To support emerging language-based applications using dispersed and heterogeneous computing resources, the hybrid language model (HLM) offers a promising architecture, where an on-device small language model (SLM) generates draft tokens that are validated and corrected by a remote large language model (LLM). However, the original HLM suffers from substantial communication overhead, as the LLM requires the SLM to upload the full vocabulary distribution for each token. Moreover, both communication and computation resources are wasted when the LLM validates tokens that are highly likely to be accepted. To overcome these limitations, we proposecommunication-efficient and uncertainty-aware HLM (CU-HLM). In CU-HLM, the SLM transmits truncated vocabulary distributions only when its output uncertainty is high. We validate the feasibility of this opportunistic transmission by discovering a strong correlation between SLM’s uncertainty and LLM’s rejection probability. Furthermore, we theoretically derive optimal uncertainty thresholds and optimal vocabulary truncation strategies. Simulation results show that, compared to standard HLM, CU-HLM achieves up to 206× higher token throughput by skipping 74.8% transmissions with 97.4% vocabulary compression, while maintaining 97.4% accuracy.

We try to maximize the energy efficiency (EE) of the heterogeneous networks (HetNets) with maintaining high quality of service (QoS). We consider HetNets with Unmanned Aerial Vehicles (UAVs) operating on ultra-high frequencies (UHF) and millimeter-wave (mmWave) frequencies. We examine the joint optimization in communication, trajectory planning and power consumption in UAV Networks applying the efficient Augmented Lagrangian Methods. The simulation results show the effectiveness of the proposed algorithms.

Due to the emergence of various wireless sensing technologies, numerous positioning algorithms have been introduced in the literature, categorized intogeometry-driven positioning(GP) anddata-driven positioning(DP). These approaches have respective limitations, e.g., a non-line-of-sight issue for GP and the lack of a high-dimensional and labeled dataset for DP, which could be complemented by integrating both methods. To this end, this paper aims to introduce a novel principle calledcombinatorial data augmentation(CDA), a catalyst for the two approaches’ seamless integration. Specifically, GP-based data samples augmented from different positioning element combinations are calledpreliminary estimated locations(PELs), which can be used as high-dimensional inputs for DP. We confirm the CDA’s effectiveness from field experiments based on WiFiround-trip times(RTTs) andinertial measurement units(IMUs) by designing several CDA-based positioning algorithms. First, we show that CDA offers various metrics quantifying each PEL’s reliability, thereby extracting important PELs for WiFi RTT positioning. Second, CDA helps compute the observation error covariance matrix of a Kalman filter for fusing two position estimates derived by WiFi RTTs and IMUs. Third, we use the important PELs and the above position estimate as the corresponding input feature and the real-time label for fingerprint-based positioning as a representative DP algorithm. It provides accurate and reliable positioning results, with an average positioning error of 1.58 (m) and a standard deviation of 0.90 (m).

By integrating recent advances in large language models (LLMs) and generative models into the emerging semantic communication (SC) paradigm, in this article we put forward to a novel framework of language-oriented semantic communication (LSC). In LSC, machines communicate using human language messages that can be interpreted and manipulated via natural language processing (NLP) techniques for SC efficiency. To demonstrate LSC’s potential, we introduce three innovative algorithms: 1) semantic source coding (SSC) which compresses a text prompt into its key head words capturing the prompt’s syntactic essence while maintaining their appearance order to keep the prompt’s context; 2) semantic channel coding (SCC) that improves robustness against errors by substituting head words with their lenghthier synonyms; and 3) semantic knowledge distillation (SKD) that produces listener-customized prompts via in-context learning the listener’s language style. In a communication task for progressive text-to-image generation, the proposed methods achieve higher perceptual similarities with fewer transmissions while enhancing robustness in noisy communication channels.

In this work, we compare emergent communication (EC) built upon multi-agent deep reinforcement learning (MADRL) and language-oriented semantic communication (LSC) empowered by a pre-trained large language model (LLM) using human language. In a multi-agent remote navigation task, with multimodal input data comprising location and channel maps, it is shown that EC incurs high training cost and struggles when using multimodal data, whereas LSC yields high inference computing cost due to the LLM's large size. To address their respective bottlenecks, we propose a novel framework of language-guided EC (LEC) by guiding the EC training using LSC via knowledge distillation (KD). Simulations corroborate that LEC achieves faster travel time while avoiding areas with poor channel conditions, as well as speeding up the MADRL training convergence by up to 61.8% compared to EC.

This paper introduces a novel approach, mobility-induced graph learning (MINGLE), to enhance the accuracy of Wi-Fi positioning. Traditional Wi-Fi positioning methods often struggle with accuracy due to obstructions and interference. MINGLE addresses these challenges by converting user movement patterns into graphs, which are then analyzed using graph neural network. This method involves creating two types of graphs, based on the time and direction of user mobility, and employs a novel cross-graph learning technique in conjunction with self-supervised learning. This approach has demonstrated significant improvements in positioning accuracy, achieving a remarkable accuracy of 1.301 (m) in an underground parking lot setting, without relying on labeled data samples.

A smartphone-based user mobility tracking could be effective in finding his/her location, while the unpredictable error therein due to low specification of built-in inertial measurement units (IMUs) rejects its standalone usage but demands the integration to another positioning technique like WiFi positioning. This paper aims to propose a novel integration technique using a graph neural network called Mobility-INduced Graph LEarning (MINGLE), which is designed based on two types of graphs made by capturing different user mobility features. Specifically, considering sequential measurement points (MPs) as nodes, a user’s regular mobility pattern allows us to connect neighbor MPs as edges, called time-driven mobility graph (TMG). Second, a user’s relatively straight transition at a constant pace when moving from one position to another can be captured by connecting the nodes on each path, called a direction-driven mobility graph (DMG). Then, we can design graph convolution network (GCN)-based cross-graph learning, where two different GCN models for TMG and DMG are jointly trained by feeding different input features created by WiFi RTTs yet sharing their weights. Besides, the loss function includes a mobility regularization term such that the differences between adjacent location estimates should be less variant due to the user’s stable moving pace. Noting that the regularization term does not require ground-truth location, MINGLE can be designed under semi- and self-supervised learning frameworks. The proposed MINGLE’s effectiveness is extensively verified through field experiments, showing a better positioning accuracy than benchmarks, say mean absolute errors (MAEs) being 1.510 (m) and 1.077 (m) for self- and semi-supervised learning cases, respectively.

In recent years, split learning (SL) has emerged as a promising distributed learning framework that can utilize big data in parallel without privacy leakage while reducing client-side computing resources. In the initial implementation of SL, however, the server serves multiple clients sequentially incurring high latency. Parallel implementation of SL can alleviate this latency problem, but existing Parallel SL algorithms compromise scalability due to its fundamental structural problem. To this end, our previous works have proposed two scalable Parallel SL algorithms, dubbed SGLR and LocFedMix-SL, by solving the aforementioned fundamental problem of the Parallel SL structure. In this article, we propose a novel Parallel SL framework, coined Mix2SFL, that can ameliorate both accuracy and communication-efficiency while still ensuring scalability. Mix2SFL first supplies more samples to the server through a manifold mixup between the smashed data uploaded to the server as in SmashMix of LocFedMix-SL, and then averages the split-layer gradient as in GradMix of SGLR, followed by local model aggregation as in SFL. Numerical evaluation corroborates that Mix2SFL achieves improved performance in both accuracy and latency compared to the state-of-the-art SL algorithm with scalability guarantees. Moreover, its convergence speed as well as privacy guarantee are validated through the experimental results.

The vision of pervasiveartificial intelligence(AI) services can be realized by training an AI model on time using real-time data collected byinternet of things(IoT) devices. To this end, IoT devices require offloading their data to an edge server in proximity. However, transmitting high-dimensional and voluminous data from energy-constrained IoT devices poses a significant challenge. To address this limitation, we propose a novel offloading architecture, calledjoint data deepening-and-prefetching(JD2P), which is feature-by-feature offloading comprising two key techniques. The first one isdata deepening, where each data sample’s features are sequentially offloaded in the order of importance determined by the data embedding technique such asprinciple component analysis(PCA). Offloading is terminated once the already transmitted features are sufficient for accurate data classification, resulting in a reduction in the amount of transmitted data. The criteria to offload data are derived for binary and multi-class classifiers, which are designed based onsupport vector machine(SVM) anddeep neural network(DNN), respectively. The second one isdata prefetching, where some features potentially required in the future are offloaded in advance, thus achieving high efficiency via precise prediction and parameter optimization. We evaluate the effectiveness of JD2P through experiments using the MNIST dataset, and the results demonstrate its significant reduction in expected energy consumption compared to several benchmarks without degrading learning accuracy.

A distributed control of vehicle platooning is referred to asdistributed consensus(DC) since manyautonomous vehicles(AVs) reach a consensus to achieve coordinated movement with the same velocity and inter-distance. For DC control to be stable, each AV utilizes other AVs’ real-time position information obtained viavehicle-to-vehicle(V2V) communications. On the other hand, too many V2V links should be simultaneously established and frequently retrained, causing a longer communication latency due to frequent packet losses and thereby hampering stable DC. This paper proposes a novel DC algorithm calledover-the-air consensus(AirCons), a joint communication-and-control design with two key features to overcome the above limitations. First, exploiting a wireless signal’s superposition and broadcasting properties renders every AV’s signal converge to a specific value. We show that the consensus value is proportional to the weighted average of participating AVs’ real-time positions and has a tight lower bound as the ground-truth average. In other words, the average position location can be directly estimated without the neighbor AVs’ positions, thereby achieving ultra-low latency data sharing. Next, the estimated average position is inputted into each AV’s controller to adjust its dynamics distributively. The average position, considered a real-time value due to its low latency, contributes to achieving the stability of vehicle platooning. We design AirCons based on New Radio architecture with its feasibility study by analyzing required radio resources, i.e., time and bandwidth. Through analytic and numerical studies, the effectiveness of the proposed AirCons is verified by showing a 16.40% control gain compared to the benchmark without the average position.

With the rise of edge computing, various AI services are expected to be available at a mobile side through the inference based on deep neural network (DNN) operated at the network edge, called edge inference (EI). On the other hand, the resulting AI quality (e.g., mean average precision in objective detection) has been regarded as a given factor, and AI quality control has yet to be explored despite its importance in addressing the diverse demands of different users. This work aims at tackling the issue by proposing a feature hierarchical EI (FHEI), comprising feature network and inference network deployed at an edge server and corresponding mobile, respectively. Specifically, feature network is designed based on feature hierarchy, a one-directional feature dependency with a different scale. A higher scale feature requires more computation and communication loads while it provides a better AI quality. The tradeoff enables FHEI to control AI quality gradually w.r t, communication and computation loads, leading to deriving a near-to-optimal solution to maximize multi-user AI quality under the constraints of uplink & downlink transmissions and edge server and mobile computation capabilities. It is verified by extensive simulations that the proposed joint communication-and-computation control on FHEI architecture always outperforms several benchmarks by differentiating each user's AI quality depending on the communication and computation conditions.

The vision of pervasive machine learning (ML) services can be realized by training an ML model on time using real-time data collected by internet of things (IoT) devices. To this end, IoT devices require offloading their data to an edge server in proximity. On the other hand, high dimensional data with a heavy volume causes a significant burden to an IoT device with a limited energy budget. To cope with the limitation, we propose a novel offloading architecture, called joint data deepening and prefetching (JD2P), which is feature-by-feature offloading comprising two key techniques. The first one is data deepening, where each data sample's features are sequentially offloaded in the order of importance determined by the data embedding technique such as principle component analysis (PCA). No more features are offloaded when the features offloaded so far are enough to classify the data, resulting in reducing the amount of offloaded data. The second one is data prefetching, where some features potentially required in the future are offloaded in advance, thus achieving high efficiency via precise prediction and parameter optimization. To verify the effectiveness of JD2P, we conduct experiments using the MNIST and fashion-MNIST dataset. Experimental results demonstrate that the JD2P can significantly reduce the expected energy consumption compared with several benchmarks without degrading learning accuracy.

A distributed control of vehicle platooning is referred to as distributed consensus (DC) since many autonomous vehicles (AVs) reach a consensus to move as one body with the same velocity and inter-distance. For DC control to be stable, other AVs' real-time position information should be inputted to each AV's controller via vehicle-to-vehicle (V2V) communications. On the other hand, too many V2V links should be simultaneously established and frequently retrained, causing frequent packet loss and longer communication latency. We propose a novel DC algorithm called over-the-air consensus (AirCons), a joint communication-and-control design with two key features to overcome the above limitations. First, exploiting a wireless signal's superposition and broadcasting properties renders all AVs' signals to converge to a specific value proportional to participating AVs' average position without individual V2V channel information. Second, the estimated average position is used to control each AV's dynamics instead of each AV's individual position. Through analytic and numerical studies, the effectiveness of the proposed AirCons designed on the state-of-the-art New Radio architecture is verified by showing a 14.22% control gain compared to the benchmark without the average position.

This paper proposes new framework of communication system leveraging promising generation capabilities of multimodal generative models. Regarding nowadays’ smart applications, successful communication can be made by conveying the perceptual meaning, which we set as text prompt. Text serves as a suitable semantic representation of image data as it has evolved to instruct an image or generate image through mutli-modal techniques, by being interpreted in a manner similar to human cogitation. Utilizing text can also reduce the overload compared to transmitting the intact data itself. The transmitter converts objective image to text through multi-model generation process and the receiver reconstructs the image using reverse process. Each word in the text sentence has each syntactic role, responsible for particular piece of information the text contains. For further efficiency in communication load, the transmitter sequentially sends words in priority of carrying the most information until reaches successful communication. Therefore, our primary focus is on the promising design of a communication system based on image-to-text transformation and the proposed schemes for sequentially transmitting word tokens. Our work is expected to pave a new road of utilizing state-of-the-art generative models to real communication systems.

Metaverse over wireless networks is an emerging use case of the sixth generation (6G) wireless systems, posing unprecedented challenges in terms of its multi-modal data transmissions with stringent latency and reliability requirements. Towards enabling this wireless metaverse, in this article we propose a novel semantic communication (SC) framework by decomposing the metaverse into human/machine agent-specific semantic multiverses (SMs). An SM stored at each agent comprises a semantic encoder and a generator, leveraging recent advances in generative artificial intelligence (AI). To improve communication efficiency, the encoder learns the semantic representations (SRs) of multi-modal data, while the generator learns how to manipulate them for locally rendering scenes and interactions in the metaverse. Since these learned SMs are biased towards local environments, their success hinges on synchronizing heterogeneous SMs in the background while communicating SRs in the foreground, turning the wireless metaverse problem into the problem of semantic multiverse communication (SMC). Based on this SMC architecture, we propose several promising algorithmic and analytic tools for modeling and designing SMC, ranging from distributed learning and multi-agent reinforcement learning (MARL) to signaling games and symbolic AI.

In this paper, we suggest a method to transform a communication protocol based on deep neural network (NN) into a semantic communication protocol. We need such transformation to alleviate the issues posed by NN's lack of interpretability and redundant parameters due to overparametrization. However, transformation process is challenging because it is difficult to disambiguate the semantics while reducing the protocol's complexity. We solve the challenge by employing NN's activation patterns and probabilistic logic. Lastly, we validate our method by transforming an NN trained for a medium access control (MAC) protocol and verifying its contention performance compared to ALOHA based protocols.

Automatic modulation classification (AMC) is a technology that identifies a modulation scheme without prior signal information and plays a vital role in various applications, including cognitive radio and link adaptation. With the development of deep learning (DL), DL-based AMC methods have emerged, while most of them focus on reducing computational complexity in a centralized structure. This centralized learning-based AMC (CentAMC) violates data privacy in the aspect of direct transmission of client-side raw data. Federated learning-based AMC (FedeAMC) can bypass this issue by exchanging model parameters, but causes large resultant latency and client-side computational load. Moreover, both CentAMC and FedeAMC are vulnerable to large-scale noise occured in the wireless channel between the client and the server. To this end, we develop a novel AMC method based on a split learning (SL) framework, coined SplitAMC, that can achieve high accuracy even in poor channel conditions, while guaranteeing data privacy and low latency. In SplitAMC, each client can benefit from data privacy leakage by exchanging smashed data and its gradient instead of raw data, and has robustness to noise with the help of high scale of smashed data. Numerical evaluations validate that SplitAMC outperforms CentAMC and FedeAMC in terms of accuracy for all SNRs as well as latency.

Classical medium access control (MAC) protocols are interpretable, yet their task-agnostic control signaling messages (CMs) are ill-suited for emerging mission-critical applications. By contrast, neural network (NN) based protocol models (NPMs) learn to generate task-specific CMs, but their rationale and impact lack interpretability. To fill this void, in this article we propose, for the first time, a semantic protocol model (SPM) constructed by transforming an NPM into an interpretable symbolic graph written in the probabilistic logic programming language (ProbLog). This transformation is viable by extracting and merging common CMs and their connections, while treating the NPM as a CM generator. By extensive simulations, we corroborate that the SPM tightly approximates its original NPM while occupying only 0.02% memory. By leveraging its interpretability and memory-efficiency, we demonstrate several SPM-enabled applications such as SPM reconfiguration for collision-avoidance, as well as comparing different SPMs via semantic entropy calculation and storing multiple SPMs to cope with non-stationary environments.

Due to the edge’s position between the cloud and the users, and the recent surge of deep neural network (DNN) applications, edge computing brings about uncertainties that must be understood separately. Particularly, the edge users’ locally specific requirements that change depending on time and location cause a phenomenon called dataset shift, defined as the difference between the training and test datasets’ representations. It renders many of the state-of-the-art approaches for resolving uncertainty insufficient. Instead of finding ways around it, we exploit such phenomenon by utilizing a new principle: AI model diversity, which is achieved when the user is allowed to opportunistically choose from multiple AI models. To utilize AI model diversity, we propose Model Diversity Network (MoDNet), and provide design guidelines and future directions for efficient learning driven communication schemes.

Split learning (SL) is a promising distributed learning framework that enables to utilize the huge data and parallel computing resources of mobile devices. SL is built upon a model-split architecture, wherein a server stores an upper model segment that is shared by different mobile clients storing its lower model segments. Without exchanging raw data, SL achieves high accuracy and fast convergence by only uploading smashed data from clients and downloading global gradients from the server. Nonetheless, the original implementation of SL sequentially serves multiple clients, incurring high latency with many clients. A parallel implementation of SL has great potential in reducing latency, yet existing parallel SL algorithms resort to compromising scalability and/or convergence speed. Motivated by this, the goal of this article is to develop a scalable parallel SL algorithm with fast convergence and low latency. As a first step, we identify that the fundamental bottleneck of existing parallel SL comes from the model-split and parallel computing architectures, under which the server-client model updates are often imbalanced, and the client models are prone to detach from the server’s model. To fix this problem, by carefully integrating local parallelism, federated learning, and mixup augmentation techniques, we propose a novel parallel SL framework, coined LocFedMix-SL. Simulation results corroborate that LocFedMix-SL achieves improved scalability, convergence speed, and latency, compared to sequential SL as well as the state-of-the-art parallel SL algorithms such as SplitFed and LocSplitFed.

Recently, round-trip time (RTT) measured by a fine-timing measurement protocol has received great attention in the area of WiFi positioning. It provides an acceptable ranging accuracy in favorable environments when a line-of-sight (LOS) path exists. Otherwise, a signal is detoured along with non-LOS (NLOS) paths, making the resultant ranging results different from the ground truth, called an RTT bias, which is the main reason for poor positioning performance. To address it, we aim at leveraging the user mobility trajectory detected by a smartphone’s inertial measurement units, called pedestrian dead reckoning (PDR). Specifically, PDR provides the geographic relation among adjacent locations, guiding the resultant positioning estimates’ sequence not to deviate from the user trajectory. To this end, we describe their relations as multiple geometric equations, enabling us to render a novel positioning algorithm with acceptable accuracy. Depending on the mobility pattern being linear or arbitrary, we develop different algorithms divided into two phases. First, we can jointly estimate an RTT bias of each access point (AP) and the user’s step length by leveraging the geometric relation mentioned above. It enables us to construct a user’s relative trajectory defined on the concerned AP’s local coordinate system. Second, we align every AP’s relative trajectory into a single one, calledtrajectory alignment, equivalent to transformation to the global coordinate system. As a result, we can estimate the sequence of the user’s absolute locations from the aligned trajectory. Various field experiments extensively verify the proposed algorithm’s effectiveness that the average positioning error is approximately 0.369 (m) and 1.705 (m) in LOS and NLOS environments, respectively.

To minimize enormous havoc from disasters, permanent environment monitoring is necessarily required. Thus, we propose a novel energy management protocol for energy harvesting wireless sensor networks, named the adaptive sensor node management protocol (ASMP). The proposed protocol makes system components to systematically control their performance to conserve the energy. Through this protocol, sensor nodes autonomously activate an additional energy conservation algorithm. ASMP embeds three sampling algorithms. For the optimized environment sampling, we proposed the adaptive sampling algorithm for monitoring (ASA-m). ASA-m estimates the expected time period to occur meaningful change. The meaningful change refers to the distance between two target data for the monitoring Quality of Service. Therefore, ASA-m merely gathers the data the system demands. The continuous adaptive sampling algorithm (CASA) solves the problem to be continuously decreasing energy despite of ASA-m. When the monitored environment shows a linear trend property, the sensor node in CASA rests a sampling process, and the server generates predicted data at the estimated time slot. For guaranteeing the self-sustainability, ASMP uses the recoverable adaptive sampling algorithm (RASA). RASA makes consumed energy smaller than harvested energy by utilizing the predicted data. RASA recharges the energy of the sensor node. Through this method, ASMP achieves both energy conservation and service quality.

Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication systems and beyond. By imbuing intelligence into the network edge, edge nodes can proactively carry out decision-making and, thereby, react to local environmental changes and disturbances while experiencing zero communication latency. To achieve this goal, it is essential to cater for high ML inference accuracy at scale under the time-varying channel and network dynamics, by continuously exchanging fresh data and ML model updates in a distributed way. Taming this new kind of data traffic boils down to improving the communication efficiency of distributed learning by optimizing communication payload types, transmission techniques, and scheduling, as well as ML architectures, algorithms, and data processing methods. To this end, this article aims to provide a holistic overview of relevant communication and ML principles and, thereby, present communication-efficient and distributed learning frameworks with selected use cases.

With recent deployments of the fifth generation (5G) network and advances in unmanned aerial vehicles, diverse airborne missions that provide high resolution aerial imagery in real-time are possible. However, a reliable inference of 3D cellular coverage is required to provide seamless imagery. As a part of intercontinental 5G testbed activities within Korea-EU 5G Project (PriMO-5G), we construct 3D 5G coverage map manually, to present insights regarding 3D coverage and how to construct its map efficiently. We then devise algorithms for constructing 3D coverage map simultaneously with real-time airborne missions in a cost-effective manner.

Despite the widespread popularity of stochastic geometry analysis for cellular networks, most analytical results lack the perspective of channel-adaptive user scheduling. This study presents a stochastic geometry analysis of the SINR distribution and scheduling gain of normalized SNR-based scheduling in an uplink Poisson cellular network, in which the per-user truncated fractional transmit power control is performed. Because the effects of multi-user diversity depend on the number of candidate users to be scheduled, which is a random variable in a Poisson cellular network, the number distribution of candidate users is a major factor in analyzing the SINR distribution of user scheduling. However, the maximum transmit power constraint of users complicates the distribution of candidate users. This study provides the number distribution of candidate users in a general form, which is obtained by modeling the area of the existing range of candidate users using a beta distribution. Based on this result, this study successfully obtains the uplink SINR distribution under channel-adaptive user scheduling, including cases in which edge users are both allowed and not allowed to transmit at the maximum transmit power. Numerical evaluations reveal that the scheduling gain varies depending on the SNR and the fraction of edge users.

To support wireless communication traffic of Internet-of-Things (IoT) systems in terms of massive connectivity, dynamic spectrum access (DSA) is important issue. This paper proposes spectrum sensor-aided DSA system based on a reinforcement learning (RL) algorithm that aims at efficient spectrum usage for IoT network over the incumbent network. Due to small-form-factor of IoT devices, they do not have spectrum sensing capability. To support DSA of IoT devices, we introduce sensor-aided DSA system that enhances spatial spectrum reusability by means of RL algorithm. With the RL algorithm, proposed DSA system provides self-organizing feature for massive number of IoT devices. We show that the performance of proposed RL based DSA system in various densities of IoT devices utilizing slotted ALOHA protocol that has spectrum access probability learned by proposed DSA system. We also present the performance of proposed RL based DSA system surpass that of distributed Carrier Sensing Multiple Access with Collision Avoidance (CSMA/CA) protocol for channel access coordination. We also present the consistent performance of incumbent user when the IoT devices access to the spectrum band with learned spectrum access probability.

As the number of smartphone users exploded, the demand for Location-Based Service (LBS) has increased. It is important for the LBS to specify the user location by utilizing the sensor built in the smartphone. Unlike outdoor localization, which can employ GPS, there are many challenging issues in indoor localization including non-line-of-sight (NLOS) and multipath effect. In our paper, we focus on Wi-Fi Fine Timing Measurement (FTM) which is a new function of the Android Pie Operating System (OS). We propose line-of-sight (LOS) identification algorithms applicable to Wi-Fi FTM and apply these algorithms to indoor localization based on multilateration methods. We utilize a hypothesis test framework and Support Vector Machine (SVM) to identify LOS signals. We divide LOS/NLOS signals as low and high-quality signals according to the degree of multipath error. We achieve high-quality signals identification rate of 92.4% on average in the sample size 99 and of 78.3% on average in the sample size 29. Therefore, we obtain a 24.4% localization performance improvement compared to the perfect LOS detector by using only high-quality signals to localization.

Cognitive radio (CR) is a key enabler realizing future networks to achieve higher spectral efficiency by allowing spectrum sharing between different wireless networks. It is important to explore whether spectrum access opportunities are available, but conventional CR based on transmitter (TX) sensing cannot be used to this end because the paired receiver (RX) may experience different levels of interference, according to the extent of their separation, blockages, and beam directions. To address this problem, this paper proposes a novel form of medium access control (MAC) termed sense-and-predict (SaP), whereby each secondary TX predicts the interference level at the RX based on the sensed interference at the TX; this can be quantified in terms of a spatial interference correlation between the two locations. Using stochastic geometry, the spatial interference correlation can be expressed in the form of a conditional coverage probability, such that the signal-to-interference ratio at the RX is no less than a predetermined threshold given the sensed interference at the TX, defined as an opportunistic probability (OP). The secondary TX randomly accesses the spectrum depending on OP. We optimize the SaP framework to maximize the area spectral efficiencies (ASEs) of secondary networks while guaranteeing the service quality of the primary networks. The testbed experiments using universal software radio peripheral (USRP) and MATLAB simulations show that SaP affords higher ASEs compared with CR without prediction.

This paper provides the signal-to-interference-plus-noise ratio (SINR) complimentary cumulative distribution function (CCDF) and average data rate of the normalized SNR-based scheduling in an uplink cellular network using stochastic geometry. The uplink analysis is essentially different from the downlink analysis in that the per-user transmit power control is performed and that the interferers are composed of at most one transmitting user in each cell other than the target cell. In addition, as the effect of multi-user diversity varies from cell to cell depending on the number of users involved in the scheduling, the distribution of the number of users is required to obtain the averaged performance of the scheduling. This paper derives the SINR CCDF relative to the typical scheduled user by focusing on two incompatible cases, where the scheduler selects a user from all the users in the corresponding Voronoi cell or does not select users near cell edges. In each case, the SINR CCDF is marginalized over the distribution of the number of users involved in the scheduling, which is asymptotically correct if the BS density is sufficiently large or small. Through the simulations, the accuracies of the analytical results are validated for both cases, and the scheduling gains are evaluated to confirm the multi-user diversity gain.

This paper provides a bargaining game-based band- width allocation scheme in multi-operator shared millimeter-wave (mmWave) radio access network (RAN). We consider mobile network operators (MNOs) that mutually share their mmWave base stations (BSs) to expand coverage such that the subscribers of one MNO can be associated with the mmWave BSs of other MNOs. Since MNOs are also competitive in nature, there is a necessity for MNOs to negotiate the amount of bandwidth to be allocated to the users of each other. We first evaluate how the amount of allocated bandwidth enhances the success probability both theoretically and through simulations. Then, by using the evolutionary game theory to model the subscription of users, the feasible region of bandwidth is mapped to the feasible region of market state, enabling the MNOs to negotiate based the evolution of the market state. The Nash bargaining solution yields an allocation scheme that maximizes the product of MNOs' increments in the market share.

Welcome to the IEEE JSAC special issue onAirborne Communication Networks. The goal of this special issue is to disseminate the contributions in the field of airborne communication networks.

Detecting the spectrum access opportunity (OP) of a secondary user is a key technique in cognitive radio (CR) networks. Especially in a CR scenario where dedicated spectrum sensors are installed to check the spectrum utilization, the OP at locations where the sensor is not installed cannot be estimated. To cope with the issue, this paper proposes an OP map, in which a centralized server estimates the OP of secondary users based on the interference measurements of sensors. The OP is estimated by analyzing the spatial correlation of interference. The accuracy of OP detection is validated through the MATLAB simulations in conjunction with testbed experiments using universal software radio peripherals (USRPs) in Yonsei university, Seoul, South Korea.

This paper investigates a cellular edge caching design under an extremely large number of small base stations (SBSs) and users. In this ultra-dense edge caching network (UDCN), SBS-user distances shrink, and each user can request a cached content from multiple SBSs. Unfortunately, the complexity of existing caching controls' mechanisms increases with the number of SBSs, making them inapphcable for solving the fundamental caching problem: How to maximize local caching gain while minimizing the replicated content caching? Furthermore, spatial dynamics of interference is no longer negligible in UDCNs due to the surge in interference. In addition, the caching control should consider temporal dynamics of user demands. To overcome such difficulties, we propose a novel caching algorithm weaving together notions of mean-field game theory and stochastic geometry. These enable our caching algorithm to become independent of the number of SBSs and users, while incorporating spatial interference dynamics as well as temporal dynamics of content popularity and storage constraints. Numerical evaluation validates the fact that the proposed algorithm reduces not only the long run average cost by at least 24% but also the number of replicated content by 56% compared to a popularity-based algorithm.

Conventional mobile computation offloading relies on offline prefetching that fetches user-specific data to the cloud prior to computing. For computing depending on real-time inputs, the offline operation can result in fetching large volumes of redundant data over wireless channels and unnecessarily consumes mobile-transmission energy. To address this issue, we propose the novel technique of online prefetching for a large-scale program with numerous tasks, which seamlessly integrates task-level computation prediction and real-time prefetching within the program runtime. The technique not only reduces mobile-energy consumption by avoiding excessive fetching but also shortens the program runtime by parallel fetching and computing enabled by prediction. By modeling the sequential task transition in an offloaded program as a Markov chain, stochastic optimization is applied to design the online-fetching policies to minimize mobile-energy consumption for transmitting fetched data over fading channels under a deadline constraint. The optimal policies for slow and fast fading are shown to have a similar threshold-based structure that selects candidates for the next task by applying a threshold on their likelihoods and furthermore uses them controlling the corresponding sizes of prefetched data. In addition, computation prediction for online prefetching is shown theoretically to always achieve energy reduction.

Recently, millimeter-wave (mmWave) communications have received great attention due to the availability of large spectrum resources. Nevertheless, their impact on TCP performance has been overlooked, which is observed that the said TCP performance collapse occurs owing to the significant difference in signal quality between LOS and NLOS links. We propose a novel TCP design for mmWave communications, a mmWave performance enhancing proxy (mmPEP), enabling not only to overcome TCP performance collapse but also exploit the properties of mmWave channels. The base station installs the TCP proxy to operate the two functionalities called Ack management and batch retransmission. Specifically, the proxy sends the said early-Ack to the server not to decrease its sending rate even in the NLOS status. In addition, when a packet-loss is detected, the proxy retransmits not only lost packets but also the certain number of the following packets expected to be lost too. It is verified by ns-3 simulation that compared with benchmark, mmPEP enhances the end-to-end rate and packet delivery ratio by maintaining high sending rate with decreasing the loss recovery time.

This paper focuses on cognitive radio (CR) internet- of-things (IoT) networks where spectrum sensors are deployed for IoT CR devices, which do not have enough hardware capability to identify an unoccupied spectrum by themselves. In this sensor- enabled IoT CR network, the CR devices and the sensors are separated. It induces that spectrum occupancies at locations of CR devices and sensors could be different. To handle this difference, we investigate a conditional interference distribution (CID) at the CR device for a given measured interference at the sensor. We can observe a spatial correlation of the aggregate interference distribution through the CID. Reflecting the CID, we devise a cognitive random access scheme which adaptively adjusts transmission probability with respect to the interference measurement of the sensor. Our scheme improves area spectral efficiency (ASE) compared to conventional ALOHA and an adaptive transmission scheme which attempts to send data when the sensor measurement is lower than an interference threshold.

One of the goals of 5G wireless systems stated by the NGMN alliance is to provide moderate rates (50+ Mbps) everywhere and with very high reliability. We term this service Ultra-Reliable Ubiquitous-Rate Communication (UR2C). This paper investigates the role of frequency reuse in supporting UR2C in the downlink. To this end, two frequency reuse schemes are considered: user-specific frequency reuse (FRu) and BS-specific frequency reuse (FRb). For a given unit frequency channel, FRureduces the number of serving user equipments (UEs), whereas FRb directly decreases the number of interfering base stations (BSs). This increases the distance from the interfering BSs and the signal-to-interference ratio (SIR) attains ultra-reliability, e.g. 99% SIR coverage at a randomly picked UE. The ultra-reliability is, however, achieved at the cost of the reduced frequency allocation, which may degrade overall downlink rate. To fairly capture this reliability-rate tradeoff, we propose ubiquitous rate defined as the maximum downlink rate whose required SIR can be achieved with ultra-reliability. By using stochastic geometry, we derive closed-form ubiquitous rate as well as the optimal frequency reuse rules for UR2C.

The business potential of device-to-device (D2D) communication including public safety and vehicular communications will be realized only if direct communication between devices subscribed to different mobile operators (OPs) is supported. One possible way to implement inter-operator D2D communication may use the licensed spectrum of the OPs, i.e., OPs agree to share spectrum in a co-primary manner, and inter-operator D2D communication is allocated over spectral resources contributed from both parties. In this paper, we consider a spectrum sharing scenario, where a number of OPs construct a spectrum pool dedicated to support inter-operator D2D communication. OPs negotiate in the form of a non-cooperative game about how much spectrum each OP contributes to the spectrum pool. OPs submit proposals to each other in parallel until a consensus is reached. When every OP has a concave utility function on the box-constrained region, we identify the conditions guaranteeing the existence of a unique equilibrium point. We show that the iterative algorithm based on the OP's best response might not converge to the equilibrium point due to myopically overreacting to the response of the other OPs, while the Jacobi-play strategy update algorithm can converge with an appropriate selection of update parameter. Using the Jacobi-play update algorithm, we illustrate that asymmetric OPs contribute an unequal amount of resources to the spectrum pool; however, all participating OPs may experience significant performance gains compared with the scheme without spectrum sharing.

Mobile computation offloading refers to techniques for offloading computation intensive tasks from mobile devices to the cloud so as to lengthen the formers' battery lives and enrich their features. The conventional designs fetch (transfer) user-specific data from mobiles to the cloud prior to computing, called offline prefetching. However, this approach can potentially result in excessive fetching of large volumes of data and cause heavy loads on radio-access networks. To solve this problem, the novel technique of live prefetching, which seamlessly integrates the task-level computation prediction and prefetching within the cloud-computing process of a large program with numerous tasks, is proposed in this paper. The technique avoids excessive fetching but retains the feature of leveraging prediction to reduce the program runtime and mobile transmission energy. By modeling the tasks in an offloaded program as a stochastic sequence, stochastic optimization is applied to design fetching policies to minimize mobile energy consumption under a deadline constraint. The policies enable real-time control of the prefetched-data sizes of candidates for future tasks. For slow fading, the optimal policy is derived and shown to have a threshold-based structure, selecting candidate tasks for prefetching and controlling their prefetched data based on their likelihoods. The result is extended to design close-to-optimal prefetching policies to fast fading channels. Compared with fetching without prediction, live prefetching is shown theoretically to always achieve reduction on mobile energy consumption.

This article investigates the mobility management of an ultra dense cellular network (UDN) from an energy-efficiency (EE) point of view. Many dormant base stations (BSs) in a UDN do not transmit signals, and thus a received power based handover (HO) approach as in traditional cellular networks is hardly applicable. In addition, the limited front/backhaul capacity compared to a huge number of BSs makes it difficult to implement a centralized HO and power control. For these reasons, a novel user-centric association rule is proposed, which jointly optimizes HO and power control for maximizing EE. The proposed mobility management is able to cope not only with the spatial randomness of user movement but also with temporally correlated wireless channels. The proposed approach is implemented over a HO time window and tractable power con- trol policy by exploiting mean-field game (MFG) and stochastic geometry (SG). Compared to a baseline with a fixed HO interval and transmit power, the proposed approach achieves the 1.2 times higher long-term average EE at a typical active BS.

This article investigates the performance of an ultra-dense network (UDN) from an energy-efficiency (EE) standpoint leveraging the interplay between stochastic geometry (SG) and mean-field game (MFG) theory. In this setting, base stations (BSs) (resp. users) are uniformly distributed over a two-dimensional plane as two independent homogeneous Poisson point processes (PPPs), where users associate to their nearest BSs. The goal of every BS is to maximize its own energy efficiency subject to channel uncertainty, random BS location, and interference levels. Due to the coupling in interference, the problem is solved in the mean-field (MF) regime where each BS interacts with the whole BS population via time- varying MF interference. As a main contribution, the asymptotic convergence of MF interference to zero is rigorously proved in a UDN with multiple transmit antennas. It allows us to derive a closed-form EE representation, yielding a tractable EE optimal power control policy. This proposed power control achieves more than 1.5 times higher EE compared to a fixed power baseline.

Reverse pricing has been recognized as an effective tool to handle demand uncertainty in the travel industry (e.g., airlines and hotels). To investigate its viability for communication networks, we study the practical limitations of (operator-driven) time-dependent pricing that has been recently introduced, taking into account demand uncertainty. Compared to (operator-driven) time-dependent pricing, we show that the proposed pricing scheme can achieve “triple-win” solutions: an increase in the total average revenue of the operator; higher average resource utilization efficiency; and an increment in the total average payoff of the users. Our findings provide a new outlook on resource allocation, and design guidelines for adopting the reverse pricing scheme.

A strong motivation of charging depleted battery can be an enabler for network capacity increase. In this light we propose a spatial attraction cellular network (SAN) consisting of macro cells overlaid with small cell base stations that wirelessly charge user batteries. Such a network makes battery depleting users move toward the vicinity of small cell base stations. With a fine adjustment of charging power, this user spatial attraction (SA) improves in spectral efficiency as well as load balancing. We jointly optimize both enhancements thanks to SA, and derive the corresponding optimal charging power in a closed form by using a stochastic geometric approach.

Gamal et al. showed that the end-to-end delay is$n$times the end-to-end throughput under the centralized TDMA scheduling[4]where$n$is the number of nodes in the network, and defined this relationship as the optimal tradeoff between the end-to-end throughput and the end-to-end delay. The main purpose of this paper is to show whether this tradeoff relationship is established when IEEE 802.11 DCF is used. We mathematically express the end-to-end throughput and the end-to-end delay as a function of carrier sensing range and packet generation rate. We optimally control them in order to derive a delay-constrained capacity, the maximum value among the end-to-end throughput in which the end-to-end delay requirement is satisfied. As a result, we show that IEEE 802.11 DCF can establish the optimal tradeoff relationship in[4]. This indicates that the optimally controlled parameters can compensate the loss from the difference between the centralized TDMA scheduling and IEEE 802.11 DCF.

The forthcoming 5G cellular network is expected to overlay millimeter-wave (mmW) transmissions with the incumbent micro-wave (μW) architecture. The overall mm-μW resource management should, therefore, harmonize with each other. This paper aims at maximizing the overall downlink (DL) rate with a minimum uplink (UL) rate constraint, and concludes: mmW tends to focus more on DL transmissions while μW has high priority for complementing UL, under time-division duplex (TDD) mmW operations. Such UL dedication of μW results from the limited use of mmW UL bandwidth due to excessive power consumption and/or high peak-to-average power ratio (PAPR) at mobile users. To further relieve this UL bottleneck, we propose mmW UL decoupling that allows each legacy μW base station (BS) to receive mmW signals. Its impact on mm-μW resource management is provided in a tractable way by virtue of a novel closed-form mm-μW spectral efficiency (SE) derivation. In an ultra-dense cellular network (UDN), our derivation verifies mmW (or μW) SE is a logarithmic function of BS-to-user density ratio. This strikingly simple yet practically valid analysis is enabled by exploiting stochastic geometry in conjunction with real three-dimensional (3-D) building blockage statistics in Seoul, South Korea.

In this paper, we discuss the potential for improvement of the simple random access scheme by utilizing local information such as the received signal-to-interference-plus-noise-ratio (SINR). We propose a spatially adaptive random access (SARA) scheme in which the transmitters in the network utilize different transmit probabilities depending on the local situation. In our proposed scheme, the transmit probability is adaptively updated by the ratio of the received SINR and the target SINR. We investigate the performance of the SARA scheme. For comparison, we derive an optimal transmit probability of ALOHA scheme in which all transmitters use the same transmit probability. We illustrate the performance of the SARA scheme through simulations. We show that the performance of the proposed scheme surpasses that of the optimal ALOHA scheme and is comparable with the CSMA/CA scheme.

This paper investigates the relationship between base station (BS) density and average spectral efficiency (SE) in the downlink of a cellular network. This relationship has been well known for sparse deployment, i.e. when the number of BSs is small compared to the number of users. In this case the SE is independent of BS density. As BS density grows, on the other hand, it has previously been shown that increasing the BS density increases the SE, but no tractable form for the SE-BS density relationship has yet been derived. In this paper we derive such a closed-form result that reveals the SE is asymptotically a logarithmic function of BS density as the density grows. Further, we study the impact of this result on the network operator's profit when user demand varies, and derive the profit maximizing BS density and the optimal amount of spectrum to be utilized in closed forms. In addition, we provide deployment planning guidelines that will aid the operator in his decision if he should invest in densifying his network or in acquiring more spectrum.

Device-to-device (D2D) communication enables us to spread information in the local area without infrastructure support. In this paper, we focus on information spreading in mobile wireless networks where all nodes move around. The source nodes deliver a given information packet to mobile users using D2D communication as an underlay to the cellular uplink. By stochastic geometry, we derive the average number of nodes that have successfully received a given information packet as a function of the transmission power and the number of transmissions. Based on these results, we formulate a redundancy minimization problem under the maximum transmission power and delay constraints. By solving the problem, we provide an optimal rule for the transmission power of the source node.

The Long Term Evolution (LTE) broadcast is a promising solution to cope with exponentially increasing user traffic by broadcasting common user requests over the same frequency channels. In this paper, we propose a novel network framework provisioning broadcast and unicast services simultaneously. For each serving file to users, a cellular base station determines either to broadcast or unicast the file based on user demand prediction examining the file's content specific characteristics such as: file size, delay tolerance, price sensitivity. In a network operator's revenue maximization perspective while not inflicting any user payoff degradation, we jointly optimize resource allocation, pricing, and file scheduling. In accordance with the state of the art LTE specifications, the proposed network demonstrates up to 32% increase in revenue for a single cell and more than a 7-fold increase for a 7 cell coordinated LTE broadcast network, compared to the conventional unicast cellular networks.

We study bidding and pricing competition between two spiteful mobile network operators (MNOs) with considering their existing spectrum holdings. Given asymmetric-valued spectrum blocks are auctioned off to them via a first-price sealed-bid auction, we investigate the interactions between two spiteful MNOs and users as a three-stage dynamic game and characterize the dynamic game's equilibria. We show an asymmetric pricing structure and different market share between two spiteful MNOs. Perhaps counter-intuitively, our results show that the MNO who acquires the less-valued spectrum block always lowers his service price despite providing double-speed LTE service to users. We also show that the MNO who acquires the high-valued spectrum block, despite charing a higher price, still achieves more market share than the other MNO. We further show that the competition between two MNOs leads to some loss of their revenues. By investigating a cross-over point at which the MNOs' profits are switched, it serves as the benchmark of practical auction designs.

In mobile communication services, users wish to subscribe to high-quality service at a low price level, which leads to competition between mobile network operators (MNOs). The MNOs compete with each other by service prices after deciding the extent of investment to improve quality of service. Unfortunately, the theoretic backgrounds of price dynamics are not known to us, and as a result, effective network planning and regulative actions are hard to make in the competitive market. To explain this competition in more detail, we formulate and solve an optimization problem applying the two-stage Cournot and Bertrand competition model. Consequently, we derive price dynamics that the MNOs increase and decrease their service prices periodically, which completely explains the subsidy dynamics in the real world. Moving forward, to avoid this instability and inefficiency, we suggest a simple regulation rule, which leads to a Pareto-optimal equilibrium point. Moreover, we suggest regulator's optimal actions corresponding to user welfare and the regulator's revenue.

One of the challenges facing the next-generation wireless networks is to cope with the expected demand for data. This calls for an efficient spectrum regulation that can enable mobile subscribers to support high quality of service (QoS) and mobile network operators (MNOs) to leverage their profit streams. In this paper, we present a new spectrum allocation policy in a monopoly situation. The problem is formulated as a Stackelberg game. We show that the conventional spectrum leasing contract may lead to the unprecedented scenario in which costs outweigh their revenues. On the other hand, our proposed spectrum leasing contract can not only maximize user welfare but also leverage MNO's profit streams. We show that our spectrum leasing contract can increase user welfare and MNO's profit up to 75% and 20%, respectively, relative to the conventional spectrum leasing contract. Thus, regulators must rewrite their spectrum allocation policy in order to maximize user welfare and leverage MNO's profit streams.

Main purpose of this paper is to investigate the frequency allocation schemes combined with a downlink Coordinated Multi-Point (CoMP) joint transmission system. We suggest 6-sector directional antenna and according sector based frequency reuse scheme for CoMP system, and compare the edge user performance of conventional Fractional Frequency Reuse (FFR) systems and suggested CoMP system. The numerical results show that the suggested scheme is better than the conventional FFR systems in the performance and the energy efficiency perspectives.

In recent years, to satisfy people's need for wireless services, the number of access points of the 3G-based systems, WiFi and WiMAX has been increased exponentially by wireless service providers (WSPs). As a result, there are many WSPs coexisting in the same hotspot area, which drives price competition among WSPs. Existing researche shows that each WSP will lower its price to increase revenue or market share, and this kind of price competition will eventually damage every WSP with the revenue decrease. However in this paper, we show that there is another type of price competition, where the WSPs' decreasing or increasing price levels occurs periodically and there is no equilibrium point. We illustrate it by using an example of the duopoly price competition and suggest a simple regulation rule that leads to an equilibrium point. Moreover, we show that the equilibrium point is Pareto-optimal and is well balanced in the aspects of total revenue, fairness and social welfare.

In this paper, we consider a way to deploy a wireless sensor network under the dynamic spectrum sharing. The dynamic spectrum sharing technique can help a sensor network overcome the shortage of radio resources because of getting crowded unlicensed ISM bands commonly used by it. For the purpose of this, we analyze the aggregate interference to a primary network from the sensor network as a secondary network. Base on the analysis, we propose a cost-efficient deployment algorithm. The proposed algorithm consists of the power control framework to avoid harmful interference and the inhomogeneous deployment strategy to guarantee k-coverage and k-connectivity with the least number of additional sensor nodes reducing the deployment cost. Through the numerical analysis to verify the performance of the proposed algorithm, we show the power control framework makes it possible that the sensor network is deployed with a primary network even under more restricted conditions, and the inhomogeneous deployment strategy saves the number of additional sensor nodes needed to cover a given sensing area by about 20%.

Cognitive Radios (CR) technology gives network operators more opportunities to create new communication services by sharing under-utilized spectrum owned by the licensed users. Despite of the advantages from CR, since secondary users should protect the primary service during their communication, the channel access of the secondary user is very restricted and opportunistic. This restriction occasionally makes the secondary users hard to meet the required QoS of them in some situations. As a resolution of this performance restriction, in this paper, we propose a scheme to create additional transmission opportunity for the secondary users and efficiently allocate the opportunity to maximize the network throughput considering two overlaid wireless ad hoc networks. Using the acquirable geometric and statistical information about the primary system, additional transmission opportunity is created by means of a power control and estimated as the opportunistic rate. The opportunistic rate is optimally assigned to the flows among the secondary users to maximize the throughput with an optimization method. Simulation results show that the proposed opportunity estimation and allocation scheme considerably improve the secondary network performance without degrading the primary service quality.

In this paper, we consider the CSMA/CA multihop networks where the two end-nodes transmit their packets to each other and each intermediate node adopts network coding for delivering bidirectional flows. In addition, the neighbor nodes are randomly uniformly deployed with the Poisson Point Process. By varying the combination of the physical carrier-sensing range of the transmitter node and the target signal-to-interference ratio (SIR) set by the receiver node, we can control the interference level in the network and the degree of spatial reuse of a frequency band. The larger the carrier-sensing range is, the smaller the interference level, while the smaller the opportunity of getting a channel by a node. Similarly, the higher the target SIR value is, the more probable the retransmission (by the exponential random backoff) is, while the better the link quality on successful transmission is getting. Under this tradeoff context, we find the optimal combinations of these two factors that make the end-to-end throughput of the flow maximal for three different retransmission schemes.

In this paper, we investigate the optimal power and time allocation for a wireless relay network, called BCFR (Broadcasting Channel with Fixed Relay), where the relay node is limited by the half-duplex operation. Introducing a relay in two-node broadcasting channel for a node which has a link of bad quality or requires higher transmission rate enlarges the rate region with well-designed power and time allocation. Also, BCFR is a common component of many relay networks, in which either orthogonal or non-orthogonal transmission can be adopted at the information source node. In particular, for the non-orthogonal transmission, the layered transmission with superposition coding is applied at the source node. By deriving the optimal power and time-fraction allocation at each node of BCFR, we show that the non-orthogonal structure enhances the capacity region and achieves higher log-sum capacity than the orthogonal transmission.

In this paper we investigate the opportunistic spectrum access in temporal domain where a secondary user shares a radio channel with a primary user during the OFF period of the primary user. We consider practical ON/OFF traffic models whose bursty natures are not properly described by a Markovian assumption. An optimal strategy to determine the transmission power of the secondary user is proposed, which can be adapted to any source traffic model of the primary user. This strategy will maximize the spectrum utilization of the secondary user while keeping interference violations to the primary user below a threshold. Numerical results show that the transmission power of the secondary user depends on the probability distribution of the primary traffic as well as the elapsed time of the OFF period.

The topology control for guaranteeing QoS requirements of nodes might not have any solution because of the limited resources of nodes, e.g., the maximum transmitting power. In this case, the mobility of nodes can compensate for the shortage of resources and then makes a topology feasible. We propose the energy efficient mobility control algorithms for the QoS topology control with the least movement. Through extensive simulations, we compare the performance of the proposed heuristic algorithms with that of the restricted distance optimal method. The simulation results show proposed algorithms make the network load uniformly distributed over the network.

The purpose of this paper is to show the possibility of combining slotted ALOHA with network coding in wireless multihop networks. In particular, we focus on a star topology in which outer nodes exchange data with each other through a center node. The question is how much the throughput increases by adopting network coding at the center node. To answer this question, we analyze the performance of slotted ALOHA for a star topology. In our analysis, there are two versions of slotted ALOHA: conventional ALOHA, and so-called network coded ALOHA, where the center node makes a network coding with the XOR operation to encode bi-directional traffic of the outer nodes. By analyzing the star topology, we can understand how to control the congested node in a wireless multihop network, where a lot of traffic passes through the node. In our analysis, we make cross- layer optimization over physical and MAC layers. Our conclusion is that network coded ALOHA is a good alternative to support the congested node, compared to the other wireless MAC, e.g., CSMA/CA Index.

The XOR network code is widely used in the conventional network coding. However, when the channel is noisy, the XOR code is suboptimal in terms of minimizing distortion in the source signal. We propose to use a new network code for the two-way relay channel, designed to minimize signal distortion due to the channel noise. We assume the source signal X is encoded by a source encoder and the sequence of symbols is transmitted over the two-way relay channel with noise. The received sequence of symbols may be corrupted by the channel noise, and Xcirc, the estimation of X, may be different from X. We design a new network code which minimizes the distortion between X and Xcirc. To provide an algorithm to obtain a network code with minimum distortion, we define the expected distortion associated with a network code. Starting with the XOR network code, we iteratively optimize the network code to obtain smaller expected distortion, while maintaining the Latin square constraint of the network code. New network code achieves substantial performance gain over the XOR network code.

The multicarrier system has received great attention as a solution to transmitting high-rate data over wireless channels with severe inter-symbol interference, where the optimal power allocation scheme is known as the well-known waterfilling. In this paper, the so called block waterfilling (BW) is proposed, which is a hybrid of waterfilling and constant waterfilling. Within BW, there are three channel blocks, being divided from each other by two thresholds: waterfilling block, equal power block and non-power allocation block. For BW, we propose to use the power borrowing, which plays an important role in minimizing the duality gap, releasing us from fine-tuning of the two thresholds. From numerical examples, we have found that BW shows superior performance to the constant water- filling in terms of throughput enhancement with small amount of additional computational complexity. The main idea behind BW is to parameterize computational burden of the classical waterfilling, which makes us trade the complexity with the solution quality.

In this paper, we focus on the optimal power allocation problem in the multi-user uplink OFDMA system. For the purpose, based on the KKT conditions of the problem, we design suboptimal algorithms that may be implementable in an iterative and distributive manner. An important feature of our algorithm is that it takes the generated interference to the system into account. Numerical examples show that our algorithms outperform the others, in terms of total throughput, outage probability and energy efficiency.

We analyze how the capacity of the random wireless network scales with node density for stationary nodes, taking jointly into account link adaptation, media access control (MAC), routing and retransmission for error recovery (ARQ). For the purpose, we propose a generic per-hop-based routing scheme, in which relay probability is a key parameter, and use the routing scheme as a basis for our analysis. By jointly optimizing the above factors, we derive the capacity of the random network. Our analysis shows that the per-node throughput of a static random wireless network composed of n source-destination pairs is O(1/radic(n log n)). This capacity estimation is similar to that of Gupta and Kumar, even if the assumptions are quite different.We also use simulations to investigate how node mobility affects the capacity of a random network. We have found that mobility can increase network capacity, by giving the nodes more chances to be close to each other. Our simulation results empirically show that the capacity of the random network under a mobility condition declines in the order of O(1/radicn).

We consider spectral and energy efficiency achieved by a novel relay communication in a cellular system, in which the so-called relay-beneficial mobile nodes communicate with base stations through appropriate relay nodes. The relay node uses the network coding to mix up- and downlink traffic; moreover, the mobile nodes and base stations adopt Maximal Ratio Combining (MRC) scheme to take advantage of cooperative transmission. For this system, we derive an information-theoretical condition, under which the cooperative transmission (combined with network coding) is more beneficial than the direct transmission. Using that condition, we suggest a two-phase algorithm to select the relay nodes (Phase 1) and to determine the transmission powers of those relay nodes (Phase 2). The numerical examples using this algorithm show improved energy efficiency compared to a plain system.

We consider the throughput-maximization problem for both up- and downlink in the wireless network. For the purpose, we design an iterative and distributive uplink algorithm based on Lagrangian relaxation. Using the Lagrange multipliers and the network duality, we make throughput-maximization in the downlink. Our analysis and computational tests assume that channels are symmetric between up- and downlink. The network duality we proved here is a generalized version of previous researches by Jindal et al. and Catrein et al. Computational test shows that the performance of up- and downlink throughput for our algorithms is close to the optimal value for the channel orthogonality factor between 0.5 and 1. In particular, our duality-based approach gives 97-98% throughput of the optimal uplink algorithm proposed by Kumaran and Quian, and a downlink heuristic algorithm (MPA-1) proposed by Mo and Kim, when the channel orthogonality factor is a value between 0.5 and 1. On the other hand, when channels are rather orthogonal (between 0 and 0.5), we have observed some throughput degradation in the downlink, which is about 86% of MPA-1. Considering the complexity of the existing algorithms, we conclude that these results are quite encouraging in terms of both performance and practical applicability of the generalized duality theorem.

In this paper, we consider the following problem in the wireless ad hoc network: Given a set of paths between source and destination, how to divide the data flow among the paths and how to control the transmission rates, times, and powers of the individual links in order to maximize the operation time of the worst network node. If all nodes are of equal importance, the operation time of the worst node is also the lifetime of the network. By solving the problem, our aim is to investigate how the network lifetime is affected by link conditions such as the maximum transmission power of a node and the peak data rate of a link. For the purpose, we start from a system model that incorporates the carrier to interference ratio (CIR) into a variable data rate of a link. With this, we can develop an iterative algorithm for the lifetime maximization, which resembles to the distributed power control in cellular systems. Numerical examples on the iterative algorithm are included to illustrate the network lifetime as a function of the maximum transmission power and the peak data rate.

We suggest an iterative power and greedy subcarrier allocation algorithm to improve rate-sum capacity in an uplink OFDMA system. For the downlink, it has been accepted as an optimal solution that each subcarrier is allocated to the user with the best channel condition and power is allocated by water-filling over subcarriers. However, it is not true in an uplink OFDMA system which has distributive power constraints. We formulate the optimization problem having constraints power and subcarriers in uplink, then draw two necessary conditions for optimality. Using the conditions, we propose a greedy subcarrier allocation algorithm based a marginal rate junction and iterative power allocation algorithm based on water-filling. Simulation results show that the enhanced system capacity is achieved by our proposed scheme.

The quality of service in direct-sequence code-division multiple access (DS-CDMA) can be controlled by a suitable selection of processing gain and transmission powers. In this paper, distributed control of rate and power for best effort data services is considered. In particular, we elaborate on the problem of how to control the transmission rates for maximizing system throughput while simultaneously minimizing the transmission powers. We assume a practical scenario, where every user has a finite set of discrete transmission rates and propose a simple heuristic rate allocation scheme, greedy rate packing (GRP), applicable in both up- and downlink. The scheme can be interpreted as a practical form of water-filling, in the sense that high transmission rates are allocated to users having high link gains and low interference. We show that under certain conditions, GRP will give maximum throughput and that it can be extended to guarantee a minimum data rate while maximizing network excess capacity. We suggest and analyze a distributed power control algorithm to control the intercell interference when GRP is applied to a multicellular system. Numerical results show that the proposed transmission scheme can significantly decrease the power levels while maintaining high throughput.

In power control, convergence rate is one of the most important criteria that can determine the practical applicability of a given algorithm. The convergence rate of power control is especially important when propagation and traffic conditions are changing rapidly. To track these changes, the power control algorithm must converge quickly. The purpose of this paper is to generalized the existing power control framework such that we can utilize partially known link gain information in improving the convergence speed. For this purpose, block power control (BPC) is suggested with its convergence properties. BPC is centralized within each block in the sense that it exchanges link gain information within the same block. However, it is distributed in a block-wise manner and no information is exchanged between different blocks. Depending on availability of link gain information, a block can be any set of users, and even consist of a single user. Computational experiments are carried out on a DS-CDMA system, illustrating how BPC utilizes available link gain information in increasing the convergence speed of the power control.

The performance of DS-CDMA systems depends on the success in managing interference arising from both intercell and intracell transmissions. Interference management in terms of power control for real time data services like voice has been widely studied and shown to be a crucial component for the functionality of such systems. In this work we consider the problem of supporting downlink nonreal time data services, where in addition to power control, there is also the possibility of controlling the interference by means of transmission scheduling. One such decentralized schedule is to use time division so that users transmit in a one-by-one fashion within each cell. We show that this has merits in terms of saving energy and increasing system capacity. We combine this form of intracell scheduling with a suggested distributed power control algorithm for the intercell interference management. We address its rate of convergence and show that the algorithm converges to a power allocation that supports the nonreal time data users, using the minimum required power while meeting requirements on average data rate. Numerical results indicate a big potential of increased capacity and that a significant amount of energy can be saved with the proposed transmission scheme.

This paper proposes a distributed power control algorithm that uses power levels of both current and previous iterations for power update. The algorithm is developed by applying the successive overrelaxation method to the power control problem. The gain from such a second-order algorithm is in faster convergence. Convergence analysis of the algorithm in case of feasible systems is provided in this paper. Using the distributed constrained power control (DCPC) as a reference algorithm, we carried out computational experiments on a DS-CDMA system. The results indicate that our algorithm significantly enhances the convergence speed of power control. A practical version of the proposed algorithm is provided and compared with the bang-bang type algorithm used in the IS-95 and the WCDMA systems. The results show that our algorithm also has a high potential for increasing the radio network capacity. Our analysis assumes that the system is feasible in the sense that we can support every active user by an optimal power control. When the system becomes infeasible because of high traffic load, it calls for other actions such as transmitter removal, which is beyond the scope of the present paper.

We propose a transmitter power control scheme that considers service priority in removal of transmitters under heavy traffic situations. This scheme is for the next generation PCS supporting different classes of wireless services. In developing the scheme we have formulated the prioritized transmitter removal into a manageable optimization problem and used the Lagrangian relaxation technique. Computational experiments show that our algorithm is a strong alternative to the prioritized removal algorithm recently proposed by Kim (1997). The effect of our algorithm appears clearly when there is a small priority level difference between classes.