Hand gesture recognition enhances human-computer interaction in areas like virtual reality and smart homes. Wireless signal-based methods, particularly frequency modulated continuous wave (FMCW) millimeter-wave radar, can capture range and velocity with high spatial resolution, providing high-quality data compared to other methods. This makes them ideal for precise and robust gesture recognition. In this paper, we propose a device-free hand gesture recognition system using the Google Soli radar sensor, an FMCW millimeter-wave radar designed for low-power, miniature gesture sensing technology. The system preprocesses radar signals by generating image data, removing noise and outliers, and standardizing the frame count across gesture sequences. To effectively distinguish subtle motion differences between gestures, frame difference images are generated, and edge detection algorithm is applied to further emphasize these differences. A deep learning (DL) model, combining convolutional neural network (CNN) for spatial feature extraction and long short-term memory (LSTM) for temporal pattern learning, is used to classify the preprocessed data into specific gestures. The proposed system achieves state-of-the-art classification accuracy of 98.91% among studies using the same dataset for evaluation. Furthermore, it demonstrates robustness with 94.91% accuracy in leave-one-user-out cross validation, highlighting its capability to generalize across different users.

Gesture recognition using wireless signals has gained significant attention as a promising approach for contactless human-computer interaction. Among various wireless sensing tools, millimeter-wave (mmWave) frequency-modulated continuous wave (FMCW) radar provides rich information such as range, angle, and velocity, making it well-suited for human motion sensing. However, existing studies often underutilize the trajectory information of human body parts, and the transformation of radar data through image processing or resampling often leads to a loss of inherent spatio-temporal patterns. To address these limitations, we propose a graph construction method that effectively preserves spatial and temporal information by representing a point-cloud data of FMCW radar as a directed graph. Based on this representation, we design a lightweight trajectory-aware graph encoder that efficiently processes a directed graph. The proposed graph encoder is readily compatible with various classifiers, making it applicable to broader tasks such as openset recognition which requires detection of unknown classes. Extensive experiments on public datasets demonstrate that our trajectory-aware gesture recognition scheme greatly outperforms existing state-of-the-art schemes under various cross-domain situations.

Gesture recognition can provide a natural and user-friendly interface to a variety of systems. However, in order for the real-world applications to apply gesture recognition as a tool for human-computer interaction (HCI), it is necessary not only to accurately classify predefined gestures but also to correctly identify undefined gestures. In this article, we design a novel open-set gesture recognition (OSR) system which leverages ultra-wideband (UWB) impulse radar-based wireless sensing and deep learning technology to effectively detect both known and unknown gestures. In the proposed system, the OSR task is decomposed to two steps of closed-set classification and deep inspection. The first closed-set classification step extracts the features from the channel impulse response (CIR) matrix of UWB radar and predicts one of the known gestures. The second deep inspection step investigates more deeply whether the predicted known gesture is correct or an unknown gesture. The feature extractor is composed of two Transformers, which extract temporal features and spatial features unique to each gesture from the CIR matrix, respectively. The second stage consists of multiple binary deep inspectors, each of which is assigned to one of the known gestures, and this method not only greatly simplifies the training process of deep inspection but also remarkably enhances the OSR performance. Extensive experimental results conducted on a public dataset demonstrate that our system significantly outperforms the state-of-the-art OSR systems.

Radio frequency (RF)-based fall detection systems have been actively studied over the past decade due to their low privacy concerns and convenient non-wearable nature. In particular, WiFi channel state information (CSI)-based solutions offer the additional benefit of requiring no specific hardware due to the widespread deployment of WiFi. However, WiFi CSI-based systems typically suffer from severe performance degradation in cross-domain scenarios, non-line-of-sight (NLOS) and/or through-the-wall (TTW) environments. For successful real-world deployment, a fall detection system must be able to provide reasonably good performance even in such poor conditions. In this paper, we propose a new WiFi CSI amplitude-based fall detection system (called AmFall) that effectively addresses the aforementioned challenges. To cope with NLOS/TTW situations, AmFall selectively uses the subcarriers and principal components containing valuable information. Considering that the continuous wavelet transform (CWT) is suitable for time-frequency analysis of non-stationary signals, such as those generated during falls, AmFall generates a scalogram by applying CWT to the resulting CSI signal with a novel denoising algorithm and extracts the speed information for segmentation. The segmented scalograms are fed into a deep learning-based classifier. In this paper, we also suggest a simple yet effective dataset augmentation method to generate multiple scalograms from a single CSI observation. The proposed system, implemented on commercial WiFi devices, achieves a high fall detection accuracy of 95.28% to 99.67% in TTW and cross-domain environments, outperforming state-of-the-art WiFi-based methods.

In dense wireless local area networks (WLANs), users may experience unsatisfactory services owing to severe contention and interference caused by channel sharing among neighboring access points (APs). We propose a deep reinforcement learning (DRL)-based resource allocation (RA) scheme that can practically tackle the performance degradation problem by allocating appropriate channel and transmit (tx) power to each AP. To compensate for insufficient computing power of APs to train deep neural networks (DNN), the proposed scheme takes a hybrid approach of centralized training and distributed execution. Since the channel environment of neighboring APs can be very similar, when all APs determine their own channels at once, it is feasible for multiple neighboring APs to select the same channel and this can incur the performance degradation of the entire network. To avoid this phenomenon, the proposed scheme makes a DRL-based sequential decision, i.e., one AP at a time in a round-robin order determines its channel and tx power. Furthermore, the proposed scheme uses local observations that can be gathered from legacy 802.11 APs and stations (STAs), which may be a useful property because the local observations can be obtained even when there coexist uncontrolled APs in the environment and no additional functions need to be implemented in the STAs. We evaluate the performance of the proposed scheme using the average delay and the power consumption as metrics under unsaturated traffic condition. The experimental results show that the proposed scheme achieves better delay performance while consuming much lower power than comparison schemes in dense WLAN scenarios.

WiFi-based smart sensing applications are receiving a lot of attention, and among them, intruder detection is being actively studied as security concerns increase. Recent studies on WiFi-based intruder detection systems put channel state information (CSI) data into a machine learning (ML) model and then use the output of the model to determine whether or not an intruder is present based on predefined thresholds. However, threshold-based intruder detection systems have an inherent problem in that when strict thresholds are used to increase intruder detection rate, false alarm rate that judge known users as intruders increases, and vice versa when less strict thresholds are used to reduce false alarm rate. To tackle this challenge, we propose Wi-Sniffer that increases the intruder detection rate, which is more critical than the false alarm rate by using a strict threshold, and compensates for the increased false alarm rate by putting user association (UA) information from the WiFi access point (AP) and the inference result of the ML model together in a decision tree. The UA information can be easily retrieved from the AP without additional implementation in the mobile devices. We test Wi-Sniffer in real time, and the evaluation results show that Wi-Sniffer can achieve both high intruder detection rate and low false alarm rate with only a small number of training samples and low computational complexity.

Among various human-computer interaction (HCI) techniques, gesture recognition can be a good solution because it is simple and intuitive for humans. Even though the camera-based and the wearable sensor-based gesture recognition has achieved high accuracy, there are some limitations such as the privacy issue and the uncomfortable wearable sensors. Recently, the WiFi-based gesture recognition using channel state information (CSI) gets attention of many researchers since it is device-free and does not capture private information of users as images. Previous works on WiFi-based gesture recognition typically utilize deep learning (DL) models that provide good performance on classification. However, the DL models require large memory and high computational complexity due to many parameters such as weights and biases. Such requirements are undesirable for real-time systems and usual commercial access points (APs) that have small memory and low computational capability. In this paper, we propose a new machine learning (ML)-based gesture recognition scheme which exploits a prior-step of gesture categorization. The experimental results show that the proposed scheme can achieves a high accuracy while reduces significantly the computational complexity and the memory consumption.

Since Internet of Things (IoT) traffic in cellular networks tends to be explosively concentrated on uplink for a short period of time, the random access (RA) channel may be frequently congested. The 3GPP uses a$p$-persistent access scheme called access class barring (ACB), which mitigates congestion by redistributing bursty access attempts over time. The ACB scheme can effectively control the congestion problem but treats equally all user equipments (UEs) in initial access. Thus, when a variety of UEs with different service requirements (e.g., for mission-critical applications and for delay-tolerant applications) coexist, the Quality of Service (QoS) of some UEs may not be guaranteed. In this article, we propose a new RA scheme that effectively alleviates a congestion situation of the RA channel, while providing the differentiated RA service to UEs in accordance with their QoS requirements. The proposed RA scheme consists of the load estimation and the optimization of an access probability based on the estimated load. Considering that the base station estimates the system loads by observing the status of received preambles, we formulate the proposed RA scheme as a partially observable Markov decision process (POMDP) framework. By using simulations, we demonstrate that the proposed scheme guarantees a short access delay requirement of high-priority UEs without any additional energy consumption of low-priority UEs during the access procedure.

Ultra-wideband (UWB) ranging-based indoor positioning system (IPS) is suggested as one of the solutions to satisfy the requirement of decimeter level accuracy in indoor environments. However, UWB ranging error caused in non-line-of-sight (NLOS) environments is inevitable, which reduces accuracy of positioning. To tackle this problem, in this paper, an indoor positioning scheme that combines UWB positioning with pedestrian dead reckoning (PDR) is designed and proposed. First, the proposed scheme improves the performance of PDR utilizing UWB positioning in order to achieve the effect of parameter adaptation used in PDR. To this end, step detection and stride length estimation in traditional PDR are substituted with a deep learning-based speed estimation. In addition, heading estimation is improved by calibrating tilt effect of smartphone with the aid of UWB positioning. Then, with the UWB-assisted PDR (U-PDR), we also propose UWB positioning and U-PDR fusion algorithm using Kalman filter (KF). The proposed fusion algorithm complements UWB positioning and U-PDR based positioning, which improves the accuracy of positioning. Experimental results demonstrates that the performance of the proposed algorithm is better than that of UWB positioning or PDR only algorithms.

Recently, many researchers and global vendors of mobile devices have paid attention to the ranging technique using IEEE 802.15.4z ultra-wideband (UWB), because it can support a highly precise spatial awareness and indoor localization. In the Internet of Things (IoT) environments, the interval of the ranging attempts for a ranging device (RDEV) affects largely the performance of the RDEV. The proper ranging interval for an RDEV varies according to the IoT applications using the ranging results as well as the type and state of the RDEV (e.g., moving speed and battery status). In the IEEE 802.15.4z, since the ranging for a group of multiple RDEVs is carried out once every ranging block, the ranging interval can be represented by the block length, which should be determined based on the various requirements of RDEVs within the group. In this article, we analyze the requirements on the block length of RDEVs, from the viewpoint of the energy efficiency and the degree of service satisfaction. Considering these requirements, we design a novel decision scheme of the ranging block length, based on the Markov decision process. The simulation results show that the proposed scheme enhances remarkably the energy efficiency and the satisfaction of RDEVs.

As the number of Internet-of-Things (IoT) devices explosively increases, developing the IoT standard is essential to control those devices efficiently. To do this, many vendors and IT companies have established IoT standard organizations. In particular, the Open Connectivity Foundation (OCF), the most influential IoT standard organization, has been actively developing the OCF standard and an open-source software framework called IoTivity. The OCF standard supports interoperability through bridging techniques toward non-OCF devices of other ecosystems, such as ZigBee, Bluetooth low energy, oneM2M, and AllJoyn. Among several bridging techniques, we focus on developing an OCF-to-ZigBee (O2Z) bridging technique which is very important to provide the scalability of the OCF standard. This is because there are many ZigBee devices to construct IoT applications (e.g., lightening, heating, and sensing), and the number of them is steadily increasing. In this article, we introduce the O2Z bridging technique adopted in the OCF standard, and describe how to implement an O2Z bridging plugin as a software component of IoTivity. This work would provide a good guideline for developing other bridging techniques toward other IoT ecosystems.

Long-range wide-area networks (LoRaWANs) have been designed to provide low-power long-range communication service to Internet-of-Things (IoT) applications. To efficiently support a massive number of IoT devices in a wide area with limited resources, the LoRaWAN utilizes various adaptation techniques, including rate and power control. In this article, we propose a very simple and energy-efficient rate control scheme for LoRa uplink transmission. With the proposed scheme, a LoRa end device increases its rate (rate_up) when recognizing that the uplink channel quality gets better and, when recognizing the transmission failure under an uncongested network situation, the device decreases the rate (rate_down) or increases the transmission power. The proposed scheme also has a simple mechanism for avoiding the repeated alternation between rate_up and rate_down, called a ping-pong situation. We evaluate the performance of the proposed scheme, by modeling it as a continuous-time Markov chain. In addition, we compare the performances of the proposed scheme and some existing schemes by using simulation. The simulation results demonstrate that the proposed scheme greatly outperforms the others, in the transmission success probability, effective transmission rate, frame transmission delay, and energy consumption.

Recently, video traffic has rapidly increased, and a considerable portion of that is caused by wireless devices. Since video streaming services now require high data rates, IEEE 802.11 has been widely used to support them in the wireless environment. On the other hand, in densely populated areas (e.g., conference rooms), multiple video streaming services using a unicast method can result in exceeding the available capacity. Instead, a multicast method can be considered for video streaming services in these latter conditions, since many users can be served at the same time with a relatively small resource. However, the legacy multicast in IEEE 802.11 does not perform the Automatic Repeat reQuest (ARQ), which leads to many packet losses and the degradation of video quality. To tackle this problem, we investigate the reliable video multicast using the application layer-forward error correction (AL-FEC) technique without the ARQ mechanism. Specifically, based on traffic characteristics of H.264/AVC video streaming, we propose a practical adaptation scheme, which determines the robustness of the FEC and a physical transmission rate of wireless access point. Moreover, we implement a prototype system in practice using the proposed scheme. The experimental results show that the proposed scheme greatly supports the coexistence with unicast transmissions, as well as the reliable video multicast, while satisfying the video quality.

Indoor localization technique is very helpful for rescuing people in disaster relief. However, since the existing communication facilities may shut down and the indoor environment can be changed (damaged) under disaster conditions, the traditional localization scheme is likely to cause a large estimation error and even may not work. In this paper, we propose a new indoor localization scheme for disaster relief. The proposed localization scheme is composed of two phases: in the first (off-line) phase, the target area is divided into several subareas; in the second (online) phase, the scheme finds out the subarea where the target node locates. Because the proposed scheme does not require any pre-measurement of radio characteristics between points in the target building, it is very useful from the practical point of view. The simulation results show that the proposed scheme provides a higher localization accuracy compared with the existing approach. In addition, the experimental results verify the feasibility of the proposed scheme in practice.

Bluetooth Low Energy (BLE) is one of the promising technologies to enable Internet of Things. Many potential BLE applications are delay-sensitive which demand a fast data delivery. However, since BLE coexist with other technologies such as WiFi, ZigBee, microwave, the BLE may experience severe interference. The interference can cause the BLE connection event to be unexpectedly closed which forces the device to enter sleep mode even though there is more data to be sent. This negatively affects the performance of the BLE in terms of transmission delay which makes the BLE not suitable for delay-sensitive applications. To address this issue, we propose an extension of channel access scheme of the BLE in connection state. The proposed scheme is based on the listen-before-talk mechanism where the device should sense the channel before using it. By using Markov analysis, several performance measures are derived. Through numerical result, we show that the proposed channel access scheme can reduce the average transmission delay.

In cellular systems, the relaying technique is used for increasing service coverage, system capacity, and cell- edge user throughput at a relatively low cost. However, the introduction of relay stations (RSs) leads to a more complex intra-cell and inter-cell interference problem between the base station (BS) and RSs. The fractional frequency reuse (FFR) is one of the most simple and efficient (thus, practical) techniques to solve this problem. To heighten the performance of cellular systems with the relaying/FFR combination, the deployment (i.e., the positioning) of RSs should be managed carefully. In this paper, we focus on energy efficiency (EE) as a performance index, which is quantified as the number of bits that can be transmitted per unit of energy consumption. We first derive the EE as a function of the distance between BS and RS. Then, we find out the optimal distance using optimization approach. Based on the numerical results, we provide a guideline in the deployment of RSs.

We propose an efficient resource allocation (RA) scheme for downlink of relay-aided orthogonal frequency division multiple access systems. To mitigate excessive computational complexity of the centralized RA where the base station (BS) is responsible for resource allocation of the entire cell, we take a distributed RA strategy with which each relay station (RS) and BS perform resource allocation for their respective receivers by themselves. The proposed RA scheme is composed of two parts, RS resource allocation (RS-RA) and BS resource allocation (BS-RA). Considering that multiple RSs compete for radio resource in a distributive manner, we design the RS-RA scheme according to noncooperative game framework. The proposed RS-RA scheme aims at allocating less resource to two-hop transmission via RS so that more resource can be used for one-hop (direct) transmission. This RA strategy greatly heightens the system performance. Simulation results show that the proposed RA scheme provides the high cell throughput and the low outage probability. In addition, we devise a heuristic RA scheme that can lower the computational complexity further with much smaller reporting overhead and also suggest an iterative algorithm that can easily implement the proposed RA scheme.

In cellular networks, the device-to-device (D2D) communication increases the network capacity by spatial reuse of radio resources and prolongs the battery life of devices by reducing the transmission power. In this paper, we propose a two-stage semi-distributed resource management framework for the D2D communication. At the first stage of the framework, the base station (BS) allocates resource blocks (RBs) to BS-to-user device (B2D) links and D2D links, in a centralized manner. At the second stage, the BS schedules the transmission using the RBs allocated to B2D links, while the primary user device of each D2D link carries out link adaptation on the RBs allocated to the D2D link, in a distributed fashion. The proposed framework has the advantages of both centralized and distributed design approaches, i.e., high network capacity and low control/computational overhead, respectively. We formulate the problems of RB allocation to maximize the radio resources efficiency, taking account of two different policies on the spatial reuse of RBs. To solve these problems, we suggest a greedy algorithm and a column generation-based algorithm. By simulation, it is shown that the proposed scheme achieves the near-optimal performance while reducing the control/computational overhead.

Recently, device-to-device (D2D) communication in cellular networks has gained much attention for enabling a base station (BS) to offload traffic to direct D2D links and for facilitating new network services. In this paper, we propose a semi-distributed resource allocation scheme for the D2D communication where the BS allocates radio resources for cellular user links and D2D links in a centralized manner while the modulation and coding scheme (MCS) level and the transmission power of D2D links are distributively decided by the device of each D2D link. We first formulate a resource allocation problem of which the objective is to maximize the spatial reuse of radio resources by allowing the simultaneous transmission of D2D links on the same resources. To solve the problem, we present a suboptimal greedy algorithm. By simulation, we show that the cellular networks with the proposed scheme achieve a higher network throughput by maximizing the spatial reuse of radio resources.

The authors propose an adaptive transmission scheme for cooperative communication networks. The cooperative network with the proposed scheme chooses the transmission rate and decides to involve the relay in transmission, adapting to the channel state estimated from limited feedback information (e.g. positive/negative acknowledgement (ACK/NACK) feedback). Considering that the limited feedback information provides only partial knowledge about the actual channel states, the authors design a decision‐making algorithm on cooperative transmission by using a partially observable Markov decision process framework. The simulation results show that, when the feedback overhead is taken into account, the proposed scheme outperforms even the scheme with which the states of all relevant channels are estimated and fed back.

Many cognitive radio (CR) networks adopt the collaborative sensing strategy that combines the sensing results of multiple CR stations, for getting the better primary user (PU) detection performance. In the practical CR networks with collaborative sensing, since a station reports the sensing result to a fusion center through a message, the reporting overhead is not negligible. In this paper, we design a sensing management scheme for CR networks, while taking this reporting overhead into account. First, we formulate an optimization problem that aims at finding the optimal number of sensing stations which collaborate to maximize the PU detection probability for given sensing/reporting time. Then, we propose a heuristic scheme to efficiently select the collaborative sensing stations, without a priori information for the locations of stations. Furthermore, we determine the optimal sensing interval for minimizing the PU detection delay while maintaining the CR channel utilization to a target value, by using a Markov analysis. The simulation results show that the proposed sensing management scheme significantly improves the PU detection performance.

In this paper, we propose a downlink resource sharing strategy in two-tier networks with the co-channel femtocell deployment. The proposed resource sharing strategy adopts the fractional frequency reuse (FFR) framework to maximize the spectral efficiency and overcome the femto-to-macro interference (FMI) problem at the same time. In the proposed strategy, resource units in each frequency partition (FP) are divided into a macro-privileged group and a femto-dedicated group. To protect the macrocell from the FMI, the resource usage of the femtocells is controlled according to the FP assignments of the nearby macrocell users. In addition, the per-FP resource division is determined to maximize the spectral efficiency while balancing macrocell/femtocell user throughputs. Simulation results show that the proposed resource sharing strategy can achieve high spectral efficiency while suppressing the FMI effectively.

Routing is one of the most important issues in multihop ad hoc networks. In the routing for mobile cognitive radio (CR) networks, the constraints on residual energy of each user and the requirements for primary user protection should be additionally taken into account. This study proposes a spectrum and energy-aware routing (SER) protocol for CR ad hoc networks, which is based on the dynamic source routing. To establish routes on-demand with reserved bandwidth, the protocol involves route selection and channel–timeslot allocation jointly. The proposed SER protocol can balance energy consumption, reduce routing overhead, eliminate contention between nodes and decompose traffics over different channels/timeslots. Extensive simulation shows that the proposed protocol achieves good performance in system throughput, network lifetime and mean message delay.

This paper proposes a novel medium access control (MAC) scheme for multichannel cognitive radio (CR) ad hoc networks, which achieves high throughput of CR system while protecting primary users (PUs) effectively. In designing the MAC scheme, we consider that the PU signal may cover only a part of the network and the nodes can have the different sensing result for the same PU even on the same channel. By allowing the nodes to use the channel on which the PU exists as long as their transmissions do not disturb the PU, the proposed MAC scheme fully utilizes the spectrum access opportunity. To mitigate the hidden PU problem inherent to multichannel CR networks where the PU signal is detectable only to some nodes, the proposed MAC scheme adjusts the sensing priorities of channels at each node with the PU detection information of other nodes and also limits the transmission power of a CR node to the maximum allowable power for guaranteeing the quality of service requirement of PU. The performance of the proposed MAC scheme is evaluated by using simulation. The simulation results show that the CR system with the proposed MAC accomplishes good performance in throughput and packet delay, while protecting PUs properly.

A practical resource allocation in cellular networks should take into consideration inter-cell information exchange overhead, user channel feedback overhead, and adaptability to network status variations such as user distribution and traffic load. This paper presents two practical resource allocation techniques for the fractional frequency reuse (FFR) operation in IEEE 802.16m networks. Firstly, we propose a power allocation technique where the transmission powers across frequency partitions (FPs) are gradually adapted in a distributed way. The gradual power adaptation is derived from analysis of the optimal power allocation problem. Secondly, base station guided FP selection is proposed to obtain load balanced FP assignment with minimal feedback overhead. Simulation results show that the proposed techniques can enhance the spectral efficiency for the FFR under various network status.

In order to guarantee the high quality of service (QoS) even for cell edge users and deeply shadowed users, future cellular networks are expected to deploy relay stations (RSs). For fully exploiting the newly deployed RSs, the centralized scheduling at the base station (BS) have been widely studied due to its ability to provide optimal use of wireless resources and RSs. However, severe burden for channel reporting can be imposed by the centralized scheduling since the BS should collect every channel gain not only between BS and mobile stations (MSs) but also between each RS and MSs. To cope with this high overhead problem, we propose a distributed scheduling scheme which exploits the frequency reuse and provides high QoS to MSs, based on the game theoretic framework. The simulation results show that the proposed scheme can obtain higher throughput than the centralized scheme while the outage probability of the proposed scheme is similar to that of the centralized scheme.

Recently, the IEEE 802 working groups for regional, local, metropolitan, and personal area networks undertake standardization of cognitive radio (CR) networks exploiting TV white spaces. Thus, in the near future, various types of IEEE 802 CR systems may operate on the same TV channel simultaneously. A CR system can coexist more effectively with other CR systems on the same TV channel if it can identifies the types of them. In this paper we propose a system identification scheme for distinguishing the coexisting IEEE 802 CR networks, which exploits the unique features of each system. Simulation results show that the proposed scheme identifies IEEE 802 CR systems accurately and efficiently.

Due to the license-exempt nature of the cognitive radio (CR), multiple CR networks may operate (coexist) on the same channel. In this paper, we first discuss some techniques for self-coexistence of CR local and personal area networks, where the self-coexistence means the coexistence of homogeneous CR networks using the same PHY/MAC protocol. Then, we propose a self-coexistence etiquette for the ECMA TC48-TG1 system, which is a CR personal area network specification in standardization. Simulation results show that the proposed etiquette supports effectively the self-coexistence of ECMA TC48-TG1 networks.

Cognitive radio (CR) is a promising technology that can be a solution for the frequency scarcity problem of the wireless mesh network operating on multiple channels. In this paper, we investigate the joint channel assignment and routing in CR-based wireless mesh networks (CRWMNs) for better coexistence with primary users and other CR networks. We formulate an optimization problem with the objective of minimizing the number of operating channels in CRWMNs while satisfying the given traffic demand. Since the formulated problem is NP-hard, we develop a heuristic algorithm to solve the problem. Simulation results show that the proposed scheme helps CRWMNs operate stably under the environment that the coexistence with PUs and other CR networks is required.

This paper proposes a load-aware routing scheme for wireless mesh networks (WMNs). In a WMN, the traffic load tends to be unevenly distributed over the network. In this situation, the load-aware routing scheme can balance the load, and consequently, enhance the overall network capacity. We design a routing scheme which maximizes the utility, i.e., the degree of user satisfaction, by using the dual decomposition method. The structure of this method makes it possible to implement the proposed routing scheme in a fully distributed way. With the proposed scheme, a WMN is divided into multiple clusters for load control. A cluster head estimates traffic load in its cluster. As the estimated load gets higher, the cluster head increases the routing metrics of the routes passing through the cluster. Based on the routing metrics, user traffic takes a detour to avoid overloaded areas, and as a result, the WMN achieves global load balancing. We present the numerical results showing that the proposed scheme effectively balances the traffic load and outperforms the routing algorithm using the expected transmission time (ETT) as a routing metric.

For supporting the high quality of service (QoS) in relay network, this paper proposes a new scheduling scheme that can be adopted in the orthogonal frequency division multiple access (OFDMA) cellular systems. In relay-aided network, a relay station should receive data from base station for forwarding data to its serving mobile stations. In addition, since the channel quality of each hop cannot be the same, the transmission time of each hop should be adjusted to harmonize the achievable transmission rate on each hop. In this paper, we first formulate the optimal scheduling problem and suggest a simple and fast heuristic scheme that provides enough subchannels to each hop for guaranteeing QoS and also adjusts the transmission time of each hop. With the proposed scheme, the QoS of each mobile station can be guaranteed and the channel utilization is increased. By using the simulation, we show that the throughput of the proposed scheme is higher than that of the scheme with fixed transmission time and the scheme not using relay stations. Moreover, since the proposed scheme not only allocates subchannels adequately but also adjusts the transmission time of each hop, the lowest outage rate can be achieved.

We propose QoS-aware mesh deterministic access opportunities (MDAOPs) assignment algorithm for the IEEE 802.11s wireless mesh networks (WMNs). The WMNs can benefit from the mesh deterministic access (MDA) in terms of guaranteeing the QoS of the delay bounded traffic, mitigating the exposed node problem, and providing the fairness among best effort traffic flows. However, it is not considered that how much size of MDAOPs is assigned to each mesh link. In order to search the mesh links which need an additional duration of MDAOPs, we focus on the packet link delay and the required data rate of traffic flows. Based on these measures, the QoS-related objective function is designed. MRs periodically decide the duration of MDAOPs to minimize the value of the objective function. And MRs update the MDAOPs by the MDA request/reply packet exchange. By using simulation, we evaluate the performance of the proposed scheme. The results show that the proposed scheme guarantees the QoS of various traffic flows and provides the fair transmission among TCP traffic flows.

The cyclostationary feature detector is a viable candidate for a primary user (PU) detection method of the cognitive radio (CR) system. However, it requires very long detection time, which leads to inefficient spectrum utilization. To reduce the detection time, we propose to apply the sequential detection framework to the cyclostationary feature detector. Unfortunately, a straightforward application cannot achieve a sufficient gain, which is expected with the sequential detection. To solve this problem, we design a novel detector, taking account of the cyclic phase of the cyclostationary signal. The simulation results show that the proposed detector reduces the average detection time almost in half. The proposed detector can well be applied to the CR systems that operate in the frequency bands, where the PUs have long interarrival and sojourn time. For example, the CR systems equipped with the proposed detector can efficiently exploit the white space in the VHF/UHF TV bands.

Cognitive radio (CR) is one of the most promising solutions to the scarcity of radio spectrum. In CR systems, the channel sensing technique to detect the appearance of a primary user (PU) directly affects the performances of both CR user and PU. In this paper, we propose a nonquiet channel sensing scheme for the orthogonal frequency division multiple access (OFDMA)- based CR systems. The proposed nonquiet PU detection scheme is based on the reasoning that a CR user can detect the PU even though the signal transmitted from other CR user interferes with the detection, if the interference power is sufficiently low. Since the interference power decreases as the channel condition between the transmitter and receiver CR users gets worse, the proposed scheme regards the sensing results with deeper channel fading as more reliable, given that the received signal power from PU is unknown. We design a linear combining scheme which integrates multiple sensing results to obtain spectral, temporal, and spatial diversity gain. We also suggest a system-wide PU detection mechanism based on the proposed combining scheme. The simulation results show that the proposed scheme can detect the appearance of PU effectively while achieving high utilization of the CR system.

It is expected for multi-channel ad hoc networks to adopt cognitive radio (CR) technology since the CR technology can provide enough channels for high speed data transmission. These CR ad hoc networks should maintain accurate channel state information not only for protecting primary users (PUs) but also for obtaining enough channels. To cope with this, we propose a novel dissemination scheme for channel state information and an adjustment scheme of sensing priorities. According to the proposed dissemination scheme, a CR node can inform the PU presence or a request for a temporary quiet period on a channel for its sensing. When a CR node receives the PU presence on a channel, the CR node adjusts the sensing priority of a channel in order to confirm the availability of the channel by its own sensing. With these schemes, CR nodes cooperate together to accurately maintain the channel state information while each CR node grasps available channels in its own location. Using a simulation, we show that the proposed scheme provides more channels while guaranteeing the right of PUs. Since the proposed scheme uses only two bit channel map for informing the channel status and sends it on top of control packets, the performance gain can be achieved by low overhead.

In cognitive radio (CR) systems, the channel sensing scheme for detecting the presence of primary user directly affects the quality-of-service of CR users and primary user. In this letter, we propose a sensing scheme that consists of a series of consecutive energy detections followed by feature detection, where the energy detection time is much shorter than the feature detection time. With the proposed scheme, multiple energy detections decrease the feature detection due to false alarm and the overall channel sensing time. The performance evaluation using Markov analysis shows that the proposed scheme can heighten the maximum channel utilization of CR users, while maintaining the detection delay of primary user under a predefined value.

In the cognitive radio (CR) systems, channel sensing policy to detect the presence of primary user directly affects the quality-of-service (QoS) of both primary and CR users. In this paper, we investigate the efficient channel sensing scheme for the orthogonal frequency division multiple access (OFDMA) based CR systems. To improve the overall system performance, the proposed scheme exploits the multiuser-diversity in the frequency-selective fading channel, considering various types of the primary users. Performance of the proposed scheme is evaluated by simulation. Numerical results show that the proposed scheme can increase the utilization of the CR system while reducing the waiting time of the primary user.

Adaptive modulation and coding (AMC) scheme is widely used in wireless and mobile communications systems supporting high-speed Internet services. A system using AMC can be modeled as that with time-varying capacity. In this paper, we propose a generalized framework of packet scheduler for wireless systems with time-varying capacity. First, we develop the fundamental scheduling algorithm (FSA) that maximizes the system throughput under given constraints by using the dual optimization technique. Even though FSA can be used as a stand-alone scheduler supporting only nonreal-time traffic, it mainly plays a role in this paper as a basic building block of the advanced scheduling algorithm (ASA). We design ASA so that it supports real-time and nonreal-time traffic simultaneously. Since ASA operates on the basis of connections, we also suggest a connection admission control algorithm that matches well with the proposed scheduler. Finally, we show an application example of the proposed scheduling algorithm to the time-division multiple access system using AMC

We investigate the cross-layer design for the packet scheduling and resource allocation in the orthogonal frequency division multiple access (OFDMA) wireless networks. Our focus is the efficiency of the resource management scheme from the practical point of view. For physical (PHY) layer, an efficient resource allocation algorithm with low computational complexity is proposed to cope with the frequency selective fading. A packet scheduler at medium access control (MAC) is also designed in order to utilize the properties of packet data system. The resource allocator and the scheduler are tightly coupled together through the cross-layer approach. The simulation results show that the proposed scheme utilizes the resources effectively while guaranteeing the quality of service (QoS).

Battery power is an important and limited resource in ubiquitous mobile communications. Energy consumption can be reduced by turning off the radio during idle times. In most power saving schemes, a node decides whether to put its wireless interfaces in the sleep state or in an awake state, based only on the presence of packets destined for the node. However, by considering other factors related to the node status such as the remaining energy, the node can achieve more power savings for the given delay constraints. In this paper, we propose a p-persistent power saving scheme for mobile devices (nodes) in ubiquitous mobile communications systems. The proposed scheme first computes the sleep probability, considering the node status-related factors, including the channel state between nodes, the number of packets queued, the constraint on packet delay, the intensity of load, and the remaining available battery energy. Then, using the probability, the proposed scheme decides the node state. By using simulation, we compare the proposed scheme with IEEE 802.11 power saving mode. The results show that the proposed scheme saves remarkably more energy for the given delay constraints.

Sleep mode operation is widely used in wireless communications systems to reduce power consumption of portable devices. The optimal length of sleep period is affected by the message interarrival time that varies over time. In this letter, the adaptive schemes to control sleep period is investigated. We first generalize the scheme in 2004 and then suggest another variant of the generalized scheme, where the sleep period increases or decreases exponentially depending on whether a message arrives or not. The performance of these two schemes is evaluated by using Markov analysis. Numerical results show that the schemes operate effectively when the system is lightly loaded, which is the operating environment of sleep mode in practice

Internet services are becoming essential in the third- and fourth-generation cellular mobile networks. In order to provide effectively the packet data service with scarce radio resource, several systems, including the High Speed Downlink Packet Access (HSDPA) enhancement of wideband code-division multiple access (WCDMA), have been proposed. In this paper, we design an Internet Protocol (IP) packet scheduler for high-speed packet data services using the wireless packet access system. We design the scheduler so that its performance is maximized from the standpoint of end-to-end application level rather than from the standpoint of radio interface. To do so, the scheduler aims at avoiding excessive packet delays that may invoke the slow-start on the Transmission Control Protocol (TCP) connections. The performance of the proposed scheme is evaluated by extensive simulation and compared with the performances of the existing three schedulers. The results show that the proposed scheduler has remarkably better performance than the baseline schedulers for performance comparison.

We investigate the load sharing problem between the umbrella microcell and the overlaid picocells in a hierarchical cell structure. A load sharing strategy is proposed and its performance is evaluated by simulation when it is used for transmission of mobile Internet traffic. The results show that, with the proposed strategy, the microcell backs up the picocell well, especially when a specific picocell cluster suffers an unusually heavy load condition. By using the strategy, we can reduce the installation cost otherwise needed for the system capacity of every picocell cluster to cope with the unusually heavy load.

This paper proposes a packet scheduler for high speed downlink packet access (HSDPA) systems. In designing the scheduler, we consider the delay constraint of packets from the user's point of view and throughput performance in whole systems, as well as the throughput fairness between mobiles. The performance of the proposed scheme is evaluated by simulation.

The call admission control (CAC) belongs to the category of resource management. Since the radio spectrum is very scarce resource, CAC is one of the most important engineering issues for mobile communications. In this paper, we propose a CAC scheme for direct sequence code-division multiple-access cellular systems supporting mobile multimedia communications services. There are multiple call classes in multimedia services and the required signal-to-interference ratio (SIR) varies with call classes. Call admission decision in the proposed scheme is based on SIR measurement. We take account of the traffic asymmetry between uplink and downlink, which is the most important characteristic of multimedia traffic. In addition, the proposed scheme guarantees the priority of handoff call requests over new call requests. We evaluate the performance of the proposed scheme using Markov analysis. The performance measures which we focus on are the system throughput and the blocking probabilities of handoff calls and new calls. The outage probability of a call in progress is also calculated, which is the probability that the measured bit energy-to-noise density ratio of the call is smaller than the required value for maintaining adequate transmission quality. We present some numerical examples with practically meaningful parameter values and, as a result, show that the proposed CAC scheme can operate well in the mobile multimedia systems such as the International Mobile Telecommunications-2000 (IMT-2000) systems.

The traffic (load) asymmetry between uplink and downlink is a remarkable traffic characteristic in cellular mobile multimedia communications. The code division multiple access system with time division duplex mode (CDMA/TDD system), adopting unbalanced slot allocation between uplink and downlink, is a good solution for this traffic asymmetry. However, the level of traffic asymmetry may be significantly different from cell to cell. In this paper, we investigate a slot allocation strategy (DA strategy), by which each cell has its own slot allocation according to the level of traffic asymmetry. We compute the system capacity with DA strategy and find out the optimal slot allocation for the system. We also compare the maximum capacity to that with another strategy (SA strategy), by which all cells have the same slot allocation. As a result, this paper shows that the system with DA strategy outperforms the system with SA strategy in the aspect of capacity.

This paper proposes a code division multiple access (CDMA) time division duplex (TDD) system for wireless multimedia services with traffic unbalance between uplink and downlink. In the proposed system, the number of uplink time slots in a TDD frame differs from that of downlink. Moreover, the difference can be reset by the network operator according to the traffic pattern. We evaluate the performance of the proposed system under multimedia environment using Markov analysis and computer simulation. The results show that the frequency utilization is maximized even when the uplink and downlink traffic volumes are unbalanced. This, in turn, reduces drastically the blocking rate of multimedia calls (connections) in the proposed system compared with that in the traditional CDMA systems where the uplink and downlink use equal bandwidth.

This paper proposes a CDMA/TDD system for wireless multimedia services with traffic unbalance between uplink and downlink. In the proposed system, the number of uplink time slots in a TDD frame differs from that of the downlink. Moreover, the difference can be reset by the network operator according to the traffic pattern. We evaluate the performance of the proposed system under multimedia environments. The results show that the frequency utilization is maximized even when the uplink and downlink traffic volumes are unbalanced. This, in turn, reduces drastically the blocking rate of multimedia calls (connections) in the proposed system compared with that in the traditional CDMA systems where the uplink and downlink use equal bandwidth.

This paper proposes a new medium access control protocol for wireless data communications in local area, called the reservation-based multiple access with variable frame length (RMAV). We design RMAV under the consideration that the population of wireless terminals and the system load frequently change and are almost unpredictable in wireless data communications. RMAV is based on the slot reservation scheme and adopts a frame structure with variable length. The frame length increases as the number of active terminals and/or the system load increases. We evaluate the performance of RMAV by analysis and computer simulation. Due to its adaptability to traffic patterns, RMAV offers short delay in light load conditions and high throughput in heavy load conditions.