theoretical background and literature review of RIS-aided PLS are discussed, and design solutions related to resource allocation, beamforming, artificial noise, and cooperative communication are presented. We provide simulation results to show the effectiveness of RIS in terms of PLS. In addition, we examine the research issues and possible solutions for RIS modeling, channel modeling and estimation, optimization, and machine learning. Finally, we discuss recent advances, including simultaneous transmitting and reflecting-RIS and malicious RIS.

Recently, unmanned aerial vehicle (UAV)-aided cellular networks have emerged as one of the core technologies for the realization of the forthcoming B5G and 6G mobile communications. However, the development of the battery capacity of small devices (such as UAVs) is too slow compared to that of wireless communication technologies. It causes the frequent replacement of UAV-base stations (UBS) in the UBS networks, which makes providing seamless and reliable services difficult. In this paper, we aim to control UBSs to maximize the energy efficiency of the UBS network in which the ground users (GUs) are moving at a practical speed. Specifically, we propose a deep reinforcement learning (DRL)-based centralized UBS control approach considering the mobility of GUs. First, we introduce a sequential control deep Q-network (SC-DQN). This method is free from a large action size that is problematic in the centralized DQN as controlling the UBS in a round-robin manner. However, since UBSs must wait to be controlled until their turn, it is inefficient. Ultimately, we propose a multi- UBS selective-k control DQN (MUSK-DQN) method to complement the problem. Since the MUSK-DQN controls$k$UBSs that can contribute to the performance increment of network-wide energy efficiency according to the network situation, the MUSK-DQN can efficiently control UBSs more than the SC-DQN method. Through intensive simulations, it is verified that the proposed MUSK-DQN method achieves higher energy efficiency than the conventional methods including SC-DQN, and the performance of the proposal is robust according to the moving speed of GUs.

We propose a novel practical access point (AP) deployment technique for 6G ultra-reliable industrial Internet-of-things (IIoT) networks. In particular, a cell-free massive multiple-input multiple-output (CF -mMIMO) architecture is adopted to improve reliability with macro-diversity, where a station (STA) is simultaneously associated with and served by multiple APs. We first mathematically formulate an integer linear programming (ILP)-based optimization problem that minimizes the number of required APs while satisfying a certain quality-of-service (QoS) of IIoT networks, such as the minimum number of concurrent communication links and the minimum required signal-to-noise ratio (SNR). However, the optimal technique requires significant computational complexity and time, unfortunately. Hence, we propose a low-complexity AP deployment algorithm based on parallel search methods, named PRADA. The proposed algorithm sufficiently reduces the number of required APs in a computationally efficient manner while satisfying the QoS constraints.

Optimizing the placement and transmit power control of unmanned aerial vehicle-base stations (UAV-BSs) is a key priority in 6G air-to-ground communication networks. A multi-UAV network brings many advantages, such as high line-of-sight (LoS) probability, three-dimensional (3D) connectivity, flexible mobility, and cost-effectiveness. However, it still has a problem of severe battery constraints. To overcome this battery problem, the concept of a tethered UAV (TUAV) is introduced, and it receives sufficient battery power from the ground through a tether. Therefore, we propose a decentralized Q-learning-based optimal placement and transmit power control algorithm (DQ-OPP) to maximize the individual data rate of each TUAV-BS. Through simulations, we show that the proposed DQ-OPP algorithm outperforms the conventional algorithms.

In wireless communication networks including unmanned aerial vehicles (UAVs), joint communication and radar (JCR) using a single waveform for both communication and sensing functions has been considered. In the JCR, the power allocation to the pilot and data parts can be optimized in terms of communication and sensing performance metrics. Furthermore, to serve ground users effectively, the location of UAVs, which receive the transmit signal from a base-station (BS) and forward to ground users, should be optimized. In multi-UAV environments, the optimization of signal power and UAV's position becomes too complicated to solve with a conventional optimization framework. Therefore, a reinforcement learning approach, i.e., multi-agent Q-learning, is adopted to optimize the UAV-assisted JCR networks.

Massive Internet of Things (IoT) networks have a wide range of applications, including but not limited to the rapid delivery of emergency and disaster messages. Although various benchmark algorithms have been developed to date for message delivery in such applications, they pose several practical challenges, such as insufficient network coverage and/or highly redundant transmissions to expand the coverage area, resulting in considerable energy consumption for each IoT device. To overcome this problem, we first characterize a new performance metric, forwarding efficiency, which is defined as the ratio of the coverage probability to the average number of transmissions per device, to evaluate the data dissemination performance more appropriately. Then, we propose a novel and effective forwarding method, fishbone forwarding (FiFo), which aims to improve the forwarding efficiency with acceptable computational complexity. OurFiFomethod completes two tasks: 1) it clusters devices based on the unweighed pair group method with the arithmetic average and 2) it creates the main axis and subaxes of each cluster using both the expectation-maximization algorithm for the Gaussian mixture model and principal component analysis. We demonstrate the superiority ofFiFoby using a real-world data set. Through intensive and comprehensive simulations, we show that the proposedFiFomethod outperforms benchmark algorithms in terms of the forwarding efficiency.

In air-to-ground communications, the network lifetime depends on the operation time of unmanned aerial vehicle-base stations (UAV-BSs) owing to the restricted battery capacity. Therefore, the maximization of energy efficiency and the minimization of outage ground users are important metrics of network performance. To achieve these two objectives, the location and transmit power of the UAV-BSs in the network must be optimized. This optimization problem may not be tractable in the conventional optimization framework because multiple UAV-BSs interact in a complicated manner. Hence, we formulate the problem as a Markov decision process and develop an algorithm to obtain a solution in a reinforcement learning framework. To avoid a central controller and high computational complexity, we employ a multiagent distributed${Q}$-learning algorithm to obtain a solution. Specifically, we propose a multiagent${Q}$-learning-based UAV-BS deployment and power control strategy to maximize energy efficiency and minimize the number of outage users in multi-UAV wireless networks. Through intensive simulations, it is demonstrated that the proposed algorithm can outperform benchmark algorithms in terms of average energy efficiency and number of average outage users in multi-UAV wireless networks.

An unmanned aerial vehicle-mounted base station (UAV-BS) is a promising technology for the forthcoming sixth-generation wireless networks, owing to its flexibility and cost effectiveness. Besides, the limited network operation time of UAV-BS networks can be overcome with the concept of tethered unmanned aerial vehicles (TUAVs), which are powered from an energy source in the ground. Along with this trend, the optimal deployment (i.e., trajectory control) of TUAVs to maximize throughput in multicell environments has been studied. As the problem is modeled by a Markov decision process, a multiagent$Q$-learning (QL) algorithm was developed to obtain a solution. When considering the limited inter-UAV link capacity and computing power of each UAV, the proposed multiagent QL algorithm can be a practical approach. Intensive simulations were conducted to evaluate the performance of the proposed algorithm with respect to various metrics, such as sum or individual rates, fairness, and computational complexity in multicell air-to-ground (A2G) networks. Our proposed algorithm achieves superior performance compared to conventional algorithms, such as random action, QL-based altitude control algorithms (QAC), and centralized QL algorithm.

For industrial Internet of Things network operators, undesired and abrupt network failure is a critical problem to be resolved quickly. In order to provide reliable communication services to devices in faulty cells, in this article, we propose a distributed autonomous self-healing mechanism that allows a random-access-based instantaneous communication to the neighbor cells. The design of the self-healing mechanism is challenged by the diverse device locations and the different available number of channels provided by the neighbor cells due to their intracell traffic load. By estimating the number of devices communicating with each neighbor cell in an online manner, our proposed mechanism can control the channel access probability of each cell to maximize throughput. In addition, the devices can reselect their serving cells in a distributed manner while realizing maximum but fair throughput among devices. Through extensive simulations, we show that our proposed mechanism can provide effective performance for autonomous self-healing.

This paper investigates achievable rates of a multi- user mode-division multiplexing (MDM) system using orbital angular momentum (OAM), where multiple transceivers exploit the same OAM modes. In general, multiple independent signals with different OAM modes are mutually orthogonal between perfectly aligned transmitter and receiver antennas, but the OAM signals among multiple users may interfere with each other. Note that we analyze the achievable rate of multi-user MDM system that utilizes the microwave OAM for the first time. Through extensive computer simulations, we analyze the achievable rate of the multi-user OAM-MDM system by considering the multi-user interference. It is worth noting that the communication scenario we consider in this paper has not been investigated in literature so far.

In wireless broadcast environments, a system performance can be improved with random linear network coding (RLNC). In RLNC, a source transmits a set of coding coefficients and a coded packet together. The coded packet is a combination of the coding coefficients and uncoded packets. Using the key feature of RLNC, we herein propose a new RLNC scheme to minimize outage probability with a feedback channel. In addition, the outage probability of the proposed scheme is analyzed in a Rayleigh fading channel. Through the numerical analysis and Monte Carlo simulations, we demonstrate that the outage probability of the proposed scheme is lower than that of a conventional RLNC scheme.

Recently, self-healing has been actively investigated for mitigating an unforeseen network failure. In particular, to enable self-healing operations in indoor wireless communications systems, an autonomous mechanism to resolve unforeseen network failure problems should be considered. Therefore, we here address the issue of autonomous self-healing, in which continuous connectivity can be provided to users by resolving unexpected network failures. To overcome this problem, we propose a low-complexity resource allocation algorithm based on an optimization approach with reduced message passing. In the proposed algorithm, normal base stations perform subchannel and power allocations with a minimum amount of information sharing (NH(M-1) to provide reliable service to users in faulty cells autonomously. We also show that the proposed algorithm converges to a unique fixed point in the low-interference region by using a contraction mapping technique. Through simulation results, we demonstrate that the proposed algorithm achieves good performances with respect to the average cell capacity, user fairness, and outage probability while reducing the message passing overhead and computational complexity.

In social commerce services, if the purchase condition for the minimum number of ordered users is satisfied, customers can get a great deal of discount for the corresponding products. The convergence of D2D communications and the social commerce services may create a synergistic effect because the D2D user can spontaneously relay the advertisement messages to their neighbors. Accordingly, we here propose Wi-Fi Direct based voluntary advertisement dissemination scenario and algorithm (VADA) for social commerce services. By using our proposed VADA algorithm, the small business owners are able to transmit advertisement messages to many local D2D users cost-effectively. Through intensive simulations, we evaluate the performance excellency of our proposed algorithm with respect to total number of successfully received users, average number of relay users, and transmission efficiency compared with conventional algorithms and optimal algorithm based on exhaustive search.

For the purpose of automating network management, self-organizing network (SON) technology is currently being investigated. There are three important issues in SON: self-configuration, self-optimization, and self-healing. This paper focuses on self-healing, in order to resolve the problem of unexpected network faults and improve network throughput simultaneously. To deal with this problem, we propose a healing channel selection and a cooperative beamforming-based iterative resource allocation algorithms. We utilize the cooperative beamforming in the healing channel based on phase pre-adjustment, and this cooperative beamforming can be performed without power cooperation between distributed nodes. Moreover, we derive the sub-optimality and convergence of the proposed algorithm in weak interference condition by using contraction mapping. Finally, simulation results demonstrate that the proposed algorithm improves average cell capacity and user fairness while repairing network failure effectively.

The main objectives of a self-organizing network (SON) technology are autonomous network deployment, network performance optimization and real-time adaptation to environmental changes. There are three functionalities in SON, such as self-configuration, self-optimization and self-healing. In this paper, we focus on the self-healing issue with respect to abrupt network faults. In order to solve this problem, we design a healing channel (HC) and propose a collaborative resource allocation (CRA) algorithm based on a modified iterative water-filling (MIWF) algorithm. Through intensive simulations, we show that CRA efficiently supports users in disabled femtocell base stations (FBS) with the little degradation in system capacity.

We propose a fault-management channel (FMC) to address the problem of indoor-RAS (radio access station) faults and analyze it by using an optimization method. To reduce the complexity of the optimization problem, we also propose a suboptimal algorithm that is based on the FMC: an equal power allocation algorithm (EPA). Our main contributions are as follows: 1) proposing a solution for indoor-RAS faults; and 2) presenting an efficient suboptimal algorithm (EPA) for the improvement of fairness. Through intensive simulations, we evaluate the performance of our proposed algorithm with respect to fairness, spectral efficiency, and a new proposed metric that considers spectral efficiency and fairness together.

A new algorithm is proposed for wireless voice over IP (VoIP) codecs that allocates radio resources in consideration of the variations in packet size and packet-generation period. This algorithm allows resources to be utilized more efficiently in mobile-WiMAX systems. The outstanding performance of our proposed algorithm with respect to average throughput and average packet dropping probability is demonstrated through intensive numerical analysis and simulations. VoIP capacity is compared with respect to the application of different VoIP codecs (G.729B, adaptive multirate (AMR)) and VoIP header compression schemes (payload header suppression (PHS) and robust header compression (ROHC)).

We review previous resource allocation algorithms for voice over IP (VoIP) services in Mobile WiMAX based on the IEEE 802.16e-2005 standard, and propose a new algorithm that allocates radio resources in the consideration of packet-size and packet-generation period variations of wireless VoIP codecs; hence, we can improve the efficiency of resource utilization in Mobile WiMAX. Through simulations, we demonstrate the outstanding performance of our proposed algorithm with respect to packet dropping probability and VoIP capacity for various VoIP codecs, such as adaptive multi-rate wideband (AMR-WB) codec, enhanced variable rate codec (EVRC), and G.729B codec. In addition, we compare VoIP capacity of each algorithm according to the application of VoIP header compression schemes, such as payload header suppression (PHS) and robust header compression (ROHC).

To support dynamic traffic-asymmetry property in future wireless communication systems, we propose a hybrid- TDD scheme, combination of static-TDD and dynamic-TDD. By using adaptive power control, inner/outer scheduling and hybrid-link for guaranteeing safe downlink/uplink time-slots, we can effectively solve the interference problems of the dynamic- TDD scheme. Especially, an adaptive downlink power control strategy in hybrid-link region can efficiently reduce severe BS- BS interference compared with other conventional schemes. Through numerical analysis and simulation results, we prove that our proposed scheme has the best performance compared with other conventional schemes in view of spectral efficiency and downlink/uplink outage probability.

There are several scheduling algorithms for Voice over IP (VoIP) services in IEEE 802.16 systems, such as unsolicited grant service (UGS), real-time polling service (rtPS), UGS with Activity Detection (UGS-AD), and Lee's algorithm using Grant-Me bit of the generic MAC header. However, these algorithms have some problems of a waste of uplink resources, additional access delay, and MAC overhead for supporting VoIP services with variable data rates and silence suppression. To solve these problems, we propose a novel uplink scheduling algorithm (Extended-rtPS) for the VoIP services in IEEE 802.16 systems. Through the performance analysis and simulation results of resource utilization, VoIP capacity, total throughput, and packet transmission delay, we show that our proposed algorithm can solve the problems of the conventional algorithms, and has the best performance among these algorithms. In addition, with simulation results of packet transmission delay, we prove that our proposed algorithm can support more 74%, 24%, and 9% voice users compared with the UGS, rtPS, and UGS-AD (Lee's) algorithms, respectively.

There are several scheduling algorithms for voice over IP (VoIP) services in IEEE 802.16e systems, such as unsolicited grant service (UGS), real-time polling service (rtPS), and extended real-time polling service (ertPS). The ertPS is a new scheduling algorithm for VoIP services with variable data rates and silence suppression, and this algorithm is recently proposed and accepted in the IEEE 802.16e standard. In this paper, we analyze and discuss the performance of the scheduling algorithms recommended in IEEE 802.16e systems including the ertPS algorithm. Through the analysis of resource utilization efficiency and VoIP capacity, we show that the UGS and rtPS algorithms have some problems, which are the waste of uplink resources in the UGS algorithm, and additional access delay and MAC overhead due to bandwidth request process in the rtPS algorithm, to support the VoIP services. In addition, for analysis of VoIP capacity, we utilize OPNET simulation, and show that the ertPS algorithm can support more 21% and 35% voice users compared with the UGS and rtPS algorithms, respectively

Handover has been the most important thing for supporting the mobility and has been researched in various wireless communication systems. Handover schemes in systems with multiple antennas, the technology highlighted for 4G wireless communications to increase the capacity, have frequently been made and considered to be very important. However, handover decision method using the number of multiple antennas has not been researched. In this paper, we propose a new handover decision method using the number of detected antennas and list requirements and distinctive features for this scheme. Simulation results show that our proposed scheme and conventional scheme trigger handovers at the almost same point. Since our proposed scheme is simpler than the conventional scheme, it has the better performance in view of feedback quantity.