With the emergence of time-sensitive applications, such as virtual reality and online gaming, low latency has become a critical requirement for wireless communication systems. In wireless LANs (WLANs), the target wake time (TWT), introduced in the IEEE 802.11ax standard, has attracted attention as a mechanism to achieve low latency. In this study, through a measurement campaign involving multiple access points (APs), we verified the feasibility of TWT-based multi-AP coordinated scheduling for low-latency WLANs, currently under discussion for standardization as IEEE 802.11bn. Since the TWT mechanism schedules communication service periods for each station (STA), we investigated the impact of multi-STA TWT scheduling on the latency and throughput. The experimental results revealed that eliminating the overlap of communication service periods with TWT is effective for achieving low-latency WLANs, regardless of whether it is a single AP or multiple APs.

Recomposing channel state information (CSI) from the beamforming feedback matrix (BFM), which is a compressed version of CSI and can be captured because of its lack of encryption, is an alternative way of implementing firmware-agnostic WiFi sensing. In this study, we propose the use of camera images toward the accuracy enhancement of CSI recomposition from BFM. The key motivation for this vision-aided CSI recomposition is to draw a first-hand insight that the BFM does not fully involve spatial information to recompose CSI and that this could be compensated by camera images. To leverage the camera images, we use multimodal deep learning. We conducted experiments using IEEE 802.11ac devices and revealed that the recomposition accuracy of the proposed multimodal framework is improved compared to the single-modal framework only using images or BFMs.

Most channel state information (CSI)-based moving target localization and tracking techniques use CSI acquisition at over 100 Hz, which may result in degradation of the communication capability. To address this, this study proposes a device-free walking-direction estimation method using low-frequency CSI acquisition at approximately 10 Hz. The key enabling idea is to utilize the relationship between the standard deviation of the CSI and length of the reflected path. In detail, we prove that the inversely proportional relationship between them. The experimental evaluation revealed that the accuracy of the binary classification of the walking direction, i.e., approaching or receding, was 81.3%.

This paper experimentally demonstrates a novel blockage prediction based on exhaustive beam-pair scans in millimeter wave (mmWave) networks toward 6G. In particular, the prediction uses the signal strength of all transmit/receive beam-pairs through a two-sided scan rather than that of all transmit beams through the one-sided scan assumed in existing studies. It is also possible to predict future line-of-sight (LOS) blockages. The mapping from the signal strength of all beam pairs to future blockages was learned with LightGBM. The experimental results confirm that the blockage prediction through a two-sided scan of all beam pairs is more accurate than that through a one-sided scan.

Line-of-sight (LOS) identification methods based on WiFi devices have used time series of channel state information (CSI) instead of the frequency domain of CSI owing to limited bandwidth. However, the use of IEEE 802.11ac/ax devices allows one to obtain CSI with a wider bandwidth. In this paper, a LOS identification method using the frequency domain of CSI for WiFi devices is proposed. The proposed method focuses on the number of valleys observed in the shape of the frequency series of CSI amplitude and exploits the fact that the number is greater in LOS environments than in non-line-of-sight (NLOS) environments. Because the proposed method utilizes the shape of CSI, it performs well for normalized CSI obtained using typical CSI capture tools for IEEE 802.11ac/ax. The proposed method is experimentally evaluated in a real environment. The experimental results show that the proposed LOS identification method achieves an accuracy of 95%.

This paper discusses the device-free localization and communication in intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) systems. In particular, the localization accuracy and the communication performance are dependent on the configuration of IRS, that is, the direction of IRS-reflected beam, which mitigates the blockage effect in millimeter-wave (mmWave) communications. Localization is realized by using the angular power profile (APP) obtained through beam search. Results of experimental evaluations show that high localization accuracy is achieved by steering the IRS-reflected beam toward the sensing area. In contrast, high communication performance is obtained by adjusting the IRS-reflected beam toward the receiver. These results confirm that in order to achieve high localization accuracy, the IRS-reflected beam should be reconfigured to be pointed in a different direction from that when communicating.

As a technology for 6G wireless communications, Intelligent Reflecting Surfaces (IRSs) are considered as a promising solution to boost the network capacity, spectrum and coverage in multiusers’ downlink communication systems. The users in blockage and cell edge areas can utilize this technology for data transfer purpose. In this paper, a machine learning-based policy optimization for downlink communication in distributed IRS aided multiple-input single-output (MISO) systems is proposed. Three categories of users are considered, namely, users who can utilize only the direct links, blockage area users who can utilize only the IRS links, and cell edge or poor link quality of users who can utilize both the direct and IRS links. The sum rate maximization problem is formulated to derive the optimal policy (i.e. communication link, IRS selection, power allocation and reflection coefficients) for those users, considering the IRS selection, link quality, power allocation and IRS reflection constraints. The proposed methods to achieve the optimal policy include reinforcement learning-based model with binary decision tree-based user categories, maximum posterior probability-based IRS selection, fractional programming method-based power and IRS coefficient allocation, and value function-based policy optimization. Through simulations, the sum data rate and energy efficiency performances of different categories of users are obtained and discussed.

This study develops a federated learning (FL) framework overcoming largely incremental communication costs due to model sizes in typical frameworks without compromising model performance. To this end, based on the idea of leveraging an unlabeled open dataset, we propose a distillation-based semi-supervised FL (DS-FL) algorithm that exchanges the outputs of local models among mobile devices, instead of model parameter exchange employed by the typical frameworks. In DS-FL, the communication cost depends only on the output dimensions of the models and does not scale up according to the model size. The exchanged model outputs are used to label each sample of the open dataset, which creates an additionally labeled dataset. Based on the new dataset, local models are further trained, and model performance is enhanced owing to the data augmentation effect. We further highlight that in DS-FL, the heterogeneity of the devices’ dataset leads to ambiguous of each data sample and lowing of the training convergence. To prevent this, we propose entropy reduction averaging, where the aggregated model outputs are intentionally sharpened. Moreover, extensive experiments show that DS-FL reduces communication costs up to 99 percent relative to those of the FL benchmark while achieving similar or higher classification accuracy.

With regard to the implementation of WiFi sensing agnostic according to the availability of channel state information (CSI), we investigate the possibility of estimating a CSI matrix based on its compressed version, which is known as beamforming feedback matrix (BFM). Being different from the CSI matrix that is processed and discarded in physical layer components, the BFM can be captured using a medium-access-layer frame-capturing technique because this is exchanged among an access point (AP) and stations (STAs) over the air. This indicates that WiFi sensing that leverages the BFM matrix is more practical to implement using the pre-installed APs. However, the ability of BFM-based sensing has been evaluated in a few tasks, and more general insights into its performance should be provided. To fill this gap, we propose a CSI estimation method based on BFM, approximating the estimation function with a machine learning model. In addition, to improve the estimation accuracy, we leverage the inter-subcarrier dependency using the BFMs at multiple subcarriers in orthogonal frequency division multiplexing transmissions. Our simulation evaluation reveals that the estimated CSI matches the ground-truth amplitude. Moreover, compared to CSI estimation at each individual subcarrier, the effect of the BFMs at multiple subcarriers on the CSI estimation accuracy is validated.

In IEEE 802.11bc, the broadcast mode on wireless local area networks (WLANs), data rate control that is based on acknowledgement (ACK) mechanism similar to the one in the current IEEE 802.11 WLANs is not applicable because the ACK mechanism is not implemented. This paper addresses this challenge by proposing ACK-less data rate adaptation methods by capturing non-broadcast uplink frames of STAs. In IEEE 802.11bc, a use case is assumed, where a part of STAs in the broadcast recipients is also associated with non-broadcast APs, and such STAs periodically transmit uplink frames including ACK frames. The proposed method is based on the idea that by overhearing such uplink frames, the broadcast AP surveys channel conditions at partial STAs, thereby setting appropriate data rates for the STAs. Furthermore, to avoid reception failures in a large portion of STAs, this paper proposes deep reinforcement learning (DRL)-based data rate adaptation framework that uses a sim-to-real approach. Therein, information of reception success/failure at broadcast recipient STAs, that could not be notified to the broadcast AP in real deployments, is made available by simulations beforehand, thereby forming data rate adaptation strategies. Numerical results show that utilizing overheard uplink frames of recipients makes it feasible to manage data rates in ACK-less broadcast WLANs, and using the sim-to-real DRL framework can decrease reception failures.

This paper presents a method that estimates the respiratory rate based on the frame capturing of wireless local area networks. The method uses beamforming feedback matrices (BFMs) contained in the captured frames, which is a rotation matrix of channel state information (CSI). BFMs are transmitted unencrypted and easily obtained using frame capturing, requiring no specific firmware or WiFi chipsets, unlike the methods that use CSI. Such properties of BFMs allow us to apply frame capturing to various sensing tasks, e.g., vital sensing. In the proposed method, principal component analysis is applied to BFMs to isolate the effect of the chest movement of the subject, and then, discrete Fourier transform is performed to extract respiratory rates in a frequency domain. Experimental evaluation results confirm that the frame-capture-based respiratory rate estimation can achieve estimation error lower than 3.5 breaths/minute.

The IEEE 802.11ax has introduced the concurrent transmissions among neighboring wireless local area networks (WLANs) to facilitate spatial reuse. As a side effect, the scheduling problem has become more challenging due to the appearance of unmanaged co-channel interference. This paper proposes a latency-aware fair resource scheduling scheme in dense WLANs with Lyapunov optimization. In the scheduling scheme, we formulate a scheduling problem for WLANs with the stabilization of the transmission queue, which is one of the quality of service (QoS) issues and the fairness of the instantaneous data rate as a stochastic optimization problem. This problem is solved by using Lyapunov optimization, which eliminates the time average constraints of the problem. We performed numerical simulations in dense WLANs, and the results confirm that the proposed scheme guarantees an allowable queue size. the results also show that the proposed scheme achieves a higher fairness index and a smaller queue size than compared methods.

The distributed inference framework is an emerging technology for real-time applications empowered by cutting-edge deep machine learning (ML) on resource-constrained Internet of things (IoT) devices. In distributed inference, computational tasks are offloaded from the IoT device to other devices or the edge server via lossy IoT networks. However, narrow-band and lossy IoT networks cause non-negligible packet losses and re-transmissions, resulting in non-negligible communication latency. This study solves the problem of the incremental retransmission latency caused by packet loss in a lossy IoT network. We propose a split inference with no retransmissions (SI-NR) method that achieves high accuracy without any retransmissions, even when packet loss occurs. In SI-NR, the key idea is to train the ML model by emulating the packet loss by a dropout method, which randomly drops the output of hidden units in a neural network layer. This enables the SI-NR system to obtain robustness against packet losses. Our ML experimental evaluation reveals that SI-NR obtains accurate predictions without packet retransmission at a packet loss rate of 60%.

We propose a sophisticated network selection scheme based on multi-armed bandits and stochastic geometry for heterogeneous cellular networks. In the system model, a user seeking the best network tries to estimate the density of active interferers for every network through the repeated observation of signal-to-interference power ratio (SIR), which shows the randomness induced by randomized interference sources and fading effects. The purpose of this study is to enable the user to identify the network with the lowest density of active interferers while considering the communication quality during exploration. In order to resolve the trade-off between getting more observations on uncertain networks and using a network that seems better so far, we employ a bandit algorithm called Thompson sampling (TS), which is known for its empirical effectiveness. We take two ideas into consideration to enhance TS. First, noticing that the statistical SIR model given by stochastic geometry is useful for capturing the relationship between observed SIR and density of active interferers, we propose to incorporate the statistical model into TS. Second, TS requires us to sample from the posterior distribution of the density parameter for each network, while the distribution obtained through stochastic geometry is much more complicated to generate samples than well-known distribution; we reveal that such a sampling process is achieved with the help of the Markov chain Monte Carlo method. The simulation results show that the proposed method enables a user to find the best network more efficiently than well-known bandit algorithms such as an ϵ-greedy strategy.

For reliable millimeter-wave (mmWave) networks, this paper proposes cooperative sensing with multi-camera operation in an image-to-decision proactive handover framework that directly maps images to a handover decision. In the framework, camera images are utilized to allow for the prediction of blockage effects in a mmWave link, whereby a network controller triggers a handover in a proactive fashion. Furthermore, direct mapping allows for the scalability of the number of pedestrians. This paper experimentally investigates the feasibility of adopting cooperative sensing with multiple cameras that can compensate for one another's blind spots. The optimal mapping is learned via deep reinforcement learning to resolve the high dimensionality of images from multiple cameras. An evaluation based on experimentally obtained images and received powers verifies that a mapping that enhances channel capacity can be learned in a multi-camera operation. The results indicate that our proposed framework with multi-camera operation outperforms a conventional framework with single-camera operation in terms of the average capacity.

We propose a sequential channel allocation method based on the multi-objective multi-armed bandit (MOMAB) problem to identify the best channel set among model-based solutions. A solution obtained from pre-designed objective functions cannot always be the best channel set due to external factors in the wireless environment. It is difficult to take into account external factors in advance, so we propose to allocate channels based on several performance metrics that can only be measured by operating access points. The fine-tuning during actual operation involves a trade-off between exploration and exploitation for the best channel set. In addition, we should utilize a channel set that performs not so good on some metrics but well on other metrics. By using MOMAB, we can balance between exploration and exploitation of Pareto optimal channel sets for multiple metrics. The experimental results demonstrate that the proposed method successfully identifies a Pareto optimal channel set.

In this study, we extended a contextual bandit algorithm, LinUCB, to facilitate cooperative learning of an optimal strategy with intermittent information sharing. We then applied the algorithm to a Wi-Fi basic service set (BSS) selection problem. The BSS selection problem, in which a mobile user selects a BSS that provides maximal throughput based on information observed, still remains a topic of debate. Reinforcement learning, specifically the multi-armed bandit algorithm, enables mobile users to learn an optimal strategy for selecting a good BSS in their environments. This paper proposes a cooperative contextual bandit algorithm, called Cooperative LinUCB (CoopLinUCB), to address the BSS selection problem. Conventional cooperative bandit algorithms require to share experiences such as context and payoffs for every action, and the information sharing increases communication costs. The proposed algorithm enables mobile users to learn strategies using a limited amount of information that is shared intermittently. The learned strategies are then guaranteed to be equivalent to the strategies that are updated using user experience information. Simulation evaluation based on measured throughput and received signal strength indication fingerprint demonstrates that CoopLinUCB-based BSS selection learns a BSS selection strategy faster and reduces the cumulative regret compared to BSS selection without cooperation.

Towards ultra-reliable and low-latency communications, this paper proposes a packet mapping algorithm in an enhanced distributed channel access (EDCA) scheme using policy gradient reinforcement learning (RL). The EDCA scheme provides higher priority packets with more transmission opportunities by mapping packets to a predefined access category (AC); thereby, the EDCA scheme supports a higher quality of service in wireless local area networks. In this paper, it is noted that by mapping high priority packets to lower priority ACs, the one-packet delay of a high priority packet can be reduced. In contrast, the mapping algorithm cannot minimize the multiple-packets delay because the mapping algorithm is based on the current status. This is because, from a long-term perspective, mapping high priority packets is required as a countermeasure for collisions, to minimize the multiple-packets delay. As a solution, this paper proposes a new mapping algorithm using RL because RL is suitable for maximizing the reward from a long-term perspective. The key idea is to design the state such that the state involves the number of packets having arrived at each AP in the past, which is an indicator expressing past status. In the designed RL task, the reward, i.e., the multiple-packets delay depends on an overall sequence of states and actions; hence, the recursive value function-based RL algorithms are not compatible. To solve this problem, this paper utilizes policy gradient RL, which learns the packet mapping policy from an overall state-action sequence and a consequent multiple-packets delay. The simulation result reveals that the transmission delay of the proposed mapping algorithm is 13.8% shorter than that of the conventional EDCA mapping algorithm.

Over-the-air computation (AirComp)-based federated learning (FL) enables low-latency uploads and the aggregation of machine learning models by exploiting simultaneous co-channel transmission and the resultant waveform superposition. This study aims at realizing secure AirComp-based FL against various privacy attacks where malicious central servers infer clients' private data from aggregated global models. To this end, a differentially private AirComp-based FL is designed in this study, where the key idea is to harness receiver noise perturbation injected to aggregated global models inherently, thereby preventing the inference of clients' private data. However, the variance of the inherent receiver noise is often uncontrollable, which renders the process of injecting an appropriate noise perturbation to achieve a desired privacy level quite challenging. Hence, this study designs transmit power control across clients, wherein the received signal level is adjusted intentionally to control the noise perturbation levels effectively, thereby achieving the desired privacy level. It is observed that a higher privacy level requires lower transmit power, which indicates the tradeoff between the privacy level and signal-to-noise ratio (SNR). To understand this tradeoff more fully, the closed-form expressions of SNR (with respect to the privacy level) are derived, and the tradeoff is analytically demonstrated. The analytical results also demonstrate that among the configurable parameters, the number of participating clients is a key parameter that enhances the received SNR under the aforementioned tradeoff. The analytical results are validated through numerical evaluations.

This paper proposes a cooperative mechanism for mitigating the performance degradation due to non-independent and-identically-distributed (non-IID) data in collaborative machine learning (ML), namely federated learning (FL), which trains an ML model using the rich data and computational resources of mobile clients without gathering their data to central systems. The data of mobile clients is typically non-IID owing to diversity among mobile clients' interests and usage, and FL with non-IID data could degrade the model performance. Therefore, to mitigate the degradation induced by non-IID data, we assume that a limited number (e.g., less than 1%) of clients allow their data to be uploaded to a server, and we propose a hybrid learning mechanism referred to as Hybrid-FL, wherein the server updates the model using the data gathered from the clients and aggregates the model with the models trained by clients. The HybridFL solves both client- and data-selection problems via heuristic algorithms, which try to select the optimal sets of clients who train models with their own data, clients who upload their data to the server, and data uploaded to the server. The algorithms increase the number of clients participating in FL and make more data gather in the server IID, thereby improving the prediction accuracy of the aggregated model. Evaluations, which consist of network simulations and ML experiments, demonstrate that the proposed scheme achieves a 13.5% higher classification accuracy than those of the previously proposed schemes for the non-IID case.

Federated learning (FL) enables a neural network (NN) to be trained using privacy-sensitive data on mobile devices while retaining all the data on their local storages. However, FL asks the mobile devices to perform heavy communication and computation tasks, i.e., devices are requested to upload and download large-volume NN models and train them. This paper proposes a novel unsupervised pre-training method adapted for FL, which aims to reduce both the communication and computation costs through model compression. Since the communication and computation costs are highly dependent on the volume of NN models, reducing the volume without decreasing model performance can reduce these costs. The proposed pretraining method leverages unlabeled data, which is expected to be obtained from the Internet or data repository much more easily than labeled data. The key idea of the proposed method is to obtain a "good" subnetwork from the original NN using the unlabeled data based on the lottery hypothesis. The proposed method trains an original model using a denoising auto encoder with the unlabeled data and then prunes small-magnitude parameters of the original model to generate a small but good subnetwork. The proposed method is evaluated using an image classification task. The results show that the proposed method requires 35% less traffic and computation time than previous methods when achieving a certain test accuracy.

This paper proposes a method to predict received power in urban area deterministically, which can learn a prediction model from small amount of measurement data by a simulation-aided transfer learning and data augmentation. Recent development in machine learning such as artificial neural network (ANN) enables us to predict radio propagation and path loss accurately. However, training a high-performance ANN model requires a significant number of data, which are difficult to obtain in real environments. The main motivation for this work was to facilitate accurate prediction using small amount of measurement data. To this end, we propose a transfer learning-based prediction method with data augmentation. The proposed method pre-trains a prediction model using data generated from ray-tracing simulations, increases the number of data using simulation-assisted data augmentation, and then fine-tunes a model using the augmented data to fit the target environment. Experiments using Wi-Fi devices were conducted, and the results demonstrate that the proposed method predicts received power with 50% (or less) of the RMS error of conventional methods.

This paper proposes a robust adversarial reinforcement learning (RARL)-based multi-access point (AP) coordination method that is robust even against unexpected decentralized operations of uncoordinated APs. Multi-AP coordination is a promising technique towards IEEE 802.11be, and there are studies that use RL for multi-AP coordination. Indeed, a simple RL-based multi-AP coordination method diminishes the collision probability among the APs; therefore, the method is a promising approach to improve time-resource efficiency. However, this method is vulnerable to frame transmissions of uncoordinated APs that are less aware of frame transmissions of other coordinated APs. To help the central agent experience even such unexpected frame transmissions, in addition to the central agent, the proposed method also competitively trains an adversarial AP that disturbs coordinated APs by causing frame collisions intensively. Besides, we propose to exploit a history of frame losses of a coordinated AP to promote reasonable competition between the central agent and adversarial AP. The simulation results indicate that the proposed method can avoid uncoordinated interference and thereby improve the minimum sum of the throughputs in the system compared to not considering the uncoordinated AP.

To achieve reliable small cell millimeter-wave wireless backhauls, this study installs small cell base stations (SBSs) on overhead messenger wires to gain flexibility in physical deployments of SBSs ensuring in the line-of-sight connections between SBSs and gateway BSs. These installations pose challenges in aligning directional beams, whereby complicated wind-forced dynamics in on-wire SBSs require frequent beam training, and consequently, a large signaling overhead. To address this, this study aims at demonstrating the feasibility of learning-based beam tracking where a beam tracking policy is learned a priori to fix beam misalignment caused by the wind-forced dynamics. Because wind-forced dynamics in SBSs can be three-dimensional (3D), the proposed beam tracking newly exploits the 3D position/velocity of the SBS as state information. As a solution to fix beam misalignment, the beam tracking policy is learned via deep reinforcement learning wherein the 3D information and beam direction are regarded as a state and an action, respectively, and the received signal power at a gateway BS is maximized. The simulation results depict the feasibility of learning an appropriate beam tracking policy to prevent beam misalignment induced by wind-forced 3D dynamics in on-wire SBSs.

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

In this study, we conducted an analysis of the system performance of a wireless local area network in which access points (APs) dynamically adjust the carrier sense threshold (CST) based on the individual average received power to determine the optimal CST. Adjustment of the CST is a promising approach to improve spatial reuse and proposed for IEEE 802.11ax standard. Here, assuming to adopt the inversely proportional setting of the CST and transmission power, we can make the carrier sensing relationship symmetric, restraining throughput starvation. This paper analytically derives the density of successful transmissions (DST) on the basis of stochastic geometry. The DST is a system performance metric which expresses the number of APs whose transmission is successful based on signal-to-interference-plus-noise power ratio. We show that both results of the analytically derived DST and Monte Carlo simulation have the same trend. From the perspective of the derived DST, the optimal parameter setting is also discussed.

This paper proposes a camera-based geo-fencing system for wireless local area networks (WLANs) which enables geo-location based wireless access control to intuitively manage the area where the WLANs are available. The proposed system leverages camera to localize WLAN users accurately and estimates the proximity of users to objects in the real world. Meanwhile, conventional geo-location based access control suffers from low accuracy of RSSI based localization. As an example of geo-location based access control, we execute a WLAN activation control which allows STAs to pre-activate WLAN and associates with access points (APs) so that the power consumption and time to obtain contents are reduced. Experimental results show the feasibility of camera-based geo-fencing.

This paper proposes a design for a primary exclusive region (PER) based on a cylindrical grid model in unmanned aerial vehicle (UAV) networks. UAV communications require using additional frequency bands shared with other systems. When they use these bands, their communications must not interfere with the communications of the primary users (e.g., radar systems). To avoid this interference, a PER should be designed. This paper proposes a complex-shaped PER design based on the radar's antenna pattern to maximize the number of transmitting UAVs, and presents a stochastic geometry analysis of interference in UAV networks. On the assumption that the distribution of UAVs in each grid follows an inhomogeneous Poisson point process, the radar's outage probability is derived. From this analysis, an optimization problem of PER is formulated to maximize the number of transmitting UAVs. Subsequently, the solution of this problem is numerically evaluated for a keyhole antenna model. The results show that the complex-shaped PER is designed corresponding to the radar's antenna pattern and the number of transmitting UAVs increases with an increase in the number of grid divisions.

This paper proposes a novel channel allocation scheme based on the replica exchange Monte Carlo method (REMCMC). Some distributed channel allocation schemes in the literature formulate the channel allocation problem as a potential game, in which the unilateral improvement dynamics is guaranteed to converge to a Nash equilibrium. In general, spatial adaptive play (SAP), which is one of the representative learning algorithms in the potential game-based approach, can reach an optimal Nash equilibrium stochastically. However, this is inefficient for the channel allocation and SAP tends to be stuck in a sub-optimal Nash equilibrium in a limited time. To assist in finding the optimal Nash equilibrium for this kind of channel allocation problem, we apply the REMCMC to the existing potential game-based channel allocation. We show that SAP can be considered as a sampling process of the Boltzmann- Gibbs distribution and sampling methods can be utilized. We evaluated the proposed algorithm through simulations and the results show that the proposed algorithm can find the optimal Nash equilibrium quickly.

This paper proposes a pre-training method for a deep- neural-network (DNN) based received power prediction scheme leveraging transfer learning for millimeter-wave (mmWave) networks. The received power prediction scheme has been proposed for proactive network control, which accurately predicts the received power 500 ms ahead using depth- camera images and a DNN. However, the prediction scheme requires a large number of training datasets and computational resources to prepare the accurate prediction model. In this paper, we propose a pre-training method that reduces the preparation time by leveraging the use of 3D model simulations with signal propagation simulations and transfer learning. The proposed method generates a dataset for pre- training using computer simulations, and trains a prediction model. The pre-trained model is transferred and fine-tuned by using a dataset obtained in a place where the system is actually used so that the model fits to the place. The experimental results show that the computational time of the proposed scheme with an RMS error of less than 5 dB is reduced by 78% compared with the previous work when using the dataset obtained in 60 s.

Starvation problems in a dense IEEE 802.11 wireless local area network (WLAN) seriously degrade fairness among links in the network. Some transmitters enjoy the full opportunities to access their channels, while others have very few opportunities to transmit, especially when network traffic is saturated. This paper focuses on the joint channel selection and spatial reuse problems in IEEE 802.11ax WLANs. Our main objective is to improve throughput while mitigating starvation. We formulate a non-cooperative game and proved it to be an exact potential game (EPG). We design a joint channel selection and spatial reuse algorithm with only local information exchange, based on the proposed game model. Convergence of the proposed algorithm is guaranteed by the property of potential games when unilateral improvement dynamics is used as a learning algorithm.

This study demonstrates the feasibility of proactive received power prediction by leveraging spatiotemporal visual sensing information towards reliable millimeter-wave (mmWave) networks. As the received power on a mmWave link can attenuate aperiodically owing to human blockages, a long-term series of the future received power cannot be predicted by analyzing the received signals prior to the blockage occurring. We propose a novel mechanism that predicts the time series of received power from the next moment to as many as several hundred milliseconds ahead. The key idea is to leverage camera imagery and machine learning (ML). Time-sequential images may involve the spatial geometry and mobility of obstacles representing mmWave signal propagation. ML is used to construct a prediction model from a dataset of sequential images labeled with received power in several hundred milliseconds ahead of the time at which each image is obtained. The simulation and experimental evaluations conducted using IEEE 802.11ad devices and a depth camera demonstrated that the proposed mechanism employing convolutional long short-term memory predicted a time series of received power up to 500 ms ahead, with an inference time of less than 3 ms and a root-mean-square error of 3.4 dB.

Camera-assisted millimeter-wave (mmWave) network is a new paradigm for mmWave communications where mobility of obstacles is captured by using RGB and depth cameras and conducts network operations by considering the captured information. For camera-assisted mmWave networks, this paper proposes a recurrent neural network (RNN)-based received signal strength (RSS) estimation scheme using depth camera images. This scheme enables us to estimate the RSS of any mmWave links, including links where nodes are not transmitting frames. An RNN enables us to model the relationship between current RSS and an image time series, which includes information regarding the mobility of nodes and obstacles. Simulation results demonstrate that the RNN-based estimation scheme achieves higher accuracy than that of a multi-layer perceptron.

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

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

This paper presents the stochastic geometry analysis and design of a primary exclusive region (PER) for cognitive radio vehicular ad hoc networks (CR-VANETs). Recently, to satisfy the increasing demands for vehicular communications, cognitive radio (CR) technology has been applied to broaden the vehicular communication spectrum. However, while utilizing the licensed spectrum opportunistically as secondary transmitters (STs), vehicles must avoid harmful interference with primary receivers (PRs), e.g., TV broadcasters. In contrast to cooperative spectrum sensing and stand-alone spectrum sensing in most existing works, in this paper, we proposed a geo-location-database-driven opportunistic spectrum access (OSA) approach to maximize the spectrum opportunity of SUs. For the proposed OSA approach, this paper presents a line segment model for CR-VANETs and introduces the allowable transmission probability of STs in each line segment. We theoretically analyze the outage probability of the primary user using stochastic geometry. From these analyses, we introduce the optimization problem to maximize the spectrum opportunity of STs while ensuring a minimal outage threshold for the PR. Subsequently, we numerically evaluate the solution of the optimization problem. Our results show that a PER for CR-VANETs has been successfully designed. With the decrease in the length of the line segment and further detailed information, a smaller and more complex-shaped PER can be achieved.

This paper experimentally finds the optimum number of input images of a machine learning-based mmWave received signal strength (RSS) value prediction scheme from depth images. By modeling the relationships between time-sequential depth images and RSS values based on machine learning, it is possible to predict the future RSS values, and thereby, a predictive handover makes a moment of degradation of the RSS value avoidable. As prediction models of RSS value, three machine learning models are compared: the convolutional neural networks (CNN), the combination of CNN and convolutional long short-term memory (CNN+ConvLSTM), and random forest. As the number of input images increases, the prediction accuracy generally improves, however, too numerous input images may make the prediction accuracy worse because of over-fitting. Experimental results reveal that the number of input images that are input in order to predict the RSS value the most accurately is 16.

In millimeter wave (mmWave) vehicle-toinfrastructure (V2I) communications for autonomous vehicles, the small coverage of road side units (RSUs) is an open problem. We propose a multi-hop relaying method using dynamic vehicle deployment in order to increase the coverage of RSUs. The key idea of our method is to leverage the movement controllability of autonomous vehicles to extend the multi- hop relay distance. The proposed deployment method considers blockage because it is a crucial problem in mmWave communications, although it is not a crucial problem in microwave communications. We formulate the deployment problem as an optimization problem and obtain its lower and upper bounds performances. We also introduce a mmWave connectivity graph, from which the vehicle position that achieves the lower bound performance can be obtained by solving a shortest-path problem. Simulation results demonstrate that even when only 7.5% of all vehicles'' positions are controllable, the proposed deployment method can achieve a coverage of 80%, which is more than twice the coverage achieved by the relaying without deployment.

Cooperative perceptions for autonomous vehicles by sharing image sensor data enhance traffic security. For sharing a large amount of sensor data, millimeter-wave (mmWave) communication is expected to be an enabler of high-throughput communications because of its wide band width and its efficiency in spatial reuse. This paper proposes a concurrent scheduling for sensor data dissemination in mmWave vehicular networks at an intersection. The proposed algorithm improves the region covered by shared data in situations where dissemination time is limited and not all data are disseminated. Improvement is realized by prioritizing data to be forwarded considering the geographical information. The priority control enlarges the average of the area of covered region by 15% at maximum. Meanwhile, it is proved that when sufficient time is available for dissemination, the proposed algorithm guarantees that all vehicles can share their data with each other.

In most stochastic geometry analyses of cellular networks, a receiving user is selected randomly. Thus, the network performance is equivalent to that obtained with the use of a round-robin scheduler; in other words, channel-aware user scheduling is not applied. The first objective of this study is to clarify the type of channel-aware user scheduling that can be utilized with stochastic geometry. In both Poisson bipolar networks and Poisson cellular networks, the signal-to-interference power ratio (SIR) distribution, average data rate, and scheduling gain of a normalized SNR scheduler are derived. The scheduler selects the user with the largest SNR normalized by the short-term average SNR, and it is equivalent to the well-known proportional fair scheduler when the data rate is proportional to the SNR. The second objective is to discuss the achievable scheduling gain with stochastic geometry when compared with round-robin scheduling. The value of the scheduling gain is examined using numerical integration results.

This paper discusses a measurement method of time‐variant attenuation of IEEE 802.11ad wireless LAN signals in the 60 GHz band induced by human blockage. The IEEE 802.11ad access point (AP) transmits frames intermittently, not continuously. Thus, to obtain the time‐varying signal attenuation, it is required to estimate the duration in which the AP transmitted signals. To estimate whether the AP transmitted signals or not at each sampling point, this paper applies a simple two‐state hidden Markov model. In addition, the validity of the model is tested based on Bayesian information criterion in order to prevent model overfitting and consequent invalid results. The measurement method is validated in that the distribution of the time duration in which the signal attenuates by 5 dB is consistent with the existing statistical model and the range of the measured time duration in which the signal attenuation decreases from 5 dB to 0 dB is similar to that in the previous report.

In this paper, an asymptotic analysis of the inversely proportional setting (IPS) of carrier sense threshold (CST) and transmission power in densely deployed wireless local area networks (WLANs) is presented. In densely deployed WLANs, CST adjustment is a crucial technology to enhance spatial channel reuse, but it can starve surrounding transmitters due to an asymmetric carrier sensing relationship. In order for the carrier sensing relationship to be symmetric, the IPS of the CST and transmission power is a promising approach, i.e., each transmitter jointly adjusts the CST and transmission power in order for their product to be equal to those of others. By assuming that the set of potential transmitters follows a Poisson point process, the impact of the IPS on throughput is formulated based on stochastic geometry in two scenarios: an adjustment of a single transmitter and an identical adjustment of all transmitters. The asymptotic expression of the throughput in dense WLANs is derived and an explicit solution of the optimal CST is achieved as a function of the number of neighboring potential transmitters and signal-to-interference power ratio using approximations. This solution was confirmed through numerical results, where the explicit solution achieved throughput with a loss of less than 8% compared to the numerically evaluated optimal solution.

A potential game based distributed channel selection scheme is proposed in this paper to mitigate the flow-in-the-middle (FIM) throughput starvation problem that frequently occurs in dense wireless local area networks (WLANs). The FIM throughput starvation occurs when neighbors of a given node are not within the carrier sense ranges of each other. Since they spatially reuse the channel and at least one of them transmits with a high probability, the node in the middle would detect the channel being occupied for a prolonged time and therefore experience extremely low throughput. The basic idea of the proposed scheme is to let each access point (AP) select the channel that reduces the number of three-node chain topologies on its two-hop neighborhood contention graph. The proposed scheme is proved to be a potential game, i.e., the proposed scheme is guaranteed to converge. Graph-based simulation shows that starvation occurs on 20% of nodes when nodes randomly select their frequency channels. The proposed scheme significantly reduces the number of starved nodes along with iterations, outperforming the compared traditional potential game based scheme.

In database-driven spectrum sharing for 5G mobile networks, a primary user (PU) may experience harmful interference caused by unpredictable propagation paths, even when secondary users (SUs) follow a spectrum sharing policy established on the basis of a database. A framework for determining the optimal radius of a circular primary exclusive region (PER) on the basis of SU's information has been proposed. However, a practical PER can be complex-shaped and should be designed on the basis of the directivity of the PU antenna, and the SU information in each region. In this paper, we present a stochastic geometry analysis in a spatial grid-based spectrum database, and propose a design for an optimal complex-shaped PER. The database determines the transmission probability of the SUs on each divided annular sector. By regarding the SU's locations on each annular sector as an inhomogeneous Poisson point process, we analytically derive a PU's outage probability (OP), where the PU's OP is defined as the probability that the aggregate interference power at a PU from the SUs exceeds a threshold. Using the derived expression, we formulate an optimization problem to maximize the number of transmitting SUs, which optimizes the SU's transmission probability on each annular sector. Then, we numerically evaluate the solution of the optimization problem in various scenarios. The results show that the accuracy of the PER improves as the grid size decreases. In addition, we successfully design a complex-shaped PER with holes in which the SUs are permitted to transmit.

Secure communication is expected to be achieved by locating interfering transmitters near a transmitter sending confidential messages. This is because the transmitters in this arrangement would cause harmful interference to eavesdroppers. To investigate this proposal, this paper theoretically analyzes the secrecy outage probability (SOP) in clustered wireless networks. The SOP is formulated on the basis of the stochastic geometry approach, considering the case when the transmitters are distributed according to a Neyman-Scott cluster process. Numerical results reveal the impact of various clustered distributions of transmitters on the SOP. In addition, this paper conducts a comparative evaluation of the SOP between the clustered distribution and a uniform random distribution. It is revealed that the clustered distribution, in particular consisting of small size clusters and a large number of transmitters in each cluster, can reduce SOP compared to the uniform random distribution.

This paper presents a measurement of time-varying attenuation of IEEE802.11ad wireless LAN (WLAN) signals in 60GHz band induced by human blockage. The present measurement is a novel approach to obtain the attenuation, where a commercially available IEEE802.11ad access point (AP) and station (STA) are employed and the measurement is conducted under intermittent packet transmission. This paper also presents a hidden Markov model (HMM)-based signal power estimation scheme so that the attenuation is estimated from data obtained with a microwave spectrum analyzer which cannot detect signals of IEEE 802.11ad WLAN in itself. In this scheme, whether 11ad WLAN signals exist or not at each sampling instant is estimated based on HMM. Before the application of HMM, the scheme detects the number of HMM states via Bayesian information criterion and, thereby, prevents model over-fitting and consequent invalid power estimation. Our experiment revealed that the IEEE802.11ad WLAN signal attenuates by 5dB in a duration of 51.5ms when a human moves across the path between the AP and the STA at a velocity of 0.5m/s. This result is consistent with a previous report about an IEEE 802.11ad WLAN channel model.

The human blockage problem is an open issue in next- generation wireless access networks using millimeter-wave (mmWave) communications. A proactive base station (BS) handover system leveraging RGB and depth (RGB-D) cameras is proposed for solving the human blockage problem. RGB-D cameras observe mmWave communication ranges, and BS handover is conducted proactively before a human blockage causes serious performance degradation. However, this system must rely on a scheme that provides a guideline for selecting a BS to which the transfer can be done. In this paper, we propose a mmWave throughput estimation scheme using an online machine learning algorithm and depth images obtained by the RGB-D camera. The algorithm learns the relationship between depth images and measured throughputs, and estimates throughput from depth images. The scheme enables the handover system to estimate throughput quickly and adaptively to the wireless environment without transmitting any control frames. We conducted a proof-of-concept experiment by using a testbed consisting of IEEE 802.11ad mmWave wireless local area network devices and an RGB-D camera. The experiment confirmed that the proposed scheme estimates throughput from a depth image with an RMS error of 114-178 Mbit/s in real time.

In database-driven spectrum sharing, despite the spectrum sharing policy given by a database, harmful interference can occur between a primary user (PU) and a secondary user (SU) due to the unexpected propagation paths. In a previous study, a primary exclusive region (PER) centered at a PU, wherein the SUs are forbidden to use the spectrum, has been proposed. However, the PER figure that efficiently covers the regions where interference occurs, cannot be circular. In this paper, we propose a framework for updating the PER adaptively with machine learning, when interference occurs. The framework employs undersampling and oversampling schemes considering the propagation characteristics and shadow fading in order to solve an imbalanced data problem degrading estimation accuracy of appropriate shape of PER. Our simulation results demonstrate that the area of PER with the proposed framework is smaller by 54% than that of the fixed circular PER setting, and the proposed sampling scheme achieves a 1% interference probability with 21% fewer iterations and a 6% smaller area compared to the existing sampling schemes.

In database-driven spectrum sharing for 5G mobile networks, a primary user (PU) can experience harmful interference due to unexpected propagation paths, even when the secondary user (SU) follows the spectrum sharing policy stored in a database, during operations. A framework for deriving the optimal radius of a circular primary exclusive region (PER) has been proposed. However, a practical PER can be non-circular and should be designed on the basis of the geographical information and directivity of the PU antenna. To design a non-circular PER, in this paper, we introduce a spatial grid-based spectrum database; the database permits or forbids SU transmissions for each annular sector divided by a spatial grid. Considering the random locations of the SUs on each annular sector as an inhomogeneous Poisson point process, we analytically derive the PU's outage probability (OP) using stochastic geometry, where the PU's OP is defined as the probability that the aggregate interference power at a PU, from the SUs, exceeds a threshold. Numerical results show that a non- circular PER results in a lower PU OP compared to a circular PER.

This paper proposes a decentralized scheme to improve the energy efficiency (EE) in dense wireless local area networks (WLANs), based on inversely proportional setting (IPS) of carrier sense threshold (CST) and transmit power. Jointly adjusting the CST and transmit power in an inversely proportional manner, i.e., keeping their product to be a constant, is a promising technique to increase transmit opportunities in dense WLANs, without generating unfairness issue of asymmetric carrier sensing. The proposed scheme is formulated in the non-cooperative game framework. Two utility functions are designed, where the first utility function is an approximation of the EE. To guarantee the existence of the equilibrium, and to ensure the convergence of the proposed scheme, we further incorporate potential games, based on which the second utility function is de- signed. The second utility function can be considered as assigning a cost on the first utility function. Simulation results show that the proposed scheme provides considerable improvement in EE and average throughput under various node densities. The second utility function outperforms the first utility function, while the first one requires only local information to be calculated.

This paper provides an appropriate allocation scheme of channel resources for drone communications. In recent years, the usage of drones has been increasing for a wide range of applications, and their communications require using additional frequency bands. However, their communications must avoid interference with primary users (e.g., radar systems) when they use additional frequency bands. We assume that their communications use two frequency bands, the main band and the backup band, to avoid interference. The proposed resource allocation method, which determines whether each drone should use the main or the backup bands, enables drone communications to use the main band efficiently without causing interference. For the proposed resource allocation scheme, this paper presents a stochastic geometry analysis of interference in drone networks. Based on the assumption that the distribution of drones follows a 3D Poisson point process, we analytically derive the radar's outage probability (OP) and the drone's OP. From these analyses, an optimization problem is formulated to maximize the number of drones using the main band, since drones can transmit massive amounts of data by using the main band. Then, we numerically evaluate the solution of the optimization problem. Our results show that the maximum ratio of the number of drones using the main band to the total number of all drones increases along with the size of the primary exclusive region.

This paper proposes a modified bat algorithm for optimal resource allocation scheme in closed access femtocell networks. Bat algorithm is a new nature-inspired metaheuristic algorithm which is inspired by the behaviour of bats echolocation. The main objective of the proposed algorithm is to select the best combination of resource blocks for femto users so as to minimize the co- and cross-tier interference in macrocell-femtocell network. To deal with a discrete solution in resource allocation problem, the original bat algorithm updating rule is modified using nearest integer method. The simulation results show that the proposed algorithm improves the throughput of the femtocell networks compared to that of random allocation scheme.

In machine-type communication (MTC) over LTE cellular network, resource allocation problem becomes a challenging issue as MTC devices compete with LTE users for the same radio resources. Compared to the LTE users, MTC devices generate more uplink traffic requests and signalling which results in congestion arises in uplink transmission when a large number of devices send connection requests simultaneously. In this paper, we consider the resource allocation problem for MTC over LTE networks in which LTE users, MTC devices, and relay nodes co-exist. Firstly, we derive an analytical model which detects overload condition in the base station (eNB) and estimates available resources for MTC devices. We propose a relay-assisted radio resource allocation (R3A) scheme for MTC devices which utilize dynamic access class barring method in overload situations when the number of resource blocks are less than the MTC devices. In the case when the number of MTC devices is less than the available resources than we use relay nodes to maximize the throughput of MTC system. Numerical results demonstrate the significance of proposed R3A method. The results are evaluated in terms of access success probability, access drop percentage, and MTC channel capacity.

Inversely proportional setting (IPS) of the transmission power and carrier sense threshold (CST) to a portion of access points/stations is discussed through numerical evaluation and experiments to examine coexistence with legacy devices. In densely deployed wireless local area networks (WLANs), tuning CST is a promising approach to facilitate spatial channel reuse. Particularly, IPS of the transmission power and CST keeps symmetric carrier sensing relationship between any two transceivers, and thus provides a novel solution for the starvation due to asymmetric carrier sensing relationship. We first model the throughput of two transmitter-receiver pairs when applying IPS. In addition, through experiments, we verify the throughput model and confirm that IPS does not cause throughput starvation even when applying IPS only to some APs and/or STAs.

In millimeter wave (mmWave) communications, when a pedestrian blocks the path of the line of sight (LOS) between a station (STA) and a base station (BS), the quality of communication sharply deteriorates. In order to cope with such human blockage, an RGB and depth (RGB-D) camera-assisted proactive handover scheme is proposed in this paper. The scheme uses images from an RGB-D camera to predict human blockage, and allows the selection of an appropriate BS based on this prediction. To clarify the impact of the accuracy of blockage prediction on throughput performance and assess the ideal performance of the proposed scheme, we propose the performance modeling of both proactive and reactive handover schemes based on the received power level. Numerical evaluation results revealed conditions under which the proactive handover scheme yields higher spectral efficiency than the reactive scheme. We conducted simulations to verify the performance gain of the proposed scheme in a realistic scenario where the LOS paths between a mobile STA and the BS were stochastically blocked. The results show that the proactive handover scheme can reduce the duration of outages due to human blockage, and increased system throughput by 12.1% over the reactive handover scheme.

To realize high-speed and high-capacity 5G mobile networks, secondary users (SUs) may opportunistically use licensed spectrum allocated to primary users (PUs). This paper presents a framework to deal with the situation that occurs when a PU suffers harmful interference caused by the secondary use of the spectrum allocated to it. Here, the PU is assumed to be a radar system. In our proposed framework, the PU informs the database that it is suffering harmful interference. Receiving this information, the database updates the primary exclusive region (PER), where SUs are prohibited from using the licensed spectrum. The updated PER depends on the current knowledge of the SUs, which is stored in the database. We assume a circular PER centered at the primary receiver (PR) and derive its optimal radius using stochastic geometry. For each type of SU knowledge stored in the database, we evaluate the optimal PER radius for a target probability at which the PU suffers harmful interference. The results show that more detailed knowledge of the SU density and transmission powers leads to a smaller updated PER radius. Hence, a more efficient spatial reuse of the licensed spectrum is achieved.

This paper proposes frame length optimization for wireless local area networks (WLANs) using an in-band full-duplex system that enables a WLAN access point and stations to transmit and receive frames at the same time on the same frequency channel. In in-band full-duplex WLANs, a primary sender which captures the channel transmits a frame to the intended receiver called a secondary sender and then the secondary sender transmits a frame reacting to the primary sender's transmission. The difference of time length of frames transmitted by the primary sender and the secondary sender wastes the frequency channel where more frames could be transmitted. The wasted time decreases the system throughput performance of the in-band full-duplex system. In order to solve this problem, we propose a scheme where the secondary sender adjusts the length of its frame to the length of the primary sender's frame by selecting frames used for frame aggregation properly. We evaluate the average delay, the average wasted time and the system throughput performance by computer simulations. The simulation results show that the proposed optimization reduces the delay by 49%, reduces the wasted time by 99.9% and improves the system throughput performance by 15% when the traffic is saturated.

Interference management is indispensable for dense wireless networks to improve system performance. Reduction in spatial channel overlap is one of the most effective approaches, which leads to an increase in the transmission opportunity of each access point (AP) and mitigation of channel interference at a reception point. In this paper, coverage overlap is modeled as a subset of two-dimensional Euclidean plane, and a joint decentralized scheme of transmission power control (TPC) and dynamic channel assignment (DCA) is described for reduction in the coverage overlap. In particular, the DCA scheme can be formulated as a potential game in which unilateral improvement dynamics are guaranteed to converge to a Nash equilibrium. The novel feature of this paper is that spatial adaptive play (SAP) is employed for channel update algorithm to achieve more improvement of the reduction in the total channel overlap area. Simulation results show that the scheme based on SAP is more effective to reduce the total channel overlap area than best response based learning algorithm.

This paper presents implementation of a reactive base station selection scheme for millimeter-wave (mmWave) communications. In mmWave communications, the frame loss rate increases and the throughput sharply decreases when a pedestrian blocks a line-of-sight (LOS) path. To alleviate this human blockage problem, base stations can be selected so as to maintain LOS paths on the basis of communication quality. In this paper, we build a testbed using off the shelf IEEE 802.11ad based wireless local area network (WLAN) devices, and implement a reactive base station selection scheme on the testbed. To the best of our knowledge, there is no existing work which experimentally evaluates human blockage detection system using actual IEEE 802.11ad devices. Our prototype system monitors the throughput measured at each base station and detects human blockage when the throughput decreases below a threshold. The human blockage detection triggers the base station switching. Our experimental results show that the reactive base station selection scheme decreases the duration in which human blockage degrades throughput performance, and the total amount of received data increases by 21%.

In-band full-duplex (IBFD) operation can potentially double the spectral efficiency of wireless networks. For IBFD operation, self-interference is a critical issue. In addition, in full-duplex cellular (FDC) networks, particularly when the cell size is small, inter-user interference would be another limiting factor for the performance. To overcome these issues, the scheduling scheme proposed in this paper is to adaptively utilize bidirectional IBFD in addition to half-duplex (HD) and unidirectional IBFD in FDC networks according to the residual self-interference after interference cancellation and inter-user interference. The proposed scheme is based on generalized proportional fair scheduling by using a fairness parameter. Extensive simulations are conducted to analyze the impact of the cell size. Simulation results revealed that the use of bidirectional IBFD is extremely effective for a small cell with few users if self-interference is sufficiently canceled because inter-user interference is large in small cell and the scheduler tends to select bidirectional IBFD in the cell with few users.

Wireless local area network (WLAN) using millimeter wave (mmWave) communications is one of enabler of next generation wireless access networks since huge bandwidth is available in mmWave bands and it enables beyond Gbit/s communications. This paper experimentally evaluate the PHY rate and the throughput performance of off the shelf mmWave WLAN devices that are compatible with IEEE 802.11ad standard. Our experimental evaluation shows that the association can be established until the distance between an access point (AP) and a station (STA) is 27 m and even if a STA is located at opposite side of an AP, the association still be established until the distance is smaller than 5 m. Our interference evaluation reveals an unfairness of throughput between two pairs of the AP and the STA.

This paper proposes an RGBD cameras-based wireless environment prediction and presents an implementation of a proactive traffic control system using the prediction results. In millimeter-wave and terahertz communications, frame loss rate increases and throughput is decreased tremendously when pedestrians block line-of-sight paths. Such serious human blockage does not occur in microwave communications. By using the RGBD cameras, we can predict human blockage easily as the cameras can detect obstacles and their mobility. We implement a prototype of a proactive traffic control system using camera-based human blockage prediction to demonstrate the feasibility of the proposed system.

This paper proposes a proactive base station selection system for millimeter-wave (mmWave) communications based on human blockage prediction using RGB and depth (RGB-D) cameras. In mmWave communications, the frame loss rate increases and the throughput sharply decreases if a pedestrian blocks a line-of-sight (LOS) path between a station and a base station.To address this human blockage problem, multiple base stations can be arranged so as to maintain at least one LOS path.For base station selection in particular, RGB-D camera images can be used to estimate the mobility of pedestrians and to predict when blockage of LOS paths will occur. Using IEEE 802.11ad-based wireless local area network (WLAN) devices, a testbed for implementing the proposed system was built. The results of experiments on the influence of human blockage confirmed the presence of significant throughput degradation due to human blockage.Furthermore, the innovative experimental results demonstrated that the proactive base station selection system can considerably reduce the duration of human blockage-induced degradation of throughput performance relative to reactive base station selection systems based on throughput performance.

Dense wireless networks require advanced interference management for the performance improvement. In carrier sense multiple access with collision avoidance (CSMA/CA) networks, the mutual interference can be modeled as the overlap area, and range adjustment, such as transmission power control (TPC) and dynamic channel assignment (DCA), is effective to reduce the overlap area. A game-theoretic framework is utilized to construct a joint TPC and DCA scheme which reduces the total overlap area. Although the evaluation of the overlap area requires topology of neighboring APs, i.e., received power levels among neighboring APs are required, the proposed scheme does not require such information because decomposed overlap areas between any two APs are utilized. Moreover, unilateral improvement dynamics of channel assignment are guaranteed to converge to a Nash equilibrium because the game is shown to be a potential game. We evaluate the proposed scheme through simulations and experiments. Simulation results reveal that the proposed joint TPC and DCA scheme effectively reduces the total overlap area compared to another game-theoretic interference management scheme particularly when the number of APs is large. It is because the proposed scheme can efficiently manage the coverage overlap even when there is a variation in transmission power level. Experimental results demonstrate the convergence of the proposed scheme in a real environment.

In dense wireless local area network (WLAN) environments, the haphazard deployment of WLAN devices causes them to overlap. In such situations, a coverage adaptation through transmission power control (TPC) is required. At the same time, coverage holes should be taken into consideration for connectivity maintenance among devices. In this paper, a TPC scheme is proposed that reduces overlaps in an autonomous manner, without generating target coverage holes. The problem is formulated as a target coverage problem, in which wireless stations (STAs) are treated as target points, and the transmission power is appropriately controlled by the proposed scheme. In addition, we prove that the target coverage game is a potential game, and the proposed method is guaranteed to converge to a Nash equilibrium after a finite number of iterations. In order to prevent the occurrence of coverage holes that cannot be detected by the available STA information, we propose the exclusion of coverage boundary access points (APs) from reducing transmission power, with the idea that each boundary AP always contains the region covered by it alone. The coverage boundary APs are determined through Delaunay triangulation of all APs. The proposed scheme is evaluated by performing some simulations. The simulation results indicate that the proposed TPC scheme is effective in reducing coverage overlaps without generating coverage holes.

An adaptive channel allocation scheme for bidirectional in-band full-duplex networks is proposed by modifying a channel allocation scheme for half-duplex networks on the basis of the property of bilateral symmetric interaction. To adapt schemes for half-duplex networks to full-duplex networks, we modify them so that channel update process successfully converges to a Nash equilibrium, i.e., it does not cycle. Specifically, the sum of products of transmission power and received interference power (instead of the sum of received interference power) at a communication pair is used as the novel payoff function. Then, the proposed channel allocation scheme is proved to be a potential game, and thus, the game is guaranteed to converge in a finite number of steps. Simulation results reveal that the proposed scheme is able to effectively allocate channels in bi-directional in-band full-duplex networks.

This paper investigates the achievable throughput of a multi-antenna two-hop relay link under hybrid full/half-duplex operation. The analysis is facilitated by realistic waveform simulations, which explicitly model all the essential circuit impairments occurring in the relay transceiver together with degrading channel estimation and self-interference cancellation. The obtained results indicate that pure full-duplex operation does not ensure optimal performance but additional half-duplex transmission periods are usually needed to maximize the end-to-end throughput. Especially, it is shown that the estimation of the self-interference channel within the relay should be performed when the source is not transmitting anything while also the source should be allowed to transmit alone to avoid making the first hop a bottleneck. These findings form a solid basis for optimizing the full-duplex MIMO relay deployments in future mobile networks.

This paper studies the idea and performance of cyclostationary spectrum sensing in cognitive full-duplex radios, when secondary transmission and spectrum sensing are done simultaneously at the same channel. The paper starts by briefly introducing the ideas of cognitive full-duplex radio and the cyclostationary spectrum sensing in the presence of self-interference. The idea of changing the cyclic features of the secondary signal is then proposed and its impact on the spectrum sensing under self-interference is analysed. The cyclic features can be changed by changing the length of the cyclic prefix of the OFDM signal or by changing the amount of subcarriers. The effects of both approaches are evaluated with comprehensive performance simulations. It is shown that changing the cyclic features of the secondary signal can provide significant improvements in the sensing results of the primary signal, and that in general, reliable inchannel spectrum sensing while transmitting is feasible. This can enable enhance coexistence mechanisms, e.g., for LTE-Unlicensed technology at ISM band.

Relays can be used to improve the coverage in blind spots as well as increase the signal strength at locations where the signal from the access point undergoes fading. In-band full-duplex relaying is a promising technique which improves the spectral efficiency over traditional half-duplex relaying by relaying at the same time slot and frequency. Relays are expected to play a major role in IEEE 802.11ah systems for below 1 GHz WLAN in order to cover a large area of 1 km around the access point. In this contribution, the impact of deploying in-band full- duplex relays is studied over traditional half- duplex relays in improving the coverage and throughput at the stations for M2M applications. System-level realistic evaluation of in-band full- duplex relaying is performed using the COST-2100 channel model to model the propagation and self- interference channels at 920 MHz. The channel is combined with realistic antenna design to determine the end-to-end throughput at the location of each station in a 1 sq.km area. For the downlink scenario, it is shown that such in-band full-duplex relays can increase the number of users for a given throughput over half-duplex relays. Also, a single in-band full-duplex relay can decrease the outage by 0.9% compared to half-duplex relays and 12.6% without a relay to cover 97.8% of the stations in a 1 sq.km area with downlink throughput of 100 kbps.

In this paper, the problem of sampling-jitter estimation in OFDM full-duplex radio transceivers is addressed. The sampling-jitter estimation is very challenging, because the effects are very small, but still high enough to be a problem in full-duplex radios. The proposed sampling-jitter estimation method is based on estimating only the most significant frequency components of the sampling jitter. The performance of the estimation algorithm is evaluated with simulations by studying how much self-interference cancellation is achievable when the self-interference is mitigated using the estimates of the sampling jitter samples.

Inserting attenuators between transceivers and antennas is proposed to improve spatial channel reuse in carrier sense multiple access with collision avoidance based WLANs. Using attenuators enables the access points or stations to decrease the powers of transmission signals and received signals, which equivalently results in the increase of the carrier sense threshold. Thus, the attenuation value control enables inversely proportional setting of transmission power and carrier sense threshold, which is known to provide a novel solution for the unfairness problem caused by variable transmission power or variable carrier sense threshold. We first derive a simple and useful sufficient condition such that the aggregate spectral efficiency is higher when attenuators are used than they are not by using some approximations. Through a numerical evaluation as well as a testbed for using attenuators, the sufficient condition is shown to be valid despite the approximations and throughput improvement is verified. The result from the testbed also discloses a new unfairness problem due to an individual difference in transmission power or carrier sense threshold.

In this paper, the sampling jitter effect on self- interference cancellation in full-duplex radio transceiver is studied. The goal is to find if sampling jitter is a problem or not in transceivers utilizing full-duplex radio technology, and also to study how the effects of the sampling jitter can be mitigated. In the paper, first, full-duplex transceiver model is described with sampling jitter, and then the corresponding signal model is derived. After this, a technique for sampling jitter mitigation as part of the digital linear cancellation of the self-interference is proposed. Finally, the initial question whether the sampling jitter is a problem or not is answered and the mitigation performance of the proposed technique is studied with extensive computer simulations.

In the present paper, a self-organization scheme for joint base station and resource block selection in an orthogonal frequency-division multiple access (OFDMA) cellular network is proposed. Inspired by the cognitive radio technology, each mobile node selects the most appropriate base station and resource blocks for uplink transmission in a decentralized manner in order to improve the throughput performance and manage the interference. A utility function is defined for each mobile node, which takes into account the cross- and co-tier interference, and incentive to choose the base station based on the link quality. Such a self-organization scheme can be modeled as a potential game, which is guaranteed to converge to a Nash equilibrium. The simulation results show that the proposed self-organization scheme facilitates the improvement of the system capacity in LTE heterogeneous networks by offloading the macrocell traffic and mitigate the cross- and co-tier interference.

In this paper, microwave power and sensing data transmission scheduling for machine-to-machine (M2M) wireless networks comprising enormous numbers of sensors is discussed. Due to the large numbers involved, the sensors are required to be battery-less so that they can be maintenance-free. When microwave power and sensing data are simultaneously transmitted, they interfere with each other at the receiver. As a result, separate timing schedules are needed for power and data transmissions in these networks. In addition, we discuss the number of frames successfully received and the maximum number of sensing data frames that can be collected. If the power transmission duty ratio is too low, the sensors cannot get adequate energy, resulting in a decrease in the number of sensors that try to transmit sensing data. On the other hand, if the power transmission duty ratio is too high, it is difficult for the sensors to transmit all their sensing data in the time available for data transmission. Hence, the power and data transmission duty ratios have to be determined appropriately. We also clarify characteristics associated with the number of successfully received frames using computer simulations.

This paper reports a feasibility study including experimental implementation of a battery-less wireless sensor whose power is supplied by a microwave power transmitter. In particular, both microwave power transmissions (MPTs) and wireless communications using wireless local area networks (WLANs) are performed on the same frequency band, 2.4 GHz, to achieve high spectral efficiency. In this case, time scheduling of MPT and wireless communication is necessary to avoid interference. In addition, for stable operation, the duty ratio, which is defined as the ratio of the active duration to the whole period, is set depending on the charge remaining in the capacitor of the sensor. In the initial experimental setup, a single battery-less wireless sensor is successfully operated, and then the feasible transmission range of the designed time scheduling system is discussed.

A transmission experiment of a multi-user MIMO system with user selection and linear spatial precoding is conducted and the results are reported. As a low-complexity user selection algorithm, the chordal distance-based user selection (CDUS) algorithm is employed. The testbed used in the experiment is implemented by taking full advantage of the software-defined radio. An amplify-and-forward-based simple channel estimation method is adopted. As a result of the experiment in a conference room, the CDUS algorithm improved both the power efficiency and the interference suppression accuracy compared to the round-robin user selection.

In wireless sensor networks, individual nodes must be batteryless to make them maintenance-free, which is particularly important for networks that have an enormous number of nodes. When these wireless batteryless nodes are microwave-powered devices, they would necessitate scheduling of power and data transmission. In addition, to ensure network sustainability, a constant amount of energy should be stored in the capacitors of the batteryless nodes. In the experiment conducted in this study, we confirm the necessity of scheduling. In addition, sleep periods are scheduled to ensure that a constant amount of energy remains in the capacitor.

As a promising wireless access standard for machine-to-machine (M2M) networks, the IEEE 802.11 task group ah has been discussing a new standard which is based on the wireless local area network (WLAN) standard. This new standard will support an enormous number of stations (STAs) such as 6000 STAs. To mitigate degradation of the throughput and delay performance in WLANs that employ a carrier sense multiple access with collision avoidance (CSMA/CA) protocol, this paper proposes a virtual grouping method which exploits the random arbitration interframe space number scheme. This method complies with the CSMA/CA protocol, which employs distributed medium access control. Moreover, power saving is another important issue for M2M networks, where most STAs are operated by primary or secondary batteries. This paper proposes a new power saving method for the IEEE 802.11ah based M2M network employing the proposed virtual grouping method. With the proposed virtual grouping and power saving methods, the STAs can save their power by as much as 90% and maintain good throughput and delay performance.

In this paper, a method for a multi-operator mobile relay node (RN) for cellular networks on a bus or train is proposed. The introduction of RNs enables an improvement in the spectral efficiency because an antenna with higher gain than that of user equipment (UE) can be installed in an RN. However, installing different RNs for different operators is not efficient because of the large amount of space needed to install multiple RNs in a bus. This problem can be solved by sharing the RN among multiple operators. In addition, we propose a system of dynamic spectrum allocation among the operators for RN-UEs communication. By allocating bandwidth to operators depending on the link quality, the effective use of radio resources can be achieved. This resource allocation problem can be discussed in terms of the theory of bargaining. We evaluate dynamic spectrum allocation, based on the Nash bargaining solution (NBS), using computer simulations. As a result, considering fairness among the operators, the total throughput can be improved by approximately 20% compared with the situation where multiple operators install different RNs individually.

A practical and efficient vector quantization called partitioned vector quantization (P-VQ) based non-linear precoder for MIMO downlink broadcasting channels has been analyzed in this paper. P- VQ has been found to be an efficient way of reducing the memory requirements and search complexity in conventional VQ systems, especially for large MIMO. Simulation results reveal that P-VQ technique can enhance the overall quantization performance in terms of achieved capacity, bit error rate (BER) at a cost of reasonable additional complexity under certain feedback budgets. Tomlinson-Harashima precoding (THP), a power and complexity efficient precoding technique along with data dependent vector perturbation (VP) has been employed to meet the power constraint requirements of our system. Least-square (LS) channel estimation is performed at the user terminals to acquire their channel state information (CSI).

In this paper, the end-to-end packet error rate (PER) performance of a space-time block code (STBC) based multi-hop cooperative relaying system is discussed. This system consists of a source, a destination, and two STBC based cooperative relays in each hop. These relays decode their received packets and forward the packets only when no error is found. In this system, there are conditions where the end-to-end PER performance improves with the number of hops. The theoretical end-to-end PER performance of this system have been derived analytically. However, in real-world wireless systems, various practical issues, e.g., timing synchronization and imperfect channel estimation, have a large impact on the performance. To confirm the end-to-end PER performance of this system in actual setup, an in-lab experiment using a fading emulator and four transceivers is performed under Rayleigh fading environment. The experimental results clarify the PER performance of the STBC based multi-hop cooperative relaying system. In addition, the theoretical PER performance is also verified from the experimental results.

In this paper, implementation of a multi-user MIMO experimental system is presented. Linear spatial precoding and a simple two- way channel estimation technique are adopted in the experimental system. In-lab and field transmission experiments are carried out and the performance is evaluated. The impact of channel estimation error under average channel gain discrepancy between two mobile stations is analyzed by means of computer simulation. It is shown that channel estimation error has a greater influence on the mobile station with greater average channel gain.

In this paper, we propose a receiver using the concept of a "virtual channel" and Turbo codes for multiuser multiple-input multiple-output (MU-MIMO) systems with cooperating transmitters. In order to reduce feedback information from receivers, a precoding method with reduced channel state information (CSI) is needed. Although signals interfere with each other in such a system, the proposed receiver enables high-speed transmission by detecting signals iteratively. In the proposed iterative scheme, soft information is exchanged between virtual channel detectors and Turbo decoders to improve the bit error rate (BER) performance. We evaluate the performance of the proposed receiver in systems with two transmitters, each equipped with four antennas, and two receivers, each equipped with two antennas. The results show that the proposed receiver can improve the system performance owing to the iteration scheme.

In multi-hop cooperative communications, multiple relay stations are used at each hop in order to enhance transmission performance. In previous studies, transceivers have been used to evaluate the transmission performance of multi-hop cooperative communications involving the use of a space-time block code, in real propagation channels. In the present study, an experiment involving multi-hop cooperative networks is conducted with transceivers. The transceivers are implemented on field programmable gate arrays, which perform real-time signal processing. Synchronization of the timing and carrier frequency among wireless stations is carried out on the basis of received signals. The transmission performance of cooperative communications is compared to the performance of non-cooperative multi-hop communications involving the use of only one relay station. The performances of the relay schemes are measured quasi-simultaneously. The transmission performance is evaluated in terms of the cumulative distribution function (CDF) of the bit error ratio.

This paper proposes a game-theoretic approach for self-organized resource allocation in OFDMA femtocells with closed access configuration. The objective of the proposed scheme is to overcome the interference problem caused by co-channel operation of femtocells in an existing macrocell network using a self-organization capability of femto users. Inspired by the emerging cognitive radio technology, each femto user acts as an autonomous entity and attempts to select the most appropriate subset of resource blocks in a decentralized manner in order to mitigate the cross- and co-tier interference. Such a self-organized resource allocation scheme can be modeled as a potential game, which guarantees the convergence to a Nash equilibrium as long as distributed sequential play based on the best response strategy is adopted. Simulation results show that the proposed scheme improves the capacity of the femtocell network, while minimizing the performance degradation of the macrocell network.

In this paper, we propose a virtual layered space-frequency receiver with an ordered successive detector for multiuser multiple-input multiple-output (MU-MIMO) systems where multiple transmitters cooperate with one another. The proposed receiver enables high-speed transmission in the systems even if the transmitters know only partial channel state information (CSI). Actually, the receiver shows such superior performance when the concept of "virtual channel" is applied to cooperative MU-MIMO systems. Moreover, we propose an iterative detection scheme for the receiver; in this scheme, some signals from the Viterbi decoder are fed back to the ordered successive detector. The proposed iterative scheme helps improve the transmission speed when the number of cooperating transmitters is increased. We evaluate the performance of MIMO systems in which the transmitters have four antennas each and two users have two antennas. The receiver can double the transmission speed at the cost of 3 dB in Eb/N0at a BER of 10-4. Furthermore, the receiver can triple the transmission speed at the cost of 7 dB in Eb/N0at a BER of 10-4. When the system has three transmitters, we observe an error floor. However, the proposed iterative method can suppress the error to a value below BER = 10-7.

In cognitive radios, reliable detection of wireless systems is crucial since these radios share the frequency spectrum with other wireless systems. In a heterogeneous environment, systems with different priorities have different protection requirements and operating parameters.The cognitive radio must guarantee highly reliable communication in the primary systems, but need not necessarily guarantee reliable communication in the secondary systems. Therefore, classification of the signals in a cognitive radio is necessary to fulfill the above mentioned protection requirements. Cyclostationary detection allows for the classification of coexisting cyclostationary signals with different parameters, whereas energy detection cannot be used for distinguishing primary signals from secondary signals. This method has been theoretically investigated, but it has not been applied to an actual field. This paper presents the experimental results obtained from field trials on the use of cyclic-feature-based methods for distributed signal detection and signal classification.

Recently, the concept of self-organization has drawn considerable attention for its possible use in distributed interference management. To realize self-organized interference management, a minority game (MG) is a promising tool because it facilitates self-organized decision-making. Although the existing interference management scheme that involves the use of an MG achieves transmission control aware of the system priority, it cannot actively control the outage probability of a higher-priority system. In the present study, the existing scheme is modified to control the outage probability for a predefined target value. It is proposed that the criterion for deciding whether or not to transmit should be changed adaptively, depending on the interference power in the higher-priority system. In this way, robustness against the location of stations is also achieved. Numerical simulation confirms the above advantage of the proposed scheme.

In multi-hop cooperative communications, multiple relay stations are used at each hop in order to enhance transmission performance. Transceivers for multi-hop cooperative communications using space-time block code are developed in order to evaluate the transmission performance in real propagation channels. In the present paper, an experiment involving multi-hop cooperative networks is conducted with this equipment. The transceivers are implemented on field programmable gate arrays, which perform real-time signal processing. Synchronization of timing and carrier frequency among wireless stations is established based on the received signals. The transmission performance of cooperative communications is compared to the performance of non-cooperative multi-hop communications using one relay station. The performances of the relay schemes are measured quasi-simultaneously. The performance is evaluated in terms of the cumulative distribution function (CDF) of the bit error ratio.

In the present paper, a game theoretic framework of joint channel selection and power allocation for spectrum sharing in distributed cognitive radio networks is proposed. The objective of the proposed scheme is to investigate the performance of the lower- priority system in spectrum sharing where the lower-priority users utilize the spectrum by selecting the channel and transmit power while aware of the interference to the higher-priority users. The utility function that captures the cooperative behavior to minimize the interference and the satisfaction to improve the throughput is defined. The proposed joint channel selection and power allocation game can be formulated as a potential game and it is guaranteed to converge to a Nash equilibrium when the best response dynamic is performed. The simulation results verify the convergence of the proposed potential game and reveal that the proposed utility function with coefficient adjustment improves the throughput performance of the lower-priority system compared to that of without coefficient adjustment, while ensuring the Quality of Service (QoS) constraint at the higher-priority system.

Adaptive radio resource management policy aware of the demand for wireless services is termed dynamic spectrum access (DSA). In order to evaluate the effectiveness of DSA, dynamics of the demand should be considered. In the present paper, dynamics of the spectrum market, which is a way to realize DSA, are evaluated from the economic perspective. Modeling the spectrum market system by using game theory, the convergence characteristics of the system are analyzed from two points of view: allocation interval and prediction period. The available spectrum is allocated at regular time intervals and the length of this allocation interval naturally has a significant impact on the dynamics of the system. Also, available spectrum is allocated based on the prediction of the time variation of the demand for wireless services. This prediction thus influences the dynamics of the system. Simulation results reveal that short allocation interval and long prediction period are required to improve the convergence property of the system. Simulation results also reveal that long prediction period leads to the increase in the revenue of spectrum holder.

The introduction of adaptive array antennas to wireless ad hoc networks is effective for increasing the overall network throughput because of its interference suppression capability. The network throughput can be further improved by controlling antenna-pattern, channel, and power simultaneously. In this paper, the asynchronous distributed pricing (ADP) and multi-channel ADP (MADP) algorithms, which are joint channel selection and power control schemes, are modified to also select antenna-pattern. Simulation results show that the proposed scheme achieves high network throughput.

Reliable detection of other wireless systems is crucial for the systems that share the same frequency band. In wireless communication channels, there is uncertainty in the received signal level due to multipath fading and shadowing. Cooperative sensing techniques in which wireless stations share their sensing information can improve the detection probability of other systems. This paper presents the field trial results of a cooperative sensing scheme with energy detection and hard decision cooperation. The results demonstrate that the cooperation of wireless stations can relax the stringent sensitivity requirement of energy detection.

The present paper focuses the application of the base station cooperation (BSC) technique in fractional frequency reuse (FFR) networks. Fractional frequency reuse is considered to be a promising scheme for avoiding the inter-cell interference problem in OFDMA cellular systems, such as WiMAX, in which the edge mobile stations (MSs) of adjacent cells use different subchannels for separate transmission. However, the problem of FFR is that the cell edge spectral efficiency (SE) is much lower compared to that of the cell center. The BSC technique, in which adjacent BSs perform cooperative transmission for one cell edge MS with the same channel, may improve the cell edge SE. However, since more BSs transmit signals for one cell edge MS, the use of BSC can also increase the inter-cell interference, which might degrade the network performance. In the present paper, with a focus on this tradeoff, we propose an adaptive BSC scheme in which BSC is only performed for the cell edge MSs that can achieve a significant capacity increase with only a slight increase in inter-cell interference. Moreover, a channel reallocation scheme is proposed in order to further improve the performance of the adaptive BSC scheme. The simulation results reveal that, compared to the conventional FFR scheme, the proposed schemes are effective for improving the performance of FFR networks.

This paper investigates the performance of a multiuser distributed antenna system (DAS) with spatial precoding in which mobile stations (MSs) just send back the received signals along with their training sequences. In this method, base stations (BSs) can obtain the channel state information of both the uplink and downlink without estimating the channels in MSs. This two-way multiple-input multiple-output (MIMO) channel estimation technique have been proposed and mathematically analyzed in the literature. However, implementation aspects of this method, such as forward gain and dynamic range, are not studied yet. In this paper, these aspects are investigated in order to confirm its feasibility in a multi-user DAS where a precoder is employed in BSs. Computer simulation results are presented to demonstrate the required hardware specs for an amplify and forward (AF) operation in MSs by examining the bit error rate (BER) performance. It is shown that an 8-bit A/D converter together with a fixed gain amplifier brings almost no degradation to the BER performance. Furthermore, a performance comparison with a vector feedback method is given.

A distributed power, channel, and route selection scheme for multi-hop radio networks is proposed by extending multichannel asynchronous distributed pricing (MADP) algorithm, which is a power and channel selection scheme for single-hop radio networks. MADP algorithm achieves suboptimal system throughput in single-hop radio networks, however, since MADP algorithm allocates more resources to links with good quality, there may be a large difference in throughput among communication links. Particularly in multi-hop radio networks, MADP algorithm might not necessarily achieve high system throughput because the link with the lowest throughput limits the end-to-end throughput of a given multi-hop route. In order to achieve high system throughput in multi-hop radio networks, a modification of MADP algorithm named end-to-end throughput aware MADP (ETA-MADP) algorithm is proposed. It is proved that all links in a given multi-hop route have the same throughput through the use of ETA-MADP algorithm. Simulation results reveal that the introduction of the proposed algorithm achieves high spectral efficiency in multi-hop radio networks even with lower transmit power compared to that in single-hop radio networks.

Multi-hop relaying can extend communication range using limited transmit power, and improve area spectral efficiency. However, they might cause considerable degradation of end-to-end error performance due to error accumulation. Cooperative relaying, in which multiple stations forward the received signal using transmit diversity techniques, can recover the errors unless all the relay stations do not receive the signal correctly, and improve the transmission performance. In this paper, the FER performance of multi-hop cooperative networks is analyzed by using state transition. Theoretical analysis reveals that the FER performance can be kept almost constant, or even improved with increasing the number of hops. The computer simulation results closely agree with the analysis.

Cooperative relaying is a promising technique that improves the end-to-end error performance in multihop vehicular networks. When cyclic delay diversity (CDD) is employed as transmit diversity at each hop, the control of the delay time introduced at each transmitting station improves the diversity gain. In this paper, a novel distributed delay time selection algorithm for cooperative multi-hop vehicular networks with CDD is proposed. In this algorithm, neither dedicated control packets nor an external controller for the delay time selection is required since the delay time is selected by overhearing the transmission in the next hop. Simulation results show that the proposed algorithm can achieve the end-to-end FER performance close to that of fixed delay time allocation, and is not sensitive to vehicles movements.

Cooperative relaying is a promising technique for multihop wireless networks to exploit spatial diversity. Most of the studies of multihop relaying assumed a simple i.i.d. Rayleigh fading model. In this case, there is no correlation of phase and amplitude among the received signals. However, this assumption is not always fulfilled in practice. For instance, the transmission distance in multihop wireless transmission is supposed to be a close range and the shadowing effect needs to be taken into account. The ray launching method based on geometrical optics is a technique for estimating a deterministic propagation channel. In this paper, 3D ray launching method is used for wireless propagation prediction. By computer simulations, the performance of 2-hop cooperative relaying with two relay stations is investigated. The simulation-based performance analysis confirms that the cooperative relaying scheme has an advantage of diversity gain thus improving the bit error ratio performance.

In this paper, the performance of decentralized adaptive route selection problem in cooperative relaying networks in which each source independently chooses a route to enhance its own throughput is studied. Cooperative relaying is a new technique to enhance spectral efficiency by using multiple stations. However, when multiple stations transmit simultaneously, it also increases the number of interference signals. For this reason, if each source independently and selfishly transmits, it is not clear whether cooperative relaying always increases the network capacity. In order to investigate the capacity of cooperative relaying under the decentralized adaptive route selection, the game-theoretic approach is applied. Simulation results reveal that the introduction of the cooperative relaying always increases the capacity under centralized control, however that it may reduce the capacity under decentralized control due to the increase in the number of interference signals.

This paper analyzes distributed asynchronous power and rate control for wireless ad hoc networks. Importantly, all network transmitters are considered to be independent of any management infrastructure and to have the freedom to choose their own arbitrary control rules, using as input only information on local interference and achieved carrier signal-to-interference ratio (CIR). Such an approach respects diverse user preferences of on quality of service (QoS) and allows them to adapt to local network conditions in contrast with conventional cellular systems, whose users must follow centralized control commands from serving base stations. For this purpose, we develop a general non-cooperative game-theoretic framework and characterize the resulting power and rate allocation dynamics in terms of its convergence to network-wide acceptable equilibrium states under stochastic communication channels. Chief among the attractive features of our proposed framework is the fact that it is developed in an entirely abstract way without any particular technological or architectural assumptions, which are typically made in related works. Numerical simulations prove the potential of our approach to provide for fair, robust and comparably better CIR allocation in ad hoc networks with varying topology and user density.

In cognitive radio, reliable detection of the primary system is crucial for the secondary system. However, there is an uncertainty in the received power level due to shadowing. The cooperative sensing where the stations in the secondary system share their sensing information can improve the detection accuracy of the primary system. In this paper, a new secondary system for reducing interference to the primary system is proposed. In the proposed system, a secondary transmitter receives sensing information by using multi-hop transmission from all secondary stations which detect the primary system, and makes decision of its transmission based on the received sensing information. Moreover, it controls the transmit power by the received CINR information from the secondary sensing stations. Simulation results assuming spatially independent shadowing and ideal multi-hop transmission in the secondary system reveal that the proposed system is helpful to protect the primary receivers which have no transmission ability.

This paper analyzes distributed asynchronous power and rate control for wireless ad hoc networks with stochastic channels. In contrast to conventional cellular systems, all network transmitters are assumed to be independent of any management infrastructure and, importantly, to have the freedom to choose their own arbitrary control rules, using as input only the information on local interference and achieved signal-to-interference and noise ratio (SINR). This approach respects link's different local network conditions and preferences on quality of service. With the purpose of finding network-wide acceptable equilibria for such an individually defined power/rate allocation dynamics, the authors discuss an entirely general asynchronous and distributed algorithm, whereby stochastic channels are assumed. Moreover, optimum admission scheme for linear/linearized models is given. Numerical simulations show the efficiency of our approach to allocate comparably higher SINRs in random ad hoc networks with changing topologies and user density.

There arises a need to increase the network throughput by broadening the bandwidth. Currently, in IEEE 802.11 wireless LAN standardizations, two approaches are taken to effectively utilize multiple existing channels. One is multi-channel technique in which multiple separate frequency channels are used for communications as seen in IEEE 802.11s. The other is channel-bonding technique in which multiple frequency channels are bonded into a single broadband channel as seen in IEEE 802.11n. The former one can mitigate traffic congestion, while the latter one can increase the transmission rates. Here, in this paper, these two approaches are analytically compared and the advantages of the respective approach are clarified in terms of the network throughput and delay performances assuming the same total bandwidth and a CSMA protocol. Our numerical and simulation results indicate that under low-traffic conditions, using channel-bonding technique can achieve a low delay, while under high-traffic conditions, the performances of both throughput and delay can be improved by using multi-channel technique.

The impact of spatial correlation of shadowing on the coverage of TDMA multihop cellular systems is investigated in single-cell environments. The introduction of relaying capabilities to cellular systems may enhance the cell coverage as follows. Changing single-hop transmission to multihop transmission reduces per-hop path loss. In addition, since a multihop route is selected among a number of alternatives, the use of mobile stations suffering from severe shadowing can be decreased. In most studies on multihop cellular systems, shadowing is modeled as a location-independent log-normal random variable; however, adjacent shadowing values are spatially correlated because shadowing is caused by terrain configuration or obstacles between the transmitter and receiver. Thus, coverage enhancement due to relaying capabilities may not be achieved as expected. In this paper, first, according to the commonality between multihop transmission and symbol rate control, a similar methodology to formulate the spectral efficiency (SE) and outage probability of rate-adaptive cellular systems is used to estimate these performances of multihop cellular systems. Second, we investigate the impact of decorrelation distance, whose typical value for the urban environment is 20 meters, on the coverage of multihop cellular systems. By using a model for spatially correlated shadowing, numerical results reveal that when the coverage of single-hop cellular systems is ten times larger than the decorrelation distance, the introduction of relaying capability may enhance the coverage without significant degradation due to the spatial correlation of shadowing.

In performance evaluations of wireless ad hoc networks, both successful data reception and carrier detection probabilities are assumed to be 1 within a circle with certain radius and 0 outside the circle. In real systems, however, those probabilities are gradually changes from 1 to 0 as the distance between a terminal and access point increases. In this paper, we evaluate the throughput of wireless ad hoc networks assuming successful data reception and carrier detection probabilities to be continuous functions of the distance between a terminal and access point. We also show that new additional factors which decrease the throughput will appear under these assumptions, compared to the case where only probabilities 1 and 0 are assumed

This paper presents a novel game-theoretic framework for distributed adaptive power control in wireless ad-hoc networks. It first shows the equivalency of two recent approaches to this noncooperative field - the QoS utility maximization approach and the best-response approach. We then choose to follow the analytically more intuitive best-response approach and analyze in an abstract way general conditions for existence of optimal outcomes (Nash equilibria) of best-response power control dynamics. Consequently, we characterize conditions for global convergence to such states without any particular technical assumption. Our work provides a mathematically more general insight to game-theoretic power control compared to recent related works. Using the herein developed framework, we discuss CIR-based utility function maximization and show conditions for applicability of linear (linearized) best-response power control in connection with illustrative simulations

This paper presents a novel framework for distributed power control for ad-hoc wireless networks. We analyze dynamic adaptive power allocation assuming that transmit power is adjusted with respect to experienced interference based on general best-response functions. For this purpose, we develop a general non-cooperative game-theoretic framework in order to characterize optimal equilibrium states and convergence of distributed power control dynamics to such states. Our work provides a more general insight to game-theoretic power control compared to most of recent works in this field. Moreover, our framework is developed in an abstract way without any technical assumption on particular modulation, coding, QoS measure definition or network architecture. To demonstrate an application of our framework, we show that stable linear best-response power control converges exponentially to a unique Nash equilibrium for any initial condition, which we confirm by numerical simulations.

Much recent interest has focused on applying multihop relaying to conventional cellular communication systems. Multihop relaying can broaden the cell coverage and enhance the capacity of cellular systems. However, as is well-known, cell coverage can also be broadened simply by lowering the transmission rate in a conventional cellular system. According to this commonality, we theoretically formulate the performance of cellular systems with multihop relaying in terms of outage probability and spectral efficiency using the same methodology to formulate the performance of cellular systems with rate adaptation. Simulation results reveal that multihop relaying and rate adaptation have very similar effects on the performance of cellular systems

The capacity region of ad hoc networks with optimal routing or scheduling has been investigated. Since distributed network control algorithms can be applied without centralized information, the resulting network can be scalable. On the other hand, selfish nodes can severely degrade network performance, for example, the network capacity. We attempt to analyze the obtained network capacity region based upon distributed route selection by using game theory. Simulation results reveal that in some situations, even with optimal routing, each rational selfish node cannot determine a unique route under the assumption that nodes know not only their own end-to-end throughput, but also those of all the other nodes, as a result of their own, and all the other nodes', route selections.