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.

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.

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.

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.

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 proposes a dynamic channel selection method for direct spectrum division transmission systems, which are autonomous decentralized control systems like CSMA/CA systems, in order to use frequency resources efficiently. In the method each terminal monitors the usage status of the whole system bandwidth, then searches for and memorizes all combinations of currently vacant channels. When the terminal transmits data it selects the combination having the lowest occupancy rate for the transmission. The method's effectiveness was confirmed in discrete event simulations with probabilistic resource sharing between terminals. The aggregate throughput improvement compared with simple random selection achieved in the simulations ranged between 2.5% and 11%.

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.

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, 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.

This paper proposes a novel heterodyne multiband multiple-input multiple-output (MIMO) receiver with baseband automatic gain control (AGC) for cognitive radios. In the receiver, the analog baseband AGC is introduced to avoid the baseband signals exceeding the voltage reference of analog-to-digital converters (ADCs). This paper proposes a novel technique to estimate the analog devices imperfection in the heterodyne multiband MIMO receiver with the baseband AGC. The proposed technique does not require any high precision analog devices. The performance of the proposed receiver is verified by using computer simulations. As a result, the required resolution of the ADC is nine bits in the proposed receiver. Moreover, the proposed receiver has less computational complexity than that with the baseband interference cancellation unless a frequency band is changed once in nine packets or less.

This paper describes a QoS control scheme for a wide area ubiquitous wireless network which is designed to accommodate many wireless terminals (WTs), such as sensors and actuators, in a large cell area. The purpose of this paper is to establish a QoS control scheme in a media access control (MAC) layer that can hold the transmission delay time of high priority (HP) class traffic within a predefined value regardless of how much low priority (LP) class traffic there is. Several QoS control schemes for wireless communication use have been proposed. However, in the wide area ubiquitous wireless network, an access point (AP) accommodates many WTs and the AP traffic volume often drastically changes. Therefore, conventional schemes sometimes cannot control the QoS of HP traffic. To solve this problem, we propose a QoS control scheme that calculates a suitable initial back-off window size (IWS) of random access (RA) for each QoS class by using equations derived from a Markov chain behavior model. The proposed scheme adjusts the window size so as to prevent increased transmission delay of HP traffic. The scheme's performance is clarified by computer simulation.

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.

The spread of the Internet has given rise to strong demands for real-time services such as voice and streaming video. Since these services require guarantees that may include transmission rate, delay, and delay variance, the network, including the wireless access networks, must offer quality of service (QoS) guarantees. However, no radio access system fully supports QoS controls. In this paper, we propose a new QoS control scheme that uses back pressure traffic control and alters the Inter Frame Space (IFS) length of stations (STAs) according to the traffic circumstances of the wireless local area network (WLAN). It measures traffic of each user and controls the amount of data transmitted in order to guarantee the throughput of each user. This paper shows that the problem of “priority inversion” occurs and that the QoS is degraded when conventional schemes are employed in the medium access control (MAC) layer. The effectiveness of the proposed scheme is validated by comparison against conventional schemes. The results reveal that the proposed scheme can guarantee QoS in terms of throughput even if there is lot of best effort traffic and improve transmission delay performance.

This paper proposes a novel radio frequency (RF) devices imperfection estimation scheme for the heterodyne multiband multiple-input multiple-output (MIMO) receiver with the baseband automatic gain control (AGC) for cognitive radios. It is quantitatively shown that the proposed estimation scheme allows analog-to-digital converters (ADCs) with lower resolution to be applied to the receiver than the conventional estimation technique. Also, the proposed scheme is shown to attain rapid convergence, and to keep superior bit error rate (BER) performance in spite of the power of image-band interference. It is shown that the proposed estimation scheme makes the receiver with 8 bits ADCs attain the BER performance even in the presence of strong image-band interference, e.g., CIR=-40 dB.

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.

Cognitive radios are of interest because they are capable of high frequency utilization efficiency. Multiband multiple-input multiple-output (MIMO) receivers based on heterodyne reception have been proposed for use in cognitive radios. This paper proposes a reduced-complexity multiband MIMO receiver that can achieve high performance by correcting the imperfection of the Hilbert transformer in the intermediate frequency (IF) band. However, the imperfection of the analog devices in the feedback loop causes poor performance. Therefore, a scheme for correcting the imperfection of the analog devices is proposed.

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.

Cognitive radios are of interest because they are capable of high frequency utilization efficiency. In a cognitive radio, multiband receivers are required to make use of unused frequency bands. On the other hand, MIMO (multiple-input multiple-output) spatial multiplexing is also used to increase transmission capacity. This paper proposes a multiband MIMO receiver with ordered successive detection based on heterodyne reception. Because the use of an RF-BPF (radio frequency-bandpass filter) with a wide pass band is required in the receiver for multiband reception, the heterodyne receiver suffers from image-band interference. Therefore, the proposed receiver is designed not only to detect spatially multiplexed signals but also to suppress image-band interference. Computer simulation shows that the proposed receiver achieves superior BER performance even in the presence of strong image-band interference signals.

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.

This paper considers two-hop wireless systems employing network coding and a carrier sense multiple access (CSMA) protocol. Network coding is a recent and highly regarded technology for the capacity enhancement of multiple unicast and multisource multicast networks. The two-hop wireless CSMA systems are often involved with the hidden node problem, but the impact of the hidden nodes on network coding has not been analyzed in theory. This paper provides explicit expressions of the throughput for single-relay two-hop wireless CSMA systems both without and with network coding. The throughput can be obtained from these expressions for given system parameters even when end nodes via the relay node are hidden each other. Furthermore it is shown that the transmit probability of the relay node is a design parameter that is crucial for maximizing the achievable throughput for CSMA systems with network coding. It is clarified that the throughput for CSMA systems can be enhanced as compared with that for slotted ALOHA systems in case of non-hidden end nodes whereas it deteriorates considerably in case of hidden end nodes.

In this paper, we introduce, a new method named “Complexity Reduction Method based on Compensation Distance” in order to reduce the complexity of the detection in virtual layered space-time receivers in multiple-input multiple-output (MIMO) systems. Moreover, this method is practical even when the number of transmitting streams is greater than that of receiving antennas. Computer simulations indicate that the proposed method can be used to reduce the complexity of detection to approximately 20% in [8×4] (the transmitter is equipped with 8 antennas and the receiver is equipped with 4 antennas) systems.

A novel method is proposed in this paper to cancel the image-band interference for multi-mode/band receivers with baseband AGC. The Hilbert transformer, which plays an important part in heterodyne multi-mode receivers described here, has the imperfection which results in image-band interference in baseband, causing severe deterioration of transmission performance. The method proposed here cancels the image-band interference by estimating and canceling imperfection of the analog Hilbert transformer consisting of the phase error and the amplitude error by correlation matrices calculation. Furthermore, filtering is employed here to improve the estimation accuracy. This paper also shows the error estimation performance of the proposed method and the BER performance of the interference cancellation by computer simulation under the circumstance of CIR =-20 dB, and compares them with that of the conventional method.

In this study, we develop a virtual layered space-frequency communication architecture. This architecture enables the decoding of transmitted OFDM (orthogonal frequency division multiplexing)-QPSK signals even if the number of antennas at the transmitter is twice the number of antennas at the receiver over multipath fading channels. Furthermore, we propose two detection schemes called "hard replica" and "soft replica." In addition, in order to improve the performance of this architecture, we propose an iteration in which the receiver decodes the detected signals; through such iterations, this architecture can obtain path diversity and achieve high performance. When the transmitter has four antennas and receiver has two antennas, the proposed architecture achieves BER of 8 × 10-4using the soft replica at Eb/N0= 15 dB. Furthermore, by performing more iterations, it provides additional gains of 3 dB. The performance of the proposed architecture is verified using computer simulations.

This paper deals with two representative bidirectional traffic cases in two-hop wireless relay access systems employing network coding and a slotted ALOHA protocol. Network coding is a recent and highly regarded technology for capacity enhancement of multiple unicast and multisource multicast networks. The relay nodes are generally involved with unbalanced multidirectional traffic, but the impact of the unbalanced traffic on network coding has not been analyzed. This paper provides closed-form expressions for the throughput and packet delay for two-hop bidirectional traffic cases both with and without network coding even if the buffers on nodes are unsaturated. The analytical results are mainly derived by solving queueing systems for the buffer behavior at the relay node. The results show that the transmission probability of the relay node is a design parameter that is crucial for maximizing the achievable throughput of slotted ALOHA systems with network coding in two-hop bidirectional traffic cases.

In this study, we develop a virtual layered space-frequency communication architecture. This architecture enables the decoding of transmitted OFDM (Orthogonal Frequency Division Multiplexing)-QPSK signals even if the number of antennas at the transmitter is twice the number of antennas at the receiver over multipath fading channels. Furthermore, in order to improve the performance of this architecture, we propose an iteration with prior probability in which the receiver decodes the detected signals; through such iteration, this architecture can obtain path diversity and achieve high performance. When the transmitter has four antennas and receiver has two antennas, the proposed architecture achieves BER of 8×10-4at Eb/N0= 13.5 dB. Furthermore, by performing more iterations, it provides additional gains of 5 dB. The performance of the proposed architecture is verified using computer simulations.

This paper proposes a novel receiver architecture named as "Virtual layered space time reception" for MIMO wireless communication systems. In the proposed reception architecture, a transmission signal is regarded to comprise a sign signal and fractional phase shift. The phase shift combined with wireless channels are dealt as a virtual channel. Then, the sign signal which is assumed to be transmitted over the virtual channel is detected by a serial interference canceler (SIC) like V-BLAST. The receiver based on the proposed architecture can detect more data streams than the number of received antennas, even though the architecture applies the SIC. This means that the receiver based on the proposed architecture can form more communications channels between the transmitter and the receiver than the number of received antennas. Therefore, the proposed architecture has potential of increasing channel capacity. Actually, computer simulation result shows that the proposed receiver attains superior performance even when the number of transmit antenna is twice as many as that of the received antennas.

We propose an architecture which reduce the circuit size to concurrently cancel co-channel interference and image- band interference. We adopt analogue Hilbert transformers to cancel image-band interference and adaptive array antennas to cancel co-channel interference. Furthermore, we can miniaturize the receiver by mixing all signals at the IF stage. We also show performance of the proposed system by computer simulation. As a result, image-band interference caused by some imperfection of Hilbert transformer can be completely canceled. Moreover, co-channel interference can be simultaneously canceled.

This paper deals with two representative unbalanced traffic cases for two-hop wireless relay access systems employing network coding and a slotted ALOHA protocol. Network coding is a recent and highly regarded technology for capacity enhancement with multiple unicast and multisource multicast networks. We have analyzed the performance of network coding on a two-hop wireless relay access system employing the slotted ALOHA under a balanced bidirectional traffic. The relay nodes will generally undergo this unbalanced multidirectional traffic but the impact of this unbalanced traffic on network coding has not been analyzed. This paper provides closed-form expressions for the throughput and packet delay for two-hop unbalanced bidirectional traffic cases both with and without network coding even if the buffers on nodes are unsaturated. The analytical results are mainly derived by solving queueing systems for the buffer behavior at the relay node. The results show that the transmission probability of the relay node is a design parameter that is crucial to maximizing the achievable throughput of wireless network coding in slotted ALOHA on two-hop unbalanced traffic cases. Furthermore, we show that the throughput is enhanced even if the traffic at the relay node is unbalanced.

This paper deals with a simple but essential two-hop wireless relay network employing network coding and a slotted ALOHA protocol. Network coding is a recent and highly regarded technology for capacity enhancement on multiple unicast and multisource multicast networks. However, the performance of network coding on wireless relay networks employing random access protocols has not been analyzed. This paper provides closed-form expressions for the throughput and delay on two- hop wireless relay networks both with and without network coding from a theoretical perspective of the slotted ALOHA protocol. The analytical results are derived by solving queueing systems for the buffer activity at the relay node. The results show that the transmission probability of the relay node is a design parameter that is crucial to maximizing the achievable throughput of slotted ALOHA with network coding on a two-hop wireless relay network. Furthermore, this paper clarifies that the throughput is enhanced by controlling the transmission probability of the relay node such that it has considerable opportunity for network coding.

VoIP (voice over IP) is one of the real time applications which demand that wireless LAN systems achieve severe quality requirements in terms of delay time, jitter and packet loss. However, it is difficult for CSMA/CA (carrier sense multiple access with collision avoidance) to achieve the service quality demanded by VoIP if voice and data traffic coexist, so some form of priority control is needed. This paper proposes a novel multiple access protocol that allows wireless LANs based on autonomous distributed control to satisfy the VoIP requirements. This new protocol suits both VoIP and data traffic and executes priority control dynamically according to whether the VoIP packet collides with a data packet or another VoIP packet. The results of a theoretical analysis and computer simulations indicate its excellent performance. This proposed protocol reduces the delay time of VoIP packets by 54 to 70% compared with conventional CSMA/CA, even if the traffic load increases, provided that the packet loss probability is less than 3%.

An adaptive phase tracking scheme for orthogonal frequency division multiplexing (OFDM) signals can provide superior PER performance in channels that exhibit a variety of phase noise power. It is an effective technique with which to achieve high-rate and high quality wireless transmission. This paper proposes a new simple adaptive phase tracking scheme for OFDM in order to realize high-rate wireless local area networks (LANs). The proposed scheme measures the integrated phase rotation in order to set appropriately the properties of the FIR filter in the phase tracking circuits. This scheme is based on the fact that the integrated phase rotation is correlated to the phase noise power. Assuming an RMS delay spread of 100 ns, computer simulations show that the proposed scheme offers superior required E/sub b//N/sub 0/ performance (versus the phase noise power) compared to the conventional fixed-tap scheme, where the phase-noise-to-signal power ratios are lower than -18 dB. It also offers excellent PER performance at the packet length of 1000 bytes, unlike the conventional schemes which suffer degraded PER performance.

This paper proposes a new effective data transfer method for IEEE 802.11 wireless LANs by integrating priority control and multirate mechanism. The IEEE 802.11 PHY layer supports a multirate mechanism with dynamic rate switching and an appropriate data rate is selected in transmitting a frame. However, the multirate mechanism is used with the CSMA/CA (carrier sense multiple access with collision avoidance) protocol, low rate transmissions need much longer time than high rate transmissions to finish sending a frame. As a result, the system capacity is decreased. The proposed method assumes the same number of priority levels as the data rates, and a data rate is associated to a priority level. The priority of a transmission goes up with the used data rate. For this purpose, we have modified the CSMA/CA protocol to support prioritized transmission. By selecting the appropriate priority depending on the data rate and giving more transmission opportunities for high rate transmission, the system capacity is increased. The effect of the proposed mechanism is confirmed by computer simulations.

This paper presents a novel slotted ALOHA that has an enhanced capture effect even if heavy traffic is offered. Capture event success probability is improved by controlling the probability of high power transmission permission. This makes it possible to capture the other packets if only one station increases its transmission power. This control method is the key issue of this paper since the capture effect is one of the most promising ways to achieve high throughput without interference (collision) detection/carrier sense. We modified the conventional slotted ALOHA to yield a practical and useful packet transmission scheme. This modified slotted ALOHA has a finite buffer that stores backlog packets. Heavy traffic may cause buffer overflow which implies packet loss. When a station of a wireless LAN or another data terminal intends to send a packet, its transmission power is controlled by the number of stations in an area that covers the same cell shaped by an access point. As a result, only one of the packets can be successfully sent by capturing the other packets. This capture effect is improved significantly by the proposed scheme. This modified and enhanced slotted ALOHA achieves the maximum throughput of 0.74, which is higher than the 0.47 achieved by transmission power control in a previous paper. This is double the throughput of the original slotted ALOHA.

This paper proposes a maximal ratio combining (MRC) frequency diversity automatic-repeat-request (ARQ) scheme which is suitable for high data rate orthogonal frequency division multiplexing (OFDM) signals. The proposed scheme regularly changes the subcarrier assignment pattern at every retransmission. This scheme sharply reduces the number of ARQ retransmissions in slow fading environments by virtue of the frequency diversity effect. Computer simulation results show that the proposed scheme reduces the number of retransmissions to about 3 at the accumulative correct packet reception rate (ACPRR) of 0.99 while the conventional schemes lead to more than 10.

Diversity combining methods for mobile satellite communication systems employing convolutional encoding and soft-decision Viterbi decoding are evaluated. Computer simulation clarifies that the pre-Viterbi-decoding maximal ratio combining method has better performance than other methods in Rician fading channels. The simulation results agree with the Pe (bit error probability) performance derived from the numerical analysis for slow fading using the approximate Pe performance of Viterbi decoding in AWGN (additive white Gaussian noise) channels and the probability density function of Rician fading. Applying this diversity method to the multi-beam mobile satellite communication systems, a satellite beam diversity reception scheme is proposed. A computer simulation result shows that the proposed scheme decreases the packet error rate of the control signals to less than 1/100 around the satellite beam boundary.>

The authors propose a satellite video signal transmission system that uses a time division multiple access (TDMA) scheme for digital video signal transmission and a code division multiple access (CDMA) scheme for voice signal transmission from a video signal reception-only earth station (backward voice signal transmission). The adoption of a TDMA scheme makes it possible to transmit video signals from various places and to communicate in multipoint-to-(multi)point mode. For backward voice channel transmission from video signal reception-only earth stations, a superposed CDMA voice signal transmission over TDMA video signals by using the same transponder is proposed. The employment of high coding gain forward error correction and development of a cochannel interference cancellation technique have made it possible for the proposed system to transmit a practical number of voice channels. The performance of the proposed system has been experimentally evaluated and the results show the proposed scheme can transmit about 20 CDMA voice channels simultaneously.>

The authors propose a TDMA (time-division multiple-access) satellite communication system suitable for both basic and primary ISDN services in addition to B-channel transmission lines among ISDN switches. To provide these services economically and efficiently, the proposed system uses an offset quadrature phase-shift keying (QPSK) burst model coupled with high-coding gain and rate-forward-error correction, demand assignment technology for each data burst, and a transmission and reception transponder (carrier) hopping scheme. To realize cost-effective transmission and subscriber lines and to increase the reliability of TDMA terminals, the major functions of TDMA terminals have been implemented in large-scale integrated circuits (LSICs), monolithic ICs (MICs) and hybrid ICs (HICs). The experimental results show narrow spectrum transmission with little bit error probability degradation for the offset QPSK burst modem and perfect operation of demand assignment and carrier hopping capability of the system.>

A DCF (dual carrier filter) reverse-modulation-type carrier recovery circuit is proposed to achieve a low carrier skipping rate and satisfactory phase tracking performance for coherent detection of PSK (phase shift keying) signals in fast Rician fading channels. The proposed scheme employs both narrow and wide bandwidth carrier filters simultaneously for the reverse-modulation-type carrier recovery circuit. It is clarified by computer simulation that the Pe performance of a QPSK (quadriphase shift keying) modem employing the proposed scheme shows an improvement of 1.5 dB in required E/sub s//N/sub O/ at Pe=10/sup 4/ (after Viterbi decoding (R=7/8, K=7), C/M (direct-to-multipath signal power ratio)=10 dB, interleaving size=64*64), compared with conventional coherent detection employing the reverse modulation tank-limiter scheme or the Costas loop scheme.>

A DCF (dual carrier filter) reverse-modulation-type carrier recovery circuit is proposed to achieve a low carrier skipping rate and satisfactory phase tracking performance for coherent detection of PSK (phase shift keying) signals in fast Rician fading channels. The proposed scheme employs both narrow and wide bandwidth carrier filters simultaneously for the reverse-modulation-type carrier recovery circuit. It is clarified by computer simulation that the Pe performance of a QPSK (quadriphase shift keying) modem employing the proposed scheme shows an improvement of 1.5 dB in required E/sub s//N/sub O/ at Pe=10/sup 4/ (after Viterbi decoding (R=7/8, K=7), C/M (direct-to-multipath signal power ratio)=10 dB, interleaving size=64*64), compared with conventional coherent detection employing the reverse modulation tank-limiter scheme or the Costas loop scheme. >

An architecture is proposed for TDMA (time division multiple access) equipment and functional-module realization in microelectronics to increase reliability and to reduce hardware size and development time. The approach described basically involves digitization of analog circuits (allowing their realization using digital LSI circuits) and analog IC implementation for high-speed circuits. In order to use general-purpose LSI circuits and ICs, TDMA equipment is reconfigured into a hardware-oriented and simplified architecture. Using this architecture and optimal function assigning to each module, six types of general-purpose synchronization unit LSI circuits have been developed in addition to eleven types of LSI circuits and ICs, i.e. three types of digital LSI circuits, four types of MAICs (monolithic analog ICs), and four types of HICs (hybrid ICs) for a burst modem. As a result of this LSI circuit and IC implementation, the hardware size of TDMA equipment has been reduced to one-fifth of the conventional size, and maintenance-free capability has been achieved.>