In mobile edge computing (MEC) networks, the limited computational capabilities of edge devices present challenges in achieving energy-efficient and high-accuracy inference, highlighting the critical role of cooperative inference with an edge server. This study proposes a cooperative inference framework in which edge devices employ dual confidence thresholds to filter ambiguous images, which are stored in task buffer queues, and offloaded to the edge server for more accurate inferences. We conduct a numerical analysis of the inference accuracy and energy consumption for the proposed cooperative inference method and formulate a joint optimization problem to determine the optimal confidence thresholds, offloading ratio, transmit power, and transmission/reception time, aiming to minimize energy consumption while ensuring accuracy and queue stability. We employ a Lyapunov-based optimization approach to convert this optimization problem into a time-independent real-time decision problem. Subsequently, we decompose it into tractable subproblems and propose an iterative algorithm based on the block coordinate descent method to derive suboptimal variables efficiently. The experimental results reveal a trade-off between inference accuracy and cooperation penalty depending on the confidence thresholds. In particular, optimizing these thresholds in conjunction with radio resources significantly reduces energy consumption and queue backlog while maintaining the required accuracy in diverse MEC environments, compared with the conventional inference methods.

This paper proposes a Transformer-basedshared embedding (SE)scheme as an artificial intelligence (AI)-native multiple access for semantic communications. In SE, multiple users jointly occupy a high-dimensional embedding space and are separated by learnable user-specific positional masks together with attention-driven demultiplexing, so that inter-user interference is suppressed directly in the embedding domain. The resulting interference structure closely resembles interference alignment (IA), wherein residual multiuser energy is compressed into a low-rank subspace that the decoder can effectively attenuate. We develop closed-form expressions for signal power, effective signal-to-interference-plus-noise ratio (SINR), symbol error rate (SER), and per-user capacity, explicitly characterizing the roles of intra-user coherence and an alignment coefficient that quantifies attention-induced interference suppression. These formulas yield theoretical baselines that situate SE performance between a no-alignment lower bound and an ideal non-orthogonal multiple access (NOMA)-inspired upper bound and explain capacity gains in dense regimes via statistical multiplexing and alignment. Experimental evaluations are carried out under Rayleigh fading channels, and the results show that SE consistently outperforms the dedicated embedding (DE) scheme, which assigns each user an exclusive portion of the embedding space, across different channel conditions and user densities. The empirical SE curves match the theoretical predictions with fitted alignment factors and approach the NOMA-inspired bound as the embedding dimension increases, thereby showing that the embedding space can function as a new axis of multiple access complementary to time, frequency, and code domains. Finally, we outline adaptive and generalized training strategies that extend beyond symbol-level recovery to semantic-level classification and reconstruction tasks for next generation networks.

In this paper, we propose a new cooperative infer-ence method between the end device and the edge server for intelligent surveillance services. In this method, the end device with a small neural network (NN) model operates with dual confidence thresholds to filter ambiguous input images, which are then forwarded to the edge server and reevaluated by a large NN model. We numerically analyze the performance of the proposed method in terms of accuracy and end-to-end latency, taking into account the confidence scores derived from both positive images and negative images that induce false alarms. Subsequently, we identify the optimal confidence thresholds for both the end device and the edge server to minimize the end-to-end latency while ensuring the required accuracy. The simulation and analysis results show that a tradeoff exists between accuracy and latency according to the confidence thresholds, and the selection of optimal confidence thresholds significantly reduces the latency while satisfying the required accuracy. Accordingly, the proposed method achieves higher accuracy than the device-only inference and lower latency than the server-only inference. This highlights the importance of employing cooperative inference with optimal confidence thresholds in surveillance systems to avoid the fate of ‘The Boy Who Cried Wolf.’

To overcome the limitations of standalone inference that relies on either an edge device or a server, this study proposes a new cooperative inference method between the edge device and the edge server for intelligent surveillance services. In this method, the edge device equipped with a small neural network (NN) model operates with dual confidence thresholds to filter ambiguous input images, which are then forwarded to the edge server and reevaluated by a large NN model. We numerically analyze the performance of the proposed method in terms of inference accuracy and end-to-end latency, taking into account the distribution of confidence scores resulting from positive images as well as negative images that may induce false alarms. Subsequently, we formulate an optimization problem to minimize the end-to-end latency while ensuring the required accuracy, and propose a greedy search algorithm to find the optimal confidence thresholds with low complexity in a nonconvex problem. We also present an operational framework to utilize the proposed cooperative inference method in a practical on-site environment. The simulation and analysis results show that a tradeoff exists between accuracy and latency according to the confidence thresholds, and the selection of optimal confidence thresholds significantly reduces the latency while satisfying the required accuracy. Therefore, the proposed cooperative inference achieves higher accuracy than the device-only inference and much lower latency than the server-only inference across various system parameters. This verifies the importance of optimizing confidence thresholds when applying a cooperative inference method to mobile edge networks.

This study investigates an interference coordination for multiple unmanned aerial vehicle (UAV)-enabled communications under a probabilistic Line-of-Sight (LoS) channel model. Given that the LoS probability of a wireless channel is determined by the elevation angle between the UAV and ground node (GN), we jointly optimize the 3-D trajectory, user scheduling, and transmit power of the UAVs to maximize the minimum average spectral efficiency (SE) among GNs. We first present an effective lower bound for the channel-state-dependent SE to handle its complex form that inevitably arises from the probabilistic LoS channel model. To solve the nonconvex optimization problem under consideration, we also divide the original problem into four subproblems and apply a successive convex approximation to make each subproblem convex for a relevant optimization variable. We then propose an iterative algorithm based on a block coordinate descent method to efficiently find the optimal solution for each convex subproblem. The results of extensive simulation show that to mitigate interference with other UAV networks, each UAV optimizes its horizontal and vertical trajectories, along with its radio resources, to establish LoS for desired channels and Non-LoS for interference channels. The proposed scheme is also verified to be superior to conventional schemes in terms of average SE by effectively adjusting co-channel interference between different UAV networks.

Accurate and real-time three-dimensional (3D) pose estimation is challenging in resource-constrained and dynamic environments owing to its high computational complexity. To address this issue, this study proposes a novel cooperative inference method for real-time 3D human pose estimation in mobile edge computing (MEC) networks. In the proposed method, multiple end devices equipped with lightweight inference models employ dual confidence thresholds to filter ambiguous images. Only the filtered images are offloaded to an edge server with a more powerful inference model for re-evaluation, thereby improving the estimation accuracy under computational and communication constraints. We numerically analyze the performance of the proposed inference method in terms of the inference accuracy and end-to-end delay and formulate a joint optimization problem to derive the optimal confidence thresholds and transmission time for each device, with the objective of minimizing the mean per-joint position error (MPJPE) while satisfying the required end-to-end delay constraint. To solve this problem, we demonstrate that minimizing the MPJPE is equivalent to maximizing the sum of the inference accuracies for all devices, decompose the problem into manageable subproblems, and present a low-complexity optimization algorithm to obtain a near-optimal solution. The experimental results show that a trade-off exists between the MPJPE and end-to-end delay depending on the confidence thresholds. Furthermore, the results confirm that the proposed cooperative inference method achieves a significant reduction in the MPJPE through the optimal selection of confidence thresholds and transmission times, while consistently satisfying the end-to-end delay requirement in various MEC 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.

In multibeam satellites, transmit power is a limited resource shared among beams, and allocating higher power to certain beams may cause more interference with others. Moreover, beam placement is the issue of determining the center position of each beam and potentially leads to interbeam interference depending on the locations of ground nodes. Therefore, in this study, we investigate the joint optimization problem of beam placement and transmit power to maximize the sum spectral efficiency in multibeam low-Earth-orbit satellite communication systems, taking into account the interbeam interference and user distribution. We solve the optimization problem using the gradient ascent method and quadratic transform, and then propose an optimization-based algorithm that iteratively searches for the beam center positions and transmit power levels. To reduce the complexity of this iterative algorithm, we present a deep neural network (DNN) architecture and a training method for approximating optimal solutions, and propose a deep-learning (DL)-based algorithm that quickly infers the optimal values using the pretrained DNN. Simulation results show that the two proposed algorithms have a clear tradeoff in performance between the spectral efficiency and the computation time. In particular, the DL-based algorithm achieves 3% lower spectral efficiency than the optimization-based algorithm; however, the computation time can be significantly reduced. Furthermore, both schemes achieve at least 10% higher spectral efficiency than the benchmark schemes without joint optimization by optimally adjusting both the beam center position and transmit power according to the node distribution and satellite environments.

This study considers an unmanned aerial vehicle (UAV)-assisted simultaneous wireless information and power transfer (SWIPT) system in which the UAV broadcasts wireless signals to ground nodes (GNs) that receive information and harvest energy simultaneously using a policy of power splitting (PS) or time switching (TS). While taking into account the throughput and fairness of GNs, we investigate a joint optimization of the trajectory, transmit power of the UAV, and the energy harvesting (EH) ratio of the GNs to maximize the sum of the logarithmic average throughput of the GNs while ensuring the average harvested energy requirement for each GN in terms of both linear and nonlinear EH models. We employ a successive convex approximation method to address the nonconvex nature of this problem, which stems from incorporating a nonlinear EH model. This approach allows us to approximate the problem as convex for each control parameter. Thereafter, we propose a respective iterative algorithm based on the block coordinate descent method to identify the optimal solution for each convex problem under each PS or TS policy. Extensive simulations confirm that the proposed method improves average throughput and fairness index while satisfying the EH constraint by effectively optimizing the three control parameters, thereby achieving a near-optimal performance that is superior to existing baseline methods. Our results also reveal significant differences in the UAV trajectory and resource allocation patterns between linear and nonlinear EH models under the PS and TS policies. Furthermore, we explore the practical aspects of EH by comparing both EH models, such as the limitations of the linear EH model in satisfying the EH requirement in real-world SWIPT environments. The findings underscore the importance of considering nonlinear EH models in practical UAV-assisted SWIPT environments.

This paper considers a multiple unmanned aerial vehicle (UAV)-enabled wireless powered communication network (WPCN). In this WPCN, UAVs broadcast radio frequency (RF) signals to facilitate a wireless power transfer (WPT) during the downlink phase, and ground nodes (GNs) harvest energy from these RF signals and transmit data to their respective UAVs in the uplink phase. To maximize the minimum uplink throughput of GNs, we jointly optimize the scheduling, transmit power of GNs, and trajectory of UAVs, while satisfying the energy neutrality of GNs and the mobility constraints of UAVs. To solve this non-convex optimization problem, we apply a successive convex approximation to divide the original problem into subproblems and make each of them convex for each optimization variable. Subsequently, we propose an iterative algorithm based on a block coordinate descent technique and efficiently find the optimal solution for each convex subproblem. The simulation result reveals that resource allocation and the trajectory of UAVs are strongly influenced by the interference level within the network. Furthermore, the result verifies that the proposed optimization approach significantly outperforms existing baseline schemes by properly coordinating co-channel and cross-link interferences between distinct UAV networks.

In this paper, we consider an unmanned aerial vehicle (UAV)-powered wireless sensor network (WSN) in which a UAV transfers wireless energy to ground sensor (GS) nodes using a directional antenna, and the GS nodes use the harvested energy to transmit sensing information back to the UAV. Considering the tradeoff aspect in adjusting the antenna beamwidth and altitude of the UAV, we jointly optimize the altitude and beamwidth to collect sensing data from all GSs as quickly as possible. The numerical analysis verifies the existence of unique altitude and beamwidth values that minimize the data collection time. The simulation result indicates that the use of a directional antenna and the joint optimization of altitude and beamwidth are essential for maximizing the performance of UAV-powered WSN systems.1

In this article, we consider wireless-powered two-way communication in an$N$-user interference channel with imperfect channel state information (CSI). In the system considered, the receivers harvest energy and receive information simultaneously from data signals sent by transmitters using a time switching (TS) policy, before transmitting response signals back to the transmitters in a subsequent phase using the harvested energy. We aim to find the resource allocation that allows the transmit power and TS ratio to be determined jointly to maximize the sum rate of the response links while guaranteeing a predetermined rate requirement for each data link, even in the presence of errors in the estimated CSI. To deal with the nonconvexity of our optimization problem, we first introduce a gradient algorithm with a barrier function that finds suboptimal solutions heuristically. Moreover, to overcome the limitations of the gradient algorithm, e.g., its high computational complexity and vulnerability to channel error, we devise a robust strategy for resource allocation based on deep learning, in which artificially distorted CSI is fed into the deep neural network (DNN) during training to compensate for the incompleteness of the derived solutions caused by channel error. The performances of the considered schemes are examined through simulations, in which the proposed DNN scheme achieves a near-optimal performance with respect to the sum rate of the response links and outage probability under imperfect CSI, which validates its usefulness and robustness.

To extend the lifetime of a multihop network by addressing energy shortages in wireless nodes, we apply wireless energy transfer (WET) technology to the multihop transmission. Considering a linear multihop topology, we establish a system model for a WET-enabled multihop transmission and formulate an optimization problem that obtains the optimal WET time of each node to maximize the lifetime of multihop networks. To solve this problem, we adopt a flocking model inspired by the similarity between flocking behaviors and WET-enabled multihop transmissions. Applying the underlying principles of the flocking model, we propose a bioinspired cooperative WET (BiCoWET) algorithm, in which each node adjusts its own WET time to equalize the lifetime of all nodes in a distributed manner. Theoretical analysis verifies that the proposed BiCoWET algorithm achieves optimality with exponential convergence. The intensive simulation shows that the proposed BiCoWET outperforms the conventional multihop transmission methods without WET and maximizes the network lifetime by equalizing the lifetime of all nodes.

To extend the lifetime of multihop communication suffering from energy shortages in wireless nodes, we apply the concept of wireless energy sharing (WES) to bidirectional linear multihop transmission and formulate an optimization problem to determine the amount of energy shared in each node that maximizes the lifetime of the multihop path. To solve this problem analytically, we first verify that the lifetimes of multihop nodes have the solidarity property and reveal that the lifetime of the multihop path is maximized when the lifetimes of all the constituting nodes become equal. Based on this property, we then convert the considered optimization problem to a tractable linear programming (LP) problem and obtain the optimal amount of energy shared in each node by solving this LP problem in a centralized manner. Considering the control overhead and complexity in this centralized WES, we also propose a distributed WES operation in which each node autonomously determines the amount of energy shared by matching its lifetime with its neighbors' lifetime without a central coordinator. Thereafter, we prove that the proposed distributed WES algorithm always guarantees convergence, and numerically analyze the control overhead in both centralized and distributed WESs. Intensive simulations in various environments demonstrate that the proposed WES algorithm maximizes the lifetime of the multihop path by equalizing the lifetimes of all the nodes and, thus, increasing the path lifetime almost twice as much as that of the typical one-way wireless energy transfer. Moreover, distributed WES achieves a near-optimal performance and exhibits a smaller control overhead than centralized WES as the number of hops increases.

To solve security vulnerability and energy scarcity problems in relay, we propose two secure relaying protocols, power splitting-based relaying (PSR) and time switching-based relaying (TSR), in a wireless-powered relay network with destination-assisted cooperative jamming. In these protocols, the relay adaptively controls the amount of energy harvested from the received signals using PS or TS policy, considering information leakage to the eavesdropper. We first prove the convexity of the secrecy outage probability with respect to the PS ratio ($\rho $) and TS ratio ($\alpha $), and then derive the closed-form expressions of the optimal$\rho $and$\alpha $for minimizing secrecy outage under the signal-to-noise ratio (SNR) assumption. Numerical results reveal that the proposed PSR and TSR protocols using the derived$\rho $and$\alpha $can achieve near-optimal performance in terms of secrecy outage. It is observed that the optimal$\rho $and$\alpha $do not depend on the eavesdropping channels in a high SNR regime such that the near-optimal secrecy outage can be achieved practically without knowledge of the eavesdropper location. Furthermore, intensive simulations reveal that it is advantageous to allocate more power to energy harvesting for PSR, whereas more time to signal processing for TSR to minimize secrecy outage.

In this paper, we consider a wireless-powered two-way communication, calledtransmit-harvest-respond, with co-channel interference. The two-way communication considered here comprises three steps: i) transmitters send data signals, ii) receivers decode information and harvest energy simultaneously from the received signals using a policy of time switching (TS) or power splitting (PS), and iii) receivers transmit responses back to transmitters using this harvested energy. We aim to find the transmit power and energy harvesting ratios that maximize the sum rate of the forward links while ensuring a minimum rate requirement for each backward link. Due to the non-convexity and NP hardness of the optimization problem considered here, we first derive suboptimal solutions using an iterative algorithm (IA) on the basis of asymptotic strong duality. In view of the high computation time of the IA, we then design an efficient deep neural network (DNN) framework and novel training strategy as a means of combining supervised and unsupervised training. Specifically, DNNs are pre-trained using the suboptimal solutions obtained by the IA in a supervised manner, as a means of initialization; further training is then applied to DNNs using a well-designed loss function in an unsupervised manner to enhance performance. Simulation results reveal that the pre-training technique using IA solutions is beneficial for improving the performance of the DNN. The proposed hybrid scheme thus achieves near-optimal performances with a lower computation time, compared with the use of IA or DNN alone.

We consider a sectorized wireless powered sensor network (WPSN) where sensor nodes transmit data to their cluster head (CH) using the energy harvested from the hybrid access point (HAP) and the CH transmits the aggregated sensing data to the HAP using the harvested energy. We construct a system model for this sectorized WPSN operation and formulate an optimization problem to find an optimal CH that maximizes the achievable rate of sensing data. To find the optimal CH with low complexity, we perform an asymptotic geometric analysis (GA) that informs the optimal position of CH in the sector area and enables us to find the optimal CH near this optimal position. Simulation results show that the proposed GA-based CH selection method approaches the optimal performance obtained by exhaustive search while significantly reducing overhead.

For energy-neutral operation (ENO) of wireless sensor networks (WSNs), we consider a WSN deployed in a wireless powered communication network. In this network, sensor nodes with high harvesting energies and good link budgets have energy remaining after sending their data to the cluster head (CH), whereas the CH suffers from energy scarcity. Thus, we apply the simultaneous wireless information and power transfer (SWIPT) technique so that the sensor nodes can transfer their remaining energy to the CH while transmitting data in a cooperative manner. To maximize the achievable rate of sensing data while guaranteeing ENO, we propose a novel ENO framework, which provides a frame structure for SWIPT operation, rate improvement subject to ENO, SWIPT ratio optimization, as well as clustering and CH selection algorithm. The results of extensive simulations demonstrate that the proposed scheme based on SWIPT significantly improves the achievable rate while guaranteeing ENO, in comparison with the conventional schemes without SWIPT.

Low-power wide-area (LPWA) networks have been proposed to satisfy the features of massive machine-type communication (mMTC) of Internet-of-Things (IoT) networks with the large number of end devices, such as low data rates, high network scalability, wide area coverage, and delay tolerance with very low energy cost. The LoRa wide-area network (LoRaWAN) is one of the leading technologies among LPWA networks (LPWANs) and supports three types of medium access control (MAC) options: Class A, Class B, and Class C, each of which are used to address different needs of various applications. Specifically, Class B is designed to reduce downlink frame transmission delay while the end device maintains a relatively low energy consumption. In this article, we propose an analytical model of LoRaWAN Class B mode, focusing on the delay, the data throughput, and the energy consumption for downlink frame transmission using the M/G/1 queueing model. Based on the analytical model, a cost function considering both the average waiting time of the frame in the gateway and the average energy consumption of an end device is proposed. Using the cost function, we derive the optimal number of ping slots which maximizes the value of the cost function. Results show the tradeoff relation between the waiting time of the frame in the gateway and the energy consumption of an end device, and it is verified that the optimal number of ping slots increases as the traffic density increases.

We investigate the effect of outdated channel state information (CSI) on the secrecy performance of wireless-powered untrusted relay networks, in which the relay is a potential eavesdropper. To keep the information secret from this untrusted relay, the destination sends a jamming signal to the relay when the source transmits an information signal. At the same time, the relay harvests energy from the radio-frequency power, and forwards the received signals to the destination using this harvested energy. To determine the proportions of energy harvesting and information processing, the relay makes use of relaying based on power splitting or time switching policy. Although the destination tries to remove the jamming signal from the relaying signal, it cannot cancel out the jamming signal perfectly due to imperfect channel reciprocity caused by the outdated CSI; this residual jamming signal therefore has a negative impact on secrecy performance. In this scenario, we derive the closed-form expressions for the outage probability and average secrecy rate, and find the jamming power ratio, power splitting ratio, and time switching ratio to optimize these secrecy performance metrics. The numerical results demonstrate the accuracy of our analysis, and show that the proposed secure relaying protocols achieve a near-optimal secrecy performance, as well as outperforming the conventional scheme without the jamming power control.

This paper considers a two-way relay network, in which two sources exchange data through a relay and a cooperative jammer transmits an artificial noise (AN) while a number of nearby eavesdroppers overhear to recover data from both sources. The relay harvests energy from the two source signals and the AN, and then, uses this harvested energy to forward the received signals to the two sources. Each source eliminates its own signal from the relaying signal by self-cancellation and then decodes the data signal received from the other source. For this wireless-powered two-way relay system, we propose two secure relay protocols based on power splitting and time switching techniques. The two protocols are power splitting-based two-way relaying (PS-TWR) and time switching-based two-way relaying (TS-TWR), in which the relay, respectively, controls the power splitting ratio (p) and time switching ratio (α), in order to achieve a balance between the data receiving and the energy harvesting. The optimal values of p and α for each protocol are found analytically to maximize the minimum guaranteed secrecy capacity (CSmin) considering multiple eavesdroppers in high signal-to-noise ratio environments. Numerical results show that both the PS-TWR and TS-TWR protocols using the optimized values of p and α achieve the near-optimal CSminno matter how many eavesdroppers exist anywhere. Comparisons of the two protocols in various scenarios also show that PS-TWR achieves better CSminthan TS-TWR because PS-TWR inherently has a shorter vulnerable time for eavesdropping than TS-TWR.

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.

A two-hop relay network is considered, in which an eavesdropper can overhear the relaying signal. To prevent the eavesdropper from decoding this signal, a destination transmits a jamming noise while a source transmits the data signal to the relay. At the same time, the relay can harvest energy from both the source signal and the jamming noise, and use this harvested energy to forward the received signal to the destination. In such a wireless-powered relay system with cooperative jamming, we propose two adaptive relaying schemes based on power splitting and time switching techniques. In the proposed power splitting-based relaying (PSR) and time switching-based relaying (TSR) schemes, the relay controls the power splitting ratio (p) and time switching ratio (α), respectively, in order to achieve a balance between signal processing and energy harvesting. We find analytically the optimal values of p and α in each scheme to maximize the secrecy capacity under the assumption of high signal-to-noise ratio (SNR). Interestingly, although the eavesdropper's channel state information (CSI) is used in the derivation of the optimal control parameters (p and α), they are shown not to be affected by the eavesdropper's CSI in a high SNR regime. This implies that the proposed schemes can be effective even for practical environments where there is no eavesdropper's CSI. Furthermore, simulation results show that they well coincide with the exact solutions in practical environments even though the closed-form solutions are obtained with a high SNR assumption. Moreover, the comparisons of PSR and TSR in various scenarios show that the two relaying schemes have complementary performances depending on the network conditions. Specifically, PSR achieves greater secrecy capacity than TSR when the channel condition is unfavorable to the eavesdropper for wiretapping.

This paper studies full-duplex (FD) cellular networks in which a base station (BS) operated in FD mode with multiple antennas supports multiple uplink and downlink users simultaneously in the same wireless channel. Two typical FD cellular scenarios are considered, one with half-duplex (HD) users and the other with FD users along with the FD BS. For both the cases, a novel constructive method is developed for finding a closed-form interference alignment (IA) solution, namedcyclic IA. The core idea behind this approach is to construct a set of loop-equations enabling IA in acyclicmanner, so that beamforming vectors are sequentially determined by solving an eigenvalue problem. It is shown analytically that the proposed cyclic IA can achieve theoptimalsum degrees-of-freedom (DoF) when the number of user antennas is large enough to meet the derived conditions. In particular, it is shown that the proposed scheme achieves a twofold DoF gain compared with conventional HD cellular networks even in the presence of inter-link interference, provided the number of users becomes large enough compared with the ratio of the number of BSs and user antennas. Simulation results demonstrate that not only are the analytical DoF results valid, but under a practical multi-cell scenario, the proposed cyclic IA offers significant throughput gains depending on the cell radius.

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 fourth generation wireless communication standards, the throughput enhancement for cell edge users is a main issue so that various inter-cell interference (ICI) coordination techniques have been suggested. Since the coordination between different cells requires the resources of cooperating cell, its use should be determined carefully. In this paper, we consider two basic handover schemes (Fast Cell Selection and Soft Handover) for the ICI coordination and propose a new handover decision algorithm from a viewpoint of the improvement of cell edge throughput. For the proposed algorithm, a new measurement parameter named Interference to other-Interferences-plus-Noise Ratio (IINR) is defined and some practical issues are addressed in the OFDMA-based cellular system. Compared to the legacy SINR-based decision algorithm, the proposed IINR-based decision algorithm improves the cell edge throughput with reduced feedback overhead, because it optimally selects mobile stations that have cooperation gains more than cooperation costs.

Recent broadband wireless access systems apply voice over Internet protocol (VoIP) for their voice services, so a mobile station (MS) requires an efficient energy-saving algorithm for VoIP services to prolong its battery lifetime. The IEEE 802.16e system calledMobileWiMAXprovides the power saving class (PSC) of type II as an energy-saving algorithm for VoIP service, but it is not originally designed to consider silent periods of VoIP traffic. Therefore, we propose a new hybrid energy-saving algorithm suitable for VoIP services with silence suppression, which basically follows PSC II during talk-spurt periods, but uses the truncated binary exponential algorithm of PSC I during silent periods. The analysis and simulation results show that the proposed hybrid scheme reduces power consumption effectively during silent periods while satisfying the maximum allowable end-to-end delay and low packet drop probability constraints with respect to the quality of service (QoS) of VoIP.

In multihop cellular network environments, the mobility is a major factor that disturbs finding a reliable routing path between mobile node (MN) and access node (AN). In order to minimize the effect of mobility, we propose fast route discovery protocols, which decrease the variation of link costs during the route discovery period and so achieve the reliable route setup. The proposed schemes use inhibit access control, priority access control, or hybrid control for the fast and reliable route discovery in view of cross-layer design. Simulation results show that the proposed route discovery protocols can decrease route discovery time and increase total received power compared with the conventional scheme.

In this paper, we present a practical UMTS-WLAN interworking architecture based on 3GPP standards, and propose a seamless handoff scheme that guarantees low delay and low packet loss during UMTS-WLAN handoff. For low handoff delay, the proposed handoff scheme performs pre-registration and pre-authentication processes before the layer 2 handoff. Moreover, it uses packet buffering and forwarding functions in order to reduce packet loss during the handoff period. Numerical and simulation results show that the proposed scheme performs well with respect to signaling cost, handoff delay, and packet loss compared with conventional schemes.

The HiperLAN type2 is one of the representative wireless LAN and supports high speed transmission with mobility. Its MAC protocol is based on TDMA/TDD and provides access control and resource allocation schemes. These schemes are very important to prevent waste of resources and to improve the MAC performance in various traffic conditions. In this paper, we propose a scheme that provides adaptive random access and resource allocation according to traffic load. The proposed scheme supports the efficient resource allocation and the effective access control by using access probability. The analysis and simulation results show that the proposed scheme performs better than the conventional scheme and supports priority service easily.

The access scheme is required to have fast access and reliable transmission in satellite communications because of the long propagation delay and different channel characteristics. In this paper we propose a hybrid access scheme based on one phase preamble and channel monitoring/allocation for satellite communications. For fast access, we use an access scheme of 3GPP2 using one phase preamble instead of 3GPP using two phase preambles, and for more reliable transmission, the CM/CA scheme of 3GPP is applied. We investigate the performance of these proposed schemes through simulation. According to the simulation results, we could improve the random access scheme and could support smaller delay in satellite environments.