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.

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).

In this paper, we propose a two-dimensional signal path classification (2D-SPC) for reconfigurable intelligent surface (RIS)-assisted near-field (NF) localization. In the NF regime, multiple RIS-driven signal paths (SPs) can contribute to precise localization if these are decomposable and the reflected locations on the RIS are known, referred to as SP decomposition (SPD) and SP labeling (SPL), respectively. To this end, each RIS element modulates the incoming SP’s phase by shifting it by one of the values in the phase shift profile (PSP) lists satisfying resolution requirements. By interworking with a conventional orthogonal frequency division multiplexing (OFDM) waveform, the user equipment can construct a 2D spectrum map that couples each SP’s time-of-arrival (ToA) and PSP. Then, we design SPL by mapping the SPs with the corresponding reflected RIS elements when they share the same PSP. Given two unlabeled SPs, we derive a geometric discriminant by checking whether the current label is correct. It can be extended to more than three SPs by sorting them using pairwise geometric discriminants between adjacent ones. From simulation results, it has been demonstrated that the proposed 2D-SPC achieves consistent localization accuracy while poor accuracy in a benchmark, even if insufficient PSPs are given.

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.

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.

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.

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.

Reconfigurable intelligent surface (RIS), one core element in 6G, opens a new opportunity to design near-field (NF) localization since a signal's propagation distance can be different depending on the reflected points on large RIS. For the design of NF localization to be effective, signals reflected on distinct RIS points should be profiled without interfering with the others, called signal classification (SC). In this paper, we propose a simple yet novel SC technique, called linear phase modulation (LPM), where sequences of distant RIS elements' phases are linearly modulated with different rates. Along with the conventional or-thogonal frequency division multiplexing waveform, LPM makes it possible to classify the reflected signals by distant RIS elements as well as estimate their propagation distances using a two-dimensional Fourier transform (2D-FT) technique. It exploits one more signal dimension for performance improvement than conventional one-dimensional FT (1D-FT) based SC. Through analytic and numerical studies, we verify the effectiveness of the proposed SC using LPM by comparing its localization accuracy with several benchmarks designed based on 1D-FT.

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.

Precise positioning has become one core topic in wireless communications by facilitating candidate techniques of beyond 5G and 6G. Nevertheless, most existing positioning algorithms, categorized into geometry-driven and data-driven approaches, fail to simultaneously fulfill diversified requirements for practical use, e.g., accuracy, real-time operation, scalability, maintenance, etc. This article aims at introducing a new principle, called combinatorial data augmentation (CDA), a catalyst for tightly integrating geometry and data-driven approaches. We first explain the concept of CDA and its critical advantages over the two standalone approaches, followed by validating its effectiveness by field experiments with WiFi round-trip time and inertial measurement units.

Mobile edge computing(MEC) has emerged as an attractive solution by executing computation-intensive services at a powerful edge server instead of mobiles. Two types of data are necessary to this end. One is user-specific data acquired from mobiles, calledcomputation offloading(CO). The other is service-specific data downloaded from a central cloud, calledservice caching(SC). It is noteworthy that CO and SC decisions are coupled when each user’sservice preference(SP) is personalized. Specifically, noting that the optimal SC is to cache services likely to be requested more frequently, the resultant SC tends to be biased to the SP of the user whose offloading rate is high. On the other hand, such an SC decision causes longer computing latency of users with a relatively low offloading rate, which ultimately limits a CO decision for agile MEC services. This work tackles this issue from a sum-utility maximization perspective under radio-resource and computation-latency constraints. The average computation latency is first derived in closed-form by modeling a computation as a stochastic process following a hyper-exponential distribution. Based on it, we first consider the case for homogeneous SP where CO and SC decisions are decoupled. Thus, SC can be deterministically controlled using the homogeneous SP, while CO decision is independently determined, lying between water-filling and channel-inversion allocations. Next, we design a joint CO-and-SC policy for heterogeneous SP. CO and SC decisions are iteratively optimized with the other fixed by leveraging the homogeneous SP’s result. The optimal stopping rules are derived, guaranteeing the sum-utility enhancement. The proposed algorithm’s effectiveness is verified by simulations that the proposed CO-and-SC design for heterogenous SP always outperforms that for homogeneous SP.

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.

Multi-point vehicular positioning is an essential operation for autonomous vehicles. However, the state-of-the-art positioning technologies, relying on reflected signals from a target (i.e., RADAR and LIDAR), cannot work without line-of-sight (LoS). Besides, it takes significant time for environment scanning and object recognition with potential detection inaccuracy, especially in complex urban situations. Some recent fatal accidents involving autonomous vehicles further expose such limitations. In this article, we aim at overcoming these limitations by proposing a novel relative positioning approach, called Cooperative Multi-point Positioning (COMPOP). The COMPOP establishes cooperation between a target vehicle (TV) and a sensing vehicle (SV) if a LoS path exists, where a TV explicitly lets an SV to know the TV's existence by transmitting positioning waveforms. This cooperation makes it possible to remove the time-consuming scanning and target recognizing processes, facilitating real-time positioning. One prerequisite for the cooperation is a clock synchronization between a pair of TV and SV. To this end, we use a phase-differential-of-arrival (PDoA) based approach to remove the TV-SV clock difference from the received signal. With clock difference correction, the TV's position can be obtained via peak detection over a 3D power spectrum constructed by a Fourier transform (FT) based algorithm. The COMPOP also incorporates nearby vehicles, without knowing their locations, into the above cooperation for the case without a LoS path. Specifically, several strong non-LoS (NLoS) links from the TV to the SV can be generated via mirror-like reflections over the neighboring vehicles' metal surfaces. Following the same procedures in the LoS case, virtual TVs mirrored by nearby vehicles can be detected. By exploiting the geometric relation between the virtual and actual TVs, COMPOP can be achieved by intelligently combining the virtual TVs to position the actual TV. The effectiveness of the COMPOP is verified by several simulations concerning practical channel parameters.

Multi-point detection of the full-scale environment is an important issue in autonomous driving. The state-of- the-art positioning technologies (such as RADAR and LIDAR) are incapable of real-time detection without \emph{line-of-sight} (LoS). To address this issue, this paper presents a novel multi-point vehicular positioning technology via \emph{millimeter-wave} (mmWave) transmission that exploits multi-path reflection from a \emph{target vehicle} (TV) to a \emph{sensing vehicle} (SV), which enables the SV to fast capture both the shape and location information of the TV in \emph{non-LoS} (NLoS) under the assistance of multi-path reflections. A \emph{phase-difference-of- arrival} (PDoA) based hyperbolic positioning algorithm is designed to achieve the synchronization between the TV and SV. The \emph{stepped-frequency-continuous-wave} (SFCW) is utilized as signals for multi-point detection of the TVs. Transceiver separation enables our approach to work in NLoS conditions and achieve much lower latency compared with conventional positioning techniques.

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.

Next-generation wireless networks will provide users ubiquitous low-latency computing services using devices at the network edge, called mobile edge computing (MEC). The key operation of MEC is to offload computation intensive tasks from users. Since each edge device comprises an access point (AP) and a computer server (CS), a MEC network can be decomposed as a radio access network (RAN) cascaded with a CS network (CSN). Based on the architecture, we investigate network constrained latency performance, namely communication latency (comm- latency) and computation latency (comp-latency) under the constraints of RAN coverage and CSN stability. To this end, a spatial random network is constructed featuring random node distribution, parallel computing, non-orthogonal multiple access, and random computation-task generation. Based on the model and the network constraints, we derive the scaling laws of comm-latency and comp-latency with respect to network-load parameters and network-resource parameters. Essentially, the analysis involves the interplay of stochastic geometry, queueing, and parallel computing. Combining the derived scaling laws quantifies the tradeoffs between the latency, network coverage and network stability.

This paper presents a technology of sensing hidden vehicles by exploiting multi-path vehicle-to-vehicle (V2V) communication. This overcomes the limitation of existing RADAR technologies that requires line-of-sight (LoS), thereby enabling more intelligent manoeuvre in autonomous driving and improving its safety. The proposed technology relies on transmission of orthogonal waveforms over different antennas at the target (hidden) vehicle. Even without LoS, the resultant received signal enables the sensing vehicle to detect the position, shape, and driving direction of the hidden vehicle by jointly analyzing the geometry (AoA/AoD/propagation distance) of individual propagation path. The accuracy of the proposed technique is validated by realistic simulation including both highway and rural scenarios.

Next-generation wireless networks will provide users ubiquitous low-latency computing services using devices at the network edge, called mobile edge computing (MEC). The key operation of MEC is to offload computation intensive tasks from users. Since each edge device comprises an access point (AP) and a computer server (CS), an MEC network can be decomposed as a radio access network cascaded with a CS network. Based on the architecture, we investigate network-constrained latency performance, namely communication latency and computation latency, under the constraints of radio-access connectivity and CS stability. To this end, a spatial random network is modeled featuring random node distribution, parallel computing, non-orthogonal multiple access, and random computation-task generation. Given the model and the said network constraints, we derive the scaling laws of communication latency and computation latency with respect to network-load parameters (density of mobiles and their task-generation rates) and network-resource parameters (bandwidth, density of APs/CSs, and CS computation rate). Essentially, the analysis involves the interplay of the theories of stochastic geometry, queueing, and parallel computing. Combining the derived scaling laws quantifies the tradeoffs between the latencies, network connectivity, and network stability. The results provide useful guidelines for MEC-network provisioning and planning by avoiding either of the cascaded radio access network or CS network being a performance bottleneck.

Attaining the vision of Smart Cities requires the deployment of an enormous number of sensors for monitoring various conditions of the environment. Backscatter sensors have emerged to be a promising solution due to the uninterruptible energy supply and relative simple hardwares. On the other hand, backscatter sensors with limited signal processing capabilities are unable to support conventional algorithms for multiple access and channel training. Thus, the key challenge in designing backscatter sensor networks is to enable readers to accurately detect sensing values given simple ALOHA random access, primitive transmission schemes, and no knowledge of channel states. We tackle this challenge by proposing the novel framework of backscatter sensing (BackSense) featuring random encoding at sensors and statistical inference at readers. Specifically, assuming the on/off keying for backscatter transmissions, the practical random encoding scheme causes the on/off transmission of a sensor to follow a distribution parameterized by the sensing values. Facilitated by the scheme, statistical inference algorithms are designed to enable a reader to infer sensing values from randomized transmissions by multiple sensors. The specific design procedure involves the construction of Bayesian networks, namely deriving conditional distributions for relating unknown parameters and variables to signals observed by the reader. Then based on the Bayesian networks and the well-known expectation-maximization principle, inference algorithms are derived to recover sensing values. Simulation of the BackSense system demonstrates high accuracy in reader inference despite the mentioned limitations of backscatter sensors, which grows with increasing numbers of received symbols and reader antennas.

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.

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.

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.

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 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.